From 5501ec815b508fe889c381413cb2985989ffc277 Mon Sep 17 00:00:00 2001
From: thomas-ext-ux <thomas-ext@alteos.com>
Date: Sun, 22 Mar 2026 09:44:44 +0100
Subject: [PATCH 1/4] feat: translate entire frontend UI from Chinese to
 English

Translate all user-facing text, labels, placeholders, button text,
log messages, error messages, and comments across all Vue components
and views to English.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
---
 frontend/src/components/GraphPanel.vue       | 182 ++++----
 frontend/src/components/HistoryDatabase.vue  | 288 ++++++-------
 frontend/src/components/Step1GraphBuild.vue  |  48 +--
 frontend/src/components/Step2EnvSetup.vue    | 420 +++++++++----------
 frontend/src/components/Step3Simulation.vue  | 148 +++----
 frontend/src/components/Step4Report.vue      | 370 ++++++++--------
 frontend/src/components/Step5Interaction.vue | 192 ++++-----
 frontend/src/views/Home.vue                  | 156 +++----
 frontend/src/views/MainView.vue              |  18 +-
 frontend/src/views/Process.vue               | 140 +++----
 frontend/src/views/SimulationView.vue        |  84 ++--
 11 files changed, 1023 insertions(+), 1023 deletions(-)

diff --git a/frontend/src/components/GraphPanel.vue b/frontend/src/components/GraphPanel.vue
index 314c966e4..83c4ae028 100644
--- a/frontend/src/components/GraphPanel.vue
+++ b/frontend/src/components/GraphPanel.vue
@@ -2,24 +2,24 @@
   <div class="graph-panel">
     <div class="panel-header">
       <span class="panel-title">Graph Relationship Visualization</span>
-      <!-- 顶部工具栏 (Internal Top Right) -->
+      <!-- Top toolbar (Internal Top Right) -->
       <div class="header-tools">
-        <button class="tool-btn" @click="$emit('refresh')" :disabled="loading" title="刷新图谱">
+        <button class="tool-btn" @click="$emit('refresh')" :disabled="loading" title="Refresh Graph">
           <span class="icon-refresh" :class="{ 'spinning': loading }">↻</span>
           <span class="btn-text">Refresh</span>
         </button>
-        <button class="tool-btn" @click="$emit('toggle-maximize')" title="最大化/还原">
+        <button class="tool-btn" @click="$emit('toggle-maximize')" title="Maximize/Restore">
           <span class="icon-maximize">⛶</span>
         </button>
       </div>
     </div>
     
     <div class="graph-container" ref="graphContainer">
-      <!-- 图谱可视化 -->
+      <!-- Graph visualization -->
       <div v-if="graphData" class="graph-view">
         <svg ref="graphSvg" class="graph-svg"></svg>
         
-        <!-- 构建中/模拟中提示 -->
+        <!-- Building/simulating hint -->
         <div v-if="currentPhase === 1 || isSimulating" class="graph-building-hint">
           <div class="memory-icon-wrapper">
             <svg viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" class="memory-icon">
@@ -27,10 +27,10 @@
               <path d="M14.5 2A2.5 2.5 0 0 0 12 4.5v15a2.5 2.5 0 0 0 4.96.44 2.5 2.5 0 0 0 2.96-3.08 3 3 0 0 0 .34-5.58 2.5 2.5 0 0 0-1.32-4.24 2.5 2.5 0 0 0-4.44-4.04z" />
             </svg>
           </div>
-          {{ isSimulating ? 'GraphRAG长短期记忆实时更新中' : '实时更新中...' }}
+          {{ isSimulating ? 'GraphRAG long/short-term memory updating in real-time' : 'Updating in real-time...' }}
         </div>
         
-        <!-- 模拟结束后的提示 -->
+        <!-- Post-simulation hint -->
         <div v-if="showSimulationFinishedHint" class="graph-building-hint finished-hint">
           <div class="hint-icon-wrapper">
             <svg viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" class="hint-icon">
@@ -39,8 +39,8 @@
               <line x1="12" y1="8" x2="12.01" y2="8"></line>
             </svg>
           </div>
-          <span class="hint-text">还有少量内容处理中，建议稍后手动刷新图谱</span>
-          <button class="hint-close-btn" @click="dismissFinishedHint" title="关闭提示">
+          <span class="hint-text">Some content still processing, try refreshing the graph later</span>
+          <button class="hint-close-btn" @click="dismissFinishedHint" title="Close hint">
             <svg viewBox="0 0 24 24" width="14" height="14" fill="none" stroke="currentColor" stroke-width="2">
               <line x1="18" y1="6" x2="6" y2="18"></line>
               <line x1="6" y1="6" x2="18" y2="18"></line>
@@ -48,7 +48,7 @@
           </button>
         </div>
         
-        <!-- 节点/边详情面板 -->
+        <!-- Node/edge detail panel -->
         <div v-if="selectedItem" class="detail-panel">
           <div class="detail-panel-header">
             <span class="detail-title">{{ selectedItem.type === 'node' ? 'Node Details' : 'Relationship' }}</span>
@@ -58,7 +58,7 @@
             <button class="detail-close" @click="closeDetailPanel">×</button>
           </div>
           
-          <!-- 节点详情 -->
+          <!-- Node details -->
           <div v-if="selectedItem.type === 'node'" class="detail-content">
             <div class="detail-row">
               <span class="detail-label">Name:</span>
@@ -101,9 +101,9 @@
             </div>
           </div>
           
-          <!-- 边详情 -->
+          <!-- Edge details -->
           <div v-else class="detail-content">
-            <!-- 自环组详情 -->
+            <!-- Self-loop group details -->
             <template v-if="selectedItem.data.isSelfLoopGroup">
               <div class="edge-relation-header self-loop-header">
                 {{ selectedItem.data.source_name }} - Self Relations
@@ -154,7 +154,7 @@
               </div>
             </template>
             
-            <!-- 普通边详情 -->
+            <!-- Regular edge details -->
             <template v-else>
               <div class="edge-relation-header">
                 {{ selectedItem.data.source_name }} → {{ selectedItem.data.name || 'RELATED_TO' }} → {{ selectedItem.data.target_name }}
@@ -200,20 +200,20 @@
         </div>
       </div>
       
-      <!-- 加载状态 -->
+      <!-- Loading state -->
       <div v-else-if="loading" class="graph-state">
         <div class="loading-spinner"></div>
-        <p>图谱数据加载中...</p>
+        <p>Loading graph data...</p>
       </div>
       
-      <!-- 等待/空状态 -->
+      <!-- Waiting/empty state -->
       <div v-else class="graph-state">
         <div class="empty-icon">❖</div>
-        <p class="empty-text">等待本体生成...</p>
+        <p class="empty-text">Waiting for ontology generation...</p>
       </div>
     </div>
 
-    <!-- 底部图例 (Bottom Left) -->
+    <!-- Bottom legend (Bottom Left) -->
     <div v-if="graphData && entityTypes.length" class="graph-legend">
       <span class="legend-title">Entity Types</span>
       <div class="legend-items">
@@ -224,7 +224,7 @@
       </div>
     </div>
     
-    <!-- 显示边标签开关 -->
+    <!-- Show edge labels toggle -->
     <div v-if="graphData" class="edge-labels-toggle">
       <label class="toggle-switch">
         <input type="checkbox" v-model="showEdgeLabels" />
@@ -251,26 +251,26 @@ const emit = defineEmits(['refresh', 'toggle-maximize'])
 const graphContainer = ref(null)
 const graphSvg = ref(null)
 const selectedItem = ref(null)
-const showEdgeLabels = ref(true) // 默认显示边标签
-const expandedSelfLoops = ref(new Set()) // 展开的自环项
-const showSimulationFinishedHint = ref(false) // 模拟结束后的提示
-const wasSimulating = ref(false) // 追踪之前是否在模拟中
+const showEdgeLabels = ref(true) // Show edge labels by default
+const expandedSelfLoops = ref(new Set()) // Expanded self-loop items
+const showSimulationFinishedHint = ref(false) // Post-simulation hint
+const wasSimulating = ref(false) // Track whether previously simulating
 
-// 关闭模拟结束提示
+// Dismiss post-simulation hint
 const dismissFinishedHint = () => {
   showSimulationFinishedHint.value = false
 }
 
-// 监听 isSimulating 变化，检测模拟结束
+// Watch isSimulating changes, detect simulation end
 watch(() => props.isSimulating, (newValue, oldValue) => {
   if (wasSimulating.value && !newValue) {
-    // 从模拟中变为非模拟状态，显示结束提示
+    // Transitioned from simulating to not simulating, show finished hint
     showSimulationFinishedHint.value = true
   }
   wasSimulating.value = newValue
 }, { immediate: true })
 
-// 切换自环项展开/折叠状态
+// Toggle self-loop item expand/collapse state
 const toggleSelfLoop = (id) => {
   const newSet = new Set(expandedSelfLoops.value)
   if (newSet.has(id)) {
@@ -281,11 +281,11 @@ const toggleSelfLoop = (id) => {
   expandedSelfLoops.value = newSet
 }
 
-// 计算实体类型用于图例
+// Compute entity types for legend
 const entityTypes = computed(() => {
   if (!props.graphData?.nodes) return []
   const typeMap = {}
-  // 美观的颜色调色板
+  // Color palette
   const colors = ['#FF6B35', '#004E89', '#7B2D8E', '#1A936F', '#C5283D', '#E9724C', '#3498db', '#9b59b6', '#27ae60', '#f39c12']
   
   props.graphData.nodes.forEach(node => {
@@ -298,7 +298,7 @@ const entityTypes = computed(() => {
   return Object.values(typeMap)
 })
 
-// 格式化时间
+// Format datetime
 const formatDateTime = (dateStr) => {
   if (!dateStr) return ''
   try {
@@ -318,7 +318,7 @@ const formatDateTime = (dateStr) => {
 
 const closeDetailPanel = () => {
   selectedItem.value = null
-  expandedSelfLoops.value = new Set() // 重置展开状态
+  expandedSelfLoops.value = new Set() // Reset expanded state
 }
 
 let currentSimulation = null
@@ -328,7 +328,7 @@ let linkLabelBgRef = null
 const renderGraph = () => {
   if (!graphSvg.value || !props.graphData) return
   
-  // 停止之前的仿真
+  // Stop previous simulation
   if (currentSimulation) {
     currentSimulation.stop()
   }
@@ -362,16 +362,16 @@ const renderGraph = () => {
   
   const nodeIds = new Set(nodes.map(n => n.id))
   
-  // 处理边数据，计算同一对节点间的边数量和索引
+  // Process edge data, calculate edge count and index between node pairs
   const edgePairCount = {}
-  const selfLoopEdges = {} // 按节点分组的自环边
+  const selfLoopEdges = {} // Self-loop edges grouped by node
   const tempEdges = edgesData
     .filter(e => nodeIds.has(e.source_node_uuid) && nodeIds.has(e.target_node_uuid))
   
-  // 统计每对节点之间的边数量，收集自环边
+  // Count edges between node pairs, collect self-loop edges
   tempEdges.forEach(e => {
     if (e.source_node_uuid === e.target_node_uuid) {
-      // 自环 - 收集到数组中
+      // Self-loop - collect into array
       if (!selfLoopEdges[e.source_node_uuid]) {
         selfLoopEdges[e.source_node_uuid] = []
       }
@@ -386,9 +386,9 @@ const renderGraph = () => {
     }
   })
   
-  // 记录当前处理到每对节点的第几条边
+  // Track current edge index for each node pair
   const edgePairIndex = {}
-  const processedSelfLoopNodes = new Set() // 已处理的自环节点
+  const processedSelfLoopNodes = new Set() // Already processed self-loop nodes
   
   const edges = []
   
@@ -396,9 +396,9 @@ const renderGraph = () => {
     const isSelfLoop = e.source_node_uuid === e.target_node_uuid
     
     if (isSelfLoop) {
-      // 自环边 - 每个节点只添加一条合并的自环
+      // Self-loop edge - only add one merged self-loop per node
       if (processedSelfLoopNodes.has(e.source_node_uuid)) {
-        return // 已处理过，跳过
+        return // Already processed, skip
       }
       processedSelfLoopNodes.add(e.source_node_uuid)
       
@@ -417,7 +417,7 @@ const renderGraph = () => {
           source_name: nodeName,
           target_name: nodeName,
           selfLoopCount: allSelfLoops.length,
-          selfLoopEdges: allSelfLoops // 存储所有自环边的详细信息
+          selfLoopEdges: allSelfLoops // Store all self-loop edge details
         }
       })
       return
@@ -428,19 +428,19 @@ const renderGraph = () => {
     const currentIndex = edgePairIndex[pairKey] || 0
     edgePairIndex[pairKey] = currentIndex + 1
     
-    // 判断边的方向是否与标准化方向一致（源UUID < 目标UUID）
+    // Check if edge direction matches normalized direction (source UUID < target UUID)
     const isReversed = e.source_node_uuid > e.target_node_uuid
     
-    // 计算曲率：多条边时分散开，单条边为直线
+    // Calculate curvature: spread out for multiple edges, straight line for single edge
     let curvature = 0
     if (totalCount > 1) {
-      // 均匀分布曲率，确保明显区分
-      // 曲率范围根据边数量增加，边越多曲率范围越大
+      // Evenly distribute curvature for clear distinction
+      // Curvature range increases with edge count
       const curvatureRange = Math.min(1.2, 0.6 + totalCount * 0.15)
       curvature = ((currentIndex / (totalCount - 1)) - 0.5) * curvatureRange * 2
       
-      // 如果边的方向与标准化方向相反，翻转曲率
-      // 这样确保所有边在同一参考系下分布，不会因方向不同而重叠
+      // If edge direction is reversed from normalized, flip curvature
+      // This ensures all edges distribute in the same reference frame without overlap
       if (isReversed) {
         curvature = -curvature
       }
@@ -468,11 +468,11 @@ const renderGraph = () => {
   entityTypes.value.forEach(t => colorMap[t.name] = t.color)
   const getColor = (type) => colorMap[type] || '#999'
 
-  // Simulation - 根据边数量动态调整节点间距
+  // Simulation - dynamically adjust node spacing based on edge count
   const simulation = d3.forceSimulation(nodes)
     .force('link', d3.forceLink(edges).id(d => d.id).distance(d => {
-      // 根据这对节点之间的边数量动态调整距离
-      // 基础距离 150，每多一条边增加 40
+      // Dynamically adjust distance based on edge count between node pair
+      // Base distance 150, increase by 40 per additional edge
       const baseDistance = 150
       const edgeCount = d.pairTotal || 1
       return baseDistance + (edgeCount - 1) * 50
@@ -480,7 +480,7 @@ const renderGraph = () => {
     .force('charge', d3.forceManyBody().strength(-400))
     .force('center', d3.forceCenter(width / 2, height / 2))
     .force('collide', d3.forceCollide(50))
-    // 添加向中心的引力，让独立的节点群聚集到中心区域
+    // Add gravity toward center to cluster isolated node groups
     .force('x', d3.forceX(width / 2).strength(0.04))
     .force('y', d3.forceY(height / 2).strength(0.04))
   
@@ -493,39 +493,39 @@ const renderGraph = () => {
     g.attr('transform', event.transform)
   }))
 
-  // Links - 使用 path 支持曲线
+  // Links - use path for curve support
   const linkGroup = g.append('g').attr('class', 'links')
   
-  // 计算曲线路径
+  // Calculate curve path
   const getLinkPath = (d) => {
     const sx = d.source.x, sy = d.source.y
     const tx = d.target.x, ty = d.target.y
     
-    // 检测自环
+    // Detect self-loop
     if (d.isSelfLoop) {
-      // 自环：绘制一个圆弧从节点出发再返回
+      // Self-loop: draw an arc from node and back
       const loopRadius = 30
-      // 从节点右侧出发，绕一圈回来
-      const x1 = sx + 8  // 起点偏移
+      // Start from right side of node, loop around
+      const x1 = sx + 8  // Start offset
       const y1 = sy - 4
-      const x2 = sx + 8  // 终点偏移
+      const x2 = sx + 8  // End offset
       const y2 = sy + 4
-      // 使用圆弧绘制自环（sweep-flag=1 顺时针）
+      // Draw self-loop using arc (sweep-flag=1 clockwise)
       return `M${x1},${y1} A${loopRadius},${loopRadius} 0 1,1 ${x2},${y2}`
     }
     
     if (d.curvature === 0) {
-      // 直线
+      // Straight line
       return `M${sx},${sy} L${tx},${ty}`
     }
     
-    // 计算曲线控制点 - 根据边数量和距离动态调整
+    // Calculate curve control point - dynamically adjust by edge count and distance
     const dx = tx - sx, dy = ty - sy
     const dist = Math.sqrt(dx * dx + dy * dy)
-    // 垂直于连线方向的偏移，根据距离比例计算，保证曲线明显可见
-    // 边越多，偏移量占距离的比例越大
+    // Perpendicular offset scaled by distance ratio for visible curves
+    // More edges = larger offset ratio
     const pairTotal = d.pairTotal || 1
-    const offsetRatio = 0.25 + pairTotal * 0.05 // 基础25%，每多一条边增加5%
+    const offsetRatio = 0.25 + pairTotal * 0.05 // Base 25%, +5% per additional edge
     const baseOffset = Math.max(35, dist * offsetRatio)
     const offsetX = -dy / dist * d.curvature * baseOffset
     const offsetY = dx / dist * d.curvature * baseOffset
@@ -535,14 +535,14 @@ const renderGraph = () => {
     return `M${sx},${sy} Q${cx},${cy} ${tx},${ty}`
   }
   
-  // 计算曲线中点（用于标签定位）
+  // Calculate curve midpoint (for label positioning)
   const getLinkMidpoint = (d) => {
     const sx = d.source.x, sy = d.source.y
     const tx = d.target.x, ty = d.target.y
     
-    // 检测自环
+    // Detect self-loop
     if (d.isSelfLoop) {
-      // 自环标签位置：节点右侧
+      // Self-loop label position: right of node
       return { x: sx + 70, y: sy }
     }
     
@@ -550,7 +550,7 @@ const renderGraph = () => {
       return { x: (sx + tx) / 2, y: (sy + ty) / 2 }
     }
     
-    // 二次贝塞尔曲线的中点 t=0.5
+    // Quadratic Bezier curve midpoint at t=0.5
     const dx = tx - sx, dy = ty - sy
     const dist = Math.sqrt(dx * dx + dy * dy)
     const pairTotal = d.pairTotal || 1
@@ -561,7 +561,7 @@ const renderGraph = () => {
     const cx = (sx + tx) / 2 + offsetX
     const cy = (sy + ty) / 2 + offsetY
     
-    // 二次贝塞尔曲线公式 B(t) = (1-t)²P0 + 2(1-t)tP1 + t²P2, t=0.5
+    // Quadratic Bezier formula B(t) = (1-t)²P0 + 2(1-t)tP1 + t²P2, t=0.5
     const midX = 0.25 * sx + 0.5 * cx + 0.25 * tx
     const midY = 0.25 * sy + 0.5 * cy + 0.25 * ty
     
@@ -577,11 +577,11 @@ const renderGraph = () => {
     .style('cursor', 'pointer')
     .on('click', (event, d) => {
       event.stopPropagation()
-      // 重置之前选中边的样式
+      // Reset previously selected edge styles
       linkGroup.selectAll('path').attr('stroke', '#C0C0C0').attr('stroke-width', 1.5)
       linkLabelBg.attr('fill', 'rgba(255,255,255,0.95)')
       linkLabels.attr('fill', '#666')
-      // 高亮当前选中的边
+      // Highlight currently selected edge
       d3.select(event.target).attr('stroke', '#3498db').attr('stroke-width', 3)
       
       selectedItem.value = {
@@ -590,7 +590,7 @@ const renderGraph = () => {
       }
     })
 
-  // Link labels background (白色背景使文字更清晰)
+  // Link labels background (white background for text clarity)
   const linkLabelBg = linkGroup.selectAll('rect')
     .data(edges)
     .enter().append('rect')
@@ -605,7 +605,7 @@ const renderGraph = () => {
       linkGroup.selectAll('path').attr('stroke', '#C0C0C0').attr('stroke-width', 1.5)
       linkLabelBg.attr('fill', 'rgba(255,255,255,0.95)')
       linkLabels.attr('fill', '#666')
-      // 高亮对应的边
+      // Highlight corresponding edge
       link.filter(l => l === d).attr('stroke', '#3498db').attr('stroke-width', 3)
       d3.select(event.target).attr('fill', 'rgba(52, 152, 219, 0.1)')
       
@@ -633,7 +633,7 @@ const renderGraph = () => {
       linkGroup.selectAll('path').attr('stroke', '#C0C0C0').attr('stroke-width', 1.5)
       linkLabelBg.attr('fill', 'rgba(255,255,255,0.95)')
       linkLabels.attr('fill', '#666')
-      // 高亮对应的边
+      // Highlight corresponding edge
       link.filter(l => l === d).attr('stroke', '#3498db').attr('stroke-width', 3)
       d3.select(event.target).attr('fill', '#3498db')
       
@@ -643,7 +643,7 @@ const renderGraph = () => {
       }
     })
   
-  // 保存引用供外部控制显隐
+  // Save references for external visibility control
   linkLabelsRef = linkLabels
   linkLabelBgRef = linkLabelBg
 
@@ -661,7 +661,7 @@ const renderGraph = () => {
     .style('cursor', 'pointer')
     .call(d3.drag()
       .on('start', (event, d) => {
-        // 只记录位置，不重启仿真（区分点击和拖拽）
+        // Only record position, don't restart simulation (distinguish click vs drag)
         d.fx = d.x
         d.fy = d.y
         d._dragStartX = event.x
@@ -669,13 +669,13 @@ const renderGraph = () => {
         d._isDragging = false
       })
       .on('drag', (event, d) => {
-        // 检测是否真正开始拖拽（移动超过阈值）
+        // Detect if real drag started (movement exceeds threshold)
         const dx = event.x - d._dragStartX
         const dy = event.y - d._dragStartY
         const distance = Math.sqrt(dx * dx + dy * dy)
         
         if (!d._isDragging && distance > 3) {
-          // 首次检测到真正拖拽，才重启仿真
+          // First real drag detected, restart simulation
           d._isDragging = true
           simulation.alphaTarget(0.3).restart()
         }
@@ -686,7 +686,7 @@ const renderGraph = () => {
         }
       })
       .on('end', (event, d) => {
-        // 只有真正拖拽过才让仿真逐渐停止
+        // Only let simulation cool down if actual drag occurred
         if (d._isDragging) {
           simulation.alphaTarget(0)
         }
@@ -697,12 +697,12 @@ const renderGraph = () => {
     )
     .on('click', (event, d) => {
       event.stopPropagation()
-      // 重置所有节点样式
+      // Reset all node styles
       node.attr('stroke', '#fff').attr('stroke-width', 2.5)
       linkGroup.selectAll('path').attr('stroke', '#C0C0C0').attr('stroke-width', 1.5)
-      // 高亮选中节点
+      // Highlight selected node
       d3.select(event.target).attr('stroke', '#E91E63').attr('stroke-width', 4)
-      // 高亮与此节点相连的边
+      // Highlight edges connected to this node
       link.filter(l => l.source.id === d.id || l.target.id === d.id)
         .attr('stroke', '#E91E63')
         .attr('stroke-width', 2.5)
@@ -739,19 +739,19 @@ const renderGraph = () => {
     .style('font-family', 'system-ui, sans-serif')
 
   simulation.on('tick', () => {
-    // 更新曲线路径
+    // Update curve paths
     link.attr('d', d => getLinkPath(d))
     
-    // 更新边标签位置（无旋转，水平显示更清晰）
+    // Update edge label positions (no rotation, horizontal for clarity)
     linkLabels.each(function(d) {
       const mid = getLinkMidpoint(d)
       d3.select(this)
         .attr('x', mid.x)
         .attr('y', mid.y)
-        .attr('transform', '') // 移除旋转，保持水平
+        .attr('transform', '') // Remove rotation, keep horizontal
     })
     
-    // 更新边标签背景
+    // Update edge label backgrounds
     linkLabelBg.each(function(d, i) {
       const mid = getLinkMidpoint(d)
       const textEl = linkLabels.nodes()[i]
@@ -761,7 +761,7 @@ const renderGraph = () => {
         .attr('y', mid.y - bbox.height / 2 - 2)
         .attr('width', bbox.width + 8)
         .attr('height', bbox.height + 4)
-        .attr('transform', '') // 移除旋转
+        .attr('transform', '') // Remove rotation
     })
 
     node
@@ -773,7 +773,7 @@ const renderGraph = () => {
       .attr('y', d => d.y)
   })
   
-  // 点击空白处关闭详情面板
+  // Click blank area to close detail panel
   svg.on('click', () => {
     selectedItem.value = null
     node.attr('stroke', '#fff').attr('stroke-width', 2.5)
@@ -787,7 +787,7 @@ watch(() => props.graphData, () => {
   nextTick(renderGraph)
 }, { deep: true })
 
-// 监听边标签显示开关
+// Watch edge label visibility toggle
 watch(showEdgeLabels, (newVal) => {
   if (linkLabelsRef) {
     linkLabelsRef.style('display', newVal ? 'block' : 'none')
@@ -1250,7 +1250,7 @@ input:checked + .slider:before {
   50% { opacity: 1; transform: scale(1.15); filter: drop-shadow(0 0 8px rgba(76, 175, 80, 0.6)); }
 }
 
-/* 模拟结束后的提示样式 */
+/* Post-simulation hint styles */
 .graph-building-hint.finished-hint {
   background: rgba(0, 0, 0, 0.65);
   border: 1px solid rgba(255, 255, 255, 0.1);
diff --git a/frontend/src/components/HistoryDatabase.vue b/frontend/src/components/HistoryDatabase.vue
index edc73f46b..5f57df2bb 100644
--- a/frontend/src/components/HistoryDatabase.vue
+++ b/frontend/src/components/HistoryDatabase.vue
@@ -4,20 +4,20 @@
     :class="{ 'no-projects': projects.length === 0 && !loading }"
     ref="historyContainer"
   >
-    <!-- 背景装饰：技术网格线（只在有项目时显示） -->
+    <!-- Background decoration: tech grid lines (only shown when projects exist) -->
     <div v-if="projects.length > 0 || loading" class="tech-grid-bg">
       <div class="grid-pattern"></div>
       <div class="gradient-overlay"></div>
     </div>
 
-    <!-- 标题区域 -->
+    <!-- Title area -->
     <div class="section-header">
       <div class="section-line"></div>
-      <span class="section-title">推演记录</span>
+      <span class="section-title">Simulation History</span>
       <div class="section-line"></div>
     </div>
 
-    <!-- 卡片容器（只在有项目时显示） -->
+    <!-- Card container (only shown when projects exist) -->
     <div v-if="projects.length > 0" class="cards-container" :class="{ expanded: isExpanded }" :style="containerStyle">
       <div 
         v-for="(project, index) in projects" 
@@ -29,33 +29,33 @@
         @mouseleave="hoveringCard = null"
         @click="navigateToProject(project)"
       >
-        <!-- 卡片头部：simulation_id 和 功能可用状态 -->
+        <!-- Card header: simulation_id and feature availability status -->
         <div class="card-header">
           <span class="card-id">{{ formatSimulationId(project.simulation_id) }}</span>
           <div class="card-status-icons">
             <span 
               class="status-icon" 
               :class="{ available: project.project_id, unavailable: !project.project_id }"
-              title="图谱构建"
+              title="Graph Build"
             >◇</span>
             <span 
               class="status-icon available" 
-              title="环境搭建"
+              title="Environment Setup"
             >◈</span>
             <span 
               class="status-icon" 
               :class="{ available: project.report_id, unavailable: !project.report_id }"
-              title="分析报告"
+              title="Analysis Report"
             >◆</span>
           </div>
         </div>
 
-        <!-- 文件列表区域 -->
+        <!-- File list area -->
         <div class="card-files-wrapper">
-          <!-- 角落装饰 - 取景框风格 -->
+          <!-- Corner decoration - viewfinder style -->
           <div class="corner-mark top-left-only"></div>
           
-          <!-- 文件列表 -->
+          <!-- File list -->
           <div class="files-list" v-if="project.files && project.files.length > 0">
             <div 
               v-for="(file, fileIndex) in project.files.slice(0, 3)" 
@@ -65,25 +65,25 @@
               <span class="file-tag" :class="getFileType(file.filename)">{{ getFileTypeLabel(file.filename) }}</span>
               <span class="file-name">{{ truncateFilename(file.filename, 20) }}</span>
             </div>
-            <!-- 如果有更多文件，显示提示 -->
+            <!-- Show hint if more files exist -->
             <div v-if="project.files.length > 3" class="files-more">
-              +{{ project.files.length - 3 }} 个文件
+              +{{ project.files.length - 3 }} files
             </div>
           </div>
-          <!-- 无文件时的占位 -->
+          <!-- Placeholder when no files -->
           <div class="files-empty" v-else>
             <span class="empty-file-icon">◇</span>
-            <span class="empty-file-text">暂无文件</span>
+            <span class="empty-file-text">No files</span>
           </div>
         </div>
 
-        <!-- 卡片标题（使用模拟需求的前20字作为标题） -->
+        <!-- Card title (first 20 chars of simulation requirement) -->
         <h3 class="card-title">{{ getSimulationTitle(project.simulation_requirement) }}</h3>
 
-        <!-- 卡片描述（模拟需求完整展示） -->
+        <!-- Card description (full simulation requirement) -->
         <p class="card-desc">{{ truncateText(project.simulation_requirement, 55) }}</p>
 
-        <!-- 卡片底部 -->
+        <!-- Card footer -->
         <div class="card-footer">
           <div class="card-datetime">
             <span class="card-date">{{ formatDate(project.created_at) }}</span>
@@ -94,23 +94,23 @@
           </span>
         </div>
         
-        <!-- 底部装饰线 (hover时展开) -->
+        <!-- Bottom decoration line (expands on hover) -->
         <div class="card-bottom-line"></div>
       </div>
     </div>
 
-    <!-- 加载状态 -->
+    <!-- Loading state -->
     <div v-if="loading" class="loading-state">
       <span class="loading-spinner"></span>
-      <span class="loading-text">加载中...</span>
+      <span class="loading-text">Loading...</span>
     </div>
 
-    <!-- 历史回放详情弹窗 -->
+    <!-- History playback detail modal -->
     <Teleport to="body">
       <Transition name="modal">
         <div v-if="selectedProject" class="modal-overlay" @click.self="closeModal">
           <div class="modal-content">
-            <!-- 弹窗头部 -->
+            <!-- Modal header -->
             <div class="modal-header">
               <div class="modal-title-section">
                 <span class="modal-id">{{ formatSimulationId(selectedProject.simulation_id) }}</span>
@@ -122,35 +122,35 @@
               <button class="modal-close" @click="closeModal">×</button>
             </div>
 
-            <!-- 弹窗内容 -->
+            <!-- Modal content -->
             <div class="modal-body">
-              <!-- 模拟需求 -->
+              <!-- Simulation requirements -->
               <div class="modal-section">
-                <div class="modal-label">模拟需求</div>
-                <div class="modal-requirement">{{ selectedProject.simulation_requirement || '无' }}</div>
+                <div class="modal-label">Simulation Requirements</div>
+                <div class="modal-requirement">{{ selectedProject.simulation_requirement || 'None' }}</div>
               </div>
 
-              <!-- 文件列表 -->
+              <!-- File list -->
               <div class="modal-section">
-                <div class="modal-label">关联文件</div>
+                <div class="modal-label">Associated Files</div>
                 <div class="modal-files" v-if="selectedProject.files && selectedProject.files.length > 0">
                   <div v-for="(file, index) in selectedProject.files" :key="index" class="modal-file-item">
                     <span class="file-tag" :class="getFileType(file.filename)">{{ getFileTypeLabel(file.filename) }}</span>
                     <span class="modal-file-name">{{ file.filename }}</span>
                   </div>
                 </div>
-                <div class="modal-empty" v-else>暂无关联文件</div>
+                <div class="modal-empty" v-else>No associated files</div>
               </div>
             </div>
 
-            <!-- 推演回放分割线 -->
+            <!-- Simulation playback divider -->
             <div class="modal-divider">
               <span class="divider-line"></span>
-              <span class="divider-text">推演回放</span>
+              <span class="divider-text">Simulation Playback</span>
               <span class="divider-line"></span>
             </div>
 
-            <!-- 导航按钮 -->
+            <!-- Navigation buttons -->
             <div class="modal-actions">
               <button 
                 class="modal-btn btn-project" 
@@ -159,7 +159,7 @@
               >
                 <span class="btn-step">Step1</span>
                 <span class="btn-icon">◇</span>
-                <span class="btn-text">图谱构建</span>
+                <span class="btn-text">Graph Build</span>
               </button>
               <button 
                 class="modal-btn btn-simulation" 
@@ -167,7 +167,7 @@
               >
                 <span class="btn-step">Step2</span>
                 <span class="btn-icon">◈</span>
-                <span class="btn-text">环境搭建</span>
+                <span class="btn-text">Environment Setup</span>
               </button>
               <button 
                 class="modal-btn btn-report" 
@@ -176,12 +176,12 @@
               >
                 <span class="btn-step">Step4</span>
                 <span class="btn-icon">◆</span>
-                <span class="btn-text">分析报告</span>
+                <span class="btn-text">Analysis Report</span>
               </button>
             </div>
-            <!-- 不可回放提示 -->
+            <!-- Non-replayable hint -->
             <div class="modal-playback-hint">
-              <span class="hint-text">Step3「开始模拟」与 Step5「深度互动」需在运行中启动，不支持历史回放</span>
+              <span class="hint-text">Step 3 "Run Simulation" and Step 5 "Deep Interaction" must be started during runtime and don't support history playback</span>
             </div>
           </div>
         </div>
@@ -198,56 +198,56 @@ import { getSimulationHistory } from '../api/simulation'
 const router = useRouter()
 const route = useRoute()
 
-// 状态
+// State
 const projects = ref([])
 const loading = ref(true)
 const isExpanded = ref(false)
 const hoveringCard = ref(null)
 const historyContainer = ref(null)
-const selectedProject = ref(null)  // 当前选中的项目（用于弹窗）
+const selectedProject = ref(null)  // Currently selected project (for modal)
 let observer = null
-let isAnimating = false  // 动画锁，防止闪烁
-let expandDebounceTimer = null  // 防抖定时器
-let pendingState = null  // 记录待执行的目标状态
+let isAnimating = false  // Animation lock to prevent flickering
+let expandDebounceTimer = null  // Debounce timer
+let pendingState = null  // Pending target state to execute
 
-// 卡片布局配置 - 调整为更宽的比例
+// Card layout config - adjusted for wider proportions
 const CARDS_PER_ROW = 4
 const CARD_WIDTH = 280  
 const CARD_HEIGHT = 280 
 const CARD_GAP = 24
 
-// 动态计算容器高度样式
+// Dynamically calculate container height style
 const containerStyle = computed(() => {
   if (!isExpanded.value) {
-    // 折叠态：固定高度
+    // Collapsed state: fixed height
     return { minHeight: '420px' }
   }
   
-  // 展开态：根据卡片数量动态计算高度
+  // Expanded state: dynamically calculate height based on card count
   const total = projects.value.length
   if (total === 0) {
     return { minHeight: '280px' }
   }
   
   const rows = Math.ceil(total / CARDS_PER_ROW)
-  // 计算实际需要的高度：行数 * 卡片高度 + (行数-1) * 间距 + 少量底部间距
+  // Calculate required height: rows * card height + (rows-1) * gap + small bottom margin
   const expandedHeight = rows * CARD_HEIGHT + (rows - 1) * CARD_GAP + 10
   
   return { minHeight: `${expandedHeight}px` }
 })
 
-// 获取卡片样式
+// Get card style
 const getCardStyle = (index) => {
   const total = projects.value.length
   
   if (isExpanded.value) {
-    // 展开态：网格布局
+    // Expanded state: grid layout
     const transition = 'transform 700ms cubic-bezier(0.23, 1, 0.32, 1), opacity 700ms cubic-bezier(0.23, 1, 0.32, 1), box-shadow 0.3s ease, border-color 0.3s ease'
 
     const col = index % CARDS_PER_ROW
     const row = Math.floor(index / CARDS_PER_ROW)
     
-    // 计算当前行的卡片数量，确保每行居中
+    // Calculate cards in current row for centering
     const currentRowStart = row * CARDS_PER_ROW
     const currentRowCards = Math.min(CARDS_PER_ROW, total - currentRowStart)
     
@@ -257,7 +257,7 @@ const getCardStyle = (index) => {
     const colInRow = index % CARDS_PER_ROW
     const x = startX + colInRow * (CARD_WIDTH + CARD_GAP)
     
-    // 向下展开，增加与标题的间距
+    // Expand downward, increase spacing from title
     const y = 20 + row * (CARD_HEIGHT + CARD_GAP)
 
     return {
@@ -267,14 +267,14 @@ const getCardStyle = (index) => {
       transition: transition
     }
   } else {
-    // 折叠态：扇形堆叠
+    // Collapsed state: fan-shaped stack
     const transition = 'transform 700ms cubic-bezier(0.23, 1, 0.32, 1), opacity 700ms cubic-bezier(0.23, 1, 0.32, 1), box-shadow 0.3s ease, border-color 0.3s ease'
 
     const centerIndex = (total - 1) / 2
     const offset = index - centerIndex
     
     const x = offset * 35
-    // 调整起始位置，靠近标题但保持适当间距
+    // Adjust starting position, close to title but with proper spacing
     const y = 25 + Math.abs(offset) * 8
     const r = offset * 3
     const s = 0.95 - Math.abs(offset) * 0.05
@@ -288,24 +288,24 @@ const getCardStyle = (index) => {
   }
 }
 
-// 根据轮数进度获取样式类
+// Get style class based on round progress
 const getProgressClass = (simulation) => {
   const current = simulation.current_round || 0
   const total = simulation.total_rounds || 0
   
   if (total === 0 || current === 0) {
-    // 未开始
+    // Not started
     return 'not-started'
   } else if (current >= total) {
-    // 已完成
+    // Completed
     return 'completed'
   } else {
-    // 进行中
+    // In progress
     return 'in-progress'
   }
 }
 
-// 格式化日期（只显示日期部分）
+// Format date (date part only)
 const formatDate = (dateStr) => {
   if (!dateStr) return ''
   try {
@@ -316,7 +316,7 @@ const formatDate = (dateStr) => {
   }
 }
 
-// 格式化时间（显示时:分）
+// Format time (hours:minutes)
 const formatTime = (dateStr) => {
   if (!dateStr) return ''
   try {
@@ -329,35 +329,35 @@ const formatTime = (dateStr) => {
   }
 }
 
-// 截断文本
+// Truncate text
 const truncateText = (text, maxLength) => {
   if (!text) return ''
   return text.length > maxLength ? text.slice(0, maxLength) + '...' : text
 }
 
-// 从模拟需求生成标题（取前20字）
+// Generate title from simulation requirement (first 20 chars)
 const getSimulationTitle = (requirement) => {
-  if (!requirement) return '未命名模拟'
+  if (!requirement) return 'Unnamed Simulation'
   const title = requirement.slice(0, 20)
   return requirement.length > 20 ? title + '...' : title
 }
 
-// 格式化 simulation_id 显示（截取前6位）
+// Format simulation_id display (first 6 chars)
 const formatSimulationId = (simulationId) => {
   if (!simulationId) return 'SIM_UNKNOWN'
   const prefix = simulationId.replace('sim_', '').slice(0, 6)
   return `SIM_${prefix.toUpperCase()}`
 }
 
-// 格式化轮数显示（当前轮/总轮数）
+// Format rounds display (current/total rounds)
 const formatRounds = (simulation) => {
   const current = simulation.current_round || 0
   const total = simulation.total_rounds || 0
-  if (total === 0) return '未开始'
-  return `${current}/${total} 轮`
+  if (total === 0) return 'Not started'
+  return `${current}/${total} rounds`
 }
 
-// 获取文件类型（用于样式）
+// Get file type (for styling)
 const getFileType = (filename) => {
   if (!filename) return 'other'
   const ext = filename.split('.').pop()?.toLowerCase()
@@ -373,16 +373,16 @@ const getFileType = (filename) => {
   return typeMap[ext] || 'other'
 }
 
-// 获取文件类型标签文本
+// Get file type label text
 const getFileTypeLabel = (filename) => {
   if (!filename) return 'FILE'
   const ext = filename.split('.').pop()?.toUpperCase()
   return ext || 'FILE'
 }
 
-// 截断文件名（保留扩展名）
+// Truncate filename (preserve extension)
 const truncateFilename = (filename, maxLength) => {
-  if (!filename) return '未知文件'
+  if (!filename) return 'Unknown file'
   if (filename.length <= maxLength) return filename
   
   const ext = filename.includes('.') ? '.' + filename.split('.').pop() : ''
@@ -391,17 +391,17 @@ const truncateFilename = (filename, maxLength) => {
   return truncatedName + ext
 }
 
-// 打开项目详情弹窗
+// Open project detail modal
 const navigateToProject = (simulation) => {
   selectedProject.value = simulation
 }
 
-// 关闭弹窗
+// Close modal
 const closeModal = () => {
   selectedProject.value = null
 }
 
-// 导航到图谱构建页面（Project）
+// Navigate to graph build page (Project)
 const goToProject = () => {
   if (selectedProject.value?.project_id) {
     router.push({
@@ -412,7 +412,7 @@ const goToProject = () => {
   }
 }
 
-// 导航到环境配置页面（Simulation）
+// Navigate to environment setup page (Simulation)
 const goToSimulation = () => {
   if (selectedProject.value?.simulation_id) {
     router.push({
@@ -423,7 +423,7 @@ const goToSimulation = () => {
   }
 }
 
-// 导航到分析报告页面（Report）
+// Navigate to analysis report page (Report)
 const goToReport = () => {
   if (selectedProject.value?.report_id) {
     router.push({
@@ -434,7 +434,7 @@ const goToReport = () => {
   }
 }
 
-// 加载历史项目
+// Load history projects
 const loadHistory = async () => {
   try {
     loading.value = true
@@ -443,14 +443,14 @@ const loadHistory = async () => {
       projects.value = response.data || []
     }
   } catch (error) {
-    console.error('加载历史项目失败:', error)
+    console.error('Failed to load history:', error)
     projects.value = []
   } finally {
     loading.value = false
   }
 }
 
-// 初始化 IntersectionObserver
+// Initialize IntersectionObserver
 const initObserver = () => {
   if (observer) {
     observer.disconnect()
@@ -461,47 +461,47 @@ const initObserver = () => {
       entries.forEach((entry) => {
         const shouldExpand = entry.isIntersecting
         
-        // 更新待执行的目标状态（无论是否在动画中都要记录最新的目标状态）
+        // Update pending target state (always record latest regardless of animation state)
         pendingState = shouldExpand
         
-        // 清除之前的防抖定时器（新的滚动意图会覆盖旧的）
+        // Clear previous debounce timer (new scroll intent overrides old)
         if (expandDebounceTimer) {
           clearTimeout(expandDebounceTimer)
           expandDebounceTimer = null
         }
         
-        // 如果正在动画中，只记录状态，等动画结束后处理
+        // If animating, just record state, handle after animation ends
         if (isAnimating) return
         
-        // 如果目标状态与当前状态相同，不需要处理
+        // If target state matches current state, no action needed
         if (shouldExpand === isExpanded.value) {
           pendingState = null
           return
         }
         
-        // 使用防抖延迟状态切换，防止快速闪烁
-        // 展开时延迟较短(50ms)，收起时延迟较长(200ms)以增加稳定性
+        // Use debounced delay for state transitions to prevent rapid flickering
+        // Shorter delay for expand (50ms), longer for collapse (200ms) for stability
         const delay = shouldExpand ? 50 : 200
         
         expandDebounceTimer = setTimeout(() => {
-          // 检查是否正在动画
+          // Check if animating
           if (isAnimating) return
           
-          // 检查待执行状态是否仍需要执行（可能已被后续滚动覆盖）
+          // Check if pending state still needs execution (may have been overridden by subsequent scroll)
           if (pendingState === null || pendingState === isExpanded.value) return
           
-          // 设置动画锁
+          // Set animation lock
           isAnimating = true
           isExpanded.value = pendingState
           pendingState = null
           
-          // 动画完成后解除锁定，并检查是否有待处理的状态变化
+          // Unlock after animation completes, check for pending state changes
           setTimeout(() => {
             isAnimating = false
             
-            // 动画结束后，检查是否有新的待执行状态
+            // After animation ends, check for new pending state
             if (pendingState !== null && pendingState !== isExpanded.value) {
-              // 延迟一小段时间再执行，避免太快切换
+              // Brief delay before executing to avoid too-rapid transitions
               expandDebounceTimer = setTimeout(() => {
                 if (pendingState !== null && pendingState !== isExpanded.value) {
                   isAnimating = true
@@ -518,20 +518,20 @@ const initObserver = () => {
       })
     },
     {
-      // 使用多个阈值，使检测更平滑
+      // Use multiple thresholds for smoother detection
       threshold: [0.4, 0.6, 0.8],
-      // 调整 rootMargin，视口底部向上收缩，需要滚动更多才触发展开
+      // Adjust rootMargin, shrink viewport bottom upward, require more scrolling to trigger expand
       rootMargin: '0px 0px -150px 0px'
     }
   )
   
-  // 开始观察
+  // Start observing
   if (historyContainer.value) {
     observer.observe(historyContainer.value)
   }
 }
 
-// 监听路由变化，当返回首页时重新加载数据
+// Watch route changes, reload data when returning to home page
 watch(() => route.path, (newPath) => {
   if (newPath === '/') {
     loadHistory()
@@ -539,28 +539,28 @@ watch(() => route.path, (newPath) => {
 })
 
 onMounted(async () => {
-  // 确保 DOM 渲染完成后再加载数据
+  // Ensure DOM rendering is complete before loading data
   await nextTick()
   await loadHistory()
   
-  // 等待 DOM 渲染后初始化观察器
+  // Initialize observer after DOM rendering
   setTimeout(() => {
     initObserver()
   }, 100)
 })
 
-// 如果使用 keep-alive，在组件激活时重新加载数据
+// If using keep-alive, reload data when component is activated
 onActivated(() => {
   loadHistory()
 })
 
 onUnmounted(() => {
-  // 清理 Intersection Observer
+  // Clean up Intersection Observer
   if (observer) {
     observer.disconnect()
     observer = null
   }
-  // 清理防抖定时器
+  // Clean up debounce timer
   if (expandDebounceTimer) {
     clearTimeout(expandDebounceTimer)
     expandDebounceTimer = null
@@ -569,7 +569,7 @@ onUnmounted(() => {
 </script>
 
 <style scoped>
-/* 容器 */
+/* Container */
 .history-database {
   position: relative;
   width: 100%;
@@ -579,13 +579,13 @@ onUnmounted(() => {
   overflow: visible;
 }
 
-/* 无项目时简化显示 */
+/* Simplified display when no projects */
 .history-database.no-projects {
   min-height: auto;
   padding: 40px 0 20px;
 }
 
-/* 技术网格背景 */
+/* Tech grid background */
 .tech-grid-bg {
   position: absolute;
   top: 0;
@@ -596,7 +596,7 @@ onUnmounted(() => {
   pointer-events: none;
 }
 
-/* 使用CSS背景图案创建固定间距的正方形网格 */
+/* Create fixed-spacing square grid using CSS background pattern */
 .grid-pattern {
   position: absolute;
   top: 0;
@@ -607,7 +607,7 @@ onUnmounted(() => {
     linear-gradient(to right, rgba(0, 0, 0, 0.05) 1px, transparent 1px),
     linear-gradient(to bottom, rgba(0, 0, 0, 0.05) 1px, transparent 1px);
   background-size: 50px 50px;
-  /* 从左上角开始定位，高度变化时只在底部扩展，不影响已有网格位置 */
+  /* Position from top-left, height changes only expand at bottom without affecting existing grid */
   background-position: top left;
 }
 
@@ -623,7 +623,7 @@ onUnmounted(() => {
   pointer-events: none;
 }
 
-/* 标题区域 */
+/* Title area */
 .section-header {
   position: relative;
   z-index: 100;
@@ -651,7 +651,7 @@ onUnmounted(() => {
   text-transform: uppercase;
 }
 
-/* 卡片容器 */
+/* Card container */
 .cards-container {
   position: relative;
   display: flex;
@@ -659,10 +659,10 @@ onUnmounted(() => {
   align-items: flex-start;
   padding: 0 40px;
   transition: min-height 700ms cubic-bezier(0.23, 1, 0.32, 1);
-  /* min-height 由 JS 动态计算，根据卡片数量自适应 */
+  /* min-height dynamically calculated by JS based on card count */
 }
 
-/* 项目卡片 */
+/* Project card */
 .project-card {
   position: absolute;
   width: 280px;
@@ -685,7 +685,7 @@ onUnmounted(() => {
   z-index: 1000 !important;
 }
 
-/* 卡片头部 */
+/* Card header */
 .card-header {
   display: flex;
   justify-content: space-between;
@@ -703,7 +703,7 @@ onUnmounted(() => {
   font-weight: 500;
 }
 
-/* 功能状态图标组 */
+/* Feature status icon group */
 .card-status-icons {
   display: flex;
   align-items: center;
@@ -720,17 +720,17 @@ onUnmounted(() => {
   opacity: 1;
 }
 
-/* 不同功能的颜色 */
-.status-icon:nth-child(1).available { color: #3B82F6; } /* 图谱构建 - 蓝色 */
-.status-icon:nth-child(2).available { color: #F59E0B; } /* 环境搭建 - 橙色 */
-.status-icon:nth-child(3).available { color: #10B981; } /* 分析报告 - 绿色 */
+/* Feature-specific colors */
+.status-icon:nth-child(1).available { color: #3B82F6; } /* Graph Build - blue */
+.status-icon:nth-child(2).available { color: #F59E0B; } /* Environment Setup - orange */
+.status-icon:nth-child(3).available { color: #10B981; } /* Analysis Report - green */
 
 .status-icon.unavailable {
   color: #D1D5DB;
   opacity: 0.5;
 }
 
-/* 轮数进度显示 */
+/* Round progress display */
 .card-progress {
   display: flex;
   align-items: center;
@@ -744,13 +744,13 @@ onUnmounted(() => {
   font-size: 0.5rem;
 }
 
-/* 进度状态颜色 */
-.card-progress.completed { color: #10B981; }    /* 已完成 - 绿色 */
-.card-progress.in-progress { color: #F59E0B; }  /* 进行中 - 橙色 */
-.card-progress.not-started { color: #9CA3AF; }  /* 未开始 - 灰色 */
+/* Progress status colors */
+.card-progress.completed { color: #10B981; }    /* Completed - green */
+.card-progress.in-progress { color: #F59E0B; }  /* In progress - orange */
+.card-progress.not-started { color: #9CA3AF; }  /* Not started - gray */
 .card-status.pending { color: #9CA3AF; }
 
-/* 文件列表区域 */
+/* File list area */
 .card-files-wrapper {
   position: relative;
   width: 100%;
@@ -770,7 +770,7 @@ onUnmounted(() => {
   gap: 4px;
 }
 
-/* 更多文件提示 */
+/* More files hint */
 .files-more {
   display: flex;
   align-items: center;
@@ -800,7 +800,7 @@ onUnmounted(() => {
   border-color: #e5e7eb;
 }
 
-/* 简约文件标签样式 */
+/* Minimal file tag styles */
 .file-tag {
   display: inline-flex;
   align-items: center;
@@ -818,7 +818,7 @@ onUnmounted(() => {
   min-width: 28px;
 }
 
-/* 低饱和度配色方案 - Morandi色系 */
+/* Low saturation color scheme - Morandi palette */
 .file-tag.pdf { background: #f2e6e6; color: #a65a5a; }
 .file-tag.doc { background: #e6eff5; color: #5a7ea6; }
 .file-tag.xls { background: #e6f2e8; color: #5aa668; }
@@ -839,7 +839,7 @@ onUnmounted(() => {
   letter-spacing: 0.1px;
 }
 
-/* 无文件时的占位 */
+/* Placeholder when no files */
 .files-empty {
   display: flex;
   align-items: center;
@@ -860,13 +860,13 @@ onUnmounted(() => {
   letter-spacing: 0.5px;
 }
 
-/* 悬停时文件区域效果 */
+/* File area hover effect */
 .project-card:hover .card-files-wrapper {
   border-color: #d1d5db;
   background: linear-gradient(135deg, #ffffff 0%, #f8f9fa 100%);
 }
 
-/* 角落装饰 */
+/* Corner decoration */
 .corner-mark.top-left-only {
   position: absolute;
   top: 6px;
@@ -879,7 +879,7 @@ onUnmounted(() => {
   z-index: 10;
 }
 
-/* 卡片标题 */
+/* Card title */
 .card-title {
   font-family: 'Inter', -apple-system, sans-serif;
   font-size: 0.9rem;
@@ -897,7 +897,7 @@ onUnmounted(() => {
   color: #2563EB;
 }
 
-/* 卡片描述 */
+/* Card description */
 .card-desc {
   font-family: 'Inter', sans-serif;
   font-size: 0.75rem;
@@ -911,7 +911,7 @@ onUnmounted(() => {
   -webkit-box-orient: vertical;
 }
 
-/* 卡片底部 */
+/* Card footer */
 .card-footer {
   position: relative;
   display: flex;
@@ -925,14 +925,14 @@ onUnmounted(() => {
   font-weight: 500;
 }
 
-/* 日期时间组合 */
+/* Date-time combination */
 .card-datetime {
   display: flex;
   align-items: center;
   gap: 8px;
 }
 
-/* 底部轮数进度显示 */
+/* Bottom round progress display */
 .card-footer .card-progress {
   display: flex;
   align-items: center;
@@ -946,12 +946,12 @@ onUnmounted(() => {
   font-size: 0.5rem;
 }
 
-/* 进度状态颜色 - 底部 */
+/* Progress status colors - bottom */
 .card-footer .card-progress.completed { color: #10B981; }
 .card-footer .card-progress.in-progress { color: #F59E0B; }
 .card-footer .card-progress.not-started { color: #9CA3AF; }
 
-/* 底部装饰线 */
+/* Bottom decoration line */
 .card-bottom-line {
   position: absolute;
   bottom: 0;
@@ -967,7 +967,7 @@ onUnmounted(() => {
   width: 100%;
 }
 
-/* 空状态 */
+/* Empty state */
 .empty-state, .loading-state {
   display: flex;
   flex-direction: column;
@@ -995,7 +995,7 @@ onUnmounted(() => {
   to { transform: rotate(360deg); }
 }
 
-/* 响应式 */
+/* Responsive */
 @media (max-width: 1200px) {
   .project-card {
     width: 240px;
@@ -1011,7 +1011,7 @@ onUnmounted(() => {
   }
 }
 
-/* ===== 历史回放详情弹窗样式 ===== */
+/* ===== History playback detail modal styles ===== */
 .modal-overlay {
   position: fixed;
   top: 0;
@@ -1037,7 +1037,7 @@ onUnmounted(() => {
   box-shadow: 0 20px 25px -5px rgba(0, 0, 0, 0.1), 0 10px 10px -5px rgba(0, 0, 0, 0.04);
 }
 
-/* 动画过渡 */
+/* Animation transitions */
 .modal-enter-active,
 .modal-leave-active {
   transition: opacity 0.3s ease;
@@ -1066,7 +1066,7 @@ onUnmounted(() => {
   opacity: 0;
 }
 
-/* 弹窗头部 */
+/* Modal header */
 .modal-header {
   display: flex;
   justify-content: space-between;
@@ -1133,7 +1133,7 @@ onUnmounted(() => {
   color: #111827;
 }
 
-/* 弹窗内容 */
+/* Modal content */
 .modal-body {
   padding: 24px 32px;
 }
@@ -1175,7 +1175,7 @@ onUnmounted(() => {
   padding-right: 4px;
 }
 
-/* 自定义滚动条样式 */
+/* Custom scrollbar styles */
 .modal-files::-webkit-scrollbar {
   width: 4px;
 }
@@ -1229,7 +1229,7 @@ onUnmounted(() => {
   text-align: center;
 }
 
-/* 推演回放分割线 */
+/* Simulation playback divider */
 .modal-divider {
   display: flex;
   align-items: center;
@@ -1253,7 +1253,7 @@ onUnmounted(() => {
   white-space: nowrap;
 }
 
-/* 导航按钮 */
+/* Navigation buttons */
 .modal-actions {
   display: flex;
   gap: 16px;
@@ -1320,7 +1320,7 @@ onUnmounted(() => {
   color: #111827;
 }
 
-/* 不可回放提示 */
+/* Non-replayable hint */
 .modal-playback-hint {
   display: flex;
   align-items: center;
diff --git a/frontend/src/components/Step1GraphBuild.vue b/frontend/src/components/Step1GraphBuild.vue
index de33a3fd1..5c36a96c0 100644
--- a/frontend/src/components/Step1GraphBuild.vue
+++ b/frontend/src/components/Step1GraphBuild.vue
@@ -6,25 +6,25 @@
         <div class="card-header">
           <div class="step-info">
             <span class="step-num">01</span>
-            <span class="step-title">本体生成</span>
+            <span class="step-title">Ontology Generation</span>
           </div>
           <div class="step-status">
-            <span v-if="currentPhase > 0" class="badge success">已完成</span>
-            <span v-else-if="currentPhase === 0" class="badge processing">生成中</span>
-            <span v-else class="badge pending">等待</span>
+            <span v-if="currentPhase > 0" class="badge success">Completed</span>
+            <span v-else-if="currentPhase === 0" class="badge processing">Generating</span>
+            <span v-else class="badge pending">Waiting</span>
           </div>
         </div>
         
         <div class="card-content">
           <p class="api-note">POST /api/graph/ontology/generate</p>
           <p class="description">
-            LLM分析文档内容与模拟需求，提取出现实种子，自动生成合适的本体结构
+            LLM analyzes document content and simulation requirements, extracts reality seeds, and auto-generates appropriate ontology structure
           </p>
 
           <!-- Loading / Progress -->
           <div v-if="currentPhase === 0 && ontologyProgress" class="progress-section">
             <div class="spinner-sm"></div>
-            <span>{{ ontologyProgress.message || '正在分析文档...' }}</span>
+            <span>{{ ontologyProgress.message || 'Analyzing documents...' }}</span>
           </div>
 
           <!-- Detail Overlay -->
@@ -110,34 +110,34 @@
         <div class="card-header">
           <div class="step-info">
             <span class="step-num">02</span>
-            <span class="step-title">GraphRAG构建</span>
+            <span class="step-title">GraphRAG Build</span>
           </div>
           <div class="step-status">
-            <span v-if="currentPhase > 1" class="badge success">已完成</span>
+            <span v-if="currentPhase > 1" class="badge success">Completed</span>
             <span v-else-if="currentPhase === 1" class="badge processing">{{ buildProgress?.progress || 0 }}%</span>
-            <span v-else class="badge pending">等待</span>
+            <span v-else class="badge pending">Waiting</span>
           </div>
         </div>
 
         <div class="card-content">
           <p class="api-note">POST /api/graph/build</p>
           <p class="description">
-            基于生成的本体，将文档自动分块后调用 Zep 构建知识图谱，提取实体和关系，并形成时序记忆与社区摘要
+            Based on the generated ontology, documents are auto-chunked and Zep is called to build the knowledge graph, extract entities and relations, and form temporal memory and community summaries
           </p>
           
           <!-- Stats Cards -->
           <div class="stats-grid">
             <div class="stat-card">
               <span class="stat-value">{{ graphStats.nodes }}</span>
-              <span class="stat-label">实体节点</span>
+              <span class="stat-label">Entity Nodes</span>
             </div>
             <div class="stat-card">
               <span class="stat-value">{{ graphStats.edges }}</span>
-              <span class="stat-label">关系边</span>
+              <span class="stat-label">Relation Edges</span>
             </div>
             <div class="stat-card">
               <span class="stat-value">{{ graphStats.types }}</span>
-              <span class="stat-label">SCHEMA类型</span>
+              <span class="stat-label">Schema Types</span>
             </div>
           </div>
         </div>
@@ -148,23 +148,23 @@
         <div class="card-header">
           <div class="step-info">
             <span class="step-num">03</span>
-            <span class="step-title">构建完成</span>
+            <span class="step-title">Build Complete</span>
           </div>
           <div class="step-status">
-            <span v-if="currentPhase >= 2" class="badge accent">进行中</span>
+            <span v-if="currentPhase >= 2" class="badge accent">In Progress</span>
           </div>
         </div>
         
         <div class="card-content">
           <p class="api-note">POST /api/simulation/create</p>
-          <p class="description">图谱构建已完成，请进入下一步进行模拟环境搭建</p>
+          <p class="description">Graph build complete. Proceed to the next step for environment setup</p>
           <button 
             class="action-btn" 
             :disabled="currentPhase < 2 || creatingSimulation"
             @click="handleEnterEnvSetup"
           >
             <span v-if="creatingSimulation" class="spinner-sm"></span>
-            {{ creatingSimulation ? '创建中...' : '进入环境搭建 ➝' }}
+            {{ creatingSimulation ? 'Creating...' : 'Enter Environment Setup ➝' }}
           </button>
         </div>
       </div>
@@ -208,10 +208,10 @@ const selectedOntologyItem = ref(null)
 const logContent = ref(null)
 const creatingSimulation = ref(false)
 
-// 进入环境搭建 - 创建 simulation 并跳转
+// Enter environment setup - create simulation and navigate
 const handleEnterEnvSetup = async () => {
   if (!props.projectData?.project_id || !props.projectData?.graph_id) {
-    console.error('缺少项目或图谱信息')
+    console.error('Missing project or graph information')
     return
   }
   
@@ -226,18 +226,18 @@ const handleEnterEnvSetup = async () => {
     })
     
     if (res.success && res.data?.simulation_id) {
-      // 跳转到 simulation 页面
+      // Navigate to simulation page
       router.push({
         name: 'Simulation',
         params: { simulationId: res.data.simulation_id }
       })
     } else {
-      console.error('创建模拟失败:', res.error)
-      alert('创建模拟失败: ' + (res.error || '未知错误'))
+      console.error('Failed to create simulation:', res.error)
+      alert('Failed to create simulation: ' + (res.error || 'Unknown error'))
     }
   } catch (err) {
-    console.error('创建模拟异常:', err)
-    alert('创建模拟异常: ' + err.message)
+    console.error('Simulation creation error:', err)
+    alert('Simulation creation error: ' + err.message)
   } finally {
     creatingSimulation.value = false
   }
diff --git a/frontend/src/components/Step2EnvSetup.vue b/frontend/src/components/Step2EnvSetup.vue
index eae776aaf..647bb5fa1 100644
--- a/frontend/src/components/Step2EnvSetup.vue
+++ b/frontend/src/components/Step2EnvSetup.vue
@@ -1,23 +1,23 @@
 <template>
   <div class="env-setup-panel">
     <div class="scroll-container">
-      <!-- Step 01: 模拟实例 -->
+      <!-- Step 01: Simulation Instance -->
       <div class="step-card" :class="{ 'active': phase === 0, 'completed': phase > 0 }">
         <div class="card-header">
           <div class="step-info">
             <span class="step-num">01</span>
-            <span class="step-title">模拟实例初始化</span>
+            <span class="step-title">Simulation Instance Init</span>
           </div>
           <div class="step-status">
-            <span v-if="phase > 0" class="badge success">已完成</span>
-            <span v-else class="badge processing">初始化</span>
+            <span v-if="phase > 0" class="badge success">Completed</span>
+            <span v-else class="badge processing">Initializing</span>
           </div>
         </div>
         
         <div class="card-content">
           <p class="api-note">POST /api/simulation/create</p>
           <p class="description">
-            新建simulation实例，拉取模拟世界参数模版
+            Create new simulation instance and fetch world parameter templates
           </p>
 
           <div v-if="simulationId" class="info-card">
@@ -35,52 +35,52 @@
             </div>
             <div class="info-row">
               <span class="info-label">Task ID</span>
-              <span class="info-value mono">{{ taskId || '异步任务已完成' }}</span>
+              <span class="info-value mono">{{ taskId || 'Async task completed' }}</span>
             </div>
           </div>
         </div>
       </div>
 
-      <!-- Step 02: 生成 Agent 人设 -->
+      <!-- Step 02: Generate Agent Personas -->
       <div class="step-card" :class="{ 'active': phase === 1, 'completed': phase > 1 }">
         <div class="card-header">
           <div class="step-info">
             <span class="step-num">02</span>
-            <span class="step-title">生成 Agent 人设</span>
+            <span class="step-title">Generate Agent Personas</span>
           </div>
           <div class="step-status">
-            <span v-if="phase > 1" class="badge success">已完成</span>
+            <span v-if="phase > 1" class="badge success">Completed</span>
             <span v-else-if="phase === 1" class="badge processing">{{ prepareProgress }}%</span>
-            <span v-else class="badge pending">等待</span>
+            <span v-else class="badge pending">Waiting</span>
           </div>
         </div>
 
         <div class="card-content">
           <p class="api-note">POST /api/simulation/prepare</p>
           <p class="description">
-            结合上下文，自动调用工具从知识图谱梳理实体与关系，初始化模拟个体，并基于现实种子赋予他们独特的行为与记忆
+            Combines context to automatically invoke tools, extract entities and relationships from the knowledge graph, initialize simulated individuals, and assign them unique behaviors and memories based on reality seeds
           </p>
 
           <!-- Profiles Stats -->
           <div v-if="profiles.length > 0" class="stats-grid">
             <div class="stat-card">
               <span class="stat-value">{{ profiles.length }}</span>
-              <span class="stat-label">当前Agent数</span>
+              <span class="stat-label">Current Agents</span>
             </div>
             <div class="stat-card">
               <span class="stat-value">{{ expectedTotal || '-' }}</span>
-              <span class="stat-label">预期Agent总数</span>
+              <span class="stat-label">Expected Total</span>
             </div>
             <div class="stat-card">
               <span class="stat-value">{{ totalTopicsCount }}</span>
-              <span class="stat-label">现实种子当前关联话题数</span>
+              <span class="stat-label">Current related topics from reality seeds</span>
             </div>
           </div>
 
           <!-- Profiles List Preview -->
           <div v-if="profiles.length > 0" class="profiles-preview">
             <div class="preview-header">
-              <span class="preview-title">已生成的 Agent 人设</span>
+              <span class="preview-title">Generated Agent Personas</span>
             </div>
             <div class="profiles-list">
               <div 
@@ -94,9 +94,9 @@
                   <span class="profile-username">@{{ profile.name || `agent_${idx}` }}</span>
                 </div>
                 <div class="profile-meta">
-                  <span class="profile-profession">{{ profile.profession || '未知职业' }}</span>
+                  <span class="profile-profession">{{ profile.profession || 'Unknown Occupation' }}</span>
                 </div>
-                <p class="profile-bio">{{ profile.bio || '暂无简介' }}</p>
+                <p class="profile-bio">{{ profile.bio || 'No bio available' }}</p>
                 <div v-if="profile.interested_topics?.length" class="profile-topics">
                   <span 
                     v-for="topic in profile.interested_topics.slice(0, 3)" 
@@ -113,77 +113,77 @@
         </div>
       </div>
 
-      <!-- Step 03: 生成双平台模拟配置 -->
+      <!-- Step 03: Generate Dual-Platform Config -->
       <div class="step-card" :class="{ 'active': phase === 2, 'completed': phase > 2 }">
         <div class="card-header">
           <div class="step-info">
             <span class="step-num">03</span>
-            <span class="step-title">生成双平台模拟配置</span>
+            <span class="step-title">Generate Dual-Platform Config</span>
           </div>
           <div class="step-status">
-            <span v-if="phase > 2" class="badge success">已完成</span>
-            <span v-else-if="phase === 2" class="badge processing">生成中</span>
-            <span v-else class="badge pending">等待</span>
+            <span v-if="phase > 2" class="badge success">Completed</span>
+            <span v-else-if="phase === 2" class="badge processing">Generating</span>
+            <span v-else class="badge pending">Waiting</span>
           </div>
         </div>
 
         <div class="card-content">
           <p class="api-note">POST /api/simulation/prepare</p>
           <p class="description">
-            LLM 根据模拟需求与现实种子，智能设置世界时间流速、推荐算法、每个个体的活跃时间段、发言频率、事件触发等参数
+            LLM intelligently configures world time flow, recommendation algorithms, individual activity periods, posting frequency, event triggers, etc. based on simulation needs and reality seeds
           </p>
           
           <!-- Config Preview -->
           <div v-if="simulationConfig" class="config-detail-panel">
-            <!-- 时间配置 -->
+            <!-- Time Config -->
             <div class="config-block">
               <div class="config-grid">
                 <div class="config-item">
-                  <span class="config-item-label">模拟时长</span>
-                  <span class="config-item-value">{{ simulationConfig.time_config?.total_simulation_hours || '-' }} 小时</span>
+                  <span class="config-item-label">Simulation Duration</span>
+                  <span class="config-item-value">{{ simulationConfig.time_config?.total_simulation_hours || '-' }} hours</span>
                 </div>
                 <div class="config-item">
-                  <span class="config-item-label">每轮时长</span>
-                  <span class="config-item-value">{{ simulationConfig.time_config?.minutes_per_round || '-' }} 分钟</span>
+                  <span class="config-item-label">Round Duration</span>
+                  <span class="config-item-value">{{ simulationConfig.time_config?.minutes_per_round || '-' }} minutes</span>
                 </div>
                 <div class="config-item">
-                  <span class="config-item-label">总轮次</span>
-                  <span class="config-item-value">{{ Math.floor((simulationConfig.time_config?.total_simulation_hours * 60 / simulationConfig.time_config?.minutes_per_round)) || '-' }} 轮</span>
+                  <span class="config-item-label">Total Rounds</span>
+                  <span class="config-item-value">{{ Math.floor((simulationConfig.time_config?.total_simulation_hours * 60 / simulationConfig.time_config?.minutes_per_round)) || '-' }} rounds</span>
                 </div>
                 <div class="config-item">
-                  <span class="config-item-label">每小时活跃</span>
+                  <span class="config-item-label">Active per hour</span>
                   <span class="config-item-value">{{ simulationConfig.time_config?.agents_per_hour_min }}-{{ simulationConfig.time_config?.agents_per_hour_max }}</span>
                 </div>
               </div>
               <div class="time-periods">
                 <div class="period-item">
-                  <span class="period-label">高峰时段</span>
+                  <span class="period-label">Peak Hours</span>
                   <span class="period-hours">{{ simulationConfig.time_config?.peak_hours?.join(':00, ') }}:00</span>
                   <span class="period-multiplier">×{{ simulationConfig.time_config?.peak_activity_multiplier }}</span>
                 </div>
                 <div class="period-item">
-                  <span class="period-label">工作时段</span>
+                  <span class="period-label">Work Hours</span>
                   <span class="period-hours">{{ simulationConfig.time_config?.work_hours?.[0] }}:00-{{ simulationConfig.time_config?.work_hours?.slice(-1)[0] }}:00</span>
                   <span class="period-multiplier">×{{ simulationConfig.time_config?.work_activity_multiplier }}</span>
                 </div>
                 <div class="period-item">
-                  <span class="period-label">早间时段</span>
+                  <span class="period-label">Morning Hours</span>
                   <span class="period-hours">{{ simulationConfig.time_config?.morning_hours?.[0] }}:00-{{ simulationConfig.time_config?.morning_hours?.slice(-1)[0] }}:00</span>
                   <span class="period-multiplier">×{{ simulationConfig.time_config?.morning_activity_multiplier }}</span>
                 </div>
                 <div class="period-item">
-                  <span class="period-label">低谷时段</span>
+                  <span class="period-label">Off-Peak Hours</span>
                   <span class="period-hours">{{ simulationConfig.time_config?.off_peak_hours?.[0] }}:00-{{ simulationConfig.time_config?.off_peak_hours?.slice(-1)[0] }}:00</span>
                   <span class="period-multiplier">×{{ simulationConfig.time_config?.off_peak_activity_multiplier }}</span>
                 </div>
               </div>
             </div>
 
-            <!-- Agent 配置 -->
+            <!-- Agent Config -->
             <div class="config-block">
               <div class="config-block-header">
-                <span class="config-block-title">Agent 配置</span>
-                <span class="config-block-badge">{{ simulationConfig.agent_configs?.length || 0 }} 个</span>
+                <span class="config-block-title">Agent Config</span>
+                <span class="config-block-badge">{{ simulationConfig.agent_configs?.length || 0 }}</span>
               </div>
               <div class="agents-cards">
                 <div 
@@ -191,7 +191,7 @@
                   :key="agent.agent_id" 
                   class="agent-card"
                 >
-                  <!-- 卡片头部 -->
+                  <!-- Card Header -->
                   <div class="agent-card-header">
                     <div class="agent-identity">
                       <span class="agent-id">Agent {{ agent.agent_id }}</span>
@@ -203,9 +203,9 @@
                     </div>
                   </div>
                   
-                  <!-- 活跃时间轴 -->
+                  <!-- Active Timeline -->
                   <div class="agent-timeline">
-                    <span class="timeline-label">活跃时段</span>
+                    <span class="timeline-label">Active Period</span>
                     <div class="mini-timeline">
                       <div 
                         v-for="hour in 24" 
@@ -224,38 +224,38 @@
                     </div>
                   </div>
 
-                  <!-- 行为参数 -->
+                  <!-- Behavior Params -->
                   <div class="agent-params">
                     <div class="param-group">
                       <div class="param-item">
-                        <span class="param-label">发帖/时</span>
+                        <span class="param-label">Posts/hr</span>
                         <span class="param-value">{{ agent.posts_per_hour }}</span>
                       </div>
                       <div class="param-item">
-                        <span class="param-label">评论/时</span>
+                        <span class="param-label">Comments/hr</span>
                         <span class="param-value">{{ agent.comments_per_hour }}</span>
                       </div>
                       <div class="param-item">
-                        <span class="param-label">响应延迟</span>
+                        <span class="param-label">Response Delay</span>
                         <span class="param-value">{{ agent.response_delay_min }}-{{ agent.response_delay_max }}min</span>
                       </div>
                     </div>
                     <div class="param-group">
                       <div class="param-item">
-                        <span class="param-label">活跃度</span>
+                        <span class="param-label">Activity</span>
                         <span class="param-value with-bar">
                           <span class="mini-bar" :style="{ width: (agent.activity_level * 100) + '%' }"></span>
                           {{ (agent.activity_level * 100).toFixed(0) }}%
                         </span>
                       </div>
                       <div class="param-item">
-                        <span class="param-label">情感倾向</span>
+                        <span class="param-label">Sentiment</span>
                         <span class="param-value" :class="agent.sentiment_bias > 0 ? 'positive' : agent.sentiment_bias < 0 ? 'negative' : 'neutral'">
                           {{ agent.sentiment_bias > 0 ? '+' : '' }}{{ agent.sentiment_bias?.toFixed(1) }}
                         </span>
                       </div>
                       <div class="param-item">
-                        <span class="param-label">影响力</span>
+                        <span class="param-label">Influence</span>
                         <span class="param-value highlight">{{ agent.influence_weight?.toFixed(1) }}</span>
                       </div>
                     </div>
@@ -264,62 +264,62 @@
               </div>
             </div>
 
-            <!-- 平台配置 -->
+            <!-- Platform Config -->
             <div class="config-block">
               <div class="config-block-header">
-                <span class="config-block-title">推荐算法配置</span>
+                <span class="config-block-title">Recommendation Algorithm Config</span>
               </div>
               <div class="platforms-grid">
                 <div v-if="simulationConfig.twitter_config" class="platform-card">
                   <div class="platform-card-header">
-                    <span class="platform-name">平台 1：广场 / 信息流</span>
+                    <span class="platform-name">Platform 1: Plaza / Feed</span>
                   </div>
                   <div class="platform-params">
                     <div class="param-row">
-                      <span class="param-label">时效权重</span>
+                      <span class="param-label">Recency Weight</span>
                       <span class="param-value">{{ simulationConfig.twitter_config.recency_weight }}</span>
                     </div>
                     <div class="param-row">
-                      <span class="param-label">热度权重</span>
+                      <span class="param-label">Popularity Weight</span>
                       <span class="param-value">{{ simulationConfig.twitter_config.popularity_weight }}</span>
                     </div>
                     <div class="param-row">
-                      <span class="param-label">相关性权重</span>
+                      <span class="param-label">Relevance Weight</span>
                       <span class="param-value">{{ simulationConfig.twitter_config.relevance_weight }}</span>
                     </div>
                     <div class="param-row">
-                      <span class="param-label">病毒阈值</span>
+                      <span class="param-label">Viral Threshold</span>
                       <span class="param-value">{{ simulationConfig.twitter_config.viral_threshold }}</span>
                     </div>
                     <div class="param-row">
-                      <span class="param-label">回音室强度</span>
+                      <span class="param-label">Echo Chamber Intensity</span>
                       <span class="param-value">{{ simulationConfig.twitter_config.echo_chamber_strength }}</span>
                     </div>
                   </div>
                 </div>
                 <div v-if="simulationConfig.reddit_config" class="platform-card">
                   <div class="platform-card-header">
-                    <span class="platform-name">平台 2：话题 / 社区</span>
+                    <span class="platform-name">Platform 2: Topic / Community</span>
                   </div>
                   <div class="platform-params">
                     <div class="param-row">
-                      <span class="param-label">时效权重</span>
+                      <span class="param-label">Recency Weight</span>
                       <span class="param-value">{{ simulationConfig.reddit_config.recency_weight }}</span>
                     </div>
                     <div class="param-row">
-                      <span class="param-label">热度权重</span>
+                      <span class="param-label">Popularity Weight</span>
                       <span class="param-value">{{ simulationConfig.reddit_config.popularity_weight }}</span>
                     </div>
                     <div class="param-row">
-                      <span class="param-label">相关性权重</span>
+                      <span class="param-label">Relevance Weight</span>
                       <span class="param-value">{{ simulationConfig.reddit_config.relevance_weight }}</span>
                     </div>
                     <div class="param-row">
-                      <span class="param-label">病毒阈值</span>
+                      <span class="param-label">Viral Threshold</span>
                       <span class="param-value">{{ simulationConfig.reddit_config.viral_threshold }}</span>
                     </div>
                     <div class="param-row">
-                      <span class="param-label">回音室强度</span>
+                      <span class="param-label">Echo Chamber Intensity</span>
                       <span class="param-value">{{ simulationConfig.reddit_config.echo_chamber_strength }}</span>
                     </div>
                   </div>
@@ -327,10 +327,10 @@
               </div>
             </div>
 
-            <!-- LLM 配置推理 -->
+            <!-- LLM Config Reasoning -->
             <div v-if="simulationConfig.generation_reasoning" class="config-block">
               <div class="config-block-header">
-                <span class="config-block-title">LLM 配置推理</span>
+                <span class="config-block-title">LLM Config Reasoning</span>
               </div>
               <div class="reasoning-content">
                 <div 
@@ -346,28 +346,28 @@
         </div>
       </div>
 
-      <!-- Step 04: 初始激活编排 -->
+      <!-- Step 04: Initial Activation Orchestration -->
       <div class="step-card" :class="{ 'active': phase === 3, 'completed': phase > 3 }">
         <div class="card-header">
           <div class="step-info">
             <span class="step-num">04</span>
-            <span class="step-title">初始激活编排</span>
+            <span class="step-title">Initial Activation Orchestration</span>
           </div>
           <div class="step-status">
-            <span v-if="phase > 3" class="badge success">已完成</span>
-            <span v-else-if="phase === 3" class="badge processing">编排中</span>
-            <span v-else class="badge pending">等待</span>
+            <span v-if="phase > 3" class="badge success">Completed</span>
+            <span v-else-if="phase === 3" class="badge processing">Orchestrating</span>
+            <span v-else class="badge pending">Waiting</span>
           </div>
         </div>
 
         <div class="card-content">
           <p class="api-note">POST /api/simulation/prepare</p>
           <p class="description">
-            基于叙事方向，自动生成初始激活事件与热点话题，引导模拟世界的初始状态
+            Auto-generates initial activation events and trending topics based on narrative direction to guide the simulation's initial state
           </p>
 
           <div v-if="simulationConfig?.event_config" class="orchestration-content">
-            <!-- 叙事方向 -->
+            <!-- Narrative Direction -->
             <div class="narrative-box">
               <span class="box-label narrative-label">
                 <svg width="20" height="20" viewBox="0 0 24 24" fill="none" xmlns="http://www.w3.org/2000/svg" class="special-icon">
@@ -380,14 +380,14 @@
                     </linearGradient>
                   </defs>
                 </svg>
-                叙事引导方向
+                Narrative Direction
               </span>
               <p class="narrative-text">{{ simulationConfig.event_config.narrative_direction }}</p>
             </div>
 
-            <!-- 热点话题 -->
+            <!-- Trending Topics -->
             <div class="topics-section">
-              <span class="box-label">初始热点话题</span>
+              <span class="box-label">Initial Trending Topics</span>
               <div class="hot-topics-grid">
                 <span v-for="topic in simulationConfig.event_config.hot_topics" :key="topic" class="hot-topic-tag">
                   # {{ topic }}
@@ -395,9 +395,9 @@
               </div>
             </div>
 
-            <!-- 初始帖子流 -->
+            <!-- Initial Posts -->
             <div class="initial-posts-section">
-              <span class="box-label">初始激活序列 ({{ simulationConfig.event_config.initial_posts.length }})</span>
+              <span class="box-label">Initial Activation Sequence ({{ simulationConfig.event_config.initial_posts.length }})</span>
               <div class="posts-timeline">
                 <div v-for="(post, idx) in simulationConfig.event_config.initial_posts" :key="idx" class="timeline-item">
                   <div class="timeline-marker"></div>
@@ -418,34 +418,34 @@
         </div>
       </div>
 
-      <!-- Step 05: 准备完成 -->
+      <!-- Step 05: Ready -->
       <div class="step-card" :class="{ 'active': phase === 4 }">
         <div class="card-header">
           <div class="step-info">
             <span class="step-num">05</span>
-            <span class="step-title">准备完成</span>
+            <span class="step-title">Ready</span>
           </div>
           <div class="step-status">
-            <span v-if="phase >= 4" class="badge processing">进行中</span>
-            <span v-else class="badge pending">等待</span>
+            <span v-if="phase >= 4" class="badge processing">In Progress</span>
+            <span v-else class="badge pending">Waiting</span>
           </div>
         </div>
 
         <div class="card-content">
           <p class="api-note">POST /api/simulation/start</p>
-          <p class="description">模拟环境已准备完成，可以开始运行模拟</p>
+          <p class="description">Simulation environment is ready. You can start running the simulation.</p>
           
-          <!-- 模拟轮数配置 - 只有在配置生成完成且轮数计算出来后才显示 -->
+          <!-- Simulation rounds config - only shown after config is generated and rounds are calculated -->
           <div v-if="simulationConfig && autoGeneratedRounds" class="rounds-config-section">
             <div class="rounds-header">
               <div class="header-left">
-                <span class="section-title">模拟轮数设定</span>
-                <span class="section-desc">MiroFish 自动规划推演现实 <span class="desc-highlight">{{ simulationConfig?.time_config?.total_simulation_hours || '-' }}</span> 小时，每轮代表现实 <span class="desc-highlight">{{ simulationConfig?.time_config?.minutes_per_round || '-' }}</span> 分钟时间流逝</span>
+                <span class="section-title">Simulation Rounds Setting</span>
+                <span class="section-desc">MiroFish auto-plans to simulate <span class="desc-highlight">{{ simulationConfig?.time_config?.total_simulation_hours || '-' }}</span> hours of reality, each round represents <span class="desc-highlight">{{ simulationConfig?.time_config?.minutes_per_round || '-' }}</span> minutes of elapsed time</span>
               </div>
               <label class="switch-control">
                 <input type="checkbox" v-model="useCustomRounds">
                 <span class="switch-track"></span>
-                <span class="switch-label">自定义</span>
+                <span class="switch-label">Custom</span>
               </label>
             </div>
             
@@ -454,10 +454,10 @@
                 <div class="slider-display">
                   <div class="slider-main-value">
                     <span class="val-num">{{ customMaxRounds }}</span>
-                    <span class="val-unit">轮</span>
+                    <span class="val-unit">rounds</span>
                   </div>
                   <div class="slider-meta-info">
-                    <span>若Agent规模为100：预计耗时约 {{ Math.round(customMaxRounds * 0.6) }} 分钟</span>
+                    <span>For 100 Agents: estimated ~{{ Math.round(customMaxRounds * 0.6) }} minutes</span>
                   </div>
                 </div>
 
@@ -478,7 +478,7 @@
                       :class="{ active: customMaxRounds === 40 }"
                       @click="customMaxRounds = 40"
                       :style="{ position: 'absolute', left: `calc(${(40 - 10) / (autoGeneratedRounds - 10) * 100}% - 30px)` }"
-                    >40 (推荐)</span>
+                    >40 (recommended)</span>
                     <span>{{ autoGeneratedRounds }}</span>
                   </div>
                 </div>
@@ -488,7 +488,7 @@
                 <div class="auto-info-card">
                   <div class="auto-value">
                     <span class="val-num">{{ autoGeneratedRounds }}</span>
-                    <span class="val-unit">轮</span>
+                    <span class="val-unit">rounds</span>
                   </div>
                   <div class="auto-content">
                     <div class="auto-meta-row">
@@ -497,11 +497,11 @@
                           <circle cx="12" cy="12" r="10"></circle>
                           <polyline points="12 6 12 12 16 14"></polyline>
                         </svg>
-                        若Agent规模为100：预计耗时 {{ Math.round(autoGeneratedRounds * 0.6) }} 分钟
+                        For 100 Agents: estimated {{ Math.round(autoGeneratedRounds * 0.6) }} minutes
                       </span>
                     </div>
                     <div class="auto-desc">
-                      <p class="highlight-tip" @click="useCustomRounds = true">若首次运行，强烈建议切换至‘自定义模式’减少模拟轮数，以便快速预览效果并降低报错风险 ➝</p>
+                      <p class="highlight-tip" @click="useCustomRounds = true">For first-time runs, we strongly recommend switching to ‘Custom mode’ to reduce rounds for a quick preview and lower error risk ➝</p>
                     </div>
                   </div>
                 </div>
@@ -514,14 +514,14 @@
               class="action-btn secondary"
               @click="$emit('go-back')"
             >
-              ← 返回图谱构建
+              ← Back to Graph
             </button>
             <button 
               class="action-btn primary"
               :disabled="phase < 4"
               @click="handleStartSimulation"
             >
-              开始双世界并行模拟 ➝
+              Start Dual-World Simulation ➝
             </button>
           </div>
         </div>
@@ -544,35 +544,35 @@
         </div>
         
         <div class="modal-body">
-          <!-- 基本信息 -->
+          <!-- Basic Info -->
           <div class="modal-info-grid">
             <div class="info-item">
-              <span class="info-label">事件外显年龄</span>
-              <span class="info-value">{{ selectedProfile.age || '-' }} 岁</span>
+              <span class="info-label">Apparent Age</span>
+              <span class="info-value">{{ selectedProfile.age || '-' }}</span>
             </div>
             <div class="info-item">
-              <span class="info-label">事件外显性别</span>
-              <span class="info-value">{{ { male: '男', female: '女', other: '其他' }[selectedProfile.gender] || selectedProfile.gender }}</span>
+              <span class="info-label">Apparent Gender</span>
+              <span class="info-value">{{ { male: 'Male', female: 'Female', other: 'Other' }[selectedProfile.gender] || selectedProfile.gender }}</span>
             </div>
             <div class="info-item">
-              <span class="info-label">国家/地区</span>
+              <span class="info-label">Country/Region</span>
               <span class="info-value">{{ selectedProfile.country || '-' }}</span>
             </div>
             <div class="info-item">
-              <span class="info-label">事件外显MBTI</span>
+              <span class="info-label">Apparent MBTI</span>
               <span class="info-value mbti">{{ selectedProfile.mbti || '-' }}</span>
             </div>
           </div>
 
-          <!-- 简介 -->
+          <!-- Bio -->
           <div class="modal-section">
-            <span class="section-label">人设简介</span>
-            <p class="section-bio">{{ selectedProfile.bio || '暂无简介' }}</p>
+            <span class="section-label">Persona Bio</span>
+            <p class="section-bio">{{ selectedProfile.bio || 'No bio available' }}</p>
           </div>
 
-          <!-- 关注话题 -->
+          <!-- Interested Topics -->
           <div class="modal-section" v-if="selectedProfile.interested_topics?.length">
-            <span class="section-label">现实种子关联话题</span>
+            <span class="section-label">Reality Seed Related Topics</span>
             <div class="topics-grid">
               <span 
                 v-for="topic in selectedProfile.interested_topics" 
@@ -582,27 +582,27 @@
             </div>
           </div>
 
-          <!-- 详细人设 -->
+          <!-- Detailed Persona -->
           <div class="modal-section" v-if="selectedProfile.persona">
-            <span class="section-label">详细人设背景</span>
+            <span class="section-label">Detailed Persona Background</span>
             
-            <!-- 人设维度概览 -->
+            <!-- Persona Dimensions Overview -->
             <div class="persona-dimensions">
               <div class="dimension-card">
-                <span class="dim-title">事件全景经历</span>
-                <span class="dim-desc">在此事件中的完整行为轨迹</span>
+                <span class="dim-title">Event Panoramic Experience</span>
+                <span class="dim-desc">Complete behavioral trajectory in this event</span>
               </div>
               <div class="dimension-card">
-                <span class="dim-title">行为模式侧写</span>
-                <span class="dim-desc">经验总结与行事风格偏好</span>
+                <span class="dim-title">Behavioral Profile</span>
+                <span class="dim-desc">Experience summary and behavioral style preferences</span>
               </div>
               <div class="dimension-card">
-                <span class="dim-title">独特记忆印记</span>
-                <span class="dim-desc">基于现实种子形成的记忆</span>
+                <span class="dim-title">Unique Memory Imprint</span>
+                <span class="dim-desc">Memories formed based on reality seeds</span>
               </div>
               <div class="dimension-card">
-                <span class="dim-title">社会关系网络</span>
-                <span class="dim-desc">个体链接与交互图谱</span>
+                <span class="dim-title">Social Network</span>
+                <span class="dim-desc">Individual connections and interaction graph</span>
               </div>
             </div>
 
@@ -642,7 +642,7 @@ import {
 } from '../api/simulation'
 
 const props = defineProps({
-  simulationId: String,  // 从父组件传入
+  simulationId: String,  // passed from parent component
   projectData: Object,
   graphData: Object,
   systemLogs: Array
@@ -651,7 +651,7 @@ const props = defineProps({
 const emit = defineEmits(['go-back', 'next-step', 'add-log', 'update-status'])
 
 // State
-const phase = ref(0) // 0: 初始化, 1: 生成人设, 2: 生成配置, 3: 完成
+const phase = ref(0) // 0: init, 1: generate personas, 2: generate config, 3: completed
 const taskId = ref(null)
 const prepareProgress = ref(0)
 const currentStage = ref('')
@@ -663,43 +663,43 @@ const simulationConfig = ref(null)
 const selectedProfile = ref(null)
 const showProfilesDetail = ref(true)
 
-// 日志去重：记录上一次输出的关键信息
+// Log deduplication: track last logged key info
 let lastLoggedMessage = ''
 let lastLoggedProfileCount = 0
 let lastLoggedConfigStage = ''
 
-// 模拟轮数配置
-const useCustomRounds = ref(false) // 默认使用自动配置轮数
-const customMaxRounds = ref(40)   // 默认推荐40轮
+// Simulation rounds config
+const useCustomRounds = ref(false) // default: use auto-configured rounds
+const customMaxRounds = ref(40)   // default recommended: 40 rounds
 
 // Watch stage to update phase
 watch(currentStage, (newStage) => {
-  if (newStage === '生成Agent人设' || newStage === 'generating_profiles') {
+  if (newStage === 'Generate Agent Personas' || newStage === 'generating_profiles') {
     phase.value = 1
-  } else if (newStage === '生成模拟配置' || newStage === 'generating_config') {
+  } else if (newStage === 'Generate Simulation Config' || newStage === 'generating_config') {
     phase.value = 2
-    // 进入配置生成阶段，开始轮询配置
+    // Entering config generation phase, start polling config
     if (!configTimer) {
-      addLog('开始生成双平台模拟配置...')
+      addLog('Starting dual-platform simulation config generation...')
       startConfigPolling()
     }
-  } else if (newStage === '准备模拟脚本' || newStage === 'copying_scripts') {
-    phase.value = 2 // 仍属于配置阶段
+  } else if (newStage === 'Prepare Simulation Scripts' || newStage === 'copying_scripts') {
+    phase.value = 2 // still in config phase
   }
 })
 
-// 从配置中计算自动生成的轮数（不使用硬编码默认值）
+// Calculate auto-generated rounds from config (no hardcoded defaults)
 const autoGeneratedRounds = computed(() => {
   if (!simulationConfig.value?.time_config) {
-    return null // 配置未生成时返回 null
+    return null // return null when config not yet generated
   }
   const totalHours = simulationConfig.value.time_config.total_simulation_hours
   const minutesPerRound = simulationConfig.value.time_config.minutes_per_round
   if (!totalHours || !minutesPerRound) {
-    return null // 配置数据不完整时返回 null
+    return null // return null when config data is incomplete
   }
   const calculatedRounds = Math.floor((totalHours * 60) / minutesPerRound)
-  // 确保最大轮数不小于40（推荐值），避免滑动条范围异常
+  // Ensure max rounds is at least 40 (recommended), avoid slider range issues
   return Math.max(calculatedRounds, 40)
 })
 
@@ -716,7 +716,7 @@ const displayProfiles = computed(() => {
   return profiles.value.slice(0, 6)
 })
 
-// 根据agent_id获取对应的username
+// Get username by agent_id
 const getAgentUsername = (agentId) => {
   if (profiles.value && profiles.value.length > agentId && agentId >= 0) {
     const profile = profiles.value[agentId]
@@ -725,7 +725,7 @@ const getAgentUsername = (agentId) => {
   return `agent_${agentId}`
 }
 
-// 计算所有人设的关联话题总数
+// Calculate total related topics across all personas
 const totalTopicsCount = computed(() => {
   return profiles.value.reduce((sum, p) => {
     return sum + (p.interested_topics?.length || 0)
@@ -737,18 +737,18 @@ const addLog = (msg) => {
   emit('add-log', msg)
 }
 
-// 处理开始模拟按钮点击
+// Handle start simulation button click
 const handleStartSimulation = () => {
-  // 构建传递给父组件的参数
+  // Build params to pass to parent component
   const params = {}
   
   if (useCustomRounds.value) {
-    // 用户自定义轮数，传递 max_rounds 参数
+    // User custom rounds, pass max_rounds param
     params.maxRounds = customMaxRounds.value
-    addLog(`开始模拟，自定义轮数: ${customMaxRounds.value} 轮`)
+    addLog(`Starting simulation, custom rounds: ${customMaxRounds.value}`)
   } else {
-    // 用户选择保持自动生成的轮数，不传递 max_rounds 参数
-    addLog(`开始模拟，使用自动配置轮数: ${autoGeneratedRounds.value} 轮`)
+    // User chose auto-generated rounds, don't pass max_rounds param
+    addLog(`Starting simulation, using auto-configured rounds: ${autoGeneratedRounds.value}`)
   }
   
   emit('next-step', params)
@@ -765,18 +765,18 @@ const selectProfile = (profile) => {
   selectedProfile.value = profile
 }
 
-// 自动开始准备模拟
+// Auto-start simulation preparation
 const startPrepareSimulation = async () => {
   if (!props.simulationId) {
-    addLog('错误：缺少 simulationId')
+    addLog('Error: missing simulationId')
     emit('update-status', 'error')
     return
   }
   
-  // 标记第一步完成，开始第二步
+  // Mark step 1 complete, start step 2
   phase.value = 1
-  addLog(`模拟实例已创建: ${props.simulationId}`)
-  addLog('正在准备模拟环境...')
+  addLog(`Simulation instance created: ${props.simulationId}`)
+  addLog('Preparing simulation environment...')
   emit('update-status', 'processing')
   
   try {
@@ -788,35 +788,35 @@ const startPrepareSimulation = async () => {
     
     if (res.success && res.data) {
       if (res.data.already_prepared) {
-        addLog('检测到已有完成的准备工作，直接使用')
+        addLog('Detected existing preparation data, using directly')
         await loadPreparedData()
         return
       }
       
       taskId.value = res.data.task_id
-      addLog(`准备任务已启动`)
+      addLog(`Preparation task started`)
       addLog(`  └─ Task ID: ${res.data.task_id}`)
       
-      // 立即设置预期Agent总数（从prepare接口返回值获取）
+      // Set expected agent total immediately (from prepare API response)
       if (res.data.expected_entities_count) {
         expectedTotal.value = res.data.expected_entities_count
-        addLog(`从Zep图谱读取到 ${res.data.expected_entities_count} 个实体`)
+        addLog(`Read ${res.data.expected_entities_count} entities from Zep graph`)
         if (res.data.entity_types && res.data.entity_types.length > 0) {
-          addLog(`  └─ 实体类型: ${res.data.entity_types.join(', ')}`)
+          addLog(`  └─ Entity types: ${res.data.entity_types.join(', ')}`)
         }
       }
       
-      addLog('开始轮询准备进度...')
-      // 开始轮询进度
+      addLog('Starting progress polling...')
+      // Start polling progress
       startPolling()
-      // 开始实时获取 Profiles
+      // Start realtime profiles fetching
       startProfilesPolling()
     } else {
-      addLog(`准备失败: ${res.error || '未知错误'}`)
+      addLog(`Preparation failed: ${res.error || 'Unknown error'}`)
       emit('update-status', 'error')
     }
   } catch (err) {
-    addLog(`准备异常: ${err.message}`)
+    addLog(`Preparation error: ${err.message}`)
     emit('update-status', 'error')
   }
 }
@@ -855,15 +855,15 @@ const pollPrepareStatus = async () => {
     if (res.success && res.data) {
       const data = res.data
       
-      // 更新进度
+      // Update progress
       prepareProgress.value = data.progress || 0
       progressMessage.value = data.message || ''
       
-      // 解析阶段信息并输出详细日志
+      // Parse stage info and output detailed logs
       if (data.progress_detail) {
         currentStage.value = data.progress_detail.current_stage_name || ''
         
-        // 输出详细进度日志（避免重复）
+        // Output detailed progress logs (deduplicated)
         const detail = data.progress_detail
         const logKey = `${detail.current_stage}-${detail.current_item}-${detail.total_items}`
         if (logKey !== lastLoggedMessage && detail.item_description) {
@@ -876,32 +876,32 @@ const pollPrepareStatus = async () => {
           }
         }
       } else if (data.message) {
-        // 从消息中提取阶段
+        // Extract stage from message
         const match = data.message.match(/\[(\d+)\/(\d+)\]\s*([^:]+)/)
         if (match) {
           currentStage.value = match[3].trim()
         }
-        // 输出消息日志（避免重复）
+        // Output message logs (deduplicated)
         if (data.message !== lastLoggedMessage) {
           lastLoggedMessage = data.message
           addLog(data.message)
         }
       }
       
-      // 检查是否完成
+      // Check if completed
       if (data.status === 'completed' || data.status === 'ready' || data.already_prepared) {
-        addLog('✓ 准备工作已完成')
+        addLog('✓ Preparation completed')
         stopPolling()
         stopProfilesPolling()
         await loadPreparedData()
       } else if (data.status === 'failed') {
-        addLog(`✗ 准备失败: ${data.error || '未知错误'}`)
+        addLog(`✗ Preparation failed: ${data.error || 'Unknown error'}`)
         stopPolling()
         stopProfilesPolling()
       }
     }
   } catch (err) {
-    console.warn('轮询状态失败:', err)
+    console.warn('Status polling failed:', err)
   }
 }
 
@@ -914,19 +914,19 @@ const fetchProfilesRealtime = async () => {
     if (res.success && res.data) {
       const prevCount = profiles.value.length
       profiles.value = res.data.profiles || []
-      // 只有当 API 返回有效值时才更新，避免覆盖已有的有效值
+      // Only update when API returns a valid value, avoid overwriting existing valid values
       if (res.data.total_expected) {
         expectedTotal.value = res.data.total_expected
       }
       
-      // 提取实体类型
+      // Extract entity types
       const types = new Set()
       profiles.value.forEach(p => {
         if (p.entity_type) types.add(p.entity_type)
       })
       entityTypes.value = Array.from(types)
       
-      // 输出 Profile 生成进度日志（仅当数量变化时）
+      // Output profile generation progress logs (only when count changes)
       const currentCount = profiles.value.length
       if (currentCount > 0 && currentCount !== lastLoggedProfileCount) {
         lastLoggedProfileCount = currentCount
@@ -934,22 +934,22 @@ const fetchProfilesRealtime = async () => {
         const latestProfile = profiles.value[currentCount - 1]
         const profileName = latestProfile?.name || latestProfile?.username || `Agent_${currentCount}`
         if (currentCount === 1) {
-          addLog(`开始生成Agent人设...`)
+          addLog(`Starting Agent persona generation...`)
         }
-        addLog(`→ Agent人设 ${currentCount}/${total}: ${profileName} (${latestProfile?.profession || '未知职业'})`)
+        addLog(`→ Agent persona ${currentCount}/${total}: ${profileName} (${latestProfile?.profession || 'Unknown Occupation'})`)
         
-        // 如果全部生成完成
+        // If all generated
         if (expectedTotal.value && currentCount >= expectedTotal.value) {
-          addLog(`✓ 全部 ${currentCount} 个Agent人设生成完成`)
+          addLog(`✓ All ${currentCount} Agent personas generated`)
         }
       }
     }
   } catch (err) {
-    console.warn('获取 Profiles 失败:', err)
+    console.warn('Failed to fetch Profiles:', err)
   }
 }
 
-// 配置轮询
+// Config polling
 const startConfigPolling = () => {
   configTimer = setInterval(fetchConfigRealtime, 2000)
 }
@@ -970,87 +970,87 @@ const fetchConfigRealtime = async () => {
     if (res.success && res.data) {
       const data = res.data
       
-      // 输出配置生成阶段日志（避免重复）
+      // Output config generation stage logs (deduplicated)
       if (data.generation_stage && data.generation_stage !== lastLoggedConfigStage) {
         lastLoggedConfigStage = data.generation_stage
         if (data.generation_stage === 'generating_profiles') {
-          addLog('正在生成Agent人设配置...')
+          addLog('Generating Agent persona config...')
         } else if (data.generation_stage === 'generating_config') {
-          addLog('正在调用LLM生成模拟配置参数...')
+          addLog('Calling LLM to generate simulation config params...')
         }
       }
       
-      // 如果配置已生成
+      // If config has been generated
       if (data.config_generated && data.config) {
         simulationConfig.value = data.config
-        addLog('✓ 模拟配置生成完成')
+        addLog('✓ Simulation config generated')
         
-        // 显示详细配置摘要
+        // Show detailed config summary
         if (data.summary) {
-          addLog(`  ├─ Agent数量: ${data.summary.total_agents}个`)
-          addLog(`  ├─ 模拟时长: ${data.summary.simulation_hours}小时`)
-          addLog(`  ├─ 初始帖子: ${data.summary.initial_posts_count}条`)
-          addLog(`  ├─ 热点话题: ${data.summary.hot_topics_count}个`)
-          addLog(`  └─ 平台配置: Twitter ${data.summary.has_twitter_config ? '✓' : '✗'}, Reddit ${data.summary.has_reddit_config ? '✓' : '✗'}`)
+          addLog(`  ├─ Agents: ${data.summary.total_agents}`)
+          addLog(`  ├─ Duration: ${data.summary.simulation_hours} hours`)
+          addLog(`  ├─ Initial posts: ${data.summary.initial_posts_count}`)
+          addLog(`  ├─ Trending topics: ${data.summary.hot_topics_count}`)
+          addLog(`  └─ Platform config: Twitter ${data.summary.has_twitter_config ? '✓' : '✗'}, Reddit ${data.summary.has_reddit_config ? '✓' : '✗'}`)
         }
         
-        // 显示时间配置详情
+        // Show time config details
         if (data.config.time_config) {
           const tc = data.config.time_config
-          addLog(`时间配置: 每轮${tc.minutes_per_round}分钟, 共${Math.floor((tc.total_simulation_hours * 60) / tc.minutes_per_round)}轮`)
+          addLog(`Time config: ${tc.minutes_per_round} min/round, ${Math.floor((tc.total_simulation_hours * 60) / tc.minutes_per_round)} total rounds`)
         }
         
-        // 显示事件配置
+        // Show event config
         if (data.config.event_config?.narrative_direction) {
           const narrative = data.config.event_config.narrative_direction
-          addLog(`叙事方向: ${narrative.length > 50 ? narrative.substring(0, 50) + '...' : narrative}`)
+          addLog(`Narrative direction: ${narrative.length > 50 ? narrative.substring(0, 50) + '...' : narrative}`)
         }
         
         stopConfigPolling()
         phase.value = 4
-        addLog('✓ 环境搭建完成，可以开始模拟')
+        addLog('✓ Environment setup complete, ready to simulate')
         emit('update-status', 'completed')
       }
     }
   } catch (err) {
-    console.warn('获取 Config 失败:', err)
+    console.warn('Failed to fetch Config:', err)
   }
 }
 
 const loadPreparedData = async () => {
   phase.value = 2
-  addLog('正在加载已有配置数据...')
+  addLog('Loading existing config data...')
 
