281 lines
11 KiB
Markdown
281 lines
11 KiB
Markdown
# Open Multi-Agent
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Open Multi-Agent is an open-source multi-agent orchestration framework. Build autonomous AI agent teams that can collaborate, communicate, schedule tasks with dependencies, and execute complex multi-step workflows — all model-agnostic.
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Unlike single-agent SDKs like `@anthropic-ai/claude-agent-sdk` which run one agent per process, Open Multi-Agent orchestrates **multiple specialized agents** working together in-process — deploy anywhere: cloud servers, serverless functions, Docker containers, CI/CD pipelines.
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[](https://www.npmjs.com/package/open-multi-agent)
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[](./LICENSE)
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[](https://www.typescriptlang.org/)
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## Features
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- **Multi-Agent Teams** — Create teams of specialized agents that collaborate toward a shared goal
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- **Automatic Orchestration** — Describe a goal in plain English; the framework decomposes it into tasks and assigns them
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- **Task Dependencies** — Define tasks with `dependsOn` chains; the `TaskQueue` resolves them topologically
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- **Inter-Agent Communication** — Agents message each other via `MessageBus` and share knowledge through `SharedMemory`
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- **Model Agnostic** — Works with Anthropic Claude, OpenAI GPT, or any custom `LLMAdapter`
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- **Tool Framework** — Define custom tools with Zod schemas, or use 5 built-in tools (bash, file_read, file_write, file_edit, grep)
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- **Parallel Execution** — Independent tasks run concurrently with configurable `maxConcurrency`
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- **4 Scheduling Strategies** — Round-robin, least-busy, capability-match, dependency-first
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- **Streaming** — Stream incremental text deltas from any agent via `AsyncGenerator<StreamEvent>`
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- **Full Type Safety** — Strict TypeScript with Zod validation throughout
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## Quick Start
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```bash
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npm install open-multi-agent
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```
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```typescript
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import { OpenMultiAgent } from 'open-multi-agent'
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const orchestrator = new OpenMultiAgent({ defaultModel: 'claude-sonnet-4-6' })
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// One agent, one task
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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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Set `ANTHROPIC_API_KEY` (and optionally `OPENAI_API_KEY`) in your environment before running.
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## Usage
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### Multi-Agent Team
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```typescript
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import { OpenMultiAgent } from 'open-multi-agent'
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import type { AgentConfig } from '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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// Describe a goal — the framework breaks it into tasks and orchestrates execution
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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(`Success: ${result.success}`)
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console.log(`Tokens: ${result.totalTokenUsage.output_tokens} output tokens`)
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```
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### Task Pipeline
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Use `runTasks()` when you want explicit control over the task graph and assignments:
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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'], // 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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### Custom Tools
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```typescript
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import { z } from 'zod'
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import { defineTool, Agent, ToolRegistry, ToolExecutor, registerBuiltInTools } from '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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### Multi-Model Teams
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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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### Streaming Output
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```typescript
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import { Agent, ToolRegistry, ToolExecutor, registerBuiltInTools } from '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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## Architecture
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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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## Built-in Tools
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| Tool | Description |
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|------|-------------|
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| `bash` | Execute shell commands. Returns stdout + stderr. Supports timeout and cwd. |
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| `file_read` | Read file contents at an absolute path. Supports offset/limit for large files. |
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| `file_write` | Write or create a file. Auto-creates parent directories. |
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| `file_edit` | Edit a file by replacing an exact string match. |
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| `grep` | Search file contents with regex. Uses ripgrep when available, falls back to Node.js. |
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## Design Inspiration
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The architecture draws from common multi-agent orchestration patterns seen in modern AI coding tools.
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| Pattern | open-multi-agent | What it does |
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|---------|-----------------|--------------|
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| Conversation loop | `AgentRunner` | Drives the model → tool → model turn loop |
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| Tool definition | `defineTool()` | Typed tool definition with Zod validation |
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| Coordinator | `OpenMultiAgent` | Decomposes goals, assigns tasks, manages concurrency |
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| Team / sub-agent | `Team` + `MessageBus` | Inter-agent communication and shared state |
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| Task scheduling | `TaskQueue` | Topological task scheduling with dependency resolution |
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## Star History
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[](https://star-history.com/#JackChen-me/open-multi-agent&Date)
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## License
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MIT
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