Open Multi-Agent Orchestration

Skill by ara.so — Daily 2026 Skills collection.

open-multi-agent is a TypeScript framework for building AI agent teams where agents with different roles, models, and tools collaborate on complex goals. The framework handles task dependency resolution (DAG scheduling), parallel execution, shared memory, and inter-agent communication — all in-process with no subprocess overhead.

Installation

npm install @jackchen_me/open-multi-agent
# or
pnpm add @jackchen_me/open-multi-agent

Set environment variables:

export ANTHROPIC_API_KEY=your_key_here
export OPENAI_API_KEY=your_key_here   # optional, only if using OpenAI models

Core Concepts

Concept	Description
OpenMultiAgent	Top-level orchestrator — entry point for all operations
Team	A named group of agents sharing a message bus, task queue, and optional shared memory
AgentConfig	Defines an agent's name, model, provider, system prompt, and allowed tools
Task	A unit of work with a title, description, assignee, and optional dependsOn list
LLMAdapter	Pluggable interface — built-in adapters for Anthropic and OpenAI
ToolRegistry	Registry of available tools; built-ins + custom tools via defineTool()

Quick Start — Single Agent

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)

Multi-Agent Team

import { OpenMultiAgent } from '@jackchen_me/open-multi-agent'
import type { AgentConfig } from '@jackchen_me/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,
})

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(`Output tokens: ${result.totalTokenUsage.output_tokens}`)

Task Pipeline — Explicit DAG Control

Use runTasks() when you need precise control over task ordering, assignments, and parallelism:

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'], // runs in parallel with "Write tests"
  },
])

Tasks with no unresolved dependsOn entries run in parallel automatically. The framework cascades failures — if a task fails, dependent tasks are skipped.

Multi-Model Teams (Claude + GPT)

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.')

Custom Tools with Zod Schemas

import { z } from 'zod'
import {
  defineTool,
  Agent,
  ToolRegistry,
  ToolExecutor,
  registerBuiltInTools,
} from '@jackchen_me/open-multi-agent'

// Define the tool
const weatherTool = defineTool({
  name: 'get_weather',
  description: 'Get current weather for a city.',
  inputSchema: z.object({
    city: z.string().describe('The city name.'),
    units: z.enum(['celsius', 'fahrenheit']).optional().describe('Temperature units.'),
  }),
  execute: async ({ city, units = 'celsius' }) => {
    // Replace with your actual weather API call
    const data = await fetchWeatherAPI(city, units)
    return { data: JSON.stringify(data), isError: false }
  },
})

// Wire up registry
const registry = new ToolRegistry()
registerBuiltInTools(registry)        // adds bash, file_read, file_write, file_edit, grep
registry.register(weatherTool)        // add your custom tool

const executor = new ToolExecutor(registry)
const agent = new Agent(
  {
    name: 'weather-agent',
    model: 'claude-sonnet-4-6',
    tools: ['get_weather', 'file_write'],
  },
  registry,
  executor,
)

const result = await agent.run('Get the weather for Tokyo and save a report to /tmp/weather.txt')

Streaming Output

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 dependency injection in two paragraphs.')) {
  if (event.type === 'text' && typeof event.data === 'string') {
    process.stdout.write(event.data)
  }
}

Progress Monitoring

const orchestrator = new OpenMultiAgent({
  defaultModel: 'claude-sonnet-4-6',
  onProgress: (event) => {
    switch (event.type) {
      case 'task:start':
        console.log(`▶ Task started: ${event.task}`)
        break
      case 'task:complete':
        console.log(`✓ Task done: ${event.task}`)
        break
      case 'task:failed':
        console.error(`✗ Task failed: ${event.task}`)
        break
      case 'agent:thinking':
        console.log(`  [${event.agent}] thinking...`)
        break
      case 'agent:tool_use':
        console.log(`  [${event.agent}] using tool: ${event.tool}`)
        break
    }
  },
})

