283 lines
10 KiB
Markdown
283 lines
10 KiB
Markdown
# Open Multi-Agent
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构建能协同工作的 AI 智能体团队。一个智能体负责规划，一个负责实现，一个负责审查——框架自动处理任务调度、依赖关系和智能体间通信。
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[](https://github.com/JackChen-me/open-multi-agent/stargazers)
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[](./LICENSE)
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[](https://www.typescriptlang.org/)
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[English](./README.md) | **中文**
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## 为什么选择 Open Multi-Agent？
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- **多智能体团队** — 定义不同角色、工具甚至不同模型的智能体。它们通过消息总线和共享内存协作。
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- **任务 DAG 调度** — 任务之间存在依赖关系。框架进行拓扑排序——有依赖的任务等待，无依赖的任务并行执行。
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- **模型无关** — Claude 和 GPT 可以在同一个团队中使用。每个智能体可以单独配置模型。你也可以为任何 LLM 编写自己的适配器。
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- **进程内执行** — 没有子进程开销。所有内容在一个 Node.js 进程中运行。可部署到 Serverless、Docker、CI/CD。
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## 快速开始
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```bash
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npm install @jackchen_me/open-multi-agent
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```
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在环境变量中设置 `ANTHROPIC_API_KEY`（以及可选的 `OPENAI_API_KEY`）。
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```typescript
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import { OpenMultiAgent } from '@jackchen_me/open-multi-agent'
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const orchestrator = new OpenMultiAgent({ defaultModel: 'claude-sonnet-4-6' })
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// 一个智能体，一个任务
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const result = await orchestrator.runAgent(
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{
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name: 'coder',
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model: 'claude-sonnet-4-6',
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tools: ['bash', 'file_write'],
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},
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'Write a TypeScript function that reverses a string, save it to /tmp/reverse.ts, and run it.',
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)
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console.log(result.output)
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```
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## 多智能体团队
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这才是有意思的地方。三个智能体，一个目标：
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```typescript
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import { OpenMultiAgent } from '@jackchen_me/open-multi-agent'
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import type { AgentConfig } from '@jackchen_me/open-multi-agent'
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const architect: AgentConfig = {
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name: 'architect',
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model: 'claude-sonnet-4-6',
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systemPrompt: 'You design clean API contracts and file structures.',
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tools: ['file_write'],
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}
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const developer: AgentConfig = {
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name: 'developer',
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model: 'claude-sonnet-4-6',
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systemPrompt: 'You implement what the architect designs.',
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tools: ['bash', 'file_read', 'file_write', 'file_edit'],
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}
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const reviewer: AgentConfig = {
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name: 'reviewer',
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model: 'claude-sonnet-4-6',
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systemPrompt: 'You review code for correctness and clarity.',
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tools: ['file_read', 'grep'],
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}
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const orchestrator = new OpenMultiAgent({
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defaultModel: 'claude-sonnet-4-6',
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onProgress: (event) => console.log(event.type, event.agent ?? event.task ?? ''),
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})
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const team = orchestrator.createTeam('api-team', {
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name: 'api-team',
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agents: [architect, developer, reviewer],
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sharedMemory: true,
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})
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// 描述一个目标——框架将其拆解为任务并编排执行
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const result = await orchestrator.runTeam(team, 'Create a REST API for a todo list in /tmp/todo-api/')
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console.log(`成功: ${result.success}`)
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console.log(`Token 用量: ${result.totalTokenUsage.output_tokens} output tokens`)
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```
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## 更多示例
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<details>
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<summary><b>任务流水线</b> — 显式控制任务图和分配</summary>
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```typescript
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const result = await orchestrator.runTasks(team, [
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{
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title: 'Design the data model',
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description: 'Write a TypeScript interface spec to /tmp/spec.md',
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assignee: 'architect',
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},
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{
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title: 'Implement the module',
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description: 'Read /tmp/spec.md and implement the module in /tmp/src/',
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assignee: 'developer',
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dependsOn: ['Design the data model'], // 等待设计完成后才开始
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},
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{
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title: 'Write tests',
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description: 'Read the implementation and write Vitest tests.',
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assignee: 'developer',
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dependsOn: ['Implement the module'],
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},
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{
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title: 'Review code',
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description: 'Review /tmp/src/ and produce a structured code review.',
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assignee: 'reviewer',
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dependsOn: ['Implement the module'], // 可以和测试并行执行
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},
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])
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```
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</details>
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<details>
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<summary><b>自定义工具</b> — 使用 Zod schema 定义工具</summary>
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```typescript
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import { z } from 'zod'
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import { defineTool, Agent, ToolRegistry, ToolExecutor, registerBuiltInTools } from '@jackchen_me/open-multi-agent'
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const searchTool = defineTool({
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name: 'web_search',
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description: 'Search the web and return the top results.',
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inputSchema: z.object({
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query: z.string().describe('The search query.'),
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maxResults: z.number().optional().describe('Number of results (default 5).'),
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}),
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execute: async ({ query, maxResults = 5 }) => {
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const results = await mySearchProvider(query, maxResults)
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return { data: JSON.stringify(results), isError: false }
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},
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})
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const registry = new ToolRegistry()
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registerBuiltInTools(registry)
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registry.register(searchTool)
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const executor = new ToolExecutor(registry)
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const agent = new Agent(
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{ name: 'researcher', model: 'claude-sonnet-4-6', tools: ['web_search'] },
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registry,
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executor,
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)
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const result = await agent.run('Find the three most recent TypeScript releases.')
