docs: replace inline code examples with examples/ index table
Remove ~160 lines of duplicated code snippets from both READMEs. Link to the runnable scripts in examples/ instead — single source of truth, type-checked by npm run lint.
This commit is contained in:
JackChen
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31a0fa4951
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168
README.md
168
README.md
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@ -104,165 +104,23 @@ Tokens: 12847 output tokens
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<img src="https://contrib.rocks/image?repo=JackChen-me/open-multi-agent" />
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<img src="https://contrib.rocks/image?repo=JackChen-me/open-multi-agent" />
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</a>
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</a>
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## More Examples
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## Examples
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<details>
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All examples are runnable scripts in [`examples/`](./examples/). Run any of them with `npx tsx`:
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<summary><b>Single Agent</b> — one agent, one prompt</summary>
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```typescript
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```bash
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import { OpenMultiAgent } from '@jackchen_me/open-multi-agent'
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npx tsx examples/01-single-agent.ts
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const orchestrator = new OpenMultiAgent({ defaultModel: 'claude-sonnet-4-6' })
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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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```
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</details>
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| Example | What it shows |
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|---------|---------------|
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<details>
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| [01 — Single Agent](examples/01-single-agent.ts) | `runAgent()` one-shot, `stream()` streaming, `prompt()` multi-turn |
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<summary><b>Task Pipeline</b> — explicit control over task graph and assignments</summary>
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| [02 — Team Collaboration](examples/02-team-collaboration.ts) | `runTeam()` auto-orchestration with coordinator pattern |
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| [03 — Task Pipeline](examples/03-task-pipeline.ts) | `runTasks()` explicit dependency graph (design → implement → test + review) |
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```typescript
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| [04 — Multi-Model Team](examples/04-multi-model-team.ts) | `defineTool()` custom tools, mixed Anthropic + OpenAI providers, `AgentPool` |
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const result = await orchestrator.runTasks(team, [
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| [05 — Copilot](examples/05-copilot-test.ts) | GitHub Copilot as an LLM provider |
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{
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| [06 — Local Model](examples/06-local-model.ts) | Ollama + Claude in one pipeline via `baseURL` (works with vLLM, LM Studio, etc.) |
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title: 'Design the data model',
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| [07 — Fan-Out / Aggregate](examples/07-fan-out-aggregate.ts) | `runParallel()` MapReduce — 3 analysts in parallel, then synthesize |
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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'], // blocked until design completes
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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'], // can run in parallel with tests
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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>Custom Tools</b> — define tools with Zod schemas</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>Multi-Model Teams</b> — mix Claude, GPT, and local models in one workflow</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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// Any OpenAI-compatible API — Ollama, vLLM, LM Studio, etc.
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const localAgent: AgentConfig = {
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name: 'reviewer',
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model: 'llama3.1',
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provider: 'openai',
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baseURL: 'http://localhost:11434/v1',
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apiKey: 'ollama',
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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 team = orchestrator.createTeam('mixed-team', {
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name: 'mixed-team',
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agents: [claudeAgent, gptAgent, localAgent],
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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>Streaming Output</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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## Architecture
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## Architecture
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168
README_zh.md
168
README_zh.md
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@ -108,165 +108,23 @@ Tokens: 12847 output tokens
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<img src="https://contrib.rocks/image?repo=JackChen-me/open-multi-agent" />
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<img src="https://contrib.rocks/image?repo=JackChen-me/open-multi-agent" />
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</a>
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</a>
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## 更多示例
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## 示例
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<details>
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所有示例都是可运行脚本，位于 [`examples/`](./examples/) 目录。使用 `npx tsx` 运行：
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<summary><b>单智能体</b> — 一个智能体，一个提示词</summary>
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```typescript
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```bash
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import { OpenMultiAgent } from '@jackchen_me/open-multi-agent'
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npx tsx examples/01-single-agent.ts
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const orchestrator = new OpenMultiAgent({ defaultModel: 'claude-sonnet-4-6' })
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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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```
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</details>
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| 示例 | 展示内容 |
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|------|----------|
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<details>
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| [01 — 单智能体](examples/01-single-agent.ts) | `runAgent()` 单次调用、`stream()` 流式输出、`prompt()` 多轮对话 |
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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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| [02 — 团队协作](examples/02-team-collaboration.ts) | `runTeam()` 自动编排 + 协调者模式 |
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| [03 — 任务流水线](examples/03-task-pipeline.ts) | `runTasks()` 显式依赖图（设计 → 实现 → 测试 + 评审） |
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```typescript
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| [04 — 多模型团队](examples/04-multi-model-team.ts) | `defineTool()` 自定义工具、Anthropic + OpenAI 混合、`AgentPool` |
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const result = await orchestrator.runTasks(team, [
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| [05 — Copilot](examples/05-copilot-test.ts) | GitHub Copilot 作为 LLM 提供者 |
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{
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| [06 — 本地模型](examples/06-local-model.ts) | Ollama + Claude 混合流水线，通过 `baseURL` 接入（兼容 vLLM、LM Studio 等） |
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title: 'Design the data model',
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| [07 — 扇出聚合](examples/07-fan-out-aggregate.ts) | `runParallel()` MapReduce — 3 个分析师并行，然后综合 |
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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、GPT 和本地模型</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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// 任何 OpenAI 兼容 API — Ollama、vLLM、LM Studio 等
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const localAgent: AgentConfig = {
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name: 'reviewer',
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model: 'llama3.1',
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provider: 'openai',
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baseURL: 'http://localhost:11434/v1',
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apiKey: 'ollama',
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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 team = orchestrator.createTeam('mixed-team', {
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name: 'mixed-team',
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agents: [claudeAgent, gptAgent, localAgent],
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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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## 架构
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