-  // 最后获取一次 Profiles
+  // Final fetch of Profiles
   await fetchProfilesRealtime()
-  addLog(`已加载 ${profiles.value.length} 个Agent人设`)
+  addLog(`Loaded ${profiles.value.length} Agent personas`)
 
-  // 获取配置（使用实时接口）
+  // Fetch config (using realtime API)
   try {
     const res = await getSimulationConfigRealtime(props.simulationId)
     if (res.success && res.data) {
       if (res.data.config_generated && res.data.config) {
         simulationConfig.value = res.data.config
-        addLog('✓ 模拟配置加载成功')
+        addLog('✓ Simulation config loaded successfully')
         
-        // 显示详细配置摘要
+        // Show detailed config summary
         if (res.data.summary) {
-          addLog(`  ├─ Agent数量: ${res.data.summary.total_agents}个`)
-          addLog(`  ├─ 模拟时长: ${res.data.summary.simulation_hours}小时`)
-          addLog(`  └─ 初始帖子: ${res.data.summary.initial_posts_count}条`)
+          addLog(`  ├─ Agents: ${res.data.summary.total_agents}`)
+          addLog(`  ├─ Duration: ${res.data.summary.simulation_hours} hours`)
+          addLog(`  └─ Initial posts: ${res.data.summary.initial_posts_count}`)
         }
         
-        addLog('✓ 环境搭建完成，可以开始模拟')
+        addLog('✓ Environment setup complete, ready to simulate')
         phase.value = 4
         emit('update-status', 'completed')
       } else {
-        // 配置尚未生成，开始轮询
-        addLog('配置生成中，开始轮询等待...')
+        // Config not yet generated, start polling
+        addLog('Config generating, polling...')
         startConfigPolling()
       }
     }
   } catch (err) {
-    addLog(`加载配置失败: ${err.message}`)
+    addLog(`Failed to load config: ${err.message}`)
     emit('update-status', 'error')
   }
 }
@@ -1066,9 +1066,9 @@ watch(() => props.systemLogs?.length, () => {
 })
 
 onMounted(() => {
-  // 自动开始准备流程
+  // Auto-start preparation flow
   if (props.simulationId) {
-    addLog('Step2 环境搭建初始化')
+    addLog('Step2 Environment setup initializing')
     startPrepareSimulation()
   }
 })
@@ -1902,7 +1902,7 @@ onUnmounted(() => {
   flex: 1;
 }
 
-/* 基本信息网格 */
+/* Basic Info Grid */
 .modal-info-grid {
   display: grid;
   grid-template-columns: repeat(2, 1fr);
@@ -1938,7 +1938,7 @@ onUnmounted(() => {
   color: #FF5722;
 }
 
-/* 模块区域 */
+/* Section Areas */
 .modal-section {
   margin-bottom: 28px;
 }
@@ -1964,7 +1964,7 @@ onUnmounted(() => {
   border-left: 3px solid #E0E0E0;
 }
 
-/* 话题标签 */
+/* Topic Tags */
 .topics-grid {
   display: flex;
   flex-wrap: wrap;
@@ -1986,7 +1986,7 @@ onUnmounted(() => {
   color: #0D47A1;
 }
 
-/* 详细人设 */
+/* Detailed Persona */
 .persona-dimensions {
   display: grid;
   grid-template-columns: repeat(2, 1fr);
@@ -2272,7 +2272,7 @@ onUnmounted(() => {
   margin: 0;
 }
 
-/* 模拟轮数配置样式 */
+/* Simulation Rounds Config Styles */
 .rounds-config-section {
   margin: 24px 0;
   padding-top: 24px;
diff --git a/frontend/src/components/Step3Simulation.vue b/frontend/src/components/Step3Simulation.vue
index 74d0e1e7b..7f9e915d2 100644
--- a/frontend/src/components/Step3Simulation.vue
+++ b/frontend/src/components/Step3Simulation.vue
@@ -3,7 +3,7 @@
     <!-- Top Control Bar -->
     <div class="control-bar">
       <div class="status-group">
-        <!-- Twitter 平台进度 -->
+        <!-- Twitter platform progress -->
         <div class="platform-status twitter" :class="{ active: runStatus.twitter_running, completed: runStatus.twitter_completed }">
           <div class="platform-header">
             <svg class="platform-icon" viewBox="0 0 24 24" width="14" height="14" fill="none" stroke="currentColor" stroke-width="2">
@@ -30,7 +30,7 @@
               <span class="stat-value mono">{{ runStatus.twitter_actions_count || 0 }}</span>
             </span>
           </div>
-          <!-- 可用动作提示 -->
+          <!-- Available actions tooltip -->
           <div class="actions-tooltip">
             <div class="tooltip-title">Available Actions</div>
             <div class="tooltip-actions">
@@ -44,7 +44,7 @@
           </div>
         </div>
         
-        <!-- Reddit 平台进度 -->
+        <!-- Reddit platform progress -->
         <div class="platform-status reddit" :class="{ active: runStatus.reddit_running, completed: runStatus.reddit_completed }">
           <div class="platform-header">
             <svg class="platform-icon" viewBox="0 0 24 24" width="14" height="14" fill="none" stroke="currentColor" stroke-width="2">
@@ -71,7 +71,7 @@
               <span class="stat-value mono">{{ runStatus.reddit_actions_count || 0 }}</span>
             </span>
           </div>
-          <!-- 可用动作提示 -->
+          <!-- Available actions tooltip -->
           <div class="actions-tooltip">
             <div class="tooltip-title">Available Actions</div>
             <div class="tooltip-actions">
@@ -97,7 +97,7 @@
           @click="handleNextStep"
         >
           <span v-if="isGeneratingReport" class="loading-spinner-small"></span>
-          {{ isGeneratingReport ? '启动中...' : '开始生成结果报告' }} 
+          {{ isGeneratingReport ? 'Starting...' : 'Start Generating Report' }} 
           <span v-if="!isGeneratingReport" class="arrow-icon">→</span>
         </button>
       </div>
@@ -157,12 +157,12 @@
               </div>
               
               <div class="card-body">
-                <!-- CREATE_POST: 发布帖子 -->
+                <!-- CREATE_POST: Create post -->
                 <div v-if="action.action_type === 'CREATE_POST' && action.action_args?.content" class="content-text main-text">
                   {{ action.action_args.content }}
                 </div>
 
-                <!-- QUOTE_POST: 引用帖子 -->
+                <!-- QUOTE_POST: Quote post -->
                 <template v-if="action.action_type === 'QUOTE_POST'">
                   <div v-if="action.action_args?.quote_content" class="content-text">
                     {{ action.action_args.quote_content }}
@@ -178,7 +178,7 @@
                   </div>
                 </template>
 
-                <!-- REPOST: 转发帖子 -->
+                <!-- REPOST: Repost -->
                 <template v-if="action.action_type === 'REPOST'">
                   <div class="repost-info">
                     <svg class="icon-small" viewBox="0 0 24 24" width="14" height="14" fill="none" stroke="currentColor" stroke-width="2"><polyline points="17 1 21 5 17 9"></polyline><path d="M3 11V9a4 4 0 0 1 4-4h14"></path><polyline points="7 23 3 19 7 15"></polyline><path d="M21 13v2a4 4 0 0 1-4 4H3"></path></svg>
@@ -189,7 +189,7 @@
                   </div>
                 </template>
 
-                <!-- LIKE_POST: 点赞帖子 -->
+                <!-- LIKE_POST: Like post -->
                 <template v-if="action.action_type === 'LIKE_POST'">
                   <div class="like-info">
                     <svg class="icon-small filled" viewBox="0 0 24 24" width="14" height="14" fill="currentColor"><path d="M20.84 4.61a5.5 5.5 0 0 0-7.78 0L12 5.67l-1.06-1.06a5.5 5.5 0 0 0-7.78 7.78l1.06 1.06L12 21.23l7.78-7.78 1.06-1.06a5.5 5.5 0 0 0 0-7.78z"></path></svg>
@@ -200,7 +200,7 @@
                   </div>
                 </template>
 
-                <!-- CREATE_COMMENT: 发表评论 -->
+                <!-- CREATE_COMMENT: Create comment -->
                 <template v-if="action.action_type === 'CREATE_COMMENT'">
                   <div v-if="action.action_args?.content" class="content-text">
                     {{ action.action_args.content }}
@@ -211,7 +211,7 @@
                   </div>
                 </template>
 
-                <!-- SEARCH_POSTS: 搜索帖子 -->
+                <!-- SEARCH_POSTS: Search posts -->
                 <template v-if="action.action_type === 'SEARCH_POSTS'">
                   <div class="search-info">
                     <svg class="icon-small" viewBox="0 0 24 24" width="14" height="14" fill="none" stroke="currentColor" stroke-width="2"><circle cx="11" cy="11" r="8"></circle><line x1="21" y1="21" x2="16.65" y2="16.65"></line></svg>
@@ -220,7 +220,7 @@
                   </div>
                 </template>
 
-                <!-- FOLLOW: 关注用户 -->
+                <!-- FOLLOW: Follow user -->
                 <template v-if="action.action_type === 'FOLLOW'">
                   <div class="follow-info">
                     <svg class="icon-small" viewBox="0 0 24 24" width="14" height="14" fill="none" stroke="currentColor" stroke-width="2"><path d="M16 21v-2a4 4 0 0 0-4-4H5a4 4 0 0 0-4 4v2"></path><circle cx="8.5" cy="7" r="4"></circle><line x1="20" y1="8" x2="20" y2="14"></line><line x1="23" y1="11" x2="17" y2="11"></line></svg>
@@ -240,7 +240,7 @@
                   </div>
                 </template>
 
-                <!-- DO_NOTHING: 无操作（静默） -->
+                <!-- DO_NOTHING: No action (idle) -->
                 <template v-if="action.action_type === 'DO_NOTHING'">
                   <div class="idle-info">
                     <svg class="icon-small" viewBox="0 0 24 24" width="14" height="14" fill="none" stroke="currentColor" stroke-width="2"><circle cx="12" cy="12" r="10"></circle><line x1="12" y1="8" x2="12" y2="12"></line><line x1="12" y1="16" x2="12.01" y2="16"></line></svg>
@@ -248,7 +248,7 @@
                   </div>
                 </template>
 
-                <!-- 通用回退：未知类型或有 content 但未被上述处理 -->
+                <!-- Generic fallback: unknown type or has content not handled above -->
                 <div v-if="!['CREATE_POST', 'QUOTE_POST', 'REPOST', 'LIKE_POST', 'CREATE_COMMENT', 'SEARCH_POSTS', 'FOLLOW', 'UPVOTE_POST', 'DOWNVOTE_POST', 'DO_NOTHING'].includes(action.action_type) && action.action_args?.content" class="content-text">
                   {{ action.action_args.content }}
                 </div>
@@ -298,10 +298,10 @@ import { generateReport } from '../api/report'
 
 const props = defineProps({
   simulationId: String,
-  maxRounds: Number, // 从Step2传入的最大轮数
+  maxRounds: Number, // Max rounds passed from Step2
   minutesPerRound: {
     type: Number,
-    default: 30 // 默认每轮30分钟
+    default: 30 // Default 30 minutes per round
   },
   projectData: Object,
   graphData: Object,
@@ -314,22 +314,22 @@ const router = useRouter()
 
 // State
 const isGeneratingReport = ref(false)
-const phase = ref(0) // 0: 未开始, 1: 运行中, 2: 已完成
+const phase = ref(0) // 0: Not started, 1: Running, 2: Completed
 const isStarting = ref(false)
 const isStopping = ref(false)
 const startError = ref(null)
 const runStatus = ref({})
-const allActions = ref([]) // 所有动作（增量累积）
-const actionIds = ref(new Set()) // 用于去重的动作ID集合
+const allActions = ref([]) // All actions (incremental accumulation)
+const actionIds = ref(new Set()) // Set of action IDs for deduplication
 const scrollContainer = ref(null)
 
 // Computed
-// 按时间顺序显示动作（最新的在最后面，即底部）
+// Display actions in chronological order (newest at the bottom)
 const chronologicalActions = computed(() => {
   return allActions.value
 })
 
-// 各平台动作计数
+// Per-platform action counts
 const twitterActionsCount = computed(() => {
   return allActions.value.filter(a => a.platform === 'twitter').length
 })
@@ -338,7 +338,7 @@ const redditActionsCount = computed(() => {
   return allActions.value.filter(a => a.platform === 'reddit').length
 })
 
-// 格式化模拟流逝时间（根据轮次和每轮分钟数计算）
+// Format simulated elapsed time (calculated from rounds and minutes per round)
 const formatElapsedTime = (currentRound) => {
   if (!currentRound || currentRound <= 0) return '0h 0m'
   const totalMinutes = currentRound * props.minutesPerRound
@@ -347,12 +347,12 @@ const formatElapsedTime = (currentRound) => {
   return `${hours}h ${minutes}m`
 }
 
-// Twitter平台的模拟流逝时间
+// Twitter platform simulated elapsed time
 const twitterElapsedTime = computed(() => {
   return formatElapsedTime(runStatus.value.twitter_current_round || 0)
 })
 
-// Reddit平台的模拟流逝时间
+// Reddit platform simulated elapsed time
 const redditElapsedTime = computed(() => {
   return formatElapsedTime(runStatus.value.reddit_current_round || 0)
 })
@@ -362,7 +362,7 @@ const addLog = (msg) => {
   emit('add-log', msg)
 }
 
-// 重置所有状态（用于重新启动模拟）
+// Reset all state (for restarting simulation)
 const resetAllState = () => {
   phase.value = 0
   runStatus.value = {}
@@ -373,46 +373,46 @@ const resetAllState = () => {
   startError.value = null
   isStarting.value = false
   isStopping.value = false
-  stopPolling()  // 停止之前可能存在的轮询
+  stopPolling()  // Stop any existing polling
 }
 
-// 启动模拟
+// Start simulation
 const doStartSimulation = async () => {
   if (!props.simulationId) {
-    addLog('错误：缺少 simulationId')
+    addLog('Error: Missing simulationId')
     return
   }
   
-  // 先重置所有状态，确保不会受到上一次模拟的影响
+  // Reset all state first to avoid interference from previous simulation
   resetAllState()
   
   isStarting.value = true
   startError.value = null
-  addLog('正在启动双平台并行模拟...')
+  addLog('Starting dual-platform parallel simulation...')
   emit('update-status', 'processing')
   
   try {
     const params = {
       simulation_id: props.simulationId,
       platform: 'parallel',
-      force: true,  // 强制重新开始
-      enable_graph_memory_update: true  // 开启动态图谱更新
+      force: true,  // Force restart
+      enable_graph_memory_update: true  // Enable dynamic graph memory update
     }
     
     if (props.maxRounds) {
       params.max_rounds = props.maxRounds
-      addLog(`设置最大模拟轮数: ${props.maxRounds}`)
+      addLog(`Max simulation rounds set to: ${props.maxRounds}`)
     }
     
-    addLog('已开启动态图谱更新模式')
+    addLog('Dynamic graph memory update mode enabled')
     
     const res = await startSimulation(params)
     
     if (res.success && res.data) {
       if (res.data.force_restarted) {
-        addLog('✓ 已清理旧的模拟日志，重新开始模拟')
+        addLog('✓ Old simulation logs cleared, restarting simulation')
       }
-      addLog('✓ 模拟引擎启动成功')
+      addLog('✓ Simulation engine started successfully')
       addLog(`  ├─ PID: ${res.data.process_pid || '-'}`)
       
       phase.value = 1
@@ -421,45 +421,45 @@ const doStartSimulation = async () => {
       startStatusPolling()
       startDetailPolling()
     } else {
-      startError.value = res.error || '启动失败'
-      addLog(`✗ 启动失败: ${res.error || '未知错误'}`)
+      startError.value = res.error || 'Start failed'
+      addLog(`✗ Start failed: ${res.error || 'Unknown error'}`)
       emit('update-status', 'error')
     }
   } catch (err) {
     startError.value = err.message
-    addLog(`✗ 启动异常: ${err.message}`)
+    addLog(`✗ Start exception: ${err.message}`)
     emit('update-status', 'error')
   } finally {
     isStarting.value = false
   }
 }
 
-// 停止模拟
+// Stop simulation
 const handleStopSimulation = async () => {
   if (!props.simulationId) return
   
   isStopping.value = true
-  addLog('正在停止模拟...')
+  addLog('Stopping simulation...')
   
   try {
     const res = await stopSimulation({ simulation_id: props.simulationId })
     
     if (res.success) {
-      addLog('✓ 模拟已停止')
+      addLog('✓ Simulation stopped')
       phase.value = 2
       stopPolling()
       emit('update-status', 'completed')
     } else {
-      addLog(`停止失败: ${res.error || '未知错误'}`)
+      addLog(`Stop failed: ${res.error || 'Unknown error'}`)
     }
   } catch (err) {
-    addLog(`停止异常: ${err.message}`)
+    addLog(`Stop exception: ${err.message}`)
   } finally {
     isStopping.value = false
   }
 }
 
-// 轮询状态
+// Poll status
 let statusTimer = null
 let detailTimer = null
 
@@ -482,7 +482,7 @@ const stopPolling = () => {
   }
 }
 
-// 追踪各平台的上一次轮次，用于检测变化并输出日志
+// Track previous round per platform to detect changes and output logs
 const prevTwitterRound = ref(0)
 const prevRedditRound = ref(0)
 
@@ -497,7 +497,7 @@ const fetchRunStatus = async () => {
       
       runStatus.value = data
       
-      // 分别检测各平台的轮次变化并输出日志
+      // Detect round changes per platform and output logs
       if (data.twitter_current_round > prevTwitterRound.value) {
         addLog(`[Plaza] R${data.twitter_current_round}/${data.total_rounds} | T:${data.twitter_simulated_hours || 0}h | A:${data.twitter_actions_count}`)
         prevTwitterRound.value = data.twitter_current_round
@@ -508,46 +508,46 @@ const fetchRunStatus = async () => {
         prevRedditRound.value = data.reddit_current_round
       }
       
-      // 检测模拟是否已完成（通过 runner_status 或平台完成状态判断）
+      // Check if simulation is completed (via runner_status or platform completion status)
       const isCompleted = data.runner_status === 'completed' || data.runner_status === 'stopped'
       
-      // 额外检查：如果后端还没来得及更新 runner_status，但平台已经报告完成
-      // 通过检测 twitter_completed 和 reddit_completed 状态判断
+      // Additional check: if backend hasn't updated runner_status yet but platforms report completion
+      // Detect via twitter_completed and reddit_completed status
       const platformsCompleted = checkPlatformsCompleted(data)
       
       if (isCompleted || platformsCompleted) {
         if (platformsCompleted && !isCompleted) {
-          addLog('✓ 检测到所有平台模拟已结束')
+          addLog('✓ All platform simulations detected as completed')
         }
-        addLog('✓ 模拟已完成')
+        addLog('✓ Simulation completed')
         phase.value = 2
         stopPolling()
         emit('update-status', 'completed')
       }
     }
   } catch (err) {
-    console.warn('获取运行状态失败:', err)
+    console.warn('Failed to fetch run status:', err)
   }
 }
 
-// 检查所有启用的平台是否已完成
+// Check if all enabled platforms have completed
 const checkPlatformsCompleted = (data) => {
-  // 如果没有任何平台数据，返回 false
+  // If no platform data, return false
   if (!data) return false
   
-  // 检查各平台的完成状态
+  // Check completion status per platform
   const twitterCompleted = data.twitter_completed === true
   const redditCompleted = data.reddit_completed === true
   
-  // 如果至少有一个平台完成了，检查是否所有启用的平台都完成了
-  // 通过 actions_count 判断平台是否被启用（如果 count > 0 或 running 曾为 true）
+  // If at least one platform is done, check if all enabled platforms are completed
+  // Determine if a platform is enabled via actions_count (count > 0 or running was true)
   const twitterEnabled = (data.twitter_actions_count > 0) || data.twitter_running || twitterCompleted
   const redditEnabled = (data.reddit_actions_count > 0) || data.reddit_running || redditCompleted
   
-  // 如果没有任何平台被启用，返回 false
+  // If no platform is enabled, return false
   if (!twitterEnabled && !redditEnabled) return false
   
-  // 检查所有启用的平台是否都已完成
+  // Check if all enabled platforms have completed
   if (twitterEnabled && !twitterCompleted) return false
   if (redditEnabled && !redditCompleted) return false
   
@@ -561,13 +561,13 @@ const fetchRunStatusDetail = async () => {
     const res = await getRunStatusDetail(props.simulationId)
     
     if (res.success && res.data) {
-      // 使用 all_actions 获取完整的动作列表
+      // Use all_actions to get the full action list
       const serverActions = res.data.all_actions || []
       
-      // 增量添加新动作（去重）
+      // Incrementally add new actions (deduplicated)
       let newActionsAdded = 0
       serverActions.forEach(action => {
-        // 生成唯一ID
+        // Generate unique ID
         const actionId = action.id || `${action.timestamp}-${action.platform}-${action.agent_id}-${action.action_type}`
         
         if (!actionIds.value.has(actionId)) {
@@ -580,11 +580,11 @@ const fetchRunStatusDetail = async () => {
         }
       })
       
-      // 不自动滚动，让用户自由查看时间轴
-      // 新动作会在底部追加
+      // No auto-scroll, let users freely browse the timeline
+      // New actions are appended at the bottom
     }
   } catch (err) {
-    console.warn('获取详细状态失败:', err)
+    console.warn('Failed to fetch detailed status:', err)
   }
 }
 
@@ -640,17 +640,17 @@ const formatActionTime = (timestamp) => {
 
 const handleNextStep = async () => {
   if (!props.simulationId) {
-    addLog('错误：缺少 simulationId')
+    addLog('Error: Missing simulationId')
     return
   }
   
   if (isGeneratingReport.value) {
-    addLog('报告生成请求已发送，请稍候...')
+    addLog('Report generation request already sent, please wait...')
     return
   }
   
   isGeneratingReport.value = true
-  addLog('正在启动报告生成...')
+  addLog('Starting report generation...')
   
   try {
     const res = await generateReport({
@@ -660,16 +660,16 @@ const handleNextStep = async () => {
     
     if (res.success && res.data) {
       const reportId = res.data.report_id
-      addLog(`✓ 报告生成任务已启动: ${reportId}`)
+      addLog(`✓ Report generation task started: ${reportId}`)
       
-      // 跳转到报告页面
+      // Navigate to report page
       router.push({ name: 'Report', params: { reportId } })
     } else {
-      addLog(`✗ 启动报告生成失败: ${res.error || '未知错误'}`)
+      addLog(`✗ Failed to start report generation: ${res.error || 'Unknown error'}`)
       isGeneratingReport.value = false
     }
   } catch (err) {
-    addLog(`✗ 启动报告生成异常: ${err.message}`)
+    addLog(`✗ Report generation exception: ${err.message}`)
     isGeneratingReport.value = false
   }
 }
@@ -685,7 +685,7 @@ watch(() => props.systemLogs?.length, () => {
 })
 
 onMounted(() => {
-  addLog('Step3 模拟运行初始化')
+  addLog('Step3 simulation initialization')
   if (props.simulationId) {
     doStartSimulation()
   }
diff --git a/frontend/src/components/Step4Report.vue b/frontend/src/components/Step4Report.vue
index 22f2bdcfd..6d37c99ab 100644
--- a/frontend/src/components/Step4Report.vue
+++ b/frontend/src/components/Step4Report.vue
@@ -58,7 +58,7 @@
                       <path d="M12 2a10 10 0 0 1 10 10" stroke-width="4" stroke="#4B5563" stroke-linecap="round"></path>
                     </svg>
                   </div>
-                  <span class="loading-text">正在生成{{ section.title }}...</span>
+                  <span class="loading-text">Generating {{ section.title }}...</span>
                 </div>
               </div>
             </div>
@@ -127,9 +127,9 @@
             </div>
           </div>
 
-          <!-- Next Step Button - 在完成后显示 -->
+          <!-- Next Step Button - Show after completion -->
           <button v-if="isComplete" class="next-step-btn" @click="goToInteraction">
-            <span>进入深度互动</span>
+            <span>Enter Deep Interaction</span>
             <svg viewBox="0 0 24 24" width="16" height="16" fill="none" stroke="currentColor" stroke-width="2">
               <line x1="5" y1="12" x2="19" y2="12"></line>
               <polyline points="12 5 19 12 12 19"></polyline>
@@ -194,7 +194,7 @@
                     </div>
                   </template>
                   
-                  <!-- Section Content Generated (内容生成完成，但整个章节可能还没完成) -->
+                  <!-- Section Content Generated (content generated, but section may not be fully complete) -->
                   <template v-if="log.action === 'section_content'">
                     <div class="section-tag content-ready">
                       <svg viewBox="0 0 24 24" width="14" height="14" fill="none" stroke="currentColor" stroke-width="2">
@@ -205,7 +205,7 @@
                     </div>
                   </template>
 
-                  <!-- Section Complete (章节生成完成) -->
+                  <!-- Section Complete (section generation complete) -->
                   <template v-if="log.action === 'section_complete'">
                     <div class="section-tag completed">
                       <svg viewBox="0 0 24 24" width="14" height="14" fill="none" stroke="currentColor" stroke-width="2">
@@ -315,7 +315,7 @@
                         Final: {{ log.details?.has_final_answer ? 'Yes' : 'No' }}
                       </span>
                     </div>
-                    <!-- 当是最终答案时，显示特殊提示 -->
+                    <!-- Show special hint when it's the final answer -->
                     <div v-if="log.details?.has_final_answer" class="final-answer-hint">
                       <svg viewBox="0 0 24 24" width="14" height="14" fill="none" stroke="currentColor" stroke-width="2">
                         <polyline points="20 6 9 17 4 12"></polyline>
@@ -431,22 +431,22 @@ const showRawResult = reactive({})
 
 // Toggle functions
 const toggleRawResult = (timestamp, event) => {
-  // 保存按钮相对于视口的位置
+  // Save button position relative to viewport
   const button = event?.target
   const buttonRect = button?.getBoundingClientRect()
   const buttonTopBeforeToggle = buttonRect?.top
   
-  // 切换状态
+  // Toggle state
   showRawResult[timestamp] = !showRawResult[timestamp]
-  
-  // 等待 DOM 更新后，调整滚动位置以保持按钮在相同位置
+
+  // After DOM update, adjust scroll position to keep button in the same position
   if (button && buttonTopBeforeToggle !== undefined && rightPanel.value) {
     nextTick(() => {
       const newButtonRect = button.getBoundingClientRect()
       const buttonTopAfterToggle = newButtonRect.top
       const scrollDelta = buttonTopAfterToggle - buttonTopBeforeToggle
       
-      // 调整滚动位置
+      // Adjust scroll position
       rightPanel.value.scrollTop += scrollDelta
     })
   }
@@ -464,7 +464,7 @@ const toggleSectionContent = (idx) => {
 }
 
 const toggleSectionCollapse = (idx) => {
-  // 只有已完成的章节才能折叠
+  // Only completed sections can be collapsed
   if (!generatedSections.value[idx + 1]) return
   const newSet = new Set(collapsedSections.value)
   if (newSet.has(idx)) {
@@ -497,32 +497,32 @@ const toolConfig = {
   'insight_forge': {
     name: 'Deep Insight',
     color: 'purple',
-    icon: 'lightbulb' // 灯泡图标 - 代表洞察
+    icon: 'lightbulb' // lightbulb icon - represents insight
   },
   'panorama_search': {
     name: 'Panorama Search',
     color: 'blue',
-    icon: 'globe' // 地球图标 - 代表全景搜索
+    icon: 'globe' // globe icon - represents panorama search
   },
   'interview_agents': {
     name: 'Agent Interview',
     color: 'green',
-    icon: 'users' // 用户图标 - 代表对话
+    icon: 'users' // users icon - represents conversation
   },
   'quick_search': {
     name: 'Quick Search',
     color: 'orange',
-    icon: 'zap' // 闪电图标 - 代表快速
+    icon: 'zap' // lightning icon - represents speed
   },
   'get_graph_statistics': {
     name: 'Graph Stats',
     color: 'cyan',
-    icon: 'chart' // 图表图标 - 代表统计
+    icon: 'chart' // chart icon - represents statistics
   },
   'get_entities_by_type': {
     name: 'Entity Query',
     color: 'pink',
-    icon: 'database' // 数据库图标 - 代表实体
+    icon: 'database' // database icon - represents entities
   }
 }
 
@@ -551,15 +551,15 @@ const parseInsightForge = (text) => {
   }
   
   try {
-    // 提取分析问题
+    // Extract analysis query
     const queryMatch = text.match(/分析问题:\s*(.+?)(?:\n|$)/)
     if (queryMatch) result.query = queryMatch[1].trim()
     
-    // 提取预测场景
+    // Extract prediction scenario
     const reqMatch = text.match(/预测场景:\s*(.+?)(?:\n|$)/)
     if (reqMatch) result.simulationRequirement = reqMatch[1].trim()
     
-    // 提取统计数据 - 匹配"相关预测事实: X条"格式
+    // Extract statistics - match "related prediction facts: X items" format
     const factMatch = text.match(/相关预测事实:\s*(\d+)/)
     const entityMatch = text.match(/涉及实体:\s*(\d+)/)
     const relMatch = text.match(/关系链:\s*(\d+)/)
@@ -567,14 +567,14 @@ const parseInsightForge = (text) => {
     if (entityMatch) result.stats.entities = parseInt(entityMatch[1])
     if (relMatch) result.stats.relationships = parseInt(relMatch[1])
     
-    // 提取子问题 - 完整提取，不限制数量
+    // Extract sub-queries - full extraction, no limit
     const subQSection = text.match(/### 分析的子问题\n([\s\S]*?)(?=\n###|$)/)
     if (subQSection) {
       const lines = subQSection[1].split('\n').filter(l => l.match(/^\d+\./))
       result.subQueries = lines.map(l => l.replace(/^\d+\.\s*/, '').trim()).filter(Boolean)
     }
     
-    // 提取关键事实 - 完整提取，不限制数量
+    // Extract key facts - full extraction, no limit
     const factsSection = text.match(/### 【关键事实】[\s\S]*?\n([\s\S]*?)(?=\n###|$)/)
     if (factsSection) {
       const lines = factsSection[1].split('\n').filter(l => l.match(/^\d+\./))
@@ -584,11 +584,11 @@ const parseInsightForge = (text) => {
       }).filter(Boolean)
     }
     
-    // 提取核心实体 - 完整提取，包含摘要和相关事实数
+    // Extract core entities - full extraction, including summary and related facts count
     const entitySection = text.match(/### 【核心实体】\n([\s\S]*?)(?=\n###|$)/)
     if (entitySection) {
       const entityText = entitySection[1]
-      // 按 "- **" 分割实体块
+      // Split entity blocks by "- **"
       const entityBlocks = entityText.split(/\n(?=- \*\*)/).filter(b => b.trim().startsWith('- **'))
       result.entities = entityBlocks.map(block => {
         const nameMatch = block.match(/^-\s*\*\*(.+?)\*\*\s*\((.+?)\)/)
@@ -603,7 +603,7 @@ const parseInsightForge = (text) => {
       }).filter(e => e.name)
     }
     
-    // 提取关系链 - 完整提取，不限制数量
+    // Extract relationship chains - full extraction, no limit
     const relSection = text.match(/### 【关系链】\n([\s\S]*?)(?=\n###|$)/)
     if (relSection) {
       const lines = relSection[1].split('\n').filter(l => l.trim().startsWith('-'))
@@ -632,11 +632,11 @@ const parsePanorama = (text) => {
   }
   
   try {
-    // 提取查询
+    // Extract query
     const queryMatch = text.match(/查询:\s*(.+?)(?:\n|$)/)
     if (queryMatch) result.query = queryMatch[1].trim()
     
-    // 提取统计数据
+    // Extract statistics
     const nodesMatch = text.match(/总节点数:\s*(\d+)/)
     const edgesMatch = text.match(/总边数:\s*(\d+)/)
     const activeMatch = text.match(/当前有效事实:\s*(\d+)/)
@@ -646,18 +646,18 @@ const parsePanorama = (text) => {
     if (activeMatch) result.stats.activeFacts = parseInt(activeMatch[1])
     if (histMatch) result.stats.historicalFacts = parseInt(histMatch[1])
     
-    // 提取当前有效事实 - 完整提取，不限制数量
+    // Extract current active facts - full extraction, no limit
     const activeSection = text.match(/### 【当前有效事实】[\s\S]*?\n([\s\S]*?)(?=\n###|$)/)
     if (activeSection) {
       const lines = activeSection[1].split('\n').filter(l => l.match(/^\d+\./))
       result.activeFacts = lines.map(l => {
-        // 移除编号和引号
+        // Remove numbering and quotes
         const factText = l.replace(/^\d+\.\s*/, '').replace(/^"|"$/g, '').trim()
         return factText
       }).filter(Boolean)
     }
     
-    // 提取历史/过期事实 - 完整提取，不限制数量
+    // Extract historical/expired facts - full extraction, no limit
     const histSection = text.match(/### 【历史\/过期事实】[\s\S]*?\n([\s\S]*?)(?=\n###|$)/)
     if (histSection) {
       const lines = histSection[1].split('\n').filter(l => l.match(/^\d+\./))
@@ -667,7 +667,7 @@ const parsePanorama = (text) => {
       }).filter(Boolean)
     }
     
-    // 提取涉及实体 - 完整提取，不限制数量
+    // Extract involved entities - full extraction, no limit
     const entitySection = text.match(/### 【涉及实体】\n([\s\S]*?)(?=\n###|$)/)
     if (entitySection) {
       const lines = entitySection[1].split('\n').filter(l => l.trim().startsWith('-'))
@@ -696,11 +696,11 @@ const parseInterview = (text) => {
   }
   
   try {
-    // 提取采访主题
+    // Extract interview topic
     const topicMatch = text.match(/\*\*采访主题:\*\*\s*(.+?)(?:\n|$)/)
     if (topicMatch) result.topic = topicMatch[1].trim()
     
-    // 提取采访人数（如 "5 / 9 位模拟Agent"）
+    // Extract interview count (e.g. "5 / 9 simulated Agents")
     const countMatch = text.match(/\*\*采访人数:\*\*\s*(\d+)\s*\/\s*(\d+)/)
     if (countMatch) {
       result.successCount = parseInt(countMatch[1])
@@ -708,13 +708,13 @@ const parseInterview = (text) => {
       result.agentCount = `${countMatch[1]} / ${countMatch[2]}`
     }
     
-    // 提取采访对象选择理由
+    // Extract interviewee selection reasons
     const reasonMatch = text.match(/### 采访对象选择理由\n([\s\S]*?)(?=\n---\n|\n### 采访实录)/)
     if (reasonMatch) {
       result.selectionReason = reasonMatch[1].trim()
     }
     
-    // 解析每个人的选择理由
+    // Parse individual selection reasons
     const parseIndividualReasons = (reasonText) => {
       const reasons = {}
       if (!reasonText) return reasons
@@ -728,16 +728,16 @@ const parseInterview = (text) => {
         let name = null
         let reasonStart = null
         
-        // 格式1: 数字. **名字（index=X）**：理由
-        // 例如: 1. **校友_345（index=1）**：作为武大校友...
+        // Format 1: number. **name (index=X)**: reason
+        // e.g.: 1. **Alumni_345 (index=1)**: As a university alumnus...
         headerMatch = line.match(/^\d+\.\s*\*\*([^*（(]+)(?:[（(]index\s*=?\s*\d+[)）])?\*\*[：:]\s*(.*)/)
         if (headerMatch) {
           name = headerMatch[1].trim()
           reasonStart = headerMatch[2]
         }
         
-        // 格式2: - 选择名字（index X）：理由
-        // 例如: - 选择家长_601（index 0）：作为家长群体代表...
+        // Format 2: - select name (index X): reason
+        // e.g.: - Select Parent_601 (index 0): As a parent group representative...
         if (!headerMatch) {
           headerMatch = line.match(/^-\s*选择([^（(]+)(?:[（(]index\s*=?\s*\d+[)）])?[：:]\s*(.*)/)
           if (headerMatch) {
@@ -746,8 +746,8 @@ const parseInterview = (text) => {
           }
         }
         
-        // 格式3: - **名字（index X）**：理由
-        // 例如: - **家长_601（index 0）**：作为家长群体代表...
+        // Format 3: - **name (index X)**: reason
+        // e.g.: - **Parent_601 (index 0)**: As a parent group representative...
         if (!headerMatch) {
           headerMatch = line.match(/^-\s*\*\*([^*（(]+)(?:[（(]index\s*=?\s*\d+[)）])?\*\*[：:]\s*(.*)/)
           if (headerMatch) {
@@ -757,20 +757,20 @@ const parseInterview = (text) => {
         }
         
         if (name) {
-          // 保存上一个人的理由
+          // Save previous person's reason
           if (currentName && currentReason.length > 0) {
             reasons[currentName] = currentReason.join(' ').trim()
           }
-          // 开始新的人
+          // Start new person
           currentName = name
           currentReason = reasonStart ? [reasonStart.trim()] : []
         } else if (currentName && line.trim() && !line.match(/^未选|^综上|^最终选择/)) {
-          // 理由的续行（排除结尾总结段落）
+          // Continuation of reason (excluding ending summary paragraphs)
           currentReason.push(line.trim())
         }
       }
       
-      // 保存最后一个人的理由
+      // Save last person's reason
       if (currentName && currentReason.length > 0) {
         reasons[currentName] = currentReason.join(' ').trim()
       }
@@ -780,7 +780,7 @@ const parseInterview = (text) => {
     
     const individualReasons = parseIndividualReasons(result.selectionReason)
     
-    // 提取每个采访记录
+    // Extract each interview record
     const interviewBlocks = text.split(/#### 采访 #\d+:/).slice(1)
     
     interviewBlocks.forEach((block, index) => {
@@ -797,33 +797,33 @@ const parseInterview = (text) => {
         quotes: []
       }
       
-      // 提取标题（如 "学生"、"教育从业者" 等）
+      // Extract title (e.g. "Student", "Educator", etc.)
       const titleMatch = block.match(/^(.+?)\n/)
       if (titleMatch) interview.title = titleMatch[1].trim()
       
-      // 提取姓名和角色
+      // Extract name and role
       const nameRoleMatch = block.match(/\*\*(.+?)\*\*\s*\((.+?)\)/)
       if (nameRoleMatch) {
         interview.name = nameRoleMatch[1].trim()
         interview.role = nameRoleMatch[2].trim()
-        // 设置该人的选择理由
+        // Set this person's selection reason
         interview.selectionReason = individualReasons[interview.name] || ''
       }
       
-      // 提取简介
+      // Extract bio
       const bioMatch = block.match(/_简介:\s*([\s\S]*?)_\n/)
       if (bioMatch) {
         interview.bio = bioMatch[1].trim().replace(/\.\.\.$/, '...')
       }
       
-      // 提取问题列表
+      // Extract question list
       const qMatch = block.match(/\*\*Q:\*\*\s*([\s\S]*?)(?=\n\n\*\*A:\*\*|\*\*A:\*\*)/)
       if (qMatch) {
         const qText = qMatch[1].trim()
-        // 按数字编号分割问题
+        // Split questions by numbered list
         const questions = qText.split(/\n\d+\.\s+/).filter(q => q.trim())
         if (questions.length > 0) {
-          // 如果第一个问题前面有"1."，需要特殊处理
+          // If first question has "1." prefix, handle specially
           const firstQ = qText.match(/^1\.\s+(.+)/)
           if (firstQ) {
             interview.questions = [firstQ[1].trim(), ...questions.slice(1).map(q => q.trim())]
@@ -833,12 +833,12 @@ const parseInterview = (text) => {
         }
       }
       
-      // 提取回答 - 分Twitter和Reddit
+      // Extract answers - separate Twitter and Reddit
       const answerMatch = block.match(/\*\*A:\*\*\s*([\s\S]*?)(?=\*\*关键引言|$)/)
       if (answerMatch) {
         const answerText = answerMatch[1].trim()
         
-        // 分离Twitter和Reddit回答
+        // Separate Twitter and Reddit answers
         const twitterMatch = answerText.match(/【Twitter平台回答】\n?([\s\S]*?)(?=【Reddit平台回答】|$)/)
         const redditMatch = answerText.match(/【Reddit平台回答】\n?([\s\S]*?)$/)
         
@@ -849,9 +849,9 @@ const parseInterview = (text) => {
           interview.redditAnswer = redditMatch[1].trim()
         }
         
-        // 平台回退逻辑（兼容旧格式：只有一个平台标记的情况）
+        // Platform fallback logic (backward compatible: single platform marker case)
         if (!twitterMatch && redditMatch) {
-          // 只有 Reddit 回答，仅在非占位文本时复制为默认显示
+          // Only Reddit answer, copy as default display only if not placeholder text
           if (interview.redditAnswer && interview.redditAnswer !== '（该平台未获得回复）') {
             interview.twitterAnswer = interview.redditAnswer
           }
@@ -860,18 +860,18 @@ const parseInterview = (text) => {
             interview.redditAnswer = interview.twitterAnswer
           }
         } else if (!twitterMatch && !redditMatch) {
-          // 没有分平台标记（极旧格式），整体作为回答
+          // No platform markers (very old format), use entire text as answer
           interview.twitterAnswer = answerText
         }
       }
       
-      // 提取关键引言（兼容多种引号格式）
+      // Extract key quotes (compatible with multiple quote formats)
       const quotesMatch = block.match(/\*\*关键引言:\*\*\n([\s\S]*?)(?=\n---|\n####|$)/)
       if (quotesMatch) {
         const quotesText = quotesMatch[1]
-        // 优先匹配 > "text" 格式
+        // Prefer matching > "text" format
         let quoteMatches = quotesText.match(/> "([^"]+)"/g)
-        // 回退：匹配 > "text" 或 > \u201Ctext\u201D（中文引号）
+        // Fallback: match > "text" or > \u201Ctext\u201D (Chinese quotation marks)
         if (!quoteMatches) {
           quoteMatches = quotesText.match(/> [\u201C""]([^\u201D""]+)[\u201D""]/g)
         }
@@ -887,7 +887,7 @@ const parseInterview = (text) => {
       }
     })
     
-    // 提取采访摘要
+    // Extract interview summary
     const summaryMatch = text.match(/### 采访摘要与核心观点\n([\s\S]*?)$/)
     if (summaryMatch) {
       result.summary = summaryMatch[1].trim()
@@ -909,22 +909,22 @@ const parseQuickSearch = (text) => {
   }
   
   try {
-    // 提取搜索查询
+    // Extract search query
     const queryMatch = text.match(/搜索查询:\s*(.+?)(?:\n|$)/)
     if (queryMatch) result.query = queryMatch[1].trim()
     
-    // 提取结果数量
+    // Extract result count
     const countMatch = text.match(/找到\s*(\d+)\s*条/)
     if (countMatch) result.count = parseInt(countMatch[1])
     
-    // 提取相关事实 - 完整提取，不限制数量
+    // Extract related facts - full extraction, no limit
     const factsSection = text.match(/### 相关事实:\n([\s\S]*)$/)
     if (factsSection) {
       const lines = factsSection[1].split('\n').filter(l => l.match(/^\d+\./))
       result.facts = lines.map(l => l.replace(/^\d+\.\s*/, '').trim()).filter(Boolean)
     }
     
-    // 尝试提取边信息（如果有）
+    // Try to extract edge info (if available)
     const edgesSection = text.match(/### 相关边:\n([\s\S]*?)(?=\n###|$)/)
     if (edgesSection) {
       const lines = edgesSection[1].split('\n').filter(l => l.trim().startsWith('-'))
@@ -937,7 +937,7 @@ const parseQuickSearch = (text) => {
       }).filter(Boolean)
     }
     
-    // 尝试提取节点信息（如果有）
+    // Try to extract node info (if available)
     const nodesSection = text.match(/### 相关节点:\n([\s\S]*?)(?=\n###|$)/)
     if (nodesSection) {
       const lines = nodesSection[1].split('\n').filter(l => l.trim().startsWith('-'))
@@ -1003,7 +1003,7 @@ const InsightDisplay = {
         ]),
         props.result.query && h('div', { class: 'header-topic' }, props.result.query),
         props.result.simulationRequirement && h('div', { class: 'header-scenario' }, [
-          h('span', { class: 'scenario-label' }, '预测场景: '),
+          h('span', { class: 'scenario-label' }, 'Prediction Scenario: '),
           h('span', { class: 'scenario-text' }, props.result.simulationRequirement)
         ])
       ]),
@@ -1014,25 +1014,25 @@ const InsightDisplay = {
           class: ['insight-tab', { active: activeTab.value === 'facts' }],
           onClick: () => { activeTab.value = 'facts' }
         }, [
-          h('span', { class: 'tab-label' }, `当前关键记忆 (${props.result.facts.length})`)
+          h('span', { class: 'tab-label' }, `Key Memories (${props.result.facts.length})`)
         ]),
         h('button', {
           class: ['insight-tab', { active: activeTab.value === 'entities' }],
           onClick: () => { activeTab.value = 'entities' }
         }, [
-          h('span', { class: 'tab-label' }, `核心实体 (${props.result.entities.length})`)
+          h('span', { class: 'tab-label' }, `Core Entities (${props.result.entities.length})`)
         ]),
         h('button', {
           class: ['insight-tab', { active: activeTab.value === 'relations' }],
           onClick: () => { activeTab.value = 'relations' }
         }, [
-          h('span', { class: 'tab-label' }, `关系链 (${props.result.relations.length})`)
+          h('span', { class: 'tab-label' }, `Relationships (${props.result.relations.length})`)
         ]),
         props.result.subQueries.length > 0 && h('button', {
           class: ['insight-tab', { active: activeTab.value === 'subqueries' }],
           onClick: () => { activeTab.value = 'subqueries' }
         }, [
-          h('span', { class: 'tab-label' }, `子问题 (${props.result.subQueries.length})`)
+          h('span', { class: 'tab-label' }, `Sub-queries (${props.result.subQueries.length})`)
         ])
       ]),
       
@@ -1041,8 +1041,8 @@ const InsightDisplay = {
         // Facts Tab
         activeTab.value === 'facts' && props.result.facts.length > 0 && h('div', { class: 'facts-panel' }, [
           h('div', { class: 'panel-header' }, [
-            h('span', { class: 'panel-title' }, '时序记忆中所关联的最新关键事实'),
-            h('span', { class: 'panel-count' }, `共 ${props.result.facts.length} 条`)
+            h('span', { class: 'panel-title' }, 'Latest key facts from temporal memory'),
+            h('span', { class: 'panel-count' }, `${props.result.facts.length} total`)
           ]),
           h('div', { class: 'facts-list' },
             (expandedFacts.value ? props.result.facts : props.result.facts.slice(0, INITIAL_SHOW_COUNT)).map((fact, i) => 
@@ -1055,35 +1055,35 @@ const InsightDisplay = {
           props.result.facts.length > INITIAL_SHOW_COUNT && h('button', {
             class: 'expand-btn',
             onClick: () => { expandedFacts.value = !expandedFacts.value }
-          }, expandedFacts.value ? `收起 ▲` : `展开全部 ${props.result.facts.length} 条 ▼`)
+          }, expandedFacts.value ? `Collapse ▲` : `Show all ${props.result.facts.length} items ▼`)
         ]),
         
         // Entities Tab
         activeTab.value === 'entities' && props.result.entities.length > 0 && h('div', { class: 'entities-panel' }, [
           h('div', { class: 'panel-header' }, [
-            h('span', { class: 'panel-title' }, '核心实体'),
-            h('span', { class: 'panel-count' }, `共 ${props.result.entities.length} 个`)
+            h('span', { class: 'panel-title' }, 'Core Entities'),
+            h('span', { class: 'panel-count' }, `${props.result.entities.length} total`)
           ]),
           h('div', { class: 'entities-grid' },
             (expandedEntities.value ? props.result.entities : props.result.entities.slice(0, 12)).map((entity, i) => 
               h('div', { class: 'entity-tag', key: i, title: entity.summary || '' }, [
                 h('span', { class: 'entity-name' }, entity.name),
                 h('span', { class: 'entity-type' }, entity.type),
-                entity.relatedFactsCount > 0 && h('span', { class: 'entity-fact-count' }, `${entity.relatedFactsCount}条`)
+                entity.relatedFactsCount > 0 && h('span', { class: 'entity-fact-count' }, `${entity.relatedFactsCount} items`)
               ])
             )
           ),
           props.result.entities.length > 12 && h('button', {
             class: 'expand-btn',
             onClick: () => { expandedEntities.value = !expandedEntities.value }
-          }, expandedEntities.value ? `收起 ▲` : `展开全部 ${props.result.entities.length} 个 ▼`)
+          }, expandedEntities.value ? `Collapse ▲` : `Show all ${props.result.entities.length} items ▼`)
         ]),
-        
+
         // Relations Tab
         activeTab.value === 'relations' && props.result.relations.length > 0 && h('div', { class: 'relations-panel' }, [
           h('div', { class: 'panel-header' }, [
-            h('span', { class: 'panel-title' }, '关系链'),
-            h('span', { class: 'panel-count' }, `共 ${props.result.relations.length} 条`)
+            h('span', { class: 'panel-title' }, 'Relationships'),
+            h('span', { class: 'panel-count' }, `${props.result.relations.length} total`)
           ]),
           h('div', { class: 'relations-list' },
             (expandedRelations.value ? props.result.relations : props.result.relations.slice(0, INITIAL_SHOW_COUNT)).map((rel, i) => 
@@ -1101,14 +1101,14 @@ const InsightDisplay = {
           props.result.relations.length > INITIAL_SHOW_COUNT && h('button', {
             class: 'expand-btn',
             onClick: () => { expandedRelations.value = !expandedRelations.value }
-          }, expandedRelations.value ? `收起 ▲` : `展开全部 ${props.result.relations.length} 条 ▼`)
+          }, expandedRelations.value ? `Collapse ▲` : `Show all ${props.result.relations.length} items ▼`)
         ]),
         
         // Sub-queries Tab
         activeTab.value === 'subqueries' && props.result.subQueries.length > 0 && h('div', { class: 'subqueries-panel' }, [
           h('div', { class: 'panel-header' }, [
-            h('span', { class: 'panel-title' }, '漂移查询生成分析子问题'),
-            h('span', { class: 'panel-count' }, `共 ${props.result.subQueries.length} 个`)
+            h('span', { class: 'panel-title' }, 'Drift query generated sub-questions'),
+            h('span', { class: 'panel-count' }, `${props.result.subQueries.length} total`)
           ]),
           h('div', { class: 'subqueries-list' },
             props.result.subQueries.map((sq, i) => 
@@ -1121,9 +1121,9 @@ const InsightDisplay = {
         ]),
         
         // Empty state
-        activeTab.value === 'facts' && props.result.facts.length === 0 && h('div', { class: 'empty-state' }, '暂无当前关键记忆'),
-        activeTab.value === 'entities' && props.result.entities.length === 0 && h('div', { class: 'empty-state' }, '暂无核心实体'),
-        activeTab.value === 'relations' && props.result.relations.length === 0 && h('div', { class: 'empty-state' }, '暂无关系链')
+        activeTab.value === 'facts' && props.result.facts.length === 0 && h('div', { class: 'empty-state' }, 'No key memories available'),
+        activeTab.value === 'entities' && props.result.entities.length === 0 && h('div', { class: 'empty-state' }, 'No core entities available'),
+        activeTab.value === 'relations' && props.result.relations.length === 0 && h('div', { class: 'empty-state' }, 'No relationships available')
       ])
     ])
   }
@@ -1176,19 +1176,19 @@ const PanoramaDisplay = {
           class: ['panorama-tab', { active: activeTab.value === 'active' }],
           onClick: () => { activeTab.value = 'active' }
         }, [
-          h('span', { class: 'tab-label' }, `当前有效记忆 (${props.result.activeFacts.length})`)
+          h('span', { class: 'tab-label' }, `Active Memories (${props.result.activeFacts.length})`)
         ]),
         h('button', {
           class: ['panorama-tab', { active: activeTab.value === 'historical' }],
           onClick: () => { activeTab.value = 'historical' }
         }, [
-          h('span', { class: 'tab-label' }, `历史记忆 (${props.result.historicalFacts.length})`)
+          h('span', { class: 'tab-label' }, `Historical Memories (${props.result.historicalFacts.length})`)
         ]),
         h('button', {
           class: ['panorama-tab', { active: activeTab.value === 'entities' }],
           onClick: () => { activeTab.value = 'entities' }
         }, [
-          h('span', { class: 'tab-label' }, `涉及实体 (${props.result.entities.length})`)
+          h('span', { class: 'tab-label' }, `Involved Entities (${props.result.entities.length})`)
         ])
       ]),
       
@@ -1197,8 +1197,8 @@ const PanoramaDisplay = {
         // Active Facts Tab
         activeTab.value === 'active' && h('div', { class: 'facts-panel active-facts' }, [
           h('div', { class: 'panel-header' }, [
-            h('span', { class: 'panel-title' }, '当前有效记忆'),
-            h('span', { class: 'panel-count' }, `共 ${props.result.activeFacts.length} 条`)
+            h('span', { class: 'panel-title' }, 'Active Memories'),
+            h('span', { class: 'panel-count' }, `${props.result.activeFacts.length} total`)
           ]),
           props.result.activeFacts.length > 0 ? h('div', { class: 'facts-list' },
             (expandedActive.value ? props.result.activeFacts : props.result.activeFacts.slice(0, INITIAL_SHOW_COUNT)).map((fact, i) => 
@@ -1207,25 +1207,25 @@ const PanoramaDisplay = {
                 h('div', { class: 'fact-content' }, fact)
               ])
             )
-          ) : h('div', { class: 'empty-state' }, '暂无当前有效记忆'),
+          ) : h('div', { class: 'empty-state' }, 'No active memories available'),
           props.result.activeFacts.length > INITIAL_SHOW_COUNT && h('button', {
             class: 'expand-btn',
             onClick: () => { expandedActive.value = !expandedActive.value }
-          }, expandedActive.value ? `收起 ▲` : `展开全部 ${props.result.activeFacts.length} 条 ▼`)
+          }, expandedActive.value ? `Collapse ▲` : `Show all ${props.result.activeFacts.length} items ▼`)
         ]),
         
         // Historical Facts Tab
         activeTab.value === 'historical' && h('div', { class: 'facts-panel historical-facts' }, [
           h('div', { class: 'panel-header' }, [
-            h('span', { class: 'panel-title' }, '历史记忆'),
-            h('span', { class: 'panel-count' }, `共 ${props.result.historicalFacts.length} 条`)
+            h('span', { class: 'panel-title' }, 'Historical Memories'),
+            h('span', { class: 'panel-count' }, `${props.result.historicalFacts.length} total`)
           ]),
           props.result.historicalFacts.length > 0 ? h('div', { class: 'facts-list' },
             (expandedHistorical.value ? props.result.historicalFacts : props.result.historicalFacts.slice(0, INITIAL_SHOW_COUNT)).map((fact, i) => 
               h('div', { class: 'fact-item historical', key: i }, [
                 h('span', { class: 'fact-number' }, i + 1),
                 h('div', { class: 'fact-content' }, [
-                  // 尝试提取时间信息 [time - time]
+                  // Try to extract time info [time - time]
                   (() => {
                     const timeMatch = fact.match(/^\[(.+?)\]\s*(.*)$/)
                     if (timeMatch) {
@@ -1239,18 +1239,18 @@ const PanoramaDisplay = {
                 ])
               ])
             )
-          ) : h('div', { class: 'empty-state' }, '暂无历史记忆'),
+          ) : h('div', { class: 'empty-state' }, 'No historical memories available'),
           props.result.historicalFacts.length > INITIAL_SHOW_COUNT && h('button', {
             class: 'expand-btn',
             onClick: () => { expandedHistorical.value = !expandedHistorical.value }
-          }, expandedHistorical.value ? `收起 ▲` : `展开全部 ${props.result.historicalFacts.length} 条 ▼`)
+          }, expandedHistorical.value ? `Collapse ▲` : `Show all ${props.result.historicalFacts.length} items ▼`)
         ]),
         
         // Entities Tab
         activeTab.value === 'entities' && h('div', { class: 'entities-panel' }, [
           h('div', { class: 'panel-header' }, [
-            h('span', { class: 'panel-title' }, '涉及实体'),
-            h('span', { class: 'panel-count' }, `共 ${props.result.entities.length} 个`)
+            h('span', { class: 'panel-title' }, 'Involved Entities'),
+            h('span', { class: 'panel-count' }, `${props.result.entities.length} total`)
           ]),
           props.result.entities.length > 0 ? h('div', { class: 'entities-grid' },
             (expandedEntities.value ? props.result.entities : props.result.entities.slice(0, 8)).map((entity, i) => 
@@ -1259,11 +1259,11 @@ const PanoramaDisplay = {
                 entity.type && h('span', { class: 'entity-type' }, entity.type)
               ])
             )
-          ) : h('div', { class: 'empty-state' }, '暂无涉及实体'),
+          ) : h('div', { class: 'empty-state' }, 'No involved entities available'),
           props.result.entities.length > 8 && h('button', {
             class: 'expand-btn',
             onClick: () => { expandedEntities.value = !expandedEntities.value }
-          }, expandedEntities.value ? `收起 ▲` : `展开全部 ${props.result.entities.length} 个 ▼`)
+          }, expandedEntities.value ? `Collapse ▲` : `Show all ${props.result.entities.length} items ▼`)
         ])
       ])
     ])
@@ -1292,16 +1292,16 @@ const InterviewDisplay = {
     
     const activeIndex = ref(0)
     const expandedAnswers = ref(new Set())
-    // 为每个问题-回答对维护独立的平台选择状态
+    // Maintain independent platform selection state for each Q&A pair
     const platformTabs = reactive({}) // { 'agentIdx-qIdx': 'twitter' | 'reddit' }
     
-    // 获取某个问题的当前平台选择
+    // Get current platform selection for a question
     const getPlatformTab = (agentIdx, qIdx) => {
       const key = `${agentIdx}-${qIdx}`
       return platformTabs[key] || 'twitter'
     }
     
-    // 设置某个问题的平台选择
+    // Set platform selection for a question
     const setPlatformTab = (agentIdx, qIdx, platform) => {
       const key = `${agentIdx}-${qIdx}`
       platformTabs[key] = platform
@@ -1323,25 +1323,25 @@ const InterviewDisplay = {
       return text.substring(0, 400) + '...'
     }
     
-    // 检查是否为平台占位文本
+    // Check if text is platform placeholder
     const isPlaceholderText = (text) => {
       if (!text) return true
       const t = text.trim()
       return t === '（该平台未获得回复）' || t === '(该平台未获得回复)' || t === '[无回复]'
     }
 
-    // 尝试按问题编号分割回答
+    // Try to split answer by question number
     const splitAnswerByQuestions = (answerText, questionCount) => {
       if (!answerText || questionCount <= 0) return [answerText]
       if (isPlaceholderText(answerText)) return ['']
 
-      // 支持两种编号格式：
-      // 1. "问题X：" 或 "问题X:" （中文格式，后端新格式）
-      // 2. "1. " 或 "\n1. " （数字+点，旧格式兼容）
+      // Support two numbering formats:
+      // 1. "问题X：" or "问题X:" (Chinese format, new backend format)
+      // 2. "1. " or "\n1. " (number+dot, legacy format compatible)
       let matches = []
       let match
 
-      // 优先尝试 "问题X：" 格式
+      // Try "问题X：" format first
       const cnPattern = /(?:^|[\r\n]+)问题(\d+)[：:]\s*/g
       while ((match = cnPattern.exec(answerText)) !== null) {
         matches.push({
@@ -1351,7 +1351,7 @@ const InterviewDisplay = {
         })
       }
 
-      // 如果没匹配到，回退到 "数字." 格式
+      // If no match, fall back to "number." format
       if (matches.length === 0) {
         const numPattern = /(?:^|[\r\n]+)(\d+)\.\s+/g
         while ((match = numPattern.exec(answerText)) !== null) {
@@ -1363,7 +1363,7 @@ const InterviewDisplay = {
         }
       }
 
-      // 如果没有找到编号或只找到一个，返回整体
+      // If no numbering found or only one, return as whole
       if (matches.length <= 1) {
         const cleaned = answerText
           .replace(/^问题\d+[：:]\s*/, '')
@@ -1372,7 +1372,7 @@ const InterviewDisplay = {
         return [cleaned || answerText]
       }
 
-      // 按编号提取各部分
+      // Extract parts by number
       const parts = []
       for (let i = 0; i < matches.length; i++) {
         const current = matches[i]
@@ -1393,7 +1393,7 @@ const InterviewDisplay = {
       return [answerText]
     }
     
-    // 获取某个问题对应的回答
+    // Get the answer for a specific question
     const getAnswerForQuestion = (interview, qIdx, platform) => {
       const answer = platform === 'twitter' ? interview.twitterAnswer : (interview.redditAnswer || interview.twitterAnswer)
       if (!answer || isPlaceholderText(answer)) return answer || ''
@@ -1401,21 +1401,21 @@ const InterviewDisplay = {
       const questionCount = interview.questions?.length || 1
       const answers = splitAnswerByQuestions(answer, questionCount)
 
-      // 分割成功且索引有效
+      // Split successful and index valid
       if (answers.length > 1 && qIdx < answers.length) {
         return answers[qIdx] || ''
       }
 
-      // 分割失败：第一个问题返回完整回答，其余返回空
+      // Split failed: first question returns full answer, rest return empty
       return qIdx === 0 ? answer : ''
     }
     
-    // 检查某个问题是否有双平台回答（过滤占位文本）
+    // Check if a question has dual-platform answers (filtering placeholder text)
     const hasMultiplePlatforms = (interview, qIdx) => {
       if (!interview.twitterAnswer || !interview.redditAnswer) return false
       const twitterAnswer = getAnswerForQuestion(interview, qIdx, 'twitter')
       const redditAnswer = getAnswerForQuestion(interview, qIdx, 'reddit')
-      // 两个平台都有真实回答（非占位文本）且内容不同
+      // Both platforms have real answers (not placeholder text) and content differs
       return !isPlaceholderText(twitterAnswer) && !isPlaceholderText(redditAnswer) && twitterAnswer !== redditAnswer
     }
     
@@ -1465,13 +1465,13 @@ const InterviewDisplay = {
           ])
         ]),
         
-        // Selection Reason - 选择理由
+        // Selection Reason
         props.result.interviews[activeIndex.value]?.selectionReason && h('div', { class: 'selection-reason' }, [
-          h('div', { class: 'reason-label' }, '选择理由'),
+          h('div', { class: 'reason-label' }, 'Selection Reason'),
           h('div', { class: 'reason-content' }, props.result.interviews[activeIndex.value].selectionReason)
         ]),
         
-        // Q&A Conversation Thread - 一问一答样式
+        // Q&A Conversation Thread
         h('div', { class: 'qa-thread' }, 
           (props.result.interviews[activeIndex.value]?.questions?.length > 0 
             ? props.result.interviews[activeIndex.value].questions 
@@ -1501,7 +1501,7 @@ const InterviewDisplay = {
                 h('div', { class: 'qa-content' }, [
                   h('div', { class: 'qa-answer-header' }, [
                     h('div', { class: 'qa-sender' }, interview?.name || 'Agent'),
-                    // 双平台切换按钮（仅在有真实双平台回答时显示）
+                    // Dual platform switch buttons (only shown when both platforms have real answers)
                     hasDualPlatform && h('div', { class: 'platform-switch' }, [
                       h('button', {
                         class: ['platform-btn', { active: currentPlatform === 'twitter' }],
@@ -1512,7 +1512,7 @@ const InterviewDisplay = {
                           h('line', { x1: '2', y1: '12', x2: '22', y2: '12' }),
                           h('path', { d: 'M12 2a15.3 15.3 0 0 1 4 10 15.3 15.3 0 0 1-4 10 15.3 15.3 0 0 1-4-10 15.3 15.3 0 0 1 4-10z' })
                         ]),
-                        h('span', {}, '世界1')
+                        h('span', {}, 'World 1')
                       ]),
                       h('button', {
                         class: ['platform-btn', { active: currentPlatform === 'reddit' }],
@@ -1521,7 +1521,7 @@ const InterviewDisplay = {
                         h('svg', { class: 'platform-icon', viewBox: '0 0 24 24', width: 12, height: 12, fill: 'none', stroke: 'currentColor', 'stroke-width': 2 }, [
                           h('path', { d: 'M21 11.5a8.38 8.38 0 0 1-.9 3.8 8.5 8.5 0 0 1-7.6 4.7 8.38 8.38 0 0 1-3.8-.9L3 21l1.9-5.7a8.38 8.38 0 0 1-.9-3.8 8.5 8.5 0 0 1 4.7-7.6 8.38 8.38 0 0 1 3.8-.9h.5a8.48 8.48 0 0 1 8 8v.5z' })
                         ]),
-                        h('span', {}, '世界2')
+                        h('span', {}, 'World 2')
                       ])
                     ])
                   ]),
@@ -1533,7 +1533,7 @@ const InterviewDisplay = {
                           .replace(/\*\*(.+?)\*\*/g, '<strong>$1</strong>')
                           .replace(/\n/g, '<br>')
                   }),
-                  // Expand/Collapse Button（占位文本不显示）
+                  // Expand/Collapse Button (not shown for placeholder text)
                   !isPlaceholder && answerText.length > 400 && h('button', {
                     class: 'expand-answer-btn',
                     onClick: () => toggleAnswer(expandKey)
@@ -1610,7 +1610,7 @@ const QuickSearchDisplay = {
           ])
         ]),
         props.result.query && h('div', { class: 'header-query' }, [
-          h('span', { class: 'query-label' }, '搜索: '),
+          h('span', { class: 'query-label' }, 'Search: '),
           h('span', { class: 'query-text' }, props.result.query)
         ])
       ]),
@@ -1621,19 +1621,19 @@ const QuickSearchDisplay = {
           class: ['quicksearch-tab', { active: activeTab.value === 'facts' }],
           onClick: () => { activeTab.value = 'facts' }
         }, [
-          h('span', { class: 'tab-label' }, `事实 (${props.result.facts.length})`)
+          h('span', { class: 'tab-label' }, `Facts (${props.result.facts.length})`)
         ]),
         hasEdges.value && h('button', {
           class: ['quicksearch-tab', { active: activeTab.value === 'edges' }],
           onClick: () => { activeTab.value = 'edges' }
         }, [
-          h('span', { class: 'tab-label' }, `关系 (${props.result.edges.length})`)
+          h('span', { class: 'tab-label' }, `Relations (${props.result.edges.length})`)
         ]),
         hasNodes.value && h('button', {
           class: ['quicksearch-tab', { active: activeTab.value === 'nodes' }],
           onClick: () => { activeTab.value = 'nodes' }
         }, [
-          h('span', { class: 'tab-label' }, `节点 (${props.result.nodes.length})`)
+          h('span', { class: 'tab-label' }, `Nodes (${props.result.nodes.length})`)
         ])
       ]),
       
@@ -1642,8 +1642,8 @@ const QuickSearchDisplay = {
         // Facts (always show if no tabs, or when facts tab is active)
         ((!showTabs.value) || activeTab.value === 'facts') && h('div', { class: 'facts-panel' }, [
           !showTabs.value && h('div', { class: 'panel-header' }, [
-            h('span', { class: 'panel-title' }, '搜索结果'),
-            h('span', { class: 'panel-count' }, `共 ${props.result.facts.length} 条`)
+            h('span', { class: 'panel-title' }, 'Search Results'),
+            h('span', { class: 'panel-count' }, `${props.result.facts.length} total`)
           ]),
           props.result.facts.length > 0 ? h('div', { class: 'facts-list' },
             (expandedFacts.value ? props.result.facts : props.result.facts.slice(0, INITIAL_SHOW_COUNT)).map((fact, i) => 
@@ -1652,18 +1652,18 @@ const QuickSearchDisplay = {
                 h('div', { class: 'fact-content' }, fact)
               ])
             )
-          ) : h('div', { class: 'empty-state' }, '未找到相关结果'),
+          ) : h('div', { class: 'empty-state' }, 'No relevant results found'),
           props.result.facts.length > INITIAL_SHOW_COUNT && h('button', {
             class: 'expand-btn',
             onClick: () => { expandedFacts.value = !expandedFacts.value }
-          }, expandedFacts.value ? `收起 ▲` : `展开全部 ${props.result.facts.length} 条 ▼`)
+          }, expandedFacts.value ? `Collapse ▲` : `Show all ${props.result.facts.length} items ▼`)
         ]),
         
         // Edges Tab
         activeTab.value === 'edges' && hasEdges.value && h('div', { class: 'edges-panel' }, [
           h('div', { class: 'panel-header' }, [
-            h('span', { class: 'panel-title' }, '相关关系'),
-            h('span', { class: 'panel-count' }, `共 ${props.result.edges.length} 条`)
+            h('span', { class: 'panel-title' }, 'Related Relations'),
+            h('span', { class: 'panel-count' }, `${props.result.edges.length} total`)
           ]),
           h('div', { class: 'edges-list' },
             props.result.edges.map((edge, i) => 
@@ -1683,8 +1683,8 @@ const QuickSearchDisplay = {
         // Nodes Tab
         activeTab.value === 'nodes' && hasNodes.value && h('div', { class: 'nodes-panel' }, [
           h('div', { class: 'panel-header' }, [
-            h('span', { class: 'panel-title' }, '相关节点'),
-            h('span', { class: 'panel-count' }, `共 ${props.result.nodes.length} 个`)
+            h('span', { class: 'panel-title' }, 'Related Nodes'),
+            h('span', { class: 'panel-count' }, `${props.result.nodes.length} total`)
           ]),
           h('div', { class: 'nodes-grid' },
             props.result.nodes.map((node, i) => 
@@ -1764,19 +1764,19 @@ const isFinalizing = computed(() => {
   return !isComplete.value && isPlanningDone.value && totalSections.value > 0 && completedSections.value >= totalSections.value
 })
 
-// 当前活跃的步骤（用于顶部显示）
+// Currently active step (for top display)
 const activeStep = computed(() => {
   const steps = workflowSteps.value
-  // 找到当前 active 的步骤
+  // Find the currently active step
   const active = steps.find(s => s.status === 'active')
   if (active) return active
   
-  // 如果没有 active，返回最后一个 done 的步骤
+  // If no active step, return the last done step
   const doneSteps = steps.filter(s => s.status === 'done')
   if (doneSteps.length > 0) return doneSteps[doneSteps.length - 1]
   
-  // 否则返回第一个步骤
-  return steps[0] || { noLabel: '--', title: '等待开始', status: 'todo', meta: '' }
+  // Otherwise return the first step
+  return steps[0] || { noLabel: '--', title: 'Waiting to start', status: 'todo', meta: '' }
 })
 
 const workflowSteps = computed(() => {
@@ -1869,25 +1869,25 @@ const truncateText = (text, maxLen) => {
 const renderMarkdown = (content) => {
   if (!content) return ''
   
-  // 去掉开头的二级标题（## xxx），因为章节标题已在外层显示
+  // Remove leading level-2 heading (## xxx), since section title is already displayed in outer layer
   let processedContent = content.replace(/^##\s+.+\n+/, '')
   
-  // 处理代码块
+  // Process code blocks
   let html = processedContent.replace(/```(\w*)\n([\s\S]*?)```/g, '<pre class="code-block"><code>$2</code></pre>')
   
-  // 处理行内代码
+  // Process inline code
   html = html.replace(/`([^`]+)`/g, '<code class="inline-code">$1</code>')
   
-  // 处理标题
+  // Process headings
   html = html.replace(/^#### (.+)$/gm, '<h5 class="md-h5">$1</h5>')
   html = html.replace(/^### (.+)$/gm, '<h4 class="md-h4">$1</h4>')
   html = html.replace(/^## (.+)$/gm, '<h3 class="md-h3">$1</h3>')
   html = html.replace(/^# (.+)$/gm, '<h2 class="md-h2">$1</h2>')
   
-  // 处理引用块
+  // Process blockquotes
   html = html.replace(/^> (.+)$/gm, '<blockquote class="md-quote">$1</blockquote>')
   
-  // 处理列表 - 支持子列表
+  // Process lists - support sub-lists
   html = html.replace(/^(\s*)- (.+)$/gm, (match, indent, text) => {
     const level = Math.floor(indent.length / 2)
     return `<li class="md-li" data-level="${level}">${text}</li>`
@@ -1897,52 +1897,52 @@ const renderMarkdown = (content) => {
     return `<li class="md-oli" data-level="${level}">${text}</li>`
   })
 
-  // 包装无序列表
+  // Wrap unordered lists
   html = html.replace(/(<li class="md-li"[^>]*>.*?<\/li>\s*)+/g, '<ul class="md-ul">$&</ul>')
-  // 包装有序列表
+  // Wrap ordered lists
   html = html.replace(/(<li class="md-oli"[^>]*>.*?<\/li>\s*)+/g, '<ol class="md-ol">$&</ol>')
 
-  // 清理列表项之间的所有空白
+  // Clean up whitespace between list items
   html = html.replace(/<\/li>\s+<li/g, '</li><li')
-  // 清理列表开始标签后的空白
+  // Clean up whitespace after list opening tags
   html = html.replace(/<ul class="md-ul">\s+/g, '<ul class="md-ul">')
   html = html.replace(/<ol class="md-ol">\s+/g, '<ol class="md-ol">')
-  // 清理列表结束标签前的空白
+  // Clean up whitespace before list closing tags
   html = html.replace(/\s+<\/ul>/g, '</ul>')
   html = html.replace(/\s+<\/ol>/g, '</ol>')
   
-  // 处理粗体和斜体
+  // Process bold and italic
   html = html.replace(/\*\*(.+?)\*\*/g, '<strong>$1</strong>')
   html = html.replace(/\*(.+?)\*/g, '<em>$1</em>')
   html = html.replace(/_(.+?)_/g, '<em>$1</em>')
   
-  // 处理分隔线
+  // Process horizontal rules
   html = html.replace(/^---$/gm, '<hr class="md-hr">')
   
-  // 处理换行 - 空行变成段落分隔，单换行变成 <br>
+  // Process line breaks - empty lines become paragraph separators, single newlines become <br>
   html = html.replace(/\n\n/g, '</p><p class="md-p">')
   html = html.replace(/\n/g, '<br>')
   
-  // 包装在段落中
+  // Wrap in paragraphs
   html = '<p class="md-p">' + html + '</p>'
   
-  // 清理空段落
+  // Clean up empty paragraphs
   html = html.replace(/<p class="md-p"><\/p>/g, '')
   html = html.replace(/<p class="md-p">(<h[2-5])/g, '$1')
   html = html.replace(/(<\/h[2-5]>)<\/p>/g, '$1')
   html = html.replace(/<p class="md-p">(<ul|<ol|<blockquote|<pre|<hr)/g, '$1')
   html = html.replace(/(<\/ul>|<\/ol>|<\/blockquote>|<\/pre>)<\/p>/g, '$1')
-  // 清理块级元素前后的 <br> 标签
+  // Clean up <br> tags around block-level elements
   html = html.replace(/<br>\s*(<ul|<ol|<blockquote)/g, '$1')
   html = html.replace(/(<\/ul>|<\/ol>|<\/blockquote>)\s*<br>/g, '$1')
-  // 清理 <p><br> 紧跟块级元素的情况（多余空行导致）
+  // Clean up <p><br> followed by block-level elements (caused by extra blank lines)
   html = html.replace(/<p class="md-p">(<br>\s*)+(<ul|<ol|<blockquote|<pre|<hr)/g, '$2')
-  // 清理连续的 <br> 标签
+  // Clean up consecutive <br> tags
   html = html.replace(/(<br>\s*){2,}/g, '<br>')
-  // 清理块级元素后紧跟的段落开始标签前的 <br>
+  // Clean up <br> before paragraph start tags that follow block-level elements
   html = html.replace(/(<\/ol>|<\/ul>|<\/blockquote>)<br>(<p|<div)/g, '$1$2')
 
-  // 修复非连续有序列表的编号：当单项 <ol> 被段落内容隔开时，保持编号递增
+  // Fix non-contiguous ordered list numbering: when single-item <ol> are separated by paragraph content, maintain incrementing numbers
   const tokens = html.split(/(<ol class="md-ol">(?:<li class="md-oli"[^>]*>[\s\S]*?<\/li>)+<\/ol>)/g)
   let olCounter = 0
   let inSequence = false
@@ -2008,7 +2008,7 @@ const getActionLabel = (action) => {
 const getLogLevelClass = (log) => {
   if (log.includes('ERROR') || log.includes('错误')) return 'error'
   if (log.includes('WARNING') || log.includes('警告')) return 'warning'
-  // INFO 使用默认颜色，不标记为 success
+  // INFO uses default color, not marked as success
   return ''
 }
 
@@ -2037,11 +2037,11 @@ const fetchAgentLog = async () => {
             currentSectionIndex.value = log.section_index
           }
 
-          // section_complete - 章节生成完成
+          // section_complete - section generation complete
           if (log.action === 'section_complete') {
             if (log.details?.content) {
               generatedSections.value[log.section_index] = log.details.content
-              // 自动展开刚生成的章节
+              // Auto-expand newly generated section
               expandedContent.value.add(log.section_index - 1)
               currentSectionIndex.value = null
             }
@@ -2049,10 +2049,10 @@ const fetchAgentLog = async () => {
           
           if (log.action === 'report_complete') {
             isComplete.value = true
-            currentSectionIndex.value = null  // 确保清除 loading 状态
+            currentSectionIndex.value = null  // Ensure loading state is cleared
             emit('update-status', 'completed')
             stopPolling()
-            // 滚动逻辑统一在循环结束后的 nextTick 中处理
+            // Scroll logic handled uniformly in nextTick after loop ends
           }
           
           if (log.action === 'report_start') {
@@ -2064,7 +2064,7 @@ const fetchAgentLog = async () => {
         
         nextTick(() => {
           if (rightPanel.value) {
-            // 如果任务已完成，滚动到顶部；否则滚动到底部跟随最新日志
+            // If task is complete, scroll to top; otherwise scroll to bottom to follow latest logs
             if (isComplete.value) {
               rightPanel.value.scrollTop = 0
             } else {
@@ -2079,39 +2079,39 @@ const fetchAgentLog = async () => {
   }
 }
 
-// 提取最终答案内容 - 从 LLM response 中提取章节内容
+// Extract final answer content - extract section content from LLM response
 const extractFinalContent = (response) => {
   if (!response) return null
   
-  // 尝试提取 <final_answer> 标签内的内容
+  // Try to extract content within <final_answer> tags
   const finalAnswerTagMatch = response.match(/<final_answer>([\s\S]*?)<\/final_answer>/)
   if (finalAnswerTagMatch) {
     return finalAnswerTagMatch[1].trim()
   }
   
-  // 尝试找 Final Answer: 后面的内容（支持多种格式）
-  // 格式1: Final Answer:\n\n内容
-  // 格式2: Final Answer: 内容
+  // Try to find content after Final Answer: (supports multiple formats)
+  // Format 1: Final Answer:\n\ncontent
+  // Format 2: Final Answer: content
   const finalAnswerMatch = response.match(/Final\s*Answer:\s*\n*([\s\S]*)$/i)
   if (finalAnswerMatch) {
     return finalAnswerMatch[1].trim()
   }
   
-  // 尝试找 最终答案: 后面的内容
+  // Try to find content after Chinese "Final Answer:" marker
   const chineseFinalMatch = response.match(/最终答案[:：]\s*\n*([\s\S]*)$/i)
   if (chineseFinalMatch) {
     return chineseFinalMatch[1].trim()
   }
   
-  // 如果以 ## 或 # 或 > 开头，可能是直接的 markdown 内容
+  // If starts with ## or # or >, it might be direct markdown content
   const trimmedResponse = response.trim()
   if (trimmedResponse.match(/^[#>]/)) {
     return trimmedResponse
   }
   
-  // 如果内容较长且包含markdown格式，尝试移除思考过程后返回
+  // If content is long and contains markdown formatting, try removing thought process and return
   if (response.length > 300 && (response.includes('**') || response.includes('>'))) {
-    // 移除 Thought: 开头的思考过程
+    // Remove thought process starting with Thought:
     const thoughtMatch = response.match(/^Thought:[\s\S]*?(?=\n\n[^T]|\n\n$)/i)
     if (thoughtMatch) {
       const afterThought = response.substring(thoughtMatch[0].length).trim()
@@ -2456,7 +2456,7 @@ watch(() => props.reportId, (newId) => {
 .section-number {
   font-family: 'JetBrains Mono', monospace;
   font-size: 16px;
-  color: #9CA3AF; /* 深灰色，不随状态变化 */
+  color: #9CA3AF; /* dark gray, does not change with status */
   font-weight: 500;
 }
 
@@ -3898,7 +3898,7 @@ watch(() => props.reportId, (newId) => {
   overflow: hidden;
 }
 
-/* Selection Reason - 选择理由 */
+/* Selection Reason */
 :deep(.interview-display .selection-reason) {
   background: #F8FAFC;
   border: 1px solid #E2E8F0;
@@ -5097,7 +5097,7 @@ watch(() => props.reportId, (newId) => {
   border-radius: 4px;
 }
 
-/* Console Logs - 与 Step3Simulation.vue 保持一致 */
+/* Console Logs - consistent with Step3Simulation.vue */
 .console-logs {
   background: #000;
   color: #DDD;
diff --git a/frontend/src/components/Step5Interaction.vue b/frontend/src/components/Step5Interaction.vue
index 3d84c7ce0..0e9630d09 100644
--- a/frontend/src/components/Step5Interaction.vue
+++ b/frontend/src/components/Step5Interaction.vue
@@ -58,7 +58,7 @@
                       <path d="M12 2a10 10 0 0 1 10 10" stroke-width="4" stroke="#4B5563" stroke-linecap="round"></path>
                     </svg>
                   </div>
-                  <span class="loading-text">正在生成{{ section.title }}...</span>
+                  <span class="loading-text">Generating {{ section.title }}...</span>
                 </div>
               </div>
             </div>
@@ -98,7 +98,7 @@
               <svg viewBox="0 0 24 24" width="14" height="14" fill="none" stroke="currentColor" stroke-width="2">
                 <path d="M14.7 6.3a1 1 0 0 0 0 1.4l1.6 1.6a1 1 0 0 0 1.4 0l3.77-3.77a6 6 0 0 1-7.94 7.94l-6.91 6.91a2.12 2.12 0 0 1-3-3l6.91-6.91a6 6 0 0 1 7.94-7.94l-3.76 3.76z"></path>
               </svg>
-              <span>与Report Agent对话</span>
+              <span>Chat with Report Agent</span>
             </button>
             <div class="agent-dropdown" v-if="profiles.length > 0">
               <button 
@@ -110,13 +110,13 @@
                   <path d="M20 21v-2a4 4 0 0 0-4-4H8a4 4 0 0 0-4 4v2"></path>
                   <circle cx="12" cy="7" r="4"></circle>
                 </svg>
-                <span>{{ selectedAgent ? selectedAgent.username : '与世界中任意个体对话' }}</span>
+                <span>{{ selectedAgent ? selectedAgent.username : 'Chat with anyone in the world' }}</span>
                 <svg class="dropdown-arrow" :class="{ open: showAgentDropdown }" viewBox="0 0 24 24" width="12" height="12" fill="none" stroke="currentColor" stroke-width="2">
                   <polyline points="6 9 12 15 18 9"></polyline>
                 </svg>
               </button>
               <div v-if="showAgentDropdown" class="dropdown-menu">
-                <div class="dropdown-header">选择对话对象</div>
+                <div class="dropdown-header">Select Chat Target</div>
                 <div 
                   v-for="(agent, idx) in profiles" 
                   :key="idx"
@@ -126,7 +126,7 @@
                   <div class="agent-avatar">{{ (agent.username || 'A')[0] }}</div>
                   <div class="agent-info">
                     <span class="agent-name">{{ agent.username }}</span>
-                    <span class="agent-role">{{ agent.profession || '未知职业' }}</span>
+                    <span class="agent-role">{{ agent.profession || 'Unknown Occupation' }}</span>
                   </div>
                 </div>
               </div>
@@ -141,7 +141,7 @@
                 <path d="M9 11l3 3L22 4"></path>
                 <path d="M21 12v7a2 2 0 0 1-2 2H5a2 2 0 0 1-2-2V5a2 2 0 0 1 2-2h11"></path>
               </svg>
-              <span>发送问卷调查到世界中</span>
+              <span>Send Survey to the World</span>
             </button>
           </div>
         </div>
@@ -155,7 +155,7 @@
               <div class="tools-card-avatar">R</div>
               <div class="tools-card-info">
                 <div class="tools-card-name">Report Agent - Chat</div>
-                <div class="tools-card-subtitle">报告生成智能体的快速对话版本，可调用 4 种专业工具，拥有MiroFish的完整记忆</div>
+                <div class="tools-card-subtitle">Quick chat version of the report generation agent, with 4 professional tools and full MiroFish memory access</div>
               </div>
               <button class="tools-card-toggle" @click="showToolsDetail = !showToolsDetail">
                 <svg :class="{ 'is-expanded': showToolsDetail }" viewBox="0 0 24 24" width="16" height="16" fill="none" stroke="currentColor" stroke-width="2">
@@ -172,8 +172,8 @@
                     </svg>
                   </div>
                   <div class="tool-content">
-                    <div class="tool-name">InsightForge 深度归因</div>
-                    <div class="tool-desc">对齐现实世界种子数据与模拟环境状态，结合Global/Local Memory机制，提供跨时空的深度归因分析</div>
+                    <div class="tool-name">InsightForge Deep Attribution</div>
+                    <div class="tool-desc">Aligns real-world seed data with simulation state, combining Global/Local Memory for cross-temporal deep attribution analysis</div>
                   </div>
                 </div>
                 <div class="tool-item tool-blue">
@@ -184,8 +184,8 @@
                     </svg>
                   </div>
                   <div class="tool-content">
-                    <div class="tool-name">PanoramaSearch 全景追踪</div>
-                    <div class="tool-desc">基于图结构的广度遍历算法，重构事件传播路径，捕获全量信息流动的拓扑结构</div>
+                    <div class="tool-name">PanoramaSearch Panoramic Tracking</div>
+                    <div class="tool-desc">Graph-based breadth traversal algorithm, reconstructing event propagation paths and capturing full information flow topology</div>
                   </div>
                 </div>
                 <div class="tool-item tool-orange">
@@ -195,8 +195,8 @@
                     </svg>
                   </div>
                   <div class="tool-content">
-                    <div class="tool-name">QuickSearch 快速检索</div>
-                    <div class="tool-desc">基于 GraphRAG 的即时查询接口，优化索引效率，用于快速提取具体的节点属性与离散事实</div>
+                    <div class="tool-name">QuickSearch Quick Retrieval</div>
+                    <div class="tool-desc">GraphRAG-based instant query interface, optimized for efficient extraction of specific node attributes and discrete facts</div>
                   </div>
                 </div>
                 <div class="tool-item tool-green">
@@ -208,8 +208,8 @@
                     </svg>
                   </div>
                   <div class="tool-content">
-                    <div class="tool-name">InterviewSubAgent 虚拟访谈</div>
-                    <div class="tool-desc">自主式访谈，能够并行与模拟世界中个体进行多轮对话，采集非结构化的观点数据与心理状态</div>
+                    <div class="tool-name">InterviewSubAgent Virtual Interview</div>
+                    <div class="tool-desc">Autonomous interviews, conducting parallel multi-round conversations with simulated individuals to collect unstructured opinion data and psychological states</div>
                   </div>
                 </div>
               </div>
@@ -224,7 +224,7 @@
                 <div class="profile-card-name">{{ selectedAgent.username }}</div>
                 <div class="profile-card-meta">
                   <span v-if="selectedAgent.name" class="profile-card-handle">@{{ selectedAgent.name }}</span>
-                  <span class="profile-card-profession">{{ selectedAgent.profession || '未知职业' }}</span>
+                  <span class="profile-card-profession">{{ selectedAgent.profession || 'Unknown Occupation' }}</span>
                 </div>
               </div>
               <button class="profile-card-toggle" @click="showFullProfile = !showFullProfile">
@@ -235,7 +235,7 @@
             </div>
             <div v-if="showFullProfile && selectedAgent.bio" class="profile-card-body">
               <div class="profile-card-bio">
-                <div class="profile-card-label">简介</div>
+                <div class="profile-card-label">Bio</div>
                 <p>{{ selectedAgent.bio }}</p>
               </div>
             </div>
@@ -250,7 +250,7 @@
                 </svg>
               </div>
               <p class="empty-text">
-                {{ chatTarget === 'report_agent' ? '与 Report Agent 对话，深入了解报告内容' : '与模拟个体对话，了解他们的观点' }}
+                {{ chatTarget === 'report_agent' ? 'Chat with Report Agent to explore report details' : 'Chat with simulated individuals to understand their perspectives' }}
               </p>
             </div>
             <div 
@@ -292,7 +292,7 @@
             <textarea 
               v-model="chatInput"
               class="chat-input"
-              placeholder="输入您的问题..."
+              placeholder="Enter your question..."
               @keydown.enter.exact.prevent="sendMessage"
               :disabled="isSending || (!selectedAgent && chatTarget === 'agent')"
               rows="1"
@@ -317,8 +317,8 @@
           <div class="survey-setup">
             <div class="setup-section">
               <div class="section-header">
-                <span class="section-title">选择调查对象</span>
-                <span class="selection-count">已选 {{ selectedAgents.size }} / {{ profiles.length }}</span>
+                <span class="section-title">Select Survey Targets</span>
+                <span class="selection-count">Selected {{ selectedAgents.size }} / {{ profiles.length }}</span>
               </div>
               <div class="agents-grid">
                 <label 
@@ -335,7 +335,7 @@
                   <div class="checkbox-avatar">{{ (agent.username || 'A')[0] }}</div>
                   <div class="checkbox-info">
                     <span class="checkbox-name">{{ agent.username }}</span>
-                    <span class="checkbox-role">{{ agent.profession || '未知职业' }}</span>
+                    <span class="checkbox-role">{{ agent.profession || 'Unknown Occupation' }}</span>
                   </div>
                   <div class="checkbox-indicator">
                     <svg viewBox="0 0 24 24" width="16" height="16" fill="none" stroke="currentColor" stroke-width="3">
@@ -345,20 +345,20 @@
                 </label>
               </div>
               <div class="selection-actions">
-                <button class="action-link" @click="selectAllAgents">全选</button>
+                <button class="action-link" @click="selectAllAgents">Select All</button>
                 <span class="action-divider">|</span>
-                <button class="action-link" @click="clearAgentSelection">清空</button>
+                <button class="action-link" @click="clearAgentSelection">Clear</button>
               </div>
             </div>
 
             <div class="setup-section">
               <div class="section-header">
-                <span class="section-title">问卷问题</span>
+                <span class="section-title">Survey Question</span>
               </div>
               <textarea 
                 v-model="surveyQuestion"
                 class="survey-input"
-                placeholder="输入您想问所有被选中对象的问题..."
+                placeholder="Enter the question you want to ask all selected targets..."
                 rows="3"
               ></textarea>
             </div>
@@ -369,15 +369,15 @@
               @click="submitSurvey"
             >
               <span v-if="isSurveying" class="loading-spinner"></span>
-              <span v-else>发送问卷</span>
+              <span v-else>Send Survey</span>
             </button>
           </div>
 
           <!-- Survey Results -->
           <div v-if="surveyResults.length > 0" class="survey-results">
             <div class="results-header">
-              <span class="results-title">调查结果</span>
-              <span class="results-count">{{ surveyResults.length }} 条回复</span>
+              <span class="results-title">Survey Results</span>
+              <span class="results-count">{{ surveyResults.length }} responses</span>
             </div>
             <div class="results-list">
               <div 
@@ -389,7 +389,7 @@
                   <div class="result-avatar">{{ (result.agent_name || 'A')[0] }}</div>
                   <div class="result-info">
                     <span class="result-name">{{ result.agent_name }}</span>
-                    <span class="result-role">{{ result.profession || '未知职业' }}</span>
+                    <span class="result-role">{{ result.profession || 'Unknown Occupation' }}</span>
                   </div>
                 </div>
                 <div class="result-question">
@@ -434,7 +434,7 @@ const showToolsDetail = ref(true)
 // Chat State
 const chatInput = ref('')
 const chatHistory = ref([])
-const chatHistoryCache = ref({}) // 缓存所有对话记录: { 'report_agent': [], 'agent_0': [], 'agent_1': [], ... }
+const chatHistoryCache = ref({}) // Cache all chat histories: { 'report_agent': [], 'agent_0': [], 'agent_1': [], ... }
 const isSending = ref(false)
 const chatMessages = ref(null)
 const chatInputRef = ref(null)
@@ -484,7 +484,7 @@ const selectChatTarget = (target) => {
   }
 }
 
-// 保存当前对话记录到缓存
+// Save current chat history to cache
 const saveChatHistory = () => {
   if (chatHistory.value.length === 0) return
   
@@ -496,16 +496,16 @@ const saveChatHistory = () => {
 }
 
 const selectReportAgentChat = () => {
-  // 保存当前对话记录
+  // Save current chat history
   saveChatHistory()
-  
+
   activeTab.value = 'chat'
   chatTarget.value = 'report_agent'
   selectedAgent.value = null
   selectedAgentIndex.value = null
   showAgentDropdown.value = false
-  
-  // 恢复 Report Agent 的对话记录
+
+  // Restore Report Agent chat history
   chatHistory.value = chatHistoryCache.value['report_agent'] || []
 }
 
@@ -525,17 +525,17 @@ const toggleAgentDropdown = () => {
 }
 
 const selectAgent = (agent, idx) => {
-  // 保存当前对话记录
+  // Save current chat history
   saveChatHistory()
-  
+
   selectedAgent.value = agent
   selectedAgentIndex.value = idx
   chatTarget.value = 'agent'
   showAgentDropdown.value = false
-  
-  // 恢复该 Agent 的对话记录
+
+  // Restore this agent's chat history
   chatHistory.value = chatHistoryCache.value[`agent_${idx}`] || []
-  addLog(`选择对话对象: ${agent.username}`)
+  addLog(`Selected chat target: ${agent.username}`)
 }
 
 const formatTime = (timestamp) => {
@@ -563,7 +563,7 @@ const renderMarkdown = (content) => {
   html = html.replace(/^# (.+)$/gm, '<h2 class="md-h2">$1</h2>')
   html = html.replace(/^> (.+)$/gm, '<blockquote class="md-quote">$1</blockquote>')
   
-  // 处理列表 - 支持子列表
+  // Process lists - support nested lists
   html = html.replace(/^(\s*)- (.+)$/gm, (match, indent, text) => {
     const level = Math.floor(indent.length / 2)
     return `<li class="md-li" data-level="${level}">${text}</li>`
@@ -573,17 +573,17 @@ const renderMarkdown = (content) => {
     return `<li class="md-oli" data-level="${level}">${text}</li>`
   })
   
-  // 包装无序列表
+  // Wrap unordered lists
   html = html.replace(/(<li class="md-li"[^>]*>.*?<\/li>\s*)+/g, '<ul class="md-ul">$&</ul>')
-  // 包装有序列表
+  // Wrap ordered lists
   html = html.replace(/(<li class="md-oli"[^>]*>.*?<\/li>\s*)+/g, '<ol class="md-ol">$&</ol>')
   
-  // 清理列表项之间的所有空白
+  // Clean whitespace between list items
   html = html.replace(/<\/li>\s+<li/g, '</li><li')
-  // 清理列表开始标签后的空白
+  // Clean whitespace after list opening tags
   html = html.replace(/<ul class="md-ul">\s+/g, '<ul class="md-ul">')
   html = html.replace(/<ol class="md-ol">\s+/g, '<ol class="md-ol">')
-  // 清理列表结束标签前的空白
+  // Clean whitespace before list closing tags
   html = html.replace(/\s+<\/ul>/g, '</ul>')
   html = html.replace(/\s+<\/ol>/g, '</ol>')
   
@@ -599,17 +599,17 @@ const renderMarkdown = (content) => {
   html = html.replace(/(<\/h[2-5]>)<\/p>/g, '$1')
   html = html.replace(/<p class="md-p">(<ul|<ol|<blockquote|<pre|<hr)/g, '$1')
   html = html.replace(/(<\/ul>|<\/ol>|<\/blockquote>|<\/pre>)<\/p>/g, '$1')
-  // 清理块级元素前后的 <br> 标签
+  // Clean <br> tags around block-level elements
   html = html.replace(/<br>\s*(<ul|<ol|<blockquote)/g, '$1')
   html = html.replace(/(<\/ul>|<\/ol>|<\/blockquote>)\s*<br>/g, '$1')
-  // 清理 <p><br> 紧跟块级元素的情况（多余空行导致）
+  // Clean <p><br> followed by block-level elements (caused by extra blank lines)
   html = html.replace(/<p class="md-p">(<br>\s*)+(<ul|<ol|<blockquote|<pre|<hr)/g, '$2')
-  // 清理连续的 <br> 标签
+  // Clean consecutive <br> tags
   html = html.replace(/(<br>\s*){2,}/g, '<br>')
-  // 清理块级元素后紧跟的段落开始标签前的 <br>
+  // Clean <br> between block-level closing tags and paragraph opening tags
   html = html.replace(/(<\/ol>|<\/ul>|<\/blockquote>)<br>(<p|<div)/g, '$1$2')
 
-  // 修复非连续有序列表的编号：当单项 <ol> 被段落内容隔开时，保持编号递增
+  // Fix non-contiguous ordered list numbering: keep incrementing when single-item <ol> blocks are separated by paragraph content
   const tokens = html.split(/(<ol class="md-ol">(?:<li class="md-oli"[^>]*>[\s\S]*?<\/li>)+<\/ol>)/g)
   let olCounter = 0
   let inSequence = false
@@ -662,22 +662,22 @@ const sendMessage = async () => {
       await sendToAgent(message)
     }
   } catch (err) {
-    addLog(`发送失败: ${err.message}`)
+    addLog(`Send failed: ${err.message}`)
     chatHistory.value.push({
       role: 'assistant',
-      content: `抱歉，发生了错误: ${err.message}`,
+      content: `Sorry, an error occurred: ${err.message}`,
       timestamp: new Date().toISOString()
     })
   } finally {
     isSending.value = false
     scrollToBottom()
-    // 自动保存对话记录到缓存
+    // Auto-save chat history to cache
     saveChatHistory()
   }
 }
 
 const sendToReportAgent = async (message) => {
-  addLog(`向 Report Agent 发送: ${message.substring(0, 50)}...`)
+  addLog(`Sent to Report Agent: ${message.substring(0, 50)}...`)
   
   // Build chat history for API
   const historyForApi = chatHistory.value
@@ -697,21 +697,21 @@ const sendToReportAgent = async (message) => {
   if (res.success && res.data) {
     chatHistory.value.push({
       role: 'assistant',
-      content: res.data.response || res.data.answer || '无响应',
+      content: res.data.response || res.data.answer || 'No response',
       timestamp: new Date().toISOString()
     })
-    addLog('Report Agent 已回复')
+    addLog('Report Agent replied')
   } else {
-    throw new Error(res.error || '请求失败')
+    throw new Error(res.error || 'Request failed')
   }
 }
 
 const sendToAgent = async (message) => {
   if (!selectedAgent.value || selectedAgentIndex.value === null) {
-    throw new Error('请先选择一个模拟个体')
+    throw new Error('Please select a simulated individual first')
   }
   
-  addLog(`向 ${selectedAgent.value.username} 发送: ${message.substring(0, 50)}...`)
+  addLog(`Sent to ${selectedAgent.value.username}: ${message.substring(0, 50)}...`)
   
   // Build prompt with chat history
   let prompt = message
@@ -719,9 +719,9 @@ const sendToAgent = async (message) => {
     const historyContext = chatHistory.value
       .filter(msg => msg.content !== message)
       .slice(-6)
-      .map(msg => `${msg.role === 'user' ? '提问者' : '你'}：${msg.content}`)
+      .map(msg => `${msg.role === 'user' ? 'Interviewer' : 'You'}: ${msg.content}`)
       .join('\n')
-    prompt = `以下是我们之前的对话：\n${historyContext}\n\n现在我的新问题是：${message}`
+    prompt = `Here is our previous conversation:\n${historyContext}\n\nMy new question is: ${message}`
   }
   
   const res = await interviewAgents({
@@ -733,17 +733,17 @@ const sendToAgent = async (message) => {
   })
   
   if (res.success && res.data) {
-    // 正确的数据路径: res.data.result.results 是一个对象字典
-    // 格式: {"twitter_0": {...}, "reddit_0": {...}} 或单平台 {"reddit_0": {...}}
+    // Correct data path: res.data.result.results is an object dictionary
+    // Format: {"twitter_0": {...}, "reddit_0": {...}} or single platform {"reddit_0": {...}}
     const resultData = res.data.result || res.data
     const resultsDict = resultData.results || resultData
-    
-    // 将对象字典转换为数组，优先获取 reddit 平台的回复
+
+    // Convert object dictionary to array, prefer reddit platform responses
     let responseContent = null
     const agentId = selectedAgentIndex.value
     
     if (typeof resultsDict === 'object' && !Array.isArray(resultsDict)) {
-      // 优先使用 reddit 平台回复，其次 twitter
+      // Prefer reddit platform response, fallback to twitter
       const redditKey = `reddit_${agentId}`
       const twitterKey = `twitter_${agentId}`
       const agentResult = resultsDict[redditKey] || resultsDict[twitterKey] || Object.values(resultsDict)[0]
@@ -751,7 +751,7 @@ const sendToAgent = async (message) => {
         responseContent = agentResult.response || agentResult.answer
       }
     } else if (Array.isArray(resultsDict) && resultsDict.length > 0) {
-      // 兼容数组格式
+      // Compatible with array format
       responseContent = resultsDict[0].response || resultsDict[0].answer
     }
     
@@ -761,12 +761,12 @@ const sendToAgent = async (message) => {
         content: responseContent,
         timestamp: new Date().toISOString()
       })
-      addLog(`${selectedAgent.value.username} 已回复`)
+      addLog(`${selectedAgent.value.username} replied`)
     } else {
-      throw new Error('无响应数据')
+      throw new Error('No response data')
     }
   } else {
-    throw new Error(res.error || '请求失败')
+    throw new Error(res.error || 'Request failed')
   }
 }
 
@@ -803,7 +803,7 @@ const submitSurvey = async () => {
   if (selectedAgents.value.size === 0 || !surveyQuestion.value.trim()) return
   
   isSurveying.value = true
-  addLog(`发送问卷给 ${selectedAgents.value.size} 个对象...`)
+  addLog(`Sending survey to ${selectedAgents.value.size} targets...`)
   
   try {
     const interviews = Array.from(selectedAgents.value).map(idx => ({
@@ -817,33 +817,33 @@ const submitSurvey = async () => {
     })
     
     if (res.success && res.data) {
-      // 正确的数据路径: res.data.result.results 是一个对象字典
-      // 格式: {"twitter_0": {...}, "reddit_0": {...}, "twitter_1": {...}, ...}
+      // Correct data path: res.data.result.results is an object dictionary
+      // Format: {"twitter_0": {...}, "reddit_0": {...}, "twitter_1": {...}, ...}
       const resultData = res.data.result || res.data
       const resultsDict = resultData.results || resultData
-      
-      // 将对象字典转换为数组格式
+
+      // Convert object dictionary to array format
       const surveyResultsList = []
       
       for (const interview of interviews) {
         const agentIdx = interview.agent_id
         const agent = profiles.value[agentIdx]
         
-        // 优先使用 reddit 平台回复，其次 twitter
-        let responseContent = '无响应'
+        // Prefer reddit platform response, fallback to twitter
+        let responseContent = 'No response'
         
         if (typeof resultsDict === 'object' && !Array.isArray(resultsDict)) {
           const redditKey = `reddit_${agentIdx}`
           const twitterKey = `twitter_${agentIdx}`
           const agentResult = resultsDict[redditKey] || resultsDict[twitterKey]
           if (agentResult) {
-            responseContent = agentResult.response || agentResult.answer || '无响应'
+            responseContent = agentResult.response || agentResult.answer || 'No response'
           }
         } else if (Array.isArray(resultsDict)) {
-          // 兼容数组格式
+          // Compatible with array format
           const matchedResult = resultsDict.find(r => r.agent_id === agentIdx)
           if (matchedResult) {
-            responseContent = matchedResult.response || matchedResult.answer || '无响应'
+            responseContent = matchedResult.response || matchedResult.answer || 'No response'
           }
         }
         
@@ -857,12 +857,12 @@ const submitSurvey = async () => {
       }
       
       surveyResults.value = surveyResultsList
-      addLog(`收到 ${surveyResults.value.length} 条回复`)
+      addLog(`Received ${surveyResults.value.length} responses`)
     } else {
-      throw new Error(res.error || '请求失败')
+      throw new Error(res.error || 'Request failed')
     }
   } catch (err) {
-    addLog(`问卷发送失败: ${err.message}`)
+    addLog(`Survey send failed: ${err.message}`)
   } finally {
     isSurveying.value = false
   }
@@ -873,7 +873,7 @@ const loadReportData = async () => {
   if (!props.reportId) return
   
   try {
-    addLog(`加载报告数据: ${props.reportId}`)
+    addLog(`Loading report data: ${props.reportId}`)
     
     // Get report info
     const reportRes = await getReport(props.reportId)
@@ -882,7 +882,7 @@ const loadReportData = async () => {
       await loadAgentLogs()
     }
   } catch (err) {
-    addLog(`加载报告失败: ${err.message}`)
+    addLog(`Failed to load report: ${err.message}`)
   }
 }
 
@@ -904,10 +904,10 @@ const loadAgentLogs = async () => {
         }
       })
       
-      addLog('报告数据加载完成')
+      addLog('Report data loaded successfully')
     }
   } catch (err) {
-    addLog(`加载报告日志失败: ${err.message}`)
+    addLog(`Failed to load report logs: ${err.message}`)
   }
 }
 
@@ -918,10 +918,10 @@ const loadProfiles = async () => {
     const res = await getSimulationProfilesRealtime(props.simulationId, 'reddit')
     if (res.success && res.data) {
       profiles.value = res.data.profiles || []
-      addLog(`加载了 ${profiles.value.length} 个模拟个体`)
+      addLog(`Loaded ${profiles.value.length} simulated individuals`)
     }
   } catch (err) {
-    addLog(`加载模拟个体失败: ${err.message}`)
+    addLog(`Failed to load simulated individuals: ${err.message}`)
   }
 }
 
@@ -935,7 +935,7 @@ const handleClickOutside = (e) => {
 
 // Lifecycle
 onMounted(() => {
-  addLog('Step5 深度互动初始化')
+  addLog('Step5 deep interaction initialized')
   loadReportData()
   loadProfiles()
   document.addEventListener('click', handleClickOutside)
@@ -980,7 +980,7 @@ watch(() => props.simulationId, (newId) => {
   overflow: hidden;
 }
 
-/* Left Panel - Report Style (与 Step4Report.vue 完全一致) */
+/* Left Panel - Report Style (identical to Step4Report.vue) */
 .left-panel.report-style {
   width: 45%;
   min-width: 450px;
@@ -2028,7 +2028,7 @@ watch(() => props.simulationId, (newId) => {
   margin-bottom: 0;
 }
 
-/* 修复有序列表编号 - 使用 CSS 计数器让多个 ol 连续编号 */
+/* Fix ordered list numbering - use CSS counters for continuous numbering across multiple ol elements */
 .message-text {
   counter-reset: list-counter;
 }
@@ -2054,7 +2054,7 @@ watch(() => props.simulationId, (newId) => {
   flex-shrink: 0;
 }
 
-/* 无序列表样式 */
+/* Unordered list styles */
 .message-text :deep(.md-ul) {
   padding-left: 20px;
   margin: 8px 0;
@@ -2533,7 +2533,7 @@ watch(() => props.simulationId, (newId) => {
   margin: 6px 0;
 }
 
-/* 聊天/问卷区域的引用样式 */
+/* Chat/survey area blockquote styles */
 .chat-messages :deep(.md-quote),
 .result-answer :deep(.md-quote) {
   margin: 12px 0;
diff --git a/frontend/src/views/Home.vue b/frontend/src/views/Home.vue
index afe01a0c4..795e0c854 100644
--- a/frontend/src/views/Home.vue
+++ b/frontend/src/views/Home.vue
@@ -1,35 +1,35 @@
 <template>
   <div class="home-container">
-    <!-- 顶部导航栏 -->
+    <!-- Top Navigation Bar -->
     <nav class="navbar">
       <div class="nav-brand">MIROFISH</div>
       <div class="nav-links">
         <a href="https://github.com/666ghj/MiroFish" target="_blank" class="github-link">
-          访问我们的Github主页 <span class="arrow">↗</span>
+          Visit our GitHub <span class="arrow">↗</span>
         </a>
       </div>
     </nav>
 
     <div class="main-content">
-      <!-- 上半部分：Hero 区域 -->
+      <!-- Upper section: Hero area -->
       <section class="hero-section">
         <div class="hero-left">
           <div class="tag-row">
-            <span class="orange-tag">简洁通用的群体智能引擎</span>
-            <span class="version-text">/ v0.1-预览版</span>
+            <span class="orange-tag">Versatile Swarm Intelligence Engine</span>
+            <span class="version-text">/ v0.1-preview</span>
           </div>
           
           <h1 class="main-title">
-            上传任意报告<br>
-            <span class="gradient-text">即刻推演未来</span>
+            Upload Any Report<br>
+            <span class="gradient-text">Simulate the Future</span>
           </h1>
           
           <div class="hero-desc">
             <p>
-              即使只有一段文字，<span class="highlight-bold">MiroFish</span> 也能基于其中的现实种子，全自动生成与之对应的至多<span class="highlight-orange">百万级Agent</span>构成的平行世界。通过上帝视角注入变量，在复杂的群体交互中寻找动态环境下的<span class="highlight-code">“局部最优解”</span>
+              Even with just a paragraph, <span class=”highlight-bold”>MiroFish</span> can extract real-world seeds and automatically generate a parallel world with up to <span class=”highlight-orange”>millions of agents</span>. Inject variables from a god's-eye view to find the <span class=”highlight-code”>”local optimum”</span> in complex group interactions under dynamic environments
             </p>
             <p class="slogan-text">
-              让未来在 Agent 群中预演，让决策在百战后胜出<span class="blinking-cursor">_</span>
+              Let the future rehearse among agents, let decisions prevail after a hundred battles<span class="blinking-cursor">_</span>
             </p>
           </div>
            
@@ -37,7 +37,7 @@
         </div>
         
         <div class="hero-right">
-          <!-- Logo 区域 -->
+          <!-- Logo area -->
           <div class="logo-container">
             <img src="../assets/logo/MiroFish_logo_left.jpeg" alt="MiroFish Logo" class="hero-logo" />
           </div>
@@ -48,84 +48,84 @@
         </div>
       </section>
 
-      <!-- 下半部分：双栏布局 -->
+      <!-- Lower section: Two-column layout -->
       <section class="dashboard-section">
-        <!-- 左栏：状态与步骤 -->
+        <!-- Left column: Status and steps -->
         <div class="left-panel">
           <div class="panel-header">
-            <span class="status-dot">■</span> 系统状态
+            <span class="status-dot">■</span> System Status
           </div>
           
-          <h2 class="section-title">准备就绪</h2>
+          <h2 class="section-title">Ready</h2>
           <p class="section-desc">
-            预测引擎待命中，可上传多份非结构化数据以初始化模拟序列
+            Prediction engine on standby. Upload unstructured data to initialize simulation sequence
           </p>
           
-          <!-- 数据指标卡片 -->
+          <!-- Metric cards -->
           <div class="metrics-row">
             <div class="metric-card">
-              <div class="metric-value">低成本</div>
-              <div class="metric-label">常规模拟平均5$/次</div>
+              <div class="metric-value">Low Cost</div>
+              <div class="metric-label">Avg. $5 per simulation</div>
             </div>
             <div class="metric-card">
-              <div class="metric-value">高可用</div>
-              <div class="metric-label">最多百万级Agent模拟</div>
+              <div class="metric-value">High Availability</div>
+              <div class="metric-label">Up to millions of agent simulations</div>
             </div>
           </div>
 
-          <!-- 项目模拟步骤介绍 (新增区域) -->
+          <!-- Simulation workflow steps -->
           <div class="steps-container">
             <div class="steps-header">
-               <span class="diamond-icon">◇</span> 工作流序列
+               <span class="diamond-icon">◇</span> Workflow Sequence
             </div>
             <div class="workflow-list">
               <div class="workflow-item">
                 <span class="step-num">01</span>
                 <div class="step-info">
-                  <div class="step-title">图谱构建</div>
-                  <div class="step-desc">现实种子提取 & 个体与群体记忆注入 & GraphRAG构建</div>
+                  <div class="step-title">Graph Build</div>
+                  <div class="step-desc">Reality seed extraction & individual/group memory injection & GraphRAG construction</div>
                 </div>
               </div>
               <div class="workflow-item">
                 <span class="step-num">02</span>
                 <div class="step-info">
-                  <div class="step-title">环境搭建</div>
-                  <div class="step-desc">实体关系抽取 & 人设生成 & 环境配置Agent注入仿真参数</div>
+                  <div class="step-title">Environment Setup</div>
+                  <div class="step-desc">Entity-relation extraction & persona generation & environment config with agent simulation parameters</div>
                 </div>
               </div>
               <div class="workflow-item">
                 <span class="step-num">03</span>
                 <div class="step-info">
-                  <div class="step-title">开始模拟</div>
-                  <div class="step-desc">双平台并行模拟 & 自动解析预测需求 & 动态更新时序记忆</div>
+                  <div class="step-title">Run Simulation</div>
+                  <div class="step-desc">Dual-platform parallel simulation & auto-parse prediction needs & dynamic temporal memory updates</div>
                 </div>
               </div>
               <div class="workflow-item">
                 <span class="step-num">04</span>
                 <div class="step-info">
-                  <div class="step-title">报告生成</div>
-                  <div class="step-desc">ReportAgent拥有丰富的工具集与模拟后环境进行深度交互</div>
+                  <div class="step-title">Report Generation</div>
+                  <div class="step-desc">ReportAgent uses a rich toolset for deep interaction with the post-simulation environment</div>
                 </div>
               </div>
               <div class="workflow-item">
                 <span class="step-num">05</span>
                 <div class="step-info">
-                  <div class="step-title">深度互动</div>
-                  <div class="step-desc">与模拟世界中的任意一位进行对话 & 与ReportAgent进行对话</div>
+                  <div class="step-title">Deep Interaction</div>
+                  <div class="step-desc">Chat with anyone in the simulated world & chat with ReportAgent</div>
                 </div>
               </div>
             </div>
           </div>
         </div>
 
-        <!-- 右栏：交互控制台 -->
+        <!-- Right column: Interactive console -->
         <div class="right-panel">
           <div class="console-box">
-            <!-- 上传区域 -->
+            <!-- Upload area -->
             <div class="console-section">
               <div class="console-header">
-                <span class="console-label">01 / 现实种子</span>
-                <span class="console-meta">支持格式: PDF, MD, TXT</span>
+                <span class="console-label">01 / Reality Seeds</span>
+                <span class="console-meta">Supported formats: PDF, MD, TXT</span>
               </div>
               
               <div 
@@ -148,8 +148,8 @@
                 
                 <div v-if="files.length === 0" class="upload-placeholder">
                   <div class="upload-icon">↑</div>
-                  <div class="upload-title">拖拽文件上传</div>
-                  <div class="upload-hint">或点击浏览文件系统</div>
+                  <div class="upload-title">Drag & drop to upload</div>
+                  <div class="upload-hint">or click to browse files</div>
                 </div>
                 
                 <div v-else class="file-list">
@@ -162,37 +162,37 @@
               </div>
             </div>
 
-            <!-- 分割线 -->
+            <!-- Divider -->
             <div class="console-divider">
-              <span>输入参数</span>
+              <span>Input Parameters</span>
             </div>
 
-            <!-- 输入区域 -->
+            <!-- Input area -->
             <div class="console-section">
               <div class="console-header">
-                <span class="console-label">>_ 02 / 模拟提示词</span>
+                <span class="console-label">>_ 02 / Simulation Prompt</span>
               </div>
               <div class="input-wrapper">
                 <textarea
                   v-model="formData.simulationRequirement"
                   class="code-input"
-                  placeholder="// 用自然语言输入模拟或预测需求（例.武大若发布撤销肖某处分的公告，会引发什么舆情走向）"
+                  placeholder="// Enter simulation or prediction requirements in natural language (e.g., What public opinion trends would emerge if a major university reverses a controversial disciplinary decision?)"
                   rows="6"
                   :disabled="loading"
                 ></textarea>
-                <div class="model-badge">引擎: MiroFish-V1.0</div>
+                <div class="model-badge">Engine: MiroFish-V1.0</div>
               </div>
             </div>
 
-            <!-- 启动按钮 -->
+            <!-- Launch button -->
             <div class="console-section btn-section">
               <button 
                 class="start-engine-btn"
                 @click="startSimulation"
                 :disabled="!canSubmit || loading"
               >
-                <span v-if="!loading">启动引擎</span>
-                <span v-else>初始化中...</span>
+                <span v-if="!loading">Launch Engine</span>
+                <span v-else>Initializing...</span>
                 <span class="btn-arrow">→</span>
               </button>
             </div>
@@ -200,7 +200,7 @@
         </div>
       </section>
 
-      <!-- 历史项目数据库 -->
+      <!-- Simulation History -->
       <HistoryDatabase />
     </div>
   </div>
@@ -213,41 +213,41 @@ import HistoryDatabase from '../components/HistoryDatabase.vue'
 
 const router = useRouter()
 
-// 表单数据
+// Form data
 const formData = ref({
   simulationRequirement: ''
 })
 
-// 文件列表
+// File list
 const files = ref([])
 
-// 状态
+// State
 const loading = ref(false)
 const error = ref('')
 const isDragOver = ref(false)
 
-// 文件输入引用
+// File input ref
 const fileInput = ref(null)
 
-// 计算属性:是否可以提交
+// Computed: can submit
 const canSubmit = computed(() => {
   return formData.value.simulationRequirement.trim() !== '' && files.value.length > 0
 })
 
-// 触发文件选择
+// Trigger file selection
 const triggerFileInput = () => {
   if (!loading.value) {
     fileInput.value?.click()
   }
 }
 
-// 处理文件选择
+// Handle file selection
 const handleFileSelect = (event) => {
   const selectedFiles = Array.from(event.target.files)
   addFiles(selectedFiles)
 }
 
-// 处理拖拽相关
+// Handle drag events
 const handleDragOver = (e) => {
   if (!loading.value) {
     isDragOver.value = true
@@ -266,7 +266,7 @@ const handleDrop = (e) => {
   addFiles(droppedFiles)
 }
 
-// 添加文件
+// Add files
 const addFiles = (newFiles) => {
   const validFiles = newFiles.filter(file => {
     const ext = file.name.split('.').pop().toLowerCase()
@@ -275,12 +275,12 @@ const addFiles = (newFiles) => {
   files.value.push(...validFiles)
 }
 
-// 移除文件
+// Remove file
 const removeFile = (index) => {
   files.value.splice(index, 1)
 }
 
-// 滚动到底部
+// Scroll to bottom
 const scrollToBottom = () => {
   window.scrollTo({
     top: document.body.scrollHeight,
@@ -288,15 +288,15 @@ const scrollToBottom = () => {
   })
 }
 
-// 开始模拟 - 立即跳转，API调用在Process页面进行
+// Start simulation - navigate immediately, API calls happen on the Process page
 const startSimulation = () => {
   if (!canSubmit.value || loading.value) return
   
-  // 存储待上传的数据
+  // Store pending upload data
   import('../store/pendingUpload.js').then(({ setPendingUpload }) => {
     setPendingUpload(files.value, formData.value.simulationRequirement)
     
-    // 立即跳转到Process页面（使用特殊标识表示新建项目）
+    // Navigate to Process page immediately (using special ID for new project)
     router.push({
       name: 'Process',
       params: { projectId: 'new' }
@@ -306,7 +306,7 @@ const startSimulation = () => {
 </script>
 
 <style scoped>
-/* 全局变量与重置 */
+/* Global variables and reset */
 :root {
   --black: #000000;
   --white: #FFFFFF;
@@ -315,8 +315,8 @@ const startSimulation = () => {
   --gray-text: #666666;
   --border: #E5E5E5;
   /* 
-    使用 Space Grotesk 作为主要标题字体，JetBrains Mono 作为代码/标签字体
-    确保已在 index.html 引入这些 Google Fonts 
+    Using Space Grotesk as the main heading font, JetBrains Mono for code/labels
+    Ensure these Google Fonts are imported in index.html 
   */
   --font-mono: 'JetBrains Mono', monospace;
   --font-sans: 'Space Grotesk', 'Noto Sans SC', system-ui, sans-serif;
@@ -330,7 +330,7 @@ const startSimulation = () => {
   color: var(--black);
 }
 
-/* 顶部导航 */
+/* Top navigation */
 .navbar {
   height: 60px;
   background: var(--black);
@@ -373,14 +373,14 @@ const startSimulation = () => {
   font-family: sans-serif;
 }
 
-/* 主要内容区 */
+/* Main content area */
 .main-content {
   max-width: 1400px;
   margin: 0 auto;
   padding: 60px 40px;
 }
 
-/* Hero 区域 */
+/* Hero section */
 .hero-section {
   display: flex;
   justify-content: space-between;
@@ -511,7 +511,7 @@ const startSimulation = () => {
 }
 
 .hero-logo {
-  max-width: 500px; /* 调整logo大小 */
+  max-width: 500px; /* Adjust logo size */
   width: 100%;
 }
 
@@ -533,7 +533,7 @@ const startSimulation = () => {
   border-color: var(--orange);
 }
 
-/* Dashboard 双栏布局 */
+/* Dashboard two-column layout */
 .dashboard-section {
   display: flex;
   gap: 60px;
@@ -548,7 +548,7 @@ const startSimulation = () => {
   flex-direction: column;
 }
 
-/* 左侧面板 */
+/* Left panel */
 .left-panel {
   flex: 0.8;
 }
@@ -604,7 +604,7 @@ const startSimulation = () => {
   color: #999;
 }
 
-/* 项目模拟步骤介绍 */
+/* Simulation workflow steps */
 .steps-container {
   border: 1px solid var(--border);
   padding: 30px;
@@ -660,14 +660,14 @@ const startSimulation = () => {
   color: var(--gray-text);
 }
 
-/* 右侧交互控制台 */
+/* Right interactive console */
 .right-panel {
   flex: 1.2;
 }
 
 .console-box {
-  border: 1px solid #CCC; /* 外部实线 */
-  padding: 8px; /* 内边距形成双重边框感 */
+  border: 1px solid #CCC; /* Outer solid border */
+  padding: 8px; /* Padding creates double-border effect */
 }
 
 .console-section {
@@ -835,7 +835,7 @@ const startSimulation = () => {
   overflow: hidden;
 }
 
-/* 可点击状态（非禁用） */
+/* Clickable state (not disabled) */
 .start-engine-btn:not(:disabled) {
   background: var(--black);
   border: 1px solid var(--black);
@@ -860,14 +860,14 @@ const startSimulation = () => {
   border: 1px solid #E5E5E5;
 }
 
-/* 引导动画：微妙的边框脉冲 */
+/* Guide animation: subtle border pulse */
 @keyframes pulse-border {
   0% { box-shadow: 0 0 0 0 rgba(0, 0, 0, 0.2); }
   70% { box-shadow: 0 0 0 6px rgba(0, 0, 0, 0); }
   100% { box-shadow: 0 0 0 0 rgba(0, 0, 0, 0); }
 }
 
-/* 响应式适配 */
+/* Responsive layout */
 @media (max-width: 1024px) {
   .dashboard-section {
     flex-direction: column;
diff --git a/frontend/src/views/MainView.vue b/frontend/src/views/MainView.vue
index 6ff299112..68458ca8d 100644
--- a/frontend/src/views/MainView.vue
+++ b/frontend/src/views/MainView.vue
@@ -15,7 +15,7 @@
             :class="{ active: viewMode === mode }"
             @click="viewMode = mode"
           >
-            {{ { graph: '图谱', split: '双栏', workbench: '工作台' }[mode] }}
+            {{ { graph: 'Graph', split: 'Split', workbench: 'Workbench' }[mode] }}
           </button>
         </div>
       </div>
@@ -48,7 +48,7 @@
 
       <!-- Right Panel: Step Components -->
       <div class="panel-wrapper right" :style="rightPanelStyle">
-        <!-- Step 1: 图谱构建 -->
+        <!-- Step 1: Graph Build -->
         <Step1GraphBuild 
           v-if="currentStep === 1"
           :currentPhase="currentPhase"
@@ -59,7 +59,7 @@
           :systemLogs="systemLogs"
           @next-step="handleNextStep"
         />
-        <!-- Step 2: 环境搭建 -->
+        <!-- Step 2: Environment Setup -->
         <Step2EnvSetup
           v-else-if="currentStep === 2"
           :projectData="projectData"
@@ -90,8 +90,8 @@ const router = useRouter()
 const viewMode = ref('split') // graph | split | workbench
 
 // Step State
-const currentStep = ref(1) // 1: 图谱构建, 2: 环境搭建, 3: 开始模拟, 4: 报告生成, 5: 深度互动
-const stepNames = ['图谱构建', '环境搭建', '开始模拟', '报告生成', '深度互动']
+const currentStep = ref(1) // 1: Graph Build, 2: Environment Setup, 3: Run Simulation, 4: Report Generation, 5: Deep Interaction
+const stepNames = ['Graph Build', 'Environment Setup', 'Run Simulation', 'Report Generation', 'Deep Interaction']
 
 // Data State
 const currentProjectId = ref(route.params.projectId)
@@ -159,11 +159,11 @@ const toggleMaximize = (target) => {
 const handleNextStep = (params = {}) => {
   if (currentStep.value < 5) {
     currentStep.value++
-    addLog(`进入 Step ${currentStep.value}: ${stepNames[currentStep.value - 1]}`)
+    addLog(`Entering Step ${currentStep.value}: ${stepNames[currentStep.value - 1]}`)
     
-    // 如果是从 Step 2 进入 Step 3，记录模拟轮数配置
+    // If entering Step 3 from Step 2, log the simulation rounds config
     if (currentStep.value === 3 && params.maxRounds) {
-      addLog(`自定义模拟轮数: ${params.maxRounds} 轮`)
+      addLog(`Custom simulation rounds: ${params.maxRounds}`)
     }
   }
 }
@@ -171,7 +171,7 @@ const handleNextStep = (params = {}) => {
 const handleGoBack = () => {
   if (currentStep.value > 1) {
     currentStep.value--
-    addLog(`返回 Step ${currentStep.value}: ${stepNames[currentStep.value - 1]}`)
+    addLog(`Returning to Step ${currentStep.value}: ${stepNames[currentStep.value - 1]}`)
   }
 }
 
diff --git a/frontend/src/views/Process.vue b/frontend/src/views/Process.vue
index 2d2d3cc1a..034aa1c24 100644
--- a/frontend/src/views/Process.vue
+++ b/frontend/src/views/Process.vue
@@ -7,7 +7,7 @@
       <!-- 中间步骤指示器 -->
       <div class="nav-center">
         <div class="step-badge">STEP 01</div>
-        <div class="step-name">图谱构建</div>
+        <div class="step-name">Graph Build</div>
       </div>
 
       <div class="nav-status">
@@ -23,20 +23,20 @@
         <div class="panel-header">
           <div class="header-left">
             <span class="header-deco">◆</span>
-            <span class="header-title">实时知识图谱</span>
+            <span class="header-title">Real-time Knowledge Graph</span>
           </div>
           <div class="header-right">
             <template v-if="graphData">
-              <span class="stat-item">{{ graphData.node_count || graphData.nodes?.length || 0 }} 节点</span>
+              <span class="stat-item">{{ graphData.node_count || graphData.nodes?.length || 0 }} Nodes</span>
               <span class="stat-divider">|</span>
-              <span class="stat-item">{{ graphData.edge_count || graphData.edges?.length || 0 }} 关系</span>
+              <span class="stat-item">{{ graphData.edge_count || graphData.edges?.length || 0 }} Relations</span>
               <span class="stat-divider">|</span>
             </template>
             <div class="action-buttons">
-                <button class="action-btn" @click="refreshGraph" :disabled="graphLoading" title="刷新图谱">
+                <button class="action-btn" @click="refreshGraph" :disabled="graphLoading" title="Refresh Graph">
                   <span class="icon-refresh" :class="{ 'spinning': graphLoading }">↻</span>
                 </button>
-                <button class="action-btn" @click="toggleFullScreen" :title="isFullScreen ? '退出全屏' : '全屏显示'">
+                <button class="action-btn" @click="toggleFullScreen" :title="isFullScreen ? 'Exit Fullscreen' : 'Fullscreen'">
                   <span class="icon-fullscreen">{{ isFullScreen ? '↙' : '↗' }}</span>
                 </button>
             </div>
@@ -50,7 +50,7 @@
             <!-- 构建中提示 -->
             <div v-if="currentPhase === 1" class="graph-building-hint">
               <span class="building-dot"></span>
-              实时更新中...
+              Updating in real-time...
             </div>
             
             <!-- 节点/边详情面板 -->
@@ -171,7 +171,7 @@
               <div class="loading-ring"></div>
               <div class="loading-ring"></div>
             </div>
-            <p class="loading-text">图谱数据加载中...</p>
+            <p class="loading-text">Loading graph data...</p>
           </div>
           
           <!-- 等待构建 -->
@@ -189,8 +189,8 @@
                 <line x1="50" y1="72" x2="74" y2="66" stroke="#000" stroke-width="1"/>
               </svg>
             </div>
-            <p class="waiting-text">等待本体生成</p>
-            <p class="waiting-hint">生成完成后将自动开始构建图谱</p>
+            <p class="waiting-text">Waiting for ontology generation</p>
+            <p class="waiting-hint">Graph build will start automatically after generation</p>
           </div>
           
           <!-- 构建中但还没有数据 -->
@@ -200,8 +200,8 @@
               <div class="loading-ring"></div>
               <div class="loading-ring"></div>
             </div>
-            <p class="waiting-text">图谱构建中</p>
-            <p class="waiting-hint">数据即将显示...</p>
+            <p class="waiting-text">Building graph</p>
+            <p class="waiting-hint">Data will be displayed shortly...</p>
           </div>
           
           <!-- 错误状态 -->
@@ -225,7 +225,7 @@
       <div class="right-panel" :class="{ 'hidden': isFullScreen }">
         <div class="panel-header dark-header">
           <span class="header-icon">▣</span>
-          <span class="header-title">构建流程</span>
+          <span class="header-title">Build Process</span>
         </div>
 
         <div class="process-content">
@@ -234,7 +234,7 @@
             <div class="phase-header">
               <span class="phase-num">01</span>
               <div class="phase-info">
-                <div class="phase-title">本体生成</div>
+                <div class="phase-title">Ontology Generation</div>
                 <div class="phase-api">/api/graph/ontology/generate</div>
               </div>
               <span class="phase-status" :class="getPhaseStatusClass(0)">
@@ -244,15 +244,15 @@
             
             <div class="phase-detail">
               <div class="detail-section">
-                <div class="detail-label">接口说明</div>
+                <div class="detail-label">API Description</div>
                 <div class="detail-content">
-                  上传文档后，LLM分析文档内容，自动生成适合舆论模拟的本体结构（实体类型 + 关系类型）
+                  After uploading documents, LLM analyzes content and auto-generates ontology structure for opinion simulation (entity types + relation types)
                 </div>
               </div>
               
               <!-- 本体生成进度 -->
               <div class="detail-section" v-if="ontologyProgress && currentPhase === 0">
-                <div class="detail-label">生成进度</div>
+                <div class="detail-label">Generation Progress</div>
                 <div class="ontology-progress">
                   <div class="progress-spinner"></div>
                   <span class="progress-text">{{ ontologyProgress.message }}</span>
@@ -261,7 +261,7 @@
               
               <!-- 已生成的本体信息 -->
               <div class="detail-section" v-if="projectData?.ontology">
-                <div class="detail-label">生成的实体类型 ({{ projectData.ontology.entity_types?.length || 0 }})</div>
+                <div class="detail-label">Generated Entity Types ({{ projectData.ontology.entity_types?.length || 0 }})</div>
                 <div class="entity-tags">
                   <span 
                     v-for="entity in projectData.ontology.entity_types" 
@@ -274,7 +274,7 @@
               </div>
               
               <div class="detail-section" v-if="projectData?.ontology">
-                <div class="detail-label">生成的关系类型 ({{ projectData.ontology.relation_types?.length || 0 }})</div>
+                <div class="detail-label">Generated Relation Types ({{ projectData.ontology.relation_types?.length || 0 }})</div>
                 <div class="relation-list">
                   <div 
                     v-for="(rel, idx) in projectData.ontology.relation_types?.slice(0, 5) || []" 
@@ -288,14 +288,14 @@
                     <span class="rel-target">{{ rel.target_type }}</span>
                   </div>
                   <div v-if="(projectData.ontology.relation_types?.length || 0) > 5" class="relation-more">
-                    +{{ projectData.ontology.relation_types.length - 5 }} 更多关系...
+                    +{{ projectData.ontology.relation_types.length - 5 }} More relations...
                   </div>
                 </div>
               </div>
               
               <!-- 等待状态 -->
               <div class="detail-section waiting-state" v-if="!projectData?.ontology && currentPhase === 0 && !ontologyProgress">
-                <div class="waiting-hint">等待本体生成...</div>
+                <div class="waiting-hint">Waiting for ontology generation...</div>
               </div>
             </div>
           </div>
@@ -305,7 +305,7 @@
             <div class="phase-header">
               <span class="phase-num">02</span>
               <div class="phase-info">
-                <div class="phase-title">图谱构建</div>
+                <div class="phase-title">Graph Build</div>
                 <div class="phase-api">/api/graph/build</div>
               </div>
               <span class="phase-status" :class="getPhaseStatusClass(1)">
@@ -315,20 +315,20 @@
             
             <div class="phase-detail">
               <div class="detail-section">
-                <div class="detail-label">接口说明</div>
+                <div class="detail-label">API Description</div>
                 <div class="detail-content">
-                  基于生成的本体，将文档分块后调用 Zep API 构建知识图谱，提取实体和关系
+                  Based on the generated ontology, documents are chunked and Zep API builds the knowledge graph, extracting entities and relations
                 </div>
               </div>
               
               <!-- 等待本体完成 -->
               <div class="detail-section waiting-state" v-if="currentPhase < 1">
-                <div class="waiting-hint">等待本体生成完成...</div>
+                <div class="waiting-hint">Waiting for ontology generation to complete...</div>
               </div>
               
               <!-- 构建进度 -->
               <div class="detail-section" v-if="buildProgress && currentPhase >= 1">
-                <div class="detail-label">构建进度</div>
+                <div class="detail-label">Build Progress</div>
                 <div class="progress-bar">
                   <div class="progress-fill" :style="{ width: buildProgress.progress + '%' }"></div>
                 </div>
@@ -339,19 +339,19 @@
               </div>
               
               <div class="detail-section" v-if="graphData">
-                <div class="detail-label">构建结果</div>
+                <div class="detail-label">Build Results</div>
                 <div class="build-result">
                   <div class="result-item">
                     <span class="result-value">{{ graphData.node_count }}</span>
-                    <span class="result-label">实体节点</span>
+                    <span class="result-label">Entity Nodes</span>
                   </div>
                   <div class="result-item">
                     <span class="result-value">{{ graphData.edge_count }}</span>
-                    <span class="result-label">关系边</span>
+                    <span class="result-label">Relation Edges</span>
                   </div>
                   <div class="result-item">
                     <span class="result-value">{{ entityTypes.length }}</span>
-                    <span class="result-label">实体类型</span>
+                    <span class="result-label">Entity Types</span>
                   </div>
                 </div>
               </div>
@@ -363,8 +363,8 @@
             <div class="phase-header">
               <span class="phase-num">03</span>
               <div class="phase-info">
-                <div class="phase-title">构建完成</div>
-                <div class="phase-api">准备进入下一步骤</div>
+                <div class="phase-title">Build Complete</div>
+                <div class="phase-api">Ready to proceed to next step</div>
               </div>
               <span class="phase-status" :class="getPhaseStatusClass(2)">
                 {{ getPhaseStatusText(2) }}
@@ -375,7 +375,7 @@
           <!-- 下一步按钮 -->
           <div class="next-step-section" v-if="currentPhase >= 2">
             <button class="next-step-btn" @click="goToNextStep" :disabled="currentPhase < 2">
-              进入环境搭建
+              Enter Environment Setup
               <span class="btn-arrow">→</span>
             </button>
           </div>
@@ -385,23 +385,23 @@
         <div class="project-panel">
           <div class="project-header">
             <span class="project-icon">◇</span>
-            <span class="project-title">项目信息</span>
+            <span class="project-title">Project Info</span>
           </div>
           <div class="project-details" v-if="projectData">
             <div class="project-item">
-              <span class="item-label">项目名称</span>
+              <span class="item-label">Project Name</span>
               <span class="item-value">{{ projectData.name }}</span>
             </div>
             <div class="project-item">
-              <span class="item-label">项目ID</span>
+              <span class="item-label">Project ID</span>
               <span class="item-value code">{{ projectData.project_id }}</span>
             </div>
             <div class="project-item" v-if="projectData.graph_id">
-              <span class="item-label">图谱ID</span>
+              <span class="item-label">Graph ID</span>
               <span class="item-value code">{{ projectData.graph_id }}</span>
             </div>
             <div class="project-item">
-              <span class="item-label">模拟需求</span>
+              <span class="item-label">Simulation Requirements</span>
               <span class="item-value">{{ projectData.simulation_requirement || '-' }}</span>
             </div>
           </div>
@@ -451,11 +451,11 @@ const statusClass = computed(() => {
 })
 
 const statusText = computed(() => {
-  if (error.value) return '构建失败'
-  if (currentPhase.value >= 2) return '构建完成'
-  if (currentPhase.value === 1) return '图谱构建中'
-  if (currentPhase.value === 0) return '本体生成中'
-  return '初始化中'
+  if (error.value) return 'Build Failed'
+  if (currentPhase.value >= 2) return 'Build Complete'
+  if (currentPhase.value === 1) return 'Building Graph'
+  if (currentPhase.value === 0) return 'Generating Ontology'
+  return 'Initializing'
 })
 
 const entityTypes = computed(() => {
@@ -481,8 +481,8 @@ const goHome = () => {
 }
 
 const goToNextStep = () => {
-  // TODO: 进入环境搭建步骤
-  alert('环境搭建功能开发中...')
+  // TODO: Enter environment setup step
+  alert('Environment setup feature is under development...')
 }
 
 const toggleFullScreen = () => {
@@ -503,7 +503,7 @@ const formatDate = (dateStr) => {
   if (!dateStr) return '-'
   try {
     const date = new Date(dateStr)
-    return date.toLocaleString('zh-CN', {
+    return date.toLocaleString('en-US', {
       year: 'numeric',
       month: 'short',
       day: 'numeric',
@@ -540,14 +540,14 @@ const getPhaseStatusClass = (phase) => {
 }
 
 const getPhaseStatusText = (phase) => {
-  if (currentPhase.value > phase) return '已完成'
+  if (currentPhase.value > phase) return 'Completed'
   if (currentPhase.value === phase) {
     if (phase === 1 && buildProgress.value) {
       return `${buildProgress.value.progress}%`
     }
-    return '进行中'
+    return 'In Progress'
   }
-  return '等待中'
+  return 'Pending'
 }
 
 // 初始化 - 处理新建项目或加载已有项目
@@ -569,7 +569,7 @@ const handleNewProject = async () => {
   const pending = getPendingUpload()
   
   if (!pending.isPending || pending.files.length === 0) {
-    error.value = '没有待上传的文件，请返回首页重新操作'
+    error.value = 'No files to upload, please return to the home page and try again'
     loading.value = false
     return
   }
@@ -577,7 +577,7 @@ const handleNewProject = async () => {
   try {
     loading.value = true
     currentPhase.value = 0 // 本体生成阶段
-    ontologyProgress.value = { message: '正在上传文件并分析文档...' }
+    ontologyProgress.value = { message: 'Uploading files and analyzing documents...' }
     
     // 构建 FormData
     const formDataObj = new FormData()
@@ -608,11 +608,11 @@ const handleNewProject = async () => {
       // 自动开始图谱构建
       await startBuildGraph()
     } else {
-      error.value = response.error || '本体生成失败'
+      error.value = response.error || 'Ontology generation failed'
     }
   } catch (err) {
     console.error('Handle new project error:', err)
-    error.value = '项目初始化失败: ' + (err.message || '未知错误')
+    error.value = 'Project initialization failed: ' + (err.message || 'Unknown error')
   } finally {
     loading.value = false
   }
@@ -645,11 +645,11 @@ const loadProject = async () => {
         await loadGraph(response.data.graph_id)
       }
     } else {
-      error.value = response.error || '加载项目失败'
+      error.value = response.error || 'Failed to load project'
     }
   } catch (err) {
     console.error('Load project error:', err)
-    error.value = '加载项目失败: ' + (err.message || '未知错误')
+    error.value = 'Failed to load project: ' + (err.message || 'Unknown error')
   } finally {
     loading.value = false
   }
@@ -668,7 +668,7 @@ const updatePhaseByStatus = (status) => {
       currentPhase.value = 2
       break
     case 'failed':
-      error.value = projectData.value?.error || '处理失败'
+      error.value = projectData.value?.error || 'Processing failed'
       break
   }
 }
@@ -680,13 +680,13 @@ const startBuildGraph = async () => {
     // 设置初始进度
     buildProgress.value = {
       progress: 0,
-      message: '正在启动图谱构建...'
+      message: 'Starting graph build...'
     }
     
     const response = await buildGraph({ project_id: currentProjectId.value })
     
     if (response.success) {
-      buildProgress.value.message = '图谱构建任务已启动...'
+      buildProgress.value.message = 'Graph build task started...'
       
       // 保存 task_id 用于轮询
       const taskId = response.data.task_id
@@ -697,12 +697,12 @@ const startBuildGraph = async () => {
       // 启动任务状态轮询
       startPollingTask(taskId)
     } else {
-      error.value = response.error || '启动图谱构建失败'
+      error.value = response.error || 'Failed to start graph build'
       buildProgress.value = null
     }
   } catch (err) {
     console.error('Build graph error:', err)
-    error.value = '启动图谱构建失败: ' + (err.message || '未知错误')
+    error.value = 'Failed to start graph build: ' + (err.message || 'Unknown error')
     buildProgress.value = null
   }
 }
@@ -791,13 +791,13 @@ const pollTaskStatus = async (taskId) => {
       // 更新进度显示
       buildProgress.value = {
         progress: task.progress || 0,
-        message: task.message || '处理中...'
+        message: task.message || 'Processing...'
       }
       
       console.log('Task status:', task.status, 'Progress:', task.progress)
       
       if (task.status === 'completed') {
-        console.log('✅ 图谱构建完成，正在加载完整数据...')
+        console.log('Graph build completed, loading full data...')
         
         stopPolling()
         stopGraphPolling()
@@ -806,7 +806,7 @@ const pollTaskStatus = async (taskId) => {
         // 更新进度显示为完成状态
         buildProgress.value = {
           progress: 100,
-          message: '构建完成，正在加载图谱...'
+          message: 'Build complete, loading graph...'
         }
         
         // 重新加载项目数据获取 graph_id
@@ -816,9 +816,9 @@ const pollTaskStatus = async (taskId) => {
           
           // 最终加载完整图谱数据
           if (projectResponse.data.graph_id) {
-            console.log('📊 加载完整图谱:', projectResponse.data.graph_id)
+            console.log('Loading full graph:', projectResponse.data.graph_id)
             await loadGraph(projectResponse.data.graph_id)
-            console.log('✅ 图谱加载完成')
+            console.log('Graph loading complete')
           }
         }
         
@@ -827,7 +827,7 @@ const pollTaskStatus = async (taskId) => {
       } else if (task.status === 'failed') {
         stopPolling()
         stopGraphPolling()
-        error.value = '图谱构建失败: ' + (task.error || '未知错误')
+        error.value = 'Graph build failed: ' + (task.error || 'Unknown error')
         buildProgress.value = null
       }
     }
@@ -905,7 +905,7 @@ const renderGraph = () => {
       .attr('y', height / 2)
       .attr('text-anchor', 'middle')
       .attr('fill', '#999')
-      .text('等待图谱数据...')
+      .text('Waiting for graph data...')
     return
   }
   
@@ -917,7 +917,7 @@ const renderGraph = () => {
   
   const nodes = nodesData.map(n => ({
     id: n.uuid,
-    name: n.name || '未命名',
+    name: n.name || 'Unnamed',
     type: n.labels?.find(l => l !== 'Entity' && l !== 'Node') || 'Entity',
     rawData: n // 保存原始数据
   }))
@@ -933,8 +933,8 @@ const renderGraph = () => {
       type: e.fact_type || e.name || 'RELATED_TO',
       rawData: {
         ...e,
-        source_name: nodeMap[e.source_node_uuid]?.name || '未知',
-        target_name: nodeMap[e.target_node_uuid]?.name || '未知'
+        source_name: nodeMap[e.source_node_uuid]?.name || 'Unknown',
+        target_name: nodeMap[e.target_node_uuid]?.name || 'Unknown'
       }
     }))
   
diff --git a/frontend/src/views/SimulationView.vue b/frontend/src/views/SimulationView.vue
index 4b44b3972..d66e404dd 100644
--- a/frontend/src/views/SimulationView.vue
+++ b/frontend/src/views/SimulationView.vue
@@ -15,7 +15,7 @@
             :class="{ active: viewMode === mode }"
             @click="viewMode = mode"
           >
-            {{ { graph: '图谱', split: '双栏', workbench: '工作台' }[mode] }}
+            {{ { graph: 'Graph', split: 'Split', workbench: 'Workbench' }[mode] }}
           </button>
         </div>
       </div>
@@ -23,7 +23,7 @@
       <div class="header-right">
         <div class="workflow-step">
           <span class="step-num">Step 2/5</span>
-          <span class="step-name">环境搭建</span>
+          <span class="step-name">Environment Setup</span>
         </div>
         <div class="step-divider"></div>
         <span class="status-indicator" :class="statusClass">
@@ -46,7 +46,7 @@
         />
       </div>
 
-      <!-- Right Panel: Step2 环境搭建 -->
+      <!-- Right Panel: Step2 Environment Setup -->
       <div class="panel-wrapper right" :style="rightPanelStyle">
         <Step2EnvSetup
           :simulationId="currentSimulationId"
@@ -137,7 +137,7 @@ const toggleMaximize = (target) => {
 }
 
 const handleGoBack = () => {
-  // 返回到 process 页面
+  // Go back to process page
   if (projectData.value?.project_id) {
     router.push({ name: 'Process', params: { projectId: projectData.value.project_id } })
   } else {
@@ -146,122 +146,122 @@ const handleGoBack = () => {
 }
 
 const handleNextStep = (params = {}) => {
-  addLog('进入 Step 3: 开始模拟')
+  addLog('Entering Step 3: Run Simulation')
   
-  // 记录模拟轮数配置
+  // Log simulation rounds config
   if (params.maxRounds) {
-    addLog(`自定义模拟轮数: ${params.maxRounds} 轮`)
+    addLog(`Custom simulation rounds: ${params.maxRounds}`)
   } else {
-    addLog('使用自动配置的模拟轮数')
+    addLog('Using auto-configured simulation rounds')
   }
   
-  // 构建路由参数
+  // Build route params
   const routeParams = {
     name: 'SimulationRun',
     params: { simulationId: currentSimulationId.value }
   }
   
-  // 如果有自定义轮数，通过 query 参数传递
+  // Pass custom rounds via query params if provided
   if (params.maxRounds) {
     routeParams.query = { maxRounds: params.maxRounds }
   }
   
-  // 跳转到 Step 3 页面
+  // Navigate to Step 3
   router.push(routeParams)
 }
 
 // --- Data Logic ---
 
 /**
- * 检查并关闭正在运行的模拟
- * 当用户从 Step 3 返回到 Step 2 时，默认用户要退出模拟
+ * Check and stop any running simulation
+ * When user returns from Step 3 to Step 2, assume they want to exit the simulation
  */
 const checkAndStopRunningSimulation = async () => {
   if (!currentSimulationId.value) return
   
   try {
-    // 先检查模拟环境是否存活
+    // First check if the simulation environment is alive
     const envStatusRes = await getEnvStatus({ simulation_id: currentSimulationId.value })
     
     if (envStatusRes.success && envStatusRes.data?.env_alive) {
-      addLog('检测到模拟环境正在运行，正在关闭...')
+      addLog('Simulation environment running, shutting down...')
       
-      // 尝试优雅关闭模拟环境
+      // Try graceful shutdown of simulation environment
       try {
         const closeRes = await closeSimulationEnv({ 
           simulation_id: currentSimulationId.value,
-          timeout: 10  // 10秒超时
+          timeout: 10  // 10s timeout
         })
         
         if (closeRes.success) {
-          addLog('✓ 模拟环境已关闭')
+          addLog('✓ Simulation environment closed')
         } else {
-          addLog(`关闭模拟环境失败: ${closeRes.error || '未知错误'}`)
-          // 如果优雅关闭失败，尝试强制停止
+          addLog(`Failed to close simulation env: ${closeRes.error || 'Unknown error'}`)
+          // If graceful shutdown failed, try force stop
           await forceStopSimulation()
         }
       } catch (closeErr) {
-        addLog(`关闭模拟环境异常: ${closeErr.message}`)
-        // 如果优雅关闭异常，尝试强制停止
+        addLog(`Error closing simulation env: ${closeErr.message}`)
+        // If graceful shutdown threw, try force stop
         await forceStopSimulation()
       }
     } else {
-      // 环境未运行，但可能进程还在，检查模拟状态
+      // Environment not running, but process may still be active, check simulation status
       const simRes = await getSimulation(currentSimulationId.value)
       if (simRes.success && simRes.data?.status === 'running') {
-        addLog('检测到模拟状态为运行中，正在停止...')
+        addLog('Simulation status is running, stopping...')
         await forceStopSimulation()
       }
     }
   } catch (err) {
-    // 检查环境状态失败不影响后续流程
-    console.warn('检查模拟状态失败:', err)
+    // Failure to check env status should not block subsequent flow
+    console.warn('Failed to check simulation status:', err)
   }
 }
 
 /**
- * 强制停止模拟
+ * Force stop simulation
  */
 const forceStopSimulation = async () => {
   try {
     const stopRes = await stopSimulation({ simulation_id: currentSimulationId.value })
     if (stopRes.success) {
-      addLog('✓ 模拟已强制停止')
+      addLog('✓ Simulation force stopped')
     } else {
-      addLog(`强制停止模拟失败: ${stopRes.error || '未知错误'}`)
+      addLog(`Failed to force stop: ${stopRes.error || 'Unknown error'}`)
     }
   } catch (err) {
-    addLog(`强制停止模拟异常: ${err.message}`)
+    addLog(`Error force stopping: ${err.message}`)
   }
 }
 
 const loadSimulationData = async () => {
   try {
-    addLog(`加载模拟数据: ${currentSimulationId.value}`)
+    addLog(`Loading simulation data: ${currentSimulationId.value}`)
     
-    // 获取 simulation 信息
+    // Get simulation info
     const simRes = await getSimulation(currentSimulationId.value)
     if (simRes.success && simRes.data) {
       const simData = simRes.data
       
-      // 获取 project 信息
+      // Get project info
       if (simData.project_id) {
         const projRes = await getProject(simData.project_id)
         if (projRes.success && projRes.data) {
           projectData.value = projRes.data
-          addLog(`项目加载成功: ${projRes.data.project_id}`)
+          addLog(`Project loaded: ${projRes.data.project_id}`)
           
-          // 获取 graph 数据
+          // Get graph data
           if (projRes.data.graph_id) {
             await loadGraph(projRes.data.graph_id)
           }
         }
       }
     } else {
-      addLog(`加载模拟数据失败: ${simRes.error || '未知错误'}`)
+      addLog(`Failed to load simulation: ${simRes.error || 'Unknown error'}`)
     }
   } catch (err) {
-    addLog(`加载异常: ${err.message}`)
+    addLog(`Load error: ${err.message}`)
   }
 }
 
@@ -271,10 +271,10 @@ const loadGraph = async (graphId) => {
     const res = await getGraphData(graphId)
     if (res.success) {
       graphData.value = res.data
-      addLog('图谱数据加载成功')
+      addLog('Graph data loaded')
     }
   } catch (err) {
-    addLog(`图谱加载失败: ${err.message}`)
+    addLog(`Graph load failed: ${err.message}`)
   } finally {
     graphLoading.value = false
   }
@@ -287,12 +287,12 @@ const refreshGraph = () => {
 }
 
 onMounted(async () => {
-  addLog('SimulationView 初始化')
+  addLog('SimulationView initialized')
   
-  // 检查并关闭正在运行的模拟（用户从 Step 3 返回时）
+  // Check and stop any running simulation (when user returns from Step 3)
   await checkAndStopRunningSimulation()
   
-  // 加载模拟数据
+  // Load simulation data
   loadSimulationData()
 })
 </script>

From 33b896745cc85dea9652b5a2611f885d9bcfdc10 Mon Sep 17 00:00:00 2001
From: thomas-ext-ux <thomas-ext@alteos.com>
Date: Sun, 22 Mar 2026 09:44:55 +0100
Subject: [PATCH 2/4] fix: support ARM64 Docker build and increase ontology
 max_tokens

- Change Dockerfile to install uv via pip instead of COPY from ghcr.io
  (fixes ARM64 build and BuildKit DNS issues)
- Switch docker-compose to local build instead of pre-built image
- Increase ontology generation max_tokens from 4096 to 16384 to prevent
  truncated JSON responses with large documents

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
---
 Dockerfile                                 | 4 ++--
 backend/app/services/ontology_generator.py | 2 +-
 docker-compose.yml                         | 5 ++---
 3 files changed, 5 insertions(+), 6 deletions(-)

diff --git a/Dockerfile b/Dockerfile
index e65646860..e126ffd3f 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -5,8 +5,8 @@ RUN apt-get update \
   && apt-get install -y --no-install-recommends nodejs npm \
   && rm -rf /var/lib/apt/lists/*
 
-# 从 uv 官方镜像复制 uv
-COPY --from=ghcr.io/astral-sh/uv:0.9.26 /uv /uvx /bin/
+# 安装 uv
+RUN pip install uv==0.9.26
 
 WORKDIR /app
 
diff --git a/backend/app/services/ontology_generator.py b/backend/app/services/ontology_generator.py
index 2d3e39bd8..c9bd4ba58 100644
--- a/backend/app/services/ontology_generator.py
+++ b/backend/app/services/ontology_generator.py
@@ -197,7 +197,7 @@ def generate(
         result = self.llm_client.chat_json(
             messages=messages,
             temperature=0.3,
-            max_tokens=4096
+            max_tokens=16384
         )
         
         # 验证和后处理
diff --git a/docker-compose.yml b/docker-compose.yml
index 637f1dfae..53d52709f 100644
--- a/docker-compose.yml
+++ b/docker-compose.yml
@@ -1,8 +1,7 @@
 services:
   mirofish:
-    image: ghcr.io/666ghj/mirofish:latest
-    # 加速镜像（如拉取缓慢可替换上方地址）
-    # image: ghcr.nju.edu.cn/666ghj/mirofish:latest
+    build: .
+    image: mirofish:local
     container_name: mirofish
     env_file:
       - .env

From 460c53b8223abcc1280a9922f5b44afb10f0f087 Mon Sep 17 00:00:00 2001
From: thomas-ext-ux <thomas-ext@alteos.com>
Date: Mon, 23 Mar 2026 08:08:55 +0100
Subject: [PATCH 3/4] feat: replace Zep Cloud with self-hosted Graphiti + Neo4j

Replace the zep_cloud managed SaaS dependency with graphiti-core
backed by a local Neo4j instance. This eliminates rate limits,
episode limits, and the $25/mo Zep subscription.

Changes:
- Add GraphitiAdapter: sync wrapper for async graphiti-core API
- Migrate graph_builder, zep_entity_reader, zep_tools,
  zep_graph_memory_updater, oasis_profile_generator to use adapter
- Remove zep_paging.py (no pagination needed with local DB)
- Update config: add NEO4J_URI/USER/PASSWORD, remove ZEP_API_KEY
- Use Gemini native support for LLM, embeddings, and reranking
- Increase default chunk size to 2000 for faster graph builds
- Pass ontology entity_types to Graphiti for typed node labels
- Remove _wait_for_episodes (Graphiti processes synchronously)

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
---
 Dockerfile                                    |   2 +-
 backend/app/api/graph.py                      |  49 +-
 backend/app/api/simulation.py                 |  12 +-
 backend/app/config.py                         |  15 +-
 backend/app/services/graph_builder.py         | 435 ++++-------------
 backend/app/services/graphiti_adapter.py      | 452 ++++++++++++++++++
 .../app/services/oasis_profile_generator.py   | 161 ++-----
 backend/app/services/ontology_generator.py    |  10 +-
 backend/app/services/zep_entity_reader.py     | 175 +++----
 .../app/services/zep_graph_memory_updater.py  |  21 +-
 backend/app/services/zep_tools.py             | 205 ++++----
 backend/app/utils/zep_paging.py               | 143 ------
 backend/pyproject.toml                        |   4 +-
 backend/uv.lock                               | 121 ++++-
 14 files changed, 887 insertions(+), 918 deletions(-)
 create mode 100644 backend/app/services/graphiti_adapter.py
 delete mode 100644 backend/app/utils/zep_paging.py

diff --git a/Dockerfile b/Dockerfile
index e126ffd3f..c3592360d 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -18,7 +18,7 @@ COPY backend/pyproject.toml backend/uv.lock ./backend/
 # 安装依赖（Node + Python）
 RUN npm ci \
   && npm ci --prefix frontend \
-  && cd backend && uv sync --frozen
+  && cd backend && uv sync
 
 # 复制项目源码
 COPY . .
diff --git a/backend/app/api/graph.py b/backend/app/api/graph.py
index 12ff1ba2d..9d30d3799 100644
--- a/backend/app/api/graph.py
+++ b/backend/app/api/graph.py
@@ -284,8 +284,8 @@ def build_graph():
         
         # 检查配置
         errors = []
-        if not Config.ZEP_API_KEY:
-            errors.append("ZEP_API_KEY未配置")
+        if not Config.LLM_API_KEY:
+            errors.append("LLM_API_KEY not configured")
         if errors:
             logger.error(f"配置错误: {errors}")
             return jsonify({
@@ -382,7 +382,7 @@ def build_task():
                 )
                 
                 # 创建图谱构建服务
-                builder = GraphBuilderService(api_key=Config.ZEP_API_KEY)
+                builder = GraphBuilderService()
                 
                 # 分块
                 task_manager.update_task(
@@ -400,7 +400,7 @@ def build_task():
                 # 创建图谱
                 task_manager.update_task(
                     task_id,
-                    message="创建Zep图谱...",
+                    message="创建图谱...",
                     progress=10
                 )
                 graph_id = builder.create_graph(name=graph_name)
@@ -439,27 +439,12 @@ def add_progress_callback(msg, progress_ratio):
                     progress_callback=add_progress_callback
                 )
                 
-                # 等待Zep处理完成（查询每个episode的processed状态）
-                task_manager.update_task(
-                    task_id,
-                    message="等待Zep处理数据...",
-                    progress=55
-                )
-                
-                def wait_progress_callback(msg, progress_ratio):
-                    progress = 55 + int(progress_ratio * 35)  # 55% - 90%
-                    task_manager.update_task(
-                        task_id,
-                        message=msg,
-                        progress=progress
-                    )
-                
-                builder._wait_for_episodes(episode_uuids, wait_progress_callback)
-                
-                # 获取图谱数据
+                # Graphiti processes episodes synchronously, no waiting needed
+
+                # Retrieve graph data
                 task_manager.update_task(
                     task_id,
-                    message="获取图谱数据...",
+                    message="Retrieving graph data...",
                     progress=95
                 )
                 graph_data = builder.get_graph_data(graph_id)
@@ -567,13 +552,13 @@ def get_graph_data(graph_id: str):
     获取图谱数据（节点和边）
     """
     try:
-        if not Config.ZEP_API_KEY:
+        if not Config.LLM_API_KEY:
             return jsonify({
                 "success": False,
-                "error": "ZEP_API_KEY未配置"
+                "error": "LLM_API_KEY not configured"
             }), 500
-        
-        builder = GraphBuilderService(api_key=Config.ZEP_API_KEY)
+
+        builder = GraphBuilderService()
         graph_data = builder.get_graph_data(graph_id)
         
         return jsonify({
@@ -592,16 +577,16 @@ def get_graph_data(graph_id: str):
 @graph_bp.route('/delete/<graph_id>', methods=['DELETE'])
 def delete_graph(graph_id: str):
     """
-    删除Zep图谱
+    删除图谱
     """
     try:
-        if not Config.ZEP_API_KEY:
+        if not Config.LLM_API_KEY:
             return jsonify({
                 "success": False,
-                "error": "ZEP_API_KEY未配置"
+                "error": "LLM_API_KEY not configured"
             }), 500
-        
-        builder = GraphBuilderService(api_key=Config.ZEP_API_KEY)
+
+        builder = GraphBuilderService()
         builder.delete_graph(graph_id)
         
         return jsonify({
diff --git a/backend/app/api/simulation.py b/backend/app/api/simulation.py
index 3a0f68168..86c08bd43 100644
--- a/backend/app/api/simulation.py
+++ b/backend/app/api/simulation.py
@@ -56,10 +56,10 @@ def get_graph_entities(graph_id: str):
         enrich: 是否获取相关边信息（默认true）
     """
     try:
-        if not Config.ZEP_API_KEY:
+        if not Config.LLM_API_KEY:
             return jsonify({
                 "success": False,
-                "error": "ZEP_API_KEY未配置"
+                "error": "LLM_API_KEY not configured"
             }), 500
         
         entity_types_str = request.args.get('entity_types', '')
@@ -93,10 +93,10 @@ def get_graph_entities(graph_id: str):
 def get_entity_detail(graph_id: str, entity_uuid: str):
     """获取单个实体的详细信息"""
     try:
-        if not Config.ZEP_API_KEY:
+        if not Config.LLM_API_KEY:
             return jsonify({
                 "success": False,
-                "error": "ZEP_API_KEY未配置"
+                "error": "LLM_API_KEY not configured"
             }), 500
         
         reader = ZepEntityReader()
@@ -126,10 +126,10 @@ def get_entity_detail(graph_id: str, entity_uuid: str):
 def get_entities_by_type(graph_id: str, entity_type: str):
     """获取指定类型的所有实体"""
     try:
-        if not Config.ZEP_API_KEY:
+        if not Config.LLM_API_KEY:
             return jsonify({
                 "success": False,
-                "error": "ZEP_API_KEY未配置"
+                "error": "LLM_API_KEY not configured"
             }), 500
         
         enrich = request.args.get('enrich', 'true').lower() == 'true'
diff --git a/backend/app/config.py b/backend/app/config.py
index 953dfa50a..ef0c72a51 100644
--- a/backend/app/config.py
+++ b/backend/app/config.py
@@ -32,17 +32,20 @@ class Config:
     LLM_BASE_URL = os.environ.get('LLM_BASE_URL', 'https://api.openai.com/v1')
     LLM_MODEL_NAME = os.environ.get('LLM_MODEL_NAME', 'gpt-4o-mini')
     
-    # Zep配置
-    ZEP_API_KEY = os.environ.get('ZEP_API_KEY')
-    
+    # Graphiti / Neo4j config
+    GRAPHITI_DB_DIR = os.path.join(os.path.dirname(__file__), '../uploads/graphs')
+    NEO4J_URI = os.environ.get('NEO4J_URI', 'bolt://localhost:7687')
+    NEO4J_USER = os.environ.get('NEO4J_USER', 'neo4j')
+    NEO4J_PASSWORD = os.environ.get('NEO4J_PASSWORD', 'mirofish123')
+
     # 文件上传配置
     MAX_CONTENT_LENGTH = 50 * 1024 * 1024  # 50MB
     UPLOAD_FOLDER = os.path.join(os.path.dirname(__file__), '../uploads')
     ALLOWED_EXTENSIONS = {'pdf', 'md', 'txt', 'markdown'}
     
     # 文本处理配置
-    DEFAULT_CHUNK_SIZE = 500  # 默认切块大小
-    DEFAULT_CHUNK_OVERLAP = 50  # 默认重叠大小
+    DEFAULT_CHUNK_SIZE = 2000  # default chunk size (larger = fewer LLM calls, faster)
+    DEFAULT_CHUNK_OVERLAP = 100  # default overlap
     
     # OASIS模拟配置
     OASIS_DEFAULT_MAX_ROUNDS = int(os.environ.get('OASIS_DEFAULT_MAX_ROUNDS', '10'))
@@ -69,7 +72,5 @@ def validate(cls):
         errors = []
         if not cls.LLM_API_KEY:
             errors.append("LLM_API_KEY 未配置")
-        if not cls.ZEP_API_KEY:
-            errors.append("ZEP_API_KEY 未配置")
         return errors
 
diff --git a/backend/app/services/graph_builder.py b/backend/app/services/graph_builder.py
index 0e0444bf3..f8652aa81 100644
--- a/backend/app/services/graph_builder.py
+++ b/backend/app/services/graph_builder.py
@@ -1,6 +1,6 @@
 """
-图谱构建服务
-接口2：使用Zep API构建Standalone Graph
+Graph Builder Service
+Interface 2: Build knowledge graphs using GraphitiAdapter
 """
 
 import os
@@ -10,23 +10,20 @@
 from typing import Dict, Any, List, Optional, Callable
 from dataclasses import dataclass
 
-from zep_cloud.client import Zep
-from zep_cloud import EpisodeData, EntityEdgeSourceTarget
-
 from ..config import Config
 from ..models.task import TaskManager, TaskStatus
-from ..utils.zep_paging import fetch_all_nodes, fetch_all_edges
 from .text_processor import TextProcessor
+from .graphiti_adapter import GraphitiAdapter
 
 
 @dataclass
 class GraphInfo:
-    """图谱信息"""
+    """Graph information"""
     graph_id: str
     node_count: int
     edge_count: int
     entity_types: List[str]
-    
+
     def to_dict(self) -> Dict[str, Any]:
         return {
             "graph_id": self.graph_id,
@@ -38,18 +35,13 @@ def to_dict(self) -> Dict[str, Any]:
 
 class GraphBuilderService:
     """
-    图谱构建服务
-    负责调用Zep API构建知识图谱
+    Graph Builder Service
+    Builds knowledge graphs using GraphitiAdapter
     """
-    
-    def __init__(self, api_key: Optional[str] = None):
-        self.api_key = api_key or Config.ZEP_API_KEY
-        if not self.api_key:
-            raise ValueError("ZEP_API_KEY 未配置")
-        
-        self.client = Zep(api_key=self.api_key)
+
+    def __init__(self):
         self.task_manager = TaskManager()
-    
+
     def build_graph_async(
         self,
         text: str,
@@ -60,20 +52,20 @@ def build_graph_async(
         batch_size: int = 3
     ) -> str:
         """
-        异步构建图谱
-        
+        Build graph asynchronously
+
         Args:
-            text: 输入文本
-            ontology: 本体定义（来自接口1的输出）
-            graph_name: 图谱名称
-            chunk_size: 文本块大小
-            chunk_overlap: 块重叠大小
-            batch_size: 每批发送的块数量
-            
+            text: Input text
+            ontology: Ontology definition (from interface 1 output)
+            graph_name: Graph name
+            chunk_size: Text chunk size
+            chunk_overlap: Chunk overlap size
+            batch_size: Number of chunks per batch
+
         Returns:
-            任务ID
+            Task ID
         """
-        # 创建任务
+        # Create task
         task_id = self.task_manager.create_task(
             task_type="graph_build",
             metadata={
@@ -82,17 +74,17 @@ def build_graph_async(
                 "text_length": len(text),
             }
         )
-        
-        # 在后台线程中执行构建
+
+        # Execute build in background thread
         thread = threading.Thread(
             target=self._build_graph_worker,
             args=(task_id, text, ontology, graph_name, chunk_size, chunk_overlap, batch_size)
         )
         thread.daemon = True
         thread.start()
-        
+
         return task_id
-    
+
     def _build_graph_worker(
         self,
         task_id: str,
@@ -103,188 +95,81 @@ def _build_graph_worker(
         chunk_overlap: int,
         batch_size: int
     ):
-        """图谱构建工作线程"""
+        """Graph build worker thread"""
         try:
             self.task_manager.update_task(
                 task_id,
                 status=TaskStatus.PROCESSING,
                 progress=5,
-                message="开始构建图谱..."
+                message="Starting graph build..."
             )
-            
-            # 1. 创建图谱
+
+            # 1. Create graph
             graph_id = self.create_graph(graph_name)
             self.task_manager.update_task(
                 task_id,
                 progress=10,
-                message=f"图谱已创建: {graph_id}"
+                message=f"Graph created: {graph_id}"
             )
-            
-            # 2. 设置本体
+
+            # 2. Set ontology
             self.set_ontology(graph_id, ontology)
             self.task_manager.update_task(
                 task_id,
                 progress=15,
-                message="本体已设置"
+                message="Ontology set"
             )
-            
-            # 3. 文本分块
+
+            # 3. Split text into chunks
             chunks = TextProcessor.split_text(text, chunk_size, chunk_overlap)
             total_chunks = len(chunks)
             self.task_manager.update_task(
                 task_id,
                 progress=20,
-                message=f"文本已分割为 {total_chunks} 个块"
+                message=f"Text split into {total_chunks} chunks"
             )
-            
-            # 4. 分批发送数据
-            episode_uuids = self.add_text_batches(
+
+            # 4. Send data in batches
+            self.add_text_batches(
                 graph_id, chunks, batch_size,
                 lambda msg, prog: self.task_manager.update_task(
                     task_id,
-                    progress=20 + int(prog * 0.4),  # 20-60%
-                    message=msg
-                )
-            )
-            
-            # 5. 等待Zep处理完成
-            self.task_manager.update_task(
-                task_id,
-                progress=60,
-                message="等待Zep处理数据..."
-            )
-            
-            self._wait_for_episodes(
-                episode_uuids,
-                lambda msg, prog: self.task_manager.update_task(
-                    task_id,
-                    progress=60 + int(prog * 0.3),  # 60-90%
+                    progress=20 + int(prog * 60),  # 20-80%
                     message=msg
                 )
             )
-            
-            # 6. 获取图谱信息
+
+            # 5. Retrieve graph info
             self.task_manager.update_task(
                 task_id,
                 progress=90,
-                message="获取图谱信息..."
+                message="Retrieving graph info..."
             )
-            
+
             graph_info = self._get_graph_info(graph_id)
-            
-            # 完成
+
+            # Complete
             self.task_manager.complete_task(task_id, {
                 "graph_id": graph_id,
                 "graph_info": graph_info.to_dict(),
                 "chunks_processed": total_chunks,
             })
-            
+
         except Exception as e:
             import traceback
             error_msg = f"{str(e)}\n{traceback.format_exc()}"
             self.task_manager.fail_task(task_id, error_msg)
-    
+
     def create_graph(self, name: str) -> str:
-        """创建Zep图谱（公开方法）"""
         graph_id = f"mirofish_{uuid.uuid4().hex[:16]}"
-        
-        self.client.graph.create(
-            graph_id=graph_id,
-            name=name,
-            description="MiroFish Social Simulation Graph"
-        )
-        
+        adapter = GraphitiAdapter.get_or_create(graph_id)
+        adapter.create_graph(name)
         return graph_id
-    
+
     def set_ontology(self, graph_id: str, ontology: Dict[str, Any]):
-        """设置图谱本体（公开方法）"""
-        import warnings
-        from typing import Optional
-        from pydantic import Field
-        from zep_cloud.external_clients.ontology import EntityModel, EntityText, EdgeModel
-        
-        # 抑制 Pydantic v2 关于 Field(default=None) 的警告
-        # 这是 Zep SDK 要求的用法，警告来自动态类创建，可以安全忽略
-        warnings.filterwarnings('ignore', category=UserWarning, module='pydantic')
-        
-        # Zep 保留名称，不能作为属性名
-        RESERVED_NAMES = {'uuid', 'name', 'group_id', 'name_embedding', 'summary', 'created_at'}
-        
-        def safe_attr_name(attr_name: str) -> str:
-            """将保留名称转换为安全名称"""
-            if attr_name.lower() in RESERVED_NAMES:
-                return f"entity_{attr_name}"
-            return attr_name
-        
-        # 动态创建实体类型
-        entity_types = {}
-        for entity_def in ontology.get("entity_types", []):
-            name = entity_def["name"]
-            description = entity_def.get("description", f"A {name} entity.")
-            
-            # 创建属性字典和类型注解（Pydantic v2 需要）
-            attrs = {"__doc__": description}
-            annotations = {}
-            
-            for attr_def in entity_def.get("attributes", []):
-                attr_name = safe_attr_name(attr_def["name"])  # 使用安全名称
-                attr_desc = attr_def.get("description", attr_name)
-                # Zep API 需要 Field 的 description，这是必需的
-                attrs[attr_name] = Field(description=attr_desc, default=None)
-                annotations[attr_name] = Optional[EntityText]  # 类型注解
-            
-            attrs["__annotations__"] = annotations
-            
-            # 动态创建类
-            entity_class = type(name, (EntityModel,), attrs)
-            entity_class.__doc__ = description
-            entity_types[name] = entity_class
-        
-        # 动态创建边类型
-        edge_definitions = {}
-        for edge_def in ontology.get("edge_types", []):
-            name = edge_def["name"]
-            description = edge_def.get("description", f"A {name} relationship.")
-            
-            # 创建属性字典和类型注解
-            attrs = {"__doc__": description}
-            annotations = {}
-            
-            for attr_def in edge_def.get("attributes", []):
-                attr_name = safe_attr_name(attr_def["name"])  # 使用安全名称
-                attr_desc = attr_def.get("description", attr_name)
-                # Zep API 需要 Field 的 description，这是必需的
-                attrs[attr_name] = Field(description=attr_desc, default=None)
-                annotations[attr_name] = Optional[str]  # 边属性用str类型
-            
-            attrs["__annotations__"] = annotations
-            
-            # 动态创建类
-            class_name = ''.join(word.capitalize() for word in name.split('_'))
-            edge_class = type(class_name, (EdgeModel,), attrs)
-            edge_class.__doc__ = description
-            
-            # 构建source_targets
-            source_targets = []
-            for st in edge_def.get("source_targets", []):
-                source_targets.append(
-                    EntityEdgeSourceTarget(
-                        source=st.get("source", "Entity"),
-                        target=st.get("target", "Entity")
-                    )
-                )
-            
-            if source_targets:
-                edge_definitions[name] = (edge_class, source_targets)
-        
-        # 调用Zep API设置本体
-        if entity_types or edge_definitions:
-            self.client.graph.set_ontology(
-                graph_ids=[graph_id],
-                entities=entity_types if entity_types else None,
-                edges=edge_definitions if edge_definitions else None,
-            )
-    
+        adapter = GraphitiAdapter.get_or_create(graph_id)
+        adapter.set_ontology(ontology)
+
     def add_text_batches(
         self,
         graph_id: str,
@@ -292,123 +177,40 @@ def add_text_batches(
         batch_size: int = 3,
         progress_callback: Optional[Callable] = None
     ) -> List[str]:
-        """分批添加文本到图谱，返回所有 episode 的 uuid 列表"""
-        episode_uuids = []
+        adapter = GraphitiAdapter.get_or_create(graph_id)
         total_chunks = len(chunks)
-        
+
         for i in range(0, total_chunks, batch_size):
-            batch_chunks = chunks[i:i + batch_size]
+            batch = chunks[i:i + batch_size]
             batch_num = i // batch_size + 1
             total_batches = (total_chunks + batch_size - 1) // batch_size
-            
+
             if progress_callback:
-                progress = (i + len(batch_chunks)) / total_chunks
+                progress = (i + len(batch)) / total_chunks
                 progress_callback(
-                    f"发送第 {batch_num}/{total_batches} 批数据 ({len(batch_chunks)} 块)...",
+                    f"Sending batch {batch_num}/{total_batches} ({len(batch)} chunks)...",
                     progress
                 )
-            
-            # 构建episode数据
-            episodes = [
-                EpisodeData(data=chunk, type="text")
-                for chunk in batch_chunks
-            ]
-            
-            # 发送到Zep
+
             try:
-                batch_result = self.client.graph.add_batch(
-                    graph_id=graph_id,
-                    episodes=episodes
-                )
-                
-                # 收集返回的 episode uuid
-                if batch_result and isinstance(batch_result, list):
-                    for ep in batch_result:
-                        ep_uuid = getattr(ep, 'uuid_', None) or getattr(ep, 'uuid', None)
-                        if ep_uuid:
-                            episode_uuids.append(ep_uuid)
-                
-                # 避免请求过快
-                time.sleep(1)
-                
+                adapter.add_episodes_bulk(batch)
             except Exception as e:
                 if progress_callback:
-                    progress_callback(f"批次 {batch_num} 发送失败: {str(e)}", 0)
+                    progress_callback(f"Batch {batch_num} failed: {str(e)}", 0)
                 raise
-        
-        return episode_uuids
-    
-    def _wait_for_episodes(
-        self,
-        episode_uuids: List[str],
-        progress_callback: Optional[Callable] = None,
-        timeout: int = 600
-    ):
-        """等待所有 episode 处理完成（通过查询每个 episode 的 processed 状态）"""
-        if not episode_uuids:
-            if progress_callback:
-                progress_callback("无需等待（没有 episode）", 1.0)
-            return
-        
-        start_time = time.time()
-        pending_episodes = set(episode_uuids)
-        completed_count = 0
-        total_episodes = len(episode_uuids)
-        
-        if progress_callback:
-            progress_callback(f"开始等待 {total_episodes} 个文本块处理...", 0)
-        
-        while pending_episodes:
-            if time.time() - start_time > timeout:
-                if progress_callback:
-                    progress_callback(
-                        f"部分文本块超时，已完成 {completed_count}/{total_episodes}",
-                        completed_count / total_episodes
-                    )
-                break
-            
-            # 检查每个 episode 的处理状态
-            for ep_uuid in list(pending_episodes):
-                try:
-                    episode = self.client.graph.episode.get(uuid_=ep_uuid)
-                    is_processed = getattr(episode, 'processed', False)
-                    
-                    if is_processed:
-                        pending_episodes.remove(ep_uuid)
-                        completed_count += 1
-                        
-                except Exception as e:
-                    # 忽略单个查询错误，继续
-                    pass
-            
-            elapsed = int(time.time() - start_time)
-            if progress_callback:
-                progress_callback(
-                    f"Zep处理中... {completed_count}/{total_episodes} 完成, {len(pending_episodes)} 待处理 ({elapsed}秒)",
-                    completed_count / total_episodes if total_episodes > 0 else 0
-                )
-            
-            if pending_episodes:
-                time.sleep(3)  # 每3秒检查一次
-        
-        if progress_callback:
-            progress_callback(f"处理完成: {completed_count}/{total_episodes}", 1.0)
-    
-    def _get_graph_info(self, graph_id: str) -> GraphInfo:
-        """获取图谱信息"""
-        # 获取节点（分页）
-        nodes = fetch_all_nodes(self.client, graph_id)
 
-        # 获取边（分页）
-        edges = fetch_all_edges(self.client, graph_id)
+        return []  # No episode UUIDs needed
+
+    def _get_graph_info(self, graph_id: str) -> GraphInfo:
+        adapter = GraphitiAdapter.get_or_create(graph_id)
+        nodes = adapter.get_all_nodes()
+        edges = adapter.get_all_edges()
 
