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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.
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JackChen 2026-04-03 02:18:58 +08:00
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commit 94cccf24d7
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168
README.md
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@ -104,165 +104,23 @@ Tokens: 12847 output tokens
<img src="https://contrib.rocks/image?repo=JackChen-me/open-multi-agent" />
</a>
## More Examples
## Examples
<details>
<summary><b>Single Agent</b> — one agent, one prompt</summary>
All examples are runnable scripts in [`examples/`](./examples/). Run any of them with `npx tsx`:
```typescript
import { OpenMultiAgent } from '@jackchen_me/open-multi-agent'
const orchestrator = new OpenMultiAgent({ defaultModel: 'claude-sonnet-4-6' })
const result = await orchestrator.runAgent(
{
name: 'coder',
model: 'claude-sonnet-4-6',
tools: ['bash', 'file_write'],
},
'Write a TypeScript function that reverses a string, save it to /tmp/reverse.ts, and run it.',
)
console.log(result.output)
```bash
npx tsx examples/01-single-agent.ts
```
</details>
<details>
<summary><b>Task Pipeline</b> — explicit control over task graph and assignments</summary>
```typescript
const result = await orchestrator.runTasks(team, [
{
title: 'Design the data model',
description: 'Write a TypeScript interface spec to /tmp/spec.md',
assignee: 'architect',
},
{
title: 'Implement the module',
description: 'Read /tmp/spec.md and implement the module in /tmp/src/',
assignee: 'developer',
dependsOn: ['Design the data model'], // blocked until design completes
},
{
title: 'Write tests',
description: 'Read the implementation and write Vitest tests.',
assignee: 'developer',
dependsOn: ['Implement the module'],
},
{
title: 'Review code',
description: 'Review /tmp/src/ and produce a structured code review.',
assignee: 'reviewer',
dependsOn: ['Implement the module'], // can run in parallel with tests
},
])
```
</details>
<details>
<summary><b>Custom Tools</b> — define tools with Zod schemas</summary>
```typescript
import { z } from 'zod'
import { defineTool, Agent, ToolRegistry, ToolExecutor, registerBuiltInTools } from '@jackchen_me/open-multi-agent'
const searchTool = defineTool({
name: 'web_search',
description: 'Search the web and return the top results.',
inputSchema: z.object({
query: z.string().describe('The search query.'),
maxResults: z.number().optional().describe('Number of results (default 5).'),
}),
execute: async ({ query, maxResults = 5 }) => {
const results = await mySearchProvider(query, maxResults)
return { data: JSON.stringify(results), isError: false }
},
})
const registry = new ToolRegistry()
registerBuiltInTools(registry)
registry.register(searchTool)
const executor = new ToolExecutor(registry)
const agent = new Agent(
{ name: 'researcher', model: 'claude-sonnet-4-6', tools: ['web_search'] },
registry,
executor,
)
const result = await agent.run('Find the three most recent TypeScript releases.')
```
</details>
<details>
<summary><b>Multi-Model Teams</b> — mix Claude, GPT, and local models in one workflow</summary>
```typescript
const claudeAgent: AgentConfig = {
name: 'strategist',
model: 'claude-opus-4-6',
provider: 'anthropic',
systemPrompt: 'You plan high-level approaches.',
tools: ['file_write'],
}
const gptAgent: AgentConfig = {
name: 'implementer',
model: 'gpt-5.4',
provider: 'openai',
systemPrompt: 'You implement plans as working code.',
tools: ['bash', 'file_read', 'file_write'],
}
// Any OpenAI-compatible API — Ollama, vLLM, LM Studio, etc.
const localAgent: AgentConfig = {
name: 'reviewer',
model: 'llama3.1',
provider: 'openai',
baseURL: 'http://localhost:11434/v1',
apiKey: 'ollama',
systemPrompt: 'You review code for correctness and clarity.',
tools: ['file_read', 'grep'],
}
const team = orchestrator.createTeam('mixed-team', {
name: 'mixed-team',
agents: [claudeAgent, gptAgent, localAgent],
sharedMemory: true,
})
const result = await orchestrator.runTeam(team, 'Build a CLI tool that converts JSON to CSV.')
