Open Multi-Agent Orchestration

Skill by ara.so — Daily 2026 Skills collection.

open-multi-agent

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

OpenMultiAgent

Team

AgentConfig

Task

dependsOn

LLMAdapter

ToolRegistry

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()

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

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

bash

command

timeout

cwd

file_read

path

offset

limit

file_write

path

content

file_edit

path

old_string

new_string

grep

pattern

path

flags

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

.env

dotenv

Tasks not running in parallel Check that tasks don't share a circular

dependsOn

Agent exceeds token limit Set

maxTurns

AgentConfig

file_read

offset

limit

Tool not found error Ensure the tool name in

AgentConfig.tools[]

ToolRegistry

registerBuiltInTools(registry)

OpenAI adapter not initializing

OPENAI_API_KEY

provider: 'openai'

Type errors with

defineTool

zod

npm install zod