Open Multi-Agent

No telemetry. No hosted control plane. Your keys, your models — cloud, local (Ollama, vLLM, llama-server), or Chinese providers — your environment. Nothing stops working when the people who built it leave.

Get started

Requires Node.js 20 or newer. For production, use a currently maintained Node.js LTS release. Node.js 20 is upstream-EOL and retained only as a migration compatibility window; OMA will remove it in the next major release, no earlier than 2026-10-31.

Scaffold a PR review agent, security analysis agent, or teaching DAG:

npm create oma-app@latest my-oma

In an interactive terminal, that one command selects a starter and runtime, installs dependencies, and runs a deterministic local demo. The demo needs no API key and makes no model request: scripted model responses drive the real OMA scheduler, result aggregation, and offline dashboard.

Or add OMA to an existing backend:

npm install @open-multi-agent/core

import { OpenMultiAgent } from '@open-multi-agent/core'

const oma = new OpenMultiAgent({ defaultProvider: 'openai', defaultModel: 'gpt-5.4' })

const team = oma.createTeam('research-team', {
  name: 'research-team',
  agents: [
    { name: 'researcher', systemPrompt: 'Find the relevant facts.' },
    { name: 'analyst', systemPrompt: 'Compare evidence and identify tradeoffs.' },
  ],
  sharedMemory: true,
})

const result = await oma.runTeam(team, 'Compare three approaches and recommend one.')

// Nothing above declares a task graph. The coordinator planned one at runtime,
// and the finished run is data you can read back.
for (const task of result.tasks ?? []) {
  console.log(`[${task.status}] ${task.title} → ${task.assignee ?? 'unassigned'}`, task.dependsOn)
}

console.log(result.agentResults.get('coordinator')?.output)
console.log(result.totalTokenUsage)

Set OPENAI_API_KEY to run this example. Providers covers other hosted models, local servers, OpenAI-compatible endpoints, and AI SDK providers.

runAgent() runs a single agent, runTasks() executes an explicit pipeline, and runTeam() plans from a goal. The Core package guide walks through all three modes, provider and credential setup, and the production checklist. The example index lists every runnable example across basics, cookbook workflows, patterns, providers, and integrations.

Durable approvals

Pause before executing a plan, dispatching a task, or calling a tool for human approval. With persistent storage, a restarted process can continue from the reviewed content. Your application supplies the approval interface and notifications. Guide

Verifiable journal

Enable a run journal to record model inputs, tool calls, and results for later inspection. verifyRun() checks the recorded sources and content for consistency offline; it does not prove the journal was never edited. Guide

Governance floor

Declare required roles and their execution order, then check whether the actual run met those requirements. A successful run can still leave governance requirements unmet, so applications must check governanceConclusion separately. Guide

Runs where you run

• No telemetry, no hosted control plane. A library with no OMA backend or account, and none planned. It makes no analytics, license, update, or phone-home request. Self-hosting
• Your keys, your models. Built-in adapters for Anthropic, OpenAI, Azure OpenAI, Bedrock, Gemini, Grok, NVIDIA, and Copilot, and for DeepSeek, Doubao, Hunyuan, MiniMax, MiMo, and Qiniu; Ollama, vLLM, and llama-server through baseURL; any OpenAI-compatible endpoint and Vercel AI SDK providers. Providers
• Egress policy. offline or allowlist, checked before a built-in adapter connects. A child policy can only tighten its parent, an unenforceable transport fails closed, and process and ACP backends sit outside it. LLM egress policy

Built with OMA

open-multi-agent launched 2026-04-01 under MIT. Known users and integrations to date:

• temodar-agent by Ali Sünbül. WordPress security analysis platform running OMA's built-in tools (bash, file_*, grep) inside a Docker runtime. Confirmed production use.
• Mark Galyan runs OMA fully offline on local quantized models, using the coordinator and context compaction to keep autonomous agent loops alive under tight VRAM limits. Contributor since the framework's first month.
• Engram: "Git for AI memory." Syncs knowledge across agents instantly and flags conflicts. (repo)

Users

• PR-Copilot by kidoom. AI pull-request review assistant running an OMA review team, with defineTool repo-context tools and a custom ContextStrategy for token-aware diff compression.
• StuFlow by znc15. Terminal AI coding assistant on OMA's orchestration core, driving runAgent / runTasks / runTeam with a custom coordinator, paired with DeepSeek.
• Reports to Charts Studio. Turns documents and research tables into slide-ready charts, using a five-role extraction council with structured outputs and deterministic validation.
• OrchestrAI: Visual multi-agent workspace using OMA for task-graph execution and peer review, with PostgreSQL-backed checkpoints for restart recovery.

Integrations

This is the beginning of the README. Read the rest at the original. Read the full README ↗