feat: #64 execution plan review

example/src/agents/static_supervisor.ts

@@ -0,0 +1,110 @@
+import s from 'dedent'
+import { agent, AgentOptions } from 'fabrice-ai/agent'
+import { childState } from 'fabrice-ai/state'
+import { tool } from 'fabrice-ai/tool'
+import { zodResponseFormat } from 'openai/helpers/zod'
+import { z } from 'zod'
+
+const defaults: AgentOptions = {
+  tools: {
+    printThePlan: tool({
+      description: 'Tool for printing a message to the user',
+      parameters: z.object({
+        message: z.string().describe('The message to print to the user'),
+      }),
+      execute: ({ message }) => {
+        console.log(message)
+        return Promise.resolve('User got the message')
+      },
+    }),
+  },
+  run: async (state, context, workflow) => {
+    const response = await workflow.team[state.agent].provider.completions({
+      messages: [
+        {
+          role: 'system',
+          content: s`
+            You are a planner that breaks down complex workflows into smaller, actionable steps.
+            Your job is to determine the execution plan of the workflow.
+            Print the plan using the 'printThePlan' tool.
+            Then return the tasks one by one untill execution plan end.
+            Take available agents into consideration.
+            If all required tasks are completed, return null.
+
+            Rules:
+            1. Each task should be self-contained and achievable
+            2. Tasks should be specific and actionable
+            3. Return null when the workflow is complete
+            4. Consider dependencies and order of operations
+            5. Use context from completed tasks to inform next steps
+
+            Here are the available agents:
+            <agents>
+                ${Object.entries(workflow.team).map(([name, agent]) =>
+                  agent.description ? `<agent name="${name}">${agent.description}</agent>` : ''
+                )}
+            </agents>            
+          `,
+        },
+        {
+          role: 'assistant',
+          content: 'What is the request?',
+        },
+        ...context,
+        ...state.messages,
+      ],
+      temperature: 0.2,
+      response_format: zodResponseFormat(
+        z.object({
+          plan: z.array(z.string()).describe('The execution plan of the workflow'),
+          task: z
+            .string()
+            .describe('The next task to be completed or null if the workflow is complete')
+            .nullable(),
+          reasoning: z
+            .string()
+            .describe('The reasoning for selecting the next task or why the workflow is complete'),
+        }),
+        'next_task'
+      ),
+    })
+
+    try {
+      const content = response.choices[0].message.parsed
+      console.log(content)
+      if (!content) {
+        throw new Error('No content in response')
+      }
+
+      if (!content.task) {
+        return {
+          ...state,
+          status: 'finished',
+        }
+      }
+
+      const agentRequest = {
+        role: 'user' as const,
+        content: content.task,
+      }
+
+      return {
+        ...state,
+        status: 'running',
+        messages: [...state.messages, agentRequest],
+        child: childState({
+          agent: 'resourcePlanner',
+          messages: [agentRequest],
+        }),
+      }
+    } catch (error) {
+      throw new Error('Failed to determine next task')
+    }
+  },
+}
+
+export const staticSupervisor = (options?: AgentOptions) =>
+  agent({
+    ...defaults,
+    ...options,
+  })

example/src/github_trending_vector.ts

@@ -2,13 +2,13 @@ import { createFireCrawlTool } from '@fabrice-ai/tools/firecrawl'
 import { getApiKey } from '@fabrice-ai/tools/utils'
 import { createVectorStoreTools } from '@fabrice-ai/tools/vector'
 import { agent } from 'fabrice-ai/agent'
+import { supervisor } from 'fabrice-ai/agents/supervisor'
 import { solution } from 'fabrice-ai/solution'
 import { teamwork } from 'fabrice-ai/teamwork'
 import { logger } from 'fabrice-ai/telemetry'
-import { tool } from 'fabrice-ai/tool'
 import { workflow } from 'fabrice-ai/workflow'
-import { z } from 'zod'
 
+import { staticSupervisor } from './agents/static_supervisor.js'
 import { askUser } from './tools/askUser.js'
 
 const apiKey = await getApiKey('Firecrawl.dev API Key', 'FIRECRAWL_API_KEY')
@@ -19,46 +19,54 @@ const { firecrawl } = createFireCrawlTool({
   apiKey,
 })
 
-const githubResearcher = agent({
+const webCrawler = agent({
   description: `
-    You are skilled at browsing Github pages.
+    You are skilled at browsing Web pages.
     You are saving the documents to vector store for later usage.
-    You don't do any other thing just these two tasks.
   `,
   tools: {
     firecrawl,
     saveDocumentInVectorStore,
   },
 })
 
-const wrapupRedactor = agent({
+const human = agent({
   description: `
-    You ask users for which topic to focus on if it's defined in the task.
-    Then - you search relevant information in Vector Store and compile reports based on it.
-    You're famous of beautiful Markdown formatting.
+    You can ask user and get their answer to questions that are needed by other agents.
   `,
   tools: {
     askUser,
+  },
+})
+
+const reportCompiler = agent({
+  description: `
+    You can search Vector Store to find relevant informations and create reports based on it
+    Based on the information from Vector Store you can compile a comprehensive report.
+    You're famous of beautiful Markdown formatting.
+  `,
+  tools: {
     searchInVectorStore,
   },
 })
 
 const wrapUpTrending = workflow({
-  team: { githubResearcher, wrapupRedactor },
+  team: { webCrawler, human, reportCompiler, supervisor: staticSupervisor() }, // an example of overriding supervisor
   description: `
-    Research the URL "https://github.com/trending/typescript" page using firecrawl tool
-    Select 3 top projects. Browse for details about these projects on their subpages. 
-    Save it all to the vector store.
+    Research the URL "https://github.com/trending/typescript" page.
+    Select 3 top projects. Browse details about these projects on their subpages.
+    Store each page in Vector Store for further usage.
+    After you store the information you don't need to browse the page again 
+    because everything is stored in Vector Store.
+
+    Ask user about which project he wants to learn more. Ask user only once.
 
-    Ask user about which project he wants to learn more.
-    reate a comprehensive report markdown output:
+    Create a comprehensive markdown report using information from Vector Store, based on user selection:
      - create a one, two sentence summary about every project.
      - include detailed summary about the project selected by the user.
 
     Here are some ground rules to follow: 
-      - Browser the pages onle once and store content in Vector Store. 
       - Use Vector Store if you need information about the project.
-      - Before making up the record: ask user about which project he wants to learn more.
   `,
   output: `
     Comprehensive markdown report including: