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# 从 `.network()` 迁移到 supervisor Agent

使用 `Agent.stream()` 和 `Agent.generate()` 的 supervisor Agent 是协调多个 Agent 的推荐方式，可替代旧版 `.network()` API。本指南将带你完成迁移的每个步骤。

> **.network() 已弃用:** `.network()` 已弃用，并将在未来版本中移除。在此之前，现有代码仍可继续运行，但后续开发重点现已转向 supervisor Agent。请尽快迁移。

## 用 `.stream()` 或 `.generate()` 替换 `.network()`

核心变更是用 `.stream()`（流式）或 `.generate()`（非流式）替换 `.network()` 调用。Agent 配置保持不变，仍需在 Agent 上定义 `agents`、`workflows`、`tools` 和 `memory`。变化的是调用方式和结果处理方式。

使用 `.network()` 时，你会遍历 `network-execution-event-step-finish` 等自定义事件类型。使用 `.stream()` 时，则使用标准的 `textStream` 或 `fullStream` 迭代器。

**迁移前：**

```typescript
const result = await routingAgent.network('Research AI in education')

for await (const chunk of result) {
  if (chunk.type === 'network-execution-event-step-finish') {
    console.log(chunk.payload.result)
  }
}
```

**迁移后：**

```typescript
const stream = await supervisorAgent.stream('Research AI in education', {
  maxSteps: 10,
})

for await (const chunk of stream.textStream) {
  process.stdout.write(chunk)
}
```

`maxSteps` 选项限制 supervisor 可进行的迭代次数，替代 `.network()` 中的隐式迭代限制。

对于非流式用例，请使用具有相同选项的 `generate()`：

```typescript
const result = await supervisorAgent.generate('Research AI in education', {
  maxSteps: 10,
})

console.log(result.text)
```

## 编写清晰的 supervisor 指令

使用 `.network()` 时，路由 Agent 依靠通用指令和原语描述来决定调用内容。supervisor Agent 的工作方式相同，但清晰具体的指令可提高委派准确性。

supervisor 的 `instructions` 应列明可用资源及各资源的使用时机，还应说明如何协调这些资源以及如何判断任务已经完成。

**迁移前：**

```typescript
const routingAgent = new Agent({
  id: 'routing-agent',
  instructions: 'You are a network of researchers and writers...',
  agents: { researchAgent, writingAgent },
  memory: new Memory(),
})
```

**迁移后：**

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

Available resources:
- researchAgent: Gathers factual data and sources (returns bullet points)
- writingAgent: Transforms research into narrative content (returns full paragraphs)

Delegation strategy:
1. For research requests: Delegate to researchAgent first
2. For writing requests: Delegate to writingAgent (provide research if available)
3. For complex requests: Delegate to researchAgent first, then writingAgent

Success criteria:
- All user questions are fully answered
- Response is well-formatted and complete
- If information is incomplete, continue iterating`,
  agents: { researchAgent, writingAgent },
  memory: new Memory(),
})
```

## 为子 Agent 添加描述

每个子 Agent 都应提供 `description` 字段，用于说明其用途和返回格式。描述还应说明何时使用该子 Agent。supervisor 会根据这些描述决定将任务委派给哪个 Agent。

```typescript
const researchAgent = new Agent({
  id: 'research-agent',
  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.`,
})

const writingAgent = new Agent({
  id: 'writing-agent',
  description: `Transforms research material into well-structured written content.
    Produces full paragraphs and complete articles.
    Best used after research has been gathered.`,
})
```

## 更新事件处理

如果之前处理特定的 `.network()` 事件，请将其更新为使用标准流式数据块类型：

| `.network()` 事件                       | supervisor Agent 数据块                   |
| ------------------------------------- | -------------------------------------- |
| `routing-agent-start`                 | `step-start`                           |
| `routing-agent-end`                   | `step-finish`                          |
| `agent-execution-start`               | `step-start`（委派时）                      |
| `agent-execution-event-text-delta`    | `text-delta`                           |
| `agent-execution-event-finish`        | `step-finish`                          |
| `network-execution-event-step-finish` | `step-finish` + `finishReason: 'stop'` |
| `network-object`                      | `object-delta`（使用 structuredOutput）    |
| `network-object-result`               | `object`（使用 structuredOutput）          |

## 添加委派钩子

supervisor Agent 允许你接入委派生命周期，以监控、修改或拒绝委派。这些钩子可以在 Agent 的 `defaultOptions` 中配置，也可以在每次调用时传入。

`onDelegationStart` 会在 supervisor 向子 Agent 委派任务前调用。你可以修改提示词或限制子 Agent 的步骤数，该钩子也可以完全拒绝委派：

