feat(loop-detection): defer warning injection

backend/app/channels/manager.py

@@ -146,13 +146,6 @@ def _normalize_custom_agent_name(raw_value: str) -> str:
     return normalized
 
 
-def _strip_loop_warning_text(text: str) -> str:
-    """Remove middleware-authored loop warning lines from display text."""
-    if "[LOOP DETECTED]" not in text:
-        return text
-    return "\n".join(line for line in text.splitlines() if "[LOOP DETECTED]" not in line).strip()
-
-
 def _extract_response_text(result: dict | list) -> str:
     """Extract the last AI message text from a LangGraph runs.wait result.
 
@@ -162,7 +155,6 @@ def _extract_response_text(result: dict | list) -> str:
     Handles special cases:
     - Regular AI text responses
     - Clarification interrupts (``ask_clarification`` tool messages)
-    - Strips loop-detection warnings attached to tool-call AI messages
     """
     if isinstance(result, list):
         messages = result
@@ -192,12 +184,7 @@ def _extract_response_text(result: dict | list) -> str:
         # Regular AI message with text content
         if msg_type == "ai":
             content = msg.get("content", "")
-            has_tool_calls = bool(msg.get("tool_calls"))
             if isinstance(content, str) and content:
-                if has_tool_calls:
-                    content = _strip_loop_warning_text(content)
-                    if not content:
-                        continue
                 return content
             # content can be a list of content blocks
             if isinstance(content, list):
@@ -208,8 +195,6 @@ def _extract_response_text(result: dict | list) -> str:
                     elif isinstance(block, str):
                         parts.append(block)
                 text = "".join(parts)
-                if has_tool_calls:
-                    text = _strip_loop_warning_text(text)
                 if text:
                     return text
     return ""

backend/docs/middleware-execution-flow.md

@@ -4,22 +4,22 @@
 
 `create_deerflow_agent` 通过 `RuntimeFeatures` 组装的完整 middleware 链（默认全开时）：
 
-| # | Middleware | `before_agent` | `before_model` | `after_model` | `after_agent` | `wrap_tool_call` | 主 Agent | Subagent | 来源 |
-|---|-----------|:-:|:-:|:-:|:-:|:-:|:-:|:-:|------|
-| 0 | ThreadDataMiddleware | ✓ | | | | | ✓ | ✓ | `sandbox` |
-| 1 | UploadsMiddleware | ✓ | | | | | ✓ | ✗ | `sandbox` |
-| 2 | SandboxMiddleware | ✓ | | | ✓ | | ✓ | ✓ | `sandbox` |
-| 3 | DanglingToolCallMiddleware | | | ✓ | | | ✓ | ✗ | 始终开启 |
-| 4 | GuardrailMiddleware | | | | | ✓ | ✓ | ✓ | *Phase 2 纳入* |
-| 5 | ToolErrorHandlingMiddleware | | | | | ✓ | ✓ | ✓ | 始终开启 |
-| 6 | SummarizationMiddleware | | | ✓ | | | ✓ | ✗ | `summarization` |
-| 7 | TodoMiddleware | | | ✓ | | | ✓ | ✗ | `plan_mode` 参数 |
-| 8 | TitleMiddleware | | | ✓ | | | ✓ | ✗ | `auto_title` |
-| 9 | MemoryMiddleware | | | | ✓ | | ✓ | ✗ | `memory` |
-| 10 | ViewImageMiddleware | | ✓ | | | | ✓ | ✗ | `vision` |
-| 11 | SubagentLimitMiddleware | | | ✓ | | | ✓ | ✗ | `subagent` |
-| 12 | LoopDetectionMiddleware | | | ✓ | | | ✓ | ✗ | 始终开启 |
-| 13 | ClarificationMiddleware | | | ✓ | | | ✓ | ✗ | 始终最后 |
+| # | Middleware | `before_agent` | `before_model` | `after_model` | `after_agent` | `wrap_model_call` | `wrap_tool_call` | 主 Agent | Subagent | 来源 |
+|---|-----------|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|------|
+| 0 | ThreadDataMiddleware | ✓ | | | | | | ✓ | ✓ | `sandbox` |
+| 1 | UploadsMiddleware | ✓ | | | | | | ✓ | ✗ | `sandbox` |
+| 2 | SandboxMiddleware | ✓ | | | ✓ | | | ✓ | ✓ | `sandbox` |
+| 3 | DanglingToolCallMiddleware | | | | | ✓ | | ✓ | ✗ | 始终开启 |
+| 4 | GuardrailMiddleware | | | | | | ✓ | ✓ | ✓ | *Phase 2 纳入* |
+| 5 | ToolErrorHandlingMiddleware | | | | | | ✓ | ✓ | ✓ | 始终开启 |
+| 6 | SummarizationMiddleware | | ✓ | | | | | ✓ | ✗ | `summarization` |
+| 7 | TodoMiddleware | | ✓ | ✓ | | | | ✓ | ✗ | `plan_mode` 参数 |
+| 8 | TitleMiddleware | | | ✓ | | | | ✓ | ✗ | `auto_title` |
+| 9 | MemoryMiddleware | | | | ✓ | | | ✓ | ✗ | `memory` |
+| 10 | ViewImageMiddleware | | ✓ | | | | | ✓ | ✗ | `vision` |
+| 11 | SubagentLimitMiddleware | | | ✓ | | | | ✓ | ✗ | `subagent` |
+| 12 | LoopDetectionMiddleware | ✓ | | ✓ | ✓ | ✓ | | ✓ | ✗ | 始终开启 |
+| 13 | ClarificationMiddleware | | | | | | ✓ | ✓ | ✗ | 始终最后 |
 