```
</details>
<details>
<summary><b>Streaming Output</b></summary>
```typescript
import { Agent, ToolRegistry, ToolExecutor, registerBuiltInTools } from '@jackchen_me/open-multi-agent'
const registry = new ToolRegistry()
registerBuiltInTools(registry)
const executor = new ToolExecutor(registry)
const agent = new Agent(
{ name: 'writer', model: 'claude-sonnet-4-6', maxTurns: 3 },
registry,
executor,
)
for await (const event of agent.stream('Explain monads in two sentences.')) {
if (event.type === 'text' && typeof event.data === 'string') {
process.stdout.write(event.data)
}
}
```
</details>
| Example | What it shows |
|---------|---------------|
| [01 — Single Agent](examples/01-single-agent.ts) | `runAgent()` one-shot, `stream()` streaming, `prompt()` multi-turn |
| [02 — Team Collaboration](examples/02-team-collaboration.ts) | `runTeam()` auto-orchestration with coordinator pattern |
| [03 — Task Pipeline](examples/03-task-pipeline.ts) | `runTasks()` explicit dependency graph (design → implement → test + review) |
| [04 — Multi-Model Team](examples/04-multi-model-team.ts) | `defineTool()` custom tools, mixed Anthropic + OpenAI providers, `AgentPool` |
| [05 — Copilot](examples/05-copilot-test.ts) | GitHub Copilot as an LLM provider |
| [06 — Local Model](examples/06-local-model.ts) | Ollama + Claude in one pipeline via `baseURL` (works with vLLM, LM Studio, etc.) |
| [07 — Fan-Out / Aggregate](examples/07-fan-out-aggregate.ts) | `runParallel()` MapReduce — 3 analysts in parallel, then synthesize |
## Architecture

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README_zh.md
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@ -108,165 +108,23 @@ Tokens: 12847 output tokens
<img src="https://contrib.rocks/image?repo=JackChen-me/open-multi-agent" />
</a>
## 更多示例
## 示例
<details>
<summary><b>单智能体</b> — 一个智能体，一个提示词</summary>
所有示例都是可运行脚本，位于 [`examples/`](./examples/) 目录。使用 `npx tsx` 运行：
```typescript
import { OpenMultiAgent } from '@jackchen_me/open-multi-agent'
const orchestrator = new OpenMultiAgent({ defaultModel: 'claude-sonnet-4-6' })
const result = await orchestrator.runAgent(
{
name: 'coder',
model: 'claude-sonnet-4-6',
tools: ['bash', 'file_write'],
},
'Write a TypeScript function that reverses a string, save it to /tmp/reverse.ts, and run it.',
)
console.log(result.output)
```bash
npx tsx examples/01-single-agent.ts
```
</details>
<details>
<summary><b>任务流水线</b> — 显式控制任务图和分配</summary>
```typescript
const result = await orchestrator.runTasks(team, [
{
title: 'Design the data model',
description: 'Write a TypeScript interface spec to /tmp/spec.md',
assignee: 'architect',
},
{
title: 'Implement the module',
description: 'Read /tmp/spec.md and implement the module in /tmp/src/',
assignee: 'developer',
dependsOn: ['Design the data model'], // 等待设计完成后才开始
},
{
title: 'Write tests',
description: 'Read the implementation and write Vitest tests.',
assignee: 'developer',
dependsOn: ['Implement the module'],
},
{
title: 'Review code',
description: 'Review /tmp/src/ and produce a structured code review.',
assignee: 'reviewer',
dependsOn: ['Implement the module'], // 可以和测试并行执行
},
])
```
</details>
<details>
<summary><b>自定义工具</b> — 使用 Zod schema 定义工具</summary>
```typescript
import { z } from 'zod'
import { defineTool, Agent, ToolRegistry, ToolExecutor, registerBuiltInTools } from '@jackchen_me/open-multi-agent'
const searchTool = defineTool({
name: 'web_search',
description: 'Search the web and return the top results.',
inputSchema: z.object({
query: z.string().describe('The search query.'),
maxResults: z.number().optional().describe('Number of results (default 5).'),
}),
execute: async ({ query, maxResults = 5 }) => {
const results = await mySearchProvider(query, maxResults)
return { data: JSON.stringify(results), isError: false }
},
})
const registry = new ToolRegistry()
registerBuiltInTools(registry)
registry.register(searchTool)
const executor = new ToolExecutor(registry)
const agent = new Agent(
{ name: 'researcher', model: 'claude-sonnet-4-6', tools: ['web_search'] },
registry,
executor,
)
const result = await agent.run('Find the three most recent TypeScript releases.')
```
</details>
<details>
<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>
```typescript
const claudeAgent: AgentConfig = {
name: 'strategist',
model: 'claude-opus-4-6',
provider: 'anthropic',
systemPrompt: 'You plan high-level approaches.',
tools: ['file_write'],
}
const gptAgent: AgentConfig = {
name: 'implementer',
model: 'gpt-5.4',
provider: 'openai',
systemPrompt: 'You implement plans as working code.',
tools: ['bash', 'file_read', 'file_write'],
}
// 任何 OpenAI 兼容 API — Ollama、vLLM、LM Studio 等
const localAgent: AgentConfig = {
name: 'reviewer',
model: 'llama3.1',
provider: 'openai',
baseURL: 'http://localhost:11434/v1',
apiKey: 'ollama',
systemPrompt: 'You review code for correctness and clarity.',
tools: ['file_read', 'grep'],
}
const team = orchestrator.createTeam('mixed-team', {
name: 'mixed-team',
agents: [claudeAgent, gptAgent, localAgent],
sharedMemory: true,
})
const result = await orchestrator.runTeam(team, 'Build a CLI tool that converts JSON to CSV.')
```
</details>
<details>
<summary><b>流式输出</b></summary>
```typescript
import { Agent, ToolRegistry, ToolExecutor, registerBuiltInTools } from '@jackchen_me/open-multi-agent'
const registry = new ToolRegistry()
registerBuiltInTools(registry)
const executor = new ToolExecutor(registry)
const agent = new Agent(
{ name: 'writer', model: 'claude-sonnet-4-6', maxTurns: 3 },
registry,
executor,
)
for await (const event of agent.stream('Explain monads in two sentences.')) {
if (event.type === 'text' && typeof event.data === 'string') {
process.stdout.write(event.data)
}
}
```
</details>
| 示例 | 展示内容 |
|------|----------|
| [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 |
| [02 — 团队协作](examples/02-team-collaboration.ts) | `runTeam()` 自动编排 + 协调者模式 |
| [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） |
| [04 — 多模型团队](examples/04-multi-model-team.ts) | `defineTool()` 自定义工具、Anthropic + OpenAI 混合、`AgentPool` |
| [05 — Copilot](examples/05-copilot-test.ts) | GitHub Copilot 作为 LLM 提供者 |
| [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） |
| [07 — 扇出聚合](examples/07-fan-out-aggregate.ts) | `runParallel()` MapReduce — 3 个分析师并行，然后综合 |
## 架构