使用 supervisor Agent 构建研究协调器

本指南将介绍如何使用 supervisor Agent 构建一个编排多个专业 Agent 的研究协调器。协调器会将研究任务委派给研究 Agent，将写作任务委派给写作 Agent，然后将结果综合成详细报告。

你将设置职责明确的子 Agent，并配置 supervisor Agent 来协调它们。还会使用委派 hook 控制执行，并使用 scorer 验证任务完成情况。

前提条件

• 已安装 Node.js v22.13.0 或更高版本
• 受支持的模型 Provider所提供的 API 密钥
• 现有 Mastra 项目（按照安装指南设置新项目）

创建研究 Agent

研究 Agent 专门收集任意主题的事实信息，并以简洁的项目符号摘要返回关键事实和来源。

创建新文件 src/mastra/agents/research-agent.ts：

src/mastra/agents/research-agent.ts

import { Agent } from '@mastra/core/agent'

export const researchAgent = new Agent({
  id: 'research-agent',
  name: 'Research Specialist',
  description:
    'Specializes in gathering factual information and data on any topic. ' +
    'Returns concise bullet-point summaries with key facts and sources. ' +
    'Does not write full articles or narrative content.',
  instructions:
    'You are a research specialist. When given a topic, gather key facts, ' +
    'statistics, and information. Present findings as clear bullet points. ' +
    'Include sources when possible. Focus on accuracy and completeness.',
  model: 'openai/gpt-5-mini',
})

description 字段非常关键，它帮助 supervisor 判断何时将任务委派给此 Agent。清晰的描述可以提高委派准确性。

创建写作 Agent

写作 Agent 将研究内容转换成结构良好、段落完整且行文流畅的文章。

创建新文件 src/mastra/agents/writing-agent.ts：

src/mastra/agents/writing-agent.ts

import { Agent } from '@mastra/core/agent'

export const writingAgent = new Agent({
  id: 'writing-agent',
  name: 'Writing Specialist',
  description:
    'Transforms research material into well-structured written content. ' +
    'Produces full paragraphs and complete articles with proper flow. ' +
    'Best used after research has been gathered.',
  instructions:
    'You are a writing specialist. Transform research and information into ' +
    'well-written articles. Use complete paragraphs, clear structure, and ' +
    'engaging language. Maintain a professional yet accessible tone. ' +
    'Ensure the content flows naturally from introduction to conclusion.',
  model: 'openai/gpt-5-mini',
})

创建 supervisor Agent

Supervisor 负责协调研究和写作任务。其 instructions 定义委派策略，包括何时使用各个子 Agent 以及如何综合结果。Memory 直接在 Agent 上配置。

创建新文件 src/mastra/agents/supervisor-agent.ts：

src/mastra/agents/supervisor-agent.ts

import { Agent } from '@mastra/core/agent'
import { Memory } from '@mastra/memory'
import { LibSQLStore } from '@mastra/libsql'
import { researchAgent } from './research-agent'
import { writingAgent } from './writing-agent'

export const supervisorAgent = new Agent({
  id: 'supervisor-agent',
  name: 'Research Coordinator',
  instructions: `You coordinate research and writing tasks using specialized agents.

Available resources:
- research-agent: Gathers factual data and sources (returns bullet points)
- writing-agent: Transforms research into well-structured articles (returns full paragraphs)

Delegation strategy:
1. For research requests: Delegate to research-agent first to gather facts
2. For writing requests: Delegate to writing-agent with any available research context
3. For comprehensive reports: Delegate to research-agent first, then writing-agent
4. Always ensure you have gathered sufficient information before producing final output

Success criteria:
- All aspects of the user's request are addressed
- Information is accurate and well-sourced
- Final output is well-formatted and complete
- If anything is missing or uncertain, continue gathering information`,
  model: 'openai/gpt-5.6-sol',
  agents: {
    researchAgent,
    writingAgent,
  },
  memory: new Memory({
    storage: new LibSQLStore({
      id: 'mastra-storage',
      url: 'file:mastra.db',
    }),
  }),
  defaultOptions: {
    maxSteps: 10,