 主 agent **14 个** middleware（`make_lead_agent`），subagent **4 个**（ThreadData、Sandbox、Guardrail、ToolErrorHandling）。`create_deerflow_agent` Phase 1 实现 **13 个**（Guardrail 仅支持自定义实例，无内置默认）。
 
@@ -35,42 +35,50 @@ graph TB
 
     subgraph BA ["<b>before_agent</b> 正序 0→N"]
         direction TB
-        TD["[0] ThreadData<br/>创建线程目录"] --> UL["[1] Uploads<br/>扫描上传文件"] --> SB["[2] Sandbox<br/>获取沙箱"]
+        TD["[0] ThreadData<br/>创建线程目录"] --> UL["[1] Uploads<br/>扫描上传文件"] --> SB["[2] Sandbox<br/>获取沙箱"] --> LD_BA["[12] LoopDetection<br/>清理 stale warning"]
     end
 
     subgraph BM ["<b>before_model</b> 正序 0→N"]
         direction TB
         VI["[10] ViewImage<br/>注入图片 base64"]
     end
 
-    SB --> VI
-    VI --> M["<b>MODEL</b>"]
+    subgraph WM ["<b>wrap_model_call</b>"]
+        direction TB
+        DTC_WM["[3] DanglingToolCall<br/>补悬空 ToolMessage"] --> LD_WM["[12] LoopDetection<br/>注入当前 run warning"]
+    end
+
+    LD_BA --> VI
+    VI --> DTC_WM
+    LD_WM --> M["<b>MODEL</b>"]
 
     subgraph AM ["<b>after_model</b> 反序 N→0"]
         direction TB
-        CL["[13] Clarification<br/>拦截 ask_clarification"] --> LD["[12] LoopDetection<br/>检测循环"] --> SL["[11] SubagentLimit<br/>截断多余 task"] --> TI["[8] Title<br/>生成标题"] --> SM["[6] Summarization<br/>上下文压缩"] --> DTC["[3] DanglingToolCall<br/>补缺失 ToolMessage"]
+        LD["[12] LoopDetection<br/>检测循环/排队 warning"] --> SL["[11] SubagentLimit<br/>截断多余 task"] --> TI["[8] Title<br/>生成标题"]
     end
 
-    M --> CL
+    M --> LD
 
     subgraph AA ["<b>after_agent</b> 反序 N→0"]
         direction TB
-        SBR["[2] Sandbox<br/>释放沙箱"] --> MEM["[9] Memory<br/>入队记忆"]
+        LD_CLEAN["[12] LoopDetection<br/>清理 pending warning"] --> MEM["[9] Memory<br/>入队记忆"] --> SBR["[2] Sandbox<br/>释放沙箱"]
     end
 
-    DTC --> SBR
-    MEM --> END(["response"])
+    TI --> LD_CLEAN
+    SBR --> END(["response"])
 
     classDef beforeNode fill:#a0a8b5,stroke:#636b7a,color:#2d3239
     classDef modelNode fill:#b5a8a0,stroke:#7a6b63,color:#2d3239
+    classDef wrapModelNode fill:#a8a0b5,stroke:#6b637a,color:#2d3239
     classDef afterModelNode fill:#b5a0a8,stroke:#7a636b,color:#2d3239
     classDef afterAgentNode fill:#a0b5a8,stroke:#637a6b,color:#2d3239
     classDef terminalNode fill:#a8b5a0,stroke:#6b7a63,color:#2d3239
 