```typescript
const stream = await supervisorAgent.stream('Research AI in education', {
  maxSteps: 10,
  delegation: {
    onDelegationStart: async context => {
      console.log(`Delegating to: ${context.primitiveId}`)

      if (context.primitiveId === 'research-agent') {
        return {
          proceed: true,
          modifiedPrompt: `${context.prompt}\n\nFocus on 2024-2025 data.`,
          modifiedMaxSteps: 5,
        }
      }

      if (context.iteration > 8) {
        return {
          proceed: false,
          rejectionReason: 'Max iterations reached. Synthesize current findings.',
        }
      }

      return { proceed: true }
    },
  },
})
```

`onDelegationComplete` 会在委派完成后调用。请检查结果，并在应停止 supervisor 循环时调用 `context.bail()`。你还可以返回将保存到 supervisor Memory 的反馈：

```typescript
const stream = await supervisorAgent.stream('Research AI in education', {
  maxSteps: 10,
  delegation: {
    onDelegationComplete: async context => {
      if (context.error) {
        context.bail() // Stop further delegations
        return {
          feedback: `Delegation to ${context.primitiveId} failed: ${context.error}. Try a different approach.`,
        }
      }
    },
  },
})
```

## 添加消息过滤

默认情况下，子 Agent 会从 supervisor 接收完整的对话上下文。使用 `messageFilter` 可控制共享哪些消息，例如移除敏感数据或限制消息数量：

```typescript
const stream = await supervisorAgent.stream('Research AI in education', {
  maxSteps: 10,
  delegation: {
    messageFilter: ({ messages, primitiveId, prompt }) => {
      return messages
        .filter(msg => {
          const content =
            typeof msg.content === 'string' ? msg.content : JSON.stringify(msg.content)
          return !content.includes('confidential')
        })
        .slice(-10)
    },
  },
})
```

## 添加迭代监控

`onIterationComplete` 会在 supervisor 循环的每次迭代后调用。可用它记录进度或提供反馈以引导 Agent。该钩子也可以提前停止执行：

```typescript
const stream = await supervisorAgent.stream('Research AI in education', {
  maxSteps: 10,
  onIterationComplete: async context => {
    console.log(`Iteration ${context.iteration}/${context.maxIterations}`)

    if (!context.text.includes('recommendations')) {
      return {
        continue: true,
        feedback: 'Please include specific recommendations in your analysis.',
      }
    }

    if (context.text.length > 1000 && context.finishReason === 'stop') {
      return { continue: false }
    }

    return { continue: true }
  },
})
```

## 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。如果验证失败，supervisor 会继续迭代。未通过 scorer 的反馈会加入对话上下文，使子 Agent 能够了解缺少了什么：

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

const taskCompleteScorer = createScorer({
  id: 'task-complete',
  name: 'Task Completeness',
}).generateScore(async context => {
  const text = (context.run.output || '').toString()
  const hasAnalysis = text.includes('analysis')
  const hasRecommendations = text.includes('recommendation')
  return hasAnalysis && hasRecommendations ? 1 : 0
})

const stream = await supervisorAgent.stream('Research AI in education', {
  maxSteps: 10,
  isTaskComplete: {
    scorers: [taskCompleteScorer],
    strategy: 'all',
    onComplete: async result => {
      console.log('Task complete:', result.complete)
    },
  },
})
```

## 另请参阅

- [supervisor Agent](https://mastra.zisheng.pro/docs/capabilities/subagents)
- [Agent 网络](https://mastra.zisheng.pro/docs/agents/networks)
- [Agent.stream() 参考](https://mastra.zisheng.pro/reference/streaming/agents/stream)
- [Agent.generate() 参考](https://mastra.zisheng.pro/reference/agents/generate)
- [Agent 审批](https://mastra.zisheng.pro/docs/agents/agent-approval)
- [指南：研究协调器](https://mastra.zisheng.pro/guides/guide/research-coordinator)