Open Multi-Agent

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.

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.

Features

• Multi-Agent Teams — Create teams of specialized agents that collaborate toward a shared goal
• Automatic Orchestration — Describe a goal in plain English; the framework decomposes it into tasks and assigns them
• Task Dependencies — Define tasks with dependsOn chains; the TaskQueue resolves them topologically
• Inter-Agent Communication — Agents message each other via MessageBus and share knowledge through SharedMemory
• Model Agnostic — Works with Anthropic Claude, OpenAI GPT, or any custom LLMAdapter
• Tool Framework — Define custom tools with Zod schemas, or use 5 built-in tools (bash, file_read, file_write, file_edit, grep)
• Parallel Execution — Independent tasks run concurrently with configurable maxConcurrency
• 4 Scheduling Strategies — Round-robin, least-busy, capability-match, dependency-first
• Streaming — Stream incremental text deltas from any agent via AsyncGenerator<StreamEvent>
• Full Type Safety — Strict TypeScript with Zod validation throughout

Quick Start

npm install open-multi-agent

import { OpenMultiAgent } from 'open-multi-agent'

const orchestrator = new OpenMultiAgent({ defaultModel: 'claude-sonnet-4-6' })

// One agent, one task
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)

Set ANTHROPIC_API_KEY (and optionally OPENAI_API_KEY) in your environment before running.

Usage

Multi-Agent Team

import { OpenMultiAgent } from 'open-multi-agent'
import type { AgentConfig } from 'open-multi-agent'

const architect: AgentConfig = {
  name: 'architect',
  model: 'claude-sonnet-4-6',
  systemPrompt: 'You design clean API contracts and file structures.',
  tools: ['file_write'],
}

const developer: AgentConfig = {
  name: 'developer',
  model: 'claude-sonnet-4-6',
  systemPrompt: 'You implement what the architect designs.',
  tools: ['bash', 'file_read', 'file_write', 'file_edit'],
}

const reviewer: AgentConfig = {
  name: 'reviewer',
  model: 'claude-sonnet-4-6',
  systemPrompt: 'You review code for correctness and clarity.',
  tools: ['file_read', 'grep'],
}

const orchestrator = new OpenMultiAgent({
  defaultModel: 'claude-sonnet-4-6',
  onProgress: (event) => console.log(event.type, event.agent ?? event.task ?? ''),
})

const team = orchestrator.createTeam('api-team', {
  name: 'api-team',
  agents: [architect, developer, reviewer],
  sharedMemory: true,
})

// Describe a goal — the framework breaks it into tasks and orchestrates execution
const result = await orchestrator.runTeam(team, 'Create a REST API for a todo list in /tmp/todo-api/')

console.log(`Success: ${result.success}`)
console.log(`Tokens: ${result.totalTokenUsage.output_tokens} output tokens`)

Task Pipeline

Use runTasks() when you want explicit control over the task graph and assignments:

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
  },
])

Custom Tools

import { z } from 'zod'
import { defineTool, Agent, ToolRegistry, ToolExecutor, registerBuiltInTools } from '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.')

Multi-Model Teams

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'],
}

const team = orchestrator.createTeam('mixed-team', {
  name: 'mixed-team',
  agents: [claudeAgent, gptAgent],
  sharedMemory: true,
})

const result = await orchestrator.runTeam(team, 'Build a CLI tool that converts JSON to CSV.')

Streaming Output

import { Agent, ToolRegistry, ToolExecutor, registerBuiltInTools } from '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)
  }
}

Architecture

┌─────────────────────────────────────────────────────────────────┐
│  OpenMultiAgent (Orchestrator)                                  │
│                                                                 │
│  createTeam()  runTeam()  runTasks()  runAgent()  getStatus()   │
└──────────────────────┬──────────────────────────────────────────┘
                       │
            ┌──────────▼──────────┐
            │  Team               │
            │  - AgentConfig[]    │
            │  - MessageBus       │
            │  - TaskQueue        │
            │  - SharedMemory     │
            └──────────┬──────────┘
                       │
         ┌─────────────┴─────────────┐
         │                           │
┌────────▼──────────┐    ┌───────────▼───────────┐
│  AgentPool        │    │  TaskQueue             │
│  - Semaphore      │    │  - dependency graph    │
│  - runParallel()  │    │  - auto unblock        │
└────────┬──────────┘    │  - cascade failure     │
         │               └───────────────────────┘
┌────────▼──────────┐
│  Agent            │
│  - run()          │    ┌──────────────────────┐
│  - prompt()       │───►│  LLMAdapter          │
│  - stream()       │    │  - AnthropicAdapter  │
└────────┬──────────┘    │  - OpenAIAdapter     │
         │               └──────────────────────┘
┌────────▼──────────┐
│  AgentRunner      │    ┌──────────────────────┐
│  - conversation   │───►│  ToolRegistry        │
│    loop           │    │  - defineTool()      │
│  - tool dispatch  │    │  - 5 built-in tools  │
└───────────────────┘    └──────────────────────┘

Built-in Tools

Tool	Description
bash	Execute shell commands. Returns stdout + stderr. Supports timeout and cwd.
file_read	Read file contents at an absolute path. Supports offset/limit for large files.
file_write	Write or create a file. Auto-creates parent directories.
file_edit	Edit a file by replacing an exact string match.
grep	Search file contents with regex. Uses ripgrep when available, falls back to Node.js.

Design Inspiration

The architecture draws from common multi-agent orchestration patterns seen in modern AI coding tools.

Pattern	open-multi-agent	What it does
Conversation loop	AgentRunner	Drives the model → tool → model turn loop
Tool definition	defineTool()	Typed tool definition with Zod validation
Coordinator	OpenMultiAgent	Decomposes goals, assigns tasks, manages concurrency
Team / sub-agent	Team + MessageBus	Inter-agent communication and shared state
Task scheduling	TaskQueue	Topological task scheduling with dependency resolution

License

MIT