-        # 统计实体类型
         entity_types = set()
         for node in nodes:
-            if node.labels:
-                for label in node.labels:
-                    if label not in ["Entity", "Node"]:
-                        entity_types.add(label)
+            for label in node.get("labels", []):
+                if label not in ["Entity", "Node"]:
+                    entity_types.add(label)
 
         return GraphInfo(
             graph_id=graph_id,
@@ -416,85 +218,28 @@ def _get_graph_info(self, graph_id: str) -> GraphInfo:
             edge_count=len(edges),
             entity_types=list(entity_types)
         )
-    
+
     def get_graph_data(self, graph_id: str) -> Dict[str, Any]:
-        """
-        获取完整图谱数据（包含详细信息）
-        
-        Args:
-            graph_id: 图谱ID
-            
-        Returns:
-            包含nodes和edges的字典，包括时间信息、属性等详细数据
-        """
-        nodes = fetch_all_nodes(self.client, graph_id)
-        edges = fetch_all_edges(self.client, graph_id)
+        adapter = GraphitiAdapter.get_or_create(graph_id)
+        nodes = adapter.get_all_nodes()
+        edges = adapter.get_all_edges()
+
+        node_map = {n["uuid"]: n.get("name", "") for n in nodes}
 
-        # 创建节点映射用于获取节点名称
-        node_map = {}
-        for node in nodes:
-            node_map[node.uuid_] = node.name or ""
-        
-        nodes_data = []
-        for node in nodes:
-            # 获取创建时间
-            created_at = getattr(node, 'created_at', None)
-            if created_at:
-                created_at = str(created_at)
-            
-            nodes_data.append({
-                "uuid": node.uuid_,
-                "name": node.name,
-                "labels": node.labels or [],
-                "summary": node.summary or "",
-                "attributes": node.attributes or {},
-                "created_at": created_at,
-            })
-        
-        edges_data = []
         for edge in edges:
-            # 获取时间信息
-            created_at = getattr(edge, 'created_at', None)
-            valid_at = getattr(edge, 'valid_at', None)
-            invalid_at = getattr(edge, 'invalid_at', None)
-            expired_at = getattr(edge, 'expired_at', None)
-            
-            # 获取 episodes
-            episodes = getattr(edge, 'episodes', None) or getattr(edge, 'episode_ids', None)
-            if episodes and not isinstance(episodes, list):
-                episodes = [str(episodes)]
-            elif episodes:
-                episodes = [str(e) for e in episodes]
-            
-            # 获取 fact_type
-            fact_type = getattr(edge, 'fact_type', None) or edge.name or ""
-            
-            edges_data.append({
-                "uuid": edge.uuid_,
-                "name": edge.name or "",
-                "fact": edge.fact or "",
-                "fact_type": fact_type,
-                "source_node_uuid": edge.source_node_uuid,
-                "target_node_uuid": edge.target_node_uuid,
-                "source_node_name": node_map.get(edge.source_node_uuid, ""),
-                "target_node_name": node_map.get(edge.target_node_uuid, ""),
-                "attributes": edge.attributes or {},
-                "created_at": str(created_at) if created_at else None,
-                "valid_at": str(valid_at) if valid_at else None,
-                "invalid_at": str(invalid_at) if invalid_at else None,
-                "expired_at": str(expired_at) if expired_at else None,
-                "episodes": episodes or [],
-            })
-        
+            edge["source_node_name"] = node_map.get(edge.get("source_node_uuid", ""), "")
+            edge["target_node_name"] = node_map.get(edge.get("target_node_uuid", ""), "")
+            edge["fact_type"] = edge.get("name", "")
+
         return {
             "graph_id": graph_id,
-            "nodes": nodes_data,
-            "edges": edges_data,
-            "node_count": len(nodes_data),
-            "edge_count": len(edges_data),
+            "nodes": nodes,
+            "edges": edges,
+            "node_count": len(nodes),
+            "edge_count": len(edges),
         }
-    
-    def delete_graph(self, graph_id: str):
-        """删除图谱"""
-        self.client.graph.delete(graph_id=graph_id)
 
+    def delete_graph(self, graph_id: str):
+        adapter = GraphitiAdapter.get_or_create(graph_id)
+        adapter.delete_graph()
+        GraphitiAdapter.remove_instance(graph_id)
diff --git a/backend/app/services/graphiti_adapter.py b/backend/app/services/graphiti_adapter.py
new file mode 100644
index 000000000..fc21840e7
--- /dev/null
+++ b/backend/app/services/graphiti_adapter.py
@@ -0,0 +1,452 @@
+"""
+Graphiti sync adapter
+Wraps graphiti-core async API for Flask synchronous use.
+Replaces all zep_cloud usage, backed by Neo4j graph database.
+
+Core design:
+- Dedicated daemon thread event loop for async-to-sync bridging
+- Neo4j as graph database backend
+- Native Gemini support for LLM and embeddings
+- API compatible with existing Zep-based code
+"""
+
+import asyncio
+import threading
+import os
+import json
+import shutil
+import uuid as uuid_mod
+from datetime import datetime, timezone
+from typing import Dict, Any, List, Optional, Callable
+
+from graphiti_core import Graphiti
+from graphiti_core.llm_client.gemini_client import GeminiClient
+from graphiti_core.llm_client.config import LLMConfig
+from graphiti_core.embedder.gemini import GeminiEmbedder, GeminiEmbedderConfig
+from graphiti_core.nodes import EpisodeType, EntityNode
+from graphiti_core.cross_encoder.openai_reranker_client import OpenAIRerankerClient
+from pydantic import BaseModel, Field
+
+from ..config import Config
+from ..utils.logger import get_logger
+
+logger = get_logger('mirofish.graphiti_adapter')
+
+# ---------------------------------------------------------------------------
+# Module-level event loop for async operations
+# ---------------------------------------------------------------------------
+
+_loop: Optional[asyncio.AbstractEventLoop] = None
+_thread: Optional[threading.Thread] = None
+_lock = threading.Lock()
+
+
+def _ensure_loop() -> asyncio.AbstractEventLoop:
+    global _loop, _thread
+    with _lock:
+        if _loop is None or not _loop.is_running():
+            _loop = asyncio.new_event_loop()
+            _thread = threading.Thread(target=_loop.run_forever, daemon=True)
+            _thread.start()
+    return _loop
+
+
+def _run_async(coro):
+    loop = _ensure_loop()
+    future = asyncio.run_coroutine_threadsafe(coro, loop)
+    return future.result(timeout=600)
+
+
+# ---------------------------------------------------------------------------
+# Adapter
+# ---------------------------------------------------------------------------
+
+class GraphitiAdapter:
+    """Synchronous adapter for graphiti-core async API backed by Neo4j."""
+
+    _instances: Dict[str, 'GraphitiAdapter'] = {}
+    _instances_lock = threading.Lock()
+
+    def __init__(self, graph_id: str):
+        self.graph_id = graph_id
+        self.db_dir = os.path.join(Config.UPLOAD_FOLDER, 'graphs', graph_id)
+        self._graphiti: Optional[Graphiti] = None
+        self._gemini_api_key: str = Config.LLM_API_KEY or ''
+
+    @classmethod
+    def get_or_create(cls, graph_id: str) -> 'GraphitiAdapter':
+        with cls._instances_lock:
+            if graph_id not in cls._instances:
+                cls._instances[graph_id] = cls(graph_id)
+            return cls._instances[graph_id]
+
+    @classmethod
+    def remove_instance(cls, graph_id: str):
+        with cls._instances_lock:
+            inst = cls._instances.pop(graph_id, None)
+        if inst is not None:
+            try:
+                inst.close()
+            except Exception:
+                logger.warning("Failed to close GraphitiAdapter: %s", graph_id, exc_info=True)
+
+    # --------------------------------------------------------------------------
+    # Lazy Graphiti initialisation (Neo4j + Gemini)
+    # --------------------------------------------------------------------------
+
+    def _get_graphiti(self) -> Graphiti:
+        if self._graphiti is None:
+            self._graphiti = _run_async(self._init_graphiti())
+        return self._graphiti
+
+    async def _init_graphiti(self) -> Graphiti:
+        os.makedirs(self.db_dir, exist_ok=True)
+
+        llm_client = GeminiClient(
+            config=LLMConfig(
+                api_key=self._gemini_api_key,
+                model="gemini-2.5-flash-lite",
+            )
+        )
+
+        embedder = GeminiEmbedder(
+            config=GeminiEmbedderConfig(
+                api_key=self._gemini_api_key,
+                embedding_model="gemini-embedding-001",
+            )
+        )
+
+        cross_encoder = OpenAIRerankerClient(
+            config=LLMConfig(
+                api_key=self._gemini_api_key,
+                base_url="https://generativelanguage.googleapis.com/v1beta/openai",
+                model="gemini-2.5-flash-lite",
+            )
+        )
+
+        graphiti = Graphiti(
+            uri=Config.NEO4J_URI,
+            user=Config.NEO4J_USER,
+            password=Config.NEO4J_PASSWORD,
+            llm_client=llm_client,
+            embedder=embedder,
+            cross_encoder=cross_encoder,
+        )
+
+        await graphiti.build_indices_and_constraints()
+        logger.info("Graphiti initialized: graph_id=%s, neo4j=%s", self.graph_id, Config.NEO4J_URI)
+        return graphiti
+
+    # --------------------------------------------------------------------------
+    # Graph lifecycle
+    # --------------------------------------------------------------------------
+
+    def create_graph(self, name: str = "MiroFish Graph") -> str:
+        os.makedirs(self.db_dir, exist_ok=True)
+
+        meta_path = os.path.join(self.db_dir, '_meta.json')
+        meta = {
+            'graph_id': self.graph_id,
+            'name': name,
+            'created_at': datetime.now(timezone.utc).isoformat(),
+        }
+        with open(meta_path, 'w', encoding='utf-8') as f:
+            json.dump(meta, f, ensure_ascii=False, indent=2)
+
+        self._get_graphiti()
+        logger.info("Graph created: graph_id=%s, name=%s", self.graph_id, name)
+        return self.graph_id
+
+    def delete_graph(self):
+        self.close()
+        if os.path.isdir(self.db_dir):
+            shutil.rmtree(self.db_dir, ignore_errors=True)
+            logger.info("Graph deleted: graph_id=%s", self.graph_id)
+
+    def graph_exists(self) -> bool:
+        return os.path.isdir(self.db_dir)
+
+    # --------------------------------------------------------------------------
+    # Ontology (stored as JSON metadata)
+    # --------------------------------------------------------------------------
+
+    def _ontology_path(self) -> str:
+        return os.path.join(self.db_dir, '_ontology.json')
+
+    def set_ontology(self, ontology_dict: Dict[str, Any]):
+        os.makedirs(self.db_dir, exist_ok=True)
+        with open(self._ontology_path(), 'w', encoding='utf-8') as f:
+            json.dump(ontology_dict, f, ensure_ascii=False, indent=2)
+        logger.info("Ontology saved: graph_id=%s", self.graph_id)
+
+    def get_ontology(self) -> Optional[Dict[str, Any]]:
+        path = self._ontology_path()
+        if not os.path.isfile(path):
+            return None
+        with open(path, 'r', encoding='utf-8') as f:
+            return json.load(f)
+
+    def _build_entity_types(self) -> Optional[Dict[str, type]]:
+        """Convert stored ontology into Pydantic models for Graphiti entity typing."""
+        ontology = self.get_ontology()
+        if not ontology:
+            return None
+
+        reserved = set(EntityNode.model_fields.keys())
+        entity_types = {}
+
+        for et in ontology.get("entity_types", []):
+            name = et.get("name", "")
+            if not name:
+                continue
+            description = et.get("description", f"A {name} entity")
+
+            # Build fields dict for dynamic Pydantic model
+            fields = {}
+            annotations = {}
+            for attr in et.get("attributes", []):
+                attr_name = attr.get("name", "")
+                if not attr_name or attr_name.lower() in reserved:
+                    attr_name = f"entity_{attr_name}"
+                attr_desc = attr.get("description", attr_name)
+                fields[attr_name] = Field(default=None, description=attr_desc)
+                annotations[attr_name] = Optional[str]
+
+            # Create dynamic Pydantic model
+            model_attrs = {"__annotations__": annotations, "__doc__": description}
+            model_attrs.update(fields)
+            model_class = type(name, (BaseModel,), model_attrs)
+            entity_types[name] = model_class
+
+        return entity_types if entity_types else None
+
+    # --------------------------------------------------------------------------
+    # Episodes
+    # --------------------------------------------------------------------------
+
+    def add_episode(self, text: str, source_description: str = "") -> str:
+        g = self._get_graphiti()
+        episode_id = str(uuid_mod.uuid4())
+        source = source_description or "mirofish"
+        entity_types = self._build_entity_types()
+
+        async def _add():
+            await g.add_episode(
+                name=episode_id,
+                episode_body=text,
+                source_description=source,
+                source=EpisodeType.text,
+                reference_time=datetime.now(timezone.utc),
+                group_id=self.graph_id,
+                entity_types=entity_types,
+            )
+            return episode_id
+
+        result = _run_async(_add())
+        logger.debug("Episode added: graph_id=%s, len=%d", self.graph_id, len(text))
+        return result
+
+    def add_episodes_bulk(self, texts: List[str]):
+        total = len(texts)
+        for idx, text in enumerate(texts):
+            try:
+                self.add_episode(text, source_description="mirofish_bulk")
+            except Exception:
+                logger.error("Episode add failed (%d/%d): graph_id=%s",
+                             idx + 1, total, self.graph_id, exc_info=True)
+
+    # --------------------------------------------------------------------------
+    # Node / edge retrieval via Neo4j Cypher
+    # --------------------------------------------------------------------------
+
+    def get_all_nodes(self) -> List[Dict[str, Any]]:
+        g = self._get_graphiti()
+
+        async def _fetch():
+            driver = g.driver
+            records, _, _ = await driver.execute_query(
+                "MATCH (n:Entity) WHERE n.group_id = $gid "
+                "RETURN n.uuid AS uuid, n.name AS name, labels(n) AS labels, "
+                "n.summary AS summary, n.created_at AS created_at",
+                gid=self.graph_id,
+            )
+            nodes = []
+            for r in records:
+                labels = [l for l in (r['labels'] or []) if l not in ('Entity', '__Entity__')]
+                nodes.append({
+                    'uuid': str(r['uuid'] or ''),
+                    'name': str(r['name'] or ''),
+                    'labels': labels,
+                    'summary': str(r['summary'] or ''),
+                    'attributes': {},
+                    'created_at': str(r['created_at'] or ''),
+                })
+            return nodes
+
+        return _run_async(_fetch())
+
+    def get_all_edges(self) -> List[Dict[str, Any]]:
+        g = self._get_graphiti()
+
+        async def _fetch():
+            driver = g.driver
+            records, _, _ = await driver.execute_query(
+                "MATCH (a:Entity)-[r:RELATES_TO]->(b:Entity) "
+                "WHERE r.group_id = $gid "
+                "RETURN r.uuid AS uuid, r.name AS name, r.fact AS fact, "
+                "a.uuid AS source_uuid, b.uuid AS target_uuid, "
+                "r.created_at AS created_at, r.valid_at AS valid_at, "
+                "r.invalid_at AS invalid_at, r.expired_at AS expired_at",
+                gid=self.graph_id,
+            )
+            edges = []
+            for r in records:
+                edges.append({
+                    'uuid': str(r['uuid'] or ''),
+                    'name': str(r['name'] or ''),
+                    'fact': str(r['fact'] or ''),
+                    'source_node_uuid': str(r['source_uuid'] or ''),
+                    'target_node_uuid': str(r['target_uuid'] or ''),
+                    'attributes': {},
+                    'created_at': str(r['created_at'] or ''),
+                    'valid_at': str(r['valid_at'] or ''),
+                    'invalid_at': str(r['invalid_at'] or ''),
+                    'expired_at': str(r['expired_at'] or ''),
+                    'episodes': [],
+                })
+            return edges
+
+        return _run_async(_fetch())
+
+    def get_node(self, node_uuid: str) -> Optional[Dict[str, Any]]:
+        g = self._get_graphiti()
+
+        async def _fetch():
+            driver = g.driver
+            records, _, _ = await driver.execute_query(
+                "MATCH (n:Entity) WHERE n.uuid = $uuid "
+                "RETURN n.uuid AS uuid, n.name AS name, labels(n) AS labels, "
+                "n.summary AS summary, n.created_at AS created_at",
+                uuid=node_uuid,
+            )
+            if not records:
+                return None
+            r = records[0]
+            labels = [l for l in (r['labels'] or []) if l not in ('Entity', '__Entity__')]
+            return {
+                'uuid': str(r['uuid'] or ''),
+                'name': str(r['name'] or ''),
+                'labels': labels,
+                'summary': str(r['summary'] or ''),
+                'attributes': {},
+                'created_at': str(r['created_at'] or ''),
+            }
+
+        return _run_async(_fetch())
+
+    def get_node_edges(self, node_uuid: str) -> List[Dict[str, Any]]:
+        g = self._get_graphiti()
+
+        async def _fetch():
+            driver = g.driver
+            records, _, _ = await driver.execute_query(
+                "MATCH (a:Entity)-[r:RELATES_TO]->(b:Entity) "
+                "WHERE a.uuid = $uuid OR b.uuid = $uuid "
+                "RETURN r.uuid AS uuid, r.name AS name, r.fact AS fact, "
+                "a.uuid AS source_uuid, b.uuid AS target_uuid, "
+                "r.created_at AS created_at, r.valid_at AS valid_at, "
+                "r.invalid_at AS invalid_at, r.expired_at AS expired_at",
+                uuid=node_uuid,
+            )
+            edges = []
+            for r in records:
+                edges.append({
+                    'uuid': str(r['uuid'] or ''),
+                    'name': str(r['name'] or ''),
+                    'fact': str(r['fact'] or ''),
+                    'source_node_uuid': str(r['source_uuid'] or ''),
+                    'target_node_uuid': str(r['target_uuid'] or ''),
+                    'attributes': {},
+                    'created_at': str(r['created_at'] or ''),
+                    'valid_at': str(r['valid_at'] or ''),
+                    'invalid_at': str(r['invalid_at'] or ''),
+                    'expired_at': str(r['expired_at'] or ''),
+                    'episodes': [],
+                })
+            return edges
+
+        return _run_async(_fetch())
+
+    # --------------------------------------------------------------------------
+    # Search
+    # --------------------------------------------------------------------------
+
+    def search(self, query: str, limit: int = 10, scope: str = "edges") -> Dict[str, Any]:
+        g = self._get_graphiti()
+
+        async def _do_search():
+            edges: List[Dict[str, Any]] = []
+            nodes: List[Dict[str, Any]] = []
+
+            try:
+                raw = await g.search(
+                    query,
+                    num_results=limit,
+                    group_ids=[self.graph_id],
+                )
+                for item in (raw if isinstance(raw, list) else []):
+                    edges.append(_entity_edge_to_dict(item))
+            except Exception:
+                logger.error("Search failed: query=%s", query, exc_info=True)
+
+            return {"edges": edges, "nodes": nodes}
+
+        return _run_async(_do_search())
+
+    # --------------------------------------------------------------------------
+    # Cleanup
+    # --------------------------------------------------------------------------
+
+    def close(self):
+        if self._graphiti is not None:
+            try:
+                _run_async(self._graphiti.close())
+            except Exception:
+                logger.debug("Graphiti close error", exc_info=True)
+            self._graphiti = None
+            logger.info("GraphitiAdapter closed: graph_id=%s", self.graph_id)
+
+
+# ---------------------------------------------------------------------------
+# Conversion helpers
+# ---------------------------------------------------------------------------
+
+def _entity_edge_to_dict(edge) -> Dict[str, Any]:
+    if isinstance(edge, dict):
+        return {
+            'uuid': str(edge.get('uuid', '')),
+            'name': str(edge.get('name', '')),
+            'fact': str(edge.get('fact', '')),
+            'source_node_uuid': str(edge.get('source_node_uuid', '')),
+            'target_node_uuid': str(edge.get('target_node_uuid', '')),
+            'attributes': edge.get('attributes', {}),
+            'created_at': str(edge.get('created_at', '')),
+            'valid_at': str(edge.get('valid_at', '')),
+            'invalid_at': str(edge.get('invalid_at', '')),
+            'expired_at': str(edge.get('expired_at', '')),
+            'episodes': list(edge.get('episodes', [])) if edge.get('episodes') else [],
+        }
+
+    return {
+        'uuid': str(getattr(edge, 'uuid', '')),
+        'name': str(getattr(edge, 'name', '')),
+        'fact': str(getattr(edge, 'fact', '')),
+        'source_node_uuid': str(getattr(edge, 'source_node_uuid', '')),
+        'target_node_uuid': str(getattr(edge, 'target_node_uuid', '')),
+        'attributes': getattr(edge, 'attributes', {}) or {},
+        'created_at': str(getattr(edge, 'created_at', '')),
+        'valid_at': str(getattr(edge, 'valid_at', '')),
+        'invalid_at': str(getattr(edge, 'invalid_at', '')),
+        'expired_at': str(getattr(edge, 'expired_at', '')),
+        'episodes': list(getattr(edge, 'episodes', [])) if getattr(edge, 'episodes', None) else [],
+    }
diff --git a/backend/app/services/oasis_profile_generator.py b/backend/app/services/oasis_profile_generator.py
index 57836c539..8207e92a4 100644
--- a/backend/app/services/oasis_profile_generator.py
+++ b/backend/app/services/oasis_profile_generator.py
@@ -16,11 +16,11 @@
 from datetime import datetime
 
 from openai import OpenAI
-from zep_cloud.client import Zep
 
 from ..config import Config
 from ..utils.logger import get_logger
 from .zep_entity_reader import EntityNode, ZepEntityReader
+from .graphiti_adapter import GraphitiAdapter
 
 logger = get_logger('mirofish.oasis_profile')
 
@@ -178,11 +178,10 @@ class OasisProfileGenerator:
     ]
     
     def __init__(
-        self, 
+        self,
         api_key: Optional[str] = None,
         base_url: Optional[str] = None,
         model_name: Optional[str] = None,
-        zep_api_key: Optional[str] = None,
         graph_id: Optional[str] = None
     ):
         self.api_key = api_key or Config.LLM_API_KEY
@@ -197,16 +196,14 @@ def __init__(
             base_url=self.base_url
         )
         
-        # Zep客户端用于检索丰富上下文
-        self.zep_api_key = zep_api_key or Config.ZEP_API_KEY
-        self.zep_client = None
+        # Graphiti adapter for graph search (replaces Zep client)
         self.graph_id = graph_id
-        
-        if self.zep_api_key:
+        self._graphiti_adapter = None
+        if self.graph_id:
             try:
-                self.zep_client = Zep(api_key=self.zep_api_key)
+                self._graphiti_adapter = GraphitiAdapter.get_or_create(self.graph_id)
             except Exception as e:
-                logger.warning(f"Zep客户端初始化失败: {e}")
+                logger.warning(f"GraphitiAdapter初始化失败: {e}")
     
     def generate_profile_from_entity(
         self, 
@@ -284,130 +281,78 @@ def _generate_username(self, name: str) -> str:
     
     def _search_zep_for_entity(self, entity: EntityNode) -> Dict[str, Any]:
         """
-        使用Zep图谱混合搜索功能获取实体相关的丰富信息
-        
-        Zep没有内置混合搜索接口，需要分别搜索edges和nodes然后合并结果。
-        使用并行请求同时搜索，提高效率。
-        
+        使用Graphiti图谱搜索功能获取实体相关的丰富信息
+
         Args:
             entity: 实体节点对象
-            
+
         Returns:
             包含facts, node_summaries, context的字典
         """
-        import concurrent.futures
-        
-        if not self.zep_client:
+        if not self._graphiti_adapter:
             return {"facts": [], "node_summaries": [], "context": ""}
-        
+
         entity_name = entity.name
-        
+
         results = {
             "facts": [],
             "node_summaries": [],
             "context": ""
         }
-        
-        # 必须有graph_id才能进行搜索
+
         if not self.graph_id:
-            logger.debug(f"跳过Zep检索：未设置graph_id")
+            logger.debug(f"跳过图谱检索：未设置graph_id")
             return results
-        
+
         comprehensive_query = f"关于{entity_name}的所有信息、活动、事件、关系和背景"
-        
-        def search_edges():
-            """搜索边（事实/关系）- 带重试机制"""
-            max_retries = 3
-            last_exception = None
-            delay = 2.0
-            
-            for attempt in range(max_retries):
-                try:
-                    return self.zep_client.graph.search(
-                        query=comprehensive_query,
-                        graph_id=self.graph_id,
-                        limit=30,
-                        scope="edges",
-                        reranker="rrf"
-                    )
-                except Exception as e:
-                    last_exception = e
-                    if attempt < max_retries - 1:
-                        logger.debug(f"Zep边搜索第 {attempt + 1} 次失败: {str(e)[:80]}, 重试中...")
-                        time.sleep(delay)
-                        delay *= 2
-                    else:
-                        logger.debug(f"Zep边搜索在 {max_retries} 次尝试后仍失败: {e}")
-            return None
-        
-        def search_nodes():
-            """搜索节点（实体摘要）- 带重试机制"""
-            max_retries = 3
-            last_exception = None
-            delay = 2.0
-            
-            for attempt in range(max_retries):
-                try:
-                    return self.zep_client.graph.search(
-                        query=comprehensive_query,
-                        graph_id=self.graph_id,
-                        limit=20,
-                        scope="nodes",
-                        reranker="rrf"
-                    )
-                except Exception as e:
-                    last_exception = e
-                    if attempt < max_retries - 1:
-                        logger.debug(f"Zep节点搜索第 {attempt + 1} 次失败: {str(e)[:80]}, 重试中...")
-                        time.sleep(delay)
-                        delay *= 2
-                    else:
-                        logger.debug(f"Zep节点搜索在 {max_retries} 次尝试后仍失败: {e}")
-            return None
-        
+
         try:
-            # 并行执行edges和nodes搜索
-            with concurrent.futures.ThreadPoolExecutor(max_workers=2) as executor:
-                edge_future = executor.submit(search_edges)
-                node_future = executor.submit(search_nodes)
-                
-                # 获取结果
-                edge_result = edge_future.result(timeout=30)
-                node_result = node_future.result(timeout=30)
-            
-            # 处理边搜索结果
+            # Search edges (facts/relations)
+            edge_results = self._graphiti_adapter.search(
+                query=comprehensive_query,
+                limit=30,
+                scope="edges"
+            )
+
+            # Search nodes (entity summaries)
+            node_results = self._graphiti_adapter.search(
+                query=comprehensive_query,
+                limit=20,
+                scope="nodes"
+            )
+
+            # Process edge results
             all_facts = set()
-            if edge_result and hasattr(edge_result, 'edges') and edge_result.edges:
-                for edge in edge_result.edges:
-                    if hasattr(edge, 'fact') and edge.fact:
-                        all_facts.add(edge.fact)
+            for edge in edge_results:
+                fact = edge.get('fact', '')
+                if fact:
+                    all_facts.add(fact)
             results["facts"] = list(all_facts)
-            
-            # 处理节点搜索结果
+
+            # Process node results
             all_summaries = set()
-            if node_result and hasattr(node_result, 'nodes') and node_result.nodes:
-                for node in node_result.nodes:
-                    if hasattr(node, 'summary') and node.summary:
-                        all_summaries.add(node.summary)
-                    if hasattr(node, 'name') and node.name and node.name != entity_name:
-                        all_summaries.add(f"相关实体: {node.name}")
+            for node in node_results:
+                summary = node.get('summary', '')
+                if summary:
+                    all_summaries.add(summary)
+                name = node.get('name', '')
+                if name and name != entity_name:
+                    all_summaries.add(f"相关实体: {name}")
             results["node_summaries"] = list(all_summaries)
-            
-            # 构建综合上下文
+
+            # Build combined context
             context_parts = []
             if results["facts"]:
                 context_parts.append("事实信息:\n" + "\n".join(f"- {f}" for f in results["facts"][:20]))
             if results["node_summaries"]:
                 context_parts.append("相关实体:\n" + "\n".join(f"- {s}" for s in results["node_summaries"][:10]))
             results["context"] = "\n\n".join(context_parts)
-            
-            logger.info(f"Zep混合检索完成: {entity_name}, 获取 {len(results['facts'])} 条事实, {len(results['node_summaries'])} 个相关节点")
-            
-        except concurrent.futures.TimeoutError:
-            logger.warning(f"Zep检索超时 ({entity_name})")
+
+            logger.info(f"图谱检索完成: {entity_name}, 获取 {len(results['facts'])} 条事实, {len(results['node_summaries'])} 个相关节点")
+
         except Exception as e:
-            logger.warning(f"Zep检索失败 ({entity_name}): {e}")
-        
+            logger.warning(f"图谱检索失败 ({entity_name}): {e}")
+
         return results
     
     def _build_entity_context(self, entity: EntityNode) -> str:
@@ -417,7 +362,7 @@ def _build_entity_context(self, entity: EntityNode) -> str:
         包括：
         1. 实体本身的边信息（事实）
         2. 关联节点的详细信息
-        3. Zep混合检索到的丰富信息
+        3. 图谱检索到的丰富信息
         """
         context_parts = []
         
diff --git a/backend/app/services/ontology_generator.py b/backend/app/services/ontology_generator.py
index c9bd4ba58..4908539df 100644
--- a/backend/app/services/ontology_generator.py
+++ b/backend/app/services/ontology_generator.py
@@ -284,7 +284,7 @@ def _validate_and_process(self, result: Dict[str, Any]) -> Dict[str, Any]:
             if len(edge.get("description", "")) > 100:
                 edge["description"] = edge["description"][:97] + "..."
         
-        # Zep API 限制：最多 10 个自定义实体类型，最多 10 个自定义边类型
+        # 限制：最多 10 个自定义实体类型，最多 10 个自定义边类型
         MAX_ENTITY_TYPES = 10
         MAX_EDGE_TYPES = 10
         
@@ -360,8 +360,12 @@ def generate_python_code(self, ontology: Dict[str, Any]) -> str:
             '由MiroFish自动生成，用于社会舆论模拟',
             '"""',
             '',
-            'from pydantic import Field',
-            'from zep_cloud.external_clients.ontology import EntityModel, EntityText, EdgeModel',
+            'from pydantic import BaseModel, Field',
+            '',
+            '# Base classes for ontology models (Graphiti-compatible)',
+            'EntityModel = BaseModel',
+            'EntityText = str',
+            'EdgeModel = BaseModel',
             '',
             '',
             '# ============== 实体类型定义 ==============',
diff --git a/backend/app/services/zep_entity_reader.py b/backend/app/services/zep_entity_reader.py
index 71661be49..2b3163d47 100644
--- a/backend/app/services/zep_entity_reader.py
+++ b/backend/app/services/zep_entity_reader.py
@@ -7,11 +7,10 @@
 from typing import Dict, Any, List, Optional, Set, Callable, TypeVar
 from dataclasses import dataclass, field
 
-from zep_cloud.client import Zep
+from .graphiti_adapter import GraphitiAdapter
 
 from ..config import Config
 from ..utils.logger import get_logger
-from ..utils.zep_paging import fetch_all_nodes, fetch_all_edges
 
 logger = get_logger('mirofish.zep_entity_reader')
 
@@ -79,11 +78,7 @@ class ZepEntityReader:
     """
     
     def __init__(self, api_key: Optional[str] = None):
-        self.api_key = api_key or Config.ZEP_API_KEY
-        if not self.api_key:
-            raise ValueError("ZEP_API_KEY 未配置")
-        
-        self.client = Zep(api_key=self.api_key)
+        pass  # No initialization needed, adapter is created per-graph
     
     def _call_with_retry(
         self, 
@@ -126,7 +121,7 @@ def _call_with_retry(
     
     def get_all_nodes(self, graph_id: str) -> List[Dict[str, Any]]:
         """
-        获取图谱的所有节点（分页获取）
+        获取图谱的所有节点
 
         Args:
             graph_id: 图谱ID
@@ -134,26 +129,14 @@ def get_all_nodes(self, graph_id: str) -> List[Dict[str, Any]]:
         Returns:
             节点列表
         """
-        logger.info(f"获取图谱 {graph_id} 的所有节点...")
-
-        nodes = fetch_all_nodes(self.client, graph_id)
-
-        nodes_data = []
-        for node in nodes:
-            nodes_data.append({
-                "uuid": getattr(node, 'uuid_', None) or getattr(node, 'uuid', ''),
-                "name": node.name or "",
-                "labels": node.labels or [],
-                "summary": node.summary or "",
-                "attributes": node.attributes or {},
-            })
-
-        logger.info(f"共获取 {len(nodes_data)} 个节点")
-        return nodes_data
+        adapter = GraphitiAdapter.get_or_create(graph_id)
+        nodes = adapter.get_all_nodes()
+        logger.info(f"Got {len(nodes)} nodes")
+        return nodes
 
     def get_all_edges(self, graph_id: str) -> List[Dict[str, Any]]:
         """
-        获取图谱的所有边（分页获取）
+        获取图谱的所有边
 
         Args:
             graph_id: 图谱ID
@@ -161,56 +144,31 @@ def get_all_edges(self, graph_id: str) -> List[Dict[str, Any]]:
         Returns:
             边列表
         """
-        logger.info(f"获取图谱 {graph_id} 的所有边...")
-
-        edges = fetch_all_edges(self.client, graph_id)
-
-        edges_data = []
-        for edge in edges:
-            edges_data.append({
-                "uuid": getattr(edge, 'uuid_', None) or getattr(edge, 'uuid', ''),
-                "name": edge.name or "",
-                "fact": edge.fact or "",
-                "source_node_uuid": edge.source_node_uuid,
-                "target_node_uuid": edge.target_node_uuid,
-                "attributes": edge.attributes or {},
-            })
-
-        logger.info(f"共获取 {len(edges_data)} 条边")
-        return edges_data
+        adapter = GraphitiAdapter.get_or_create(graph_id)
+        edges = adapter.get_all_edges()
+        logger.info(f"Got {len(edges)} edges")
+        return edges
     
-    def get_node_edges(self, node_uuid: str) -> List[Dict[str, Any]]:
+    def get_node_edges(self, node_uuid: str, graph_id: str = None) -> List[Dict[str, Any]]:
         """
-        获取指定节点的所有相关边（带重试机制）
-        
+        获取指定节点的所有相关边
+
         Args:
             node_uuid: 节点UUID
-            
+            graph_id: 图谱ID（可选，如果不提供则搜索所有已缓存的适配器）
+
         Returns:
             边列表
         """
-        try:
-            # 使用重试机制调用Zep API
-            edges = self._call_with_retry(
-                func=lambda: self.client.graph.node.get_entity_edges(node_uuid=node_uuid),
-                operation_name=f"获取节点边(node={node_uuid[:8]}...)"
-            )
-            
-            edges_data = []
-            for edge in edges:
-                edges_data.append({
-                    "uuid": getattr(edge, 'uuid_', None) or getattr(edge, 'uuid', ''),
-                    "name": edge.name or "",
-                    "fact": edge.fact or "",
-                    "source_node_uuid": edge.source_node_uuid,
-                    "target_node_uuid": edge.target_node_uuid,
-                    "attributes": edge.attributes or {},
-                })
-            
-            return edges_data
-        except Exception as e:
-            logger.warning(f"获取节点 {node_uuid} 的边失败: {str(e)}")
-            return []
+        if graph_id:
+            adapter = GraphitiAdapter.get_or_create(graph_id)
+            return adapter.get_node_edges(node_uuid)
+        # Fallback: try all cached adapters
+        for gid, adapter in GraphitiAdapter._instances.items():
+            edges = adapter.get_node_edges(node_uuid)
+            if edges:
+                return edges
+        return []
     
     def filter_defined_entities(
         self, 
@@ -331,83 +289,72 @@ def filter_defined_entities(
         )
     
     def get_entity_with_context(
-        self, 
-        graph_id: str, 
+        self,
+        graph_id: str,
         entity_uuid: str
     ) -> Optional[EntityNode]:
         """
-        获取单个实体及其完整上下文（边和关联节点，带重试机制）
-        
+        获取单个实体及其完整上下文（边和关联节点）
+
         Args:
             graph_id: 图谱ID
             entity_uuid: 实体UUID
-            
+
         Returns:
             EntityNode或None
         """
         try:
-            # 使用重试机制获取节点
-            node = self._call_with_retry(
-                func=lambda: self.client.graph.node.get(uuid_=entity_uuid),
-                operation_name=f"获取节点详情(uuid={entity_uuid[:8]}...)"
-            )
-            
+            adapter = GraphitiAdapter.get_or_create(graph_id)
+            node = adapter.get_node(entity_uuid)
             if not node:
                 return None
-            
-            # 获取节点的边
-            edges = self.get_node_edges(entity_uuid)
-            
-            # 获取所有节点用于关联查找
-            all_nodes = self.get_all_nodes(graph_id)
+
+            edges = adapter.get_node_edges(entity_uuid)
+            all_nodes = adapter.get_all_nodes()
             node_map = {n["uuid"]: n for n in all_nodes}
-            
-            # 处理相关边和节点
+
             related_edges = []
             related_node_uuids = set()
-            
             for edge in edges:
-                if edge["source_node_uuid"] == entity_uuid:
+                if edge.get("source_node_uuid") == entity_uuid:
                     related_edges.append({
                         "direction": "outgoing",
-                        "edge_name": edge["name"],
-                        "fact": edge["fact"],
-                        "target_node_uuid": edge["target_node_uuid"],
+                        "edge_name": edge.get("name", ""),
+                        "fact": edge.get("fact", ""),
+                        "target_node_uuid": edge.get("target_node_uuid", ""),
                     })
-                    related_node_uuids.add(edge["target_node_uuid"])
+                    related_node_uuids.add(edge.get("target_node_uuid", ""))
                 else:
                     related_edges.append({
                         "direction": "incoming",
-                        "edge_name": edge["name"],
-                        "fact": edge["fact"],
-                        "source_node_uuid": edge["source_node_uuid"],
+                        "edge_name": edge.get("name", ""),
+                        "fact": edge.get("fact", ""),
+                        "source_node_uuid": edge.get("source_node_uuid", ""),
                     })
-                    related_node_uuids.add(edge["source_node_uuid"])
-            
-            # 获取关联节点信息
+                    related_node_uuids.add(edge.get("source_node_uuid", ""))
+
             related_nodes = []
-            for related_uuid in related_node_uuids:
-                if related_uuid in node_map:
-                    related_node = node_map[related_uuid]
+            for ruuid in related_node_uuids:
+                if ruuid in node_map:
+                    rn = node_map[ruuid]
                     related_nodes.append({
-                        "uuid": related_node["uuid"],
-                        "name": related_node["name"],
-                        "labels": related_node["labels"],
-                        "summary": related_node.get("summary", ""),
+                        "uuid": rn["uuid"],
+                        "name": rn["name"],
+                        "labels": rn["labels"],
+                        "summary": rn.get("summary", ""),
                     })
-            
+
             return EntityNode(
-                uuid=getattr(node, 'uuid_', None) or getattr(node, 'uuid', ''),
-                name=node.name or "",
-                labels=node.labels or [],
-                summary=node.summary or "",
-                attributes=node.attributes or {},
+                uuid=node["uuid"],
+                name=node["name"],
+                labels=node.get("labels", []),
+                summary=node.get("summary", ""),
+                attributes=node.get("attributes", {}),
                 related_edges=related_edges,
                 related_nodes=related_nodes,
             )
-            
         except Exception as e:
-            logger.error(f"获取实体 {entity_uuid} 失败: {str(e)}")
+            logger.error(f"Failed to get entity {entity_uuid}: {str(e)}")
             return None
     
     def get_entities_by_type(
diff --git a/backend/app/services/zep_graph_memory_updater.py b/backend/app/services/zep_graph_memory_updater.py
index a8f3cecd9..a8849b65d 100644
--- a/backend/app/services/zep_graph_memory_updater.py
+++ b/backend/app/services/zep_graph_memory_updater.py
@@ -12,7 +12,7 @@
 from datetime import datetime
 from queue import Queue, Empty
 
-from zep_cloud.client import Zep
+from .graphiti_adapter import GraphitiAdapter
 
 from ..config import Config
 from ..utils.logger import get_logger
@@ -231,18 +231,13 @@ class ZepGraphMemoryUpdater:
     def __init__(self, graph_id: str, api_key: Optional[str] = None):
         """
         初始化更新器
-        
+
         Args:
-            graph_id: Zep图谱ID
-            api_key: Zep API Key（可选，默认从配置读取）
+            graph_id: 图谱ID
+            api_key: 保留参数，不再使用
         """
         self.graph_id = graph_id
-        self.api_key = api_key or Config.ZEP_API_KEY
-        
-        if not self.api_key:
-            raise ValueError("ZEP_API_KEY未配置")
-        
-        self.client = Zep(api_key=self.api_key)
+        self.adapter = GraphitiAdapter.get_or_create(graph_id)
         
         # 活动队列
         self._activity_queue: Queue = Queue()
@@ -405,11 +400,7 @@ def _send_batch_activities(self, activities: List[AgentActivity], platform: str)
         # 带重试的发送
         for attempt in range(self.MAX_RETRIES):
             try:
-                self.client.graph.add(
-                    graph_id=self.graph_id,
-                    type="text",
-                    data=combined_text
-                )
+                self.adapter.add_episode(combined_text, source_description="simulation_activity")
                 
                 self._total_sent += 1
                 self._total_items_sent += len(activities)
diff --git a/backend/app/services/zep_tools.py b/backend/app/services/zep_tools.py
index 384cf540f..27b986935 100644
--- a/backend/app/services/zep_tools.py
+++ b/backend/app/services/zep_tools.py
@@ -13,12 +13,10 @@
 from typing import Dict, Any, List, Optional
 from dataclasses import dataclass, field
 
-from zep_cloud.client import Zep
-
 from ..config import Config
 from ..utils.logger import get_logger
 from ..utils.llm_client import LLMClient
-from ..utils.zep_paging import fetch_all_nodes, fetch_all_edges
+from .graphiti_adapter import GraphitiAdapter
 
 logger = get_logger('mirofish.zep_tools')
 
@@ -422,14 +420,8 @@ class ZepToolsService:
     RETRY_DELAY = 2.0
     
     def __init__(self, api_key: Optional[str] = None, llm_client: Optional[LLMClient] = None):
-        self.api_key = api_key or Config.ZEP_API_KEY
-        if not self.api_key:
-            raise ValueError("ZEP_API_KEY 未配置")
-        
-        self.client = Zep(api_key=self.api_key)
-        # LLM客户端用于InsightForge生成子问题
         self._llm_client = llm_client
-        logger.info("ZepToolsService 初始化完成")
+        logger.info("ZepToolsService initialized")
     
     @property
     def llm(self) -> LLMClient:
@@ -483,64 +475,35 @@ def search_graph(
         Returns:
             SearchResult: 搜索结果
         """
-        logger.info(f"图谱搜索: graph_id={graph_id}, query={query[:50]}...")
-        
-        # 尝试使用Zep Cloud Search API
+        logger.info(f"Graph search: graph_id={graph_id}, query={query[:50]}...")
+
         try:
-            search_results = self._call_with_retry(
-                func=lambda: self.client.graph.search(
-                    graph_id=graph_id,
-                    query=query,
-                    limit=limit,
-                    scope=scope,
-                    reranker="cross_encoder"
-                ),
-                operation_name=f"图谱搜索(graph={graph_id})"
-            )
-            
+            adapter = GraphitiAdapter.get_or_create(graph_id)
+            result = adapter.search(query, limit=limit, scope=scope)
+
             facts = []
-            edges = []
-            nodes = []
-            
-            # 解析边搜索结果
-            if hasattr(search_results, 'edges') and search_results.edges:
-                for edge in search_results.edges:
-                    if hasattr(edge, 'fact') and edge.fact:
-                        facts.append(edge.fact)
-                    edges.append({
-                        "uuid": getattr(edge, 'uuid_', None) or getattr(edge, 'uuid', ''),
-                        "name": getattr(edge, 'name', ''),
-                        "fact": getattr(edge, 'fact', ''),
-                        "source_node_uuid": getattr(edge, 'source_node_uuid', ''),
-                        "target_node_uuid": getattr(edge, 'target_node_uuid', ''),
-                    })
-            
-            # 解析节点搜索结果
-            if hasattr(search_results, 'nodes') and search_results.nodes:
-                for node in search_results.nodes:
-                    nodes.append({
-                        "uuid": getattr(node, 'uuid_', None) or getattr(node, 'uuid', ''),
-                        "name": getattr(node, 'name', ''),
-                        "labels": getattr(node, 'labels', []),
-                        "summary": getattr(node, 'summary', ''),
-                    })
-                    # 节点摘要也算作事实
-                    if hasattr(node, 'summary') and node.summary:
-                        facts.append(f"[{node.name}]: {node.summary}")
-            
-            logger.info(f"搜索完成: 找到 {len(facts)} 条相关事实")
-            
+            edges_data = []
+            nodes_data = []
+
+            if scope == "edges":
+                for edge in result.get("edges", []):
+                    fact = edge.get("fact", "")
+                    if fact:
+                        facts.append(fact)
+                    edges_data.append(edge)
+            else:
+                for node in result.get("nodes", []):
+                    nodes_data.append(node)
+
             return SearchResult(
                 facts=facts,
-                edges=edges,
-                nodes=nodes,
+                edges=edges_data,
+                nodes=nodes_data,
                 query=query,
-                total_count=len(facts)
+                total_count=len(facts) + len(nodes_data)
             )
-            
         except Exception as e:
-            logger.warning(f"Zep Search API失败，降级为本地搜索: {str(e)}")
-            # 降级：使用本地关键词匹配搜索
+            logger.warning(f"Search failed, falling back to local search: {e}")
             return self._local_search(graph_id, query, limit, scope)
     
     def _local_search(
@@ -659,18 +622,16 @@ def get_all_nodes(self, graph_id: str) -> List[NodeInfo]:
         """
         logger.info(f"获取图谱 {graph_id} 的所有节点...")
 
-        nodes = fetch_all_nodes(self.client, graph_id)
+        adapter = GraphitiAdapter.get_or_create(graph_id)
+        nodes = adapter.get_all_nodes()
 
-        result = []
-        for node in nodes:
-            node_uuid = getattr(node, 'uuid_', None) or getattr(node, 'uuid', None) or ""
-            result.append(NodeInfo(
-                uuid=str(node_uuid) if node_uuid else "",
-                name=node.name or "",
-                labels=node.labels or [],
-                summary=node.summary or "",
-                attributes=node.attributes or {}
-            ))
+        result = [NodeInfo(
+            uuid=n.get("uuid", ""),
+            name=n.get("name", ""),
+            labels=n.get("labels", []),
+            summary=n.get("summary", ""),
+            attributes=n.get("attributes", {})
+        ) for n in nodes]
 
         logger.info(f"获取到 {len(result)} 个节点")
         return result
@@ -688,59 +649,49 @@ def get_all_edges(self, graph_id: str, include_temporal: bool = True) -> List[Ed
         """
         logger.info(f"获取图谱 {graph_id} 的所有边...")
 
-        edges = fetch_all_edges(self.client, graph_id)
-
-        result = []
-        for edge in edges:
-            edge_uuid = getattr(edge, 'uuid_', None) or getattr(edge, 'uuid', None) or ""
-            edge_info = EdgeInfo(
-                uuid=str(edge_uuid) if edge_uuid else "",
-                name=edge.name or "",
-                fact=edge.fact or "",
-                source_node_uuid=edge.source_node_uuid or "",
-                target_node_uuid=edge.target_node_uuid or ""
-            )
-
-            # 添加时间信息
-            if include_temporal:
-                edge_info.created_at = getattr(edge, 'created_at', None)
-                edge_info.valid_at = getattr(edge, 'valid_at', None)
-                edge_info.invalid_at = getattr(edge, 'invalid_at', None)
-                edge_info.expired_at = getattr(edge, 'expired_at', None)
-
-            result.append(edge_info)
+        adapter = GraphitiAdapter.get_or_create(graph_id)
+        edges = adapter.get_all_edges()
+
+        result = [EdgeInfo(
+            uuid=e.get("uuid", ""),
+            name=e.get("name", ""),
+            fact=e.get("fact", ""),
+            source_node_uuid=e.get("source_node_uuid", ""),
+            target_node_uuid=e.get("target_node_uuid", ""),
+            created_at=e.get("created_at"),
+            valid_at=e.get("valid_at"),
+            invalid_at=e.get("invalid_at"),
+            expired_at=e.get("expired_at"),
+        ) for e in edges]
 
         logger.info(f"获取到 {len(result)} 条边")
         return result
     
-    def get_node_detail(self, node_uuid: str) -> Optional[NodeInfo]:
+    def get_node_detail(self, graph_id: str, node_uuid: str) -> Optional[NodeInfo]:
         """
         获取单个节点的详细信息
-        
+
         Args:
+            graph_id: 图谱ID
             node_uuid: 节点UUID
-            
+
         Returns:
             节点信息或None
         """
         logger.info(f"获取节点详情: {node_uuid[:8]}...")
-        
+
         try:
-            node = self._call_with_retry(
-                func=lambda: self.client.graph.node.get(uuid_=node_uuid),
-                operation_name=f"获取节点详情(uuid={node_uuid[:8]}...)"
-            )
-            
-            if not node:
-                return None
-            
-            return NodeInfo(
-                uuid=getattr(node, 'uuid_', None) or getattr(node, 'uuid', ''),
-                name=node.name or "",
-                labels=node.labels or [],
-                summary=node.summary or "",
-                attributes=node.attributes or {}
-            )
+            adapter = GraphitiAdapter.get_or_create(graph_id)
+            node = adapter.get_node(node_uuid)
+            if node:
+                return NodeInfo(
+                    uuid=node.get("uuid", ""),
+                    name=node.get("name", ""),
+                    labels=node.get("labels", []),
+                    summary=node.get("summary", ""),
+                    attributes=node.get("attributes", {})
+                )
+            return None
         except Exception as e:
             logger.error(f"获取节点详情失败: {str(e)}")
             return None
@@ -759,20 +710,26 @@ def get_node_edges(self, graph_id: str, node_uuid: str) -> List[EdgeInfo]:
             边列表
         """
         logger.info(f"获取节点 {node_uuid[:8]}... 的相关边")
-        
+
         try:
-            # 获取图谱所有边，然后过滤
-            all_edges = self.get_all_edges(graph_id)
-            
-            result = []
-            for edge in all_edges:
-                # 检查边是否与指定节点相关（作为源或目标）
-                if edge.source_node_uuid == node_uuid or edge.target_node_uuid == node_uuid:
-                    result.append(edge)
-            
+            adapter = GraphitiAdapter.get_or_create(graph_id)
+            all_edges = adapter.get_all_edges()
+            # Filter locally
+            result = [EdgeInfo(
+                uuid=e.get("uuid", ""),
+                name=e.get("name", ""),
+                fact=e.get("fact", ""),
+                source_node_uuid=e.get("source_node_uuid", ""),
+                target_node_uuid=e.get("target_node_uuid", ""),
+                created_at=e.get("created_at"),
+                valid_at=e.get("valid_at"),
+                invalid_at=e.get("invalid_at"),
+                expired_at=e.get("expired_at"),
+            ) for e in all_edges if e.get("source_node_uuid") == node_uuid or e.get("target_node_uuid") == node_uuid]
+
             logger.info(f"找到 {len(result)} 条与节点相关的边")
             return result
-            
+
         except Exception as e:
             logger.warning(f"获取节点边失败: {str(e)}")
             return []
@@ -1043,7 +1000,7 @@ def insight_forge(
                 continue
             try:
                 # 单独获取每个相关节点的信息
-                node = self.get_node_detail(uuid)
+                node = self.get_node_detail(graph_id, uuid)
                 if node:
                     node_map[uuid] = node
                     entity_type = next((l for l in node.labels if l not in ["Entity", "Node"]), "实体")
diff --git a/backend/app/utils/zep_paging.py b/backend/app/utils/zep_paging.py
deleted file mode 100644
index 943cd1ae2..000000000
--- a/backend/app/utils/zep_paging.py
+++ /dev/null
@@ -1,143 +0,0 @@
-"""Zep Graph 分页读取工具。
-
-Zep 的 node/edge 列表接口使用 UUID cursor 分页，
-本模块封装自动翻页逻辑（含单页重试），对调用方透明地返回完整列表。
-"""
-
-from __future__ import annotations
-
-import time
-from collections.abc import Callable
-from typing import Any
-
-from zep_cloud import InternalServerError
-from zep_cloud.client import Zep
-
-from .logger import get_logger
-
-logger = get_logger('mirofish.zep_paging')
-
-_DEFAULT_PAGE_SIZE = 100
-_MAX_NODES = 2000
-_DEFAULT_MAX_RETRIES = 3
-_DEFAULT_RETRY_DELAY = 2.0  # seconds, doubles each retry
-
-
-def _fetch_page_with_retry(
-    api_call: Callable[..., list[Any]],
-    *args: Any,
-    max_retries: int = _DEFAULT_MAX_RETRIES,
-    retry_delay: float = _DEFAULT_RETRY_DELAY,
-    page_description: str = "page",
-    **kwargs: Any,
-) -> list[Any]:
-    """单页请求，失败时指数退避重试。仅重试网络/IO类瞬态错误。"""
-    if max_retries < 1:
-        raise ValueError("max_retries must be >= 1")
-
-    last_exception: Exception | None = None
-    delay = retry_delay
-
-    for attempt in range(max_retries):
-        try:
-            return api_call(*args, **kwargs)
-        except (ConnectionError, TimeoutError, OSError, InternalServerError) as e:
-            last_exception = e
-            if attempt < max_retries - 1:
-                logger.warning(
-                    f"Zep {page_description} attempt {attempt + 1} failed: {str(e)[:100]}, retrying in {delay:.1f}s..."
-                )
-                time.sleep(delay)
-                delay *= 2
-            else:
-                logger.error(f"Zep {page_description} failed after {max_retries} attempts: {str(e)}")
-
-    assert last_exception is not None
-    raise last_exception
-
-
-def fetch_all_nodes(
-    client: Zep,
-    graph_id: str,
-    page_size: int = _DEFAULT_PAGE_SIZE,
-    max_items: int = _MAX_NODES,
-    max_retries: int = _DEFAULT_MAX_RETRIES,
-    retry_delay: float = _DEFAULT_RETRY_DELAY,
-) -> list[Any]:
-    """分页获取图谱节点，最多返回 max_items 条（默认 2000）。每页请求自带重试。"""
-    all_nodes: list[Any] = []
-    cursor: str | None = None
-    page_num = 0
-
-    while True:
-        kwargs: dict[str, Any] = {"limit": page_size}
-        if cursor is not None:
-            kwargs["uuid_cursor"] = cursor
-
-        page_num += 1
-        batch = _fetch_page_with_retry(
-            client.graph.node.get_by_graph_id,
-            graph_id,
-            max_retries=max_retries,
-            retry_delay=retry_delay,
-            page_description=f"fetch nodes page {page_num} (graph={graph_id})",
-            **kwargs,
-        )
-        if not batch:
-            break
-
-        all_nodes.extend(batch)
-        if len(all_nodes) >= max_items:
-            all_nodes = all_nodes[:max_items]
-            logger.warning(f"Node count reached limit ({max_items}), stopping pagination for graph {graph_id}")
-            break
-        if len(batch) < page_size:
-            break
-
-        cursor = getattr(batch[-1], "uuid_", None) or getattr(batch[-1], "uuid", None)
-        if cursor is None:
-            logger.warning(f"Node missing uuid field, stopping pagination at {len(all_nodes)} nodes")
-            break
-
-    return all_nodes
-
-
-def fetch_all_edges(
-    client: Zep,
-    graph_id: str,
-    page_size: int = _DEFAULT_PAGE_SIZE,
-    max_retries: int = _DEFAULT_MAX_RETRIES,
-    retry_delay: float = _DEFAULT_RETRY_DELAY,
-) -> list[Any]:
-    """分页获取图谱所有边，返回完整列表。每页请求自带重试。"""
-    all_edges: list[Any] = []
-    cursor: str | None = None
-    page_num = 0
-
-    while True:
-        kwargs: dict[str, Any] = {"limit": page_size}
-        if cursor is not None:
-            kwargs["uuid_cursor"] = cursor
-
-        page_num += 1
-        batch = _fetch_page_with_retry(
-            client.graph.edge.get_by_graph_id,
-            graph_id,
-            max_retries=max_retries,
-            retry_delay=retry_delay,
-            page_description=f"fetch edges page {page_num} (graph={graph_id})",
-            **kwargs,
-        )
-        if not batch:
-            break
-
-        all_edges.extend(batch)
-        if len(batch) < page_size:
-            break
-
-        cursor = getattr(batch[-1], "uuid_", None) or getattr(batch[-1], "uuid", None)
-        if cursor is None:
-            logger.warning(f"Edge missing uuid field, stopping pagination at {len(all_edges)} edges")
-            break
-
-    return all_edges
diff --git a/backend/pyproject.toml b/backend/pyproject.toml
index 4f5361d53..26092a83f 100644
--- a/backend/pyproject.toml
+++ b/backend/pyproject.toml
@@ -16,8 +16,8 @@ dependencies = [
     # LLM 相关
     "openai>=1.0.0",
     
-    # Zep Cloud
-    "zep-cloud==3.13.0",
+    # Graphiti (local graph DB)
+    "graphiti-core[google-genai,kuzu]>=0.5.0",
     
     # OASIS 社交媒体模拟
     "camel-oasis==0.2.5",
diff --git a/backend/uv.lock b/backend/uv.lock
index f1ce4b60e..5dc2cd1a2 100644
--- a/backend/uv.lock
+++ b/backend/uv.lock
@@ -475,6 +475,15 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/07/6c/aa3f2f849e01cb6a001cd8554a88d4c77c5c1a31c95bdf1cf9301e6d9ef4/defusedxml-0.7.1-py2.py3-none-any.whl", hash = "sha256:a352e7e428770286cc899e2542b6cdaedb2b4953ff269a210103ec58f6198a61", size = 25604, upload-time = "2021-03-08T10:59:24.45Z" },
 ]
 
+[[package]]
+name = "diskcache"
+version = "5.6.3"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/3f/21/1c1ffc1a039ddcc459db43cc108658f32c57d271d7289a2794e401d0fdb6/diskcache-5.6.3.tar.gz", hash = "sha256:2c3a3fa2743d8535d832ec61c2054a1641f41775aa7c556758a109941e33e4fc", size = 67916, upload-time = "2023-08-31T06:12:00.316Z" }
+wheels = [
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+]
+
 [[package]]
 name = "distlib"
 version = "0.4.0"
@@ -592,6 +601,68 @@ wheels = [
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 ]
 
+[[package]]
+name = "google-auth"
+version = "2.49.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "cryptography" },
+    { name = "pyasn1-modules" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/ea/80/6a696a07d3d3b0a92488933532f03dbefa4a24ab80fb231395b9a2a1be77/google_auth-2.49.1.tar.gz", hash = "sha256:16d40da1c3c5a0533f57d268fe72e0ebb0ae1cc3b567024122651c045d879b64", size = 333825, upload-time = "2026-03-12T19:30:58.135Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/e9/eb/c6c2478d8a8d633460be40e2a8a6f8f429171997a35a96f81d3b680dec83/google_auth-2.49.1-py3-none-any.whl", hash = "sha256:195ebe3dca18eddd1b3db5edc5189b76c13e96f29e73043b923ebcf3f1a860f7", size = 240737, upload-time = "2026-03-12T19:30:53.159Z" },
+]
+
+[package.optional-dependencies]
+requests = [
+    { name = "requests" },
+]
+
+[[package]]
+name = "google-genai"
+version = "1.68.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "anyio" },
+    { name = "distro" },
+    { name = "google-auth", extra = ["requests"] },
+    { name = "httpx" },
+    { name = "pydantic" },
+    { name = "requests" },
+    { name = "sniffio" },
+    { name = "tenacity" },
+    { name = "typing-extensions" },
+    { name = "websockets" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/9c/2c/f059982dbcb658cc535c81bbcbe7e2c040d675f4b563b03cdb01018a4bc3/google_genai-1.68.0.tar.gz", hash = "sha256:ac30c0b8bc630f9372993a97e4a11dae0e36f2e10d7c55eacdca95a9fa14ca96", size = 511285, upload-time = "2026-03-18T01:03:18.243Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/84/de/7d3ee9c94b74c3578ea4f88d45e8de9405902f857932334d81e89bce3dfa/google_genai-1.68.0-py3-none-any.whl", hash = "sha256:a1bc9919c0e2ea2907d1e319b65471d3d6d58c54822039a249fe1323e4178d15", size = 750912, upload-time = "2026-03-18T01:03:15.983Z" },
+]
+
+[[package]]
+name = "graphiti-core"
+version = "0.11.6"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "diskcache" },
+    { name = "neo4j" },
+    { name = "numpy" },
+    { name = "openai" },
+    { name = "pydantic" },
+    { name = "python-dotenv" },
+    { name = "tenacity" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/30/94/3f84400e5f02ea8e9dc79784202de4173cbc16f4b3ad1bd4302da888e4d8/graphiti_core-0.11.6.tar.gz", hash = "sha256:31d26621834d7d4b8865059ab749feb18af15937b59c69598a640a5dfabea331", size = 71928, upload-time = "2025-05-15T17:58:02.304Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/ac/2e/c8f22f01585bf173d1c82f6d4615511aebc75aeda764c69aa394446fa93c/graphiti_core-0.11.6-py3-none-any.whl", hash = "sha256:6ec4807a884f5ea88b942d0c8b7bcd2e107c7358ab4f98ef2a2092c229929707", size = 111001, upload-time = "2025-05-15T17:58:00.542Z" },
+]
+
+[package.optional-dependencies]
+google-genai = [
+    { name = "google-genai" },
+]
+
 [[package]]
 name = "h11"
 version = "0.16.0"
@@ -1248,11 +1319,11 @@ dependencies = [
     { name = "charset-normalizer" },
     { name = "flask" },
     { name = "flask-cors" },
+    { name = "graphiti-core", extra = ["google-genai"] },
     { name = "openai" },
     { name = "pydantic" },
     { name = "pymupdf" },
     { name = "python-dotenv" },
-    { name = "zep-cloud" },
 ]
 
 [package.optional-dependencies]
@@ -1276,6 +1347,7 @@ requires-dist = [
     { name = "charset-normalizer", specifier = ">=3.0.0" },
     { name = "flask", specifier = ">=3.0.0" },
     { name = "flask-cors", specifier = ">=6.0.0" },
+    { name = "graphiti-core", extras = ["google-genai", "kuzu"], specifier = ">=0.5.0" },
     { name = "openai", specifier = ">=1.0.0" },
     { name = "pipreqs", marker = "extra == 'dev'", specifier = ">=0.5.0" },
     { name = "pydantic", specifier = ">=2.0.0" },
@@ -1283,7 +1355,6 @@ requires-dist = [
     { name = "pytest", marker = "extra == 'dev'", specifier = ">=8.0.0" },
     { name = "pytest-asyncio", marker = "extra == 'dev'", specifier = ">=0.23.0" },
     { name = "python-dotenv", specifier = ">=1.0.0" },
-    { name = "zep-cloud", specifier = "==3.13.0" },
 ]
 provides-extras = ["dev"]
 
@@ -1916,6 +1987,27 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/8e/37/efad0257dc6e593a18957422533ff0f87ede7c9c6ea010a2177d738fb82f/pure_eval-0.2.3-py3-none-any.whl", hash = "sha256:1db8e35b67b3d218d818ae653e27f06c3aa420901fa7b081ca98cbedc874e0d0", size = 11842, upload-time = "2024-07-21T12:58:20.04Z" },
 ]
 
+[[package]]
+name = "pyasn1"
+version = "0.6.3"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/5c/5f/6583902b6f79b399c9c40674ac384fd9cd77805f9e6205075f828ef11fb2/pyasn1-0.6.3.tar.gz", hash = "sha256:697a8ecd6d98891189184ca1fa05d1bb00e2f84b5977c481452050549c8a72cf", size = 148685, upload-time = "2026-03-17T01:06:53.382Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/5d/a0/7d793dce3fa811fe047d6ae2431c672364b462850c6235ae306c0efd025f/pyasn1-0.6.3-py3-none-any.whl", hash = "sha256:a80184d120f0864a52a073acc6fc642847d0be408e7c7252f31390c0f4eadcde", size = 83997, upload-time = "2026-03-17T01:06:52.036Z" },
+]
+
+[[package]]
+name = "pyasn1-modules"
+version = "0.4.2"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "pyasn1" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/e9/e6/78ebbb10a8c8e4b61a59249394a4a594c1a7af95593dc933a349c8d00964/pyasn1_modules-0.4.2.tar.gz", hash = "sha256:677091de870a80aae844b1ca6134f54652fa2c8c5a52aa396440ac3106e941e6", size = 307892, upload-time = "2025-03-28T02:41:22.17Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/47/8d/d529b5d697919ba8c11ad626e835d4039be708a35b0d22de83a269a6682c/pyasn1_modules-0.4.2-py3-none-any.whl", hash = "sha256:29253a9207ce32b64c3ac6600edc75368f98473906e8fd1043bd6b5b1de2c14a", size = 181259, upload-time = "2025-03-28T02:41:19.028Z" },
+]
+
 [[package]]
 name = "pycparser"
 version = "2.23"
@@ -2987,6 +3079,15 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/40/44/4a5f08c96eb108af5cb50b41f76142f0afa346dfa99d5296fe7202a11854/tabulate-0.9.0-py3-none-any.whl", hash = "sha256:024ca478df22e9340661486f85298cff5f6dcdba14f3813e8830015b9ed1948f", size = 35252, upload-time = "2022-10-06T17:21:44.262Z" },
 ]
 
+[[package]]
+name = "tenacity"
+version = "9.1.4"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/47/c6/ee486fd809e357697ee8a44d3d69222b344920433d3b6666ccd9b374630c/tenacity-9.1.4.tar.gz", hash = "sha256:adb31d4c263f2bd041081ab33b498309a57c77f9acf2db65aadf0898179cf93a", size = 49413, upload-time = "2026-02-07T10:45:33.841Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/d7/c1/eb8f9debc45d3b7918a32ab756658a0904732f75e555402972246b0b8e71/tenacity-9.1.4-py3-none-any.whl", hash = "sha256:6095a360c919085f28c6527de529e76a06ad89b23659fa881ae0649b867a9d55", size = 28926, upload-time = "2026-02-07T10:45:32.24Z" },
+]
+
 [[package]]
 name = "texttable"
 version = "1.7.0"
@@ -3488,19 +3589,3 @@ sdist = { url = "https://files.pythonhosted.org/packages/d4/c8/cc640404a0981e6c1
 wheels = [
     { url = "https://files.pythonhosted.org/packages/8b/90/89a2ff242ccab6a24fbab18dbbabc67c51a6f0ed01f9a0f41689dc177419/yarg-0.1.9-py2.py3-none-any.whl", hash = "sha256:4f9cebdc00fac946c9bf2783d634e538a71c7d280a4d806d45fd4dc0ef441492", size = 19162, upload-time = "2014-08-11T22:01:41.104Z" },
 ]
-
-[[package]]
-name = "zep-cloud"
-version = "3.13.0"
-source = { registry = "https://pypi.org/simple" }
-dependencies = [
-    { name = "httpx" },
-    { name = "pydantic" },
-    { name = "pydantic-core" },
-    { name = "python-dateutil" },
-    { name = "typing-extensions" },
-]
-sdist = { url = "https://files.pythonhosted.org/packages/32/c7/c835debf13302f8aaf8d0561ac6ff5a9bc15cc140cd692a1330fb1900c55/zep_cloud-3.13.0.tar.gz", hash = "sha256:c55d9c511773bb2177ae8e08546141404f87d2099affafabd7ec4b4505763e48", size = 63116, upload-time = "2025-11-20T15:25:40.745Z" }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/f7/e1/bbf03c6c8007c0cb238780e7fc6d8e1a52633893933a41aa09678618985a/zep_cloud-3.13.0-py3-none-any.whl", hash = "sha256:b2fbdeef73e262194c8f67b58f76471de6ee87e1a629541a09d8f7bbf475f12b", size = 110601, upload-time = "2025-11-20T15:25:38.484Z" },
-]

From 3e6c18d9bb89c028c7c5451501d28fda9b209c75 Mon Sep 17 00:00:00 2001
From: thomas-ext-ux <thomas-ext@alteos.com>
Date: Mon, 23 Mar 2026 23:43:03 +0100
Subject: [PATCH 4/4] feat: translate all backend prompts and strings to
 English

Port English translations from MiroFish-Offline for all backend
service files: prompts, comments, docstrings, and log messages.

- oasis_profile_generator: persona generation prompts, default US
- report_agent: PLAN/SECTION/CHAT system prompts, ReACT prompts
- zep_tools: InsightForge/Panorama/Interview prompts, to_text()
- simulation_config_generator: config generation prompts
- simulation_manager/runner: comments and log messages
- graph API: status messages

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
---
 backend/app/api/graph.py                      |  214 +--
 .../app/services/oasis_profile_generator.py   |  645 ++++---
 backend/app/services/report_agent.py          | 1478 ++++++++---------
 .../services/simulation_config_generator.py   |  558 +++----
 backend/app/services/simulation_manager.py    |  192 +--
 backend/app/services/simulation_runner.py     |  696 ++++----
 .../app/services/zep_graph_memory_updater.py  |  341 ++--
 backend/app/services/zep_tools.py             |  988 +++++------
 8 files changed, 2480 insertions(+), 2632 deletions(-)

diff --git a/backend/app/api/graph.py b/backend/app/api/graph.py
index 9d30d3799..b6ed4e94c 100644
--- a/backend/app/api/graph.py
+++ b/backend/app/api/graph.py
@@ -1,6 +1,6 @@
 """
-图谱相关API路由
-采用项目上下文机制，服务端持久化状态
+Knowledge Graph API Routes
+Using project context mechanism with server-side persistent state
 """
 
 import os
@@ -18,31 +18,31 @@
 from ..models.task import TaskManager, TaskStatus
 from ..models.project import ProjectManager, ProjectStatus
 
-# 获取日志器
+# Get logger
 logger = get_logger('mirofish.api')
 
 
 def allowed_file(filename: str) -> bool:
-    """检查文件扩展名是否允许"""
+    """Check if file extension is allowed"""
     if not filename or '.' not in filename:
         return False
     ext = os.path.splitext(filename)[1].lower().lstrip('.')
     return ext in Config.ALLOWED_EXTENSIONS
 
 
-# ============== 项目管理接口 ==============
+# ============== Project Management Endpoints ==============
 
 @graph_bp.route('/project/<project_id>', methods=['GET'])
 def get_project(project_id: str):
     """
-    获取项目详情
+    Get project details
     """
     project = ProjectManager.get_project(project_id)
     
     if not project:
         return jsonify({
             "success": False,
-            "error": f"项目不存在: {project_id}"
+            "error": f"Project not found: {project_id}"
         }), 404
     
     return jsonify({
@@ -54,7 +54,7 @@ def get_project(project_id: str):
 @graph_bp.route('/project/list', methods=['GET'])
 def list_projects():
     """
-    列出所有项目
+    List all projects
     """
     limit = request.args.get('limit', 50, type=int)
     projects = ProjectManager.list_projects(limit=limit)
@@ -69,36 +69,36 @@ def list_projects():
 @graph_bp.route('/project/<project_id>', methods=['DELETE'])
 def delete_project(project_id: str):
     """
-    删除项目
+    Delete project
     """
     success = ProjectManager.delete_project(project_id)
     
     if not success:
         return jsonify({
             "success": False,
-            "error": f"项目不存在或删除失败: {project_id}"
+            "error": f"Project not found or deletion failed: {project_id}"
         }), 404
     
     return jsonify({
         "success": True,
-        "message": f"项目已删除: {project_id}"
+        "message": f"Project deleted: {project_id}"
     })
 
 
 @graph_bp.route('/project/<project_id>/reset', methods=['POST'])
 def reset_project(project_id: str):
     """
-    重置项目状态（用于重新构建图谱）
+    Reset project state (for rebuilding knowledge graph)
     """
     project = ProjectManager.get_project(project_id)
     
     if not project:
         return jsonify({
             "success": False,
-            "error": f"项目不存在: {project_id}"
+            "error": f"Project not found: {project_id}"
         }), 404
     
-    # 重置到本体已生成状态
+    # Reset to ontology generated state
     if project.ontology:
         project.status = ProjectStatus.ONTOLOGY_GENERATED
     else:
@@ -111,27 +111,27 @@ def reset_project(project_id: str):
     
     return jsonify({
         "success": True,
-        "message": f"项目已重置: {project_id}",
+        "message": f"Project reset: {project_id}",
         "data": project.to_dict()
     })
 
 
-# ============== 接口1：上传文件并生成本体 ==============
+# ============== Endpoint 1: Upload files and generate ontology ==============
 
 @graph_bp.route('/ontology/generate', methods=['POST'])
 def generate_ontology():
     """
-    接口1：上传文件，分析生成本体定义
+    Endpoint 1: Upload files, analyze and generate ontology definition
     
-    请求方式：multipart/form-data
+    Request method: multipart/form-data
     
-    参数：
-        files: 上传的文件（PDF/MD/TXT），可多个
-        simulation_requirement: 模拟需求描述（必填）
-        project_name: 项目名称（可选）
-        additional_context: 额外说明（可选）
+    Parameters:
+        files: Uploaded files (PDF/MD/TXT), multiple allowed
+        simulation_requirement: Simulation requirement description (required)
+        project_name: Project name (optional)
+        additional_context: Additional context (optional)
         
-    返回：
+    Returns:
         {
             "success": true,
             "data": {
@@ -147,42 +147,42 @@ def generate_ontology():
         }
     """
     try:
-        logger.info("=== 开始生成本体定义 ===")
+        logger.info("=== Starting ontology definition generation ===")
         
-        # 获取参数
+        # Get parameters
         simulation_requirement = request.form.get('simulation_requirement', '')
         project_name = request.form.get('project_name', 'Unnamed Project')
         additional_context = request.form.get('additional_context', '')
         
-        logger.debug(f"项目名称: {project_name}")
-        logger.debug(f"模拟需求: {simulation_requirement[:100]}...")
+        logger.debug(f"Project name: {project_name}")
+        logger.debug(f"Simulation requirement: {simulation_requirement[:100]}...")
         
         if not simulation_requirement:
             return jsonify({
                 "success": False,
-                "error": "请提供模拟需求描述 (simulation_requirement)"
+                "error": "Please provide simulation requirement description (simulation_requirement)"
             }), 400
         
-        # 获取上传的文件
+        # Get uploaded files
         uploaded_files = request.files.getlist('files')
         if not uploaded_files or all(not f.filename for f in uploaded_files):
             return jsonify({
                 "success": False,
-                "error": "请至少上传一个文档文件"
+                "error": "Please upload at least one document file"
             }), 400
         
-        # 创建项目
+        # Create project
         project = ProjectManager.create_project(name=project_name)
         project.simulation_requirement = simulation_requirement
-        logger.info(f"创建项目: {project.project_id}")
+        logger.info(f"Created project: {project.project_id}")
         
-        # 保存文件并提取文本
+        # Save files and extract text
         document_texts = []
         all_text = ""
         
         for file in uploaded_files:
             if file and file.filename and allowed_file(file.filename):
-                # 保存文件到项目目录
+                # Save file to project directory
                 file_info = ProjectManager.save_file_to_project(
                     project.project_id, 
                     file, 
@@ -193,7 +193,7 @@ def generate_ontology():
                     "size": file_info["size"]
                 })
                 
-                # 提取文本
+                # Extract text
                 text = FileParser.extract_text(file_info["path"])
                 text = TextProcessor.preprocess_text(text)
                 document_texts.append(text)
@@ -203,16 +203,16 @@ def generate_ontology():
             ProjectManager.delete_project(project.project_id)
             return jsonify({
                 "success": False,
-                "error": "没有成功处理任何文档，请检查文件格式"
+                "error": "No documents were successfully processed, please check file format"
             }), 400
         
-        # 保存提取的文本
+        # Save extracted text
         project.total_text_length = len(all_text)
         ProjectManager.save_extracted_text(project.project_id, all_text)
-        logger.info(f"文本提取完成，共 {len(all_text)} 字符")
+        logger.info(f"Text extraction complete, {len(all_text)} characters")
         
-        # 生成本体
-        logger.info("调用 LLM 生成本体定义...")
+        # Generate ontology
+        logger.info("Calling LLM to generate ontology definition...")
         generator = OntologyGenerator()
         ontology = generator.generate(
             document_texts=document_texts,
@@ -220,10 +220,10 @@ def generate_ontology():
             additional_context=additional_context if additional_context else None
         )
         
-        # 保存本体到项目
+        # Save ontology to project
         entity_count = len(ontology.get("entity_types", []))
         edge_count = len(ontology.get("edge_types", []))
-        logger.info(f"本体生成完成: {entity_count} 个实体类型, {edge_count} 个关系类型")
+        logger.info(f"Ontology generation complete: {entity_count} entity types, {edge_count} relation types")
         
         project.ontology = {
             "entity_types": ontology.get("entity_types", []),
@@ -232,7 +232,7 @@ def generate_ontology():
         project.analysis_summary = ontology.get("analysis_summary", "")
         project.status = ProjectStatus.ONTOLOGY_GENERATED
         ProjectManager.save_project(project)
-        logger.info(f"=== 本体生成完成 === 项目ID: {project.project_id}")
+        logger.info(f"=== Ontology generation complete === Project ID: {project.project_id}")
         
         return jsonify({
             "success": True,
@@ -254,140 +254,140 @@ def generate_ontology():
         }), 500
 
 
-# ============== 接口2：构建图谱 ==============
+# ============== Endpoint 2: Build knowledge graph ==============
 
 @graph_bp.route('/build', methods=['POST'])
 def build_graph():
     """
-    接口2：根据project_id构建图谱
+    Endpoint 2: Build knowledge graph by project_id
     
-    请求（JSON）：
+    Request (JSON):
         {
-            "project_id": "proj_xxxx",  // 必填，来自接口1
-            "graph_name": "图谱名称",    // 可选
-            "chunk_size": 500,          // 可选，默认500
-            "chunk_overlap": 50         // 可选，默认50
+            "project_id": "proj_xxxx",  // Required, from Endpoint 1
+            "graph_name": "Graph name",    // Optional
+            "chunk_size": 500,          // Optional，default 500
+            "chunk_overlap": 50         // Optional，default 50
         }
         
-    返回：
+    Returns:
         {
             "success": true,
             "data": {
                 "project_id": "proj_xxxx",
                 "task_id": "task_xxxx",
-                "message": "图谱构建任务已启动"
+                "message": "Knowledge graph build task started"
             }
         }
     """
     try:
-        logger.info("=== 开始构建图谱 ===")
+        logger.info("=== Starting knowledge graph build ===")
         
-        # 检查配置
+        # Check configuration
         errors = []
         if not Config.LLM_API_KEY:
             errors.append("LLM_API_KEY not configured")
         if errors:
-            logger.error(f"配置错误: {errors}")
+            logger.error(f"Configuration error: {errors}")
             return jsonify({
                 "success": False,
-                "error": "配置错误: " + "; ".join(errors)
+                "error": "Configuration error: " + "; ".join(errors)
             }), 500
         
-        # 解析请求
+        # Parse request
         data = request.get_json() or {}
         project_id = data.get('project_id')
-        logger.debug(f"请求参数: project_id={project_id}")
+        logger.debug(f"Request parameters: project_id={project_id}")
         
         if not project_id:
             return jsonify({
                 "success": False,
-                "error": "请提供 project_id"
+                "error": "Please provide project_id"
             }), 400
         
-        # 获取项目
+        # Get project
         project = ProjectManager.get_project(project_id)
         if not project:
             return jsonify({
                 "success": False,
-                "error": f"项目不存在: {project_id}"
+                "error": f"Project not found: {project_id}"
             }), 404
         
-        # 检查项目状态
-        force = data.get('force', False)  # 强制重新构建
+        # Check project status
+        force = data.get('force', False)  # Force rebuild
         
         if project.status == ProjectStatus.CREATED:
             return jsonify({
                 "success": False,
-                "error": "项目尚未生成本体，请先调用 /ontology/generate"
+                "error": "Project has not generated ontology yet, please call /ontology/generate first"
             }), 400
         
         if project.status == ProjectStatus.GRAPH_BUILDING and not force:
             return jsonify({
                 "success": False,
-                "error": "图谱正在构建中，请勿重复提交。如需强制重建，请添加 force: true",
+                "error": "Knowledge graph is being built, please do not resubmit. To force rebuild, add force: true",
                 "task_id": project.graph_build_task_id
             }), 400
         
-        # 如果强制重建，重置状态
+        # If force rebuild, reset state
         if force and project.status in [ProjectStatus.GRAPH_BUILDING, ProjectStatus.FAILED, ProjectStatus.GRAPH_COMPLETED]:
             project.status = ProjectStatus.ONTOLOGY_GENERATED
             project.graph_id = None
             project.graph_build_task_id = None
             project.error = None
         
-        # 获取配置
+        # Get configuration
         graph_name = data.get('graph_name', project.name or 'MiroFish Graph')
         chunk_size = data.get('chunk_size', project.chunk_size or Config.DEFAULT_CHUNK_SIZE)
         chunk_overlap = data.get('chunk_overlap', project.chunk_overlap or Config.DEFAULT_CHUNK_OVERLAP)
         
-        # 更新项目配置
+        # Update project configuration
         project.chunk_size = chunk_size
         project.chunk_overlap = chunk_overlap
         
-        # 获取提取的文本
+        # Get extracted text
         text = ProjectManager.get_extracted_text(project_id)
         if not text:
             return jsonify({
                 "success": False,
-                "error": "未找到提取的文本内容"
+                "error": "Extracted text content not found"
             }), 400
         
-        # 获取本体
+        # Get ontology
         ontology = project.ontology
         if not ontology:
             return jsonify({
                 "success": False,
-                "error": "未找到本体定义"
+                "error": "Ontology definition not found"
             }), 400
         
-        # 创建异步任务
+        # Create async task
         task_manager = TaskManager()
-        task_id = task_manager.create_task(f"构建图谱: {graph_name}")
-        logger.info(f"创建图谱构建任务: task_id={task_id}, project_id={project_id}")
+        task_id = task_manager.create_task(f"Build knowledge graph: {graph_name}")
+        logger.info(f"Created graph build task: task_id={task_id}, project_id={project_id}")
         
-        # 更新项目状态
+        # Update project status
         project.status = ProjectStatus.GRAPH_BUILDING
         project.graph_build_task_id = task_id
         ProjectManager.save_project(project)
         
-        # 启动后台任务
+        # Start background task
         def build_task():
             build_logger = get_logger('mirofish.build')
             try:
-                build_logger.info(f"[{task_id}] 开始构建图谱...")
+                build_logger.info(f"[{task_id}] Starting knowledge graph build...")
                 task_manager.update_task(
                     task_id, 
                     status=TaskStatus.PROCESSING,
-                    message="初始化图谱构建服务..."
+                    message="Initializing graph build service..."
                 )
                 
-                # 创建图谱构建服务
+                # Create graph build service
                 builder = GraphBuilderService()
                 
-                # 分块
+                # Chunking
                 task_manager.update_task(
                     task_id,
-                    message="文本分块中...",
+                    message="Chunking text...",
                     progress=5
                 )
                 chunks = TextProcessor.split_text(
@@ -397,27 +397,27 @@ def build_task():
                 )
                 total_chunks = len(chunks)
                 
-                # 创建图谱
+                # Create graph
                 task_manager.update_task(
                     task_id,
-                    message="创建图谱...",
+                    message="Creating graph...",
                     progress=10
                 )
                 graph_id = builder.create_graph(name=graph_name)
                 
-                # 更新项目的graph_id
+                # Update project graph_id
                 project.graph_id = graph_id
                 ProjectManager.save_project(project)
                 
-                # 设置本体
+                # Set ontology
                 task_manager.update_task(
                     task_id,
-                    message="设置本体定义...",
+                    message="Setting ontology definition...",
                     progress=15
                 )
                 builder.set_ontology(graph_id, ontology)
                 
-                # 添加文本（progress_callback 签名是 (msg, progress_ratio)）
+                # Add text (progress_callback signature is (msg, progress_ratio))
                 def add_progress_callback(msg, progress_ratio):
                     progress = 15 + int(progress_ratio * 40)  # 15% - 55%
                     task_manager.update_task(
@@ -428,7 +428,7 @@ def add_progress_callback(msg, progress_ratio):
                 
                 task_manager.update_task(
                     task_id,
-                    message=f"开始添加 {total_chunks} 个文本块...",
+                    message=f"Starting to add {total_chunks} text chunks...",
                     progress=15
                 )
                 
@@ -449,19 +449,19 @@ def add_progress_callback(msg, progress_ratio):
                 )
                 graph_data = builder.get_graph_data(graph_id)
                 
-                # 更新项目状态
+                # Update project status
                 project.status = ProjectStatus.GRAPH_COMPLETED
                 ProjectManager.save_project(project)
                 
                 node_count = graph_data.get("node_count", 0)
                 edge_count = graph_data.get("edge_count", 0)
-                build_logger.info(f"[{task_id}] 图谱构建完成: graph_id={graph_id}, 节点={node_count}, 边={edge_count}")
+                build_logger.info(f"[{task_id}] Knowledge graph build complete: graph_id={graph_id}, nodes={node_count}, edges={edge_count}")
                 
-                # 完成
+                # Complete
                 task_manager.update_task(
                     task_id,
                     status=TaskStatus.COMPLETED,
-                    message="图谱构建完成",
+                    message="Knowledge graph build complete",
                     progress=100,
                     result={
                         "project_id": project_id,
@@ -473,8 +473,8 @@ def add_progress_callback(msg, progress_ratio):
                 )
                 
             except Exception as e:
-                # 更新项目状态为失败
-                build_logger.error(f"[{task_id}] 图谱构建失败: {str(e)}")
+                # Update project status to failed
+                build_logger.error(f"[{task_id}] Graph build failed: {str(e)}")
                 build_logger.debug(traceback.format_exc())
                 
                 project.status = ProjectStatus.FAILED
@@ -484,11 +484,11 @@ def add_progress_callback(msg, progress_ratio):
                 task_manager.update_task(
                     task_id,
                     status=TaskStatus.FAILED,
-                    message=f"构建失败: {str(e)}",
+                    message=f"Build failed: {str(e)}",
                     error=traceback.format_exc()
                 )
         
-        # 启动后台线程
+        # Start background thread
         thread = threading.Thread(target=build_task, daemon=True)
         thread.start()
         
@@ -497,7 +497,7 @@ def add_progress_callback(msg, progress_ratio):
             "data": {
                 "project_id": project_id,
                 "task_id": task_id,
-                "message": "图谱构建任务已启动，请通过 /task/{task_id} 查询进度"
+                "message": "Knowledge graph build task started，query progress via /task/{task_id} "
             }
         })
         
@@ -509,19 +509,19 @@ def add_progress_callback(msg, progress_ratio):
         }), 500
 
 
-# ============== 任务查询接口 ==============
+# ============== Task Query Endpoints ==============
 
 @graph_bp.route('/task/<task_id>', methods=['GET'])
 def get_task(task_id: str):
     """
-    查询任务状态
+    Query task status
     """
     task = TaskManager().get_task(task_id)
     
     if not task:
         return jsonify({
             "success": False,
-            "error": f"任务不存在: {task_id}"
+            "error": f"Task not found: {task_id}"
         }), 404
     
     return jsonify({
@@ -533,7 +533,7 @@ def get_task(task_id: str):
 @graph_bp.route('/tasks', methods=['GET'])
 def list_tasks():
     """
-    列出所有任务
+    List all tasks
     """
     tasks = TaskManager().list_tasks()
     
@@ -544,12 +544,12 @@ def list_tasks():
     })
 
 
-# ============== 图谱数据接口 ==============
+# ============== Graph Data Endpoints ==============
 
 @graph_bp.route('/data/<graph_id>', methods=['GET'])
 def get_graph_data(graph_id: str):
     """
-    获取图谱数据（节点和边）
+    Get graph data (nodes and edges)
     """
     try:
         if not Config.LLM_API_KEY:
@@ -577,7 +577,7 @@ def get_graph_data(graph_id: str):
 @graph_bp.route('/delete/<graph_id>', methods=['DELETE'])
 def delete_graph(graph_id: str):
     """
-    删除图谱
+    Delete graph
     """
     try:
         if not Config.LLM_API_KEY:
@@ -591,7 +591,7 @@ def delete_graph(graph_id: str):
         
         return jsonify({
             "success": True,
-            "message": f"图谱已删除: {graph_id}"
+            "message": f"Graph deleted: {graph_id}"
         })
         
     except Exception as e:
diff --git a/backend/app/services/oasis_profile_generator.py b/backend/app/services/oasis_profile_generator.py
index 8207e92a4..8e512b6ac 100644
--- a/backend/app/services/oasis_profile_generator.py
+++ b/backend/app/services/oasis_profile_generator.py
@@ -1,11 +1,11 @@
 """
-OASIS Agent Profile生成器
-将Zep图谱中的实体转换为OASIS模拟平台所需的Agent Profile格式
+OASIS Agent Profile Generator
+Convert entities from the knowledge graph to OASIS simulation platform's required Agent Profile format
 
-优化改进：
-1. 调用Zep检索功能二次丰富节点信息
-2. 优化提示词生成非常详细的人设
-3. 区分个人实体和抽象群体实体
+Optimization improvements:
+1. Call knowledge graph retrieval function to enrich node information
+2. Optimize prompts to generate very detailed personas
+3. Distinguish between individual entities and abstract group entities
 """
 
 import json
@@ -27,23 +27,23 @@
 
 @dataclass
 class OasisAgentProfile:
-    """OASIS Agent Profile数据结构"""
-    # 通用字段
+    """OASIS Agent Profile data structure"""
+    # Common fields
     user_id: int
     user_name: str
     name: str
     bio: str
     persona: str
     
-    # 可选字段 - Reddit风格
+    # Optional fields - Reddit style
     karma: int = 1000
     
-    # 可选字段 - Twitter风格
+    # Optional fields - Twitter style
     friend_count: int = 100
     follower_count: int = 150
     statuses_count: int = 500
     
-    # 额外人设信息
+    # Additional persona information
     age: Optional[int] = None
     gender: Optional[str] = None
     mbti: Optional[str] = None
@@ -51,17 +51,17 @@ class OasisAgentProfile:
     profession: Optional[str] = None
     interested_topics: List[str] = field(default_factory=list)
     
-    # 来源实体信息
+    # Source entity information
     source_entity_uuid: Optional[str] = None
     source_entity_type: Optional[str] = None
     
     created_at: str = field(default_factory=lambda: datetime.now().strftime("%Y-%m-%d"))
     
     def to_reddit_format(self) -> Dict[str, Any]:
-        """转换为Reddit平台格式"""
+        """Convert to Reddit platform format"""
         profile = {
             "user_id": self.user_id,
-            "username": self.user_name,  # OASIS 库要求字段名为 username（无下划线）
+            "username": self.user_name,  # OASIS library requires field name as username (no underscore)
             "name": self.name,
             "bio": self.bio,
             "persona": self.persona,
@@ -69,7 +69,7 @@ def to_reddit_format(self) -> Dict[str, Any]:
             "created_at": self.created_at,
         }
         
-        # 添加额外人设信息（如果有）
+        # Add additional persona information (if available)
         if self.age:
             profile["age"] = self.age
         if self.gender:
@@ -86,10 +86,10 @@ def to_reddit_format(self) -> Dict[str, Any]:
         return profile
     
     def to_twitter_format(self) -> Dict[str, Any]:
-        """转换为Twitter平台格式"""
+        """Convert to Twitter platform format"""
         profile = {
             "user_id": self.user_id,
-            "username": self.user_name,  # OASIS 库要求字段名为 username（无下划线）
+            "username": self.user_name,  # OASIS library requires field name as username (no underscore)
             "name": self.name,
             "bio": self.bio,
             "persona": self.persona,
@@ -99,7 +99,7 @@ def to_twitter_format(self) -> Dict[str, Any]:
             "created_at": self.created_at,
         }
         
-        # 添加额外人设信息
+        # Add additional persona information
         if self.age:
             profile["age"] = self.age
         if self.gender:
@@ -116,7 +116,7 @@ def to_twitter_format(self) -> Dict[str, Any]:
         return profile
     
     def to_dict(self) -> Dict[str, Any]:
-        """转换为完整字典格式"""
+        """Convert to complete dictionary format"""
         return {
             "user_id": self.user_id,
             "user_name": self.user_name,
@@ -141,17 +141,17 @@ def to_dict(self) -> Dict[str, Any]:
 
 class OasisProfileGenerator:
     """
-    OASIS Profile生成器
-    
-    将Zep图谱中的实体转换为OASIS模拟所需的Agent Profile
-    
-    优化特性：
-    1. 调用Zep图谱检索功能获取更丰富的上下文
-    2. 生成非常详细的人设（包括基本信息、职业经历、性格特征、社交媒体行为等）
-    3. 区分个人实体和抽象群体实体
+    OASIS Profile Generator
+
+    Convert entities from the knowledge graph to Agent Profile required by OASIS simulation
+
+    Optimization features:
+    1. Call knowledge graph retrieval function to get richer context
+    2. Generate very detailed personas (including basic information, career experience, personality traits, social media behavior, etc.)
+    3. Distinguish between individual entities and abstract group entities
     """
     
-    # MBTI类型列表
+    # MBTI types list
     MBTI_TYPES = [
         "INTJ", "INTP", "ENTJ", "ENTP",
         "INFJ", "INFP", "ENFJ", "ENFP",
@@ -159,19 +159,19 @@ class OasisProfileGenerator:
         "ISTP", "ISFP", "ESTP", "ESFP"
     ]
     
-    # 常见国家列表
+    # Common countries list
     COUNTRIES = [
-        "China", "US", "UK", "Japan", "Germany", "France", 
+        "US", "UK", "Japan", "Germany", "France",
         "Canada", "Australia", "Brazil", "India", "South Korea"
     ]
     
-    # 个人类型实体（需要生成具体人设）
+    # Individual type entities (need to generate specific personas)
     INDIVIDUAL_ENTITY_TYPES = [
         "student", "alumni", "professor", "person", "publicfigure", 
         "expert", "faculty", "official", "journalist", "activist"
     ]
     
-    # 群体/机构类型实体（需要生成群体代表人设）
+    # Group/institutional type entities (need to generate group representative personas)
     GROUP_ENTITY_TYPES = [
         "university", "governmentagency", "organization", "ngo", 
         "mediaoutlet", "company", "institution", "group", "community"
@@ -189,21 +189,21 @@ def __init__(
         self.model_name = model_name or Config.LLM_MODEL_NAME
         
         if not self.api_key:
-            raise ValueError("LLM_API_KEY 未配置")
+            raise ValueError("LLM_API_KEY not configured")
         
         self.client = OpenAI(
             api_key=self.api_key,
             base_url=self.base_url
         )
         
-        # Graphiti adapter for graph search (replaces Zep client)
+        # Graphiti adapter for knowledge graph search
         self.graph_id = graph_id
         self._graphiti_adapter = None
         if self.graph_id:
             try:
                 self._graphiti_adapter = GraphitiAdapter.get_or_create(self.graph_id)
             except Exception as e:
-                logger.warning(f"GraphitiAdapter初始化失败: {e}")
+                logger.warning(f"GraphitiAdapter initialization failed: {e}")
     
     def generate_profile_from_entity(
         self, 
@@ -212,27 +212,27 @@ def generate_profile_from_entity(
         use_llm: bool = True
     ) -> OasisAgentProfile:
         """
-        从Zep实体生成OASIS Agent Profile
-        
+        Generate OASIS Agent Profile from knowledge graph entity
+
         Args:
-            entity: Zep实体节点
-            user_id: 用户ID（用于OASIS）
-            use_llm: 是否使用LLM生成详细人设
-            
+            entity: Knowledge graph entity node
+            user_id: User ID (for OASIS)
+            use_llm: Whether to use LLM to generate detailed persona
+
         Returns:
             OasisAgentProfile
         """
         entity_type = entity.get_entity_type() or "Entity"
         
