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# 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](https://mastra.zisheng.pro/models)所提供的 API 密钥
- 现有 Mastra 项目（按照[安装指南](https://mastra.zisheng.pro/guides/getting-started/quickstart)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`：

```typescript
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`：

```typescript
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`：

```typescript
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：

```typescript
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：

```typescript
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：

```typescript
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**:

```bash
npm install @mastra/evals
```

**pnpm**:

```bash
pnpm add @mastra/evals
```

**Yarn**:

```bash
yarn add @mastra/evals
```

**Bun**:

```bash
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：

```typescript
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：

```bash
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：

```text
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](https://mastra.zisheng.pro/docs/capabilities/subagents)
- [Agent.stream() 参考](https://mastra.zisheng.pro/reference/streaming/agents/stream)