-    class TD,UL,SB,VI beforeNode
+    class TD,UL,SB,LD_BA,VI beforeNode
+    class DTC_WM,LD_WM wrapModelNode
     class M modelNode
-    class CL,LD,SL,TI,SM,DTC afterModelNode
-    class SBR,MEM afterAgentNode
+    class LD,SL,TI afterModelNode
+    class LD_CLEAN,SBR,MEM afterAgentNode
     class START,END terminalNode
 ```
 
@@ -82,13 +90,12 @@ sequenceDiagram
     participant TD as ThreadDataMiddleware
     participant UL as UploadsMiddleware
     participant SB as SandboxMiddleware
+    participant LD as LoopDetectionMiddleware
     participant VI as ViewImageMiddleware
+    participant DTC as DanglingToolCallMiddleware
     participant M as MODEL
-    participant CL as ClarificationMiddleware
     participant SL as SubagentLimitMiddleware
     participant TI as TitleMiddleware
-    participant SM as SummarizationMiddleware
-    participant DTC as DanglingToolCallMiddleware
     participant MEM as MemoryMiddleware
 
     U ->> TD: invoke
@@ -103,19 +110,26 @@ sequenceDiagram
     activate SB
     Note right of SB: before_agent 获取沙箱
 
-    SB ->> VI: before_model
+    SB ->> LD: before_agent
+    activate LD
+    Note right of LD: before_agent 清理同 thread 旧 run 的 pending warning
+    LD ->> VI: before_model
     activate VI
     Note right of VI: before_model 注入图片 base64
 
-    VI ->> M: messages + tools
+    VI ->> DTC: wrap_model_call
+    activate DTC
+    Note right of DTC: wrap_model_call 补悬空 ToolMessage
+    DTC ->> LD: wrap_model_call
+    Note right of LD: wrap_model_call drain 当前 run warning 并追加到末尾
+    LD ->> M: messages + tools
     activate M
-    M -->> CL: AI response
+    M -->> LD: AI response
     deactivate M
 
-    activate CL
-    Note right of CL: after_model 拦截 ask_clarification
-    CL -->> SL: after_model
-    deactivate CL
+    Note right of LD: after_model 检测循环；warning 入队，hard-stop 清 tool_calls
+    LD -->> SL: after_model
+    deactivate LD
 
     activate SL
     Note right of SL: after_model 截断多余 task
@@ -124,31 +138,25 @@ sequenceDiagram
 
     activate TI
     Note right of TI: after_model 生成标题
-    TI -->> SM: after_model
+    TI -->> DTC: done
     deactivate TI
 
-    activate SM
-    Note right of SM: after_model 上下文压缩
-    SM -->> DTC: after_model
-    deactivate SM
-
-    activate DTC
-    Note right of DTC: after_model 补缺失 ToolMessage
-    DTC -->> VI: done
     deactivate DTC
 
     VI -->> SB: done
     deactivate VI
 
+    Note right of LD: after_agent 清理当前 run 未消费 warning
+
+    Note right of MEM: after_agent 入队记忆
+
     Note right of SB: after_agent 释放沙箱
     SB -->> UL: done
     deactivate SB
 
     UL -->> TD: done
     deactivate UL
 
-    Note right of MEM: after_agent 入队记忆
-
     TD -->> U: response
     deactivate TD
 ```
@@ -224,12 +232,12 @@ sequenceDiagram
     participant TD as ThreadData
     participant UL as Uploads
     participant SB as Sandbox
+    participant LD as LoopDetection
     participant VI as ViewImage
+    participant DTC as DanglingToolCall
     participant M as MODEL
-    participant CL as Clarification
     participant SL as SubagentLimit
     participant TI as Title
-    participant SM as Summarization
     participant MEM as Memory
 