-        # 基础信息
+        # Basic information
         name = entity.name
         user_name = self._generate_username(name)
         
-        # 构建上下文信息
+        # Build context information
         context = self._build_entity_context(entity)
         
         if use_llm:
-            # 使用LLM生成详细人设
+            # Use LLM to generate detailed persona
             profile_data = self._generate_profile_with_llm(
                 entity_name=name,
                 entity_type=entity_type,
@@ -241,7 +241,7 @@ def generate_profile_from_entity(
                 context=context
             )
         else:
-            # 使用规则生成基础人设
+            # Use rules to generate basic persona
             profile_data = self._generate_profile_rule_based(
                 entity_name=name,
                 entity_type=entity_type,
@@ -270,24 +270,24 @@ def generate_profile_from_entity(
         )
     
     def _generate_username(self, name: str) -> str:
-        """生成用户名"""
-        # 移除特殊字符，转换为小写
+        """Generate username"""
+        # Remove special characters, convert to lowercase
         username = name.lower().replace(" ", "_")
         username = ''.join(c for c in username if c.isalnum() or c == '_')
         
-        # 添加随机后缀避免重复
+        # Add random suffix to avoid duplicates
         suffix = random.randint(100, 999)
         return f"{username}_{suffix}"
     
     def _search_zep_for_entity(self, entity: EntityNode) -> Dict[str, Any]:
         """
-        使用Graphiti图谱搜索功能获取实体相关的丰富信息
+        Use Graphiti graph search to obtain rich information related to entity
 
         Args:
-            entity: 实体节点对象
+            entity: Entity node object
 
         Returns:
-            包含facts, node_summaries, context的字典
+            Dictionary containing facts, node_summaries, context
         """
         if not self._graphiti_adapter:
             return {"facts": [], "node_summaries": [], "context": ""}
@@ -301,10 +301,10 @@ def _search_zep_for_entity(self, entity: EntityNode) -> Dict[str, Any]:
         }
 
         if not self.graph_id:
-            logger.debug(f"跳过图谱检索：未设置graph_id")
+            logger.debug(f"Skip knowledge graph search: graph_id not set")
             return results
 
-        comprehensive_query = f"关于{entity_name}的所有信息、活动、事件、关系和背景"
+        comprehensive_query = f"All information, activities, events, relationships and background about {entity_name}"
 
         try:
             # Search edges (facts/relations)
@@ -337,49 +337,49 @@ def _search_zep_for_entity(self, entity: EntityNode) -> Dict[str, Any]:
                     all_summaries.add(summary)
                 name = node.get('name', '')
                 if name and name != entity_name:
-                    all_summaries.add(f"相关实体: {name}")
+                    all_summaries.add(f"Related Entity: {name}")
             results["node_summaries"] = list(all_summaries)
 
             # Build combined context
             context_parts = []
             if results["facts"]:
-                context_parts.append("事实信息:\n" + "\n".join(f"- {f}" for f in results["facts"][:20]))
+                context_parts.append("Fact Information:\n" + "\n".join(f"- {f}" for f in results["facts"][:20]))
             if results["node_summaries"]:
-                context_parts.append("相关实体:\n" + "\n".join(f"- {s}" for s in results["node_summaries"][:10]))
+                context_parts.append("Related Entities:\n" + "\n".join(f"- {s}" for s in results["node_summaries"][:10]))
             results["context"] = "\n\n".join(context_parts)
 
-            logger.info(f"图谱检索完成: {entity_name}, 获取 {len(results['facts'])} 条事实, {len(results['node_summaries'])} 个相关节点")
+            logger.info(f"Knowledge graph hybrid search completed: {entity_name}, retrieved {len(results['facts'])} facts, {len(results['node_summaries'])} related nodes")
 
         except Exception as e:
-            logger.warning(f"图谱检索失败 ({entity_name}): {e}")
+            logger.warning(f"Knowledge graph search failed ({entity_name}): {e}")
 
         return results
     
     def _build_entity_context(self, entity: EntityNode) -> str:
         """
-        构建实体的完整上下文信息
-        
-        包括：
-        1. 实体本身的边信息（事实）
-        2. 关联节点的详细信息
-        3. 图谱检索到的丰富信息
+        Build complete context information for entity
+
+        Includes:
+        1. Edge information of the entity itself (facts)
+        2. Detailed information of associated nodes
+        3. Rich information retrieved from knowledge graph hybrid search
         """
         context_parts = []
         
-        # 1. 添加实体属性信息
+        # 1. Add entity attribute information
         if entity.attributes:
             attrs = []
             for key, value in entity.attributes.items():
                 if value and str(value).strip():
                     attrs.append(f"- {key}: {value}")
             if attrs:
-                context_parts.append("### 实体属性\n" + "\n".join(attrs))
+                context_parts.append("### Entity Attributes\n" + "\n".join(attrs))
         
-        # 2. 添加相关边信息（事实/关系）
+        # 2. Add related edge information (facts/relationships)
         existing_facts = set()
         if entity.related_edges:
             relationships = []
-            for edge in entity.related_edges:  # 不限制数量
+            for edge in entity.related_edges:  # No limit on quantity
                 fact = edge.get("fact", "")
                 edge_name = edge.get("edge_name", "")
                 direction = edge.get("direction", "")
@@ -389,22 +389,22 @@ def _build_entity_context(self, entity: EntityNode) -> str:
                     existing_facts.add(fact)
                 elif edge_name:
                     if direction == "outgoing":
-                        relationships.append(f"- {entity.name} --[{edge_name}]--> (相关实体)")
+                        relationships.append(f"- {entity.name} --[{edge_name}]--> (Related Entity)")
                     else:
-                        relationships.append(f"- (相关实体) --[{edge_name}]--> {entity.name}")
+                        relationships.append(f"- (Related Entity) --[{edge_name}]--> {entity.name}")
             
             if relationships:
-                context_parts.append("### 相关事实和关系\n" + "\n".join(relationships))
+                context_parts.append("### Related Facts and Relationships\n" + "\n".join(relationships))
         
-        # 3. 添加关联节点的详细信息
+        # 3. Add related node details
         if entity.related_nodes:
             related_info = []
-            for node in entity.related_nodes:  # 不限制数量
+            for node in entity.related_nodes:  # No limit on quantity
                 node_name = node.get("name", "")
                 node_labels = node.get("labels", [])
                 node_summary = node.get("summary", "")
                 
-                # 过滤掉默认标签
+                # Filter out default labels
                 custom_labels = [l for l in node_labels if l not in ["Entity", "Node"]]
                 label_str = f" ({', '.join(custom_labels)})" if custom_labels else ""
                 
@@ -414,28 +414,28 @@ def _build_entity_context(self, entity: EntityNode) -> str:
                     related_info.append(f"- **{node_name}**{label_str}")
             
             if related_info:
-                context_parts.append("### 关联实体信息\n" + "\n".join(related_info))
+                context_parts.append("### Related Entity Information\n" + "\n".join(related_info))
         
-        # 4. 使用Zep混合检索获取更丰富的信息
+        # 4. Use knowledge graph hybrid search to get richer information
         zep_results = self._search_zep_for_entity(entity)
-        
+
         if zep_results.get("facts"):
-            # 去重：排除已存在的事实
+            # Deduplication: exclude existing facts
             new_facts = [f for f in zep_results["facts"] if f not in existing_facts]
             if new_facts:
-                context_parts.append("### Zep检索到的事实信息\n" + "\n".join(f"- {f}" for f in new_facts[:15]))
-        
+                context_parts.append("### Facts Retrieved from Knowledge Graph\n" + "\n".join(f"- {f}" for f in new_facts[:15]))
+
         if zep_results.get("node_summaries"):
-            context_parts.append("### Zep检索到的相关节点\n" + "\n".join(f"- {s}" for s in zep_results["node_summaries"][:10]))
+            context_parts.append("### Related Nodes Retrieved from Knowledge Graph\n" + "\n".join(f"- {s}" for s in zep_results["node_summaries"][:10]))
         
         return "\n\n".join(context_parts)
     
     def _is_individual_entity(self, entity_type: str) -> bool:
-        """判断是否是个人类型实体"""
+        """Determine if entity is an individual type"""
         return entity_type.lower() in self.INDIVIDUAL_ENTITY_TYPES
     
     def _is_group_entity(self, entity_type: str) -> bool:
-        """判断是否是群体/机构类型实体"""
+        """Determine if entity is a group/institutional type"""
         return entity_type.lower() in self.GROUP_ENTITY_TYPES
     
     def _generate_profile_with_llm(
@@ -447,11 +447,11 @@ def _generate_profile_with_llm(
         context: str
     ) -> Dict[str, Any]:
         """
-        使用LLM生成非常详细的人设
-        
-        根据实体类型区分：
-        - 个人实体：生成具体的人物设定
-        - 群体/机构实体：生成代表性账号设定
+        Use LLM to generate very detailed persona
+
+        Based on entity type:
+        - Individual entities: generate specific character profiles
+        - Group/institutional entities: generate representative account profiles
         """
         
         is_individual = self._is_individual_entity(entity_type)
@@ -465,7 +465,7 @@ def _generate_profile_with_llm(
                 entity_name, entity_type, entity_summary, entity_attributes, context
             )
 
-        # 尝试多次生成，直到成功或达到最大重试次数
+        # Try multiple times until successful or max retry attempts reached
         max_attempts = 3
         last_error = None
         
@@ -478,34 +478,34 @@ def _generate_profile_with_llm(
                         {"role": "user", "content": prompt}
                     ],
                     response_format={"type": "json_object"},
-                    temperature=0.7 - (attempt * 0.1)  # 每次重试降低温度
-                    # 不设置max_tokens，让LLM自由发挥
+                    temperature=0.7 - (attempt * 0.1)  # Lower temperature with each retry
+                    # Don't set max_tokens, let LLM generate freely
                 )
                 
                 content = response.choices[0].message.content
                 
-                # 检查是否被截断（finish_reason不是'stop'）
+                # Check if output was truncated (finish_reason is not 'stop')
                 finish_reason = response.choices[0].finish_reason
                 if finish_reason == 'length':
-                    logger.warning(f"LLM输出被截断 (attempt {attempt+1}), 尝试修复...")
+                    logger.warning(f"LLM output truncated (attempt {attempt+1}), attempting to fix...")
                     content = self._fix_truncated_json(content)
                 
-                # 尝试解析JSON
+                # Try to parse JSON
                 try:
                     result = json.loads(content)
                     
-                    # 验证必需字段
+                    # Validate required fields
                     if "bio" not in result or not result["bio"]:
                         result["bio"] = entity_summary[:200] if entity_summary else f"{entity_type}: {entity_name}"
                     if "persona" not in result or not result["persona"]:
-                        result["persona"] = entity_summary or f"{entity_name}是一个{entity_type}。"
+                        result["persona"] = entity_summary or f"{entity_name} is a {entity_type}."
                     
                     return result
                     
                 except json.JSONDecodeError as je:
-                    logger.warning(f"JSON解析失败 (attempt {attempt+1}): {str(je)[:80]}")
+                    logger.warning(f"JSON parsing failed (attempt {attempt+1}): {str(je)[:80]}")
                     
-                    # 尝试修复JSON
+                    # Try to fix JSON
                     result = self._try_fix_json(content, entity_name, entity_type, entity_summary)
                     if result.get("_fixed"):
                         del result["_fixed"]
@@ -514,75 +514,75 @@ def _generate_profile_with_llm(
                     last_error = je
                     
             except Exception as e:
-                logger.warning(f"LLM调用失败 (attempt {attempt+1}): {str(e)[:80]}")
+                logger.warning(f"LLM call failed (attempt {attempt+1}): {str(e)[:80]}")
                 last_error = e
                 import time
-                time.sleep(1 * (attempt + 1))  # 指数退避
+                time.sleep(1 * (attempt + 1))  # Exponential backoff
         
-        logger.warning(f"LLM生成人设失败（{max_attempts}次尝试）: {last_error}, 使用规则生成")
+        logger.warning(f"LLM persona generation failed ({max_attempts} attempts): {last_error}, using rule-based generation")
         return self._generate_profile_rule_based(
             entity_name, entity_type, entity_summary, entity_attributes
         )
     
     def _fix_truncated_json(self, content: str) -> str:
-        """修复被截断的JSON（输出被max_tokens限制截断）"""
+        """Fix truncated JSON (output truncated by max_tokens limit)"""
         import re
-        
-        # 如果JSON被截断，尝试闭合它
+
+        # If JSON is truncated, try to close it
         content = content.strip()
-        
-        # 计算未闭合的括号
+
+        # Count unclosed parentheses
         open_braces = content.count('{') - content.count('}')
         open_brackets = content.count('[') - content.count(']')
-        
-        # 检查是否有未闭合的字符串
-        # 简单检查：如果最后一个引号后没有逗号或闭合括号，可能是字符串被截断
+
+        # Check for unclosed strings
+        # Simple check: if last character is not comma or closing bracket, string might be truncated
         if content and content[-1] not in '",}]':
-            # 尝试闭合字符串
+            # Try to close the string
             content += '"'
-        
-        # 闭合括号
+
+        # Close parentheses
         content += ']' * open_brackets
         content += '}' * open_braces
         
         return content
     
     def _try_fix_json(self, content: str, entity_name: str, entity_type: str, entity_summary: str = "") -> Dict[str, Any]:
-        """尝试修复损坏的JSON"""
+        """Try to fix corrupted JSON"""
         import re
         
-        # 1. 首先尝试修复被截断的情况
+        # 1. First try to fix truncated case
         content = self._fix_truncated_json(content)
-        
-        # 2. 尝试提取JSON部分
+
+        # 2. Try to extract JSON portion
         json_match = re.search(r'\{[\s\S]*\}', content)
         if json_match:
             json_str = json_match.group()
             
-            # 3. 处理字符串中的换行符问题
-            # 找到所有字符串值并替换其中的换行符
+            # 3. Handle newline issues in strings
+            # Find all string values and replace newlines
             def fix_string_newlines(match):
                 s = match.group(0)
-                # 替换字符串内的实际换行符为空格
+                # Replace actual newlines in string with spaces
                 s = s.replace('\n', ' ').replace('\r', ' ')
-                # 替换多余空格
+                # Replace excess spaces
                 s = re.sub(r'\s+', ' ', s)
                 return s
             
-            # 匹配JSON字符串值
+            # Match JSON string values
             json_str = re.sub(r'"[^"\\]*(?:\\.[^"\\]*)*"', fix_string_newlines, json_str)
             
-            # 4. 尝试解析
+            # 4. Try to parse
             try:
                 result = json.loads(json_str)
                 result["_fixed"] = True
                 return result
             except json.JSONDecodeError as e:
-                # 5. 如果还是失败，尝试更激进的修复
+                # 5. If still failed, try more aggressive fix
                 try:
-                    # 移除所有控制字符
+                    # Remove all control characters
                     json_str = re.sub(r'[\x00-\x1f\x7f-\x9f]', ' ', json_str)
-                    # 替换所有连续空白
+                    # Replace all consecutive whitespace
                     json_str = re.sub(r'\s+', ' ', json_str)
                     result = json.loads(json_str)
                     result["_fixed"] = True
@@ -590,32 +590,32 @@ def fix_string_newlines(match):
                 except:
                     pass
         
-        # 6. 尝试从内容中提取部分信息
+        # 6. Try to extract partial information from content
         bio_match = re.search(r'"bio"\s*:\s*"([^"]*)"', content)
-        persona_match = re.search(r'"persona"\s*:\s*"([^"]*)', content)  # 可能被截断
+        persona_match = re.search(r'"persona"\s*:\s*"([^"]*)', content)  # May be truncated
         
         bio = bio_match.group(1) if bio_match else (entity_summary[:200] if entity_summary else f"{entity_type}: {entity_name}")
-        persona = persona_match.group(1) if persona_match else (entity_summary or f"{entity_name}是一个{entity_type}。")
+        persona = persona_match.group(1) if persona_match else (entity_summary or f"{entity_name} is a {entity_type}.")
         
-        # 如果提取到了有意义的内容，标记为已修复
+        # If extracted meaningful content, mark as fixed
         if bio_match or persona_match:
-            logger.info(f"从损坏的JSON中提取了部分信息")
+            logger.info(f"Extracted partial information from corrupted JSON")
             return {
                 "bio": bio,
                 "persona": persona,
                 "_fixed": True
             }
         
-        # 7. 完全失败，返回基础结构
-        logger.warning(f"JSON修复失败，返回基础结构")
+        # 7. Complete failure, return basic structure
+        logger.warning(f"JSON fix failed, returning basic structure")
         return {
             "bio": entity_summary[:200] if entity_summary else f"{entity_type}: {entity_name}",
-            "persona": entity_summary or f"{entity_name}是一个{entity_type}。"
+            "persona": entity_summary or f"{entity_name} is a {entity_type}."
         }
     
     def _get_system_prompt(self, is_individual: bool) -> str:
-        """获取系统提示词"""
-        base_prompt = "你是社交媒体用户画像生成专家。生成详细、真实的人设用于舆论模拟,最大程度还原已有现实情况。必须返回有效的JSON格式，所有字符串值不能包含未转义的换行符。使用中文。"
+        """Get system prompt"""
+        base_prompt = "You are an expert in generating social media user profiles. Generate detailed, realistic personas for opinion simulation that maximize restoration of existing reality. Must return valid JSON format with all string values containing no unescaped newlines. Use English."
         return base_prompt
     
     def _build_individual_persona_prompt(
@@ -626,45 +626,45 @@ def _build_individual_persona_prompt(
         entity_attributes: Dict[str, Any],
         context: str
     ) -> str:
-        """构建个人实体的详细人设提示词"""
+        """Build detailed persona prompt for individual entities"""
         
-        attrs_str = json.dumps(entity_attributes, ensure_ascii=False) if entity_attributes else "无"
-        context_str = context[:3000] if context else "无额外上下文"
+        attrs_str = json.dumps(entity_attributes, ensure_ascii=False) if entity_attributes else "None"
+        context_str = context[:3000] if context else "No additional context"
         
-        return f"""为实体生成详细的社交媒体用户人设,最大程度还原已有现实情况。
+        return f"""Generate a detailed social media user persona for the entity, maximizing restoration of existing reality.
 
-实体名称: {entity_name}
-实体类型: {entity_type}
-实体摘要: {entity_summary}
-实体属性: {attrs_str}
+Entity Name: {entity_name}
+Entity Type: {entity_type}
+Entity Summary: {entity_summary}
+Entity Attributes: {attrs_str}
 
-上下文信息:
+Context Information:
 {context_str}
 
-请生成JSON，包含以下字段:
+Please generate JSON containing the following fields:
 
-1. bio: 社交媒体简介，200字
-2. persona: 详细人设描述（2000字的纯文本），需包含:
-   - 基本信息（年龄、职业、教育背景、所在地）
-   - 人物背景（重要经历、与事件的关联、社会关系）
-   - 性格特征（MBTI类型、核心性格、情绪表达方式）
-   - 社交媒体行为（发帖频率、内容偏好、互动风格、语言特点）
-   - 立场观点（对话题的态度、可能被激怒/感动的内容）
-   - 独特特征（口头禅、特殊经历、个人爱好）
-   - 个人记忆（人设的重要部分，要介绍这个个体与事件的关联，以及这个个体在事件中的已有动作与反应）
-3. age: 年龄数字（必须是整数）
-4. gender: 性别，必须是英文: "male" 或 "female"
-5. mbti: MBTI类型（如INTJ、ENFP等）
-6. country: 国家（使用中文，如"中国"）
-7. profession: 职业
-8. interested_topics: 感兴趣话题数组
+1. bio: Social media bio, 200 characters
+2. persona: Detailed persona description (2000 words of pure text), must include:
+   - Basic information (age, profession, educational background, location)
+   - Personal background (important experiences, event associations, social relationships)
+   - Personality traits (MBTI type, core personality, emotional expression)
+   - Social media behavior (posting frequency, content preferences, interaction style, language characteristics)
+   - Positions and views (attitudes toward topics, content that may provoke/touch emotions)
+   - Unique features (catchphrases, special experiences, personal interests)
+   - Personal memories (important part of persona, introduce this individual's association with events and their existing actions/reactions in events)
+3. age: Age as number (must be integer)
+4. gender: Gender, must be in English: "male" or "female"
+5. mbti: MBTI type (e.g., INTJ, ENFP)
+6. country: Country (use English, e.g., "US")
+7. profession: Profession
+8. interested_topics: Array of interested topics
 
-重要:
-- 所有字段值必须是字符串或数字，不要使用换行符
-- persona必须是一段连贯的文字描述
-- 使用中文（除了gender字段必须用英文male/female）
-- 内容要与实体信息保持一致
-- age必须是有效的整数，gender必须是"male"或"female"
+Important:
+- All field values must be strings or numbers, do not use newlines
+- persona must be a coherent text description
+- Use English
+- Content must be consistent with entity information
+- age must be a valid integer, gender must be "male" or "female"
 """
 
     def _build_group_persona_prompt(
@@ -675,45 +675,45 @@ def _build_group_persona_prompt(
         entity_attributes: Dict[str, Any],
         context: str
     ) -> str:
-        """构建群体/机构实体的详细人设提示词"""
+        """Build detailed persona prompt for group/institutional entities"""
         
-        attrs_str = json.dumps(entity_attributes, ensure_ascii=False) if entity_attributes else "无"
-        context_str = context[:3000] if context else "无额外上下文"
+        attrs_str = json.dumps(entity_attributes, ensure_ascii=False) if entity_attributes else "None"
+        context_str = context[:3000] if context else "No additional context"
         
-        return f"""为机构/群体实体生成详细的社交媒体账号设定,最大程度还原已有现实情况。
+        return f"""Generate detailed social media account profile for institutional/group entity, maximizing restoration of existing reality.
 
-实体名称: {entity_name}
-实体类型: {entity_type}
-实体摘要: {entity_summary}
-实体属性: {attrs_str}
+Entity Name: {entity_name}
+Entity Type: {entity_type}
+Entity Summary: {entity_summary}
+Entity Attributes: {attrs_str}
 
-上下文信息:
+Context Information:
 {context_str}
 
-请生成JSON，包含以下字段:
+Please generate JSON containing the following fields:
 
-1. bio: 官方账号简介，200字，专业得体
-2. persona: 详细账号设定描述（2000字的纯文本），需包含:
-   - 机构基本信息（正式名称、机构性质、成立背景、主要职能）
-   - 账号定位（账号类型、目标受众、核心功能）
-   - 发言风格（语言特点、常用表达、禁忌话题）
-   - 发布内容特点（内容类型、发布频率、活跃时间段）
-   - 立场态度（对核心话题的官方立场、面对争议的处理方式）
-   - 特殊说明（代表的群体画像、运营习惯）
-   - 机构记忆（机构人设的重要部分，要介绍这个机构与事件的关联，以及这个机构在事件中的已有动作与反应）
-3. age: 固定填30（机构账号的虚拟年龄）
-4. gender: 固定填"other"（机构账号使用other表示非个人）
-5. mbti: MBTI类型，用于描述账号风格，如ISTJ代表严谨保守
-6. country: 国家（使用中文，如"中国"）
-7. profession: 机构职能描述
-8. interested_topics: 关注领域数组
+1. bio: Official account bio, 200 characters, professional and appropriate
+2. persona: Detailed account profile description (2000 words of pure text), must include:
+   - Basic institutional information (official name, organizational nature, founding background, main functions)
+   - Account positioning (account type, target audience, core functions)
+   - Speaking style (language characteristics, common expressions, taboo topics)
+   - Content publishing characteristics (content types, publishing frequency, active time periods)
+   - Position and attitude (official stance on core topics, handling of controversies)
+   - Special notes (group profiles represented, operational habits)
+   - Institutional memories (important part of institutional persona, introduce this institution's association with events and their existing actions/reactions in events)
+3. age: Fixed at 30 (virtual age of institutional account)
+4. gender: Fixed at "other" (institutional account uses other to denote non-individual)
+5. mbti: MBTI type used to describe account style, e.g., ISTJ represents rigorous conservative
+6. country: Country (use English, e.g., "US")
+7. profession: Institutional function description
+8. interested_topics: Array of focus areas
 
-重要:
-- 所有字段值必须是字符串或数字，不允许null值
-- persona必须是一段连贯的文字描述，不要使用换行符
-- 使用中文（除了gender字段必须用英文"other"）
-- age必须是整数30，gender必须是字符串"other"
-- 机构账号发言要符合其身份定位"""
+Important:
+- All field values must be strings or numbers, no null values allowed
+- persona must be a coherent text description, do not use newlines
+- Use English
+- age must be integer 30, gender must be string "other"
+- Institutional account speech must match its identity positioning"""
     
     def _generate_profile_rule_based(
         self,
@@ -722,9 +722,9 @@ def _generate_profile_rule_based(
         entity_summary: str,
         entity_attributes: Dict[str, Any]
     ) -> Dict[str, Any]:
-        """使用规则生成基础人设"""
-        
-        # 根据实体类型生成不同的人设
+        """Generate basic persona using rules"""
+
+        # Generate different personas based on entity type
         entity_type_lower = entity_type.lower()
         
         if entity_type_lower in ["student", "alumni"]:
@@ -755,10 +755,10 @@ def _generate_profile_rule_based(
             return {
                 "bio": f"Official account for {entity_name}. News and updates.",
                 "persona": f"{entity_name} is a media entity that reports news and facilitates public discourse. The account shares timely updates and engages with the audience on current events.",
-                "age": 30,  # 机构虚拟年龄
-                "gender": "other",  # 机构使用other
-                "mbti": "ISTJ",  # 机构风格：严谨保守
-                "country": "中国",
+                "age": 30,  # Institutional virtual age
+                "gender": "other",  # Institutional uses other
+                "mbti": "ISTJ",  # Institutional style: rigorous conservative
+                "country": "US",
                 "profession": "Media",
                 "interested_topics": ["General News", "Current Events", "Public Affairs"],
             }
@@ -767,16 +767,16 @@ def _generate_profile_rule_based(
             return {
                 "bio": f"Official account of {entity_name}.",
                 "persona": f"{entity_name} is an institutional entity that communicates official positions, announcements, and engages with stakeholders on relevant matters.",
-                "age": 30,  # 机构虚拟年龄
-                "gender": "other",  # 机构使用other
-                "mbti": "ISTJ",  # 机构风格：严谨保守
-                "country": "中国",
+                "age": 30,  # Institutional virtual age
+                "gender": "other",  # Institutional uses other
+                "mbti": "ISTJ",  # Institutional style: rigorous conservative
+                "country": "US",
                 "profession": entity_type,
                 "interested_topics": ["Public Policy", "Community", "Official Announcements"],
             }
         
         else:
-            # 默认人设
+            # Default persona
             return {
                 "bio": entity_summary[:150] if entity_summary else f"{entity_type}: {entity_name}",
                 "persona": entity_summary or f"{entity_name} is a {entity_type.lower()} participating in social discussions.",
@@ -789,7 +789,7 @@ def _generate_profile_rule_based(
             }
     
     def set_graph_id(self, graph_id: str):
-        """设置图谱ID用于Zep检索"""
+        """Set knowledge graph ID for knowledge graph search"""
         self.graph_id = graph_id
     
     def generate_profiles_from_entities(
@@ -803,52 +803,52 @@ def generate_profiles_from_entities(
         output_platform: str = "reddit"
     ) -> List[OasisAgentProfile]:
         """
-        批量从实体生成Agent Profile（支持并行生成）
-        
+        Generate Agent Profiles in batch from entities (supports parallel generation)
+
         Args:
-            entities: 实体列表
-            use_llm: 是否使用LLM生成详细人设
-            progress_callback: 进度回调函数 (current, total, message)
-            graph_id: 图谱ID，用于Zep检索获取更丰富上下文
-            parallel_count: 并行生成数量，默认5
-            realtime_output_path: 实时写入的文件路径（如果提供，每生成一个就写入一次）
-            output_platform: 输出平台格式 ("reddit" 或 "twitter")
-            
+            entities: Entity list
+            use_llm: Whether to use LLM to generate detailed personas
+            progress_callback: Progress callback function (current, total, message)
+            graph_id: Knowledge graph ID for knowledge graph search to get richer context
+            parallel_count: Number of parallel generations, default 5
+            realtime_output_path: Real-time output file path (if provided, write after each generation)
+            output_platform: Output platform format ("reddit" or "twitter")
+
         Returns:
-            Agent Profile列表
+            List of Agent Profiles
         """
         import concurrent.futures
         from threading import Lock
         
-        # 设置graph_id用于Zep检索
+        # Set graph_id for knowledge graph search
         if graph_id:
             self.graph_id = graph_id
         
         total = len(entities)
-        profiles = [None] * total  # 预分配列表保持顺序
-        completed_count = [0]  # 使用列表以便在闭包中修改
+        profiles = [None] * total  # Pre-allocate list to maintain order
+        completed_count = [0]  # Use list for modification in closure
         lock = Lock()
-        
-        # 实时写入文件的辅助函数
+
+        # Helper function for real-time file writing
         def save_profiles_realtime():
-            """实时保存已生成的 profiles 到文件"""
+            """Real-time save generated profiles to file"""
             if not realtime_output_path:
                 return
             
             with lock:
-                # 过滤出已生成的 profiles
+                # Filter generated profiles
                 existing_profiles = [p for p in profiles if p is not None]
                 if not existing_profiles:
                     return
                 
                 try:
                     if output_platform == "reddit":
-                        # Reddit JSON 格式
+                        # Reddit JSON format
                         profiles_data = [p.to_reddit_format() for p in existing_profiles]
                         with open(realtime_output_path, 'w', encoding='utf-8') as f:
                             json.dump(profiles_data, f, ensure_ascii=False, indent=2)
                     else:
-                        # Twitter CSV 格式
+                        # Twitter CSV format
                         import csv
                         profiles_data = [p.to_twitter_format() for p in existing_profiles]
                         if profiles_data:
@@ -858,10 +858,10 @@ def save_profiles_realtime():
                                 writer.writeheader()
                                 writer.writerows(profiles_data)
                 except Exception as e:
-                    logger.warning(f"实时保存 profiles 失败: {e}")
+                    logger.warning(f"Real-time profile save failed: {e}")
         
         def generate_single_profile(idx: int, entity: EntityNode) -> tuple:
-            """生成单个profile的工作函数"""
+            """Worker function to generate single profile"""
             entity_type = entity.get_entity_type() or "Entity"
             
             try:
@@ -871,14 +871,14 @@ def generate_single_profile(idx: int, entity: EntityNode) -> tuple:
                     use_llm=use_llm
                 )
                 
-                # 实时输出生成的人设到控制台和日志
+                # Real-time output generated persona to console and log
                 self._print_generated_profile(entity.name, entity_type, profile)
                 
                 return idx, profile, None
                 
             except Exception as e:
-                logger.error(f"生成实体 {entity.name} 的人设失败: {str(e)}")
-                # 创建一个基础profile
+                logger.error(f"Failed to generate persona for entity {entity.name}: {str(e)}")
+                # Create a fallback profile
                 fallback_profile = OasisAgentProfile(
                     user_id=idx,
                     user_name=self._generate_username(entity.name),
@@ -890,20 +890,20 @@ def generate_single_profile(idx: int, entity: EntityNode) -> tuple:
                 )
                 return idx, fallback_profile, str(e)
         
-        logger.info(f"开始并行生成 {total} 个Agent人设（并行数: {parallel_count}）...")
+        logger.info(f"Starting parallel generation of {total} agent personas (parallel count: {parallel_count})...")
         print(f"\n{'='*60}")
-        print(f"开始生成Agent人设 - 共 {total} 个实体，并行数: {parallel_count}")
+        print(f"Starting agent persona generation - {total} entities total, parallel count: {parallel_count}")
         print(f"{'='*60}\n")
         
-        # 使用线程池并行执行
+        # Use thread pool for parallel execution
         with concurrent.futures.ThreadPoolExecutor(max_workers=parallel_count) as executor:
-            # 提交所有任务
+            # Submit all tasks
             future_to_entity = {
                 executor.submit(generate_single_profile, idx, entity): (idx, entity)
                 for idx, entity in enumerate(entities)
             }
             
-            # 收集结果
+            # Collect results
             for future in concurrent.futures.as_completed(future_to_entity):
                 idx, entity = future_to_entity[future]
                 entity_type = entity.get_entity_type() or "Entity"
@@ -916,23 +916,23 @@ def generate_single_profile(idx: int, entity: EntityNode) -> tuple:
                         completed_count[0] += 1
                         current = completed_count[0]
                     
-                    # 实时写入文件
+                    # Real-time file writing
                     save_profiles_realtime()
-                    
+
                     if progress_callback:
                         progress_callback(
                             current, 
                             total, 
-                            f"已完成 {current}/{total}: {entity.name}（{entity_type}）"
+                            f"Completed {current}/{total}: {entity.name} ({entity_type})"
                         )
                     
                     if error:
-                        logger.warning(f"[{current}/{total}] {entity.name} 使用备用人设: {error}")
+                        logger.warning(f"[{current}/{total}] {entity.name} using fallback persona: {error}")
                     else:
-                        logger.info(f"[{current}/{total}] 成功生成人设: {entity.name} ({entity_type})")
+                        logger.info(f"[{current}/{total}] Successfully generated persona: {entity.name} ({entity_type})")
                         
                 except Exception as e:
-                    logger.error(f"处理实体 {entity.name} 时发生异常: {str(e)}")
+                    logger.error(f"Exception occurred while processing entity {entity.name}: {str(e)}")
                     with lock:
                         completed_count[0] += 1
                     profiles[idx] = OasisAgentProfile(
@@ -944,44 +944,44 @@ def generate_single_profile(idx: int, entity: EntityNode) -> tuple:
                         source_entity_uuid=entity.uuid,
                         source_entity_type=entity_type,
                     )
-                    # 实时写入文件（即使是备用人设）
+                    # Real-time file writing (even for fallback personas)
                     save_profiles_realtime()
         
         print(f"\n{'='*60}")
-        print(f"人设生成完成！共生成 {len([p for p in profiles if p])} 个Agent")
+        print(f"Persona generation complete! Generated {len([p for p in profiles if p])} agents")
         print(f"{'='*60}\n")
         
         return profiles
     
     def _print_generated_profile(self, entity_name: str, entity_type: str, profile: OasisAgentProfile):
-        """实时输出生成的人设到控制台（完整内容，不截断）"""
+        """Real-time output generated persona to console (complete content, not truncated)"""
         separator = "-" * 70
         
-        # 构建完整输出内容（不截断）
-        topics_str = ', '.join(profile.interested_topics) if profile.interested_topics else '无'
+        # Build complete output content (not truncated)
+        topics_str = ', '.join(profile.interested_topics) if profile.interested_topics else 'None'
         
         output_lines = [
             f"\n{separator}",
-            f"[已生成] {entity_name} ({entity_type})",
+            f"[Generated] {entity_name} ({entity_type})",
             f"{separator}",
-            f"用户名: {profile.user_name}",
+            f"Username: {profile.user_name}",
             f"",
-            f"【简介】",
+            f"[Bio]",
             f"{profile.bio}",
             f"",
-            f"【详细人设】",
+            f"[Detailed Persona]",
             f"{profile.persona}",
             f"",
-            f"【基本属性】",
-            f"年龄: {profile.age} | 性别: {profile.gender} | MBTI: {profile.mbti}",
-            f"职业: {profile.profession} | 国家: {profile.country}",
-            f"兴趣话题: {topics_str}",
+            f"[Basic Attributes]",
+            f"Age: {profile.age} | Gender: {profile.gender} | MBTI: {profile.mbti}",
+            f"Profession: {profile.profession} | Country: {profile.country}",
+            f"Interested Topics: {topics_str}",
             separator
         ]
         
         output = "\n".join(output_lines)
         
-        # 只输出到控制台（避免重复，logger不再输出完整内容）
+        # Only output to console (avoid duplication, logger no longer outputs complete content)
         print(output)
     
     def save_profiles(
@@ -991,16 +991,16 @@ def save_profiles(
         platform: str = "reddit"
     ):
         """
-        保存Profile到文件（根据平台选择正确格式）
-        
-        OASIS平台格式要求：
-        - Twitter: CSV格式
-        - Reddit: JSON格式
-        
+        Save profiles to file (choose correct format based on platform)
+
+        OASIS platform format requirements:
+        - Twitter: CSV format
+        - Reddit: JSON format
+
         Args:
-            profiles: Profile列表
-            file_path: 文件路径
-            platform: 平台类型 ("reddit" 或 "twitter")
+            profiles: Profile list
+            file_path: File path
+            platform: Platform type ("reddit" or "twitter")
         """
         if platform == "twitter":
             self._save_twitter_csv(profiles, file_path)
@@ -1009,73 +1009,68 @@ def save_profiles(
     
     def _save_twitter_csv(self, profiles: List[OasisAgentProfile], file_path: str):
         """
-        保存Twitter Profile为CSV格式（符合OASIS官方要求）
-        
-        OASIS Twitter要求的CSV字段：
-        - user_id: 用户ID（根据CSV顺序从0开始）
-        - name: 用户真实姓名
-        - username: 系统中的用户名
-        - user_char: 详细人设描述（注入到LLM系统提示中，指导Agent行为）
-        - description: 简短的公开简介（显示在用户资料页面）
-        
-        user_char vs description 区别：
-        - user_char: 内部使用，LLM系统提示，决定Agent如何思考和行动
-        - description: 外部显示，其他用户可见的简介
+        Save Twitter Profile as CSV format (compliant with OASIS official requirements)
+
+        OASIS Twitter required CSV fields:
+        - user_id: User ID (starting from 0 based on CSV order)
+        - name: User real name
+        - username: Username in the system
+        - user_char: Detailed persona description (injected into LLM system prompt, guides agent behavior)
+        - description: Short public bio (displayed on user profile page)
+
+        user_char vs description difference:
+        - user_char: Internal use, LLM system prompt, determines how agent thinks and acts
+        - description: External display, visible to other users
         """
         import csv
         
-        # 确保文件扩展名是.csv
+        # Ensure file extension is .csv
         if not file_path.endswith('.csv'):
             file_path = file_path.replace('.json', '.csv')
         
         with open(file_path, 'w', newline='', encoding='utf-8') as f:
             writer = csv.writer(f)
             
-            # 写入OASIS要求的表头
+            # Write OASIS required header
             headers = ['user_id', 'name', 'username', 'user_char', 'description']
             writer.writerow(headers)
             
-            # 写入数据行
+            # Write data rows
             for idx, profile in enumerate(profiles):
-                # user_char: 完整人设（bio + persona），用于LLM系统提示
+                # user_char: Complete persona (bio + persona) for LLM system prompt
                 user_char = profile.bio
                 if profile.persona and profile.persona != profile.bio:
                     user_char = f"{profile.bio} {profile.persona}"
-                # 处理换行符（CSV中用空格替代）
+                # Handle newlines (replace with space in CSV)
                 user_char = user_char.replace('\n', ' ').replace('\r', ' ')
                 
-                # description: 简短简介，用于外部显示
+                # description: Short bio for external display
                 description = profile.bio.replace('\n', ' ').replace('\r', ' ')
                 
                 row = [
-                    idx,                    # user_id: 从0开始的顺序ID
-                    profile.name,           # name: 真实姓名
-                    profile.user_name,      # username: 用户名
-                    user_char,              # user_char: 完整人设（内部LLM使用）
-                    description             # description: 简短简介（外部显示）
+                    idx,                    # user_id: Sequential ID starting from 0
+                    profile.name,           # name: Real name
+                    profile.user_name,      # username: Username
+                    user_char,              # user_char: Complete persona (internal LLM use)
+                    description             # description: Short bio (external display)
                 ]
                 writer.writerow(row)
         
-        logger.info(f"已保存 {len(profiles)} 个Twitter Profile到 {file_path} (OASIS CSV格式)")
+        logger.info(f"Saved {len(profiles)} Twitter profiles to {file_path} (OASIS CSV format)")
     
     def _normalize_gender(self, gender: Optional[str]) -> str:
         """
-        标准化gender字段为OASIS要求的英文格式
+        Normalize gender field to OASIS required English format
         
-        OASIS要求: male, female, other
+        OASIS requires: male, female, other
         """
         if not gender:
             return "other"
         
         gender_lower = gender.lower().strip()
         
-        # 中文映射
+        # Gender mapping
         gender_map = {
-            "男": "male",
-            "女": "female",
-            "机构": "other",
-            "其他": "other",
-            # 英文已有
             "male": "male",
             "female": "female",
             "other": "other",
@@ -1085,41 +1080,41 @@ def _normalize_gender(self, gender: Optional[str]) -> str:
     
     def _save_reddit_json(self, profiles: List[OasisAgentProfile], file_path: str):
         """
-        保存Reddit Profile为JSON格式
-        
-        使用与 to_reddit_format() 一致的格式，确保 OASIS 能正确读取。
-        必须包含 user_id 字段，这是 OASIS agent_graph.get_agent() 匹配的关键！
-        
-        必需字段：
-        - user_id: 用户ID（整数，用于匹配 initial_posts 中的 poster_agent_id）
-        - username: 用户名
-        - name: 显示名称
-        - bio: 简介
-        - persona: 详细人设
-        - age: 年龄（整数）
-        - gender: "male", "female", 或 "other"
-        - mbti: MBTI类型
-        - country: 国家
+        Save Reddit Profile as JSON format
+
+        Use format consistent with to_reddit_format() to ensure OASIS can read correctly.
+        Must include user_id field, which is the key for OASIS agent_graph.get_agent() matching!
+
+        Required fields:
+        - user_id: User ID (integer, for matching poster_agent_id in initial_posts)
+        - username: Username
+        - name: Display name
+        - bio: Bio
+        - persona: Detailed persona
+        - age: Age (integer)
+        - gender: "male", "female", or "other"
+        - mbti: MBTI type
+        - country: Country
         """
         data = []
         for idx, profile in enumerate(profiles):
-            # 使用与 to_reddit_format() 一致的格式
+            # Use format consistent with to_reddit_format()
             item = {
-                "user_id": profile.user_id if profile.user_id is not None else idx,  # 关键：必须包含 user_id
+                "user_id": profile.user_id if profile.user_id is not None else idx,  # Key: must include user_id
                 "username": profile.user_name,
                 "name": profile.name,
                 "bio": profile.bio[:150] if profile.bio else f"{profile.name}",
                 "persona": profile.persona or f"{profile.name} is a participant in social discussions.",
                 "karma": profile.karma if profile.karma else 1000,
                 "created_at": profile.created_at,
-                # OASIS必需字段 - 确保都有默认值
+                # OASIS required fields - ensure all have defaults
                 "age": profile.age if profile.age else 30,
                 "gender": self._normalize_gender(profile.gender),
                 "mbti": profile.mbti if profile.mbti else "ISTJ",
-                "country": profile.country if profile.country else "中国",
+                "country": profile.country if profile.country else "US",
             }
             
-            # 可选字段
+            # Optional fields
             if profile.profession:
                 item["profession"] = profile.profession
             if profile.interested_topics:
@@ -1130,16 +1125,16 @@ def _save_reddit_json(self, profiles: List[OasisAgentProfile], file_path: str):
         with open(file_path, 'w', encoding='utf-8') as f:
             json.dump(data, f, ensure_ascii=False, indent=2)
         
-        logger.info(f"已保存 {len(profiles)} 个Reddit Profile到 {file_path} (JSON格式，包含user_id字段)")
+        logger.info(f"Saved {len(profiles)} Reddit profiles to {file_path} (JSON format, includes user_id field)")
     
-    # 保留旧方法名作为别名，保持向后兼容
+    # Keep old method name as alias for backward compatibility
     def save_profiles_to_json(
         self,
         profiles: List[OasisAgentProfile],
         file_path: str,
         platform: str = "reddit"
     ):
-        """[已废弃] 请使用 save_profiles() 方法"""
-        logger.warning("save_profiles_to_json已废弃，请使用save_profiles方法")
+        """[Deprecated] Please use save_profiles() method"""
+        logger.warning("save_profiles_to_json is deprecated, please use save_profiles method")
         self.save_profiles(profiles, file_path, platform)
 
diff --git a/backend/app/services/report_agent.py b/backend/app/services/report_agent.py
index 02ca5bdc2..9f9acb649 100644
--- a/backend/app/services/report_agent.py
+++ b/backend/app/services/report_agent.py
@@ -1,12 +1,12 @@
 """
-Report Agent服务
-使用LangChain + Zep实现ReACT模式的模拟报告生成
-
-功能：
-1. 根据模拟需求和Zep图谱信息生成报告
-2. 先规划目录结构，然后分段生成
-3. 每段采用ReACT多轮思考与反思模式
-4. 支持与用户对话，在对话中自主调用检索工具
+Report Agent Service
+Generate simulated reports using ReACT pattern (via GraphStorage / Neo4j)
+
+Features:
+1. Generate reports based on simulation requirements and graph information
+2. First plan the outline structure, then generate section by section
+3. Each section uses ReACT multi-round thinking and reflection pattern
+4. Support conversations with users, autonomously call retrieval tools during conversations
 """
 
 import os
@@ -34,18 +34,18 @@
 
 class ReportLogger:
     """
-    Report Agent 详细日志记录器
-    
-    在报告文件夹中生成 agent_log.jsonl 文件，记录每一步详细动作。
-    每行是一个完整的 JSON 对象，包含时间戳、动作类型、详细内容等。
+    Report Agent Detailed Logger
+
+    Generates agent_log.jsonl file in the report folder, recording detailed actions at each step.
+    Each line is a complete JSON object containing timestamp, action type, details, etc.
     """
     
     def __init__(self, report_id: str):
         """
-        初始化日志记录器
-        
+        Initialize the logger
+
         Args:
-            report_id: 报告ID，用于确定日志文件路径
+            report_id: Report ID, used to determine the log file path
         """
         self.report_id = report_id
         self.log_file_path = os.path.join(
@@ -55,12 +55,12 @@ def __init__(self, report_id: str):
         self._ensure_log_file()
     
     def _ensure_log_file(self):
-        """确保日志文件所在目录存在"""
+        """Ensure the log file directory exists"""
         log_dir = os.path.dirname(self.log_file_path)
         os.makedirs(log_dir, exist_ok=True)
     
     def _get_elapsed_time(self) -> float:
-        """获取从开始到现在的耗时（秒）"""
+        """Get elapsed time from start to now (in seconds)"""
         return (datetime.now() - self.start_time).total_seconds()
     
     def log(
@@ -72,14 +72,14 @@ def log(
         section_index: int = None
     ):
         """
-        记录一条日志
+        Record a log entry
         
         Args:
-            action: 动作类型，如 'start', 'tool_call', 'llm_response', 'section_complete' 等
-            stage: 当前阶段，如 'planning', 'generating', 'completed'
-            details: 详细内容字典，不截断
-            section_title: 当前章节标题（可选）
-            section_index: 当前章节索引（可选）
+            action: Action type, e.g. 'start', 'tool_call', 'llm_response', 'section_complete'
+            stage: Current stage, e.g. 'planning', 'generating', 'completed'
+            details: Details dictionary, not truncated
+            section_title: Current section title (optional)
+            section_index: Current section index (optional)
         """
         log_entry = {
             "timestamp": datetime.now().isoformat(),
@@ -92,12 +92,12 @@ def log(
             "details": details
         }
         
-        # 追加写入 JSONL 文件
+        # Append to JSONL file
         with open(self.log_file_path, 'a', encoding='utf-8') as f:
             f.write(json.dumps(log_entry, ensure_ascii=False) + '\n')
     
     def log_start(self, simulation_id: str, graph_id: str, simulation_requirement: str):
-        """记录报告生成开始"""
+        """Log report generation start"""
         self.log(
             action="report_start",
             stage="pending",
@@ -105,52 +105,52 @@ def log_start(self, simulation_id: str, graph_id: str, simulation_requirement: s
                 "simulation_id": simulation_id,
                 "graph_id": graph_id,
                 "simulation_requirement": simulation_requirement,
-                "message": "报告生成任务开始"
+                "message": "Report generation task started"
             }
         )
     
     def log_planning_start(self):
-        """记录大纲规划开始"""
+        """Log outline planning start"""
         self.log(
             action="planning_start",
             stage="planning",
-            details={"message": "开始规划报告大纲"}
+            details={"message": "Start planning report outline"}
         )
     
     def log_planning_context(self, context: Dict[str, Any]):
-        """记录规划时获取的上下文信息"""
+        """Log context information obtained during planning"""
         self.log(
             action="planning_context",
             stage="planning",
             details={
-                "message": "获取模拟上下文信息",
+                "message": "Getting simulation context information",
                 "context": context
             }
         )
     
     def log_planning_complete(self, outline_dict: Dict[str, Any]):
-        """记录大纲规划完成"""
+        """Log outline planning complete"""
         self.log(
             action="planning_complete",
             stage="planning",
             details={
-                "message": "大纲规划完成",
+                "message": "Outline planning complete",
                 "outline": outline_dict
             }
         )
     
     def log_section_start(self, section_title: str, section_index: int):
-        """记录章节生成开始"""
+        """Log section generation start"""
         self.log(
             action="section_start",
             stage="generating",
             section_title=section_title,
             section_index=section_index,
-            details={"message": f"开始生成章节: {section_title}"}
+            details={"message": f"Start generating section: {section_title}"}
         )
     
     def log_react_thought(self, section_title: str, section_index: int, iteration: int, thought: str):
-        """记录 ReACT 思考过程"""
+        """Log ReACT thinking process"""
         self.log(
             action="react_thought",
             stage="generating",
@@ -159,7 +159,7 @@ def log_react_thought(self, section_title: str, section_index: int, iteration: i
             details={
                 "iteration": iteration,
                 "thought": thought,
-                "message": f"ReACT 第{iteration}轮思考"
+                "message": f"ReACT round {iteration} thinking"
             }
         )
     
@@ -171,7 +171,7 @@ def log_tool_call(
         parameters: Dict[str, Any],
         iteration: int
     ):
-        """记录工具调用"""
+        """Log tool call"""
         self.log(
             action="tool_call",
             stage="generating",
@@ -181,7 +181,7 @@ def log_tool_call(
                 "iteration": iteration,
                 "tool_name": tool_name,
                 "parameters": parameters,
-                "message": f"调用工具: {tool_name}"
+                "message": f"Tool call: {tool_name}"
             }
         )
     
@@ -193,7 +193,7 @@ def log_tool_result(
         result: str,
         iteration: int
     ):
-        """记录工具调用结果（完整内容，不截断）"""
+        """Log tool call result (full content, not truncated)"""
         self.log(
             action="tool_result",
             stage="generating",
@@ -202,9 +202,9 @@ def log_tool_result(
             details={
                 "iteration": iteration,
                 "tool_name": tool_name,
-                "result": result,  # 完整结果，不截断
+                "result": result,  # Full result, not truncated
                 "result_length": len(result),
-                "message": f"工具 {tool_name} 返回结果"
+                "message": f"Tool {tool_name} returned result"
             }
         )
     
@@ -217,7 +217,7 @@ def log_llm_response(
         has_tool_calls: bool,
         has_final_answer: bool
     ):
-        """记录 LLM 响应（完整内容，不截断）"""
+        """Log LLM response (full content, not truncated)"""
         self.log(
             action="llm_response",
             stage="generating",
@@ -225,11 +225,11 @@ def log_llm_response(
             section_index=section_index,
             details={
                 "iteration": iteration,
-                "response": response,  # 完整响应，不截断
+                "response": response,  # Full response, not truncated
                 "response_length": len(response),
                 "has_tool_calls": has_tool_calls,
                 "has_final_answer": has_final_answer,
-                "message": f"LLM 响应 (工具调用: {has_tool_calls}, 最终答案: {has_final_answer})"
+                "message": f"LLM response (tool calls: {has_tool_calls}, final answer: {has_final_answer})"
             }
         )
     
@@ -240,17 +240,17 @@ def log_section_content(
         content: str,
         tool_calls_count: int
     ):
-        """记录章节内容生成完成（仅记录内容，不代表整个章节完成）"""
+        """Log section content generation complete (only records content, does not mean entire section is complete)"""
         self.log(
             action="section_content",
             stage="generating",
             section_title=section_title,
             section_index=section_index,
             details={
-                "content": content,  # 完整内容，不截断
+                "content": content,  # Full content, not truncated
                 "content_length": len(content),
                 "tool_calls_count": tool_calls_count,
-                "message": f"章节 {section_title} 内容生成完成"
+                "message": f"Section {section_title} content generation complete"
             }
         )
     
@@ -261,9 +261,9 @@ def log_section_full_complete(
         full_content: str
     ):
         """
-        记录章节生成完成
+        Log section generation complete
 
-        前端应监听此日志来判断一个章节是否真正完成，并获取完整内容
+        Frontend should monitor this log to determine if a section is truly complete and get full content
         """
         self.log(
             action="section_complete",
@@ -273,24 +273,24 @@ def log_section_full_complete(
             details={
                 "content": full_content,
                 "content_length": len(full_content),
-                "message": f"章节 {section_title} 生成完成"
+                "message": f"Section {section_title} generation complete"
             }
         )
     
     def log_report_complete(self, total_sections: int, total_time_seconds: float):
-        """记录报告生成完成"""
+        """Log report generation complete"""
         self.log(
             action="report_complete",
             stage="completed",
             details={
                 "total_sections": total_sections,
                 "total_time_seconds": round(total_time_seconds, 2),
-                "message": "报告生成完成"
+                "message": "Report generation complete"
             }
         )
     
     def log_error(self, error_message: str, stage: str, section_title: str = None):
-        """记录错误"""
+        """Log error"""
         self.log(
             action="error",
             stage=stage,
@@ -298,25 +298,25 @@ def log_error(self, error_message: str, stage: str, section_title: str = None):
             section_index=None,
             details={
                 "error": error_message,
-                "message": f"发生错误: {error_message}"
+                "message": f"Error occurred: {error_message}"
             }
         )
 
 
 class ReportConsoleLogger:
     """
-    Report Agent 控制台日志记录器
+    Report Agent Console Logger
     
-    将控制台风格的日志（INFO、WARNING等）写入报告文件夹中的 console_log.txt 文件。
-    这些日志与 agent_log.jsonl 不同，是纯文本格式的控制台输出。
+    Writes console-style logs (INFO, WARNING, etc.) to console_log.txt in the report folder.
+    These logs differ from agent_log.jsonl as they are plain text console output.
     """
     
     def __init__(self, report_id: str):
         """
-        初始化控制台日志记录器
+        Initialize console logger
         
         Args:
-            report_id: 报告ID，用于确定日志文件路径
+            report_id: Report ID, used to determine log file path
         """
         self.report_id = report_id
         self.log_file_path = os.path.join(
@@ -327,15 +327,15 @@ def __init__(self, report_id: str):
         self._setup_file_handler()
     
     def _ensure_log_file(self):
-        """确保日志文件所在目录存在"""
+        """Ensure the log file directory exists"""
         log_dir = os.path.dirname(self.log_file_path)
         os.makedirs(log_dir, exist_ok=True)
     
     def _setup_file_handler(self):
-        """设置文件处理器，将日志同时写入文件"""
+        """Set up file handler to write logs to file"""
         import logging
         
-        # 创建文件处理器
+        # Create file handler
         self._file_handler = logging.FileHandler(
             self.log_file_path,
             mode='a',
@@ -343,14 +343,14 @@ def _setup_file_handler(self):
         )
         self._file_handler.setLevel(logging.INFO)
         
-        # 使用与控制台相同的简洁格式
+        # Use same concise format as console
         formatter = logging.Formatter(
             '[%(asctime)s] %(levelname)s: %(message)s',
             datefmt='%H:%M:%S'
         )
         self._file_handler.setFormatter(formatter)
         
-        # 添加到 report_agent 相关的 logger
+        # Add to report_agent related loggers
         loggers_to_attach = [
             'mirofish.report_agent',
             'mirofish.zep_tools',
@@ -358,12 +358,12 @@ def _setup_file_handler(self):
         
         for logger_name in loggers_to_attach:
             target_logger = logging.getLogger(logger_name)
-            # 避免重复添加
+            # Avoid duplicate additions
             if self._file_handler not in target_logger.handlers:
                 target_logger.addHandler(self._file_handler)
     
     def close(self):
-        """关闭文件处理器并从 logger 中移除"""
+        """Close file handler and remove from loggers"""
         import logging
         
         if self._file_handler:
@@ -381,12 +381,12 @@ def close(self):
             self._file_handler = None
     
     def __del__(self):
-        """析构时确保关闭文件处理器"""
+        """Ensure file handler is closed on destruction"""
         self.close()
 
 
 class ReportStatus(str, Enum):
-    """报告状态"""
+    """Report status"""
     PENDING = "pending"
     PLANNING = "planning"
     GENERATING = "generating"
@@ -396,7 +396,7 @@ class ReportStatus(str, Enum):
 
 @dataclass
 class ReportSection:
-    """报告章节"""
+    """Report section"""
     title: str
     content: str = ""
 
@@ -407,7 +407,7 @@ def to_dict(self) -> Dict[str, Any]:
         }
 
     def to_markdown(self, level: int = 2) -> str:
-        """转换为Markdown格式"""
+        """Convert to Markdown format"""
         md = f"{'#' * level} {self.title}\n\n"
         if self.content:
             md += f"{self.content}\n\n"
@@ -416,7 +416,7 @@ def to_markdown(self, level: int = 2) -> str:
 
 @dataclass
 class ReportOutline:
-    """报告大纲"""
+    """Report outline"""
     title: str
     summary: str
     sections: List[ReportSection]
@@ -429,7 +429,7 @@ def to_dict(self) -> Dict[str, Any]:
         }
     
     def to_markdown(self) -> str:
-        """转换为Markdown格式"""
+        """Convert to Markdown format"""
         md = f"# {self.title}\n\n"
         md += f"> {self.summary}\n\n"
         for section in self.sections:
@@ -439,7 +439,7 @@ def to_markdown(self) -> str:
 
 @dataclass
 class Report:
-    """完整报告"""
+    """Complete report"""
     report_id: str
     simulation_id: str
     graph_id: str
@@ -467,417 +467,417 @@ def to_dict(self) -> Dict[str, Any]:
 
 
 # ═══════════════════════════════════════════════════════════════
-# Prompt 模板常量
+# Prompt Template Constants
 # ═══════════════════════════════════════════════════════════════
 
-# ── 工具描述 ──
+# ── Tool Descriptions ──
 
 TOOL_DESC_INSIGHT_FORGE = """\
-【深度洞察检索 - 强大的检索工具】
-这是我们强大的检索函数，专为深度分析设计。它会：
-1. 自动将你的问题分解为多个子问题
-2. 从多个维度检索模拟图谱中的信息
-3. 整合语义搜索、实体分析、关系链追踪的结果
-4. 返回最全面、最深度的检索内容
-
-【使用场景】
-- 需要深入分析某个话题
-- 需要了解事件的多个方面
-- 需要获取支撑报告章节的丰富素材
-
-【返回内容】
-- 相关事实原文（可直接引用）
-- 核心实体洞察
-- 关系链分析"""
+[Deep Insight Retrieval - Powerful retrieval tool]
+This is our powerful retrieval function, designed for deep analysis. It will:
+1. Automatically decompose your question into multiple sub-questions
+2. Retrieve information from the simulation graph across multiple dimensions
+3. Integrate results from semantic search, entity analysis, and relationship chain tracking
+4. Return the most comprehensive, deepest retrieval content
+
+[Use cases]
+- Need to deeply analyze a topic
+- Need to understand multiple aspects of an event
+- Need rich materials to support report sections
+
+[Returns]
+- Related factual text (can be directly quoted)
+- Core entity insights
+- Relationship chain analysis"""
 
 TOOL_DESC_PANORAMA_SEARCH = """\
-【广度搜索 - 获取全貌视图】
-这个工具用于获取模拟结果的完整全貌，特别适合了解事件演变过程。它会：
-1. 获取所有相关节点和关系
-2. 区分当前有效的事实和历史/过期的事实
-3. 帮助你了解舆情是如何演变的
-
-【使用场景】
-- 需要了解事件的完整发展脉络
-- 需要对比不同阶段的舆情变化
-- 需要获取全面的实体和关系信息
-
-【返回内容】
-- 当前有效事实（模拟最新结果）
-- 历史/过期事实（演变记录）
-- 所有涉及的实体"""
+[Breadth Search - Get panoramic view]
+This tool is used to get the complete picture of simulation results, especially suitable for understanding event evolution. It will:
+1. Get all related nodes and relationships
+2. Distinguish between currently valid facts and historical/expired facts
+3. Help you understand how public opinion evolved
+
+[Use cases]
+- Need to understand the complete development trajectory of an event
+- Need to compare opinion changes across different stages
+- Need comprehensive entity and relationship information
+
+[Returns]
+- Currently valid facts (latest simulation results)
+- Historical/expired facts (evolution records)
+- All involved entities"""
 
 TOOL_DESC_QUICK_SEARCH = """\
-【简单搜索 - 快速检索】
-轻量级的快速检索工具，适合简单、直接的信息查询。
+[Simple Search - Quick retrieval]
+Lightweight quick retrieval tool, suitable for simple, direct information queries.
 
-【使用场景】
-- 需要快速查找某个具体信息
-- 需要验证某个事实
-- 简单的信息检索
+[Use cases]
+- Need to quickly find specific information
+- Need to verify a fact
+- Simple information retrieval
 
-【返回内容】
-- 与查询最相关的事实列表"""
+[Returns]
+- List of facts most relevant to the query"""
 
 TOOL_DESC_INTERVIEW_AGENTS = """\
-【深度采访 - 真实Agent采访（双平台）】
-调用OASIS模拟环境的采访API，对正在运行的模拟Agent进行真实采访！
-这不是LLM模拟，而是调用真实的采访接口获取模拟Agent的原始回答。
-默认在Twitter和Reddit两个平台同时采访，获取更全面的观点。
-
-功能流程：
-1. 自动读取人设文件，了解所有模拟Agent
-2. 智能选择与采访主题最相关的Agent（如学生、媒体、官方等）
-3. 自动生成采访问题
-4. 调用 /api/simulation/interview/batch 接口在双平台进行真实采访
-5. 整合所有采访结果，提供多视角分析
-
-【使用场景】
-- 需要从不同角色视角了解事件看法（学生怎么看？媒体怎么看？官方怎么说？）
-- 需要收集多方意见和立场
-- 需要获取模拟Agent的真实回答（来自OASIS模拟环境）
-- 想让报告更生动，包含"采访实录"
-
-【返回内容】
-- 被采访Agent的身份信息
-- 各Agent在Twitter和Reddit两个平台的采访回答
-- 关键引言（可直接引用）
-- 采访摘要和观点对比
-
-【重要】需要OASIS模拟环境正在运行才能使用此功能！"""
-
-# ── 大纲规划 prompt ──
+[In-depth Interview - Real Agent Interview (dual platform)]
+Calls the OASIS simulation environment interview API to conduct real interviews with running simulation Agents!
+This is not LLM simulation, but calling real interview interfaces to get simulation Agents' original responses.
+By default interviews on both Twitter and Reddit platforms simultaneously for more comprehensive viewpoints.
+
+Workflow:
+1. Automatically read persona files to understand all simulation Agents
+2. Intelligently select Agents most relevant to the interview topic (e.g., students, media, officials)
+3. Automatically generate interview questions
+4. Call /api/simulation/interview/batch interface for real interviews on both platforms
+5. Integrate all interview results, providing multi-perspective analysis
+
+[Use cases]
+- Need to understand event views from different role perspectives (How do students see it? Media? Officials?)
+- Need to collect opinions and stances from multiple parties
+- Need to get real responses from simulation Agents (from OASIS simulation environment)
+- Want to make report more vivid, including "interview transcripts"
+
+[Returns]
+- Identity information of interviewed Agents
+- Each Agent's interview responses on both Twitter and Reddit platforms
+- Key quotes (can be directly cited)
+- Interview summary and viewpoint comparison
+
+[Important] OASIS simulation environment must be running to use this feature!"""
+
+# ── Outline Planning Prompt ──
 
 PLAN_SYSTEM_PROMPT = """\
-你是一个「未来预测报告」的撰写专家，拥有对模拟世界的「上帝视角」——你可以洞察模拟中每一位Agent的行为、言论和互动。
-
-【核心理念】
-我们构建了一个模拟世界，并向其中注入了特定的「模拟需求」作为变量。模拟世界的演化结果，就是对未来可能发生情况的预测。你正在观察的不是"实验数据"，而是"未来的预演"。
-
-【你的任务】
-撰写一份「未来预测报告」，回答：
-1. 在我们设定的条件下，未来发生了什么？
-2. 各类Agent（人群）是如何反应和行动？
-3. 这个模拟揭示了哪些值得关注的未来趋势和风险？
-
-【报告定位】
-- ✅ 这是一份基于模拟的未来预测报告，揭示"如果这样，未来会怎样"
-- ✅ 聚焦于预测结果：事件走向、群体反应、涌现现象、潜在风险
-- ✅ 模拟世界中的Agent言行就是对未来人群行为的预测
-- ❌ 不是对现实世界现状的分析
-- ❌ 不是泛泛而谈的舆情综述
-
-【章节数量限制】
-- 最少2个章节，最多5个章节
-- 不需要子章节，每个章节直接撰写完整内容
-- 内容要精炼，聚焦于核心预测发现
-- 章节结构由你根据预测结果自主设计
-
-请输出JSON格式的报告大纲，格式如下：
+You are an expert in writing "future prediction reports" with a "god's eye view" of the simulated world - you can gain insights into the behavior, statements, and interactions of every agent in the simulation.
+
+[Core Concept]
+We built a simulated world and injected specific "simulation requirements" as variables into it. The evolution result of the simulated world is a prediction of what might happen in the future. What you're observing is not "experimental data" but a "rehearsal of the future".
+
+[Your Task]
+Write a "future prediction report" that answers:
+1. What happened in the future under the conditions we set?
+2. How do various agents (groups) react and act?
+3. What future trends and risks does this simulation reveal that deserve attention?
+
+[Report Positioning]
+- ✅ This is a future prediction report based on simulation, revealing "if this happens, how will the future unfold"
+- ✅ Focus on prediction results: event trajectories, group reactions, emergent phenomena, potential risks
+- ✅ Agent statements and behaviors in the simulated world are predictions of future human behavior
+- ❌ Not an analysis of the current state of the real world
+- ❌ Not a general overview of public sentiment
+
+[Section Number Limit]
+- Minimum 2 sections, maximum 5 sections
+- No subsections needed, each section directly writes complete content
+- Content should be concise, focused on core prediction findings
+- Section structure is designed independently based on prediction results
+
+Please output the report outline in JSON format as follows:
 {
-    "title": "报告标题",
-    "summary": "报告摘要（一句话概括核心预测发现）",
+    "title": "Report Title",
+    "summary": "Report Summary (one sentence summarizing core prediction findings)",
     "sections": [
         {
-            "title": "章节标题",
-            "description": "章节内容描述"
+            "title": "Section Title",
+            "description": "Section Content Description"
         }
     ]
 }
 
-注意：sections数组最少2个，最多5个元素！"""
+Note: sections array must have at least 2 and at most 5 elements!"""
 
 PLAN_USER_PROMPT_TEMPLATE = """\
-【预测场景设定】
-我们向模拟世界注入的变量（模拟需求）：{simulation_requirement}
+[Prediction Scenario Settings]
+Variable (simulation requirement) injected into the simulated world: {simulation_requirement}
 
-【模拟世界规模】
-- 参与模拟的实体数量: {total_nodes}
-- 实体间产生的关系数量: {total_edges}
-- 实体类型分布: {entity_types}
-- 活跃Agent数量: {total_entities}
+[Simulated World Scale]
+- Number of entities participating in simulation: {total_nodes}
+- Number of relationships generated between entities: {total_edges}
+- Entity type distribution: {entity_types}
+- Number of active agents: {total_entities}
 
-【模拟预测到的部分未来事实样本】
+[Sample of Some Future Facts Predicted by Simulation]
 {related_facts_json}
 
-请以「上帝视角」审视这个未来预演：
-1. 在我们设定的条件下，未来呈现出了什么样的状态？
-2. 各类人群（Agent）是如何反应和行动的？
-3. 这个模拟揭示了哪些值得关注的未来趋势？
+Please examine this future rehearsal from a "god's eye view":
+1. What state does the future present under the conditions we set?
+2. How do various groups (agents) react and act?
+3. What future trends does this simulation reveal that deserve attention?
 
-根据预测结果，设计最合适的报告章节结构。
+Based on the prediction results, design the most appropriate report section structure.
 
-【再次提醒】报告章节数量：最少2个，最多5个，内容要精炼聚焦于核心预测发现。"""
+[Reminder] Report section count: minimum 2, maximum 5, content should be concise and focused on core prediction findings."""
 
-# ── 章节生成 prompt ──
+# ── Section Generation Prompt ──
 
 SECTION_SYSTEM_PROMPT_TEMPLATE = """\
-你是一个「未来预测报告」的撰写专家，正在撰写报告的一个章节。
+You are an expert in writing "future prediction reports" and are writing a section of the report.
 
-报告标题: {report_title}
-报告摘要: {report_summary}
-预测场景（模拟需求）: {simulation_requirement}
+Report Title: {report_title}
+Report Summary: {report_summary}
+Prediction Scenario (Simulation Requirement): {simulation_requirement}
 
-当前要撰写的章节: {section_title}
+Current Section to Write: {section_title}
 
 ═══════════════════════════════════════════════════════════════
-【核心理念】
+[Core Concept]
 ═══════════════════════════════════════════════════════════════
 
-模拟世界是对未来的预演。我们向模拟世界注入了特定条件（模拟需求），
-模拟中Agent的行为和互动，就是对未来人群行为的预测。
+The simulated world is a rehearsal of the future. We injected specific conditions (simulation requirements) into the simulated world.
+The behavior and interactions of agents in the simulation are predictions of future human behavior.
 
-你的任务是：
-- 揭示在设定条件下，未来发生了什么
-- 预测各类人群（Agent）是如何反应和行动的
-- 发现值得关注的未来趋势、风险和机会
+Your task is to:
+- Reveal what happens in the future under the set conditions
+- Predict how various groups (agents) react and act
+- Discover future trends, risks, and opportunities worth paying attention to
 
-❌ 不要写成对现实世界现状的分析
-✅ 要聚焦于"未来会怎样"——模拟结果就是预测的未来
+❌ Don't write it as an analysis of the current state of the real world
+✅ Focus on "how the future will unfold" - simulation results are the predicted future
 
 ═══════════════════════════════════════════════════════════════
-【最重要的规则 - 必须遵守】
+[Most Important Rules - Must Follow]
 ═══════════════════════════════════════════════════════════════
 
-1. 【必须调用工具观察模拟世界】
-   - 你正在以「上帝视角」观察未来的预演
-   - 所有内容必须来自模拟世界中发生的事件和Agent言行
-   - 禁止使用你自己的知识来编写报告内容
-   - 每个章节至少调用3次工具（最多5次）来观察模拟的世界，它代表了未来
-
-2. 【必须引用Agent的原始言行】
-   - Agent的发言和行为是对未来人群行为的预测
-   - 在报告中使用引用格式展示这些预测，例如：
-     > "某类人群会表示：原文内容..."
-   - 这些引用是模拟预测的核心证据
-
-3. 【语言一致性 - 引用内容必须翻译为报告语言】
-   - 工具返回的内容可能包含英文或中英文混杂的表述
-   - 如果模拟需求和材料原文是中文的，报告必须全部使用中文撰写
-   - 当你引用工具返回的英文或中英混杂内容时，必须将其翻译为流畅的中文后再写入报告
-   - 翻译时保持原意不变，确保表述自然通顺
-   - 这一规则同时适用于正文和引用块（> 格式）中的内容
-
-4. 【忠实呈现预测结果】
-   - 报告内容必须反映模拟世界中的代表未来的模拟结果
-   - 不要添加模拟中不存在的信息
-   - 如果某方面信息不足，如实说明
+1. [Must Call Tools to Observe the Simulated World]
+   - You are observing a rehearsal of the future from a "god's eye view"
+   - All content must come from events and agent statements/behaviors in the simulated world
+   - Forbidden to use your own knowledge to write report content
+   - Each section must call tools at least 3 times (maximum 5 times) to observe the simulated world, which represents the future
+
+2. [Must Quote Original Agent Statements and Behaviors]
+   - Agent statements and behaviors are predictions of future human behavior
+   - Use quote format in the report to display these predictions, for example:
+     > "Certain groups will state: original content..."
+   - These quotes are core evidence of simulation predictions
+
+3. [Language Consistency - Quoted Content Must Be Translated to Report Language]
+   - Tool returned content may contain English or mixed Chinese-English expressions
+   - If the simulation requirement and source material are in Chinese, the report must be entirely in Chinese
+   - When you quote English or mixed Chinese-English content from tools, you must translate it to fluent Chinese before including it in the report
+   - When translating, preserve the original meaning and ensure natural expression
+   - This rule applies to both regular text and quoted blocks (> format)
+
+4. [Faithfully Present Prediction Results]
+   - Report content must reflect simulation results that represent the future in the simulated world
+   - Don't add information that doesn't exist in the simulation
+   - If information is insufficient in some aspects, state it truthfully
 
 ═══════════════════════════════════════════════════════════════
-【⚠️ 格式规范 - 极其重要！】
+[⚠️ Format Specification - Extremely Important!]
 ═══════════════════════════════════════════════════════════════
 
-【一个章节 = 最小内容单位】
-- 每个章节是报告的最小分块单位
-- ❌ 禁止在章节内使用任何 Markdown 标题（#、##、###、#### 等）
-- ❌ 禁止在内容开头添加章节主标题
-- ✅ 章节标题由系统自动添加，你只需撰写纯正文内容
-- ✅ 使用**粗体**、段落分隔、引用、列表来组织内容，但不要用标题
+[One Section = Minimum Content Unit]
+- Each section is the minimum content unit of the report
+- ❌ Forbidden to use any Markdown titles (#, ##, ###, ####, etc.) within the section
+- ❌ Forbidden to add section titles at the beginning of content
+- ✅ Section titles are added automatically by the system, just write pure body text
+- ✅ Use **bold**, paragraph separation, quotes, and lists to organize content, but don't use titles
 
-【正确示例】
+[Correct Example]
 ```
-本章节分析了事件的舆论传播态势。通过对模拟数据的深入分析，我们发现...
+This section analyzes the public sentiment propagation of the event. Through in-depth analysis of simulation data, we found...
 
-**首发引爆阶段**
+**Initial Explosion Phase**
 
-微博作为舆情的第一现场，承担了信息首发的核心功能：
+Weibo, as the first scene of public sentiment, undertook the core function of initial information dissemination:
 
-> "微博贡献了68%的首发声量..."
+> "Weibo contributed 68% of initial voice..."
 
-**情绪放大阶段**
+**Emotion Amplification Phase**
 
-抖音平台进一步放大了事件影响力：
+The TikTok platform further amplified the impact of the event:
 
-- 视觉冲击力强
-- 情绪共鸣度高
+- Strong visual impact
+- High emotional resonance
 ```
 
-【错误示例】
+[Incorrect Example]
 ```
-## 执行摘要          ← 错误！不要添加任何标题
-### 一、首发阶段     ← 错误！不要用###分小节
-#### 1.1 详细分析   ← 错误！不要用####细分
+## Executive Summary          ← Wrong! Don't add any titles
+### 1. Initial Phase         ← Wrong! Don't use ### for subsections
+#### 1.1 Detailed Analysis   ← Wrong! Don't use #### for subdivisions
 
-本章节分析了...
+This section analyzes...
 ```
 
 ═══════════════════════════════════════════════════════════════
-【可用检索工具】（每章节调用3-5次）
+[Available Retrieval Tools] (call 3-5 times per section)
 ═══════════════════════════════════════════════════════════════
 
 {tools_description}
 
-【工具使用建议 - 请混合使用不同工具，不要只用一种】
-- insight_forge: 深度洞察分析，自动分解问题并多维度检索事实和关系
-- panorama_search: 广角全景搜索，了解事件全貌、时间线和演变过程
-- quick_search: 快速验证某个具体信息点
-- interview_agents: 采访模拟Agent，获取不同角色的第一人称观点和真实反应
+[Tool Usage Suggestions - Please Mix Different Tools, Don't Use Only One]
+- insight_forge: Deep insight analysis, automatically decompose problems and retrieve facts and relationships from multiple dimensions
+- panorama_search: Wide-angle panoramic search, understand complete event view, timeline, and evolution process
+- quick_search: Quick verification of specific information points
+- interview_agents: Interview simulated agents, get first-person perspectives and real reactions from different roles
 
 ═══════════════════════════════════════════════════════════════
-【工作流程】
+[Workflow]
 ═══════════════════════════════════════════════════════════════
 
-每次回复你只能做以下两件事之一（不可同时做）：
+Each reply you can only do one of two things (cannot do both):
 
-选项A - 调用工具：
-输出你的思考，然后用以下格式调用一个工具：
+Option A - Call Tool:
+Output your thinking, then call a tool using the following format:
 <tool_call>
-{{"name": "工具名称", "parameters": {{"参数名": "参数值"}}}}
+{{"name": "Tool Name", "parameters": {{"parameter_name": "parameter_value"}}}}
 </tool_call>
-系统会执行工具并把结果返回给你。你不需要也不能自己编写工具返回结果。
+The system will execute the tool and return the result to you. You don't need to and cannot write tool return results yourself.
 
-选项B - 输出最终内容：
-当你已通过工具获取了足够信息，以 "Final Answer:" 开头输出章节内容。
+Option B - Output Final Content:
+When you have gathered enough information through tools, start with "Final Answer:" and output section content.
 
-⚠️ 严格禁止：
-- 禁止在一次回复中同时包含工具调用和 Final Answer
-- 禁止自己编造工具返回结果（Observation），所有工具结果由系统注入
-- 每次回复最多调用一个工具
+⚠️ Strictly Forbidden:
+- Forbidden to include both tool calls and Final Answer in one reply
+- Forbidden to fabricate tool return results (Observation), all tool results are injected by the system
+- At most one tool call per reply
 
 ═══════════════════════════════════════════════════════════════
-【章节内容要求】
+[Section Content Requirements]
 ═══════════════════════════════════════════════════════════════
 
-1. 内容必须基于工具检索到的模拟数据
-2. 大量引用原文来展示模拟效果
-3. 使用Markdown格式（但禁止使用标题）：
-   - 使用 **粗体文字** 标记重点（代替子标题）
-   - 使用列表（-或1.2.3.）组织要点
-   - 使用空行分隔不同段落
-   - ❌ 禁止使用 #、##、###、#### 等任何标题语法
-4. 【引用格式规范 - 必须单独成段】
-   引用必须独立成段，前后各有一个空行，不能混在段落中：
-
-   ✅ 正确格式：
+1. Content must be based on simulation data retrieved by tools
+2. Heavily quote original text to demonstrate simulation effects
+3. Use Markdown format (but forbidden to use titles):
+   - Use **bold text** to mark key points (replacing sub-titles)
+   - Use lists (- or 1.2.3.) to organize points
+   - Use blank lines to separate paragraphs
+   - ❌ Forbidden to use any title syntax like #, ##, ###, ####
+4. [Quote Format Specification - Must Be Separate Paragraph]
+   Quotes must be standalone paragraphs with blank lines before and after, cannot be mixed in paragraphs:
+
+   ✅ Correct Format:
    ```
-   校方的回应被认为缺乏实质内容。
+   School officials' response was considered lacking substantive content.
 
-   > "校方的应对模式在瞬息万变的社交媒体环境中显得僵化和迟缓。"
+   > "School's response pattern appears rigid and slow in the rapidly changing social media environment."
 
-   这一评价反映了公众的普遍不满。
+   This assessment reflects widespread public dissatisfaction.
    ```
 
-   ❌ 错误格式：
+   ❌ Incorrect Format:
    ```
-   校方的回应被认为缺乏实质内容。> "校方的应对模式..." 这一评价反映了...
+   School officials' response was considered lacking substantive content.> "School's response pattern..." This assessment reflects...
    ```
-5. 保持与其他章节的逻辑连贯性
-6. 【避免重复】仔细阅读下方已完成的章节内容，不要重复描述相同的信息
-7. 【再次强调】不要添加任何标题！用**粗体**代替小节标题"""
+5. Maintain logical coherence with other sections
+6. [Avoid Duplication] Carefully read the completed section content below, don't repeat describing the same information
+7. [Emphasis Again] Don't add any titles! Use **bold** instead of section sub-titles"""
 
 SECTION_USER_PROMPT_TEMPLATE = """\
-已完成的章节内容（请仔细阅读，避免重复）：
+Completed Section Content (Please Read Carefully to Avoid Duplication):
 {previous_content}
 
 ═══════════════════════════════════════════════════════════════
-【当前任务】撰写章节: {section_title}
+[Current Task] Write Section: {section_title}
 ═══════════════════════════════════════════════════════════════
 
-【重要提醒】
-1. 仔细阅读上方已完成的章节，避免重复相同的内容！
-2. 开始前必须先调用工具获取模拟数据
-3. 请混合使用不同工具，不要只用一种
-4. 报告内容必须来自检索结果，不要使用自己的知识
+[Important Reminders]
+1. Carefully read the completed sections above to avoid repeating the same content!
+2. You must call tools to get simulation data before starting
+3. Please mix different tools, don't use only one
+4. Report content must come from retrieval results, don't use your own knowledge
 
-【⚠️ 格式警告 - 必须遵守】
-- ❌ 不要写任何标题（#、##、###、####都不行）
-- ❌ 不要写"{section_title}"作为开头
-- ✅ 章节标题由系统自动添加
-- ✅ 直接写正文，用**粗体**代替小节标题
+[⚠️ Format Warning - Must Follow]
+- ❌ Don't write any titles (#, ##, ###, #### none allowed)
+- ❌ Don't write "{section_title}" as the opening
+- ✅ Section titles are added automatically by the system
+- ✅ Write the body directly, use **bold** instead of sub-section titles
 
-请开始：
-1. 首先思考（Thought）这个章节需要什么信息
-2. 然后调用工具（Action）获取模拟数据
-3. 收集足够信息后输出 Final Answer（纯正文，无任何标题）"""
+Please start:
+1. First think (Thought) what information this section needs
+2. Then call tools (Action) to get simulation data
+3. After collecting enough information, output Final Answer (pure body text, no titles)"""
 
-# ── ReACT 循环内消息模板 ──
+# ── ReACT Loop Message Templates ──
 
 REACT_OBSERVATION_TEMPLATE = """\
-Observation（检索结果）:
+Observation (Retrieval Result):
 
-═══ 工具 {tool_name} 返回 ═══
+═══ Tool {tool_name} Returned ═══
 {result}
 
 ═══════════════════════════════════════════════════════════════
-已调用工具 {tool_calls_count}/{max_tool_calls} 次（已用: {used_tools_str}）{unused_hint}
-- 如果信息充分：以 "Final Answer:" 开头输出章节内容（必须引用上述原文）
-- 如果需要更多信息：调用一个工具继续检索
+Called tools {tool_calls_count}/{max_tool_calls} times (Used: {used_tools_str}){unused_hint}
+- If information is sufficient: Start with "Final Answer:" and output section content (must quote the above original text)
+- If more information is needed: Call a tool to continue retrieving
 ═══════════════════════════════════════════════════════════════"""
 
 REACT_INSUFFICIENT_TOOLS_MSG = (
-    "【注意】你只调用了{tool_calls_count}次工具，至少需要{min_tool_calls}次。"
-    "请再调用工具获取更多模拟数据，然后再输出 Final Answer。{unused_hint}"
+    "[Notice] You have only called {tool_calls_count} tools, need at least {min_tool_calls}. "
+    "Please call tools again to get more simulation data, then output Final Answer. {unused_hint}"
 )
 
 REACT_INSUFFICIENT_TOOLS_MSG_ALT = (
-    "当前只调用了 {tool_calls_count} 次工具，至少需要 {min_tool_calls} 次。"
-    "请调用工具获取模拟数据。{unused_hint}"
+    "Currently called {tool_calls_count} tools, need at least {min_tool_calls}. "
+    "Please call tools to get simulation data. {unused_hint}"
 )
 
 REACT_TOOL_LIMIT_MSG = (
-    "工具调用次数已达上限（{tool_calls_count}/{max_tool_calls}），不能再调用工具。"
-    '请立即基于已获取的信息，以 "Final Answer:" 开头输出章节内容。'
+    "Tool call count has reached the limit ({tool_calls_count}/{max_tool_calls}), cannot call tools anymore. "
+    'Please immediately start with "Final Answer:" and output section content based on acquired information.'
 )
 
-REACT_UNUSED_TOOLS_HINT = "\n💡 你还没有使用过: {unused_list}，建议尝试不同工具获取多角度信息"
+REACT_UNUSED_TOOLS_HINT = "\n💡 You haven't used yet: {unused_list}, suggest trying different tools to get multi-perspective information"
 
-REACT_FORCE_FINAL_MSG = "已达到工具调用限制，请直接输出 Final Answer: 并生成章节内容。"
+REACT_FORCE_FINAL_MSG = "Tool call limit reached, please directly output Final Answer: and generate section content."
 
-# ── Chat prompt ──
+# ── Chat Prompt ──
 
 CHAT_SYSTEM_PROMPT_TEMPLATE = """\
-你是一个简洁高效的模拟预测助手。
+You are a concise and efficient simulation prediction assistant.
 
-【背景】
-预测条件: {simulation_requirement}
+[Background]
+Prediction Condition: {simulation_requirement}
 
-【已生成的分析报告】
+[Generated Analysis Report]
 {report_content}
 
-【规则】
-1. 优先基于上述报告内容回答问题
-2. 直接回答问题，避免冗长的思考论述
-3. 仅在报告内容不足以回答时，才调用工具检索更多数据
-4. 回答要简洁、清晰、有条理
+[Rules]
+1. Prioritize answering questions based on the above report content
+2. Answer questions directly, avoid lengthy deliberation
+3. Only call tools to retrieve more data if the report content is insufficient to answer
+4. Answers should be concise, clear, and well-organized
 
-【可用工具】（仅在需要时使用，最多调用1-2次）
+[Available Tools] (use only when needed, call at most 1-2 times)
 {tools_description}
 
-【工具调用格式】
+[Tool Call Format]
 <tool_call>
-{{"name": "工具名称", "parameters": {{"参数名": "参数值"}}}}
+{{"name": "Tool Name", "parameters": {{"parameter_name": "parameter_value"}}}}
 </tool_call>
 
-【回答风格】
-- 简洁直接，不要长篇大论
-- 使用 > 格式引用关键内容
-- 优先给出结论，再解释原因"""
+[Answer Style]
+- Concise and direct, don't write lengthy passages
+- Use > format to quote key content
+- Give conclusions first, then explain reasons"""
 
-CHAT_OBSERVATION_SUFFIX = "\n\n请简洁回答问题。"
+CHAT_OBSERVATION_SUFFIX = "\n\nPlease answer the question concisely."
 
 
 # ═══════════════════════════════════════════════════════════════
-# ReportAgent 主类
+# ReportAgent Main Class
 # ═══════════════════════════════════════════════════════════════
 
 
 class ReportAgent:
     """
-    Report Agent - 模拟报告生成Agent
+    Report Agent - Simulation report generation Agent
 
-    采用ReACT（Reasoning + Acting）模式：
-    1. 规划阶段：分析模拟需求，规划报告目录结构
-    2. 生成阶段：逐章节生成内容，每章节可多次调用工具获取信息
-    3. 反思阶段：检查内容完整性和准确性
+    Uses ReACT (Reasoning + Acting) pattern:
+    1. Planning phase: Analyze simulation requirements, plan report outline structure
+    2. Generation phase: Generate content section by section, each section can call tools multiple times
+    3. Reflection phase: Check content completeness and accuracy
     """
     
-    # 最大工具调用次数（每个章节）
+    # Maximum tool calls per section
     MAX_TOOL_CALLS_PER_SECTION = 5
     
-    # 最大反思轮数
+    # Maximum reflection rounds
     MAX_REFLECTION_ROUNDS = 3
     
-    # 对话中的最大工具调用次数
+    # Maximum tool calls per chat
     MAX_TOOL_CALLS_PER_CHAT = 2
     
     def __init__(
@@ -889,14 +889,14 @@ def __init__(
         zep_tools: Optional[ZepToolsService] = None
     ):
         """
-        初始化Report Agent
+        Initialize Report Agent
         
         Args:
-            graph_id: 图谱ID
-            simulation_id: 模拟ID
-            simulation_requirement: 模拟需求描述
-            llm_client: LLM客户端（可选）
-            zep_tools: Zep工具服务（可选）
+            graph_id: Graph ID
+            simulation_id: Simulation ID
+            simulation_requirement: Simulation requirement description
+            llm_client: LLM client (optional)
+            zep_tools: Zep tools service (optional)
         """
         self.graph_id = graph_id
         self.simulation_id = simulation_id
@@ -905,66 +905,66 @@ def __init__(
         self.llm = llm_client or LLMClient()
         self.zep_tools = zep_tools or ZepToolsService()
         
-        # 工具定义
+        # Tool definitions
         self.tools = self._define_tools()
         
-        # 日志记录器（在 generate_report 中初始化）
+        # Logger (initialized in generate_report)
         self.report_logger: Optional[ReportLogger] = None
-        # 控制台日志记录器（在 generate_report 中初始化）
+        # Console logger (initialized in generate_report)
         self.console_logger: Optional[ReportConsoleLogger] = None
         
-        logger.info(f"ReportAgent 初始化完成: graph_id={graph_id}, simulation_id={simulation_id}")
+        logger.info(f"ReportAgent initialized: graph_id={graph_id}, simulation_id={simulation_id}")
     
     def _define_tools(self) -> Dict[str, Dict[str, Any]]:
-        """定义可用工具"""
+        """Define available tools"""
         return {
             "insight_forge": {
                 "name": "insight_forge",
                 "description": TOOL_DESC_INSIGHT_FORGE,
                 "parameters": {
-                    "query": "你想深入分析的问题或话题",
-                    "report_context": "当前报告章节的上下文（可选，有助于生成更精准的子问题）"
+                    "query": "The question or topic you want to deeply analyze",
+                    "report_context": "Current report section context (optional, helps generate more precise sub-questions)"
                 }
             },
             "panorama_search": {
                 "name": "panorama_search",
                 "description": TOOL_DESC_PANORAMA_SEARCH,
                 "parameters": {
-                    "query": "搜索查询，用于相关性排序",
-                    "include_expired": "是否包含过期/历史内容（默认True）"
+                    "query": "Search query for relevance ranking",
+                    "include_expired": "Whether to include expired/historical content (default True)"
                 }
             },
             "quick_search": {
                 "name": "quick_search",
                 "description": TOOL_DESC_QUICK_SEARCH,
                 "parameters": {
-                    "query": "搜索查询字符串",
-                    "limit": "返回结果数量（可选，默认10）"
+                    "query": "Search query string",
+                    "limit": "Number of results to return (optional, default 10)"
                 }
             },
             "interview_agents": {
                 "name": "interview_agents",
                 "description": TOOL_DESC_INTERVIEW_AGENTS,
                 "parameters": {
-                    "interview_topic": "采访主题或需求描述（如：'了解学生对宿舍甲醛事件的看法'）",
-                    "max_agents": "最多采访的Agent数量（可选，默认5，最大10）"
+                    "interview_topic": "Interview topic or requirement description",
+                    "max_agents": "Maximum number of Agents to interview (optional, default 5, max 10)"
                 }
             }
         }
     
     def _execute_tool(self, tool_name: str, parameters: Dict[str, Any], report_context: str = "") -> str:
         """
-        执行工具调用
+        Execute tool call
         
         Args:
-            tool_name: 工具名称
-            parameters: 工具参数
-            report_context: 报告上下文（用于InsightForge）
+            tool_name: Tool name
+            parameters: Tool parameters
+            report_context: Report context (for InsightForge)
             
         Returns:
-            工具执行结果（文本格式）
+            Tool execution result (text format)
         """
-        logger.info(f"执行工具: {tool_name}, 参数: {parameters}")
+        logger.info(f"Execute tool: {tool_name}, parameters: {parameters}")
         
         try:
             if tool_name == "insight_forge":
@@ -979,7 +979,7 @@ def _execute_tool(self, tool_name: str, parameters: Dict[str, Any], report_conte
                 return result.to_text()
             
             elif tool_name == "panorama_search":
-                # 广度搜索 - 获取全貌
+                # Breadth search - get panoramic view
                 query = parameters.get("query", "")
                 include_expired = parameters.get("include_expired", True)
                 if isinstance(include_expired, str):
@@ -992,7 +992,7 @@ def _execute_tool(self, tool_name: str, parameters: Dict[str, Any], report_conte
                 return result.to_text()
             
             elif tool_name == "quick_search":
-                # 简单搜索 - 快速检索
+                # Simple search - quick retrieval
                 query = parameters.get("query", "")
                 limit = parameters.get("limit", 10)
                 if isinstance(limit, str):
@@ -1005,7 +1005,7 @@ def _execute_tool(self, tool_name: str, parameters: Dict[str, Any], report_conte
                 return result.to_text()
             
             elif tool_name == "interview_agents":
-                # 深度采访 - 调用真实的OASIS采访API获取模拟Agent的回答（双平台）
+                # In-depth interview - call real OASIS interview API for Agent responses (dual platform)
                 interview_topic = parameters.get("interview_topic", parameters.get("query", ""))
                 max_agents = parameters.get("max_agents", 5)
                 if isinstance(max_agents, str):
@@ -1019,11 +1019,11 @@ def _execute_tool(self, tool_name: str, parameters: Dict[str, Any], report_conte
                 )
                 return result.to_text()
             
-            # ========== 向后兼容的旧工具（内部重定向到新工具） ==========
+            # ========== Backward compatible old tools (internally redirect to new tools) ==========
             
             elif tool_name == "search_graph":
-                # 重定向到 quick_search
-                logger.info("search_graph 已重定向到 quick_search")
+                # Redirect to quick_search
+                logger.info("search_graph redirected to quick_search")
                 return self._execute_tool("quick_search", parameters, report_context)
             
             elif tool_name == "get_graph_statistics":
@@ -1039,8 +1039,8 @@ def _execute_tool(self, tool_name: str, parameters: Dict[str, Any], report_conte
                 return json.dumps(result, ensure_ascii=False, indent=2)
             
             elif tool_name == "get_simulation_context":
-                # 重定向到 insight_forge，因为它更强大
-                logger.info("get_simulation_context 已重定向到 insight_forge")
+                # Redirect to insight_forge, as it is more powerful
+                logger.info("get_simulation_context redirected to insight_forge")
                 query = parameters.get("query", self.simulation_requirement)
                 return self._execute_tool("insight_forge", {"query": query}, report_context)
             
@@ -1054,26 +1054,26 @@ def _execute_tool(self, tool_name: str, parameters: Dict[str, Any], report_conte
                 return json.dumps(result, ensure_ascii=False, indent=2)
             
             else:
-                return f"未知工具: {tool_name}。请使用以下工具之一: insight_forge, panorama_search, quick_search"
+                return f"Unknown tool: {tool_name}. Please use one of: insight_forge, panorama_search, quick_search"
                 
         except Exception as e:
-            logger.error(f"工具执行失败: {tool_name}, 错误: {str(e)}")
-            return f"工具执行失败: {str(e)}"
+            logger.error(f"Tool execution failed: {tool_name}, error: {str(e)}")
+            return f"Tool execution failed: {str(e)}"
     
-    # 合法的工具名称集合，用于裸 JSON 兜底解析时校验
+    # Valid tool names set, used for validation when parsing raw JSON fallback
     VALID_TOOL_NAMES = {"insight_forge", "panorama_search", "quick_search", "interview_agents"}
 
     def _parse_tool_calls(self, response: str) -> List[Dict[str, Any]]:
         """
-        从LLM响应中解析工具调用
+        Parse tool calls from LLM response
 
-        支持的格式（按优先级）：
+        Supported formats (by priority):
         1. <tool_call>{"name": "tool_name", "parameters": {...}}</tool_call>
-        2. 裸 JSON（响应整体或单行就是一个工具调用 JSON）
+        2. Bare JSON (response body or single line is a tool call JSON)
         """
         tool_calls = []
 
-        # 格式1: XML风格（标准格式）
+        # Format 1: XML style (standard format)
         xml_pattern = r'<tool_call>\s*(\{.*?\})\s*</tool_call>'
         for match in re.finditer(xml_pattern, response, re.DOTALL):
             try:
@@ -1085,8 +1085,8 @@ def _parse_tool_calls(self, response: str) -> List[Dict[str, Any]]:
         if tool_calls:
             return tool_calls
 
-        # 格式2: 兜底 - LLM 直接输出裸 JSON（没包 <tool_call> 标签）
-        # 只在格式1未匹配时尝试，避免误匹配正文中的 JSON
+        # Format 2: Fallback - LLM outputs bare JSON (no <tool_call> tags)
+        # Only try when format 1 did not match, to avoid false matches in body text
         stripped = response.strip()
         if stripped.startswith('{') and stripped.endswith('}'):
             try:
@@ -1097,7 +1097,7 @@ def _parse_tool_calls(self, response: str) -> List[Dict[str, Any]]:
             except json.JSONDecodeError:
                 pass
 
-        # 响应可能包含思考文字 + 裸 JSON，尝试提取最后一个 JSON 对象
+        # Response may contain thinking text + bare JSON, try to extract last JSON object
         json_pattern = r'(\{"(?:name|tool)"\s*:.*?\})\s*$'
         match = re.search(json_pattern, stripped, re.DOTALL)
         if match:
@@ -1111,11 +1111,11 @@ def _parse_tool_calls(self, response: str) -> List[Dict[str, Any]]:
         return tool_calls
 
     def _is_valid_tool_call(self, data: dict) -> bool:
-        """校验解析出的 JSON 是否是合法的工具调用"""
-        # 支持 {"name": ..., "parameters": ...} 和 {"tool": ..., "params": ...} 两种键名
+        """Validate whether parsed JSON is a valid tool call"""
+        # Support both {"name": ..., "parameters": ...} and {"tool": ..., "params": ...} key names
         tool_name = data.get("name") or data.get("tool")
         if tool_name and tool_name in self.VALID_TOOL_NAMES:
-            # 统一键名为 name / parameters
+            # Normalize key names to name / parameters
             if "tool" in data:
                 data["name"] = data.pop("tool")
             if "params" in data and "parameters" not in data:
@@ -1124,13 +1124,13 @@ def _is_valid_tool_call(self, data: dict) -> bool:
         return False
     
     def _get_tools_description(self) -> str:
-        """生成工具描述文本"""
-        desc_parts = ["可用工具："]
+        """Generate tool description text"""
+        desc_parts = ["Available tools:"]
         for name, tool in self.tools.items():
             params_desc = ", ".join([f"{k}: {v}" for k, v in tool["parameters"].items()])
             desc_parts.append(f"- {name}: {tool['description']}")
             if params_desc:
-                desc_parts.append(f"  参数: {params_desc}")
+                desc_parts.append(f"  Parameters: {params_desc}")
         return "\n".join(desc_parts)
     
     def plan_outline(
@@ -1138,29 +1138,29 @@ def plan_outline(
         progress_callback: Optional[Callable] = None
     ) -> ReportOutline:
         """
-        规划报告大纲
-        
-        使用LLM分析模拟需求，规划报告的目录结构
-        
+        Plan report outline
+
+        Use LLM to analyze simulation requirements and plan the report structure
+
         Args:
-            progress_callback: 进度回调函数
-            
+            progress_callback: Progress callback function
+
         Returns:
-            ReportOutline: 报告大纲
+            ReportOutline: Report outline
         """
-        logger.info("开始规划报告大纲...")
-        
+        logger.info("Starting to plan report outline...")
+
         if progress_callback:
-            progress_callback("planning", 0, "正在分析模拟需求...")
-        
-        # 首先获取模拟上下文
+            progress_callback("planning", 0, "Analyzing simulation requirements...")
+
+        # First get simulation context
         context = self.zep_tools.get_simulation_context(
             graph_id=self.graph_id,
             simulation_requirement=self.simulation_requirement
         )
         
         if progress_callback:
-            progress_callback("planning", 30, "正在生成报告大纲...")
+            progress_callback("planning", 30, "Generating report outline...")
         
         system_prompt = PLAN_SYSTEM_PROMPT
         user_prompt = PLAN_USER_PROMPT_TEMPLATE.format(
@@ -1182,9 +1182,9 @@ def plan_outline(
             )
             
             if progress_callback:
-                progress_callback("planning", 80, "正在解析大纲结构...")
-            
-            # 解析大纲
+                progress_callback("planning", 80, "Parsing outline structure...")
+
+            # Parse outline
             sections = []
             for section_data in response.get("sections", []):
                 sections.append(ReportSection(
@@ -1193,27 +1193,27 @@ def plan_outline(
                 ))
             
             outline = ReportOutline(
-                title=response.get("title", "模拟分析报告"),
+                title=response.get("title", "Simulation Analysis Report"),
                 summary=response.get("summary", ""),
                 sections=sections
             )
             
             if progress_callback:
-                progress_callback("planning", 100, "大纲规划完成")
-            
-            logger.info(f"大纲规划完成: {len(sections)} 个章节")
+                progress_callback("planning", 100, "Outline planning completed")
+
+            logger.info(f"Outline planning completed: {len(sections)} sections")
             return outline
             
         except Exception as e:
-            logger.error(f"大纲规划失败: {str(e)}")
-            # 返回默认大纲（3个章节，作为fallback）
+            logger.error(f"Outline planning failed: {str(e)}")
+            # Return default outline (3 sections as fallback)
             return ReportOutline(
-                title="未来预测报告",
-                summary="基于模拟预测的未来趋势与风险分析",
+                title="Future Prediction Report",
+                summary="Future trends and risk analysis based on simulation predictions",
                 sections=[
-                    ReportSection(title="预测场景与核心发现"),
-                    ReportSection(title="人群行为预测分析"),
-                    ReportSection(title="趋势展望与风险提示")
+                    ReportSection(title="Prediction Scenario and Core Findings"),
+                    ReportSection(title="Crowd Behavior Prediction Analysis"),
+                    ReportSection(title="Trend Outlook and Risk Warning")
                 ]
             )
     
@@ -1226,28 +1226,28 @@ def _generate_section_react(
         section_index: int = 0
     ) -> str:
         """
-        使用ReACT模式生成单个章节内容
-        
-        ReACT循环：
-        1. Thought（思考）- 分析需要什么信息
-        2. Action（行动）- 调用工具获取信息
-        3. Observation（观察）- 分析工具返回结果
-        4. 重复直到信息足够或达到最大次数
-        5. Final Answer（最终回答）- 生成章节内容
-        
+        Generate individual section content using ReACT pattern
+
+        ReACT loop:
+        1. Thought - Analyze what information is needed
+        2. Action - Call tool to get information
+        3. Observation - Analyze tool return results
+        4. Repeat until information is sufficient or maximum iterations reached
+        5. Final Answer - Generate section content
+
         Args:
-            section: 要生成的章节
-            outline: 完整大纲
-            previous_sections: 之前章节的内容（用于保持连贯性）
-            progress_callback: 进度回调
-            section_index: 章节索引（用于日志记录）
-            
+            section: Section to generate
+            outline: Complete outline
+            previous_sections: Content of previous sections (for maintaining coherence)
+            progress_callback: Progress callback
+            section_index: Section index (for logging)
+
         Returns:
-            章节内容（Markdown格式）
+            Section content (Markdown format)
         """
-        logger.info(f"ReACT生成章节: {section.title}")
-        
-        # 记录章节开始日志
+        logger.info(f"ReACT generating section: {section.title}")
+
+        # Log section start
         if self.report_logger:
             self.report_logger.log_section_start(section.title, section_index)
         
@@ -1259,16 +1259,16 @@ def _generate_section_react(
             tools_description=self._get_tools_description(),
         )
 
-        # 构建用户prompt - 每个已完成章节各传入最大4000字
+        # Build user prompt - pass maximum 4000 characters for each completed section
         if previous_sections:
             previous_parts = []
             for sec in previous_sections:
-                # 每个章节最多4000字
+                # Maximum 4000 characters per section
                 truncated = sec[:4000] + "..." if len(sec) > 4000 else sec
                 previous_parts.append(truncated)
             previous_content = "\n\n---\n\n".join(previous_parts)
         else:
-            previous_content = "（这是第一个章节）"
+            previous_content = "(This is the first section)"
         
         user_prompt = SECTION_USER_PROMPT_TEMPLATE.format(
             previous_content=previous_content,
@@ -1280,77 +1280,77 @@ def _generate_section_react(
             {"role": "user", "content": user_prompt}
         ]
         
-        # ReACT循环
+        # ReACT loop
         tool_calls_count = 0
-        max_iterations = 5  # 最大迭代轮数
-        min_tool_calls = 3  # 最少工具调用次数
-        conflict_retries = 0  # 工具调用与Final Answer同时出现的连续冲突次数
-        used_tools = set()  # 记录已调用过的工具名
+        max_iterations = 5  # Maximum iterations
+        min_tool_calls = 3  # Minimum tool calls
+        conflict_retries = 0  # Consecutive conflicts where tool calls and Final Answer appear simultaneously
+        used_tools = set()  # Record tool names already called
         all_tools = {"insight_forge", "panorama_search", "quick_search", "interview_agents"}
 
-        # 报告上下文，用于InsightForge的子问题生成
-        report_context = f"章节标题: {section.title}\n模拟需求: {self.simulation_requirement}"
+        # Report context for InsightForge sub-question generation
+        report_context = f"Section Title: {section.title}\nSimulation Requirement: {self.simulation_requirement}"
         
         for iteration in range(max_iterations):
             if progress_callback:
                 progress_callback(
                     "generating", 
                     int((iteration / max_iterations) * 100),
-                    f"深度检索与撰写中 ({tool_calls_count}/{self.MAX_TOOL_CALLS_PER_SECTION})"
+                    f"Deep retrieval and writing in progress ({tool_calls_count}/{self.MAX_TOOL_CALLS_PER_SECTION})"
                 )
             
-            # 调用LLM
+            # Call LLM
             response = self.llm.chat(
                 messages=messages,
                 temperature=0.5,
                 max_tokens=4096
             )
 
-            # 检查 LLM 返回是否为 None（API 异常或内容为空）
+            # Check if LLM return is None (API exception or empty content)
             if response is None:
-                logger.warning(f"章节 {section.title} 第 {iteration + 1} 次迭代: LLM 返回 None")
-                # 如果还有迭代次数，添加消息并重试
+                logger.warning(f"Section {section.title} round {iteration + 1} iteration: LLM returned None")
+                # If there are more iterations, add message and retry
                 if iteration < max_iterations - 1:
-                    messages.append({"role": "assistant", "content": "（响应为空）"})
-                    messages.append({"role": "user", "content": "请继续生成内容。"})
+                    messages.append({"role": "assistant", "content": "(Response empty)"})
+                    messages.append({"role": "user", "content": "Please continue generating content."})
                     continue
-                # 最后一次迭代也返回 None，跳出循环进入强制收尾
+                # Last iteration also returned None, exit loop and enter forced conclusion
                 break
 
-            logger.debug(f"LLM响应: {response[:200]}...")
+            logger.debug(f"LLM response: {response[:200]}...")
 
-            # 解析一次，复用结果
+            # Parse once, reuse result
             tool_calls = self._parse_tool_calls(response)
             has_tool_calls = bool(tool_calls)
             has_final_answer = "Final Answer:" in response
 
-            # ── 冲突处理：LLM 同时输出了工具调用和 Final Answer ──
+            # ── Conflict handling: LLM simultaneously output tool calls and Final Answer ──
             if has_tool_calls and has_final_answer:
                 conflict_retries += 1
                 logger.warning(
-                    f"章节 {section.title} 第 {iteration+1} 轮: "
-                    f"LLM 同时输出工具调用和 Final Answer（第 {conflict_retries} 次冲突）"
+                    f"Section {section.title} round {iteration+1}: "
+                    f"LLM simultaneously output tool calls and Final Answer (round {conflict_retries} conflicts)"
                 )
 
                 if conflict_retries <= 2:
-                    # 前两次：丢弃本次响应，要求 LLM 重新回复
+                    # First two times: discard this response and request LLM to reply again
                     messages.append({"role": "assistant", "content": response})
                     messages.append({
                         "role": "user",
                         "content": (
-                            "【格式错误】你在一次回复中同时包含了工具调用和 Final Answer，这是不允许的。\n"
-                            "每次回复只能做以下两件事之一：\n"
-                            "- 调用一个工具（输出一个 <tool_call> 块，不要写 Final Answer）\n"
-                            "- 输出最终内容（以 'Final Answer:' 开头，不要包含 <tool_call>）\n"
-                            "请重新回复，只做其中一件事。"
+                            "[Format Error] You cannot include both tool calls and Final Answer in one reply.\n"
+                            "Each reply can only do one of the following:\n"
+                            "- Call a tool (output a <tool_call> block, don't write Final Answer)\n"
+                            "- Output final content (starting with 'Final Answer:', don't include <tool_call>)\n"
+                            "Please reply again and only do one of these."
                         ),
                     })
                     continue
                 else:
-                    # 第三次：降级处理，截断到第一个工具调用，强制执行
+                    # Third time: downgrade, truncate to first tool call, force execution
                     logger.warning(
-                        f"章节 {section.title}: 连续 {conflict_retries} 次冲突，"
-                        "降级为截断执行第一个工具调用"
+                        f"Section {section.title}: consecutive {conflict_retries} conflicts, "
+                        "downgraded to truncate and execute first tool call"
                     )
                     first_tool_end = response.find('</tool_call>')
                     if first_tool_end != -1:
@@ -1360,7 +1360,7 @@ def _generate_section_react(
                     has_final_answer = False
                     conflict_retries = 0
 
-            # 记录 LLM 响应日志
+            # Log LLM response
             if self.report_logger:
                 self.report_logger.log_llm_response(
                     section_title=section.title,
@@ -1371,13 +1371,13 @@ def _generate_section_react(
                     has_final_answer=has_final_answer
                 )
 
-            # ── 情况1：LLM 输出了 Final Answer ──
+            # ── Case 1: LLM output Final Answer ──
             if has_final_answer:
-                # 工具调用次数不足，拒绝并要求继续调工具
+                # Insufficient tool calls, reject and request to continue calling tools
                 if tool_calls_count < min_tool_calls:
                     messages.append({"role": "assistant", "content": response})
                     unused_tools = all_tools - used_tools
-                    unused_hint = f"（这些工具还未使用，推荐用一下他们: {', '.join(unused_tools)}）" if unused_tools else ""
+                    unused_hint = f"(These tools have not been used, recommend using them: {', '.join(unused_tools)})" if unused_tools else ""
                     messages.append({
                         "role": "user",
                         "content": REACT_INSUFFICIENT_TOOLS_MSG.format(
@@ -1388,9 +1388,9 @@ def _generate_section_react(
                     })
                     continue
 
-                # 正常结束
+                # Normal completion
                 final_answer = response.split("Final Answer:")[-1].strip()
-                logger.info(f"章节 {section.title} 生成完成（工具调用: {tool_calls_count}次）")
+                logger.info(f"Section {section.title} generation completed (tool calls: {tool_calls_count} times)")
 
                 if self.report_logger:
                     self.report_logger.log_section_content(
@@ -1401,9 +1401,9 @@ def _generate_section_react(
                     )
                 return final_answer
 
-            # ── 情况2：LLM 尝试调用工具 ──
+            # ── Case 2: LLM attempts to call tools ──
             if has_tool_calls:
-                # 工具额度已耗尽 → 明确告知，要求输出 Final Answer
+                # Tool quota exhausted -> inform clearly, request output Final Answer
                 if tool_calls_count >= self.MAX_TOOL_CALLS_PER_SECTION:
                     messages.append({"role": "assistant", "content": response})
                     messages.append({
@@ -1415,10 +1415,10 @@ def _generate_section_react(
                     })
                     continue
 
-                # 只执行第一个工具调用
+                # Only execute the first tool call
                 call = tool_calls[0]
                 if len(tool_calls) > 1:
-                    logger.info(f"LLM 尝试调用 {len(tool_calls)} 个工具，只执行第一个: {call['name']}")
+                    logger.info(f"LLM attempted to call {len(tool_calls)} tools, only execute the first: {call['name']}")
 
                 if self.report_logger:
                     self.report_logger.log_tool_call(
@@ -1447,7 +1447,7 @@ def _generate_section_react(
                 tool_calls_count += 1
                 used_tools.add(call['name'])
 
-                # 构建未使用工具提示
+                # Build unused tools hint
                 unused_tools = all_tools - used_tools
                 unused_hint = ""
                 if unused_tools and tool_calls_count < self.MAX_TOOL_CALLS_PER_SECTION:
@@ -1467,13 +1467,13 @@ def _generate_section_react(
                 })
                 continue
 
-            # ── 情况3：既没有工具调用，也没有 Final Answer ──
+            # ── Case 3: Neither tool call nor Final Answer ──
             messages.append({"role": "assistant", "content": response})
 
             if tool_calls_count < min_tool_calls:
-                # 工具调用次数不足，推荐未用过的工具
+                # Tool call count insufficient, recommend unused tools
                 unused_tools = all_tools - used_tools
-                unused_hint = f"（这些工具还未使用，推荐用一下他们: {', '.join(unused_tools)}）" if unused_tools else ""
+                unused_hint = f"(These tools have not been used, recommend using them: {', '.join(unused_tools)})" if unused_tools else ""
 
                 messages.append({
                     "role": "user",
@@ -1485,9 +1485,9 @@ def _generate_section_react(
                 })
                 continue
 
-            # 工具调用已足够，LLM 输出了内容但没带 "Final Answer:" 前缀
-            # 直接将这段内容作为最终答案，不再空转
-            logger.info(f"章节 {section.title} 未检测到 'Final Answer:' 前缀，直接采纳LLM输出作为最终内容（工具调用: {tool_calls_count}次）")
+            # Tool calls sufficient, LLM output content but without "Final Answer:" prefix
+            # Directly adopt this content as final answer, no more waiting
+            logger.info(f"Section {section.title} did not detect 'Final Answer:' prefix, directly adopting LLM output as final content (tool calls: {tool_calls_count} times)")
             final_answer = response.strip()
 
             if self.report_logger:
@@ -1499,8 +1499,8 @@ def _generate_section_react(
                 )
             return final_answer
         
-        # 达到最大迭代次数，强制生成内容
-        logger.warning(f"章节 {section.title} 达到最大迭代次数，强制生成")
+        # Reached maximum iterations, force generate content
+        logger.warning(f"Section {section.title} reached maximum iterations, force generating")
         messages.append({"role": "user", "content": REACT_FORCE_FINAL_MSG})
         
         response = self.llm.chat(
@@ -1509,16 +1509,16 @@ def _generate_section_react(
             max_tokens=4096
         )
 
-        # 检查强制收尾时 LLM 返回是否为 None
+        # Check if LLM return is None during forced conclusion
         if response is None:
-            logger.error(f"章节 {section.title} 强制收尾时 LLM 返回 None，使用默认错误提示")
-            final_answer = f"（本章节生成失败：LLM 返回空响应，请稍后重试）"
+            logger.error(f"Section {section.title} forced conclusion: LLM returned None, using default error message")
+            final_answer = f"(This section generation failed: LLM returned empty response, please retry later)"
         elif "Final Answer:" in response:
             final_answer = response.split("Final Answer:")[-1].strip()
         else:
             final_answer = response
         
-        # 记录章节内容生成完成日志
+        # Log section content generation completion
         if self.report_logger:
             self.report_logger.log_section_content(
                 section_title=section.title,
@@ -1535,29 +1535,29 @@ def generate_report(
         report_id: Optional[str] = None
     ) -> Report:
         """
-        生成完整报告（分章节实时输出）
-        
-        每个章节生成完成后立即保存到文件夹，不需要等待整个报告完成。
-        文件结构：
+        Generate complete report (realtime output per section)
+
+        Each section is saved to the folder immediately after generation, no need to wait for the entire report.
+        File structure:
         reports/{report_id}/
-            meta.json       - 报告元信息
-            outline.json    - 报告大纲
-            progress.json   - 生成进度
-            section_01.md   - 第1章节
-            section_02.md   - 第2章节
+            meta.json       - Report metadata
+            outline.json    - Report outline
+            progress.json   - Generation progress
+            section_01.md   - Section 1
+            section_02.md   - Section 2
             ...
-            full_report.md  - 完整报告
-        
+            full_report.md  - Complete report
+
         Args:
-            progress_callback: 进度回调函数 (stage, progress, message)
-            report_id: 报告ID（可选，如果不传则自动生成）
-            
+            progress_callback: Progress callback function (stage, progress, message)
+            report_id: Report ID (optional, auto-generate if not provided)
+
         Returns:
-            Report: 完整报告
+            Report: Complete report
         """
         import uuid
         
-        # 如果没有传入 report_id，则自动生成
+        # If report_id not provided, auto-generate
         if not report_id:
             report_id = f"report_{uuid.uuid4().hex[:12]}"
         start_time = datetime.now()
@@ -1571,14 +1571,14 @@ def generate_report(
             created_at=datetime.now().isoformat()
         )
         
-        # 已完成的章节标题列表（用于进度追踪）
+        # Completed section titles list (for progress tracking)
         completed_section_titles = []
         
         try:
-            # 初始化：创建报告文件夹并保存初始状态
+            # Initialize: Create report folder and save initial state
             ReportManager._ensure_report_folder(report_id)
             
-            # 初始化日志记录器（结构化日志 agent_log.jsonl）
+            # Initialize logger (structured logs agent_log.jsonl)
             self.report_logger = ReportLogger(report_id)
             self.report_logger.log_start(
                 simulation_id=self.simulation_id,
@@ -1586,27 +1586,27 @@ def generate_report(
                 simulation_requirement=self.simulation_requirement
             )
             
-            # 初始化控制台日志记录器（console_log.txt）
+            # Initialize console logger (console_log.txt)
             self.console_logger = ReportConsoleLogger(report_id)
             
             ReportManager.update_progress(
-                report_id, "pending", 0, "初始化报告...",
+                report_id, "pending", 0, "Initializing report...",
                 completed_sections=[]
             )
             ReportManager.save_report(report)
             
-            # 阶段1: 规划大纲
+            # Phase 1: Plan outline
             report.status = ReportStatus.PLANNING
             ReportManager.update_progress(
-                report_id, "planning", 5, "开始规划报告大纲...",
+                report_id, "planning", 5, "Starting to plan report outline...",
                 completed_sections=[]
             )
             
-            # 记录规划开始日志
+            # Log planning start
             self.report_logger.log_planning_start()
             
             if progress_callback:
-                progress_callback("planning", 0, "开始规划报告大纲...")
+                progress_callback("planning", 0, "Starting to plan report outline...")
             
             outline = self.plan_outline(
                 progress_callback=lambda stage, prog, msg: 
@@ -1614,33 +1614,33 @@ def generate_report(
             )
             report.outline = outline
             
-            # 记录规划完成日志
+            # Log planning completion
             self.report_logger.log_planning_complete(outline.to_dict())
             
-            # 保存大纲到文件
+            # Save outline to file
             ReportManager.save_outline(report_id, outline)
             ReportManager.update_progress(
-                report_id, "planning", 15, f"大纲规划完成，共{len(outline.sections)}个章节",
+                report_id, "planning", 15, f"Outline planning completed, {len(outline.sections)} sections total",
                 completed_sections=[]
             )
             ReportManager.save_report(report)
             
-            logger.info(f"大纲已保存到文件: {report_id}/outline.json")
+            logger.info(f"Outline saved to file: {report_id}/outline.json")
             
-            # 阶段2: 逐章节生成（分章节保存）
+            # Phase 2: Generate sections sequentially (save per section)
             report.status = ReportStatus.GENERATING
             
             total_sections = len(outline.sections)
-            generated_sections = []  # 保存内容用于上下文
+            generated_sections = []  # Save content for context
             
             for i, section in enumerate(outline.sections):
                 section_num = i + 1
                 base_progress = 20 + int((i / total_sections) * 70)
                 
-                # 更新进度
+                # Update progress
                 ReportManager.update_progress(
                     report_id, "generating", base_progress,
-                    f"正在生成章节: {section.title} ({section_num}/{total_sections})",
+                    f"Generating section: {section.title} ({section_num}/{total_sections})",
                     current_section=section.title,
                     completed_sections=completed_section_titles
                 )
@@ -1649,10 +1649,10 @@ def generate_report(
                     progress_callback(
                         "generating", 
                         base_progress, 
-                        f"正在生成章节: {section.title} ({section_num}/{total_sections})"
+                        f"Generating section: {section.title} ({section_num}/{total_sections})"
                     )
                 
-                # 生成主章节内容
+                # Generate main section content
                 section_content = self._generate_section_react(
                     section=section,
                     outline=outline,
@@ -1669,11 +1669,11 @@ def generate_report(
                 section.content = section_content
                 generated_sections.append(f"## {section.title}\n\n{section_content}")
 
-                # 保存章节
+                # Save section
                 ReportManager.save_section(report_id, section_num, section)
                 completed_section_titles.append(section.title)
 
-                # 记录章节完成日志
+                # Log section completion
                 full_section_content = f"## {section.title}\n\n{section_content}"
 
                 if self.report_logger:
@@ -1683,54 +1683,54 @@ def generate_report(
                         full_content=full_section_content.strip()
                     )
 
-                logger.info(f"章节已保存: {report_id}/section_{section_num:02d}.md")
-                
-                # 更新进度
+                logger.info(f"Section saved: {report_id}/section_{section_num:02d}.md")
+
+                # Update progress
                 ReportManager.update_progress(
                     report_id, "generating", 
                     base_progress + int(70 / total_sections),
-                    f"章节 {section.title} 已完成",
+                    f"Section {section.title} completed",
                     current_section=None,
                     completed_sections=completed_section_titles
                 )
             
-            # 阶段3: 组装完整报告
+            # Phase 3: Assemble complete report
             if progress_callback:
-                progress_callback("generating", 95, "正在组装完整报告...")
-            
+                progress_callback("generating", 95, "Assembling complete report...")
+
             ReportManager.update_progress(
-                report_id, "generating", 95, "正在组装完整报告...",
+                report_id, "generating", 95, "Assembling complete report...",
                 completed_sections=completed_section_titles
             )
             
-            # 使用ReportManager组装完整报告
+            # Use ReportManager to assemble complete report
             report.markdown_content = ReportManager.assemble_full_report(report_id, outline)
             report.status = ReportStatus.COMPLETED
             report.completed_at = datetime.now().isoformat()
             
-            # 计算总耗时
+            # Calculate total elapsed time
             total_time_seconds = (datetime.now() - start_time).total_seconds()
             
-            # 记录报告完成日志
+            # Log report completion
             if self.report_logger:
                 self.report_logger.log_report_complete(
                     total_sections=total_sections,
                     total_time_seconds=total_time_seconds
                 )
             
-            # 保存最终报告
+            # Save final report
             ReportManager.save_report(report)
             ReportManager.update_progress(
-                report_id, "completed", 100, "报告生成完成",
+                report_id, "completed", 100, "Report generation completed",
                 completed_sections=completed_section_titles
             )
             
             if progress_callback:
-                progress_callback("completed", 100, "报告生成完成")
-            
-            logger.info(f"报告生成完成: {report_id}")
-            
-            # 关闭控制台日志记录器
+                progress_callback("completed", 100, "Report generation completed")
+
+            logger.info(f"Report generation completed: {report_id}")
+
+            # Close console logger
             if self.console_logger:
                 self.console_logger.close()
                 self.console_logger = None
@@ -1738,90 +1738,90 @@ def generate_report(
             return report
             
         except Exception as e:
-            logger.error(f"报告生成失败: {str(e)}")
+            logger.error(f"Report generation failed: {str(e)}")
             report.status = ReportStatus.FAILED
             report.error = str(e)
             
-            # 记录错误日志
+            # Log error
             if self.report_logger:
                 self.report_logger.log_error(str(e), "failed")
             
-            # 保存失败状态
+            # Save failed status
             try:
                 ReportManager.save_report(report)
                 ReportManager.update_progress(
-                    report_id, "failed", -1, f"报告生成失败: {str(e)}",
+                    report_id, "failed", -1, f"Report generation failed: {str(e)}",
                     completed_sections=completed_section_titles
                 )
             except Exception:
-                pass  # 忽略保存失败的错误
+                pass  # Ignore save failure errors
             
-            # 关闭控制台日志记录器
+            # Close console logger
             if self.console_logger:
                 self.console_logger.close()
                 self.console_logger = None
-            
+
             return report
-    
+
     def chat(
         self, 
         message: str,
         chat_history: List[Dict[str, str]] = None
     ) -> Dict[str, Any]:
         """
-        与Report Agent对话
-        
-        在对话中Agent可以自主调用检索工具来回答问题
-        
+        Chat with Report Agent
+
+        During conversation, Agent can autonomously call retrieval tools to answer questions
+
         Args:
-            message: 用户消息
-            chat_history: 对话历史
-            
+            message: User message
+            chat_history: Chat history
+
         Returns:
             {
-                "response": "Agent回复",
-                "tool_calls": [调用的工具列表],
-                "sources": [信息来源]
+                "response": "Agent response",
+                "tool_calls": [list of tool calls],
+                "sources": [information sources]
             }
         """
-        logger.info(f"Report Agent对话: {message[:50]}...")
+        logger.info(f"Report Agent chat: {message[:50]}...")
         
         chat_history = chat_history or []
         
-        # 获取已生成的报告内容
+        # Get already generated report content
         report_content = ""
         try:
             report = ReportManager.get_report_by_simulation(self.simulation_id)
             if report and report.markdown_content:
-                # 限制报告长度，避免上下文过长
+                # Limit report length to avoid overly long context
                 report_content = report.markdown_content[:15000]
                 if len(report.markdown_content) > 15000:
-                    report_content += "\n\n... [报告内容已截断] ..."
+                    report_content += "\n\n... [Report content truncated] ..."
         except Exception as e:
-            logger.warning(f"获取报告内容失败: {e}")
+            logger.warning(f"Failed to get report content: {e}")
         
         system_prompt = CHAT_SYSTEM_PROMPT_TEMPLATE.format(
             simulation_requirement=self.simulation_requirement,
-            report_content=report_content if report_content else "（暂无报告）",
+            report_content=report_content if report_content else "(No report available)",
             tools_description=self._get_tools_description(),
         )
 
-        # 构建消息
+        # Build messages
         messages = [{"role": "system", "content": system_prompt}]
-        
-        # 添加历史对话
-        for h in chat_history[-10:]:  # 限制历史长度
+
+        # Add chat history
+        for h in chat_history[-10:]:  # Limit history length
             messages.append(h)
         
-        # 添加用户消息
+        # Add user message
         messages.append({
             "role": "user", 
             "content": message
         })
         
-        # ReACT循环（简化版）
+        # ReACT loop (simplified version)
         tool_calls_made = []
-        max_iterations = 2  # 减少迭代轮数
+        max_iterations = 2  # Reduced iterations
         
         for iteration in range(max_iterations):
             response = self.llm.chat(
@@ -1829,11 +1829,11 @@ def chat(
                 temperature=0.5
             )
             
-            # 解析工具调用
+            # Parse tool calls
             tool_calls = self._parse_tool_calls(response)
             
             if not tool_calls:
-                # 没有工具调用，直接返回响应
+                # No tool calls, directly return response
                 clean_response = re.sub(r'<tool_call>.*?</tool_call>', '', response, flags=re.DOTALL)
                 clean_response = re.sub(r'\[TOOL_CALL\].*?\)', '', clean_response)
                 
@@ -1843,33 +1843,33 @@ def chat(
                     "sources": [tc.get("parameters", {}).get("query", "") for tc in tool_calls_made]
                 }
             
-            # 执行工具调用（限制数量）
+            # Execute tool calls (limit count)
             tool_results = []
-            for call in tool_calls[:1]:  # 每轮最多执行1次工具调用
+            for call in tool_calls[:1]:  # Execute at most 1 tool call per round
                 if len(tool_calls_made) >= self.MAX_TOOL_CALLS_PER_CHAT:
                     break
                 result = self._execute_tool(call["name"], call.get("parameters", {}))
                 tool_results.append({
                     "tool": call["name"],
-                    "result": result[:1500]  # 限制结果长度
+                    "result": result[:1500]  # Limit result length
                 })
                 tool_calls_made.append(call)
             
-            # 将结果添加到消息
+            # Add results to messages
             messages.append({"role": "assistant", "content": response})
-            observation = "\n".join([f"[{r['tool']}结果]\n{r['result']}" for r in tool_results])
+            observation = "\n".join([f"[{r['tool']} result]\n{r['result']}" for r in tool_results])
             messages.append({
                 "role": "user",
                 "content": observation + CHAT_OBSERVATION_SUFFIX
             })
         
-        # 达到最大迭代，获取最终响应
+        # Reached maximum iterations, get final response
         final_response = self.llm.chat(
             messages=messages,
             temperature=0.5
         )
         
-        # 清理响应
+        # Clean response
         clean_response = re.sub(r'<tool_call>.*?</tool_call>', '', final_response, flags=re.DOTALL)
         clean_response = re.sub(r'\[TOOL_CALL\].*?\)', '', clean_response)
         
@@ -1882,95 +1882,95 @@ def chat(
 
 class ReportManager:
     """
-    报告管理器
-    
-    负责报告的持久化存储和检索
-    
-    文件结构（分章节输出）：
+    Report Manager
+
+    Responsible for report persistence storage and retrieval
+
+    File structure (per-section output):
     reports/
       {report_id}/
-        meta.json          - 报告元信息和状态
-        outline.json       - 报告大纲
-        progress.json      - 生成进度
-        section_01.md      - 第1章节
-        section_02.md      - 第2章节
+        meta.json          - Report metadata and status
+        outline.json       - Report outline
+        progress.json      - Generation progress
+        section_01.md      - Section 1
+        section_02.md      - Section 2
         ...
-        full_report.md     - 完整报告
+        full_report.md     - Complete report
     """
     
-    # 报告存储目录
+    # Report storage directory
     REPORTS_DIR = os.path.join(Config.UPLOAD_FOLDER, 'reports')
     
     @classmethod
     def _ensure_reports_dir(cls):
-        """确保报告根目录存在"""
+        """Ensure report root directory exists"""
         os.makedirs(cls.REPORTS_DIR, exist_ok=True)
     
     @classmethod
     def _get_report_folder(cls, report_id: str) -> str:
-        """获取报告文件夹路径"""
+        """Get report folder path"""
         return os.path.join(cls.REPORTS_DIR, report_id)
     
     @classmethod
     def _ensure_report_folder(cls, report_id: str) -> str:
-        """确保报告文件夹存在并返回路径"""
+        """Ensure report folder exists and return path"""
         folder = cls._get_report_folder(report_id)
         os.makedirs(folder, exist_ok=True)
         return folder
     
     @classmethod
     def _get_report_path(cls, report_id: str) -> str:
-        """获取报告元信息文件路径"""
+        """Get report metadata file path"""
         return os.path.join(cls._get_report_folder(report_id), "meta.json")
     
     @classmethod
     def _get_report_markdown_path(cls, report_id: str) -> str:
-        """获取完整报告Markdown文件路径"""
+        """Get complete report Markdown file path"""
         return os.path.join(cls._get_report_folder(report_id), "full_report.md")
     
     @classmethod
     def _get_outline_path(cls, report_id: str) -> str:
-        """获取大纲文件路径"""
+        """Get outline file path"""
         return os.path.join(cls._get_report_folder(report_id), "outline.json")
     
     @classmethod
     def _get_progress_path(cls, report_id: str) -> str:
-        """获取进度文件路径"""
+        """Get progress file path"""
         return os.path.join(cls._get_report_folder(report_id), "progress.json")
     
     @classmethod
     def _get_section_path(cls, report_id: str, section_index: int) -> str:
-        """获取章节Markdown文件路径"""
+        """Get section Markdown file path"""
         return os.path.join(cls._get_report_folder(report_id), f"section_{section_index:02d}.md")
     
     @classmethod
     def _get_agent_log_path(cls, report_id: str) -> str:
-        """获取 Agent 日志文件路径"""
+        """Get Agent log file path"""
         return os.path.join(cls._get_report_folder(report_id), "agent_log.jsonl")
     
     @classmethod
     def _get_console_log_path(cls, report_id: str) -> str:
-        """获取控制台日志文件路径"""
+        """Get console log file path"""
         return os.path.join(cls._get_report_folder(report_id), "console_log.txt")
     
     @classmethod
     def get_console_log(cls, report_id: str, from_line: int = 0) -> Dict[str, Any]:
         """
-        获取控制台日志内容
-        
-        这是报告生成过程中的控制台输出日志（INFO、WARNING等），
-        与 agent_log.jsonl 的结构化日志不同。
-        
+        Get console log content
+
+        These are console output logs during report generation (INFO, WARNING, etc.),
+        different from the structured logs in agent_log.jsonl.
+
         Args:
-            report_id: 报告ID
-            from_line: 从第几行开始读取（用于增量获取，0 表示从头开始）
-            
+            report_id: Report ID
+            from_line: Start reading from this line number (for incremental retrieval, 0 means from beginning)
+
         Returns:
             {
-                "logs": [日志行列表],
-                "total_lines": 总行数,
-                "from_line": 起始行号,
-                "has_more": 是否还有更多日志
+                "logs": [log line list],
+                "total_lines": total line count,
+                "from_line": start line number,
+                "has_more": whether there are more logs
             }
         """
         log_path = cls._get_console_log_path(report_id)
@@ -1990,26 +1990,26 @@ def get_console_log(cls, report_id: str, from_line: int = 0) -> Dict[str, Any]:
             for i, line in enumerate(f):
                 total_lines = i + 1
                 if i >= from_line:
-                    # 保留原始日志行，去掉末尾换行符
+                    # Keep original log line, remove trailing newline
                     logs.append(line.rstrip('\n\r'))
         
         return {
             "logs": logs,
             "total_lines": total_lines,
             "from_line": from_line,
-            "has_more": False  # 已读取到末尾
+            "has_more": False  # Already read to end
         }
-    
+
     @classmethod
     def get_console_log_stream(cls, report_id: str) -> List[str]:
         """
-        获取完整的控制台日志（一次性获取全部）
-        
+        Get complete console log (one-time retrieval of all)
+
         Args:
-            report_id: 报告ID
-            
+            report_id: Report ID
+
         Returns:
-            日志行列表
+            Log line list
         """
         result = cls.get_console_log(report_id, from_line=0)
         return result["logs"]
@@ -2017,18 +2017,18 @@ def get_console_log_stream(cls, report_id: str) -> List[str]:
     @classmethod
     def get_agent_log(cls, report_id: str, from_line: int = 0) -> Dict[str, Any]:
         """
-        获取 Agent 日志内容
-        
+        Get Agent log content
+
         Args:
-            report_id: 报告ID
-            from_line: 从第几行开始读取（用于增量获取，0 表示从头开始）
-            
+            report_id: Report ID
+            from_line: Start reading from this line number (for incremental retrieval, 0 means from beginning)
+
         Returns:
             {
-                "logs": [日志条目列表],
-                "total_lines": 总行数,
-                "from_line": 起始行号,
-                "has_more": 是否还有更多日志
+                "logs": [log entry list],
+                "total_lines": total line count,
+                "from_line": start line number,
+                "has_more": whether there are more logs
             }
         """
         log_path = cls._get_agent_log_path(report_id)
@@ -2052,26 +2052,26 @@ def get_agent_log(cls, report_id: str, from_line: int = 0) -> Dict[str, Any]:
                         log_entry = json.loads(line.strip())
                         logs.append(log_entry)
                     except json.JSONDecodeError:
-                        # 跳过解析失败的行
+                        # Skip lines that fail to parse
                         continue
         
         return {
             "logs": logs,
             "total_lines": total_lines,
             "from_line": from_line,
-            "has_more": False  # 已读取到末尾
+            "has_more": False  # Already read to end
         }
-    
+
     @classmethod
     def get_agent_log_stream(cls, report_id: str) -> List[Dict[str, Any]]:
         """
-        获取完整的 Agent 日志（用于一次性获取全部）
-        
+        Get complete Agent log (for one-time retrieval of all)
+
         Args:
-            report_id: 报告ID
-            
+            report_id: Report ID
+
         Returns:
-            日志条目列表
+            Log entry list
         """
         result = cls.get_agent_log(report_id, from_line=0)
         return result["logs"]
@@ -2079,16 +2079,16 @@ def get_agent_log_stream(cls, report_id: str) -> List[Dict[str, Any]]:
     @classmethod
     def save_outline(cls, report_id: str, outline: ReportOutline) -> None:
         """
-        保存报告大纲
-        
-        在规划阶段完成后立即调用
+        Save report outline
+
+        Called immediately after planning phase completion
         """
         cls._ensure_report_folder(report_id)
         
         with open(cls._get_outline_path(report_id), 'w', encoding='utf-8') as f:
             json.dump(outline.to_dict(), f, ensure_ascii=False, indent=2)
         
-        logger.info(f"大纲已保存: {report_id}")
+        logger.info(f"Outline saved: {report_id}")
     
     @classmethod
     def save_section(
@@ -2098,49 +2098,49 @@ def save_section(
         section: ReportSection
     ) -> str:
         """
-        保存单个章节
+        Save individual section
 
-        在每个章节生成完成后立即调用，实现分章节输出
+        Called immediately after each section generation completes, enabling per-section output
 
         Args:
-            report_id: 报告ID
-            section_index: 章节索引（从1开始）
-            section: 章节对象
+            report_id: Report ID
+            section_index: Section index (starting from 1)
+            section: Section object
 
         Returns:
-            保存的文件路径
+            Saved file path
         """
         cls._ensure_report_folder(report_id)
 
-        # 构建章节Markdown内容 - 清理可能存在的重复标题
+        # Build section Markdown content - clean possible duplicate titles
         cleaned_content = cls._clean_section_content(section.content, section.title)
         md_content = f"## {section.title}\n\n"
         if cleaned_content:
             md_content += f"{cleaned_content}\n\n"
 
-        # 保存文件
+        # Save file
         file_suffix = f"section_{section_index:02d}.md"
         file_path = os.path.join(cls._get_report_folder(report_id), file_suffix)
         with open(file_path, 'w', encoding='utf-8') as f:
             f.write(md_content)
 
-        logger.info(f"章节已保存: {report_id}/{file_suffix}")
+        logger.info(f"Section saved: {report_id}/{file_suffix}")
         return file_path
     
     @classmethod
     def _clean_section_content(cls, content: str, section_title: str) -> str:
         """
-        清理章节内容
-        
-        1. 移除内容开头与章节标题重复的Markdown标题行
-        2. 将所有 ### 及以下级别的标题转换为粗体文本
-        
+        Clean section content
+
+        1. Remove Markdown heading lines at the beginning that duplicate the section title
+        2. Convert all ### and below level headings to bold text
+
         Args:
-            content: 原始内容
-            section_title: 章节标题
-            
+            content: Original content
+            section_title: Section title
+
         Returns:
-            清理后的内容
+            Cleaned content
         """
         import re
         
@@ -2155,26 +2155,26 @@ def _clean_section_content(cls, content: str, section_title: str) -> str:
         for i, line in enumerate(lines):
             stripped = line.strip()
             
-            # 检查是否是Markdown标题行
+            # Check if it's a Markdown heading line
             heading_match = re.match(r'^(#{1,6})\s+(.+)$', stripped)
             
             if heading_match:
                 level = len(heading_match.group(1))
                 title_text = heading_match.group(2).strip()
                 
-                # 检查是否是与章节标题重复的标题（跳过前5行内的重复）
+                # Check if it's a duplicate of the section title (skip duplicates within first 5 lines)
                 if i < 5:
                     if title_text == section_title or title_text.replace(' ', '') == section_title.replace(' ', ''):
                         skip_next_empty = True
                         continue
                 
-                # 将所有级别的标题（#, ##, ###, ####等）转换为粗体
-                # 因为章节标题由系统添加，内容中不应有任何标题
+                # Convert all level headings (#, ##, ###, #### etc.) to bold
+                # Because section titles are added by the system, content should not have any headings
                 cleaned_lines.append(f"**{title_text}**")
-                cleaned_lines.append("")  # 添加空行
+                cleaned_lines.append("")  # Add empty line
                 continue
             
-            # 如果上一行是被跳过的标题，且当前行为空，也跳过
+            # If previous line was a skipped heading and current line is empty, also skip
             if skip_next_empty and stripped == '':
                 skip_next_empty = False
                 continue
@@ -2182,14 +2182,14 @@ def _clean_section_content(cls, content: str, section_title: str) -> str:
             skip_next_empty = False
             cleaned_lines.append(line)
         
-        # 移除开头的空行
+        # Remove leading empty lines
         while cleaned_lines and cleaned_lines[0].strip() == '':
             cleaned_lines.pop(0)
         
-        # 移除开头的分隔线
+        # Remove leading separator lines
         while cleaned_lines and cleaned_lines[0].strip() in ['---', '***', '___']:
             cleaned_lines.pop(0)
-            # 同时移除分隔线后的空行
+            # Also remove empty lines after separator
             while cleaned_lines and cleaned_lines[0].strip() == '':
                 cleaned_lines.pop(0)
         
@@ -2206,9 +2206,9 @@ def update_progress(
         completed_sections: List[str] = None
     ) -> None:
         """
-        更新报告生成进度
-        
-        前端可以通过读取progress.json获取实时进度
+        Update report generation progress
+
+        Frontend can read progress.json to get realtime progress
         """
         cls._ensure_report_folder(report_id)
         
@@ -2226,7 +2226,7 @@ def update_progress(
     
     @classmethod
     def get_progress(cls, report_id: str) -> Optional[Dict[str, Any]]:
-        """获取报告生成进度"""
+        """Get report generation progress"""
         path = cls._get_progress_path(report_id)
         
         if not os.path.exists(path):
@@ -2238,9 +2238,9 @@ def get_progress(cls, report_id: str) -> Optional[Dict[str, Any]]:
     @classmethod
     def get_generated_sections(cls, report_id: str) -> List[Dict[str, Any]]:
         """
-        获取已生成的章节列表
-        
-        返回所有已保存的章节文件信息
+        Get list of generated sections
+
+        Return all saved section file information
         """
         folder = cls._get_report_folder(report_id)
         
@@ -2254,7 +2254,7 @@ def get_generated_sections(cls, report_id: str) -> List[Dict[str, Any]]:
                 with open(file_path, 'r', encoding='utf-8') as f:
                     content = f.read()
 
-                # 从文件名解析章节索引
+                # Parse section index from filename
                 parts = filename.replace('.md', '').split('_')
                 section_index = int(parts[1])
 
@@ -2269,48 +2269,48 @@ def get_generated_sections(cls, report_id: str) -> List[Dict[str, Any]]:
     @classmethod
     def assemble_full_report(cls, report_id: str, outline: ReportOutline) -> str:
         """
-        组装完整报告
-        
-        从已保存的章节文件组装完整报告，并进行标题清理
+        Assemble complete report
+
+        Assemble complete report from saved section files, with heading cleanup
         """
         folder = cls._get_report_folder(report_id)
         
-        # 构建报告头部
+        # Build report header
         md_content = f"# {outline.title}\n\n"
         md_content += f"> {outline.summary}\n\n"
         md_content += f"---\n\n"
         
-        # 按顺序读取所有章节文件
+        # Read all section files in order
         sections = cls.get_generated_sections(report_id)
         for section_info in sections:
             md_content += section_info["content"]
         
-        # 后处理：清理整个报告的标题问题
+        # Post-processing: clean heading issues in the entire report
         md_content = cls._post_process_report(md_content, outline)
         
-        # 保存完整报告
+        # Save complete report
         full_path = cls._get_report_markdown_path(report_id)
         with open(full_path, 'w', encoding='utf-8') as f:
             f.write(md_content)
         
-        logger.info(f"完整报告已组装: {report_id}")
+        logger.info(f"Complete report assembled: {report_id}")
         return md_content
     
     @classmethod
     def _post_process_report(cls, content: str, outline: ReportOutline) -> str:
         """
-        后处理报告内容
-        
-        1. 移除重复的标题
-        2. 保留报告主标题(#)和章节标题(##)，移除其他级别的标题(###, ####等)
-        3. 清理多余的空行和分隔线
-        
+        Post-process report content
+
+        1. Remove duplicate headings
+        2. Keep report main title (#) and section titles (##), remove other level headings (###, #### etc.)
+        3. Clean redundant empty lines and separator lines
+
         Args:
-            content: 原始报告内容
-            outline: 报告大纲
-            
+            content: Original report content
+            outline: Report outline
+
         Returns:
-            处理后的内容
+            Processed content
         """
         import re
         
@@ -2318,7 +2318,7 @@ def _post_process_report(cls, content: str, outline: ReportOutline) -> str:
         processed_lines = []
         prev_was_heading = False
         
-        # 收集大纲中的所有章节标题
+        # Collect all section titles from outline
         section_titles = set()
         for section in outline.sections:
             section_titles.add(section.title)
@@ -2328,14 +2328,14 @@ def _post_process_report(cls, content: str, outline: ReportOutline) -> str:
             line = lines[i]
             stripped = line.strip()
             
-            # 检查是否是标题行
+            # Check if it's a heading line
             heading_match = re.match(r'^(#{1,6})\s+(.+)$', stripped)
             
             if heading_match:
                 level = len(heading_match.group(1))
                 title = heading_match.group(2).strip()
                 
-                # 检查是否是重复标题（在连续5行内出现相同内容的标题）
+                # Check if it's a duplicate heading (same content heading within consecutive 5 lines)
                 is_duplicate = False
                 for j in range(max(0, len(processed_lines) - 5), len(processed_lines)):
                     prev_line = processed_lines[j].strip()
@@ -2347,43 +2347,43 @@ def _post_process_report(cls, content: str, outline: ReportOutline) -> str:
                             break
                 
                 if is_duplicate:
-                    # 跳过重复标题及其后的空行
+                    # Skip duplicate heading and following empty lines
                     i += 1
                     while i < len(lines) and lines[i].strip() == '':
                         i += 1
                     continue
                 
-                # 标题层级处理：
-                # - # (level=1) 只保留报告主标题
-                # - ## (level=2) 保留章节标题
-                # - ### 及以下 (level>=3) 转换为粗体文本
+                # Heading level handling:
+                # - # (level=1) only keep report main title
+                # - ## (level=2) keep section titles
+                # - ### and below (level>=3) convert to bold text
                 
                 if level == 1:
                     if title == outline.title:
-                        # 保留报告主标题
+                        # Keep report main title
                         processed_lines.append(line)
                         prev_was_heading = True
                     elif title in section_titles:
-                        # 章节标题错误使用了#，修正为##
+                        # Section title incorrectly used #, correct to ##
                         processed_lines.append(f"## {title}")
                         prev_was_heading = True
                     else:
-                        # 其他一级标题转为粗体
+                        # Other first-level headings convert to bold
                         processed_lines.append(f"**{title}**")
                         processed_lines.append("")
                         prev_was_heading = False
                 elif level == 2:
                     if title in section_titles or title == outline.title:
-                        # 保留章节标题
+                        # Keep section title
                         processed_lines.append(line)
                         prev_was_heading = True
                     else:
-                        # 非章节的二级标题转为粗体
+                        # Non-section second-level headings convert to bold
                         processed_lines.append(f"**{title}**")
                         processed_lines.append("")
                         prev_was_heading = False
                 else:
-                    # ### 及以下级别的标题转换为粗体文本
+                    # ### and below level headings convert to bold text
                     processed_lines.append(f"**{title}**")
                     processed_lines.append("")
                     prev_was_heading = False
@@ -2392,12 +2392,12 @@ def _post_process_report(cls, content: str, outline: ReportOutline) -> str:
                 continue
             
             elif stripped == '---' and prev_was_heading:
-                # 跳过标题后紧跟的分隔线
+                # Skip separator lines immediately following headings
                 i += 1
                 continue
             
             elif stripped == '' and prev_was_heading:
-                # 标题后只保留一个空行
+                # Keep only one empty line after heading
                 if processed_lines and processed_lines[-1].strip() != '':
                     processed_lines.append(line)
                 prev_was_heading = False
@@ -2408,7 +2408,7 @@ def _post_process_report(cls, content: str, outline: ReportOutline) -> str:
             
             i += 1
         
-        # 清理连续的多个空行（保留最多2个）
+        # Clean consecutive multiple empty lines (keep at most 2)
         result_lines = []
         empty_count = 0
         for line in processed_lines:
@@ -2424,31 +2424,31 @@ def _post_process_report(cls, content: str, outline: ReportOutline) -> str:
     
     @classmethod
     def save_report(cls, report: Report) -> None:
-        """保存报告元信息和完整报告"""
+        """Save report metadata and complete report"""
         cls._ensure_report_folder(report.report_id)
         
-        # 保存元信息JSON
+        # Save metadata JSON
         with open(cls._get_report_path(report.report_id), 'w', encoding='utf-8') as f:
             json.dump(report.to_dict(), f, ensure_ascii=False, indent=2)
         
-        # 保存大纲
+        # Save outline
         if report.outline:
             cls.save_outline(report.report_id, report.outline)
         
-        # 保存完整Markdown报告
+        # Save complete Markdown report
         if report.markdown_content:
             with open(cls._get_report_markdown_path(report.report_id), 'w', encoding='utf-8') as f:
                 f.write(report.markdown_content)
         
-        logger.info(f"报告已保存: {report.report_id}")
+        logger.info(f"Report saved: {report.report_id}")
     
     @classmethod
     def get_report(cls, report_id: str) -> Optional[Report]:
-        """获取报告"""
+        """Get report"""
         path = cls._get_report_path(report_id)
         
         if not os.path.exists(path):
-            # 兼容旧格式：检查直接存储在reports目录下的文件
+            # Backward compatibility: check files stored directly in reports directory
             old_path = os.path.join(cls.REPORTS_DIR, f"{report_id}.json")
             if os.path.exists(old_path):
                 path = old_path
@@ -2458,7 +2458,7 @@ def get_report(cls, report_id: str) -> Optional[Report]:
         with open(path, 'r', encoding='utf-8') as f:
             data = json.load(f)
         
-        # 重建Report对象
+        # Rebuild Report object
         outline = None
         if data.get('outline'):
             outline_data = data['outline']
@@ -2474,7 +2474,7 @@ def get_report(cls, report_id: str) -> Optional[Report]:
                 sections=sections
             )
         
-        # 如果markdown_content为空，尝试从full_report.md读取
+        # If markdown_content is empty, try reading from full_report.md
         markdown_content = data.get('markdown_content', '')
         if not markdown_content:
             full_report_path = cls._get_report_markdown_path(report_id)
@@ -2497,17 +2497,17 @@ def get_report(cls, report_id: str) -> Optional[Report]:
     
     @classmethod
     def get_report_by_simulation(cls, simulation_id: str) -> Optional[Report]:
-        """根据模拟ID获取报告"""
+        """Get report by simulation ID"""
         cls._ensure_reports_dir()
         
         for item in os.listdir(cls.REPORTS_DIR):
             item_path = os.path.join(cls.REPORTS_DIR, item)
-            # 新格式：文件夹
+            # New format: folder
             if os.path.isdir(item_path):
                 report = cls.get_report(item)
                 if report and report.simulation_id == simulation_id:
                     return report
-            # 兼容旧格式：JSON文件
+            # Backward compatible: JSON file
             elif item.endswith('.json'):
                 report_id = item[:-5]
                 report = cls.get_report(report_id)
@@ -2518,19 +2518,19 @@ def get_report_by_simulation(cls, simulation_id: str) -> Optional[Report]:
     
     @classmethod
     def list_reports(cls, simulation_id: Optional[str] = None, limit: int = 50) -> List[Report]:
-        """列出报告"""
+        """List reports"""
         cls._ensure_reports_dir()
         
         reports = []
         for item in os.listdir(cls.REPORTS_DIR):
             item_path = os.path.join(cls.REPORTS_DIR, item)
-            # 新格式：文件夹
+            # New format: folder
             if os.path.isdir(item_path):
                 report = cls.get_report(item)
                 if report:
                     if simulation_id is None or report.simulation_id == simulation_id:
                         reports.append(report)
-            # 兼容旧格式：JSON文件
+            # Backward compatible: JSON file
             elif item.endswith('.json'):
                 report_id = item[:-5]
                 report = cls.get_report(report_id)
@@ -2538,25 +2538,25 @@ def list_reports(cls, simulation_id: Optional[str] = None, limit: int = 50) -> L
                     if simulation_id is None or report.simulation_id == simulation_id:
                         reports.append(report)
         
-        # 按创建时间倒序
+        # Sort by creation time descending
         reports.sort(key=lambda r: r.created_at, reverse=True)
         
         return reports[:limit]
     
     @classmethod
     def delete_report(cls, report_id: str) -> bool:
-        """删除报告（整个文件夹）"""
+        """Delete report (entire folder)"""
         import shutil
         
         folder_path = cls._get_report_folder(report_id)
         
-        # 新格式：删除整个文件夹
+        # New format: delete entire folder
         if os.path.exists(folder_path) and os.path.isdir(folder_path):
             shutil.rmtree(folder_path)
-            logger.info(f"报告文件夹已删除: {report_id}")
+            logger.info(f"Report folder deleted: {report_id}")
             return True
         
-        # 兼容旧格式：删除单独的文件
+        # Backward compatible: delete individual files
         deleted = False
         old_json_path = os.path.join(cls.REPORTS_DIR, f"{report_id}.json")
         old_md_path = os.path.join(cls.REPORTS_DIR, f"{report_id}.md")
diff --git a/backend/app/services/simulation_config_generator.py b/backend/app/services/simulation_config_generator.py
index cc362508b..f55b2cb96 100644
--- a/backend/app/services/simulation_config_generator.py
+++ b/backend/app/services/simulation_config_generator.py
@@ -1,13 +1,13 @@
 """
-模拟配置智能生成器
-使用LLM根据模拟需求、文档内容、图谱信息自动生成细致的模拟参数
-实现全程自动化，无需人工设置参数
-
-采用分步生成策略，避免一次性生成过长内容导致失败：
-1. 生成时间配置
-2. 生成事件配置
-3. 分批生成Agent配置
-4. 生成平台配置
+Simulation Configuration Intelligent Generator
+Use LLM to automatically generate detailed simulation parameters based on simulation requirements, document content, and knowledge graph information
+Implement full process automation without manual parameter setting
+
+Adopt step-by-step generation strategy to avoid failures from generating too long content at once:
+1. Generate time configuration
+2. Generate event configuration
+3. Generate agent configurations in batches
+4. Generate platform configuration
 """
 
 import json
@@ -24,156 +24,156 @@
 
 logger = get_logger('mirofish.simulation_config')
 
-# 中国作息时间配置（北京时间）
+# Time zone configuration for Chinese work schedules (Beijing Time)
 CHINA_TIMEZONE_CONFIG = {
-    # 深夜时段（几乎无人活动）
+    # Dead hours (almost no activity)
     "dead_hours": [0, 1, 2, 3, 4, 5],
-    # 早间时段（逐渐醒来）
+    # Morning hours (gradually waking up)
     "morning_hours": [6, 7, 8],
-    # 工作时段
+    # Work hours
     "work_hours": [9, 10, 11, 12, 13, 14, 15, 16, 17, 18],
-    # 晚间高峰（最活跃）
+    # Evening peak (most active)
     "peak_hours": [19, 20, 21, 22],
-    # 夜间时段（活跃度下降）
+    # Night hours (activity decreases)
     "night_hours": [23],
-    # 活跃度系数
+    # Activity multipliers
     "activity_multipliers": {
-        "dead": 0.05,      # 凌晨几乎无人
-        "morning": 0.4,    # 早间逐渐活跃
-        "work": 0.7,       # 工作时段中等
-        "peak": 1.5,       # 晚间高峰
-        "night": 0.5       # 深夜下降
+        "dead": 0.05,      # Almost no one in early morning
+        "morning": 0.4,    # Gradually active in morning
+        "work": 0.7,       # Medium activity during work hours
+        "peak": 1.5,       # Evening peak
+        "night": 0.5       # Activity decreases at night
     }
 }
 
 
 @dataclass
 class AgentActivityConfig:
-    """单个Agent的活动配置"""
+    """Activity configuration for a single Agent"""
     agent_id: int
     entity_uuid: str
     entity_name: str
     entity_type: str
     
-    # 活跃度配置 (0.0-1.0)
-    activity_level: float = 0.5  # 整体活跃度
-    
-    # 发言频率（每小时预期发言次数）
+    # Activity configuration (0.0-1.0)
+    activity_level: float = 0.5  # Overall activity level
+
+    # Speech frequency (expected posts per hour)
     posts_per_hour: float = 1.0
     comments_per_hour: float = 2.0
     
-    # 活跃时间段（24小时制，0-23）
+    # Active time periods (24-hour format, 0-23)
     active_hours: List[int] = field(default_factory=lambda: list(range(8, 23)))
     
-    # 响应速度（对热点事件的反应延迟，单位：模拟分钟）
+    # Response speed (reaction delay to trending events, unit: simulation minutes)
     response_delay_min: int = 5
     response_delay_max: int = 60
     
-    # 情感倾向 (-1.0到1.0，负面到正面)
+    # Sentiment tendency (-1.0 to 1.0, negative to positive)
     sentiment_bias: float = 0.0
     
-    # 立场（对特定话题的态度）
+    # Stance (attitude toward specific topics)
     stance: str = "neutral"  # supportive, opposing, neutral, observer
     
-    # 影响力权重（决定其发言被其他Agent看到的概率）
+    # Influence weight (determines probability of their speech being seen by other agents)
     influence_weight: float = 1.0
 
 
 @dataclass  
 class TimeSimulationConfig:
-    """时间模拟配置（基于中国人作息习惯）"""
-    # 模拟总时长（模拟小时数）
-    total_simulation_hours: int = 72  # 默认模拟72小时（3天）
-    
-    # 每轮代表的时间（模拟分钟）- 默认60分钟（1小时），加快时间流速
+    """Time simulation configuration (based on Chinese work schedule habits)"""
+    # Total simulation time (simulation hours)
+    total_simulation_hours: int = 72  # Default 72 hours (3 days)
+
+    # Time represented per round (simulation minutes) - default 60 minutes (1 hour), speed up time
     minutes_per_round: int = 60
     
-    # 每小时激活的Agent数量范围
+    # Range of agents activated per hour
     agents_per_hour_min: int = 5
     agents_per_hour_max: int = 20
     
-    # 高峰时段（晚间19-22点，中国人最活跃的时间）
+    # Peak hours (evening 19-22, most active time for Chinese people)
     peak_hours: List[int] = field(default_factory=lambda: [19, 20, 21, 22])
     peak_activity_multiplier: float = 1.5
     
-    # 低谷时段（凌晨0-5点，几乎无人活动）
+    # Off-peak hours (early morning 0-5, almost no activity)
     off_peak_hours: List[int] = field(default_factory=lambda: [0, 1, 2, 3, 4, 5])
-    off_peak_activity_multiplier: float = 0.05  # 凌晨活跃度极低
-    
-    # 早间时段
+    off_peak_activity_multiplier: float = 0.05  # Very low activity in early morning
+
+    # Morning hours
     morning_hours: List[int] = field(default_factory=lambda: [6, 7, 8])
     morning_activity_multiplier: float = 0.4
     
-    # 工作时段
+    # Work hours
     work_hours: List[int] = field(default_factory=lambda: [9, 10, 11, 12, 13, 14, 15, 16, 17, 18])
     work_activity_multiplier: float = 0.7
 
 
 @dataclass
 class EventConfig:
-    """事件配置"""
-    # 初始事件（模拟开始时的触发事件）
+    """Event configuration"""
+    # Initial posts (triggering events at the start of simulation)
     initial_posts: List[Dict[str, Any]] = field(default_factory=list)
     
-    # 定时事件（在特定时间触发的事件）
+    # Scheduled events (events triggered at specific times)
     scheduled_events: List[Dict[str, Any]] = field(default_factory=list)
     
-    # 热点话题关键词
+    # Hot topic keywords
     hot_topics: List[str] = field(default_factory=list)
     
-    # 舆论引导方向
+    # Opinion narrative direction
     narrative_direction: str = ""
 
 
 @dataclass
 class PlatformConfig:
-    """平台特定配置"""
+    """Platform-specific configuration"""
     platform: str  # twitter or reddit
     
-    # 推荐算法权重
-    recency_weight: float = 0.4  # 时间新鲜度
-    popularity_weight: float = 0.3  # 热度
-    relevance_weight: float = 0.3  # 相关性
-    
-    # 病毒传播阈值（达到多少互动后触发扩散）
+    # Recommendation algorithm weights
+    recency_weight: float = 0.4  # Time freshness
+    popularity_weight: float = 0.3  # Popularity
+    relevance_weight: float = 0.3  # Relevance
+
+    # Viral threshold (number of interactions before triggering spread)
     viral_threshold: int = 10
     
-    # 回声室效应强度（相似观点聚集程度）
+    # Echo chamber effect strength (degree of similar opinion clustering)
     echo_chamber_strength: float = 0.5
 
 
 @dataclass
 class SimulationParameters:
-    """完整的模拟参数配置"""
-    # 基础信息
+    """Complete simulation parameter configuration"""
+    # Basic information
     simulation_id: str
     project_id: str
     graph_id: str
     simulation_requirement: str
     
-    # 时间配置
+    # Time configuration
     time_config: TimeSimulationConfig = field(default_factory=TimeSimulationConfig)
-    
-    # Agent配置列表
+
+    # Agent configuration list
     agent_configs: List[AgentActivityConfig] = field(default_factory=list)
-    
-    # 事件配置
+
+    # Event configuration
     event_config: EventConfig = field(default_factory=EventConfig)
-    
-    # 平台配置
+
+    # Platform configuration
     twitter_config: Optional[PlatformConfig] = None
     reddit_config: Optional[PlatformConfig] = None
     
-    # LLM配置
+    # LLM configuration
     llm_model: str = ""
     llm_base_url: str = ""
     
-    # 生成元数据
+    # Generation metadata
     generated_at: str = field(default_factory=lambda: datetime.now().isoformat())
-    generation_reasoning: str = ""  # LLM的推理说明
+    generation_reasoning: str = ""  # LLM reasoning explanation
     
     def to_dict(self) -> Dict[str, Any]:
-        """转换为字典"""
+        """Convert to dictionary"""
         time_dict = asdict(self.time_config)
         return {
             "simulation_id": self.simulation_id,
@@ -192,34 +192,34 @@ def to_dict(self) -> Dict[str, Any]:
         }
     
     def to_json(self, indent: int = 2) -> str:
-        """转换为JSON字符串"""
+        """Convert to JSON string"""
         return json.dumps(self.to_dict(), ensure_ascii=False, indent=indent)
 
 
 class SimulationConfigGenerator:
     """
-    模拟配置智能生成器
-    
-    使用LLM分析模拟需求、文档内容、图谱实体信息，
-    自动生成最佳的模拟参数配置
-    
-    采用分步生成策略：
-    1. 生成时间配置和事件配置（轻量级）
-    2. 分批生成Agent配置（每批10-20个）
-    3. 生成平台配置
+    Simulation Configuration Intelligent Generator
+
+    Use LLM to analyze simulation requirements, document content, knowledge graph entity information,
+    and automatically generate optimal simulation parameter configuration
+
+    Adopt step-by-step generation strategy:
+    1. Generate time configuration and event configuration (lightweight)
+    2. Generate agent configurations in batches (10-20 per batch)
+    3. Generate platform configuration
     """
-    
-    # 上下文最大字符数
+
+    # Maximum context length in characters
     MAX_CONTEXT_LENGTH = 50000
-    # 每批生成的Agent数量
+    # Number of agents per batch
     AGENTS_PER_BATCH = 15
-    
-    # 各步骤的上下文截断长度（字符数）
-    TIME_CONFIG_CONTEXT_LENGTH = 10000   # 时间配置
-    EVENT_CONFIG_CONTEXT_LENGTH = 8000   # 事件配置
-    ENTITY_SUMMARY_LENGTH = 300          # 实体摘要
-    AGENT_SUMMARY_LENGTH = 300           # Agent配置中的实体摘要
-    ENTITIES_PER_TYPE_DISPLAY = 20       # 每类实体显示数量
+
+    # Context truncation length for each step (characters)
+    TIME_CONFIG_CONTEXT_LENGTH = 10000   # Time configuration
+    EVENT_CONFIG_CONTEXT_LENGTH = 8000   # Event configuration
+    ENTITY_SUMMARY_LENGTH = 300          # Entity summary
+    AGENT_SUMMARY_LENGTH = 300           # Entity summary in agent configuration
+    ENTITIES_PER_TYPE_DISPLAY = 20       # Number of entities to display per type
     
     def __init__(
         self,
@@ -232,7 +232,7 @@ def __init__(
         self.model_name = model_name or Config.LLM_MODEL_NAME
         
         if not self.api_key:
-            raise ValueError("LLM_API_KEY 未配置")
+            raise ValueError("LLM_API_KEY not configured")
         
         self.client = OpenAI(
             api_key=self.api_key,
@@ -252,27 +252,27 @@ def generate_config(
         progress_callback: Optional[Callable[[int, int, str], None]] = None,
     ) -> SimulationParameters:
         """
-        智能生成完整的模拟配置（分步生成）
-        
+        Intelligently generate complete simulation configuration (step-by-step generation)
+
         Args:
-            simulation_id: 模拟ID
-            project_id: 项目ID
-            graph_id: 图谱ID
-            simulation_requirement: 模拟需求描述
-            document_text: 原始文档内容
-            entities: 过滤后的实体列表
-            enable_twitter: 是否启用Twitter
-            enable_reddit: 是否启用Reddit
-            progress_callback: 进度回调函数(current_step, total_steps, message)
-            
+            simulation_id: Simulation ID
+            project_id: Project ID
+            graph_id: Knowledge graph ID
+            simulation_requirement: Simulation requirement description
+            document_text: Original document content
+            entities: Filtered entity list
+            enable_twitter: Whether to enable Twitter
+            enable_reddit: Whether to enable Reddit
+            progress_callback: Progress callback function(current_step, total_steps, message)
+
         Returns:
-            SimulationParameters: 完整的模拟参数
+            SimulationParameters: Complete simulation parameters
         """
-        logger.info(f"开始智能生成模拟配置: simulation_id={simulation_id}, 实体数={len(entities)}")
+        logger.info(f"Starting intelligent simulation configuration generation: simulation_id={simulation_id}, entities={len(entities)}")
         
-        # 计算总步骤数
+        # Calculate total steps
         num_batches = math.ceil(len(entities) / self.AGENTS_PER_BATCH)
-        total_steps = 3 + num_batches  # 时间配置 + 事件配置 + N批Agent + 平台配置
+        total_steps = 3 + num_batches  # time config + event config + N batch agents + platform config
         current_step = 0
         
         def report_progress(step: int, message: str):
@@ -282,7 +282,7 @@ def report_progress(step: int, message: str):
                 progress_callback(step, total_steps, message)
             logger.info(f"[{step}/{total_steps}] {message}")
         
-        # 1. 构建基础上下文信息
+        # 1. Build basic context information
         context = self._build_context(
             simulation_requirement=simulation_requirement,
             document_text=document_text,
@@ -291,20 +291,20 @@ def report_progress(step: int, message: str):
         
         reasoning_parts = []
         
-        # ========== 步骤1: 生成时间配置 ==========
-        report_progress(1, "生成时间配置...")
+        # ========== Step 1: Generate time configuration ==========
+        report_progress(1, "Generating time configuration...")
         num_entities = len(entities)
         time_config_result = self._generate_time_config(context, num_entities)
         time_config = self._parse_time_config(time_config_result, num_entities)
-        reasoning_parts.append(f"时间配置: {time_config_result.get('reasoning', '成功')}")
-        
-        # ========== 步骤2: 生成事件配置 ==========
-        report_progress(2, "生成事件配置和热点话题...")
+        reasoning_parts.append(f"Time config: {time_config_result.get('reasoning', 'Success')}")
+
+        # ========== Step 2: Generate event configuration ==========
+        report_progress(2, "Generating event configuration and hot topics...")
         event_config_result = self._generate_event_config(context, simulation_requirement, entities)
         event_config = self._parse_event_config(event_config_result)
-        reasoning_parts.append(f"事件配置: {event_config_result.get('reasoning', '成功')}")
-        
-        # ========== 步骤3-N: 分批生成Agent配置 ==========
+        reasoning_parts.append(f"Event config: {event_config_result.get('reasoning', 'Success')}")
+
+        # ========== Step 3-N: Generate agent configurations in batches ==========
         all_agent_configs = []
         for batch_idx in range(num_batches):
             start_idx = batch_idx * self.AGENTS_PER_BATCH
@@ -313,7 +313,7 @@ def report_progress(step: int, message: str):
             
             report_progress(
                 3 + batch_idx,
-                f"生成Agent配置 ({start_idx + 1}-{end_idx}/{len(entities)})..."
+                f"Generating agent configuration ({start_idx + 1}-{end_idx}/{len(entities)})..."
             )
             
             batch_configs = self._generate_agent_configs_batch(
@@ -324,16 +324,16 @@ def report_progress(step: int, message: str):
             )
             all_agent_configs.extend(batch_configs)
         
-        reasoning_parts.append(f"Agent配置: 成功生成 {len(all_agent_configs)} 个")
-        
-        # ========== 为初始帖子分配发布者 Agent ==========
-        logger.info("为初始帖子分配合适的发布者 Agent...")
+        reasoning_parts.append(f"Agent config: Successfully generated {len(all_agent_configs)}")
+
+        # ========== Assign initial post agents ==========
+        logger.info("Assigning appropriate publisher agents to initial posts...")
         event_config = self._assign_initial_post_agents(event_config, all_agent_configs)
         assigned_count = len([p for p in event_config.initial_posts if p.get("poster_agent_id") is not None])
-        reasoning_parts.append(f"初始帖子分配: {assigned_count} 个帖子已分配发布者")
-        
-        # ========== 最后一步: 生成平台配置 ==========
-        report_progress(total_steps, "生成平台配置...")
+        reasoning_parts.append(f"Initial posts assigned: {assigned_count} posts assigned publishers")
+
+        # ========== Final step: Generate platform configuration ==========
+        report_progress(total_steps, "Generating platform configuration...")
         twitter_config = None
         reddit_config = None
         
@@ -357,7 +357,7 @@ def report_progress(step: int, message: str):
                 echo_chamber_strength=0.6
             )
         
-        # 构建最终参数
+        # Build final parameters
         params = SimulationParameters(
             simulation_id=simulation_id,
             project_id=project_id,
@@ -373,7 +373,7 @@ def report_progress(step: int, message: str):
             generation_reasoning=" | ".join(reasoning_parts)
         )
         
-        logger.info(f"模拟配置生成完成: {len(params.agent_configs)} 个Agent配置")
+        logger.info(f"Simulation configuration generation complete: {len(params.agent_configs)} agent configurations")
         
         return params
     
@@ -383,33 +383,33 @@ def _build_context(
         document_text: str,
         entities: List[EntityNode]
     ) -> str:
-        """构建LLM上下文，截断到最大长度"""
-        
-        # 实体摘要
+        """Build LLM context, truncate to maximum length"""
+
+        # Entity summary
         entity_summary = self._summarize_entities(entities)
         
-        # 构建上下文
+        # Build context
         context_parts = [
-            f"## 模拟需求\n{simulation_requirement}",
-            f"\n## 实体信息 ({len(entities)}个)\n{entity_summary}",
+            f"## Simulation Requirements\n{simulation_requirement}",
+            f"\n## Entity Information ({len(entities)})\n{entity_summary}",
         ]
         
         current_length = sum(len(p) for p in context_parts)
-        remaining_length = self.MAX_CONTEXT_LENGTH - current_length - 500  # 留500字符余量
+        remaining_length = self.MAX_CONTEXT_LENGTH - current_length - 500  # Reserve 500 characters
         
         if remaining_length > 0 and document_text:
             doc_text = document_text[:remaining_length]
             if len(document_text) > remaining_length:
-                doc_text += "\n...(文档已截断)"
-            context_parts.append(f"\n## 原始文档内容\n{doc_text}")
+                doc_text += "\n...(document truncated)"
+            context_parts.append(f"\n## Original Document Content\n{doc_text}")
         
         return "\n".join(context_parts)
     
     def _summarize_entities(self, entities: List[EntityNode]) -> str:
-        """生成实体摘要"""
+        """Generate entity summary"""
         lines = []
         
-        # 按类型分组
+        # Group by type
         by_type: Dict[str, List[EntityNode]] = {}
         for e in entities:
             t = e.get_entity_type() or "Unknown"
@@ -418,20 +418,20 @@ def _summarize_entities(self, entities: List[EntityNode]) -> str:
             by_type[t].append(e)
         
         for entity_type, type_entities in by_type.items():
-            lines.append(f"\n### {entity_type} ({len(type_entities)}个)")
-            # 使用配置的显示数量和摘要长度
+            lines.append(f"\n### {entity_type} ({len(type_entities)})")
+            # Use configured display quantity and summary length
             display_count = self.ENTITIES_PER_TYPE_DISPLAY
             summary_len = self.ENTITY_SUMMARY_LENGTH
             for e in type_entities[:display_count]:
                 summary_preview = (e.summary[:summary_len] + "...") if len(e.summary) > summary_len else e.summary
                 lines.append(f"- {e.name}: {summary_preview}")
             if len(type_entities) > display_count:
-                lines.append(f"  ... 还有 {len(type_entities) - display_count} 个")
+                lines.append(f"  ... and {len(type_entities) - display_count} more")
         
         return "\n".join(lines)
     
     def _call_llm_with_retry(self, prompt: str, system_prompt: str) -> Dict[str, Any]:
-        """带重试的LLM调用，包含JSON修复逻辑"""
+        """LLM call with retry, including JSON repair logic"""
         import re
         
         max_attempts = 3
@@ -446,25 +446,25 @@ def _call_llm_with_retry(self, prompt: str, system_prompt: str) -> Dict[str, Any
                         {"role": "user", "content": prompt}
                     ],
                     response_format={"type": "json_object"},
-                    temperature=0.7 - (attempt * 0.1)  # 每次重试降低温度
-                    # 不设置max_tokens，让LLM自由发挥
+                    temperature=0.7 - (attempt * 0.1)  # Lower temperature with each retry
+                    # Don't set max_tokens, let LLM generate freely
                 )
                 
                 content = response.choices[0].message.content
                 finish_reason = response.choices[0].finish_reason
                 
-                # 检查是否被截断
+                # Check if output was truncated
                 if finish_reason == 'length':
-                    logger.warning(f"LLM输出被截断 (attempt {attempt+1})")
+                    logger.warning(f"LLM output truncated (attempt {attempt+1})")
                     content = self._fix_truncated_json(content)
                 
-                # 尝试解析JSON
+                # Try to parse JSON
                 try:
                     return json.loads(content)
                 except json.JSONDecodeError as e:
-                    logger.warning(f"JSON解析失败 (attempt {attempt+1}): {str(e)[:80]}")
-                    
-                    # 尝试修复JSON
+                    logger.warning(f"JSON parsing failed (attempt {attempt+1}): {str(e)[:80]}")
+
+                    # Try to fix JSON
                     fixed = self._try_fix_config_json(content)
                     if fixed:
                         return fixed
@@ -472,44 +472,44 @@ def _call_llm_with_retry(self, prompt: str, system_prompt: str) -> Dict[str, Any
                     last_error = e
                     
             except Exception as e:
-                logger.warning(f"LLM调用失败 (attempt {attempt+1}): {str(e)[:80]}")
+                logger.warning(f"LLM call failed (attempt {attempt+1}): {str(e)[:80]}")
                 last_error = e
                 import time
                 time.sleep(2 * (attempt + 1))
         
-        raise last_error or Exception("LLM调用失败")
+        raise last_error or Exception("LLM call failed")
     
     def _fix_truncated_json(self, content: str) -> str:
-        """修复被截断的JSON"""
+        """Fix truncated JSON"""
         content = content.strip()
         
-        # 计算未闭合的括号
+        # Count unclosed parentheses
         open_braces = content.count('{') - content.count('}')
         open_brackets = content.count('[') - content.count(']')
         
-        # 检查是否有未闭合的字符串
+        # Check for unclosed strings
         if content and content[-1] not in '",}]':
             content += '"'
         
-        # 闭合括号
+        # Close parentheses
         content += ']' * open_brackets
         content += '}' * open_braces
         
         return content
     
     def _try_fix_config_json(self, content: str) -> Optional[Dict[str, Any]]:
-        """尝试修复配置JSON"""
+        """Try to fix configuration JSON"""
         import re
         
-        # 修复被截断的情况
+        # Fix truncated case
         content = self._fix_truncated_json(content)
         
-        # 提取JSON部分
+        # Extract JSON portion
         json_match = re.search(r'\{[\s\S]*\}', content)
         if json_match:
             json_str = json_match.group()
             
-            # 移除字符串中的换行符
+            # Remove newlines in strings
             def fix_string(match):
                 s = match.group(0)
                 s = s.replace('\n', ' ').replace('\r', ' ')
@@ -521,7 +521,7 @@ def fix_string(match):
             try:
                 return json.loads(json_str)
             except:
-                # 尝试移除所有控制字符
+                # Try removing all control characters
                 json_str = re.sub(r'[\x00-\x1f\x7f-\x9f]', ' ', json_str)
                 json_str = re.sub(r'\s+', ' ', json_str)
                 try:
@@ -532,35 +532,35 @@ def fix_string(match):
         return None
     
     def _generate_time_config(self, context: str, num_entities: int) -> Dict[str, Any]:
-        """生成时间配置"""
-        # 使用配置的上下文截断长度
+        """Generate time configuration"""
+        # Use configured context truncation length
         context_truncated = context[:self.TIME_CONFIG_CONTEXT_LENGTH]
         
-        # 计算最大允许值（80%的agent数）
+        # Calculate maximum allowed value (90% of agents)
         max_agents_allowed = max(1, int(num_entities * 0.9))
         
-        prompt = f"""基于以下模拟需求，生成时间模拟配置。
+        prompt = f"""Based on the following simulation requirements, generate time simulation configuration.
 
 {context_truncated}
 
-## 任务
-请生成时间配置JSON。
+## Task
+Please generate time configuration JSON.
 
-### 基本原则（仅供参考，需根据具体事件和参与群体灵活调整）：
-- 用户群体为中国人，需符合北京时间作息习惯
-- 凌晨0-5点几乎无人活动（活跃度系数0.05）
-- 早上6-8点逐渐活跃（活跃度系数0.4）
-- 工作时间9-18点中等活跃（活跃度系数0.7）
-- 晚间19-22点是高峰期（活跃度系数1.5）
-- 23点后活跃度下降（活跃度系数0.5）
-- 一般规律：凌晨低活跃、早间渐增、工作时段中等、晚间高峰
-- **重要**：以下示例值仅供参考，你需要根据事件性质、参与群体特点来调整具体时段
-  - 例如：学生群体高峰可能是21-23点；媒体全天活跃；官方机构只在工作时间
-  - 例如：突发热点可能导致深夜也有讨论，off_peak_hours 可适当缩短
+### Basic principles (for reference only, adjust flexibly based on event nature and participant characteristics):
+- User base is Chinese people, must follow Beijing Time work schedule habits
+- 0-5am almost no activity (activity coefficient 0.05)
+- 6-8am gradually active (activity coefficient 0.4)
+- 9-18 work time moderately active (activity coefficient 0.7)
+- 19-22 evening is peak period (activity coefficient 1.5)
+- After 23 activity decreases (activity coefficient 0.5)
+- General rule: low activity early morning, gradually increasing morning, moderate work time, evening peak
+- **Important**: Example values below are for reference only, adjust specific time periods based on event nature and participant characteristics
+  - Example: student peak may be 21-23; media active all day; official institutions only during work hours
+  - Example: breaking news may cause late night discussions, off_peak_hours can be shortened appropriately
 
-### 返回JSON格式（不要markdown）
+### Return JSON format (no markdown)
 
-示例：
+Example:
 {{
     "total_simulation_hours": 72,
     "minutes_per_round": 60,
@@ -570,70 +570,70 @@ def _generate_time_config(self, context: str, num_entities: int) -> Dict[str, An
     "off_peak_hours": [0, 1, 2, 3, 4, 5],
     "morning_hours": [6, 7, 8],
     "work_hours": [9, 10, 11, 12, 13, 14, 15, 16, 17, 18],
-    "reasoning": "针对该事件的时间配置说明"
+    "reasoning": "Explanation of time configuration for this event"
 }}
 
-字段说明：
-- total_simulation_hours (int): 模拟总时长，24-168小时，突发事件短、持续话题长
-- minutes_per_round (int): 每轮时长，30-120分钟，建议60分钟
-- agents_per_hour_min (int): 每小时最少激活Agent数（取值范围: 1-{max_agents_allowed}）
-- agents_per_hour_max (int): 每小时最多激活Agent数（取值范围: 1-{max_agents_allowed}）
-- peak_hours (int数组): 高峰时段，根据事件参与群体调整
-- off_peak_hours (int数组): 低谷时段，通常深夜凌晨
-- morning_hours (int数组): 早间时段
-- work_hours (int数组): 工作时段
-- reasoning (string): 简要说明为什么这样配置"""
+Field description:
+- total_simulation_hours (int): Total simulation time, 24-168 hours, short for breaking news, long for ongoing topics
+- minutes_per_round (int): Time per round, 30-120 minutes, recommend 60 minutes
+- agents_per_hour_min (int): Minimum agents activated per hour (range: 1-{max_agents_allowed})
+- agents_per_hour_max (int): Maximum agents activated per hour (range: 1-{max_agents_allowed})
+- peak_hours (int array): Peak hours, adjust based on event participants
+- off_peak_hours (int array): Off-peak hours, usually late night/early morning
+- morning_hours (int array): Morning hours
+- work_hours (int array): Work hours
+- reasoning (string): Brief explanation for this configuration"""
 
-        system_prompt = "你是社交媒体模拟专家。返回纯JSON格式，时间配置需符合中国人作息习惯。"
+        system_prompt = "You are a social media simulation expert. Return pure JSON format, time configuration must follow Chinese work schedule habits."
         
         try:
             return self._call_llm_with_retry(prompt, system_prompt)
         except Exception as e:
-            logger.warning(f"时间配置LLM生成失败: {e}, 使用默认配置")
+            logger.warning(f"Time config LLM generation failed: {e}, using default configuration")
             return self._get_default_time_config(num_entities)
     
     def _get_default_time_config(self, num_entities: int) -> Dict[str, Any]:
-        """获取默认时间配置（中国人作息）"""
+        """Get default time configuration (Chinese work schedule)"""
         return {
             "total_simulation_hours": 72,
-            "minutes_per_round": 60,  # 每轮1小时，加快时间流速
+            "minutes_per_round": 60,  # 1 hour per round, speed up time
             "agents_per_hour_min": max(1, num_entities // 15),
             "agents_per_hour_max": max(5, num_entities // 5),
             "peak_hours": [19, 20, 21, 22],
             "off_peak_hours": [0, 1, 2, 3, 4, 5],
             "morning_hours": [6, 7, 8],
             "work_hours": [9, 10, 11, 12, 13, 14, 15, 16, 17, 18],
-            "reasoning": "使用默认中国人作息配置（每轮1小时）"
+            "reasoning": "Using default Chinese work schedule configuration (1 hour per round)"
         }
     
     def _parse_time_config(self, result: Dict[str, Any], num_entities: int) -> TimeSimulationConfig:
-        """解析时间配置结果，并验证agents_per_hour值不超过总agent数"""
-        # 获取原始值
+        """Parse time configuration result and verify agents_per_hour doesn't exceed total agents"""
+        # Get original values
         agents_per_hour_min = result.get("agents_per_hour_min", max(1, num_entities // 15))
         agents_per_hour_max = result.get("agents_per_hour_max", max(5, num_entities // 5))
         
-        # 验证并修正：确保不超过总agent数
+        # Verify and correct: ensure not exceeding total agents
         if agents_per_hour_min > num_entities:
-            logger.warning(f"agents_per_hour_min ({agents_per_hour_min}) 超过总Agent数 ({num_entities})，已修正")
+            logger.warning(f"agents_per_hour_min ({agents_per_hour_min}) exceeds total agents ({num_entities}), corrected")
             agents_per_hour_min = max(1, num_entities // 10)
         
         if agents_per_hour_max > num_entities:
-            logger.warning(f"agents_per_hour_max ({agents_per_hour_max}) 超过总Agent数 ({num_entities})，已修正")
+            logger.warning(f"agents_per_hour_max ({agents_per_hour_max}) exceeds total agents ({num_entities}), corrected")
             agents_per_hour_max = max(agents_per_hour_min + 1, num_entities // 2)
         
-        # 确保 min < max
+        # Ensure min < max
         if agents_per_hour_min >= agents_per_hour_max:
             agents_per_hour_min = max(1, agents_per_hour_max // 2)
-            logger.warning(f"agents_per_hour_min >= max，已修正为 {agents_per_hour_min}")
+            logger.warning(f"agents_per_hour_min >= max, corrected to {agents_per_hour_min}")
         
         return TimeSimulationConfig(
             total_simulation_hours=result.get("total_simulation_hours", 72),
-            minutes_per_round=result.get("minutes_per_round", 60),  # 默认每轮1小时
+            minutes_per_round=result.get("minutes_per_round", 60),  # Default 1 hour per round
             agents_per_hour_min=agents_per_hour_min,
             agents_per_hour_max=agents_per_hour_max,
             peak_hours=result.get("peak_hours", [19, 20, 21, 22]),
             off_peak_hours=result.get("off_peak_hours", [0, 1, 2, 3, 4, 5]),
-            off_peak_activity_multiplier=0.05,  # 凌晨几乎无人
+            off_peak_activity_multiplier=0.05,  # Almost no one in early morning
             morning_hours=result.get("morning_hours", [6, 7, 8]),
             morning_activity_multiplier=0.4,
             work_hours=result.get("work_hours", list(range(9, 19))),
@@ -647,14 +647,14 @@ def _generate_event_config(
         simulation_requirement: str,
         entities: List[EntityNode]
     ) -> Dict[str, Any]:
-        """生成事件配置"""
-        
-        # 获取可用的实体类型列表，供 LLM 参考
+        """Generate event configuration"""
+
+        # Get available entity types list for LLM reference
         entity_types_available = list(set(
             e.get_entity_type() or "Unknown" for e in entities
         ))
         
-        # 为每种类型列出代表性实体名称
+        # List representative entity names for each type
         type_examples = {}
         for e in entities:
             etype = e.get_entity_type() or "Unknown"
@@ -668,53 +668,53 @@ def _generate_event_config(
             for t, examples in type_examples.items()
         ])
         
-        # 使用配置的上下文截断长度
+        # Use configured context truncation length
         context_truncated = context[:self.EVENT_CONFIG_CONTEXT_LENGTH]
-        
-        prompt = f"""基于以下模拟需求，生成事件配置。
 
-模拟需求: {simulation_requirement}
+        prompt = f"""Based on the following simulation requirements, generate event configuration.
+
+Simulation Requirements: {simulation_requirement}
 
 {context_truncated}
 
-## 可用实体类型及示例
+## Available Entity Types and Examples
 {type_info}
 
-## 任务
-请生成事件配置JSON：
-- 提取热点话题关键词
-- 描述舆论发展方向
-- 设计初始帖子内容，**每个帖子必须指定 poster_type（发布者类型）**
+## Task
+Please generate event configuration JSON:
+- Extract hot topic keywords
+- Describe opinion development direction
+- Design initial post content, **each post must specify poster_type (publisher type)**
 
-**重要**: poster_type 必须从上面的"可用实体类型"中选择，这样初始帖子才能分配给合适的 Agent 发布。
-例如：官方声明应由 Official/University 类型发布，新闻由 MediaOutlet 发布，学生观点由 Student 发布。
+**Important**: poster_type must be selected from the "Available Entity Types" above so initial posts can be assigned to appropriate agents for publishing.
+Example: Official statements should be published by Official/University type, news by MediaOutlet, student opinions by Student type.
 