    // Monitor progress after each iteration
    onIterationComplete: async context => {
      console.log(`\n✓ Iteration ${context.iteration} complete`)
      console.log(`  Finish reason: ${context.finishReason}`)
      console.log(`  Response length: ${context.text.length} chars\n`)

      // Continue until task is complete
      return { continue: true }
    },

    // Control delegations
    delegation: {
      onDelegationStart: async context => {
        console.log(`→ Delegating to: ${context.primitiveId}`)

        // Add context for specific agents
        if (context.primitiveId === 'research-agent') {
          return {
            proceed: true,
            modifiedPrompt: `${context.prompt}\n\nFocus on recent developments (2024-2025) and include statistics.`,
          }
        }

        return { proceed: true }
      },

      onDelegationComplete: async context => {
        console.log(`✓ Completed: ${context.primitiveId}\n`)

        // Handle errors: bail to stop execution and provide feedback
        if (context.error) {
          console.error('Delegation failed:', context.error)
          context.bail() // Stop further delegations
          return {
            feedback: `Delegation to ${context.primitiveId} failed: ${context.error}. Try a different approach.`,
          }
        }
      },

      // Only pass last 10 messages to subagents
      messageFilter: ({ messages }) => {
        return messages.slice(-10)
      },
    },
  },
})

在 src/mastra/index.ts 中向 Mastra 注册 supervisor：

src/mastra/index.ts

import { Mastra } from '@mastra/core'
import { supervisorAgent } from './agents/supervisor-agent'

export const mastra = new Mastra({
  agents: { supervisorAgent },
})

Supervisor Agent 上的 defaultOptions 用于配置委派 hook 和迭代监控：

• onDelegationStart 修改研究 Agent 的提示词，要求提供近期数据
• onDelegationComplete 记录完成状态，并在出错时停止，然后提供反馈
• messageFilter 将上下文限制为最近 10 条消息以提高效率
• onIterationComplete 在每次迭代后监控进度

测试基本 supervisor

创建 src/index.ts 文件以与 supervisor 交互：

src/index.ts

import { supervisorAgent } from './mastra/agents/supervisor-agent'

async function main() {
  const topic = 'artificial intelligence in education'
  console.log(`\nTopic: ${topic}\n`)

  const stream = await supervisorAgent.stream(
    `Research ${topic} and write a comprehensive article about it`,
  )

  // Stream the response
  console.log('📝 Final Report:\n')
  for await (const chunk of stream.textStream) {
    process.stdout.write(chunk)
  }
  console.log('\n')
}

main()

Supervisor 使用 defaultOptions 中配置的委派 hook 和迭代监控。

添加任务完成度评分

任务完成度 scorer 会自动验证任务是否完成，防止 supervisor 过早结束。

在 src/mastra/scorers/task-complete-scorer.ts 中创建 scorer：

src/mastra/scorers/task-complete-scorer.ts

import { createScorer } from '@mastra/core/evals'

export const taskCompleteScorer = createScorer({
  id: 'task-complete',
  name: 'Task Completeness',
  description: 'Checks if the research and writing task has been fully completed',
}).generateScore(async context => {
  const text = (context.run.output || '').toString()

  // Check if response contains required elements
  const hasSubstantialContent = text.length > 500
  const hasStructure = text.includes('\n\n') // Multiple paragraphs
  const hasContext = /\d{4}/.test(text) // Contains years/dates

  // Return 1 if complete, 0 if not
  if (hasSubstantialContent && hasStructure && hasContext) {
    return 1
  }

  return 0
})

安装 Evals 软件包：

npm

npm install @mastra/evals

pnpm

pnpm add @mastra/evals

Yarn

yarn add @mastra/evals

Bun

bun add @mastra/evals

更新 src/mastra/agents/supervisor-agent.ts 中的 supervisor Agent，在 defaultOptions 中加入任务完成度评分：

src/mastra/agents/supervisor-agent.ts

import { Agent } from '@mastra/core/agent'
import { Memory } from '@mastra/memory'
import { LibSQLStore } from '@mastra/libsql'
import { researchAgent } from './research-agent'
import { writingAgent } from './writing-agent'
import { taskCompleteScorer } from '../scorers/task-complete-scorer'

export const supervisorAgent = new Agent({
  id: 'supervisor-agent',
  name: 'Research Coordinator',
  instructions: `You coordinate research and writing tasks using specialized agents.