Built-in Tools Reference

Tool	Key Options	Notes
bash	command, timeout, cwd	Returns stdout + stderr
file_read	path, offset, limit	Use offset/limit for large files
file_write	path, content	Auto-creates parent directories
file_edit	path, old_string, new_string	Exact string match replacement
grep	pattern, path, flags	Uses ripgrep if available, falls back to Node.js

AgentConfig Options

interface AgentConfig {
  name: string                    // unique within a team
  model: string                   // e.g. 'claude-sonnet-4-6', 'gpt-5.4'
  provider?: 'anthropic' | 'openai'  // inferred from model name if omitted
  systemPrompt?: string           // agent's persona and instructions
  tools?: string[]                // names of tools the agent can use
  maxTurns?: number               // max conversation turns (default: unlimited)
}

Custom LLM Adapter

Implement two methods to add any LLM provider:

import type { LLMAdapter, ChatMessage, ChatResponse } from '@jackchen_me/open-multi-agent'

class OllamaAdapter implements LLMAdapter {
  async chat(messages: ChatMessage[], options?: ChatOptions): Promise<ChatResponse> {
    const response = await fetch('http://localhost:11434/api/chat', {
      method: 'POST',
      headers: { 'Content-Type': 'application/json' },
      body: JSON.stringify({ model: options?.model ?? 'llama3', messages }),
    })
    const data = await response.json()
    return {
      content: data.message.content,
      usage: { input_tokens: 0, output_tokens: 0 },
    }
  }

  async *stream(messages: ChatMessage[], options?: ChatOptions): AsyncIterable<StreamEvent> {
    // implement streaming from Ollama's /api/chat with stream:true
  }
}

Common Patterns

Pattern: Research → Write → Review pipeline

const team = orchestrator.createTeam('content-team', {
  name: 'content-team',
  agents: [
    { name: 'researcher', model: 'claude-sonnet-4-6', tools: ['bash', 'file_write'] },
    { name: 'writer', model: 'claude-sonnet-4-6', tools: ['file_read', 'file_write'] },
    { name: 'editor', model: 'claude-sonnet-4-6', tools: ['file_read', 'file_edit'] },
  ],
  sharedMemory: true,
})

await orchestrator.runTasks(team, [
  {
    title: 'Research topic',
    description: 'Research TypeScript 5.6 features, save findings to /tmp/research.md',
    assignee: 'researcher',
  },
  {
    title: 'Write article',
    description: 'Read /tmp/research.md and write a blog post to /tmp/article.md',
    assignee: 'writer',
    dependsOn: ['Research topic'],
  },
  {
    title: 'Edit article',
    description: 'Read /tmp/article.md and improve clarity and tone in-place',
    assignee: 'editor',
    dependsOn: ['Write article'],
  },
])

Pattern: Fan-out then merge

// Three agents work on separate modules in parallel, then one integrates
await orchestrator.runTasks(team, [
  { title: 'Build auth module', assignee: 'dev-1', description: '...' },
  { title: 'Build data module', assignee: 'dev-2', description: '...' },
  { title: 'Build api module',  assignee: 'dev-3', description: '...' },
  {
    title: 'Integrate modules',
    assignee: 'architect',
    description: 'Wire auth, data, and api modules together.',
    dependsOn: ['Build auth module', 'Build data module', 'Build api module'],
  },
])

Troubleshooting

ANTHROPIC_API_KEY not found Ensure the env var is exported in the shell running your script, or use a .env loader like dotenv before importing from the framework.

Tasks not running in parallel Check that tasks don't share a circular dependsOn chain. Only tasks with all dependencies resolved become eligible for parallel execution.

Agent exceeds token limit Set maxTurns on the AgentConfig to cap conversation length. For large file operations, use file_read with offset/limit instead of reading entire files.

Tool not found error Ensure the tool name in AgentConfig.tools[] exactly matches the name registered in ToolRegistry. Built-in tools are registered via registerBuiltInTools(registry).

OpenAI adapter not initializing OPENAI_API_KEY must be set when any agent uses provider: 'openai'. The framework initializes the adapter lazily but will throw if the key is missing at first use.

Type errors with defineTool Ensure zod is installed as a direct dependency (npm install zod) — the framework uses Zod for schema validation but doesn't re-export it.