     U ->> TD: invoke
@@ -238,54 +246,60 @@ sequenceDiagram
     Note right of UL: before_agent 扫描文件
     UL ->> SB: .
     Note right of SB: before_agent 获取沙箱
+    SB ->> LD: .
+    Note right of LD: before_agent 清理 stale pending warning
 
     loop 每轮对话（tool call 循环）
         SB ->> VI: .
         Note right of VI: before_model 注入图片
-        VI ->> M: messages + tools
-        M -->> CL: AI response
-        Note right of CL: after_model 拦截 ask_clarification
-        CL -->> SL: .
+        VI ->> DTC: .
+        Note right of DTC: wrap_model_call 补悬空工具结果
+        DTC ->> LD: .
+        Note right of LD: wrap_model_call 注入当前 run warning
+        LD ->> M: messages + tools
+        M -->> LD: AI response
+        Note right of LD: after_model 检测循环/排队 warning
+        LD -->> SL: .
         Note right of SL: after_model 截断多余 task
         SL -->> TI: .
         Note right of TI: after_model 生成标题
-        TI -->> SM: .
-        Note right of SM: after_model 上下文压缩
     end
 
-    Note right of SB: after_agent 释放沙箱
-    SB -->> MEM: .
+    Note right of LD: after_agent 清理当前 run pending warning
+    LD -->> MEM: .
     Note right of MEM: after_agent 入队记忆
-    MEM -->> U: response
+    MEM -->> SB: .
+    Note right of SB: after_agent 释放沙箱
+    SB -->> U: response
 ```
 
 > [!warning] 不是洋葱
-> 14 个 middleware 中只有 SandboxMiddleware 有 before/after 对称（获取/释放）。其余都是单向的：要么只在 `before_*` 做事，要么只在 `after_*` 做事。`before_agent` / `after_agent` 只跑一次，`before_model` / `after_model` 每轮循环都跑。
+> 大部分 middleware 只用一个阶段。SandboxMiddleware 使用 `before_agent`/`after_agent` 做资源获取/释放；LoopDetectionMiddleware 也使用这两个钩子，但用途是清理 run-scoped pending warnings，不是资源生命周期对称。`before_agent` / `after_agent` 只跑一次，`before_model` / `after_model` / `wrap_model_call` 每轮循环都跑。
 
 硬依赖只有 2 处：
 
 1. **ThreadData 在 Sandbox 之前** — sandbox 需要线程目录
-2. **Clarification 在列表最后** — `after_model` 反序时最先执行，第一个拦截 `ask_clarification`
+2. **Clarification 在列表最后** — `wrap_tool_call` 处理 `ask_clarification` 时优先拦截，并通过 `Command(goto=END)` 中断执行
 
 ### 结论
 
 | | 真正的洋葱 | DeerFlow 实际 |
 |---|---|---|
 | 每个 middleware | before + after 对称 | 大多只用一个钩子 |
 | 激活条 | 嵌套（外长内短） | 不嵌套（串行） |
-| 反序的意义 | 清理与初始化配对 | 仅影响 after_model 的执行优先级 |
+| 反序的意义 | 清理与初始化配对 | 影响 `after_model` / `after_agent` 的执行优先级 |
 | 典型例子 | Auth: 校验 token / 清理上下文 | ThreadData: 只创建目录，没有清理 |
 
 ## 关键设计点
 
 ### ClarificationMiddleware 为什么在列表最后？
 
-位置最后 = `after_model` 最先执行。它需要**第一个**看到 model 输出，检查是否有 `ask_clarification` tool call。如果有，立即中断（`Command(goto=END)`），后续 middleware 的 `after_model` 不再执行。
+位置最后使它在工具调用包装链中优先拦截 `ask_clarification`。如果命中，它返回 `Command(goto=END)`，把格式化后的澄清问题写成 `ToolMessage` 并中断执行。
 
 ### SandboxMiddleware 的对称性
 
 `before_agent`（正序第 3 个）获取沙箱，`after_agent`（反序第 1 个）释放沙箱。外层进入 → 外层退出，天然的洋葱对称。
 
-### 大部分 middleware 只用一个钩子
+### LoopDetectionMiddleware 为什么同时用多个钩子？
 
-14 个 middleware 中，只有 SandboxMiddleware 同时用了 `before_agent` + `after_agent`（获取/释放）。其余都只在一个阶段执行。洋葱模型的反序特性主要影响 `after_model` 阶段的执行顺序。
+`after_model` 只做检测：重复工具调用达到 warning 阈值时，把 warning 放入 `(thread_id, run_id)` 作用域的 pending 队列。真正注入发生在下一次 `wrap_model_call`：此时上一轮 `AIMessage(tool_calls)` 对应的 `ToolMessage` 已经在请求里，warning 追加在末尾，不会破坏 OpenAI/Moonshot 的 tool-call pairing。`before_agent` 清理同一 thread 下旧 run 的残留 warning，`after_agent` 清理当前 run 没被消费的 warning。