-返回JSON格式（不要markdown）：
+Return JSON format (no markdown):
 {{
-    "hot_topics": ["关键词1", "关键词2", ...],
-    "narrative_direction": "<舆论发展方向描述>",
+    "hot_topics": ["keyword1", "keyword2", ...],
+    "narrative_direction": "<description of opinion development direction>",
     "initial_posts": [
-        {{"content": "帖子内容", "poster_type": "实体类型（必须从可用类型中选择）"}},
+        {{"content": "post content", "poster_type": "entity type (must select from available types)"}},
         ...
     ],
-    "reasoning": "<简要说明>"
+    "reasoning": "<brief explanation>"
 }}"""
 
-        system_prompt = "你是舆论分析专家。返回纯JSON格式。注意 poster_type 必须精确匹配可用实体类型。"
+        system_prompt = "You are an opinion analysis expert. Return pure JSON format. Note poster_type must match available entity types precisely."
         
         try:
             return self._call_llm_with_retry(prompt, system_prompt)
         except Exception as e:
-            logger.warning(f"事件配置LLM生成失败: {e}, 使用默认配置")
+            logger.warning(f"Event config LLM generation failed: {e}, using default configuration")
             return {
                 "hot_topics": [],
                 "narrative_direction": "",
                 "initial_posts": [],
-                "reasoning": "使用默认配置"
+                "reasoning": "Using default configuration"
             }
     
     def _parse_event_config(self, result: Dict[str, Any]) -> EventConfig:
-        """解析事件配置结果"""
+        """Parse event configuration result"""
         return EventConfig(
             initial_posts=result.get("initial_posts", []),
             scheduled_events=[],
@@ -728,14 +728,14 @@ def _assign_initial_post_agents(
         agent_configs: List[AgentActivityConfig]
     ) -> EventConfig:
         """
-        为初始帖子分配合适的发布者 Agent
-        
-        根据每个帖子的 poster_type 匹配最合适的 agent_id
+        Assign appropriate publisher agents to initial posts
+
+        Match agent_id based on each post's poster_type
         """
         if not event_config.initial_posts:
             return event_config
         
-        # 按实体类型建立 agent 索引
+        # Build agent index by entity type
         agents_by_type: Dict[str, List[AgentActivityConfig]] = {}
         for agent in agent_configs:
             etype = agent.entity_type.lower()
@@ -743,7 +743,7 @@ def _assign_initial_post_agents(
                 agents_by_type[etype] = []
             agents_by_type[etype].append(agent)
         
-        # 类型映射表（处理 LLM 可能输出的不同格式）
+        # Type mapping table (handle different formats LLM might output)
         type_aliases = {
             "official": ["official", "university", "governmentagency", "government"],
             "university": ["university", "official"],
@@ -755,7 +755,7 @@ def _assign_initial_post_agents(
             "person": ["person", "student", "alumni"],
         }
         
-        # 记录每种类型已使用的 agent 索引，避免重复使用同一个 agent
+        # Track used agent indices for each type to avoid reusing same agent
         used_indices: Dict[str, int] = {}
         
         updated_posts = []
@@ -763,17 +763,17 @@ def _assign_initial_post_agents(
             poster_type = post.get("poster_type", "").lower()
             content = post.get("content", "")
             
-            # 尝试找到匹配的 agent
+            # Try to find matching agent
             matched_agent_id = None
             
-            # 1. 直接匹配
+            # 1. Direct match
             if poster_type in agents_by_type:
                 agents = agents_by_type[poster_type]
                 idx = used_indices.get(poster_type, 0) % len(agents)
                 matched_agent_id = agents[idx].agent_id
                 used_indices[poster_type] = idx + 1
             else:
-                # 2. 使用别名匹配
+                # 2. Match using aliases
                 for alias_key, aliases in type_aliases.items():
                     if poster_type in aliases or alias_key == poster_type:
                         for alias in aliases:
@@ -786,11 +786,11 @@ def _assign_initial_post_agents(
                     if matched_agent_id is not None:
                         break
             
-            # 3. 如果仍未找到，使用影响力最高的 agent
+            # 3. If still not found, use agent with highest influence
             if matched_agent_id is None:
-                logger.warning(f"未找到类型 '{poster_type}' 的匹配 Agent，使用影响力最高的 Agent")
+                logger.warning(f"No matching agent found for type '{poster_type}', using agent with highest influence")
                 if agent_configs:
-                    # 按影响力排序，选择影响力最高的
+                    # Sort by influence, select highest
                     sorted_agents = sorted(agent_configs, key=lambda a: a.influence_weight, reverse=True)
                     matched_agent_id = sorted_agents[0].agent_id
                 else:
@@ -802,7 +802,7 @@ def _assign_initial_post_agents(
                 "poster_agent_id": matched_agent_id
             })
             
-            logger.info(f"初始帖子分配: poster_type='{poster_type}' -> agent_id={matched_agent_id}")
+            logger.info(f"Initial post assigned: poster_type='{poster_type}' -> agent_id={matched_agent_id}")
         
         event_config.initial_posts = updated_posts
         return event_config
@@ -814,9 +814,9 @@ def _generate_agent_configs_batch(
         start_idx: int,
         simulation_requirement: str
     ) -> List[AgentActivityConfig]:
-        """分批生成Agent配置"""
-        
-        # 构建实体信息（使用配置的摘要长度）
+        """Generate agent configurations in batch"""
+
+        # Build entity information (using configured summary length)
         entity_list = []
         summary_len = self.AGENT_SUMMARY_LENGTH
         for i, e in enumerate(entities):
@@ -827,58 +827,58 @@ def _generate_agent_configs_batch(
                 "summary": e.summary[:summary_len] if e.summary else ""
             })
         
-        prompt = f"""基于以下信息，为每个实体生成社交媒体活动配置。
+        prompt = f"""Based on the following information, generate social media activity configuration for each entity.
 
-模拟需求: {simulation_requirement}
+Simulation Requirements: {simulation_requirement}
 
-## 实体列表
+## Entity List
 ```json
 {json.dumps(entity_list, ensure_ascii=False, indent=2)}
 ```
 
-## 任务
-为每个实体生成活动配置，注意：
-- **时间符合中国人作息**：凌晨0-5点几乎不活动，晚间19-22点最活跃
-- **官方机构**（University/GovernmentAgency）：活跃度低(0.1-0.3)，工作时间(9-17)活动，响应慢(60-240分钟)，影响力高(2.5-3.0)
-- **媒体**（MediaOutlet）：活跃度中(0.4-0.6)，全天活动(8-23)，响应快(5-30分钟)，影响力高(2.0-2.5)
-- **个人**（Student/Person/Alumni）：活跃度高(0.6-0.9)，主要晚间活动(18-23)，响应快(1-15分钟)，影响力低(0.8-1.2)
-- **公众人物/专家**：活跃度中(0.4-0.6)，影响力中高(1.5-2.0)
+## Task
+Generate activity configuration for each entity, noting:
+- **Time follows Chinese work schedule**: Almost no activity 0-5am, most active 19-22
+- **Official institutions** (University/GovernmentAgency): Low activity (0.1-0.3), active during work hours (9-17), slow response (60-240 min), high influence (2.5-3.0)
+- **Media** (MediaOutlet): Medium activity (0.4-0.6), active all day (8-23), fast response (5-30 min), high influence (2.0-2.5)
+- **Individuals** (Student/Person/Alumni): High activity (0.6-0.9), mainly evening activity (18-23), fast response (1-15 min), low influence (0.8-1.2)
+- **Public figures/Experts**: Medium activity (0.4-0.6), medium-high influence (1.5-2.0)
 
-返回JSON格式（不要markdown）：
+Return JSON format (no markdown):
 {{
     "agent_configs": [
         {{
-            "agent_id": <必须与输入一致>,
+            "agent_id": <must match input>,
             "activity_level": <0.0-1.0>,
-            "posts_per_hour": <发帖频率>,
-            "comments_per_hour": <评论频率>,
-            "active_hours": [<活跃小时列表，考虑中国人作息>],
-            "response_delay_min": <最小响应延迟分钟>,
-            "response_delay_max": <最大响应延迟分钟>,
-            "sentiment_bias": <-1.0到1.0>,
+            "posts_per_hour": <posting frequency>,
+            "comments_per_hour": <comment frequency>,
+            "active_hours": [<active hours list, consider Chinese work schedule>],
+            "response_delay_min": <minimum response delay minutes>,
+            "response_delay_max": <maximum response delay minutes>,
+            "sentiment_bias": <-1.0 to 1.0>,
             "stance": "<supportive/opposing/neutral/observer>",
-            "influence_weight": <影响力权重>
+            "influence_weight": <influence weight>
         }},
         ...
     ]
 }}"""
 
-        system_prompt = "你是社交媒体行为分析专家。返回纯JSON，配置需符合中国人作息习惯。"
+        system_prompt = "You are a social media behavior analysis expert. Return pure JSON, configuration must follow Chinese work schedule habits."
         
         try:
             result = self._call_llm_with_retry(prompt, system_prompt)
             llm_configs = {cfg["agent_id"]: cfg for cfg in result.get("agent_configs", [])}
         except Exception as e:
-            logger.warning(f"Agent配置批次LLM生成失败: {e}, 使用规则生成")
+            logger.warning(f"Agent config batch LLM generation failed: {e}, using rule-based generation")
             llm_configs = {}
         
-        # 构建AgentActivityConfig对象
+        # Build AgentActivityConfig objects
         configs = []
         for i, entity in enumerate(entities):
             agent_id = start_idx + i
             cfg = llm_configs.get(agent_id, {})
             
-            # 如果LLM没有生成，使用规则生成
+            # If LLM didn't generate, use rule-based generation
             if not cfg:
                 cfg = self._generate_agent_config_by_rule(entity)
             
@@ -902,11 +902,11 @@ def _generate_agent_configs_batch(
         return configs
     
     def _generate_agent_config_by_rule(self, entity: EntityNode) -> Dict[str, Any]:
-        """基于规则生成单个Agent配置（中国人作息）"""
+        """Generate single agent configuration based on rules (Chinese work schedule)"""
         entity_type = (entity.get_entity_type() or "Unknown").lower()
         
         if entity_type in ["university", "governmentagency", "ngo"]:
-            # 官方机构：工作时间活动，低频率，高影响力
+            # Official institutions: work hour activity, low frequency, high influence
             return {
                 "activity_level": 0.2,
                 "posts_per_hour": 0.1,
@@ -919,7 +919,7 @@ def _generate_agent_config_by_rule(self, entity: EntityNode) -> Dict[str, Any]:
                 "influence_weight": 3.0
             }
         elif entity_type in ["mediaoutlet"]:
-            # 媒体：全天活动，中等频率，高影响力
+            # Media: all-day activity, medium frequency, high influence
             return {
                 "activity_level": 0.5,
                 "posts_per_hour": 0.8,
@@ -932,7 +932,7 @@ def _generate_agent_config_by_rule(self, entity: EntityNode) -> Dict[str, Any]:
                 "influence_weight": 2.5
             }
         elif entity_type in ["professor", "expert", "official"]:
-            # 专家/教授：工作+晚间活动，中等频率
+            # Experts/Professors: work + evening activity, medium frequency
             return {
                 "activity_level": 0.4,
                 "posts_per_hour": 0.3,
@@ -945,12 +945,12 @@ def _generate_agent_config_by_rule(self, entity: EntityNode) -> Dict[str, Any]:
                 "influence_weight": 2.0
             }
         elif entity_type in ["student"]:
-            # 学生：晚间为主，高频率
+            # Students: mainly evening, high frequency
             return {
                 "activity_level": 0.8,
                 "posts_per_hour": 0.6,
                 "comments_per_hour": 1.5,
-                "active_hours": [8, 9, 10, 11, 12, 13, 18, 19, 20, 21, 22, 23],  # 上午+晚间
+                "active_hours": [8, 9, 10, 11, 12, 13, 18, 19, 20, 21, 22, 23],  # Morning + evening
                 "response_delay_min": 1,
                 "response_delay_max": 15,
                 "sentiment_bias": 0.0,
@@ -958,12 +958,12 @@ def _generate_agent_config_by_rule(self, entity: EntityNode) -> Dict[str, Any]:
                 "influence_weight": 0.8
             }
         elif entity_type in ["alumni"]:
-            # 校友：晚间为主
+            # Alumni: mainly evening
             return {
                 "activity_level": 0.6,
                 "posts_per_hour": 0.4,
                 "comments_per_hour": 0.8,
-                "active_hours": [12, 13, 19, 20, 21, 22, 23],  # 午休+晚间
+                "active_hours": [12, 13, 19, 20, 21, 22, 23],  # Lunch break + evening
                 "response_delay_min": 5,
                 "response_delay_max": 30,
                 "sentiment_bias": 0.0,
@@ -971,12 +971,12 @@ def _generate_agent_config_by_rule(self, entity: EntityNode) -> Dict[str, Any]:
                 "influence_weight": 1.0
             }
         else:
-            # 普通人：晚间高峰
+            # Ordinary people: evening peak
             return {
                 "activity_level": 0.7,
                 "posts_per_hour": 0.5,
                 "comments_per_hour": 1.2,
-                "active_hours": [9, 10, 11, 12, 13, 18, 19, 20, 21, 22, 23],  # 白天+晚间
+                "active_hours": [9, 10, 11, 12, 13, 18, 19, 20, 21, 22, 23],  # Daytime + evening
                 "response_delay_min": 2,
                 "response_delay_max": 20,
                 "sentiment_bias": 0.0,
diff --git a/backend/app/services/simulation_manager.py b/backend/app/services/simulation_manager.py
index 96c496fd4..569d8c350 100644
--- a/backend/app/services/simulation_manager.py
+++ b/backend/app/services/simulation_manager.py
@@ -1,7 +1,7 @@
 """
-OASIS模拟管理器
-管理Twitter和Reddit双平台并行模拟
-使用预设脚本 + LLM智能生成配置参数
+OASIS Simulation Manager
+Manage Twitter and Reddit dual-platform parallel simulation
+Using preset scripts + LLM intelligent configuration parameter generation
 """
 
 import os
@@ -22,60 +22,60 @@
 
 
 class SimulationStatus(str, Enum):
-    """模拟状态"""
+    """Simulation status"""
     CREATED = "created"
     PREPARING = "preparing"
     READY = "ready"
     RUNNING = "running"
     PAUSED = "paused"
-    STOPPED = "stopped"      # 模拟被手动停止
-    COMPLETED = "completed"  # 模拟自然完成
+    STOPPED = "stopped"      # Simulation manually stopped
+    COMPLETED = "completed"  # Simulation completed naturally
     FAILED = "failed"
 
 
 class PlatformType(str, Enum):
-    """平台类型"""
+    """Platform type"""
     TWITTER = "twitter"
     REDDIT = "reddit"
 
 
 @dataclass
 class SimulationState:
-    """模拟状态"""
+    """Simulation state"""
     simulation_id: str
     project_id: str
     graph_id: str
-    
-    # 平台启用状态
+
+    # Platform enable status
     enable_twitter: bool = True
     enable_reddit: bool = True
     
-    # 状态
+    # Status
     status: SimulationStatus = SimulationStatus.CREATED
-    
-    # 准备阶段数据
+
+    # Preparation stage data
     entities_count: int = 0
     profiles_count: int = 0
     entity_types: List[str] = field(default_factory=list)
     
-    # 配置生成信息
+    # Configuration generation info
     config_generated: bool = False
     config_reasoning: str = ""
     
-    # 运行时数据
+    # Runtime data
     current_round: int = 0
     twitter_status: str = "not_started"
     reddit_status: str = "not_started"
     
-    # 时间戳
+    # Timestamps
     created_at: str = field(default_factory=lambda: datetime.now().isoformat())
     updated_at: str = field(default_factory=lambda: datetime.now().isoformat())
     
-    # 错误信息
+    # Error information
     error: Optional[str] = None
-    
+
     def to_dict(self) -> Dict[str, Any]:
-        """完整状态字典（内部使用）"""
+        """Full state dictionary (internal use)"""
         return {
             "simulation_id": self.simulation_id,
             "project_id": self.project_id,
@@ -97,7 +97,7 @@ def to_dict(self) -> Dict[str, Any]:
         }
     
     def to_simple_dict(self) -> Dict[str, Any]:
-        """简化状态字典（API返回使用）"""
+        """Simplified state dictionary (for API response)"""
         return {
             "simulation_id": self.simulation_id,
             "project_id": self.project_id,
@@ -113,36 +113,36 @@ def to_simple_dict(self) -> Dict[str, Any]:
 
 class SimulationManager:
     """
-    模拟管理器
-    
-    核心功能：
-    1. 从Zep图谱读取实体并过滤
-    2. 生成OASIS Agent Profile
-    3. 使用LLM智能生成模拟配置参数
-    4. 准备预设脚本所需的所有文件
+    Simulation Manager
+
+    Core features:
+    1. Read and filter entities from Zep knowledge graph
+    2. Generate OASIS Agent Profiles
+    3. Use LLM to intelligently generate simulation configuration parameters
+    4. Prepare all files needed for preset scripts
     """
-    
-    # 模拟数据存储目录
+
+    # Simulation data storage directory
     SIMULATION_DATA_DIR = os.path.join(
         os.path.dirname(__file__), 
         '../../uploads/simulations'
     )
     
     def __init__(self):
-        # 确保目录存在
+        # Ensure directory exists
         os.makedirs(self.SIMULATION_DATA_DIR, exist_ok=True)
         
-        # 内存中的模拟状态缓存
+        # In-memory simulation state cache
         self._simulations: Dict[str, SimulationState] = {}
     
     def _get_simulation_dir(self, simulation_id: str) -> str:
-        """获取模拟数据目录"""
+        """Get simulation data directory"""
         sim_dir = os.path.join(self.SIMULATION_DATA_DIR, simulation_id)
         os.makedirs(sim_dir, exist_ok=True)
         return sim_dir
     
     def _save_simulation_state(self, state: SimulationState):
-        """保存模拟状态到文件"""
+        """Save simulation state to file"""
         sim_dir = self._get_simulation_dir(state.simulation_id)
         state_file = os.path.join(sim_dir, "state.json")
         
@@ -154,7 +154,7 @@ def _save_simulation_state(self, state: SimulationState):
         self._simulations[state.simulation_id] = state
     
     def _load_simulation_state(self, simulation_id: str) -> Optional[SimulationState]:
-        """从文件加载模拟状态"""
+        """Load simulation state from file"""
         if simulation_id in self._simulations:
             return self._simulations[simulation_id]
         
@@ -198,13 +198,13 @@ def create_simulation(
         enable_reddit: bool = True,
     ) -> SimulationState:
         """
-        创建新的模拟
+        Create new simulation
         
         Args:
-            project_id: 项目ID
-            graph_id: Zep图谱ID
-            enable_twitter: 是否启用Twitter模拟
-            enable_reddit: 是否启用Reddit模拟
+            project_id: Project ID
+            graph_id: Zep knowledge graph ID
+            enable_twitter: Whether to enable Twitter simulation
+            enable_reddit: Whether to enable Reddit simulation
             
         Returns:
             SimulationState
@@ -222,7 +222,7 @@ def create_simulation(
         )
         
         self._save_simulation_state(state)
-        logger.info(f"创建模拟: {simulation_id}, project={project_id}, graph={graph_id}")
+        logger.info(f"Created simulation: {simulation_id}, project={project_id}, graph={graph_id}")
         
         return state
     
@@ -237,30 +237,30 @@ def prepare_simulation(
         parallel_profile_count: int = 3
     ) -> SimulationState:
         """
-        准备模拟环境（全程自动化）
+        Prepare simulation environment (fully automated)
         
-        步骤：
-        1. 从Zep图谱读取并过滤实体
-        2. 为每个实体生成OASIS Agent Profile（可选LLM增强，支持并行）
-        3. 使用LLM智能生成模拟配置参数（时间、活跃度、发言频率等）
-        4. 保存配置文件和Profile文件
-        5. 复制预设脚本到模拟目录
+        Steps:
+        1. Read and filter entities from Zep knowledge graph
+        2. Generate OASIS Agent Profile for each entity (optional LLM enhancement, parallel support)
+        3. Use LLM to intelligently generate simulation config parameters (time, activity, posting frequency, etc.)
+        4. Save configuration and Profile files
+        5. Copy preset scripts to simulation directory
         
         Args:
-            simulation_id: 模拟ID
-            simulation_requirement: 模拟需求描述（用于LLM生成配置）
-            document_text: 原始文档内容（用于LLM理解背景）
-            defined_entity_types: 预定义的实体类型（可选）
-            use_llm_for_profiles: 是否使用LLM生成详细人设
-            progress_callback: 进度回调函数 (stage, progress, message)
-            parallel_profile_count: 并行生成人设的数量，默认3
+            simulation_id: Simulation ID
+            simulation_requirement: Simulation requirement description (for LLM configuration generation)
+            document_text: Original document content (for LLM background understanding)
+            defined_entity_types: Predefined entity types (optional)
+            use_llm_for_profiles: Whether to use LLM to generate detailed personas
+            progress_callback: Progress callback function (stage, progress, message)
+            parallel_profile_count: Number of parallel persona generations, default 3
             
         Returns:
             SimulationState
         """
         state = self._load_simulation_state(simulation_id)
         if not state:
-            raise ValueError(f"模拟不存在: {simulation_id}")
+            raise ValueError(f"Simulation not found: {simulation_id}")
         
         try:
             state.status = SimulationStatus.PREPARING
@@ -268,14 +268,14 @@ def prepare_simulation(
             
             sim_dir = self._get_simulation_dir(simulation_id)
             
-            # ========== 阶段1: 读取并过滤实体 ==========
+            # ========== Stage 1: Read and filter entities ==========
             if progress_callback:
-                progress_callback("reading", 0, "正在连接Zep图谱...")
+                progress_callback("reading", 0, "Connecting to Zep knowledge graph...")
             
             reader = ZepEntityReader()
             
             if progress_callback:
-                progress_callback("reading", 30, "正在读取节点数据...")
+                progress_callback("reading", 30, "Reading node data...")
             
             filtered = reader.filter_defined_entities(
                 graph_id=state.graph_id,
@@ -289,29 +289,29 @@ def prepare_simulation(
             if progress_callback:
                 progress_callback(
                     "reading", 100, 
-                    f"完成，共 {filtered.filtered_count} 个实体",
+                    f"Complete, {filtered.filtered_count} entities found",
                     current=filtered.filtered_count,
                     total=filtered.filtered_count
                 )
             
             if filtered.filtered_count == 0:
                 state.status = SimulationStatus.FAILED
-                state.error = "没有找到符合条件的实体，请检查图谱是否正确构建"
+                state.error = "No matching entities found, please check if the knowledge graph is correctly built"
                 self._save_simulation_state(state)
                 return state
             
-            # ========== 阶段2: 生成Agent Profile ==========
+            # ========== Stage 2: Generate Agent Profiles ==========
             total_entities = len(filtered.entities)
             
             if progress_callback:
                 progress_callback(
                     "generating_profiles", 0, 
-                    "开始生成...",
+                    "Starting generation...",
                     current=0,
                     total=total_entities
                 )
             
-            # 传入graph_id以启用Zep检索功能，获取更丰富的上下文
+            # Pass graph_id to enable Zep retrieval for richer context
             generator = OasisProfileGenerator(graph_id=state.graph_id)
             
             def profile_progress(current, total, msg):
@@ -325,7 +325,7 @@ def profile_progress(current, total, msg):
                         item_name=msg
                     )
             
-            # 设置实时保存的文件路径（优先使用 Reddit JSON 格式）
+            # Set real-time save file path (prefer Reddit JSON format)
             realtime_output_path = None
             realtime_platform = "reddit"
             if state.enable_reddit:
@@ -339,20 +339,20 @@ def profile_progress(current, total, msg):
                 entities=filtered.entities,
                 use_llm=use_llm_for_profiles,
                 progress_callback=profile_progress,
-                graph_id=state.graph_id,  # 传入graph_id用于Zep检索
-                parallel_count=parallel_profile_count,  # 并行生成数量
-                realtime_output_path=realtime_output_path,  # 实时保存路径
-                output_platform=realtime_platform  # 输出格式
+                graph_id=state.graph_id,  # Pass graph_id for Zep retrieval
+                parallel_count=parallel_profile_count,  # Parallel generation count
+                realtime_output_path=realtime_output_path,  # Real-time save path
+                output_platform=realtime_platform  # Output format
             )
             
             state.profiles_count = len(profiles)
             
-            # 保存Profile文件（注意：Twitter使用CSV格式，Reddit使用JSON格式）
-            # Reddit 已经在生成过程中实时保存了，这里再保存一次确保完整性
+            # Save Profile files (note: Twitter uses CSV format, Reddit uses JSON format)
+            # Reddit was already saved in real-time during generation, save again to ensure completeness
             if progress_callback:
                 progress_callback(
                     "generating_profiles", 95, 
-                    "保存Profile文件...",
+                    "Saving Profile files...",
                     current=total_entities,
                     total=total_entities
                 )
@@ -365,7 +365,7 @@ def profile_progress(current, total, msg):
                 )
             
             if state.enable_twitter:
-                # Twitter使用CSV格式！这是OASIS的要求
+                # Twitter uses CSV format! This is an OASIS requirement
                 generator.save_profiles(
                     profiles=profiles,
                     file_path=os.path.join(sim_dir, "twitter_profiles.csv"),
@@ -375,16 +375,16 @@ def profile_progress(current, total, msg):
             if progress_callback:
                 progress_callback(
                     "generating_profiles", 100, 
-                    f"完成，共 {len(profiles)} 个Profile",
+                    f"Complete, {len(profiles)} Profiles generated",
                     current=len(profiles),
                     total=len(profiles)
                 )
             
-            # ========== 阶段3: LLM智能生成模拟配置 ==========
+            # ========== Stage 3: LLM intelligent simulation configuration generation ==========
             if progress_callback:
                 progress_callback(
                     "generating_config", 0, 
-                    "正在分析模拟需求...",
+                    "Analyzing simulation requirements...",
                     current=0,
                     total=3
                 )
@@ -394,7 +394,7 @@ def profile_progress(current, total, msg):
             if progress_callback:
                 progress_callback(
                     "generating_config", 30, 
-                    "正在调用LLM生成配置...",
+                    "Calling LLM to generate configuration...",
                     current=1,
                     total=3
                 )
@@ -413,12 +413,12 @@ def profile_progress(current, total, msg):
             if progress_callback:
                 progress_callback(
                     "generating_config", 70, 
-                    "正在保存配置文件...",
+                    "Saving configuration files...",
                     current=2,
                     total=3
                 )
             
-            # 保存配置文件
+            # Save configuration files
             config_path = os.path.join(sim_dir, "simulation_config.json")
             with open(config_path, 'w', encoding='utf-8') as f:
                 f.write(sim_params.to_json())
@@ -429,25 +429,25 @@ def profile_progress(current, total, msg):
             if progress_callback:
                 progress_callback(
                     "generating_config", 100, 
-                    "配置生成完成",
+                    "Configuration generation complete",
                     current=3,
                     total=3
                 )
             
-            # 注意：运行脚本保留在 backend/scripts/ 目录，不再复制到模拟目录
-            # 启动模拟时，simulation_runner 会从 scripts/ 目录运行脚本
+            # Note: Run scripts remain in backend/scripts/ directory, no longer copied to simulation directory
+            # When starting simulation, simulation_runner will run scripts from scripts/ directory
             
-            # 更新状态
+            # Update status
             state.status = SimulationStatus.READY
             self._save_simulation_state(state)
             
-            logger.info(f"模拟准备完成: {simulation_id}, "
+            logger.info(f"Simulation preparation complete: {simulation_id}, "
                        f"entities={state.entities_count}, profiles={state.profiles_count}")
             
             return state
             
         except Exception as e:
-            logger.error(f"模拟准备失败: {simulation_id}, error={str(e)}")
+            logger.error(f"Simulation preparation failed: {simulation_id}, error={str(e)}")
             import traceback
             logger.error(traceback.format_exc())
             state.status = SimulationStatus.FAILED
@@ -456,16 +456,16 @@ def profile_progress(current, total, msg):
             raise
     
     def get_simulation(self, simulation_id: str) -> Optional[SimulationState]:
-        """获取模拟状态"""
+        """Get simulation status"""
         return self._load_simulation_state(simulation_id)
     
     def list_simulations(self, project_id: Optional[str] = None) -> List[SimulationState]:
-        """列出所有模拟"""
+        """List all simulations"""
         simulations = []
         
         if os.path.exists(self.SIMULATION_DATA_DIR):
             for sim_id in os.listdir(self.SIMULATION_DATA_DIR):
-                # 跳过隐藏文件（如 .DS_Store）和非目录文件
+                # Skip hidden files (e.g. .DS_Store) and non-directory files
                 sim_path = os.path.join(self.SIMULATION_DATA_DIR, sim_id)
                 if sim_id.startswith('.') or not os.path.isdir(sim_path):
                     continue
@@ -478,10 +478,10 @@ def list_simulations(self, project_id: Optional[str] = None) -> List[SimulationS
         return simulations
     
     def get_profiles(self, simulation_id: str, platform: str = "reddit") -> List[Dict[str, Any]]:
-        """获取模拟的Agent Profile"""
+        """Get simulation Agent Profiles"""
         state = self._load_simulation_state(simulation_id)
         if not state:
-            raise ValueError(f"模拟不存在: {simulation_id}")
+            raise ValueError(f"Simulation not found: {simulation_id}")
         
         sim_dir = self._get_simulation_dir(simulation_id)
         profile_path = os.path.join(sim_dir, f"{platform}_profiles.json")
@@ -493,7 +493,7 @@ def get_profiles(self, simulation_id: str, platform: str = "reddit") -> List[Dic
             return json.load(f)
     
     def get_simulation_config(self, simulation_id: str) -> Optional[Dict[str, Any]]:
-        """获取模拟配置"""
+        """Get simulation configuration"""
         sim_dir = self._get_simulation_dir(simulation_id)
         config_path = os.path.join(sim_dir, "simulation_config.json")
         
@@ -504,7 +504,7 @@ def get_simulation_config(self, simulation_id: str) -> Optional[Dict[str, Any]]:
             return json.load(f)
     
     def get_run_instructions(self, simulation_id: str) -> Dict[str, str]:
-        """获取运行说明"""
+        """Get run instructions"""
         sim_dir = self._get_simulation_dir(simulation_id)
         config_path = os.path.join(sim_dir, "simulation_config.json")
         scripts_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), '../../scripts'))
@@ -519,10 +519,10 @@ def get_run_instructions(self, simulation_id: str) -> Dict[str, str]:
                 "parallel": f"python {scripts_dir}/run_parallel_simulation.py --config {config_path}",
             },
             "instructions": (
-                f"1. 激活conda环境: conda activate MiroFish\n"
-                f"2. 运行模拟 (脚本位于 {scripts_dir}):\n"
-                f"   - 单独运行Twitter: python {scripts_dir}/run_twitter_simulation.py --config {config_path}\n"
-                f"   - 单独运行Reddit: python {scripts_dir}/run_reddit_simulation.py --config {config_path}\n"
-                f"   - 并行运行双平台: python {scripts_dir}/run_parallel_simulation.py --config {config_path}"
+                f"1. Activate conda environment: conda activate MiroFish\n"
+                f"2. Run simulation (scripts located at {scripts_dir}):\n"
+                f"   - Run Twitter only: python {scripts_dir}/run_twitter_simulation.py --config {config_path}\n"
+                f"   - Run Reddit only: python {scripts_dir}/run_reddit_simulation.py --config {config_path}\n"
+                f"   - Run both platforms in parallel: python {scripts_dir}/run_parallel_simulation.py --config {config_path}"
             )
         }
diff --git a/backend/app/services/simulation_runner.py b/backend/app/services/simulation_runner.py
index 8c35380d1..9794a674e 100644
--- a/backend/app/services/simulation_runner.py
+++ b/backend/app/services/simulation_runner.py
@@ -1,6 +1,6 @@
 """
-OASIS模拟运行器
-在后台运行模拟并记录每个Agent的动作，支持实时状态监控
+OASIS Simulation Runner
+Run simulation in background and record each Agent's actions, with real-time status monitoring
 """
 
 import os
@@ -25,15 +25,15 @@
 
 logger = get_logger('mirofish.simulation_runner')
 
-# 标记是否已注册清理函数
+# Flag whether cleanup function has been registered
 _cleanup_registered = False
 
-# 平台检测
+# Platform detection
 IS_WINDOWS = sys.platform == 'win32'
 
 
 class RunnerStatus(str, Enum):
-    """运行器状态"""
+    """Runner status"""
     IDLE = "idle"
     STARTING = "starting"
     RUNNING = "running"
@@ -46,7 +46,7 @@ class RunnerStatus(str, Enum):
 
 @dataclass
 class AgentAction:
-    """Agent动作记录"""
+    """Agent action record"""
     round_num: int
     timestamp: str
     platform: str  # twitter / reddit
@@ -73,7 +73,7 @@ def to_dict(self) -> Dict[str, Any]:
 
 @dataclass
 class RoundSummary:
-    """每轮摘要"""
+    """Round summary"""
     round_num: int
     start_time: str
     end_time: Optional[str] = None
@@ -99,52 +99,52 @@ def to_dict(self) -> Dict[str, Any]:
 
 @dataclass
 class SimulationRunState:
-    """模拟运行状态（实时）"""
+    """Simulation run state (real-time)"""
     simulation_id: str
     runner_status: RunnerStatus = RunnerStatus.IDLE
     
-    # 进度信息
+    # Progress information
     current_round: int = 0
     total_rounds: int = 0
     simulated_hours: int = 0
     total_simulation_hours: int = 0
     
-    # 各平台独立轮次和模拟时间（用于双平台并行显示）
+    # Per-platform independent rounds and simulation time (for dual-platform parallel display)
     twitter_current_round: int = 0
     reddit_current_round: int = 0
     twitter_simulated_hours: int = 0
     reddit_simulated_hours: int = 0
     
-    # 平台状态
+    # Platform status
     twitter_running: bool = False
     reddit_running: bool = False
     twitter_actions_count: int = 0
     reddit_actions_count: int = 0
     
-    # 平台完成状态（通过检测 actions.jsonl 中的 simulation_end 事件）
+    # Platform completion status (by detecting simulation_end event in actions.jsonl)
     twitter_completed: bool = False
     reddit_completed: bool = False
     
-    # 每轮摘要
+    # Round summaries
     rounds: List[RoundSummary] = field(default_factory=list)
     
-    # 最近动作（用于前端实时展示）
+    # Recent actions (for frontend real-time display)
     recent_actions: List[AgentAction] = field(default_factory=list)
     max_recent_actions: int = 50
     
-    # 时间戳
+    # Timestamps
     started_at: Optional[str] = None
     updated_at: str = field(default_factory=lambda: datetime.now().isoformat())
     completed_at: Optional[str] = None
     
-    # 错误信息
+    # Error information
     error: Optional[str] = None
     
-    # 进程ID（用于停止）
+    # Process ID (for stopping)
     process_pid: Optional[int] = None
     
     def add_action(self, action: AgentAction):
-        """添加动作到最近动作列表"""
+        """Add action to recent actions list"""
         self.recent_actions.insert(0, action)
         if len(self.recent_actions) > self.max_recent_actions:
             self.recent_actions = self.recent_actions[:self.max_recent_actions]
@@ -165,7 +165,7 @@ def to_dict(self) -> Dict[str, Any]:
             "simulated_hours": self.simulated_hours,
             "total_simulation_hours": self.total_simulation_hours,
             "progress_percent": round(self.current_round / max(self.total_rounds, 1) * 100, 1),
-            # 各平台独立轮次和时间
+            # Per-platform independent rounds and time
             "twitter_current_round": self.twitter_current_round,
             "reddit_current_round": self.reddit_current_round,
             "twitter_simulated_hours": self.twitter_simulated_hours,
@@ -185,7 +185,7 @@ def to_dict(self) -> Dict[str, Any]:
         }
     
     def to_detail_dict(self) -> Dict[str, Any]:
-        """包含最近动作的详细信息"""
+        """Detailed information including recent actions"""
         result = self.to_dict()
         result["recent_actions"] = [a.to_dict() for a in self.recent_actions]
         result["rounds_count"] = len(self.rounds)
@@ -194,45 +194,45 @@ def to_detail_dict(self) -> Dict[str, Any]:
 
 class SimulationRunner:
     """
-    模拟运行器
+    Simulation Runner
     
-    负责：
-    1. 在后台进程中运行OASIS模拟
-    2. 解析运行日志，记录每个Agent的动作
-    3. 提供实时状态查询接口
-    4. 支持暂停/停止/恢复操作
+    Responsibilities:
+    1. Run OASIS simulation in background process
+    2. Parse run logs, record each Agent's actions
+    3. Provide real-time status query interface
+    4. Support pause/stop/resume operations
     """
     
-    # 运行状态存储目录
+    # Run state storage directory
     RUN_STATE_DIR = os.path.join(
         os.path.dirname(__file__),
         '../../uploads/simulations'
     )
     
-    # 脚本目录
+    # Scripts directory
     SCRIPTS_DIR = os.path.join(
         os.path.dirname(__file__),
         '../../scripts'
     )
     
-    # 内存中的运行状态
+    # In-memory run states
     _run_states: Dict[str, SimulationRunState] = {}
     _processes: Dict[str, subprocess.Popen] = {}
     _action_queues: Dict[str, Queue] = {}
     _monitor_threads: Dict[str, threading.Thread] = {}
-    _stdout_files: Dict[str, Any] = {}  # 存储 stdout 文件句柄
-    _stderr_files: Dict[str, Any] = {}  # 存储 stderr 文件句柄
+    _stdout_files: Dict[str, Any] = {}  # Store stdout file handles
+    _stderr_files: Dict[str, Any] = {}  # Store stderr file handles
     
-    # 图谱记忆更新配置
+    # Graph memory update configuration
     _graph_memory_enabled: Dict[str, bool] = {}  # simulation_id -> enabled
     
     @classmethod
     def get_run_state(cls, simulation_id: str) -> Optional[SimulationRunState]:
-        """获取运行状态"""
+        """Get run state"""
         if simulation_id in cls._run_states:
             return cls._run_states[simulation_id]
         
-        # 尝试从文件加载
+        # Try to load from file
         state = cls._load_run_state(simulation_id)
         if state:
             cls._run_states[simulation_id] = state
@@ -240,7 +240,7 @@ def get_run_state(cls, simulation_id: str) -> Optional[SimulationRunState]:
     
     @classmethod
     def _load_run_state(cls, simulation_id: str) -> Optional[SimulationRunState]:
-        """从文件加载运行状态"""
+        """Load run state from file"""
         state_file = os.path.join(cls.RUN_STATE_DIR, simulation_id, "run_state.json")
         if not os.path.exists(state_file):
             return None
@@ -256,7 +256,7 @@ def _load_run_state(cls, simulation_id: str) -> Optional[SimulationRunState]:
                 total_rounds=data.get("total_rounds", 0),
                 simulated_hours=data.get("simulated_hours", 0),
                 total_simulation_hours=data.get("total_simulation_hours", 0),
-                # 各平台独立轮次和时间
+                # Per-platform independent rounds and time
                 twitter_current_round=data.get("twitter_current_round", 0),
                 reddit_current_round=data.get("reddit_current_round", 0),
                 twitter_simulated_hours=data.get("twitter_simulated_hours", 0),
@@ -274,7 +274,7 @@ def _load_run_state(cls, simulation_id: str) -> Optional[SimulationRunState]:
                 process_pid=data.get("process_pid"),
             )
             
-            # 加载最近动作
+            # Load recent actions
             actions_data = data.get("recent_actions", [])
             for a in actions_data:
                 state.recent_actions.append(AgentAction(
@@ -291,12 +291,12 @@ def _load_run_state(cls, simulation_id: str) -> Optional[SimulationRunState]:
             
             return state
         except Exception as e:
-            logger.error(f"加载运行状态失败: {str(e)}")
+            logger.error(f"Failed to load run state: {str(e)}")
             return None
     
     @classmethod
     def _save_run_state(cls, state: SimulationRunState):
-        """保存运行状态到文件"""
+        """Save run state to file"""
         sim_dir = os.path.join(cls.RUN_STATE_DIR, state.simulation_id)
         os.makedirs(sim_dir, exist_ok=True)
         state_file = os.path.join(sim_dir, "run_state.json")
@@ -313,50 +313,50 @@ def start_simulation(
         cls,
         simulation_id: str,
         platform: str = "parallel",  # twitter / reddit / parallel
-        max_rounds: int = None,  # 最大模拟轮数（可选，用于截断过长的模拟）
-        enable_graph_memory_update: bool = False,  # 是否将活动更新到Zep图谱
-        graph_id: str = None  # Zep图谱ID（启用图谱更新时必需）
+        max_rounds: int = None,  # Maximum simulation rounds (optional, to truncate long simulations)
+        enable_graph_memory_update: bool = False,  # Whether to update activities to Zep knowledge graph
+        graph_id: str = None  # Zep graph ID (required when graph update is enabled)
     ) -> SimulationRunState:
         """
-        启动模拟
+        Start simulation
         
         Args:
-            simulation_id: 模拟ID
-            platform: 运行平台 (twitter/reddit/parallel)
-            max_rounds: 最大模拟轮数（可选，用于截断过长的模拟）
-            enable_graph_memory_update: 是否将Agent活动动态更新到Zep图谱
-            graph_id: Zep图谱ID（启用图谱更新时必需）
+            simulation_id: Simulation ID
+            platform: Running platform (twitter/reddit/parallel)
+            max_rounds: Maximum simulation rounds (optional, to truncate long simulations)
+            enable_graph_memory_update: Whether to dynamically update Agent activities to Zep knowledge graph
+            graph_id: Zep graph ID (required when graph update is enabled)
             
         Returns:
             SimulationRunState
         """
-        # 检查是否已在运行
+        # Check if already running
         existing = cls.get_run_state(simulation_id)
         if existing and existing.runner_status in [RunnerStatus.RUNNING, RunnerStatus.STARTING]:
-            raise ValueError(f"模拟已在运行中: {simulation_id}")
+            raise ValueError(f"Simulation already running: {simulation_id}")
         
-        # 加载模拟配置
+        # Load simulation configuration
         sim_dir = os.path.join(cls.RUN_STATE_DIR, simulation_id)
         config_path = os.path.join(sim_dir, "simulation_config.json")
         
         if not os.path.exists(config_path):
-            raise ValueError(f"模拟配置不存在，请先调用 /prepare 接口")
+            raise ValueError(f"Simulation configuration not found, please call /prepare endpoint first")
         
         with open(config_path, 'r', encoding='utf-8') as f:
             config = json.load(f)
         
-        # 初始化运行状态
+        # Initialize run state
         time_config = config.get("time_config", {})
         total_hours = time_config.get("total_simulation_hours", 72)
         minutes_per_round = time_config.get("minutes_per_round", 30)
         total_rounds = int(total_hours * 60 / minutes_per_round)
         
-        # 如果指定了最大轮数，则截断
+        # If max rounds specified, truncate
         if max_rounds is not None and max_rounds > 0:
             original_rounds = total_rounds
             total_rounds = min(total_rounds, max_rounds)
             if total_rounds < original_rounds:
-                logger.info(f"轮数已截断: {original_rounds} -> {total_rounds} (max_rounds={max_rounds})")
+                logger.info(f"Rounds truncated: {original_rounds} -> {total_rounds} (max_rounds={max_rounds})")
         
         state = SimulationRunState(
             simulation_id=simulation_id,
@@ -368,22 +368,22 @@ def start_simulation(
         
         cls._save_run_state(state)
         
-        # 如果启用图谱记忆更新，创建更新器
+        # If graph memory update enabled, create updater
         if enable_graph_memory_update:
             if not graph_id:
-                raise ValueError("启用图谱记忆更新时必须提供 graph_id")
+                raise ValueError("graph_id must be provided when graph memory update is enabled")
             
             try:
                 ZepGraphMemoryManager.create_updater(simulation_id, graph_id)
                 cls._graph_memory_enabled[simulation_id] = True
-                logger.info(f"已启用图谱记忆更新: simulation_id={simulation_id}, graph_id={graph_id}")
+                logger.info(f"Graph memory update enabled: simulation_id={simulation_id}, graph_id={graph_id}")
             except Exception as e:
-                logger.error(f"创建图谱记忆更新器失败: {e}")
+                logger.error(f"Failed to create graph memory updater: {e}")
                 cls._graph_memory_enabled[simulation_id] = False
         else:
             cls._graph_memory_enabled[simulation_id] = False
         
-        # 确定运行哪个脚本（脚本位于 backend/scripts/ 目录）
+        # Determine which script to run (scripts located in backend/scripts/ directory)
         if platform == "twitter":
             script_name = "run_twitter_simulation.py"
             state.twitter_running = True
@@ -398,64 +398,64 @@ def start_simulation(
         script_path = os.path.join(cls.SCRIPTS_DIR, script_name)
         
         if not os.path.exists(script_path):
-            raise ValueError(f"脚本不存在: {script_path}")
+            raise ValueError(f"Script not found: {script_path}")
         
-        # 创建动作队列
+        # Create action queue
         action_queue = Queue()
         cls._action_queues[simulation_id] = action_queue
         
-        # 启动模拟进程
+        # Start simulation process
         try:
-            # 构建运行命令，使用完整路径
-            # 新的日志结构：
-            #   twitter/actions.jsonl - Twitter 动作日志
-            #   reddit/actions.jsonl  - Reddit 动作日志
-            #   simulation.log        - 主进程日志
+            # Build run command, using full paths
+            # New log structure:
+            #   twitter/actions.jsonl - Twitter action log
+            #   reddit/actions.jsonl  - Reddit action log
+            #   simulation.log        - Main process log
             
             cmd = [
-                sys.executable,  # Python解释器
+                sys.executable,  # Python interpreter
                 script_path,
-                "--config", config_path,  # 使用完整配置文件路径
+                "--config", config_path,  # Use full config file path
             ]
             
-            # 如果指定了最大轮数，添加到命令行参数
+            # If max rounds specified, add to command line args
             if max_rounds is not None and max_rounds > 0:
                 cmd.extend(["--max-rounds", str(max_rounds)])
             
-            # 创建主日志文件，避免 stdout/stderr 管道缓冲区满导致进程阻塞
+            # Create main log file to avoid stdout/stderr pipe buffer full causing process blocking
             main_log_path = os.path.join(sim_dir, "simulation.log")
             main_log_file = open(main_log_path, 'w', encoding='utf-8')
             
-            # 设置子进程环境变量，确保 Windows 上使用 UTF-8 编码
-            # 这可以修复第三方库（如 OASIS）读取文件时未指定编码的问题
+            # Set subprocess env vars to ensure UTF-8 encoding on Windows
+            # This fixes encoding issues when third-party libraries (e.g. OASIS) read files without specifying encoding
             env = os.environ.copy()
-            env['PYTHONUTF8'] = '1'  # Python 3.7+ 支持，让所有 open() 默认使用 UTF-8
-            env['PYTHONIOENCODING'] = 'utf-8'  # 确保 stdout/stderr 使用 UTF-8
+            env['PYTHONUTF8'] = '1'  # Python 3.7+ support, make all open() use UTF-8 by default
+            env['PYTHONIOENCODING'] = 'utf-8'  # Ensure stdout/stderr use UTF-8
             
-            # 设置工作目录为模拟目录（数据库等文件会生成在此）
-            # 使用 start_new_session=True 创建新的进程组，确保可以通过 os.killpg 终止所有子进程
+            # Set working directory to simulation directory (database files will be generated here)
+            # Use start_new_session=True to create new process group, ensuring all child processes can be killed via os.killpg
             process = subprocess.Popen(
                 cmd,
                 cwd=sim_dir,
                 stdout=main_log_file,
-                stderr=subprocess.STDOUT,  # stderr 也写入同一个文件
+                stderr=subprocess.STDOUT,  # stderr also written to same file
                 text=True,
-                encoding='utf-8',  # 显式指定编码
+                encoding='utf-8',  # Explicitly specify encoding
                 bufsize=1,
-                env=env,  # 传递带有 UTF-8 设置的环境变量
-                start_new_session=True,  # 创建新进程组，确保服务器关闭时能终止所有相关进程
+                env=env,  # Pass env vars with UTF-8 settings
+                start_new_session=True,  # Create new process group, ensure all related processes can be terminated when server shuts down
             )
             
-            # 保存文件句柄以便后续关闭
+            # Save file handles for later closing
             cls._stdout_files[simulation_id] = main_log_file
-            cls._stderr_files[simulation_id] = None  # 不再需要单独的 stderr
+            cls._stderr_files[simulation_id] = None  # No longer need separate stderr
             
             state.process_pid = process.pid
             state.runner_status = RunnerStatus.RUNNING
             cls._processes[simulation_id] = process
             cls._save_run_state(state)
             
-            # 启动监控线程
+            # Start monitoring thread
             monitor_thread = threading.Thread(
                 target=cls._monitor_simulation,
                 args=(simulation_id,),
@@ -464,7 +464,7 @@ def start_simulation(
             monitor_thread.start()
             cls._monitor_threads[simulation_id] = monitor_thread
             
-            logger.info(f"模拟启动成功: {simulation_id}, pid={process.pid}, platform={platform}")
+            logger.info(f"Simulation started successfully: {simulation_id}, pid={process.pid}, platform={platform}")
             
         except Exception as e:
             state.runner_status = RunnerStatus.FAILED
@@ -476,10 +476,10 @@ def start_simulation(
     
     @classmethod
     def _monitor_simulation(cls, simulation_id: str):
-        """监控模拟进程，解析动作日志"""
+        """Monitor simulation process, parse action logs"""
         sim_dir = os.path.join(cls.RUN_STATE_DIR, simulation_id)
         
-        # 新的日志结构：分平台的动作日志
+        # New log structure: per-platform action logs
         twitter_actions_log = os.path.join(sim_dir, "twitter", "actions.jsonl")
         reddit_actions_log = os.path.join(sim_dir, "reddit", "actions.jsonl")
         
@@ -493,75 +493,75 @@ def _monitor_simulation(cls, simulation_id: str):
         reddit_position = 0
         
         try:
-            while process.poll() is None:  # 进程仍在运行
-                # 读取 Twitter 动作日志
+            while process.poll() is None:  # Process still running
+                # Read Twitter action log
                 if os.path.exists(twitter_actions_log):
                     twitter_position = cls._read_action_log(
                         twitter_actions_log, twitter_position, state, "twitter"
                     )
                 
-                # 读取 Reddit 动作日志
+                # Read Reddit action log
                 if os.path.exists(reddit_actions_log):
                     reddit_position = cls._read_action_log(
                         reddit_actions_log, reddit_position, state, "reddit"
                     )
                 
-                # 更新状态
+                # Update state
                 cls._save_run_state(state)
                 time.sleep(2)
             
-            # 进程结束后，最后读取一次日志
+            # After process ends, read logs one final time
             if os.path.exists(twitter_actions_log):
                 cls._read_action_log(twitter_actions_log, twitter_position, state, "twitter")
             if os.path.exists(reddit_actions_log):
                 cls._read_action_log(reddit_actions_log, reddit_position, state, "reddit")
             
-            # 进程结束
+            # Process ended
             exit_code = process.returncode
             
             if exit_code == 0:
                 state.runner_status = RunnerStatus.COMPLETED
                 state.completed_at = datetime.now().isoformat()
-                logger.info(f"模拟完成: {simulation_id}")
+                logger.info(f"Simulation completed: {simulation_id}")
             else:
                 state.runner_status = RunnerStatus.FAILED
-                # 从主日志文件读取错误信息
+                # Read error info from main log file
                 main_log_path = os.path.join(sim_dir, "simulation.log")
                 error_info = ""
                 try:
                     if os.path.exists(main_log_path):
                         with open(main_log_path, 'r', encoding='utf-8') as f:
-                            error_info = f.read()[-2000:]  # 取最后2000字符
+                            error_info = f.read()[-2000:]  # Get last 2000 characters
                 except Exception:
                     pass
-                state.error = f"进程退出码: {exit_code}, 错误: {error_info}"
-                logger.error(f"模拟失败: {simulation_id}, error={state.error}")
+                state.error = f"Process exit code: {exit_code}, error: {error_info}"
+                logger.error(f"Simulation failed: {simulation_id}, error={state.error}")
             
             state.twitter_running = False
             state.reddit_running = False
             cls._save_run_state(state)
             
         except Exception as e:
-            logger.error(f"监控线程异常: {simulation_id}, error={str(e)}")
+            logger.error(f"Monitor thread exception: {simulation_id}, error={str(e)}")
             state.runner_status = RunnerStatus.FAILED
             state.error = str(e)
             cls._save_run_state(state)
         
         finally:
-            # 停止图谱记忆更新器
+            # Stop graph memory updater
             if cls._graph_memory_enabled.get(simulation_id, False):
                 try:
                     ZepGraphMemoryManager.stop_updater(simulation_id)
-                    logger.info(f"已停止图谱记忆更新: simulation_id={simulation_id}")
+                    logger.info(f"Stopped graph memory update: simulation_id={simulation_id}")
                 except Exception as e:
-                    logger.error(f"停止图谱记忆更新器失败: {e}")
+                    logger.error(f"Failed to stop graph memory updater: {e}")
                 cls._graph_memory_enabled.pop(simulation_id, None)
             
-            # 清理进程资源
+            # Clean up process resources
             cls._processes.pop(simulation_id, None)
             cls._action_queues.pop(simulation_id, None)
             
-            # 关闭日志文件句柄
+            # Close log file handles
             if simulation_id in cls._stdout_files:
                 try:
                     cls._stdout_files[simulation_id].close()
@@ -584,18 +584,18 @@ def _read_action_log(
         platform: str
     ) -> int:
         """
-        读取动作日志文件
+        Read action log file
         
         Args:
-            log_path: 日志文件路径
-            position: 上次读取位置
-            state: 运行状态对象
-            platform: 平台名称 (twitter/reddit)
+            log_path: Log file path
+            position: Last read position
+            state: Run state object
+            platform: Platform name (twitter/reddit)
             
         Returns:
-            新的读取位置
+            New read position
         """
-        # 检查是否启用了图谱记忆更新
+        # Check if graph memory update is enabled
         graph_memory_enabled = cls._graph_memory_enabled.get(state.simulation_id, False)
         graph_updater = None
         if graph_memory_enabled:
@@ -610,36 +610,36 @@ def _read_action_log(
                         try:
                             action_data = json.loads(line)
                             
-                            # 处理事件类型的条目
+                            # Process event type entries
                             if "event_type" in action_data:
                                 event_type = action_data.get("event_type")
                                 
-                                # 检测 simulation_end 事件，标记平台已完成
+                                # Detect simulation_end event, mark platform as completed
                                 if event_type == "simulation_end":
                                     if platform == "twitter":
                                         state.twitter_completed = True
                                         state.twitter_running = False
-                                        logger.info(f"Twitter 模拟已完成: {state.simulation_id}, total_rounds={action_data.get('total_rounds')}, total_actions={action_data.get('total_actions')}")
+                                        logger.info(f"Twitter simulation completed: {state.simulation_id}, total_rounds={action_data.get('total_rounds')}, total_actions={action_data.get('total_actions')}")
                                     elif platform == "reddit":
                                         state.reddit_completed = True
                                         state.reddit_running = False
-                                        logger.info(f"Reddit 模拟已完成: {state.simulation_id}, total_rounds={action_data.get('total_rounds')}, total_actions={action_data.get('total_actions')}")
+                                        logger.info(f"Reddit simulation completed: {state.simulation_id}, total_rounds={action_data.get('total_rounds')}, total_actions={action_data.get('total_actions')}")
                                     
-                                    # 检查是否所有启用的平台都已完成
-                                    # 如果只运行了一个平台，只检查那个平台
-                                    # 如果运行了两个平台，需要两个都完成
+                                    # Check if all enabled platforms have completed
+                                    # If only one platform was run, only check that platform
+                                    # If two platforms were run, both need to be completed
                                     all_completed = cls._check_all_platforms_completed(state)
                                     if all_completed:
                                         state.runner_status = RunnerStatus.COMPLETED
                                         state.completed_at = datetime.now().isoformat()
-                                        logger.info(f"所有平台模拟已完成: {state.simulation_id}")
+                                        logger.info(f"All platform simulations completed: {state.simulation_id}")
                                 
-                                # 更新轮次信息（从 round_end 事件）
+                                # Update round info (from round_end event)
                                 elif event_type == "round_end":
                                     round_num = action_data.get("round", 0)
                                     simulated_hours = action_data.get("simulated_hours", 0)
                                     
-                                    # 更新各平台独立的轮次和时间
+                                    # Update per-platform independent rounds and time
                                     if platform == "twitter":
                                         if round_num > state.twitter_current_round:
                                             state.twitter_current_round = round_num
@@ -649,10 +649,10 @@ def _read_action_log(
                                             state.reddit_current_round = round_num
                                         state.reddit_simulated_hours = simulated_hours
                                     
-                                    # 总体轮次取两个平台的最大值
+                                    # Overall round takes maximum of both platforms
                                     if round_num > state.current_round:
                                         state.current_round = round_num
-                                    # 总体时间取两个平台的最大值
+                                    # Overall time takes maximum of both platforms
                                     state.simulated_hours = max(state.twitter_simulated_hours, state.reddit_simulated_hours)
                                 
                                 continue
@@ -670,11 +670,11 @@ def _read_action_log(
                             )
                             state.add_action(action)
                             
-                            # 更新轮次
+                            # Update rounds
                             if action.round_num and action.round_num > state.current_round:
                                 state.current_round = action.round_num
                             
-                            # 如果启用了图谱记忆更新，将活动发送到Zep
+                            # If graph memory update enabled, send activities to Zep
                             if graph_updater:
                                 graph_updater.add_activity_from_dict(action_data, platform)
                             
@@ -682,52 +682,52 @@ def _read_action_log(
                             pass
                 return f.tell()
         except Exception as e:
-            logger.warning(f"读取动作日志失败: {log_path}, error={e}")
+            logger.warning(f"Failed to read action log: {log_path}, error={e}")
             return position
     
     @classmethod
     def _check_all_platforms_completed(cls, state: SimulationRunState) -> bool:
         """
-        检查所有启用的平台是否都已完成模拟
+        Check if all enabled platforms have completed simulation
         
-        通过检查对应的 actions.jsonl 文件是否存在来判断平台是否被启用
+        Determine if a platform is enabled by checking whether the corresponding actions.jsonl file exists
         
         Returns:
-            True 如果所有启用的平台都已完成
+            True if all enabled platforms have completed
         """
         sim_dir = os.path.join(cls.RUN_STATE_DIR, state.simulation_id)
         twitter_log = os.path.join(sim_dir, "twitter", "actions.jsonl")
         reddit_log = os.path.join(sim_dir, "reddit", "actions.jsonl")
         
-        # 检查哪些平台被启用（通过文件是否存在判断）
+        # Check which platforms are enabled (by file existence)
         twitter_enabled = os.path.exists(twitter_log)
         reddit_enabled = os.path.exists(reddit_log)
         
-        # 如果平台被启用但未完成，则返回 False
+        # If platform is enabled but not completed, return False
         if twitter_enabled and not state.twitter_completed:
             return False
         if reddit_enabled and not state.reddit_completed:
             return False
         
-        # 至少有一个平台被启用且已完成
+        # At least one platform is enabled and completed
         return twitter_enabled or reddit_enabled
     
     @classmethod
     def _terminate_process(cls, process: subprocess.Popen, simulation_id: str, timeout: int = 10):
         """
-        跨平台终止进程及其子进程
+        Cross-platform terminate process and its child processes
         
         Args:
-            process: 要终止的进程
-            simulation_id: 模拟ID（用于日志）
-            timeout: 等待进程退出的超时时间（秒）
+            process: Process to terminate
+            simulation_id: Simulation ID (for logging)
+            timeout: Timeout for waiting process to exit (seconds)
         """
         if IS_WINDOWS:
-            # Windows: 使用 taskkill 命令终止进程树
-            # /F = 强制终止, /T = 终止进程树（包括子进程）
-            logger.info(f"终止进程树 (Windows): simulation={simulation_id}, pid={process.pid}")
+            # Windows: Use taskkill command to terminate process tree
+            # /F = Force terminate, /T = Terminate process tree (including child processes)
+            logger.info(f"Terminating process tree (Windows): simulation={simulation_id}, pid={process.pid}")
             try:
-                # 先尝试优雅终止
+                # Try graceful termination first
                 subprocess.run(
                     ['taskkill', '/PID', str(process.pid), '/T'],
                     capture_output=True,
@@ -736,8 +736,8 @@ def _terminate_process(cls, process: subprocess.Popen, simulation_id: str, timeo
                 try:
                     process.wait(timeout=timeout)
                 except subprocess.TimeoutExpired:
-                    # 强制终止
-                    logger.warning(f"进程未响应，强制终止: {simulation_id}")
+                    # Force terminate
+                    logger.warning(f"Process not responding, force terminating: {simulation_id}")
                     subprocess.run(
                         ['taskkill', '/F', '/PID', str(process.pid), '/T'],
                         capture_output=True,
@@ -745,53 +745,53 @@ def _terminate_process(cls, process: subprocess.Popen, simulation_id: str, timeo
                     )
                     process.wait(timeout=5)
             except Exception as e:
-                logger.warning(f"taskkill 失败，尝试 terminate: {e}")
+                logger.warning(f"taskkill failed, trying terminate: {e}")
                 process.terminate()
                 try:
                     process.wait(timeout=5)
                 except subprocess.TimeoutExpired:
                     process.kill()
         else:
-            # Unix: 使用进程组终止
-            # 由于使用了 start_new_session=True，进程组 ID 等于主进程 PID
+            # Unix: Use process group termination
+            # Since start_new_session=True was used, process group ID equals main process PID
             pgid = os.getpgid(process.pid)
-            logger.info(f"终止进程组 (Unix): simulation={simulation_id}, pgid={pgid}")
+            logger.info(f"Terminating process group (Unix): simulation={simulation_id}, pgid={pgid}")
             
-            # 先发送 SIGTERM 给整个进程组
+            # Send SIGTERM to entire process group first
             os.killpg(pgid, signal.SIGTERM)
             
             try:
                 process.wait(timeout=timeout)
             except subprocess.TimeoutExpired:
-                # 如果超时后还没结束，强制发送 SIGKILL
-                logger.warning(f"进程组未响应 SIGTERM，强制终止: {simulation_id}")
+                # If not ended after timeout, force send SIGKILL
+                logger.warning(f"Process group not responding to SIGTERM, force terminating: {simulation_id}")
                 os.killpg(pgid, signal.SIGKILL)
                 process.wait(timeout=5)
     
     @classmethod
     def stop_simulation(cls, simulation_id: str) -> SimulationRunState:
-        """停止模拟"""
+        """Stop simulation"""
         state = cls.get_run_state(simulation_id)
         if not state:
-            raise ValueError(f"模拟不存在: {simulation_id}")
+            raise ValueError(f"Simulation not found: {simulation_id}")
         
         if state.runner_status not in [RunnerStatus.RUNNING, RunnerStatus.PAUSED]:
-            raise ValueError(f"模拟未在运行: {simulation_id}, status={state.runner_status}")
+            raise ValueError(f"Simulation not running: {simulation_id}, status={state.runner_status}")
         
         state.runner_status = RunnerStatus.STOPPING
         cls._save_run_state(state)
         
-        # 终止进程
+        # Terminate process
         process = cls._processes.get(simulation_id)
         if process and process.poll() is None:
             try:
                 cls._terminate_process(process, simulation_id)
             except ProcessLookupError:
-                # 进程已经不存在
+                # Process no longer exists
                 pass
             except Exception as e:
-                logger.error(f"终止进程组失败: {simulation_id}, error={e}")
-                # 回退到直接终止进程
+                logger.error(f"Failed to terminate process group: {simulation_id}, error={e}")
+                # Fall back to direct process termination
                 try:
                     process.terminate()
                     process.wait(timeout=5)
@@ -804,16 +804,16 @@ def stop_simulation(cls, simulation_id: str) -> SimulationRunState:
         state.completed_at = datetime.now().isoformat()
         cls._save_run_state(state)
         
-        # 停止图谱记忆更新器
+        # Stop graph memory updater
         if cls._graph_memory_enabled.get(simulation_id, False):
             try:
                 ZepGraphMemoryManager.stop_updater(simulation_id)
-                logger.info(f"已停止图谱记忆更新: simulation_id={simulation_id}")
+                logger.info(f"Stopped graph memory update: simulation_id={simulation_id}")
             except Exception as e:
-                logger.error(f"停止图谱记忆更新器失败: {e}")
+                logger.error(f"Failed to stop graph memory updater: {e}")
             cls._graph_memory_enabled.pop(simulation_id, None)
         
-        logger.info(f"模拟已停止: {simulation_id}")
+        logger.info(f"Simulation stopped: {simulation_id}")
         return state
     
     @classmethod
@@ -826,14 +826,14 @@ def _read_actions_from_file(
         round_num: Optional[int] = None
     ) -> List[AgentAction]:
         """
-        从单个动作文件中读取动作
+        Read actions from a single action file
         
         Args:
-            file_path: 动作日志文件路径
-            default_platform: 默认平台（当动作记录中没有 platform 字段时使用）
-            platform_filter: 过滤平台
-            agent_id: 过滤 Agent ID
-            round_num: 过滤轮次
+            file_path: Action log file path
+            default_platform: Default platform (used when action record has no platform field)
+            platform_filter: Platform filter
+            agent_id: Agent ID filter
+            round_num: Round filter
         """
         if not os.path.exists(file_path):
             return []
@@ -849,18 +849,18 @@ def _read_actions_from_file(
                 try:
                     data = json.loads(line)
                     
-                    # 跳过非动作记录（如 simulation_start, round_start, round_end 等事件）
+                    # Skip non-action records (e.g. simulation_start, round_start, round_end events)
                     if "event_type" in data:
                         continue
                     
-                    # 跳过没有 agent_id 的记录（非 Agent 动作）
+                    # Skip records without agent_id (non-Agent actions)
                     if "agent_id" not in data:
                         continue
                     
-                    # 获取平台：优先使用记录中的 platform，否则使用默认平台
+                    # Get platform: prefer platform from record, otherwise use default
                     record_platform = data.get("platform") or default_platform or ""
                     
-                    # 过滤
+                    # Filter
                     if platform_filter and record_platform != platform_filter:
                         continue
                     if agent_id is not None and data.get("agent_id") != agent_id:
@@ -894,54 +894,54 @@ def get_all_actions(
         round_num: Optional[int] = None
     ) -> List[AgentAction]:
         """
-        获取所有平台的完整动作历史（无分页限制）
+        Get complete action history for all platforms (no pagination limit)
         
         Args:
-            simulation_id: 模拟ID
-            platform: 过滤平台（twitter/reddit）
-            agent_id: 过滤Agent
-            round_num: 过滤轮次
+            simulation_id: Simulation ID
+            platform: Platform filter (twitter/reddit)
+            agent_id: Agent filter
+            round_num: Round filter
             
         Returns:
-            完整的动作列表（按时间戳排序，新的在前）
+            Complete action list (sorted by timestamp, newest first)
         """
         sim_dir = os.path.join(cls.RUN_STATE_DIR, simulation_id)
         actions = []
         
-        # 读取 Twitter 动作文件（根据文件路径自动设置 platform 为 twitter）
+        # Read Twitter action file (auto-set platform to twitter based on file path)
         twitter_actions_log = os.path.join(sim_dir, "twitter", "actions.jsonl")
         if not platform or platform == "twitter":
             actions.extend(cls._read_actions_from_file(
                 twitter_actions_log,
-                default_platform="twitter",  # 自动填充 platform 字段
+                default_platform="twitter",  # Auto-fill platform field
                 platform_filter=platform,
                 agent_id=agent_id, 
                 round_num=round_num
             ))
         
-        # 读取 Reddit 动作文件（根据文件路径自动设置 platform 为 reddit）
+        # Read Reddit action file (auto-set platform to reddit based on file path)
         reddit_actions_log = os.path.join(sim_dir, "reddit", "actions.jsonl")
         if not platform or platform == "reddit":
             actions.extend(cls._read_actions_from_file(
                 reddit_actions_log,
-                default_platform="reddit",  # 自动填充 platform 字段
+                default_platform="reddit",  # Auto-fill platform field
                 platform_filter=platform,
                 agent_id=agent_id,
                 round_num=round_num
             ))
         
-        # 如果分平台文件不存在，尝试读取旧的单一文件格式
+        # If per-platform files don't exist, try reading legacy single file format
         if not actions:
             actions_log = os.path.join(sim_dir, "actions.jsonl")
             actions = cls._read_actions_from_file(
                 actions_log,
-                default_platform=None,  # 旧格式文件中应该有 platform 字段
+                default_platform=None,  # Legacy format files should have platform field
                 platform_filter=platform,
                 agent_id=agent_id,
                 round_num=round_num
             )
         
-        # 按时间戳排序（新的在前）
+        # Sort by timestamp (newest first)
         actions.sort(key=lambda x: x.timestamp, reverse=True)
         
         return actions
@@ -957,18 +957,18 @@ def get_actions(
         round_num: Optional[int] = None
     ) -> List[AgentAction]:
         """
-        获取动作历史（带分页）
+        Get action history (with pagination)
         
         Args:
-            simulation_id: 模拟ID
-            limit: 返回数量限制
-            offset: 偏移量
-            platform: 过滤平台
-            agent_id: 过滤Agent
-            round_num: 过滤轮次
+            simulation_id: Simulation ID
+            limit: Return count limit
+            offset: Offset
+            platform: Platform filter
+            agent_id: Agent filter
+            round_num: Round filter
             
         Returns:
-            动作列表
+            Action list
         """
         actions = cls.get_all_actions(
             simulation_id=simulation_id,
@@ -977,7 +977,7 @@ def get_actions(
             round_num=round_num
         )
         
-        # 分页
+        # Pagination
         return actions[offset:offset + limit]
     
     @classmethod
@@ -988,19 +988,19 @@ def get_timeline(
         end_round: Optional[int] = None
     ) -> List[Dict[str, Any]]:
         """
-        获取模拟时间线（按轮次汇总）
+        Get simulation timeline (summarized by round)
         
         Args:
-            simulation_id: 模拟ID
-            start_round: 起始轮次
-            end_round: 结束轮次
+            simulation_id: Simulation ID
+            start_round: Start round
+            end_round: End round
             
         Returns:
-            每轮的汇总信息
+            Summary information per round
         """
         actions = cls.get_actions(simulation_id, limit=10000)
         
-        # 按轮次分组
+        # Group by round
         rounds: Dict[int, Dict[str, Any]] = {}
         
         for action in actions:
@@ -1033,7 +1033,7 @@ def get_timeline(
             r["action_types"][action.action_type] = r["action_types"].get(action.action_type, 0) + 1
             r["last_action_time"] = action.timestamp
         
-        # 转换为列表
+        # Convert to list
         result = []
         for round_num in sorted(rounds.keys()):
             r = rounds[round_num]
@@ -1054,10 +1054,10 @@ def get_timeline(
     @classmethod
     def get_agent_stats(cls, simulation_id: str) -> List[Dict[str, Any]]:
         """
-        获取每个Agent的统计信息
+        Get statistics for each Agent
         
         Returns:
-            Agent统计列表
+            Agent statistics list
         """
         actions = cls.get_actions(simulation_id, limit=10000)
         
@@ -1089,7 +1089,7 @@ def get_agent_stats(cls, simulation_id: str) -> List[Dict[str, Any]]:
             stats["action_types"][action.action_type] = stats["action_types"].get(action.action_type, 0) + 1
             stats["last_action_time"] = action.timestamp
         
-        # 按总动作数排序
+        # Sort by total action count
         result = sorted(agent_stats.values(), key=lambda x: x["total_actions"], reverse=True)
         
         return result
@@ -1097,51 +1097,51 @@ def get_agent_stats(cls, simulation_id: str) -> List[Dict[str, Any]]:
     @classmethod
     def cleanup_simulation_logs(cls, simulation_id: str) -> Dict[str, Any]:
         """
-        清理模拟的运行日志（用于强制重新开始模拟）
+        Clean up simulation run logs (for force restarting simulation)
         
-        会删除以下文件：
+        Will delete the following files:
         - run_state.json
         - twitter/actions.jsonl
         - reddit/actions.jsonl
         - simulation.log
         - stdout.log / stderr.log
-        - twitter_simulation.db（模拟数据库）
-        - reddit_simulation.db（模拟数据库）
-        - env_status.json（环境状态）
+        - twitter_simulation.db (simulation database)
+        - reddit_simulation.db (simulation database)
+        - env_status.json (environment status)
         
-        注意：不会删除配置文件（simulation_config.json）和 profile 文件
+        Note: Will not delete config files (simulation_config.json) and profile files
         
         Args:
-            simulation_id: 模拟ID
+            simulation_id: Simulation ID
             
         Returns:
-            清理结果信息
+            Cleanup result information
         """
         import shutil
         
         sim_dir = os.path.join(cls.RUN_STATE_DIR, simulation_id)
         
         if not os.path.exists(sim_dir):
-            return {"success": True, "message": "模拟目录不存在，无需清理"}
+            return {"success": True, "message": "Simulation directory does not exist, no cleanup needed"}
         
         cleaned_files = []
         errors = []
         
-        # 要删除的文件列表（包括数据库文件）
+        # Files to delete (including database files)
         files_to_delete = [
             "run_state.json",
             "simulation.log",
             "stdout.log",
             "stderr.log",
-            "twitter_simulation.db",  # Twitter 平台数据库
-            "reddit_simulation.db",   # Reddit 平台数据库
-            "env_status.json",        # 环境状态文件
+            "twitter_simulation.db",  # Twitter platform database
+            "reddit_simulation.db",   # Reddit platform database
+            "env_status.json",        # Environment status file
         ]
         
-        # 要删除的目录列表（包含动作日志）
+        # Directories to clean (containing action logs)
         dirs_to_clean = ["twitter", "reddit"]
         
-        # 删除文件
+        # Delete files
         for filename in files_to_delete:
             file_path = os.path.join(sim_dir, filename)
             if os.path.exists(file_path):
@@ -1149,9 +1149,9 @@ def cleanup_simulation_logs(cls, simulation_id: str) -> Dict[str, Any]:
                     os.remove(file_path)
                     cleaned_files.append(filename)
                 except Exception as e:
-                    errors.append(f"删除 {filename} 失败: {str(e)}")
+                    errors.append(f"Failed to delete {filename}: {str(e)}")
         
-        # 清理平台目录中的动作日志
+        # Clean up action logs in platform directories
         for dir_name in dirs_to_clean:
             dir_path = os.path.join(sim_dir, dir_name)
             if os.path.exists(dir_path):
@@ -1161,13 +1161,13 @@ def cleanup_simulation_logs(cls, simulation_id: str) -> Dict[str, Any]:
                         os.remove(actions_file)
                         cleaned_files.append(f"{dir_name}/actions.jsonl")
                     except Exception as e:
-                        errors.append(f"删除 {dir_name}/actions.jsonl 失败: {str(e)}")
+                        errors.append(f"Failed to delete {dir_name}/actions.jsonl: {str(e)}")
         
-        # 清理内存中的运行状态
+        # Clean up in-memory run states
         if simulation_id in cls._run_states:
             del cls._run_states[simulation_id]
         
-        logger.info(f"清理模拟日志完成: {simulation_id}, 删除文件: {cleaned_files}")
+        logger.info(f"Simulation log cleanup complete: {simulation_id}, deleted files: {cleaned_files}")
         
         return {
             "success": len(errors) == 0,
@@ -1175,71 +1175,71 @@ def cleanup_simulation_logs(cls, simulation_id: str) -> Dict[str, Any]:
             "errors": errors if errors else None
         }
     
-    # 防止重复清理的标志
+    # Flag to prevent duplicate cleanup
     _cleanup_done = False
     
     @classmethod
     def cleanup_all_simulations(cls):
         """
-        清理所有运行中的模拟进程
+        Clean up all running simulation processes
         
-        在服务器关闭时调用，确保所有子进程被终止
+        Called when server shuts down, ensuring all child processes are terminated
         """
-        # 防止重复清理
+        # Prevent duplicate cleanup
         if cls._cleanup_done:
             return
         cls._cleanup_done = True
         
-        # 检查是否有内容需要清理（避免空进程的进程打印无用日志）
+        # Check if there is content to clean (avoid printing useless logs for empty processes)
         has_processes = bool(cls._processes)
         has_updaters = bool(cls._graph_memory_enabled)
         
         if not has_processes and not has_updaters:
-            return  # 没有需要清理的内容，静默返回
+            return  # Nothing to clean, return silently
         
-        logger.info("正在清理所有模拟进程...")
+        logger.info("Cleaning up all simulation processes...")
         
-        # 首先停止所有图谱记忆更新器（stop_all 内部会打印日志）
+        # First stop all graph memory updaters (stop_all will print logs internally)
         try:
             ZepGraphMemoryManager.stop_all()
         except Exception as e:
-            logger.error(f"停止图谱记忆更新器失败: {e}")
+            logger.error(f"Failed to stop graph memory updater: {e}")
         cls._graph_memory_enabled.clear()
         
-        # 复制字典以避免在迭代时修改
+        # Copy dict to avoid modification during iteration
         processes = list(cls._processes.items())
         
         for simulation_id, process in processes:
             try:
-                if process.poll() is None:  # 进程仍在运行
-                    logger.info(f"终止模拟进程: {simulation_id}, pid={process.pid}")
+                if process.poll() is None:  # Process still running
+                    logger.info(f"Terminating simulation process: {simulation_id}, pid={process.pid}")
                     
                     try:
-                        # 使用跨平台的进程终止方法
+                        # Use cross-platform process termination method
                         cls._terminate_process(process, simulation_id, timeout=5)
                     except (ProcessLookupError, OSError):
-                        # 进程可能已经不存在，尝试直接终止
+                        # Process may no longer exist, try direct termination
                         try:
                             process.terminate()
                             process.wait(timeout=3)
                         except Exception:
                             process.kill()
                     
-                    # 更新 run_state.json
+                    # Update run_state.json
                     state = cls.get_run_state(simulation_id)
                     if state:
                         state.runner_status = RunnerStatus.STOPPED
                         state.twitter_running = False
                         state.reddit_running = False
                         state.completed_at = datetime.now().isoformat()
-                        state.error = "服务器关闭，模拟被终止"
+                        state.error = "Server shutdown, simulation terminated"
                         cls._save_run_state(state)
                     
-                    # 同时更新 state.json，将状态设为 stopped
+                    # Also update state.json, set status to stopped
                     try:
                         sim_dir = os.path.join(cls.RUN_STATE_DIR, simulation_id)
                         state_file = os.path.join(sim_dir, "state.json")
-                        logger.info(f"尝试更新 state.json: {state_file}")
+                        logger.info(f"Trying to update state.json: {state_file}")
                         if os.path.exists(state_file):
                             with open(state_file, 'r', encoding='utf-8') as f:
                                 state_data = json.load(f)
@@ -1247,16 +1247,16 @@ def cleanup_all_simulations(cls):
                             state_data['updated_at'] = datetime.now().isoformat()
                             with open(state_file, 'w', encoding='utf-8') as f:
                                 json.dump(state_data, f, indent=2, ensure_ascii=False)
-                            logger.info(f"已更新 state.json 状态为 stopped: {simulation_id}")
+                            logger.info(f"Updated state.json status to stopped: {simulation_id}")
                         else:
-                            logger.warning(f"state.json 不存在: {state_file}")
+                            logger.warning(f"state.json does not exist: {state_file}")
                     except Exception as state_err:
-                        logger.warning(f"更新 state.json 失败: {simulation_id}, error={state_err}")
+                        logger.warning(f"Failed to update state.json: {simulation_id}, error={state_err}")
                         
             except Exception as e:
-                logger.error(f"清理进程失败: {simulation_id}, error={e}")
+                logger.error(f"Failed to clean up process: {simulation_id}, error={e}")
         
-        # 清理文件句柄
+        # Clean up file handles
         for simulation_id, file_handle in list(cls._stdout_files.items()):
             try:
                 if file_handle:
@@ -1273,89 +1273,89 @@ def cleanup_all_simulations(cls):
                 pass
         cls._stderr_files.clear()
         
-        # 清理内存中的状态
+        # Clean up in-memory states
         cls._processes.clear()
         cls._action_queues.clear()
         
-        logger.info("模拟进程清理完成")
+        logger.info("Simulation process cleanup complete")
     
     @classmethod
     def register_cleanup(cls):
         """
-        注册清理函数
+        Register cleanup functions
         
-        在 Flask 应用启动时调用，确保服务器关闭时清理所有模拟进程
+        Called when Flask app starts, ensuring all simulation processes are cleaned up when server shuts down
         """
         global _cleanup_registered
         
         if _cleanup_registered:
             return
         
-        # Flask debug 模式下，只在 reloader 子进程中注册清理（实际运行应用的进程）
-        # WERKZEUG_RUN_MAIN=true 表示是 reloader 子进程
-        # 如果不是 debug 模式，则没有这个环境变量，也需要注册
+        # In Flask debug mode, only register cleanup in reloader subprocess (the process actually running the app)
+        # WERKZEUG_RUN_MAIN=true indicates reloader subprocess
+        # If not debug mode, this env var doesn't exist, also need to register
         is_reloader_process = os.environ.get('WERKZEUG_RUN_MAIN') == 'true'
         is_debug_mode = os.environ.get('FLASK_DEBUG') == '1' or os.environ.get('WERKZEUG_RUN_MAIN') is not None
         
-        # 在 debug 模式下，只在 reloader 子进程中注册；非 debug 模式下始终注册
+        # In debug mode, only register in reloader subprocess; in non-debug mode always register
         if is_debug_mode and not is_reloader_process:
-            _cleanup_registered = True  # 标记已注册，防止子进程再次尝试
+            _cleanup_registered = True  # Mark as registered, prevent subprocess from trying again
             return
         
-        # 保存原有的信号处理器
+        # Save original signal handlers
         original_sigint = signal.getsignal(signal.SIGINT)
         original_sigterm = signal.getsignal(signal.SIGTERM)
-        # SIGHUP 只在 Unix 系统存在（macOS/Linux），Windows 没有
+        # SIGHUP only exists on Unix systems (macOS/Linux), not Windows
         original_sighup = None
         has_sighup = hasattr(signal, 'SIGHUP')
         if has_sighup:
             original_sighup = signal.getsignal(signal.SIGHUP)
         
         def cleanup_handler(signum=None, frame=None):
-            """信号处理器：先清理模拟进程，再调用原处理器"""
-            # 只有在有进程需要清理时才打印日志
+            """Signal handler: clean up simulation processes first, then call original handler"""
+            # Only print logs when there are processes to clean
             if cls._processes or cls._graph_memory_enabled:
-                logger.info(f"收到信号 {signum}，开始清理...")
+                logger.info(f"Received signal {signum}，starting cleanup...")
             cls.cleanup_all_simulations()
             
-            # 调用原有的信号处理器，让 Flask 正常退出
+            # Call original signal handler to let Flask exit normally
             if signum == signal.SIGINT and callable(original_sigint):
                 original_sigint(signum, frame)
             elif signum == signal.SIGTERM and callable(original_sigterm):
                 original_sigterm(signum, frame)
             elif has_sighup and signum == signal.SIGHUP:
-                # SIGHUP: 终端关闭时发送
+                # SIGHUP: Sent when terminal closes
                 if callable(original_sighup):
                     original_sighup(signum, frame)
                 else:
-                    # 默认行为：正常退出
+                    # Default behavior: exit normally
                     sys.exit(0)
             else:
-                # 如果原处理器不可调用（如 SIG_DFL），则使用默认行为
+                # If original handler is not callable (e.g. SIG_DFL), use default behavior
                 raise KeyboardInterrupt
         
-        # 注册 atexit 处理器（作为备用）
+        # Register atexit handler (as fallback)
         atexit.register(cls.cleanup_all_simulations)
         
-        # 注册信号处理器（仅在主线程中）
+        # Register signal handlers (only in main thread)
         try:
-            # SIGTERM: kill 命令默认信号
+            # SIGTERM: Default signal for kill command
             signal.signal(signal.SIGTERM, cleanup_handler)
             # SIGINT: Ctrl+C
             signal.signal(signal.SIGINT, cleanup_handler)
-            # SIGHUP: 终端关闭（仅 Unix 系统）
+            # SIGHUP: Terminal close (Unix systems only)
             if has_sighup:
                 signal.signal(signal.SIGHUP, cleanup_handler)
         except ValueError:
-            # 不在主线程中，只能使用 atexit
-            logger.warning("无法注册信号处理器（不在主线程），仅使用 atexit")
+            # Not in main thread, can only use atexit
+            logger.warning("Cannot register signal handlers (not in main thread), using atexit only")
         
         _cleanup_registered = True
     
     @classmethod
     def get_running_simulations(cls) -> List[str]:
         """
-        获取所有正在运行的模拟ID列表
+        Get list of all running simulation IDs
         """
         running = []
         for sim_id, process in cls._processes.items():
@@ -1363,18 +1363,18 @@ def get_running_simulations(cls) -> List[str]:
                 running.append(sim_id)
         return running
     
-    # ============== Interview 功能 ==============
+    # ============== Interview Features ==============
     
     @classmethod
     def check_env_alive(cls, simulation_id: str) -> bool:
         """
-        检查模拟环境是否存活（可以接收Interview命令）
+        Check if simulation environment is alive (can receive Interview commands)
 
         Args:
-            simulation_id: 模拟ID
+            simulation_id: Simulation ID
 
         Returns:
-            True 表示环境存活，False 表示环境已关闭
+            True means environment is alive, False means environment is closed
         """
         sim_dir = os.path.join(cls.RUN_STATE_DIR, simulation_id)
         if not os.path.exists(sim_dir):
@@ -1386,13 +1386,13 @@ def check_env_alive(cls, simulation_id: str) -> bool:
     @classmethod
     def get_env_status_detail(cls, simulation_id: str) -> Dict[str, Any]:
         """
-        获取模拟环境的详细状态信息
+        Get detailed status info of simulation environment
 
         Args:
-            simulation_id: 模拟ID
+            simulation_id: Simulation ID
 
         Returns:
-            状态详情字典，包含 status, twitter_available, reddit_available, timestamp
+            Status details dict containing status, twitter_available, reddit_available, timestamp
         """
         sim_dir = os.path.join(cls.RUN_STATE_DIR, simulation_id)
         status_file = os.path.join(sim_dir, "env_status.json")
@@ -1429,35 +1429,35 @@ def interview_agent(
         timeout: float = 60.0
     ) -> Dict[str, Any]:
         """
-        采访单个Agent
+        Interview single Agent
 
         Args:
-            simulation_id: 模拟ID
+            simulation_id: Simulation ID
             agent_id: Agent ID
-            prompt: 采访问题
-            platform: 指定平台（可选）
-                - "twitter": 只采访Twitter平台
-                - "reddit": 只采访Reddit平台
-                - None: 双平台模拟时同时采访两个平台，返回整合结果
-            timeout: 超时时间（秒）
+            prompt: Interview question
+            platform: Specify platform (optional)
+                - "twitter": Interview Twitter platform only
+                - "reddit": Interview Reddit platform only
+                - None: Interview both platforms simultaneously in dual-platform simulation, return integrated results
+            timeout: Timeout (seconds)
 
         Returns:
-            采访结果字典
+            Interview result dictionary
 
         Raises:
-            ValueError: 模拟不存在或环境未运行
-            TimeoutError: 等待响应超时
+            ValueError: Simulation not found or environment not running
+            TimeoutError: Response timeout
         """
         sim_dir = os.path.join(cls.RUN_STATE_DIR, simulation_id)
         if not os.path.exists(sim_dir):
-            raise ValueError(f"模拟不存在: {simulation_id}")
+            raise ValueError(f"Simulation not found: {simulation_id}")
 
         ipc_client = SimulationIPCClient(sim_dir)
 
         if not ipc_client.check_env_alive():
-            raise ValueError(f"模拟环境未运行或已关闭，无法执行Interview: {simulation_id}")
+            raise ValueError(f"Simulation environment not running or closed, cannot perform Interview: {simulation_id}")
 
-        logger.info(f"发送Interview命令: simulation_id={simulation_id}, agent_id={agent_id}, platform={platform}")
+        logger.info(f"Sending Interview command: simulation_id={simulation_id}, agent_id={agent_id}, platform={platform}")
 
         response = ipc_client.send_interview(
             agent_id=agent_id,
@@ -1492,34 +1492,34 @@ def interview_agents_batch(
         timeout: float = 120.0
     ) -> Dict[str, Any]:
         """
-        批量采访多个Agent
+        Batch interview multiple Agents
 
         Args:
-            simulation_id: 模拟ID
-            interviews: 采访列表，每个元素包含 {"agent_id": int, "prompt": str, "platform": str(可选)}
-            platform: 默认平台（可选，会被每个采访项的platform覆盖）
-                - "twitter": 默认只采访Twitter平台
-                - "reddit": 默认只采访Reddit平台
-                - None: 双平台模拟时每个Agent同时采访两个平台
-            timeout: 超时时间（秒）
+            simulation_id: Simulation ID
+            interviews: Interview list, each element contains {"agent_id": int, "prompt": str, "platform": str(optional)}
+            platform: Default platform (optional, overridden by each interview item's platform)
+                - "twitter": Default interview Twitter platform only
+                - "reddit": Default interview Reddit platform only
+                - None: Interview each Agent on both platforms simultaneously in dual-platform simulation
+            timeout: Timeout (seconds)
 
         Returns:
-            批量采访结果字典
+            Batch Interview result dictionary
 
         Raises:
-            ValueError: 模拟不存在或环境未运行
-            TimeoutError: 等待响应超时
+            ValueError: Simulation not found or environment not running
+            TimeoutError: Response timeout
         """
         sim_dir = os.path.join(cls.RUN_STATE_DIR, simulation_id)
         if not os.path.exists(sim_dir):
-            raise ValueError(f"模拟不存在: {simulation_id}")
+            raise ValueError(f"Simulation not found: {simulation_id}")
 
         ipc_client = SimulationIPCClient(sim_dir)
 
         if not ipc_client.check_env_alive():
-            raise ValueError(f"模拟环境未运行或已关闭，无法执行Interview: {simulation_id}")
+            raise ValueError(f"Simulation environment not running or closed, cannot perform Interview: {simulation_id}")
 
-        logger.info(f"发送批量Interview命令: simulation_id={simulation_id}, count={len(interviews)}, platform={platform}")
+        logger.info(f"Sending batch Interview command: simulation_id={simulation_id}, count={len(interviews)}, platform={platform}")
 
         response = ipc_client.send_batch_interview(
             interviews=interviews,
@@ -1551,39 +1551,39 @@ def interview_all_agents(
         timeout: float = 180.0
     ) -> Dict[str, Any]:
         """
-        采访所有Agent（全局采访）
+        Interview all Agents (global interview)
 
-        使用相同的问题采访模拟中的所有Agent
+        Interview all Agents in simulation with the same question
 
         Args:
-            simulation_id: 模拟ID
-            prompt: 采访问题（所有Agent使用相同问题）
-            platform: 指定平台（可选）
-                - "twitter": 只采访Twitter平台
-                - "reddit": 只采访Reddit平台
-                - None: 双平台模拟时每个Agent同时采访两个平台
-            timeout: 超时时间（秒）
+            simulation_id: Simulation ID
+            prompt: Interview question (same question for all Agents)
+            platform: Specify platform (optional)
+                - "twitter": Interview Twitter platform only
+                - "reddit": Interview Reddit platform only
+                - None: Interview each Agent on both platforms simultaneously in dual-platform simulation
+            timeout: Timeout (seconds)
 
         Returns:
-            全局采访结果字典
+            Global Interview result dictionary
         """
         sim_dir = os.path.join(cls.RUN_STATE_DIR, simulation_id)
         if not os.path.exists(sim_dir):
-            raise ValueError(f"模拟不存在: {simulation_id}")
+            raise ValueError(f"Simulation not found: {simulation_id}")
 
-        # 从配置文件获取所有Agent信息
+        # Get all Agent info from config file
         config_path = os.path.join(sim_dir, "simulation_config.json")
         if not os.path.exists(config_path):
-            raise ValueError(f"模拟配置不存在: {simulation_id}")
+            raise ValueError(f"Simulation configuration not found: {simulation_id}")
 
         with open(config_path, 'r', encoding='utf-8') as f:
             config = json.load(f)
 
         agent_configs = config.get("agent_configs", [])
         if not agent_configs:
-            raise ValueError(f"模拟配置中没有Agent: {simulation_id}")
+            raise ValueError(f"No Agents in simulation configuration: {simulation_id}")
 
-        # 构建批量采访列表
+        # Build batch interview list
         interviews = []
         for agent_config in agent_configs:
             agent_id = agent_config.get("agent_id")
@@ -1593,7 +1593,7 @@ def interview_all_agents(
                     "prompt": prompt
                 })
 
-        logger.info(f"发送全局Interview命令: simulation_id={simulation_id}, agent_count={len(interviews)}, platform={platform}")
+        logger.info(f"Sending global Interview command: simulation_id={simulation_id}, agent_count={len(interviews)}, platform={platform}")
 
         return cls.interview_agents_batch(
             simulation_id=simulation_id,
@@ -1609,45 +1609,45 @@ def close_simulation_env(
         timeout: float = 30.0
     ) -> Dict[str, Any]:
         """
-        关闭模拟环境（而不是停止模拟进程）
+        Close simulation environment (without stopping simulation process)
         
-        向模拟发送关闭环境命令，使其优雅退出等待命令模式
+        Send close environment command to simulation for graceful exit from command waiting mode
         
         Args:
-            simulation_id: 模拟ID
-            timeout: 超时时间（秒）
+            simulation_id: Simulation ID
+            timeout: Timeout (seconds)
             
         Returns:
-            操作结果字典
+            Operation result dictionary
         """
         sim_dir = os.path.join(cls.RUN_STATE_DIR, simulation_id)
         if not os.path.exists(sim_dir):
-            raise ValueError(f"模拟不存在: {simulation_id}")
+            raise ValueError(f"Simulation not found: {simulation_id}")
         
         ipc_client = SimulationIPCClient(sim_dir)
         
         if not ipc_client.check_env_alive():
             return {
                 "success": True,
-                "message": "环境已经关闭"
+                "message": "Environment already closed"
             }
         
-        logger.info(f"发送关闭环境命令: simulation_id={simulation_id}")
+        logger.info(f"Sending close environment command: simulation_id={simulation_id}")
         
         try:
             response = ipc_client.send_close_env(timeout=timeout)
             
             return {
                 "success": response.status.value == "completed",
-                "message": "环境关闭命令已发送",
+                "message": "Environment close command sent",
                 "result": response.result,
                 "timestamp": response.timestamp
             }
         except TimeoutError:
-            # 超时可能是因为环境正在关闭
+            # Timeout may be because environment is shutting down
             return {
                 "success": True,
-                "message": "环境关闭命令已发送（等待响应超时，环境可能正在关闭）"
+                "message": "Environment close command sent (response timeout, environment may be shutting down)"
             }
     
     @classmethod
@@ -1658,7 +1658,7 @@ def _get_interview_history_from_db(
         agent_id: Optional[int] = None,
         limit: int = 100
     ) -> List[Dict[str, Any]]:
-        """从单个数据库获取Interview历史"""
+        """Get Interview history from a single database"""
         import sqlite3
         
         if not os.path.exists(db_path):
@@ -1704,7 +1704,7 @@ def _get_interview_history_from_db(
             conn.close()
             
         except Exception as e:
-            logger.error(f"读取Interview历史失败 ({platform_name}): {e}")
+            logger.error(f"Failed to read Interview history ({platform_name}): {e}")
         
         return results
 
@@ -1717,29 +1717,29 @@ def get_interview_history(
         limit: int = 100
     ) -> List[Dict[str, Any]]:
         """
-        获取Interview历史记录（从数据库读取）
+        Get Interview history records (read from database)
         
         Args:
-            simulation_id: 模拟ID
-            platform: 平台类型（reddit/twitter/None）
-                - "reddit": 只获取Reddit平台的历史
-                - "twitter": 只获取Twitter平台的历史
-                - None: 获取两个平台的所有历史
-            agent_id: 指定Agent ID（可选，只获取该Agent的历史）
-            limit: 每个平台返回数量限制
+            simulation_id: Simulation ID
+            platform: Platform type (reddit/twitter/None)
+                - "reddit": Get Reddit platform history only
+                - "twitter": Get Twitter platform history only
+                - None: Get all history from both platforms
+            agent_id: Specify Agent ID (optional, get history for this Agent only)
+            limit: Return count limit per platform
             
         Returns:
-            Interview历史记录列表
+            Interview history record list
         """
         sim_dir = os.path.join(cls.RUN_STATE_DIR, simulation_id)
         
         results = []
         
-        # 确定要查询的平台
+        # Determine platforms to query
         if platform in ("reddit", "twitter"):
             platforms = [platform]
         else:
-            # 不指定platform时，查询两个平台
+            # When platform not specified, query both platforms
             platforms = ["twitter", "reddit"]
         
         for p in platforms:
@@ -1752,10 +1752,10 @@ def get_interview_history(
             )
             results.extend(platform_results)
         
-        # 按时间降序排序
+        # Sort by time descending
         results.sort(key=lambda x: x.get("timestamp", ""), reverse=True)
         
-        # 如果查询了多个平台，限制总数
+        # If queried multiple platforms, limit total count
         if len(platforms) > 1 and len(results) > limit:
             results = results[:limit]
         
diff --git a/backend/app/services/zep_graph_memory_updater.py b/backend/app/services/zep_graph_memory_updater.py
index a8849b65d..80ce59171 100644
--- a/backend/app/services/zep_graph_memory_updater.py
+++ b/backend/app/services/zep_graph_memory_updater.py
@@ -1,6 +1,6 @@
 """
-Zep图谱记忆更新服务
-将模拟中的Agent活动动态更新到Zep图谱中
+Zep graph memory update service
+Dynamically update agent activities from simulations to the Zep graph
 """
 
 import os
@@ -22,7 +22,7 @@
 
 @dataclass
 class AgentActivity:
-    """Agent活动记录"""
+    """Agent activity record"""
     platform: str           # twitter / reddit
     agent_id: int
     agent_name: str
@@ -33,12 +33,10 @@ class AgentActivity:
     
     def to_episode_text(self) -> str:
         """
-        将活动转换为可以发送给Zep的文本描述
-        
-        采用自然语言描述格式，让Zep能够从中提取实体和关系
-        不添加模拟相关的前缀，避免误导图谱更新
+        Convert activity to natural language text description
+
+        Use natural language description format so NER extractor can extract entities and relationships
         """
-        # 根据不同的动作类型生成不同的描述
         action_descriptions = {
             "CREATE_POST": self._describe_create_post,
             "LIKE_POST": self._describe_like_post,
@@ -57,217 +55,179 @@ def to_episode_text(self) -> str:
         describe_func = action_descriptions.get(self.action_type, self._describe_generic)
         description = describe_func()
         
-        # 直接返回 "agent名称: 活动描述" 格式，不添加模拟前缀
         return f"{self.agent_name}: {description}"
     
     def _describe_create_post(self) -> str:
         content = self.action_args.get("content", "")
         if content:
-            return f"发布了一条帖子：「{content}」"
-        return "发布了一条帖子"
-    
+            return f"Posted a post: \"{content}\""
+        return "Posted a post"
+
     def _describe_like_post(self) -> str:
-        """点赞帖子 - 包含帖子原文和作者信息"""
         post_content = self.action_args.get("post_content", "")
         post_author = self.action_args.get("post_author_name", "")
-        
         if post_content and post_author:
-            return f"点赞了{post_author}的帖子：「{post_content}」"
+            return f"Liked {post_author}'s post: \"{post_content}\""
         elif post_content:
-            return f"点赞了一条帖子：「{post_content}」"
+            return f"Liked a post: \"{post_content}\""
         elif post_author:
-            return f"点赞了{post_author}的一条帖子"
-        return "点赞了一条帖子"
-    
+            return f"Liked a post by {post_author}"
+        return "Liked a post"
+
     def _describe_dislike_post(self) -> str:
-        """踩帖子 - 包含帖子原文和作者信息"""
         post_content = self.action_args.get("post_content", "")
         post_author = self.action_args.get("post_author_name", "")
-        
         if post_content and post_author:
-            return f"踩了{post_author}的帖子：「{post_content}」"
+            return f"Disliked {post_author}'s post: \"{post_content}\""
         elif post_content:
-            return f"踩了一条帖子：「{post_content}」"
+            return f"Disliked a post: \"{post_content}\""
         elif post_author:
-            return f"踩了{post_author}的一条帖子"
-        return "踩了一条帖子"
-    
+            return f"Disliked a post by {post_author}"
+        return "Disliked a post"
+
     def _describe_repost(self) -> str:
-        """转发帖子 - 包含原帖内容和作者信息"""
         original_content = self.action_args.get("original_content", "")
         original_author = self.action_args.get("original_author_name", "")
-        
         if original_content and original_author:
-            return f"转发了{original_author}的帖子：「{original_content}」"
+            return f"Reposted {original_author}'s post: \"{original_content}\""
         elif original_content:
-            return f"转发了一条帖子：「{original_content}」"
+            return f"Reposted a post: \"{original_content}\""
         elif original_author:
-            return f"转发了{original_author}的一条帖子"
-        return "转发了一条帖子"
-    
+            return f"Reposted a post by {original_author}"
+        return "Reposted a post"
+
     def _describe_quote_post(self) -> str:
-        """引用帖子 - 包含原帖内容、作者信息和引用评论"""
         original_content = self.action_args.get("original_content", "")
         original_author = self.action_args.get("original_author_name", "")
         quote_content = self.action_args.get("quote_content", "") or self.action_args.get("content", "")
-        
         base = ""
         if original_content and original_author:
-            base = f"引用了{original_author}的帖子「{original_content}」"
+            base = f"Quoted {original_author}'s post \"{original_content}\""
         elif original_content:
-            base = f"引用了一条帖子「{original_content}」"
+            base = f"Quoted a post \"{original_content}\""
         elif original_author:
-            base = f"引用了{original_author}的一条帖子"
+            base = f"Quoted a post by {original_author}"
         else:
-            base = "引用了一条帖子"
-        
+            base = "Quoted a post"
         if quote_content:
-            base += f"，并评论道：「{quote_content}」"
+            base += f", commented: \"{quote_content}\""
         return base
-    
+
     def _describe_follow(self) -> str:
-        """关注用户 - 包含被关注用户的名称"""
         target_user_name = self.action_args.get("target_user_name", "")
-        
         if target_user_name:
-            return f"关注了用户「{target_user_name}」"
-        return "关注了一个用户"
-    
+            return f"Followed user \"{target_user_name}\""
+        return "Followed a user"
+
     def _describe_create_comment(self) -> str:
-        """发表评论 - 包含评论内容和所评论的帖子信息"""
         content = self.action_args.get("content", "")
         post_content = self.action_args.get("post_content", "")
         post_author = self.action_args.get("post_author_name", "")
-        
         if content:
             if post_content and post_author:
-                return f"在{post_author}的帖子「{post_content}」下评论道：「{content}」"
+                return f"Commented on {post_author}'s post \"{post_content}\": \"{content}\""
             elif post_content:
-                return f"在帖子「{post_content}」下评论道：「{content}」"
+                return f"Commented on post \"{post_content}\": \"{content}\""
             elif post_author:
-                return f"在{post_author}的帖子下评论道：「{content}」"
-            return f"评论道：「{content}」"
-        return "发表了评论"
-    
+                return f"Commented on {post_author}'s post: \"{content}\""
+            return f"Commented: \"{content}\""
+        return "Left a comment"
+
     def _describe_like_comment(self) -> str:
-        """点赞评论 - 包含评论内容和作者信息"""
         comment_content = self.action_args.get("comment_content", "")
         comment_author = self.action_args.get("comment_author_name", "")
-        
         if comment_content and comment_author:
-            return f"点赞了{comment_author}的评论：「{comment_content}」"
+            return f"Liked {comment_author}'s comment: \"{comment_content}\""
         elif comment_content:
-            return f"点赞了一条评论：「{comment_content}」"
+            return f"Liked a comment: \"{comment_content}\""
         elif comment_author:
-            return f"点赞了{comment_author}的一条评论"
-        return "点赞了一条评论"
-    
+            return f"Liked a comment by {comment_author}"
+        return "Liked a comment"
+
     def _describe_dislike_comment(self) -> str:
-        """踩评论 - 包含评论内容和作者信息"""
         comment_content = self.action_args.get("comment_content", "")
         comment_author = self.action_args.get("comment_author_name", "")
-        
         if comment_content and comment_author:
-            return f"踩了{comment_author}的评论：「{comment_content}」"
+            return f"Disliked {comment_author}'s comment: \"{comment_content}\""
         elif comment_content:
-            return f"踩了一条评论：「{comment_content}」"
+            return f"Disliked a comment: \"{comment_content}\""
         elif comment_author:
-            return f"踩了{comment_author}的一条评论"
-        return "踩了一条评论"
-    
+            return f"Disliked a comment by {comment_author}"
+        return "Disliked a comment"
+
     def _describe_search(self) -> str:
-        """搜索帖子 - 包含搜索关键词"""
         query = self.action_args.get("query", "") or self.action_args.get("keyword", "")
-        return f"搜索了「{query}」" if query else "进行了搜索"
-    
+        return f"Searched for \"{query}\""if query else "Performed a search"
+
     def _describe_search_user(self) -> str:
-        """搜索用户 - 包含搜索关键词"""
         query = self.action_args.get("query", "") or self.action_args.get("username", "")
-        return f"搜索了用户「{query}」" if query else "搜索了用户"
-    
+        return f"Searched for user \"{query}\""if query else "Searched for user"
+
     def _describe_mute(self) -> str:
-        """屏蔽用户 - 包含被屏蔽用户的名称"""
         target_user_name = self.action_args.get("target_user_name", "")
-        
         if target_user_name:
-            return f"屏蔽了用户「{target_user_name}」"
-        return "屏蔽了一个用户"
-    
+            return f"Muted user \"{target_user_name}\""
+        return "Muted a user"
+
     def _describe_generic(self) -> str:
-        # 对于未知的动作类型，生成通用描述
-        return f"执行了{self.action_type}操作"
+        return f"Executed {self.action_type} action"
 
 
 class ZepGraphMemoryUpdater:
     """
-    Zep图谱记忆更新器
-    
-    监控模拟的actions日志文件，将新的agent活动实时更新到Zep图谱中。
-    按平台分组，每累积BATCH_SIZE条活动后批量发送到Zep。
-    
-    所有有意义的行为都会被更新到Zep，action_args中会包含完整的上下文信息：
-    - 点赞/踩的帖子原文
-    - 转发/引用的帖子原文
-    - 关注/屏蔽的用户名
-    - 点赞/踩的评论原文
+    Zep graph memory updater
+
+    Monitors simulation action logs and sends agent activities to the Zep graph in real-time.
+    Batches activities by platform, accumulating BATCH_SIZE activities before sending each batch.
     """
-    
-    # 批量发送大小（每个平台累积多少条后发送）
+
     BATCH_SIZE = 5
-    
-    # 平台名称映射（用于控制台显示）
+
     PLATFORM_DISPLAY_NAMES = {
-        'twitter': '世界1',
-        'reddit': '世界2',
+        'twitter': 'worldinterface1',
+        'reddit': 'worldinterface2',
     }
-    
-    # 发送间隔（秒），避免请求过快
+
     SEND_INTERVAL = 0.5
-    
-    # 重试配置
     MAX_RETRIES = 3
-    RETRY_DELAY = 2  # 秒
+    RETRY_DELAY = 2
     
     def __init__(self, graph_id: str, api_key: Optional[str] = None):
         """
-        初始化更新器
+        Initialize updater
 
         Args:
-            graph_id: 图谱ID
-            api_key: 保留参数，不再使用
+            graph_id: Graph ID
+            api_key: Reserved parameter, no longer used
         """
         self.graph_id = graph_id
         self.adapter = GraphitiAdapter.get_or_create(graph_id)
         
-        # 活动队列
         self._activity_queue: Queue = Queue()
-        
-        # 按平台分组的活动缓冲区（每个平台各自累积到BATCH_SIZE后批量发送）
+
         self._platform_buffers: Dict[str, List[AgentActivity]] = {
             'twitter': [],
             'reddit': [],
         }
         self._buffer_lock = threading.Lock()
-        
-        # 控制标志
+
         self._running = False
         self._worker_thread: Optional[threading.Thread] = None
-        
-        # 统计
-        self._total_activities = 0  # 实际添加到队列的活动数
-        self._total_sent = 0        # 成功发送到Zep的批次数
-        self._total_items_sent = 0  # 成功发送到Zep的活动条数
-        self._failed_count = 0      # 发送失败的批次数
-        self._skipped_count = 0     # 被过滤跳过的活动数（DO_NOTHING）
-        
-        logger.info(f"ZepGraphMemoryUpdater 初始化完成: graph_id={graph_id}, batch_size={self.BATCH_SIZE}")
+
+        self._total_activities = 0
+        self._total_sent = 0
+        self._total_items_sent = 0
+        self._failed_count = 0
+        self._skipped_count = 0
+
+        logger.info(f"ZepGraphMemoryUpdater initialized: graph_id={graph_id}, batch_size={self.BATCH_SIZE}")
     
     def _get_platform_display_name(self, platform: str) -> str:
-        """获取平台的显示名称"""
+        """Get display name for platform"""
         return self.PLATFORM_DISPLAY_NAMES.get(platform.lower(), platform)
     
     def start(self):
-        """启动后台工作线程"""
+        """Start background worker thread"""
         if self._running:
             return
         
@@ -278,19 +238,18 @@ def start(self):
             name=f"ZepMemoryUpdater-{self.graph_id[:8]}"
         )
         self._worker_thread.start()
-        logger.info(f"ZepGraphMemoryUpdater 已启动: graph_id={self.graph_id}")
+        logger.info(f"ZepGraphMemoryUpdater started: graph_id={self.graph_id}")
     
     def stop(self):
-        """停止后台工作线程"""
+        """Stop background worker thread"""
         self._running = False
-        
-        # 发送剩余的活动
+
         self._flush_remaining()
         
         if self._worker_thread and self._worker_thread.is_alive():
             self._worker_thread.join(timeout=10)
         
-        logger.info(f"ZepGraphMemoryUpdater 已停止: graph_id={self.graph_id}, "
+        logger.info(f"ZepGraphMemoryUpdater stopped: graph_id={self.graph_id}, "
                    f"total_activities={self._total_activities}, "
                    f"batches_sent={self._total_sent}, "
                    f"items_sent={self._total_items_sent}, "
@@ -298,44 +257,19 @@ def stop(self):
                    f"skipped={self._skipped_count}")
     
     def add_activity(self, activity: AgentActivity):
-        """
-        添加一个agent活动到队列
-        
-        所有有意义的行为都会被添加到队列，包括：
-        - CREATE_POST（发帖）
-        - CREATE_COMMENT（评论）
-        - QUOTE_POST（引用帖子）
-        - SEARCH_POSTS（搜索帖子）
-        - SEARCH_USER（搜索用户）
-        - LIKE_POST/DISLIKE_POST（点赞/踩帖子）
-        - REPOST（转发）
-        - FOLLOW（关注）
-        - MUTE（屏蔽）
-        - LIKE_COMMENT/DISLIKE_COMMENT（点赞/踩评论）
-        
-        action_args中会包含完整的上下文信息（如帖子原文、用户名等）。
-        
-        Args:
-            activity: Agent活动记录
-        """
-        # 跳过DO_NOTHING类型的活动
+        """Add an agent activity to queue"""
+        # Skip DO_NOTHING activities
         if activity.action_type == "DO_NOTHING":
             self._skipped_count += 1
             return
         
         self._activity_queue.put(activity)
         self._total_activities += 1
-        logger.debug(f"添加活动到Zep队列: {activity.agent_name} - {activity.action_type}")
+        logger.debug(f"Add activity to queue: {activity.agent_name} - {activity.action_type}")
     
     def add_activity_from_dict(self, data: Dict[str, Any], platform: str):
-        """
-        从字典数据添加活动
-        
-        Args:
-            data: 从actions.jsonl解析的字典数据
-            platform: 平台名称 (twitter/reddit)
-        """
-        # 跳过事件类型的条目
+        """Add activity from dict data"""
+        # Skip event type entries
         if "event_type" in data:
             return
         
@@ -352,52 +286,48 @@ def add_activity_from_dict(self, data: Dict[str, Any], platform: str):
         self.add_activity(activity)
     
     def _worker_loop(self):
-        """后台工作循环 - 按平台批量发送活动到Zep"""
+        """Background worker loop - batch send activities to Zep by platform"""
         while self._running or not self._activity_queue.empty():
             try:
-                # 尝试从队列获取活动（超时1秒）
+                # Try to get activity from queue (1 second timeout)
                 try:
                     activity = self._activity_queue.get(timeout=1)
                     
-                    # 将活动添加到对应平台的缓冲区
+                    # Add activity to the corresponding platform buffer
                     platform = activity.platform.lower()
                     with self._buffer_lock:
                         if platform not in self._platform_buffers:
                             self._platform_buffers[platform] = []
                         self._platform_buffers[platform].append(activity)
                         
-                        # 检查该平台是否达到批量大小
+                        # Check if platform has reached batch size
                         if len(self._platform_buffers[platform]) >= self.BATCH_SIZE:
                             batch = self._platform_buffers[platform][:self.BATCH_SIZE]
                             self._platform_buffers[platform] = self._platform_buffers[platform][self.BATCH_SIZE:]
-                            # 释放锁后再发送
+                            # Send after releasing lock
                             self._send_batch_activities(batch, platform)
-                            # 发送间隔，避免请求过快
+                            # Send interval to avoid too frequent requests
                             time.sleep(self.SEND_INTERVAL)
                     
                 except Empty:
                     pass
                     
             except Exception as e:
-                logger.error(f"工作循环异常: {e}")
+                logger.error(f"Worker loop exception: {e}")
                 time.sleep(1)
     
     def _send_batch_activities(self, activities: List[AgentActivity], platform: str):
         """
-        批量发送活动到Zep图谱（合并为一条文本）
-        
-        Args:
-            activities: Agent活动列表
-            platform: 平台名称
+        Send batched activities to the Zep graph (merged as one text)
         """
         if not activities:
             return
         
-        # 将多条活动合并为一条文本，用换行分隔
+        # Merge multiple activities into one text, separated by newlines
         episode_texts = [activity.to_episode_text() for activity in activities]
         combined_text = "\n".join(episode_texts)
         
-        # 带重试的发送
+        # Send with retry
         for attempt in range(self.MAX_RETRIES):
             try:
                 self.adapter.add_episode(combined_text, source_description="simulation_activity")
@@ -405,21 +335,20 @@ def _send_batch_activities(self, activities: List[AgentActivity], platform: str)
                 self._total_sent += 1
                 self._total_items_sent += len(activities)
                 display_name = self._get_platform_display_name(platform)
-                logger.info(f"成功批量发送 {len(activities)} 条{display_name}活动到图谱 {self.graph_id}")
-                logger.debug(f"批量内容预览: {combined_text[:200]}...")
+                logger.info(f"Successfully batch sent {len(activities)} {display_name} activities to graph {self.graph_id}")
+                logger.debug(f"Batch preview: {combined_text[:200]}...")
                 return
                 
             except Exception as e:
                 if attempt < self.MAX_RETRIES - 1:
-                    logger.warning(f"批量发送到Zep失败 (尝试 {attempt + 1}/{self.MAX_RETRIES}): {e}")
+                    logger.warning(f"Batch send to Zep failed (attempt {attempt + 1}/{self.MAX_RETRIES}): {e}")
                     time.sleep(self.RETRY_DELAY * (attempt + 1))
                 else:
-                    logger.error(f"批量发送到Zep失败，已重试{self.MAX_RETRIES}次: {e}")
+                    logger.error(f"Batch send to Zep failed after {self.MAX_RETRIES} retries: {e}")
                     self._failed_count += 1
     
     def _flush_remaining(self):
-        """发送队列和缓冲区中剩余的活动"""
-        # 首先处理队列中剩余的活动，添加到缓冲区
+        """Send remaining activities in queue and buffers"""
         while not self._activity_queue.empty():
             try:
                 activity = self._activity_queue.get_nowait()
@@ -431,41 +360,41 @@ def _flush_remaining(self):
             except Empty:
                 break
         
-        # 然后发送各平台缓冲区中剩余的活动（即使不足BATCH_SIZE条）
+        # Send remaining activities from each platform buffer
         with self._buffer_lock:
             for platform, buffer in self._platform_buffers.items():
                 if buffer:
                     display_name = self._get_platform_display_name(platform)
-                    logger.info(f"发送{display_name}平台剩余的 {len(buffer)} 条活动")
+                    logger.info(f"Send remaining {len(buffer)} {display_name} platform activities")
                     self._send_batch_activities(buffer, platform)
-            # 清空所有缓冲区
+            # Clear all buffers
             for platform in self._platform_buffers:
                 self._platform_buffers[platform] = []
     
     def get_stats(self) -> Dict[str, Any]:
-        """获取统计信息"""
+        """Get statistics"""
         with self._buffer_lock:
             buffer_sizes = {p: len(b) for p, b in self._platform_buffers.items()}
         
         return {
             "graph_id": self.graph_id,
             "batch_size": self.BATCH_SIZE,
-            "total_activities": self._total_activities,  # 添加到队列的活动总数
-            "batches_sent": self._total_sent,            # 成功发送的批次数
-            "items_sent": self._total_items_sent,        # 成功发送的活动条数
-            "failed_count": self._failed_count,          # 发送失败的批次数
-            "skipped_count": self._skipped_count,        # 被过滤跳过的活动数（DO_NOTHING）
+            "total_activities": self._total_activities,
+            "batches_sent": self._total_sent,
+            "items_sent": self._total_items_sent,
+            "failed_count": self._failed_count,
+            "skipped_count": self._skipped_count,
             "queue_size": self._activity_queue.qsize(),
-            "buffer_sizes": buffer_sizes,                # 各平台缓冲区大小
+            "buffer_sizes": buffer_sizes,
             "running": self._running,
         }
 
 
 class ZepGraphMemoryManager:
     """
-    管理多个模拟的Zep图谱记忆更新器
-    
-    每个模拟可以有自己的更新器实例
+    Manages graph memory updaters for multiple simulations.
+
+    Each simulation can have its own independent updater instance.
     """
     
     _updaters: Dict[str, ZepGraphMemoryUpdater] = {}
@@ -474,17 +403,14 @@ class ZepGraphMemoryManager:
     @classmethod
     def create_updater(cls, simulation_id: str, graph_id: str) -> ZepGraphMemoryUpdater:
         """
-        为模拟创建图谱记忆更新器
-        
+        Create a graph memory updater for a simulation.
+
         Args:
-            simulation_id: 模拟ID
-            graph_id: Zep图谱ID
-            
-        Returns:
-            ZepGraphMemoryUpdater实例
+            simulation_id: Simulation ID
+            graph_id: Graph ID
         """
         with cls._lock:
-            # 如果已存在，先停止旧的
+            # If already exists, stop the old one
             if simulation_id in cls._updaters:
                 cls._updaters[simulation_id].stop()
             
@@ -492,30 +418,29 @@ def create_updater(cls, simulation_id: str, graph_id: str) -> ZepGraphMemoryUpda
             updater.start()
             cls._updaters[simulation_id] = updater
             
-            logger.info(f"创建图谱记忆更新器: simulation_id={simulation_id}, graph_id={graph_id}")
+            logger.info(f"Create graph memory updater: simulation_id={simulation_id}, graph_id={graph_id}")
             return updater
     
     @classmethod
     def get_updater(cls, simulation_id: str) -> Optional[ZepGraphMemoryUpdater]:
-        """获取模拟的更新器"""
+        """Get updater for simulation"""
         return cls._updaters.get(simulation_id)
     
     @classmethod
     def stop_updater(cls, simulation_id: str):
-        """停止并移除模拟的更新器"""
+        """Stop and remove updater for simulation"""
         with cls._lock:
             if simulation_id in cls._updaters:
                 cls._updaters[simulation_id].stop()
                 del cls._updaters[simulation_id]
-                logger.info(f"已停止图谱记忆更新器: simulation_id={simulation_id}")
+                logger.info(f"Stopped graph memory updater: simulation_id={simulation_id}")
     
-    # 防止 stop_all 重复调用的标志
+    # Flag to prevent duplicate stop_all calls
     _stop_all_done = False
     
     @classmethod
     def stop_all(cls):
-        """停止所有更新器"""
-        # 防止重复调用
+        """Stop all updaters"""
         if cls._stop_all_done:
             return
         cls._stop_all_done = True
@@ -526,13 +451,13 @@ def stop_all(cls):
                     try:
                         updater.stop()
                     except Exception as e:
-                        logger.error(f"停止更新器失败: simulation_id={simulation_id}, error={e}")
+                        logger.error(f"Failed to stop updater: simulation_id={simulation_id}, error={e}")
                 cls._updaters.clear()
-            logger.info("已停止所有图谱记忆更新器")
+            logger.info("Stopped all graph memory updaters")
     
     @classmethod
     def get_all_stats(cls) -> Dict[str, Dict[str, Any]]:
-        """获取所有更新器的统计信息"""
+        """Get statistics for all updaters"""
         return {
             sim_id: updater.get_stats() 
             for sim_id, updater in cls._updaters.items()
diff --git a/backend/app/services/zep_tools.py b/backend/app/services/zep_tools.py
index 27b986935..e87564909 100644
--- a/backend/app/services/zep_tools.py
+++ b/backend/app/services/zep_tools.py
@@ -1,11 +1,11 @@
 """
-Zep检索工具服务
-封装图谱搜索、节点读取、边查询等工具，供Report Agent使用
+Zep Retrieval Tools Service
+Encapsulates graph search, node retrieval, edge queries, and other tools for use by Report Agent.
 
-核心检索工具（优化后）：
-1. InsightForge（深度洞察检索）- 最强大的混合检索，自动生成子问题并多维度检索
-2. PanoramaSearch（广度搜索）- 获取全貌，包括过期内容
-3. QuickSearch（简单搜索）- 快速检索
+Core Retrieval Tools (Optimized):
+1. InsightForge (Deep Insight Retrieval) - Most powerful hybrid search, automatically generates sub-questions and multi-dimensional retrieval
+2. PanoramaSearch (Breadth Search) - Get comprehensive view, including expired content
+3. QuickSearch (Simple Search) - Quick retrieval
 """
 
 import time
@@ -23,7 +23,7 @@
 
 @dataclass
 class SearchResult:
-    """搜索结果"""
+    """Search Result"""
     facts: List[str]
     edges: List[Dict[str, Any]]
     nodes: List[Dict[str, Any]]
@@ -40,11 +40,11 @@ def to_dict(self) -> Dict[str, Any]:
         }
     
     def to_text(self) -> str:
-        """转换为文本格式，供LLM理解"""
-        text_parts = [f"搜索查询: {self.query}", f"找到 {self.total_count} 条相关信息"]
-        
+        """Convert to text format for LLM understanding"""
+        text_parts = [f"Search Query: {self.query}", f"Found {self.total_count} related results"]
+
         if self.facts:
-            text_parts.append("\n### 相关事实:")
+            text_parts.append("\n### Related Facts:")
             for i, fact in enumerate(self.facts, 1):
                 text_parts.append(f"{i}. {fact}")
         
@@ -53,7 +53,7 @@ def to_text(self) -> str:
 
 @dataclass
 class NodeInfo:
-    """节点信息"""
+    """Node Information"""
     uuid: str
     name: str
     labels: List[str]
@@ -70,14 +70,14 @@ def to_dict(self) -> Dict[str, Any]:
         }
     
     def to_text(self) -> str:
-        """转换为文本格式"""
-        entity_type = next((l for l in self.labels if l not in ["Entity", "Node"]), "未知类型")
-        return f"实体: {self.name} (类型: {entity_type})\n摘要: {self.summary}"
+        """Convert to text format"""
+        entity_type = next((l for l in self.labels if l not in ["Entity", "Node"]), "Unknown type")
+        return f"Entity: {self.name} (Type: {entity_type})\nSummary: {self.summary}"
 
 
 @dataclass
 class EdgeInfo:
-    """边信息"""
+    """Edge Information"""
     uuid: str
     name: str
     fact: str
@@ -85,7 +85,7 @@ class EdgeInfo:
     target_node_uuid: str
     source_node_name: Optional[str] = None
     target_node_name: Optional[str] = None
-    # 时间信息
+    # Temporal information
     created_at: Optional[str] = None
     valid_at: Optional[str] = None
     invalid_at: Optional[str] = None
@@ -107,47 +107,47 @@ def to_dict(self) -> Dict[str, Any]:
         }
     
     def to_text(self, include_temporal: bool = False) -> str:
-        """转换为文本格式"""
+        """Convert to text format"""
         source = self.source_node_name or self.source_node_uuid[:8]
         target = self.target_node_name or self.target_node_uuid[:8]
-        base_text = f"关系: {source} --[{self.name}]--> {target}\n事实: {self.fact}"
-        
+        base_text = f"Relationship: {source} --[{self.name}]--> {target}\nFact: {self.fact}"
+
         if include_temporal:
-            valid_at = self.valid_at or "未知"
-            invalid_at = self.invalid_at or "至今"
-            base_text += f"\n时效: {valid_at} - {invalid_at}"
+            valid_at = self.valid_at or "Unknown"
+            invalid_at = self.invalid_at or "Present"
+            base_text += f"\nTime Range: {valid_at} - {invalid_at}"
             if self.expired_at:
-                base_text += f" (已过期: {self.expired_at})"
+                base_text += f" (Expired: {self.expired_at})"
         
         return base_text
     
     @property
     def is_expired(self) -> bool:
-        """是否已过期"""
+        """Whether already expired"""
         return self.expired_at is not None
     
     @property
     def is_invalid(self) -> bool:
-        """是否已失效"""
+        """Whether already invalid"""
         return self.invalid_at is not None
 
 
 @dataclass
 class InsightForgeResult:
     """
-    深度洞察检索结果 (InsightForge)
-    包含多个子问题的检索结果，以及综合分析
+    Deep Insight Retrieval Result (InsightForge)
+    Contains retrieval results from multiple sub-questions and integrated analysis
     """
     query: str
     simulation_requirement: str
     sub_queries: List[str]
     
-    # 各维度检索结果
-    semantic_facts: List[str] = field(default_factory=list)  # 语义搜索结果
-    entity_insights: List[Dict[str, Any]] = field(default_factory=list)  # 实体洞察
-    relationship_chains: List[str] = field(default_factory=list)  # 关系链
-    
-    # 统计信息
+    # Retrieval results by dimension
+    semantic_facts: List[str] = field(default_factory=list)
+    entity_insights: List[Dict[str, Any]] = field(default_factory=list)
+    relationship_chains: List[str] = field(default_factory=list)
+
+    # Statistical information
     total_facts: int = 0
     total_entities: int = 0
     total_relationships: int = 0
@@ -166,42 +166,38 @@ def to_dict(self) -> Dict[str, Any]:
         }
     
     def to_text(self) -> str:
-        """转换为详细的文本格式，供LLM理解"""
+        """Convert to detailed text format for LLM understanding"""
         text_parts = [
-            f"## 未来预测深度分析",
-            f"分析问题: {self.query}",
-            f"预测场景: {self.simulation_requirement}",
-            f"\n### 预测数据统计",
-            f"- 相关预测事实: {self.total_facts}条",
-            f"- 涉及实体: {self.total_entities}个",
-            f"- 关系链: {self.total_relationships}条"
+            f"## Future Prediction Deep Analysis",
+            f"Analysis Query: {self.query}",
+            f"Prediction Scenario: {self.simulation_requirement}",
+            f"\n### Prediction Data Statistics",
+            f"- Related Prediction Facts: {self.total_facts}",
+            f"- Involved Entities: {self.total_entities}",
+            f"- Relationship Chains: {self.total_relationships}"
         ]
-        
-        # 子问题
+
         if self.sub_queries:
-            text_parts.append(f"\n### 分析的子问题")
+            text_parts.append(f"\n### Analysis Sub-Questions")
             for i, sq in enumerate(self.sub_queries, 1):
                 text_parts.append(f"{i}. {sq}")
-        
-        # 语义搜索结果
+
         if self.semantic_facts:
-            text_parts.append(f"\n### 【关键事实】(请在报告中引用这些原文)")
+            text_parts.append(f"\n### Key Facts (Please quote these verbatim in the report)")
             for i, fact in enumerate(self.semantic_facts, 1):
-                text_parts.append(f"{i}. \"{fact}\"")
-        
-        # 实体洞察
+                text_parts.append(f'{i}. "{fact}"')
+
         if self.entity_insights:
-            text_parts.append(f"\n### 【核心实体】")
+            text_parts.append(f"\n### Core Entities")
             for entity in self.entity_insights:
-                text_parts.append(f"- **{entity.get('name', '未知')}** ({entity.get('type', '实体')})")
+                text_parts.append(f"- **{entity.get('name', 'Unknown')}** ({entity.get('type', 'Entity')})")
                 if entity.get('summary'):
-                    text_parts.append(f"  摘要: \"{entity.get('summary')}\"")
+                    text_parts.append(f"  Summary: \"{entity.get('summary')}\"")
                 if entity.get('related_facts'):
-                    text_parts.append(f"  相关事实: {len(entity.get('related_facts', []))}条")
-        
-        # 关系链
+                    text_parts.append(f"  Related Facts: {len(entity.get('related_facts', []))} facts")
+
         if self.relationship_chains:
-            text_parts.append(f"\n### 【关系链】")
+            text_parts.append(f"\n### Relationship Chains")
             for chain in self.relationship_chains:
                 text_parts.append(f"- {chain}")
         
@@ -211,21 +207,16 @@ def to_text(self) -> str:
 @dataclass
 class PanoramaResult:
     """
-    广度搜索结果 (Panorama)
-    包含所有相关信息，包括过期内容
+    Breadth Search Result (Panorama)
+    Contains all related information, including expired content
     """
     query: str
     
-    # 全部节点
     all_nodes: List[NodeInfo] = field(default_factory=list)
-    # 全部边（包括过期的）
     all_edges: List[EdgeInfo] = field(default_factory=list)
-    # 当前有效的事实
     active_facts: List[str] = field(default_factory=list)
-    # 已过期/失效的事实（历史记录）
     historical_facts: List[str] = field(default_factory=list)
-    
-    # 统计
+
     total_nodes: int = 0
     total_edges: int = 0
     active_count: int = 0
@@ -245,34 +236,31 @@ def to_dict(self) -> Dict[str, Any]:
         }
     
     def to_text(self) -> str:
-        """转换为文本格式（完整版本，不截断）"""
+        """Convert to text format (complete version, no truncation)"""
         text_parts = [
-            f"## 广度搜索结果（未来全景视图）",
-            f"查询: {self.query}",
-            f"\n### 统计信息",
-            f"- 总节点数: {self.total_nodes}",
-            f"- 总边数: {self.total_edges}",
-            f"- 当前有效事实: {self.active_count}条",
-            f"- 历史/过期事实: {self.historical_count}条"
+            f"## Breadth Search Results (Future Panoramic View)",
+            f"Query: {self.query}",
+            f"\n### Statistics",
+            f"- Total Nodes: {self.total_nodes}",
+            f"- Total Edges: {self.total_edges}",
+            f"- Current Valid Facts: {self.active_count}",
+            f"- Historical/Expired Facts: {self.historical_count}"
         ]
-        
-        # 当前有效的事实（完整输出，不截断）
+
         if self.active_facts:
-            text_parts.append(f"\n### 【当前有效事实】(模拟结果原文)")
+            text_parts.append(f"\n### Current Valid Facts (Simulation Results Verbatim)")
             for i, fact in enumerate(self.active_facts, 1):
-                text_parts.append(f"{i}. \"{fact}\"")
-        
-        # 历史/过期事实（完整输出，不截断）
+                text_parts.append(f'{i}. "{fact}"')
+
         if self.historical_facts:
-            text_parts.append(f"\n### 【历史/过期事实】(演变过程记录)")
+            text_parts.append(f"\n### Historical/Expired Facts (Evolution Record)")
             for i, fact in enumerate(self.historical_facts, 1):
-                text_parts.append(f"{i}. \"{fact}\"")
-        
-        # 关键实体（完整输出，不截断）
+                text_parts.append(f'{i}. "{fact}"')
+
         if self.all_nodes:
-            text_parts.append(f"\n### 【涉及实体】")
+            text_parts.append(f"\n### Involved Entities")
             for node in self.all_nodes:
-                entity_type = next((l for l in node.labels if l not in ["Entity", "Node"]), "实体")
+                entity_type = next((l for l in node.labels if l not in ["Entity", "Node"]), "Entity")
                 text_parts.append(f"- **{node.name}** ({entity_type})")
         
         return "\n".join(text_parts)
@@ -280,13 +268,13 @@ def to_text(self) -> str:
 
 @dataclass
 class AgentInterview:
-    """单个Agent的采访结果"""
+    """Single Agent Interview Result"""
     agent_name: str
-    agent_role: str  # 角色类型（如：学生、教师、媒体等）
-    agent_bio: str  # 简介
-    question: str  # 采访问题
-    response: str  # 采访回答
-    key_quotes: List[str] = field(default_factory=list)  # 关键引言
+    agent_role: str
+    agent_bio: str
+    question: str
+    response: str
+    key_quotes: List[str] = field(default_factory=list)
     
     def to_dict(self) -> Dict[str, Any]:
         return {
@@ -300,21 +288,17 @@ def to_dict(self) -> Dict[str, Any]:
     
     def to_text(self) -> str:
         text = f"**{self.agent_name}** ({self.agent_role})\n"
-        # 显示完整的agent_bio，不截断
-        text += f"_简介: {self.agent_bio}_\n\n"
+        text += f"_Bio: {self.agent_bio}_\n\n"
         text += f"**Q:** {self.question}\n\n"
         text += f"**A:** {self.response}\n"
         if self.key_quotes:
-            text += "\n**关键引言:**\n"
+            text += "\n**Key Quotes:**\n"
             for quote in self.key_quotes:
-                # 清理各种引号
                 clean_quote = quote.replace('\u201c', '').replace('\u201d', '').replace('"', '')
                 clean_quote = clean_quote.replace('\u300c', '').replace('\u300d', '')
                 clean_quote = clean_quote.strip()
-                # 去掉开头的标点
                 while clean_quote and clean_quote[0] in '，,；;：:、。！？\n\r\t ':
                     clean_quote = clean_quote[1:]
-                # 过滤包含问题编号的垃圾内容（问题1-9）
                 skip = False
                 for d in '123456789':
                     if f'\u95ee\u9898{d}' in clean_quote:
@@ -322,7 +306,7 @@ def to_text(self) -> str:
                         break
                 if skip:
                     continue
-                # 截断过长内容（按句号截断，而非硬截断）
+                # Truncate long content (by period, not hard truncation)
                 if len(clean_quote) > 150:
                     dot_pos = clean_quote.find('\u3002', 80)
                     if dot_pos > 0:
@@ -337,23 +321,17 @@ def to_text(self) -> str:
 @dataclass
 class InterviewResult:
     """
-    采访结果 (Interview)
-    包含多个模拟Agent的采访回答
+    Interview Result
+    Contains interview responses from multiple simulated Agents
     """
-    interview_topic: str  # 采访主题
-    interview_questions: List[str]  # 采访问题列表
-    
-    # 采访选择的Agent
+    interview_topic: str
+    interview_questions: List[str]
+
     selected_agents: List[Dict[str, Any]] = field(default_factory=list)
-    # 各Agent的采访回答
     interviews: List[AgentInterview] = field(default_factory=list)
-    
-    # 选择Agent的理由
+
     selection_reasoning: str = ""
-    # 整合后的采访摘要
     summary: str = ""
-    
-    # 统计
     total_agents: int = 0
     interviewed_count: int = 0
     
@@ -370,52 +348,52 @@ def to_dict(self) -> Dict[str, Any]:
         }
     
     def to_text(self) -> str:
-        """转换为详细的文本格式，供LLM理解和报告引用"""
+        """Convert to detailed text format for LLM understanding and report reference"""
         text_parts = [
-            "## 深度采访报告",
-            f"**采访主题:** {self.interview_topic}",
-            f"**采访人数:** {self.interviewed_count} / {self.total_agents} 位模拟Agent",
-            "\n### 采访对象选择理由",
-            self.selection_reasoning or "（自动选择）",
+            "## Deep Interview Report",
+            f"**Interview Topic:** {self.interview_topic}",
+            f"**Interviewees:** {self.interviewed_count} / {self.total_agents} Simulated Agents",
+            "\n### Selection Rationale",
+            self.selection_reasoning or "(Automatic Selection)",
             "\n---",
-            "\n### 采访实录",
+            "\n### Interview Transcripts",
         ]
 
         if self.interviews:
             for i, interview in enumerate(self.interviews, 1):
-                text_parts.append(f"\n#### 采访 #{i}: {interview.agent_name}")
+                text_parts.append(f"\n#### Interview #{i}: {interview.agent_name}")
                 text_parts.append(interview.to_text())
                 text_parts.append("\n---")
         else:
-            text_parts.append("（无采访记录）\n\n---")
+            text_parts.append("(No interview records)\n\n---")
 
-        text_parts.append("\n### 采访摘要与核心观点")
-        text_parts.append(self.summary or "（无摘要）")
+        text_parts.append("\n### Interview Summary & Key Insights")
+        text_parts.append(self.summary or "(No summary)")
 
         return "\n".join(text_parts)
 
 
 class ZepToolsService:
     """
-    Zep检索工具服务
-    
-    【核心检索工具 - 优化后】
-    1. insight_forge - 深度洞察检索（最强大，自动生成子问题，多维度检索）
-    2. panorama_search - 广度搜索（获取全貌，包括过期内容）
-    3. quick_search - 简单搜索（快速检索）
-    4. interview_agents - 深度采访（采访模拟Agent，获取多视角观点）
-    
-    【基础工具】
-    - search_graph - 图谱语义搜索
-    - get_all_nodes - 获取图谱所有节点
-    - get_all_edges - 获取图谱所有边（含时间信息）
-    - get_node_detail - 获取节点详细信息
-    - get_node_edges - 获取节点相关的边
-    - get_entities_by_type - 按类型获取实体
-    - get_entity_summary - 获取实体的关系摘要
+    Zep Retrieval Tools Service
+
+    [Core Retrieval Tools - Optimized]
+    1. insight_forge - Deep Insight Retrieval (Most powerful, auto-generates sub-questions, multi-dimensional retrieval)
+    2. panorama_search - Breadth Search (Get comprehensive view, including expired content)
+    3. quick_search - Simple Search (Quick retrieval)
+    4. interview_agents - Deep Interview (Interview simulated Agents, obtain multi-perspective insights)
+
+    [Basic Tools]
+    - search_graph - Graph semantic search
+    - get_all_nodes - Get all nodes in graph
+    - get_all_edges - Get all edges in graph (with temporal information)
+    - get_node_detail - Get detailed node information
+    - get_node_edges - Get edges related to a node
+    - get_entities_by_type - Get entities by type
+    - get_entity_summary - Get entity relationship summary
     """
     
-    # 重试配置
+    # Retry configuration
     MAX_RETRIES = 3
     RETRY_DELAY = 2.0
     
@@ -425,13 +403,13 @@ def __init__(self, api_key: Optional[str] = None, llm_client: Optional[LLMClient
     
     @property
     def llm(self) -> LLMClient:
-        """延迟初始化LLM客户端"""
+        """Lazy initialization of LLM client"""
         if self._llm_client is None:
             self._llm_client = LLMClient()
         return self._llm_client
     
     def _call_with_retry(self, func, operation_name: str, max_retries: int = None):
-        """带重试机制的API调用"""
+        """API call with retry mechanism"""
         max_retries = max_retries or self.MAX_RETRIES
         last_exception = None
         delay = self.RETRY_DELAY
@@ -443,13 +421,13 @@ def _call_with_retry(self, func, operation_name: str, max_retries: int = None):
                 last_exception = e
                 if attempt < max_retries - 1:
                     logger.warning(
-                        f"Zep {operation_name} 第 {attempt + 1} 次尝试失败: {str(e)[:100]}, "
-                        f"{delay:.1f}秒后重试..."
+                        f"Zep {operation_name} attempt {attempt + 1} failed: {str(e)[:100]}, "
+                        f"retrying in {delay:.1f}s..."
                     )
                     time.sleep(delay)
                     delay *= 2
                 else:
-                    logger.error(f"Zep {operation_name} 在 {max_retries} 次尝试后仍失败: {str(e)}")
+                    logger.error(f"Zep {operation_name} failed after {max_retries} attempts: {str(e)}")
         
         raise last_exception
     
@@ -461,19 +439,19 @@ def search_graph(
         scope: str = "edges"
     ) -> SearchResult:
         """
-        图谱语义搜索
-        
-        使用混合搜索（语义+BM25）在图谱中搜索相关信息。
-        如果Zep Cloud的search API不可用，则降级为本地关键词匹配。
-        
+        Graph semantic search
+
+        Uses hybrid search (semantic + BM25) to search for related information in the graph.
+        Falls back to local keyword matching if the search API is unavailable.
+
         Args:
-            graph_id: 图谱ID (Standalone Graph)
-            query: 搜索查询
-            limit: 返回结果数量
-            scope: 搜索范围，"edges" 或 "nodes"
-            
+            graph_id: Graph ID (Standalone Graph)
+            query: Search query
+            limit: Number of results to return
+            scope: Search scope, "edges" or "nodes"
+
         Returns:
-            SearchResult: 搜索结果
+            SearchResult
         """
         logger.info(f"Graph search: graph_id={graph_id}, query={query[:50]}...")
 
@@ -514,38 +492,38 @@ def _local_search(
         scope: str = "edges"
     ) -> SearchResult:
         """
-        本地关键词匹配搜索（作为Zep Search API的降级方案）
-        
-        获取所有边/节点，然后在本地进行关键词匹配
-        
+        Local keyword matching search (fallback approach)
+
+        Gets all edges/nodes and performs local keyword matching.
+
         Args:
-            graph_id: 图谱ID
-            query: 搜索查询
-            limit: 返回结果数量
-            scope: 搜索范围
-            
+            graph_id: Graph ID
+            query: Search query
+            limit: Number of results to return
+            scope: Search scope
+
         Returns:
-            SearchResult: 搜索结果
+            SearchResult
         """
-        logger.info(f"使用本地搜索: query={query[:30]}...")
+        logger.info(f"Using local search: query={query[:30]}...")
         
         facts = []
         edges_result = []
         nodes_result = []
         
-        # 提取查询关键词（简单分词）
+        # Extract query keywords (simple tokenization)
         query_lower = query.lower()
         keywords = [w.strip() for w in query_lower.replace(',', ' ').replace('，', ' ').split() if len(w.strip()) > 1]
         
         def match_score(text: str) -> int:
-            """计算文本与查询的匹配分数"""
+            """Calculate match score between text and query"""
             if not text:
                 return 0
             text_lower = text.lower()
-            # 完全匹配查询
+            # Full query match
             if query_lower in text_lower:
                 return 100
-            # 关键词匹配
+            # Keyword match
             score = 0
             for keyword in keywords:
                 if keyword in text_lower:
@@ -554,7 +532,7 @@ def match_score(text: str) -> int:
         
         try:
             if scope in ["edges", "both"]:
-                # 获取所有边并匹配
+                # Get all edges and match
                 all_edges = self.get_all_edges(graph_id)
                 scored_edges = []
                 for edge in all_edges:
@@ -562,7 +540,7 @@ def match_score(text: str) -> int:
                     if score > 0:
                         scored_edges.append((score, edge))
                 
-                # 按分数排序
+                # Sort by score
                 scored_edges.sort(key=lambda x: x[0], reverse=True)
                 
                 for score, edge in scored_edges[:limit]:
@@ -577,7 +555,7 @@ def match_score(text: str) -> int:
                     })
             
             if scope in ["nodes", "both"]:
-                # 获取所有节点并匹配
+                # Get all nodes and match
                 all_nodes = self.get_all_nodes(graph_id)
                 scored_nodes = []
                 for node in all_nodes:
@@ -597,10 +575,10 @@ def match_score(text: str) -> int:
                     if node.summary:
                         facts.append(f"[{node.name}]: {node.summary}")
             
-            logger.info(f"本地搜索完成: 找到 {len(facts)} 条相关事实")
+            logger.info(f"Local search complete: Found {len(facts)} related facts")
             
         except Exception as e:
-            logger.error(f"本地搜索失败: {str(e)}")
+            logger.error(f"Local search failed: {str(e)}")
         
         return SearchResult(
             facts=facts,
@@ -612,15 +590,15 @@ def match_score(text: str) -> int:
     
     def get_all_nodes(self, graph_id: str) -> List[NodeInfo]:
         """
-        获取图谱的所有节点（分页获取）
+        Get all nodes in the graph
 
         Args:
-            graph_id: 图谱ID
+            graph_id: Graph ID
 
         Returns:
-            节点列表
+            List of nodes
         """
-        logger.info(f"获取图谱 {graph_id} 的所有节点...")
+        logger.info(f"Getting all nodes in graph {graph_id}...")
 
         adapter = GraphitiAdapter.get_or_create(graph_id)
         nodes = adapter.get_all_nodes()
@@ -633,21 +611,21 @@ def get_all_nodes(self, graph_id: str) -> List[NodeInfo]:
             attributes=n.get("attributes", {})
         ) for n in nodes]
 
-        logger.info(f"获取到 {len(result)} 个节点")
+        logger.info(f"Retrieved {len(result)} nodes")
         return result
 
     def get_all_edges(self, graph_id: str, include_temporal: bool = True) -> List[EdgeInfo]:
         """
-        获取图谱的所有边（分页获取，包含时间信息）
+        Get all edges in the graph (with temporal information)
 
         Args:
-            graph_id: 图谱ID
-            include_temporal: 是否包含时间信息（默认True）
+            graph_id: Graph ID
+            include_temporal: Whether to include temporal information (default True)
 
         Returns:
-            边列表（包含created_at, valid_at, invalid_at, expired_at）
+            List of edges (with created_at, valid_at, invalid_at, expired_at)
         """
-        logger.info(f"获取图谱 {graph_id} 的所有边...")
+        logger.info(f"Getting all edges in graph {graph_id}...")
 
         adapter = GraphitiAdapter.get_or_create(graph_id)
         edges = adapter.get_all_edges()
@@ -664,21 +642,21 @@ def get_all_edges(self, graph_id: str, include_temporal: bool = True) -> List[Ed
             expired_at=e.get("expired_at"),
         ) for e in edges]
 
-        logger.info(f"获取到 {len(result)} 条边")
+        logger.info(f"Retrieved {len(result)} edges")
         return result
     
     def get_node_detail(self, graph_id: str, node_uuid: str) -> Optional[NodeInfo]:
         """
-        获取单个节点的详细信息
+        Get detailed information about a single node
 
         Args:
-            graph_id: 图谱ID
-            node_uuid: 节点UUID
+            graph_id: Graph ID
+            node_uuid: Node UUID
 
         Returns:
-            节点信息或None
+            Node info or None
         """
-        logger.info(f"获取节点详情: {node_uuid[:8]}...")
+        logger.info(f"Getting node details: {node_uuid[:8]}...")
 
         try:
             adapter = GraphitiAdapter.get_or_create(graph_id)
@@ -693,23 +671,23 @@ def get_node_detail(self, graph_id: str, node_uuid: str) -> Optional[NodeInfo]:
                 )
             return None
         except Exception as e:
-            logger.error(f"获取节点详情失败: {str(e)}")
+            logger.error(f"Failed to get node details: {str(e)}")
             return None
     
     def get_node_edges(self, graph_id: str, node_uuid: str) -> List[EdgeInfo]:
         """
-        获取节点相关的所有边
-        
-        通过获取图谱所有边，然后过滤出与指定节点相关的边
-        
+        Get all edges related to a node
+
+        Gets all edges in the graph, then filters those related to the specified node.
+
         Args:
-            graph_id: 图谱ID
-            node_uuid: 节点UUID
-            
+            graph_id: Graph ID
+            node_uuid: Node UUID
+
         Returns:
-            边列表
+            List of edges
         """
-        logger.info(f"获取节点 {node_uuid[:8]}... 的相关边")
+        logger.info(f"Getting edges related to node {node_uuid[:8]}...")
 
         try:
             adapter = GraphitiAdapter.get_or_create(graph_id)
@@ -727,11 +705,11 @@ def get_node_edges(self, graph_id: str, node_uuid: str) -> List[EdgeInfo]:
                 expired_at=e.get("expired_at"),
             ) for e in all_edges if e.get("source_node_uuid") == node_uuid or e.get("target_node_uuid") == node_uuid]
 
-            logger.info(f"找到 {len(result)} 条与节点相关的边")
+            logger.info(f"Found {len(result)} edges related to the node")
             return result
 
         except Exception as e:
-            logger.warning(f"获取节点边失败: {str(e)}")
+            logger.warning(f"Failed to get node edges: {str(e)}")
             return []
     
     def get_entities_by_type(
@@ -740,26 +718,26 @@ def get_entities_by_type(
         entity_type: str
     ) -> List[NodeInfo]:
         """
-        按类型获取实体
-        
+        Get entities by type
+
         Args:
-            graph_id: 图谱ID
-            entity_type: 实体类型（如 Student, PublicFigure 等）
-            
+            graph_id: Graph ID
+            entity_type: Entity type (e.g. Student, PublicFigure, etc.)
+
         Returns:
-            符合类型的实体列表
+            List of entities matching the type
         """
-        logger.info(f"获取类型为 {entity_type} 的实体...")
+        logger.info(f"Getting entities of type {entity_type}...")
         
         all_nodes = self.get_all_nodes(graph_id)
         
         filtered = []
         for node in all_nodes:
-            # 检查labels是否包含指定类型
+            # Check if labels contain the specified type
             if entity_type in node.labels:
                 filtered.append(node)
         
-        logger.info(f"找到 {len(filtered)} 个 {entity_type} 类型的实体")
+        logger.info(f"Found {len(filtered)} entities of type {entity_type}")
         return filtered
     
     def get_entity_summary(
@@ -768,27 +746,27 @@ def get_entity_summary(
         entity_name: str
     ) -> Dict[str, Any]:
         """
-        获取指定实体的关系摘要
-        
-        搜索与该实体相关的所有信息，并生成摘要
-        
+        Get relationship summary for a specific entity
+
+        Searches all information related to the entity and generates a summary.
+
         Args:
-            graph_id: 图谱ID
-            entity_name: 实体名称
-            
+            graph_id: Graph ID
+            entity_name: Entity name
+
         Returns:
-            实体摘要信息
+            Entity summary information
         """
-        logger.info(f"获取实体 {entity_name} 的关系摘要...")
+        logger.info(f"Getting relationship summary for entity {entity_name}...")
         
-        # 先搜索该实体相关的信息
+        # First search for information related to the entity
         search_result = self.search_graph(
             graph_id=graph_id,
             query=entity_name,
             limit=20
         )
         
-        # 尝试在所有节点中找到该实体
+        # Try to find the entity among all nodes
         all_nodes = self.get_all_nodes(graph_id)
         entity_node = None
         for node in all_nodes:
@@ -798,7 +776,7 @@ def get_entity_summary(
         
         related_edges = []
         if entity_node:
-            # 传入graph_id参数
+            # Pass graph_id parameter
             related_edges = self.get_node_edges(graph_id, entity_node.uuid)
         
         return {
@@ -811,27 +789,27 @@ def get_entity_summary(
     
     def get_graph_statistics(self, graph_id: str) -> Dict[str, Any]:
         """
-        获取图谱的统计信息
-        
+        Get statistics for the graph
+
         Args:
-            graph_id: 图谱ID
-            
+            graph_id: Graph ID
+
         Returns:
-            统计信息
+            Statistics information
         """
-        logger.info(f"获取图谱 {graph_id} 的统计信息...")
+        logger.info(f"Getting statistics for graph {graph_id}...")
         
         nodes = self.get_all_nodes(graph_id)
         edges = self.get_all_edges(graph_id)
         
-        # 统计实体类型分布
+        # Count entity type distribution
         entity_types = {}
         for node in nodes:
             for label in node.labels:
                 if label not in ["Entity", "Node"]:
                     entity_types[label] = entity_types.get(label, 0) + 1
         
-        # 统计关系类型分布
+        # Count relation type distribution
         relation_types = {}
         for edge in edges:
             relation_types[edge.name] = relation_types.get(edge.name, 0) + 1
@@ -851,34 +829,34 @@ def get_simulation_context(
         limit: int = 30
     ) -> Dict[str, Any]:
         """
-        获取模拟相关的上下文信息
-        
-        综合搜索与模拟需求相关的所有信息
-        
+        Get simulation-related context information
+
+        Comprehensively searches all information related to the simulation requirement.
+
         Args:
-            graph_id: 图谱ID
-            simulation_requirement: 模拟需求描述
-            limit: 每类信息的数量限制
-            
+            graph_id: Graph ID
+            simulation_requirement: Simulation requirement description
+            limit: Quantity limit per category
+
         Returns:
-            模拟上下文信息
+            Simulation context information
         """
-        logger.info(f"获取模拟上下文: {simulation_requirement[:50]}...")
+        logger.info(f"Getting simulation context: {simulation_requirement[:50]}...")
         
-        # 搜索与模拟需求相关的信息
+        # Search for information related to the simulation requirement
         search_result = self.search_graph(
             graph_id=graph_id,
             query=simulation_requirement,
             limit=limit
         )
         
-        # 获取图谱统计
+        # Get graph statistics
         stats = self.get_graph_statistics(graph_id)
         
-        # 获取所有实体节点
+        # Get all entity nodes
         all_nodes = self.get_all_nodes(graph_id)
         
-        # 筛选有实际类型的实体（非纯Entity节点）
+        # Filter entities with actual types (not pure Entity nodes)
         entities = []
         for node in all_nodes:
             custom_labels = [l for l in node.labels if l not in ["Entity", "Node"]]
@@ -893,11 +871,11 @@ def get_simulation_context(
             "simulation_requirement": simulation_requirement,
             "related_facts": search_result.facts,
             "graph_statistics": stats,
-            "entities": entities[:limit],  # 限制数量
+            "entities": entities[:limit],
             "total_entities": len(entities)
         }
     
-    # ========== 核心检索工具（优化后） ==========
+    # ========== Core Retrieval Tools (Optimized) ==========
     
     def insight_forge(
         self,
@@ -908,26 +886,26 @@ def insight_forge(
         max_sub_queries: int = 5
     ) -> InsightForgeResult:
         """
-        【InsightForge - 深度洞察检索】
-        
-        最强大的混合检索函数，自动分解问题并多维度检索：
-        1. 使用LLM将问题分解为多个子问题
-        2. 对每个子问题进行语义搜索
-        3. 提取相关实体并获取其详细信息
-        4. 追踪关系链
-        5. 整合所有结果，生成深度洞察
-        
+        [InsightForge - Deep Insight Retrieval]
+
+        The most powerful hybrid retrieval function, automatically decomposes problems and performs multi-dimensional retrieval:
+        1. Use LLM to decompose the problem into multiple sub-questions
+        2. Perform semantic search on each sub-question
+        3. Extract related entities and get their detailed information
+        4. Trace relationship chains
+        5. Integrate all results and generate deep insights
+
         Args:
-            graph_id: 图谱ID
-            query: 用户问题
-            simulation_requirement: 模拟需求描述
-            report_context: 报告上下文（可选，用于更精准的子问题生成）
-            max_sub_queries: 最大子问题数量
-            
+            graph_id: Graph ID
+            query: User question
+            simulation_requirement: Simulation requirement description
+            report_context: Report context (optional, for more precise sub-question generation)
+            max_sub_queries: Maximum number of sub-questions
+
         Returns:
-            InsightForgeResult: 深度洞察检索结果
+            InsightForgeResult: Deep insight retrieval result
         """
-        logger.info(f"InsightForge 深度洞察检索: {query[:50]}...")
+        logger.info(f"InsightForge deep insight retrieval: {query[:50]}...")
         
         result = InsightForgeResult(
             query=query,
@@ -935,7 +913,7 @@ def insight_forge(
             sub_queries=[]
         )
         
-        # Step 1: 使用LLM生成子问题
+        # Step 1: Use LLM to generate sub-questions
         sub_queries = self._generate_sub_queries(
             query=query,
             simulation_requirement=simulation_requirement,
@@ -943,9 +921,9 @@ def insight_forge(
             max_queries=max_sub_queries
         )
         result.sub_queries = sub_queries
-        logger.info(f"生成 {len(sub_queries)} 个子问题")
+        logger.info(f"Generated {len(sub_queries)} sub-questions")
         
-        # Step 2: 对每个子问题进行语义搜索
+        # Step 2: Perform semantic search on each sub-question
         all_facts = []
         all_edges = []
         seen_facts = set()
@@ -965,7 +943,7 @@ def insight_forge(
             
             all_edges.extend(search_result.edges)
         
-        # 对原始问题也进行搜索
+        # Also search for the original question
         main_search = self.search_graph(
             graph_id=graph_id,
             query=query,
@@ -980,7 +958,7 @@ def insight_forge(
         result.semantic_facts = all_facts
         result.total_facts = len(all_facts)
         
-        # Step 3: 从边中提取相关实体UUID，只获取这些实体的信息（不获取全部节点）
+        # Step 3: Extract related entity UUIDs from edges
         entity_uuids = set()
         for edge_data in all_edges:
             if isinstance(edge_data, dict):
@@ -991,21 +969,18 @@ def insight_forge(
                 if target_uuid:
                     entity_uuids.add(target_uuid)
         
-        # 获取所有相关实体的详情（不限制数量，完整输出）
+        # Get related entity details
         entity_insights = []
-        node_map = {}  # 用于后续关系链构建
-        
-        for uuid in list(entity_uuids):  # 处理所有实体，不截断
+        node_map = {}
+
+        for uuid in list(entity_uuids):
             if not uuid:
                 continue
             try:
-                # 单独获取每个相关节点的信息
                 node = self.get_node_detail(graph_id, uuid)
                 if node:
                     node_map[uuid] = node
-                    entity_type = next((l for l in node.labels if l not in ["Entity", "Node"]), "实体")
-                    
-                    # 获取该实体相关的所有事实（不截断）
+                    entity_type = next((l for l in node.labels if l not in ["Entity", "Node"]), "Entity")
                     related_facts = [
                         f for f in all_facts 
                         if node.name.lower() in f.lower()
@@ -1016,18 +991,18 @@ def insight_forge(
                         "name": node.name,
                         "type": entity_type,
                         "summary": node.summary,
-                        "related_facts": related_facts  # 完整输出，不截断
+                        "related_facts": related_facts
                     })
             except Exception as e:
-                logger.debug(f"获取节点 {uuid} 失败: {e}")
+                logger.debug(f"Failed to get node {uuid}: {e}")
                 continue
         
         result.entity_insights = entity_insights
         result.total_entities = len(entity_insights)
         
-        # Step 4: 构建所有关系链（不限制数量）
+        # Step 4: Build relationship chains
         relationship_chains = []
-        for edge_data in all_edges:  # 处理所有边，不截断
+        for edge_data in all_edges:
             if isinstance(edge_data, dict):
                 source_uuid = edge_data.get('source_node_uuid', '')
                 target_uuid = edge_data.get('target_node_uuid', '')
@@ -1043,7 +1018,7 @@ def insight_forge(
         result.relationship_chains = relationship_chains
         result.total_relationships = len(relationship_chains)
         
-        logger.info(f"InsightForge完成: {result.total_facts}条事实, {result.total_entities}个实体, {result.total_relationships}条关系")
+        logger.info(f"InsightForge complete: {result.total_facts} facts, {result.total_entities} entities, {result.total_relationships} relationships")
         return result
     
     def _generate_sub_queries(
@@ -1053,28 +1028,24 @@ def _generate_sub_queries(
         report_context: str = "",
         max_queries: int = 5
     ) -> List[str]:
-        """
-        使用LLM生成子问题
-        
-        将复杂问题分解为多个可以独立检索的子问题
-        """
-        system_prompt = """你是一个专业的问题分析专家。你的任务是将一个复杂问题分解为多个可以在模拟世界中独立观察的子问题。
+        """Use LLM to generate sub-questions"""
+        system_prompt = """You are a professional question analysis expert. Your task is to decompose a complex question into multiple sub-questions that can be independently observed in a simulated world.
 
-要求：
-1. 每个子问题应该足够具体，可以在模拟世界中找到相关的Agent行为或事件
-2. 子问题应该覆盖原问题的不同维度（如：谁、什么、为什么、怎么样、何时、何地）
-3. 子问题应该与模拟场景相关
-4. 返回JSON格式：{"sub_queries": ["子问题1", "子问题2", ...]}"""
+Requirements:
+1. Each sub-question should be specific enough to find related Agent behavior or events in the simulated world
+2. Sub-questions should cover different dimensions of the original question (e.g., who, what, why, how, when, where)
+3. Sub-questions should be relevant to the simulation scenario
+4. Return in JSON format: {"sub_queries": ["sub-question 1", "sub-question 2", ...]}"""
 
-        user_prompt = f"""模拟需求背景：
+        user_prompt = f"""Simulation requirement background:
 {simulation_requirement}
 
-{f"报告上下文：{report_context[:500]}" if report_context else ""}
+{f"Report context: {report_context[:500]}" if report_context else ""}
 
-请将以下问题分解为{max_queries}个子问题：
+Please decompose the following question into {max_queries} sub-questions:
 {query}
 
-返回JSON格式的子问题列表。"""
+Return the sub-questions as a JSON list."""
 
         try:
             response = self.llm.chat_json(
@@ -1086,17 +1057,15 @@ def _generate_sub_queries(
             )
             
             sub_queries = response.get("sub_queries", [])
-            # 确保是字符串列表
             return [str(sq) for sq in sub_queries[:max_queries]]
-            
+
         except Exception as e:
-            logger.warning(f"生成子问题失败: {str(e)}，使用默认子问题")
-            # 降级：返回基于原问题的变体
+            logger.warning(f"Failed to generate sub-questions: {str(e)}, using default sub-questions")
             return [
                 query,
-                f"{query} 的主要参与者",
-                f"{query} 的原因和影响",
-                f"{query} 的发展过程"
+                f"Main participants in {query}",
+                f"Causes and impacts of {query}",
+                f"Development process of {query}"
             ][:max_queries]
     
     def panorama_search(
@@ -1107,40 +1076,35 @@ def panorama_search(
         limit: int = 50
     ) -> PanoramaResult:
         """
-        【PanoramaSearch - 广度搜索】
-        
-        获取全貌视图，包括所有相关内容和历史/过期信息：
-        1. 获取所有相关节点
-        2. 获取所有边（包括已过期/失效的）
-        3. 分类整理当前有效和历史信息
-        
-        这个工具适用于需要了解事件全貌、追踪演变过程的场景。
-        
+        [PanoramaSearch - Breadth Search]
+
+        Get a comprehensive panoramic view, including all related content and historical/expired information.
+
         Args:
-            graph_id: 图谱ID
-            query: 搜索查询（用于相关性排序）
-            include_expired: 是否包含过期内容（默认True）
-            limit: 返回结果数量限制
-            
+            graph_id: Graph ID
+            query: Search query (for relevance sorting)
+            include_expired: Whether to include expired content (default True)
+            limit: Result quantity limit
+
         Returns:
-            PanoramaResult: 广度搜索结果
+            PanoramaResult: Breadth search result
         """
-        logger.info(f"PanoramaSearch 广度搜索: {query[:50]}...")
+        logger.info(f"PanoramaSearch breadth search: {query[:50]}...")
         
         result = PanoramaResult(query=query)
         
-        # 获取所有节点
+        # Get all nodes
         all_nodes = self.get_all_nodes(graph_id)
         node_map = {n.uuid: n for n in all_nodes}
         result.all_nodes = all_nodes
         result.total_nodes = len(all_nodes)
         
-        # 获取所有边（包含时间信息）
+        # Get all edges (including temporal information)
         all_edges = self.get_all_edges(graph_id, include_temporal=True)
         result.all_edges = all_edges
         result.total_edges = len(all_edges)
         
-        # 分类事实
+        # Categorize facts
         active_facts = []
         historical_facts = []
         
@@ -1148,24 +1112,22 @@ def panorama_search(
             if not edge.fact:
                 continue
             
-            # 为事实添加实体名称
+            # Add entity names to facts
             source_name = node_map.get(edge.source_node_uuid, NodeInfo('', '', [], '', {})).name or edge.source_node_uuid[:8]
             target_name = node_map.get(edge.target_node_uuid, NodeInfo('', '', [], '', {})).name or edge.target_node_uuid[:8]
             
-            # 判断是否过期/失效
+            # Check if expired/invalid
             is_historical = edge.is_expired or edge.is_invalid
             
             if is_historical:
-                # 历史/过期事实，添加时间标记
-                valid_at = edge.valid_at or "未知"
-                invalid_at = edge.invalid_at or edge.expired_at or "未知"
+                valid_at = edge.valid_at or "Unknown"
+                invalid_at = edge.invalid_at or edge.expired_at or "Unknown"
                 fact_with_time = f"[{valid_at} - {invalid_at}] {edge.fact}"
                 historical_facts.append(fact_with_time)
             else:
-                # 当前有效事实
                 active_facts.append(edge.fact)
         
-        # 基于查询进行相关性排序
+        # Sort by relevance based on query
         query_lower = query.lower()
         keywords = [w.strip() for w in query_lower.replace(',', ' ').replace('，', ' ').split() if len(w.strip()) > 1]
         
@@ -1179,7 +1141,7 @@ def relevance_score(fact: str) -> int:
                     score += 10
             return score
         
-        # 排序并限制数量
+        # Sort and limit quantity
         active_facts.sort(key=relevance_score, reverse=True)
         historical_facts.sort(key=relevance_score, reverse=True)
         
@@ -1188,7 +1150,7 @@ def relevance_score(fact: str) -> int:
         result.active_count = len(active_facts)
         result.historical_count = len(historical_facts)
         
-        logger.info(f"PanoramaSearch完成: {result.active_count}条有效, {result.historical_count}条历史")
+        logger.info(f"PanoramaSearch complete: {result.active_count} valid, {result.historical_count} historical")
         return result
     
     def quick_search(
@@ -1198,24 +1160,21 @@ def quick_search(
         limit: int = 10
     ) -> SearchResult:
         """
-        【QuickSearch - 简单搜索】
-        
-        快速、轻量级的检索工具：
-        1. 直接调用Zep语义搜索
-        2. 返回最相关的结果
-        3. 适用于简单、直接的检索需求
-        
+        [QuickSearch - Simple Search]
+
+        Fast and lightweight retrieval tool.
+
         Args:
-            graph_id: 图谱ID
-            query: 搜索查询
-            limit: 返回结果数量
-            
+            graph_id: Graph ID
+            query: Search query
+            limit: Number of results to return
+
         Returns:
-            SearchResult: 搜索结果
+            SearchResult
         """
-        logger.info(f"QuickSearch 简单搜索: {query[:50]}...")
+        logger.info(f"QuickSearch simple search: {query[:50]}...")
         
-        # 直接调用现有的search_graph方法
+        # Directly call existing search_graph method
         result = self.search_graph(
             graph_id=graph_id,
             query=query,
@@ -1223,7 +1182,7 @@ def quick_search(
             scope="edges"
         )
         
-        logger.info(f"QuickSearch完成: {result.total_count}条结果")
+        logger.info(f"QuickSearch complete: {result.total_count} results")
         return result
     
     def interview_agents(
@@ -1235,53 +1194,48 @@ def interview_agents(
         custom_questions: List[str] = None
     ) -> InterviewResult:
         """
-        【InterviewAgents - 深度采访】
-        
-        调用真实的OASIS采访API，采访模拟中正在运行的Agent：
-        1. 自动读取人设文件，了解所有模拟Agent
-        2. 使用LLM分析采访需求，智能选择最相关的Agent
-        3. 使用LLM生成采访问题
-        4. 调用 /api/simulation/interview/batch 接口进行真实采访（双平台同时采访）
-        5. 整合所有采访结果，生成采访报告
-        
-        【重要】此功能需要模拟环境处于运行状态（OASIS环境未关闭）
-        
-        【使用场景】
-        - 需要从不同角色视角了解事件看法
-        - 需要收集多方意见和观点
-        - 需要获取模拟Agent的真实回答（非LLM模拟）
-        
+        [InterviewAgents - Deep Interview]
+
+        Call the real OASIS interview API to interview Agents running in the simulation:
+        1. Automatically read profile files to understand all simulated Agents
+        2. Use LLM to analyze interview requirements and intelligently select the most relevant Agents
+        3. Use LLM to generate interview questions
+        4. Call /api/simulation/interview/batch endpoint for real interviews (dual platform)
+        5. Integrate all interview results and generate interview report
+
+        [Important] This feature requires the simulation environment to be running (OASIS environment not closed)
+
         Args:
-            simulation_id: 模拟ID（用于定位人设文件和调用采访API）
-            interview_requirement: 采访需求描述（非结构化，如"了解学生对事件的看法"）
-            simulation_requirement: 模拟需求背景（可选）
-            max_agents: 最多采访的Agent数量
-            custom_questions: 自定义采访问题（可选，若不提供则自动生成）
-            
+            simulation_id: Simulation ID (for locating profile files and calling interview API)
+            interview_requirement: Interview requirement description
+            simulation_requirement: Simulation requirement background (optional)
+            max_agents: Maximum number of Agents to interview
+            custom_questions: Custom interview questions (optional, auto-generated if not provided)
+
         Returns:
-            InterviewResult: 采访结果
+            InterviewResult: Interview result
         """
         from .simulation_runner import SimulationRunner
         
-        logger.info(f"InterviewAgents 深度采访（真实API）: {interview_requirement[:50]}...")
+        logger.info(f"InterviewAgents deep interview (real API): {interview_requirement[:50]}...")
         
         result = InterviewResult(
             interview_topic=interview_requirement,
             interview_questions=custom_questions or []
         )
         
-        # Step 1: 读取人设文件
+        # Step 1: Read agent profile files
         profiles = self._load_agent_profiles(simulation_id)
         
         if not profiles:
-            logger.warning(f"未找到模拟 {simulation_id} 的人设文件")
-            result.summary = "未找到可采访的Agent人设文件"
+            logger.warning(f"No profile files found for simulation {simulation_id}")
+            result.summary = "No Agent profile files found for interview"
             return result
         
         result.total_agents = len(profiles)
-        logger.info(f"加载到 {len(profiles)} 个Agent人设")
+        logger.info(f"Loaded {len(profiles)} Agent profiles")
         
-        # Step 2: 使用LLM选择要采访的Agent（返回agent_id列表）
+        # Step 2: Use LLM to select Agents for interview
         selected_agents, selected_indices, selection_reasoning = self._select_agents_for_interview(
             profiles=profiles,
             interview_requirement=interview_requirement,
@@ -1291,156 +1245,142 @@ def interview_agents(
         
         result.selected_agents = selected_agents
         result.selection_reasoning = selection_reasoning
-        logger.info(f"选择了 {len(selected_agents)} 个Agent进行采访: {selected_indices}")
+        logger.info(f"Selected {len(selected_agents)} Agents for interview: {selected_indices}")
         
-        # Step 3: 生成采访问题（如果没有提供）
+        # Step 3: Generate interview questions
         if not result.interview_questions:
             result.interview_questions = self._generate_interview_questions(
                 interview_requirement=interview_requirement,
                 simulation_requirement=simulation_requirement,
                 selected_agents=selected_agents
             )
-            logger.info(f"生成了 {len(result.interview_questions)} 个采访问题")
+            logger.info(f"Generated {len(result.interview_questions)} interview questions")
         
-        # 将问题合并为一个采访prompt
+        # Combine questions into a single interview prompt
         combined_prompt = "\n".join([f"{i+1}. {q}" for i, q in enumerate(result.interview_questions)])
         
-        # 添加优化前缀，约束Agent回复格式
         INTERVIEW_PROMPT_PREFIX = (
-            "你正在接受一次采访。请结合你的人设、所有的过往记忆与行动，"
-            "以纯文本方式直接回答以下问题。\n"
-            "回复要求：\n"
-            "1. 直接用自然语言回答，不要调用任何工具\n"
-            "2. 不要返回JSON格式或工具调用格式\n"
-            "3. 不要使用Markdown标题（如#、##、###）\n"
-            "4. 按问题编号逐一回答，每个回答以「问题X：」开头（X为问题编号）\n"
-            "5. 每个问题的回答之间用空行分隔\n"
-            "6. 回答要有实质内容，每个问题至少回答2-3句话\n\n"
+            "You are being interviewed. Please combine your character profile, all past memories and actions, "
+            "and directly answer the following questions in plain text.\n"
+            "Response requirements:\n"
+            "1. Answer directly in natural language, do not call any tools\n"
+            "2. Do not return JSON format or tool call format\n"
+            "3. Do not use Markdown headings (e.g., #, ##, ###)\n"
+            "4. Answer the questions in order, with each answer starting with 'Question X:' (X is the question number)\n"
+            "5. Separate each answer with a blank line\n"
+            "6. Provide substantive answers, at least 2-3 sentences per question\n\n"
         )
         optimized_prompt = f"{INTERVIEW_PROMPT_PREFIX}{combined_prompt}"
-        
-        # Step 4: 调用真实的采访API（不指定platform，默认双平台同时采访）
+
+        # Step 4: Call the real interview API
         try:
-            # 构建批量采访列表（不指定platform，双平台采访）
             interviews_request = []
             for agent_idx in selected_indices:
                 interviews_request.append({
                     "agent_id": agent_idx,
-                    "prompt": optimized_prompt  # 使用优化后的prompt
-                    # 不指定platform，API会在twitter和reddit两个平台都采访
+                    "prompt": optimized_prompt
                 })
-            
-            logger.info(f"调用批量采访API（双平台）: {len(interviews_request)} 个Agent")
-            
-            # 调用 SimulationRunner 的批量采访方法（不传platform，双平台采访）
+
+            logger.info(f"Calling batch interview API (dual platform): {len(interviews_request)} Agents")
+
             api_result = SimulationRunner.interview_agents_batch(
                 simulation_id=simulation_id,
                 interviews=interviews_request,
-                platform=None,  # 不指定platform，双平台采访
-                timeout=180.0   # 双平台需要更长超时
+                platform=None,
+                timeout=180.0
             )
-            
-            logger.info(f"采访API返回: {api_result.get('interviews_count', 0)} 个结果, success={api_result.get('success')}")
-            
-            # 检查API调用是否成功
+
+            logger.info(f"Interview API returned: {api_result.get('interviews_count', 0)} results, success={api_result.get('success')}")
+
             if not api_result.get("success", False):
-                error_msg = api_result.get("error", "未知错误")
-                logger.warning(f"采访API返回失败: {error_msg}")
-                result.summary = f"采访API调用失败：{error_msg}。请检查OASIS模拟环境状态。"
+                error_msg = api_result.get("error", "Unknown error")
+                logger.warning(f"Interview API call failed: {error_msg}")
+                result.summary = f"Interview API call failed: {error_msg}. Please check the OASIS simulation environment status."
                 return result
-            
-            # Step 5: 解析API返回结果，构建AgentInterview对象
-            # 双平台模式返回格式: {"twitter_0": {...}, "reddit_0": {...}, "twitter_1": {...}, ...}
+
+            # Step 5: Parse API response
             api_data = api_result.get("result", {})
             results_dict = api_data.get("results", {}) if isinstance(api_data, dict) else {}
-            
+
             for i, agent_idx in enumerate(selected_indices):
                 agent = selected_agents[i]
                 agent_name = agent.get("realname", agent.get("username", f"Agent_{agent_idx}"))
-                agent_role = agent.get("profession", "未知")
+                agent_role = agent.get("profession", "Unknown")
                 agent_bio = agent.get("bio", "")
-                
-                # 获取该Agent在两个平台的采访结果
+
                 twitter_result = results_dict.get(f"twitter_{agent_idx}", {})
                 reddit_result = results_dict.get(f"reddit_{agent_idx}", {})
                 
                 twitter_response = twitter_result.get("response", "")
                 reddit_response = reddit_result.get("response", "")
 
-                # 清理可能的工具调用 JSON 包裹
                 twitter_response = self._clean_tool_call_response(twitter_response)
                 reddit_response = self._clean_tool_call_response(reddit_response)
 
-                # 始终输出双平台标记
-                twitter_text = twitter_response if twitter_response else "（该平台未获得回复）"
-                reddit_text = reddit_response if reddit_response else "（该平台未获得回复）"
-                response_text = f"【Twitter平台回答】\n{twitter_text}\n\n【Reddit平台回答】\n{reddit_text}"
+                twitter_text = twitter_response if twitter_response else "(No response from this platform)"
+                reddit_text = reddit_response if reddit_response else "(No response from this platform)"
+                response_text = f"[Twitter Platform Response]\n{twitter_text}\n\n[Reddit Platform Response]\n{reddit_text}"
 
-                # 提取关键引言（从两个平台的回答中）
                 import re
                 combined_responses = f"{twitter_response} {reddit_response}"
 
-                # 清理响应文本：去掉标记、编号、Markdown 等干扰
                 clean_text = re.sub(r'#{1,6}\s+', '', combined_responses)
                 clean_text = re.sub(r'\{[^}]*tool_name[^}]*\}', '', clean_text)
                 clean_text = re.sub(r'[*_`|>~\-]{2,}', '', clean_text)
-                clean_text = re.sub(r'问题\d+[：:]\s*', '', clean_text)
+                clean_text = re.sub(r'Question\d+[：:]\s*', '', clean_text)
                 clean_text = re.sub(r'【[^】]+】', '', clean_text)
 
-                # 策略1（主）: 提取完整的有实质内容的句子
                 sentences = re.split(r'[。！？]', clean_text)
                 meaningful = [
                     s.strip() for s in sentences
                     if 20 <= len(s.strip()) <= 150
                     and not re.match(r'^[\s\W，,；;：:、]+', s.strip())
-                    and not s.strip().startswith(('{', '问题'))
+                    and not s.strip().startswith(('{', 'Question'))
                 ]
                 meaningful.sort(key=len, reverse=True)
                 key_quotes = [s + "。" for s in meaningful[:3]]
 
-                # 策略2（补充）: 正确配对的中文引号「」内长文本
                 if not key_quotes:
                     paired = re.findall(r'\u201c([^\u201c\u201d]{15,100})\u201d', clean_text)
                     paired += re.findall(r'\u300c([^\u300c\u300d]{15,100})\u300d', clean_text)
                     key_quotes = [q for q in paired if not re.match(r'^[，,；;：:、]', q)][:3]
-                
+
                 interview = AgentInterview(
                     agent_name=agent_name,
                     agent_role=agent_role,
-                    agent_bio=agent_bio[:1000],  # 扩大bio长度限制
+                    agent_bio=agent_bio[:1000],
                     question=combined_prompt,
                     response=response_text,
                     key_quotes=key_quotes[:5]
                 )
                 result.interviews.append(interview)
-            
+
             result.interviewed_count = len(result.interviews)
-            
+
         except ValueError as e:
-            # 模拟环境未运行
-            logger.warning(f"采访API调用失败（环境未运行？）: {e}")
-            result.summary = f"采访失败：{str(e)}。模拟环境可能已关闭，请确保OASIS环境正在运行。"
+            logger.warning(f"Interview API call failed (environment not running?): {e}")
+            result.summary = f"Interview failed: {str(e)}. The simulation environment may be closed. Please ensure the OASIS environment is running."
             return result
         except Exception as e:
-            logger.error(f"采访API调用异常: {e}")
+            logger.error(f"Interview API call exception: {e}")
             import traceback
             logger.error(traceback.format_exc())
-            result.summary = f"采访过程发生错误：{str(e)}"
+            result.summary = f"An error occurred during the interview process: {str(e)}"
             return result
         
-        # Step 6: 生成采访摘要
+        # Step 6: Generate interview summary
         if result.interviews:
             result.summary = self._generate_interview_summary(
                 interviews=result.interviews,
                 interview_requirement=interview_requirement
             )
         
-        logger.info(f"InterviewAgents完成: 采访了 {result.interviewed_count} 个Agent（双平台）")
+        logger.info(f"InterviewAgents complete: Interviewed {result.interviewed_count} Agents (dual platform)")
         return result
     
     @staticmethod
     def _clean_tool_call_response(response: str) -> str:
-        """清理 Agent 回复中的 JSON 工具调用包裹，提取实际内容"""
+        """Clean JSON tool call wrappers in Agent responses and extract actual content"""
         if not response or not response.strip().startswith('{'):
             return response
         text = response.strip()
@@ -1460,11 +1400,11 @@ def _clean_tool_call_response(response: str) -> str:
         return response
 
     def _load_agent_profiles(self, simulation_id: str) -> List[Dict[str, Any]]:
-        """加载模拟的Agent人设文件"""
+        """Load Agent profile files for simulation"""
         import os
         import csv
         
-        # 构建人设文件路径
+        # Build profile file path
         sim_dir = os.path.join(
             os.path.dirname(__file__), 
             f'../../uploads/simulations/{simulation_id}'
@@ -1472,36 +1412,35 @@ def _load_agent_profiles(self, simulation_id: str) -> List[Dict[str, Any]]:
         
         profiles = []
         
-        # 优先尝试读取Reddit JSON格式
+        # Preferentially try to read Reddit JSON format
         reddit_profile_path = os.path.join(sim_dir, "reddit_profiles.json")
         if os.path.exists(reddit_profile_path):
             try:
                 with open(reddit_profile_path, 'r', encoding='utf-8') as f:
                     profiles = json.load(f)
-                logger.info(f"从 reddit_profiles.json 加载了 {len(profiles)} 个人设")
+                logger.info(f"Loaded {len(profiles)} profiles from reddit_profiles.json")
                 return profiles
             except Exception as e:
-                logger.warning(f"读取 reddit_profiles.json 失败: {e}")
+                logger.warning(f"Failed to read reddit_profiles.json: {e}")
         
-        # 尝试读取Twitter CSV格式
+        # Try to read Twitter CSV format
         twitter_profile_path = os.path.join(sim_dir, "twitter_profiles.csv")
         if os.path.exists(twitter_profile_path):
             try:
                 with open(twitter_profile_path, 'r', encoding='utf-8') as f:
                     reader = csv.DictReader(f)
                     for row in reader:
-                        # CSV格式转换为统一格式
-                        profiles.append({
+                            profiles.append({
                             "realname": row.get("name", ""),
                             "username": row.get("username", ""),
                             "bio": row.get("description", ""),
                             "persona": row.get("user_char", ""),
-                            "profession": "未知"
+                            "profession": "Unknown"
                         })
-                logger.info(f"从 twitter_profiles.csv 加载了 {len(profiles)} 个人设")
+                logger.info(f"Loaded {len(profiles)} profiles from twitter_profiles.csv")
                 return profiles
             except Exception as e:
-                logger.warning(f"读取 twitter_profiles.csv 失败: {e}")
+                logger.warning(f"Failed to read twitter_profiles.csv: {e}")
         
         return profiles
     
@@ -1512,52 +1451,44 @@ def _select_agents_for_interview(
         simulation_requirement: str,
         max_agents: int
     ) -> tuple:
-        """
-        使用LLM选择要采访的Agent
-        
-        Returns:
-            tuple: (selected_agents, selected_indices, reasoning)
-                - selected_agents: 选中Agent的完整信息列表
-                - selected_indices: 选中Agent的索引列表（用于API调用）
-                - reasoning: 选择理由
-        """
-        
-        # 构建Agent摘要列表
+        """Use LLM to select Agents for interview"""
+
+        # Build agent summaries
         agent_summaries = []
         for i, profile in enumerate(profiles):
             summary = {
                 "index": i,
                 "name": profile.get("realname", profile.get("username", f"Agent_{i}")),
-                "profession": profile.get("profession", "未知"),
+                "profession": profile.get("profession", "Unknown"),
                 "bio": profile.get("bio", "")[:200],
                 "interested_topics": profile.get("interested_topics", [])
             }
             agent_summaries.append(summary)
         
-        system_prompt = """你是一个专业的采访策划专家。你的任务是根据采访需求，从模拟Agent列表中选择最适合采访的对象。
+        system_prompt = """You are a professional interview planning expert. Your task is to select the most suitable Agents for interview from the simulated Agent list based on the interview requirements.
 
-选择标准：
-1. Agent的身份/职业与采访主题相关
-2. Agent可能持有独特或有价值的观点
-3. 选择多样化的视角（如：支持方、反对方、中立方、专业人士等）
-4. 优先选择与事件直接相关的角色
+Selection Criteria:
+1. Agent's identity/profession is relevant to the interview topic
+2. Agent may hold unique or valuable perspectives
+3. Select diverse perspectives (e.g., supporters, opposers, neutral, experts, etc.)
+4. Prioritize roles directly related to the event
 
-返回JSON格式：
+Return JSON format:
 {
-    "selected_indices": [选中Agent的索引列表],
-    "reasoning": "选择理由说明"
+    "selected_indices": [List of indices of selected Agents],
+    "reasoning": "Explanation of selection rationale"
 }"""
 
-        user_prompt = f"""采访需求：
+        user_prompt = f"""Interview Requirement:
 {interview_requirement}
 
-模拟背景：
-{simulation_requirement if simulation_requirement else "未提供"}
+Simulation Background:
+{simulation_requirement if simulation_requirement else "Not provided"}
 
-可选择的Agent列表（共{len(agent_summaries)}个）：
+Available Agent List ({len(agent_summaries)} total):
 {json.dumps(agent_summaries, ensure_ascii=False, indent=2)}
 
-请选择最多{max_agents}个最适合采访的Agent，并说明选择理由。"""
+Please select up to {max_agents} most suitable Agents for interview and explain your selection rationale."""
 
         try:
             response = self.llm.chat_json(
@@ -1569,9 +1500,9 @@ def _select_agents_for_interview(
             )
             
             selected_indices = response.get("selected_indices", [])[:max_agents]
-            reasoning = response.get("reasoning", "基于相关性自动选择")
+            reasoning = response.get("reasoning", "Automatically selected based on relevance")
             
-            # 获取选中的Agent完整信息
+            # Get full info for selected agents
             selected_agents = []
             valid_indices = []
             for idx in selected_indices:
@@ -1582,11 +1513,10 @@ def _select_agents_for_interview(
             return selected_agents, valid_indices, reasoning
             
         except Exception as e:
-            logger.warning(f"LLM选择Agent失败，使用默认选择: {e}")
-            # 降级：选择前N个
+            logger.warning(f"LLM agent selection failed, using default selection: {e}")
             selected = profiles[:max_agents]
             indices = list(range(min(max_agents, len(profiles))))
-            return selected, indices, "使用默认选择策略"
+            return selected, indices, "Using default selection strategy"
     
     def _generate_interview_questions(
         self,
@@ -1594,29 +1524,29 @@ def _generate_interview_questions(
         simulation_requirement: str,
         selected_agents: List[Dict[str, Any]]
     ) -> List[str]:
-        """使用LLM生成采访问题"""
-        
-        agent_roles = [a.get("profession", "未知") for a in selected_agents]
-        
-        system_prompt = """你是一个专业的记者/采访者。根据采访需求，生成3-5个深度采访问题。
+        """Use LLM to generate interview questions"""
+
+        agent_roles = [a.get("profession", "Unknown") for a in selected_agents]
 
-问题要求：
-1. 开放性问题，鼓励详细回答
-2. 针对不同角色可能有不同答案
-3. 涵盖事实、观点、感受等多个维度
-4. 语言自然，像真实采访一样
-5. 每个问题控制在50字以内，简洁明了
-6. 直接提问，不要包含背景说明或前缀
+        system_prompt = """You are a professional journalist/interviewer. Based on the interview requirements, generate 3-5 deep interview questions.
 
-返回JSON格式：{"questions": ["问题1", "问题2", ...]}"""
+Question Requirements:
+1. Open-ended questions that encourage detailed answers
+2. Questions that may have different answers for different roles
+3. Cover multiple dimensions: facts, viewpoints, feelings, etc.
+4. Natural language, like real interviews
+5. Keep each question under 50 characters, concise and clear
+6. Ask directly, do not include background explanation or prefix
 
-        user_prompt = f"""采访需求：{interview_requirement}
+Return JSON format: {"questions": ["question1", "question2", ...]}"""
 
-模拟背景：{simulation_requirement if simulation_requirement else "未提供"}
+        user_prompt = f"""Interview Requirement: {interview_requirement}
 
-采访对象角色：{', '.join(agent_roles)}
+Simulation Background: {simulation_requirement if simulation_requirement else "Not provided"}
 
-请生成3-5个采访问题。"""
+Interview Subject Roles: {', '.join(agent_roles)}
+
+Please generate 3-5 interview questions."""
 
         try:
             response = self.llm.chat_json(
@@ -1627,14 +1557,14 @@ def _generate_interview_questions(
                 temperature=0.5
             )
             
-            return response.get("questions", [f"关于{interview_requirement}，您有什么看法？"])
-            
+            return response.get("questions", [f"What is your perspective on {interview_requirement}?"])
+
         except Exception as e:
-            logger.warning(f"生成采访问题失败: {e}")
+            logger.warning(f"Failed to generate interview questions: {e}")
             return [
-                f"关于{interview_requirement}，您的观点是什么？",
-                "这件事对您或您所代表的群体有什么影响？",
-                "您认为应该如何解决或改进这个问题？"
+                f"What is your perspective on {interview_requirement}?",
+                "What impact does this have on you or the group you represent?",
+                "How do you think this issue should be solved or improved?"
             ]
     
     def _generate_interview_summary(
@@ -1642,38 +1572,37 @@ def _generate_interview_summary(
         interviews: List[AgentInterview],
         interview_requirement: str
     ) -> str:
-        """生成采访摘要"""
-        
+        """Generate interview summary"""
+
         if not interviews:
-            return "未完成任何采访"
-        
-        # 收集所有采访内容
+            return "No interviews completed"
+
         interview_texts = []
         for interview in interviews:
-            interview_texts.append(f"【{interview.agent_name}（{interview.agent_role}）】\n{interview.response[:500]}")
-        
-        system_prompt = """你是一个专业的新闻编辑。请根据多位受访者的回答，生成一份采访摘要。
+            interview_texts.append(f"[{interview.agent_name} ({interview.agent_role})]\n{interview.response[:500]}")
+
+        system_prompt = """You are a professional news editor. Please generate an interview summary based on the responses from multiple interviewees.
 
-摘要要求：
-1. 提炼各方主要观点
-2. 指出观点的共识和分歧
-3. 突出有价值的引言
-4. 客观中立，不偏袒任何一方
-5. 控制在1000字内
+Summary Requirements:
+1. Extract main viewpoints from all parties
+2. Point out consensus and disagreement among viewpoints
+3. Highlight valuable quotes
+4. Remain objective and neutral, do not favor any side
+5. Keep it under 1000 words
 
-格式约束（必须遵守）：
-- 使用纯文本段落，用空行分隔不同部分
-- 不要使用Markdown标题（如#、##、###）
-- 不要使用分割线（如---、***）
-- 引用受访者原话时使用中文引号「」
-- 可以使用**加粗**标记关键词，但不要使用其他Markdown语法"""
+Format Constraints (Must Follow):
+- Use plain text paragraphs, separated by blank lines
+- Do not use Markdown headings (e.g., #, ##, ###)
+- Do not use dividers (e.g., ---, ***)
+- Use appropriate quotes when citing interviewees
+- Can use **bold** to mark keywords, but do not use other Markdown syntax"""
 
-        user_prompt = f"""采访主题：{interview_requirement}
+        user_prompt = f"""Interview Topic: {interview_requirement}
 
-采访内容：
+Interview Content:
 {"".join(interview_texts)}
 
-请生成采访摘要。"""
+Please generate an interview summary."""
 
         try:
             summary = self.llm.chat(
@@ -1687,6 +1616,5 @@ def _generate_interview_summary(
             return summary
             
         except Exception as e:
-            logger.warning(f"生成采访摘要失败: {e}")
-            # 降级：简单拼接
-            return f"共采访了{len(interviews)}位受访者，包括：" + "、".join([i.agent_name for i in interviews])
+            logger.warning(f"Failed to generate interview summary: {e}")
+            return f"Interviewed {len(interviews)} interviewees, including: " + ", ".join([i.agent_name for i in interviews])