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```
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</details>
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<details>
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<summary><b>多模型团队</b> — 在一个工作流中混合使用 Claude 和 GPT</summary>
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```typescript
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const claudeAgent: AgentConfig = {
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name: 'strategist',
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model: 'claude-opus-4-6',
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provider: 'anthropic',
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systemPrompt: 'You plan high-level approaches.',
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tools: ['file_write'],
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}
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const gptAgent: AgentConfig = {
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name: 'implementer',
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model: 'gpt-5.4',
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provider: 'openai',
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systemPrompt: 'You implement plans as working code.',
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tools: ['bash', 'file_read', 'file_write'],
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}
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const team = orchestrator.createTeam('mixed-team', {
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name: 'mixed-team',
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agents: [claudeAgent, gptAgent],
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sharedMemory: true,
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})
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const result = await orchestrator.runTeam(team, 'Build a CLI tool that converts JSON to CSV.')
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```
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</details>
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<details>
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<summary><b>流式输出</b></summary>
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```typescript
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import { Agent, ToolRegistry, ToolExecutor, registerBuiltInTools } from '@jackchen_me/open-multi-agent'
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const registry = new ToolRegistry()
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registerBuiltInTools(registry)
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const executor = new ToolExecutor(registry)
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const agent = new Agent(
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{ name: 'writer', model: 'claude-sonnet-4-6', maxTurns: 3 },
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registry,
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executor,
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)
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for await (const event of agent.stream('Explain monads in two sentences.')) {
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if (event.type === 'text' && typeof event.data === 'string') {
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process.stdout.write(event.data)
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}
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}
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```
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</details>
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## 架构
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```
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┌─────────────────────────────────────────────────────────────────┐
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│ OpenMultiAgent (Orchestrator) │
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│ │
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│ createTeam() runTeam() runTasks() runAgent() getStatus() │
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└──────────────────────┬──────────────────────────────────────────┘
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│
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┌──────────▼──────────┐
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│ Team │
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│ - AgentConfig[] │
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│ - MessageBus │
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│ - TaskQueue │
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│ - SharedMemory │
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└──────────┬──────────┘
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│
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┌─────────────┴─────────────┐
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│ │
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┌────────▼──────────┐ ┌───────────▼───────────┐
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│ AgentPool │ │ TaskQueue │
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│ - Semaphore │ │ - dependency graph │
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│ - runParallel() │ │ - auto unblock │
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└────────┬──────────┘ │ - cascade failure │
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│ └───────────────────────┘
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┌────────▼──────────┐
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│ Agent │
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│ - run() │ ┌──────────────────────┐
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│ - prompt() │───►│ LLMAdapter │
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│ - stream() │ │ - AnthropicAdapter │
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└────────┬──────────┘ │ - OpenAIAdapter │
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│ └──────────────────────┘
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┌────────▼──────────┐
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│ AgentRunner │ ┌──────────────────────┐
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│ - conversation │───►│ ToolRegistry │
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│ loop │ │ - defineTool() │
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│ - tool dispatch │ │ - 5 built-in tools │
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└───────────────────┘ └──────────────────────┘
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```
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## 内置工具
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| 工具 | 说明 |
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|------|------|
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| `bash` | 执行 Shell 命令。返回 stdout + stderr。支持超时和工作目录设置。 |
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| `file_read` | 读取指定绝对路径的文件内容。支持偏移量和行数限制以处理大文件。 |
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| `file_write` | 写入或创建文件。自动创建父目录。 |
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| `file_edit` | 通过精确字符串匹配编辑文件。 |
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| `grep` | 使用正则表达式搜索文件内容。优先使用 ripgrep，回退到 Node.js 实现。 |
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## 参与贡献
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欢迎提 Issue、功能需求和 PR。以下方向的贡献尤其有价值：
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- **LLM 适配器** — Ollama、llama.cpp、vLLM、Gemini。`LLMAdapter` 接口只需实现两个方法：`chat()` 和 `stream()`。
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- **示例** — 真实场景的工作流和用例。
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- **文档** — 指南、教程和 API 文档。
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## Star 趋势
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[](https://star-history.com/#JackChen-me/open-multi-agent&Date)
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## 许可证
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MIT
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