Available resources:
- research-agent: Gathers factual data and sources (returns bullet points)
- writing-agent: Transforms research into well-structured articles (returns full paragraphs)

Delegation strategy:
1. For research requests: Delegate to research-agent first to gather facts
2. For writing requests: Delegate to writing-agent with any available research context
3. For comprehensive reports: Delegate to research-agent first, then writing-agent
4. Always ensure you have gathered sufficient information before producing final output

Success criteria:
- All aspects of the user's request are addressed
- Information is accurate and well-sourced
- Final output is well-formatted and complete
- If anything is missing or uncertain, continue gathering information`,
  model: 'openai/gpt-5.6-sol',
  agents: {
    researchAgent,
    writingAgent,
  },
  memory: new Memory({
    storage: new LibSQLStore({
      id: 'mastra-storage',
      url: 'file:mastra.db',
    }),
  }),
  defaultOptions: {
    maxSteps: 10,

    onIterationComplete: async context => {
      console.log(`\n✓ Iteration ${context.iteration} complete`)
      console.log(`  Finish reason: ${context.finishReason}`)
      console.log(`  Response length: ${context.text.length} chars\n`)
      return { continue: true }
    },

    delegation: {
      onDelegationStart: async context => {
        console.log(`→ Delegating to: ${context.primitiveId}`)

        if (context.primitiveId === 'research-agent') {
          return {
            proceed: true,
            modifiedPrompt: `${context.prompt}\n\nFocus on recent developments (2024-2025) and include statistics.`,
          }
        }

        return { proceed: true }
      },

      onDelegationComplete: async context => {
        console.log(`✓ Completed: ${context.primitiveId}\n`)

        if (context.error) {
          console.error('Delegation failed:', context.error)
          context.bail() // Stop further delegations
          return {
            feedback: `Delegation to ${context.primitiveId} failed: ${context.error}. Try a different approach.`,
          }
        }
      },

      messageFilter: ({ messages }) => {
        return messages.slice(-10)
      },
    },

    // Validate task completion
    isTaskComplete: {
      scorers: [taskCompleteScorer],
      strategy: 'all',
      onComplete: async result => {
        console.log('\n🎯 Completion Check:')
        console.log(`  Complete: ${result.complete}`)
        console.log(`  Score: ${result.scorers[0]?.score}\n`)
      },
    },
  },
})

Scorer 会检查内容是否充实、结构是否合理以及是否包含上下文信息。如果任务尚未完成，supervisor 会继续迭代。现在，所有 hook 和任务完成度评分都已在 Agent 的 defaultOptions 中配置，并会自动应用于每次调用。

测试研究协调器

运行协调器以查看其实际效果：

npx tsx src/index.ts

你会看到 supervisor 先将任务委派给研究 Agent，再委派给写作 Agent，日志会显示委派流程：

Topic: artificial intelligence in education

→ Delegating to: research-agent
✓ Iteration 1 complete
  Finish reason: tool-calls
  Response length: 0 chars

✓ Completed: research-agent

→ Delegating to: writing-agent
✓ Iteration 2 complete
  Finish reason: tool-calls
  Response length: 0 chars

✓ Completed: writing-agent

🎯 Completion Check:
  Complete: true
  Score: 1

✓ Iteration 3 complete
  Finish reason: stop
  Response length: 1247 chars

📝 Final Report:

Artificial Intelligence in Education: Transforming Learning in 2024-2025

[The coordinator will produce a comprehensive article combining research findings with well-structured writing...]

由于 Agent 响应具有非确定性，实际输出可能不同，但委派模式会保持一致。

后续步骤

你可以通过以下方式扩展研究协调器：

• 添加更多专业 Agent（事实核查员、编辑、引用格式化工具）
• 为质量指标（可读性、来源质量）实现自定义 scorer
• 添加用于 Web 搜索或数据库访问的 Tool
• 为复杂的多步骤研究流程创建 Workflow
• 使用结构化输出生成特定格式的报告

了解更多：

• Supervisor Agent
• Agent.stream() 参考