open-multi-agent/open-multi-agent
Some data may be outdated — see provenance labels.
Self-hosted TypeScript agent runtime with durable approvals and verifiable run records. Own it, approve it, audit it. observed · 2026-10-04
Health v2 · maintenance only
81/100
- Activity 98
- Release rhythm 98
- Longevity 14
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-10-21. Adoption (stars, forks) is never an input.
- gap_med: 7
- age_days: 203
- days_rel: 17
- days_push: 17
- n_releases_24m: 28
Adoption not part of the score
6974 stars · 2434 forks observed · 2026-10-04
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded
Open Multi-Agent (OMA) is an MIT-licensed TypeScript framework for multi-agent AI orchestration that runs inside Node.js backends. A coordinator decomposes a goal into a task DAG at runtime, a deterministic scheduler executes tasks in parallel across a team of agents on any LLM provider (Claude, GPT, Gemini, DeepSeek, Ollama, or OpenAI-compatible endpoints), and every run is traceable, resumable, and replayable via an offline Run Viewer.
Use cases
- orchestrate multiple AI agents on a single goal in a Node.js backend
- decompose a goal into a parallel task DAG without hand-building the workflow graph
- run a security review or PR review as a coordinated multi-agent team
- mix models per agent, including local models via Ollama
- include external coding agents (via ACP) as team members in one task DAG
- trace, approve, budget, and replay multi-agent runs
- get schema-validated structured output from agent tasks
When to choose
- a goal benefits from multiple specialist roles and parallelizable work
- you want runtime planning instead of maintaining a fixed workflow graph
- you need orchestration embedded in an existing Node.js/TypeScript backend
- you want provider-agnostic model access with tracing and resumability
When to avoid
- one model call already solves the task
- every step and transition must be fixed and deterministic
- you only need a provider SDK or a chat UI
- you want to avoid the extra cost of a planning model call
Facets
framework · maturity active
agent-framework llm-inference mcp workflow-automation sdk large-language-models developer-tools backend cross-platform self-hosted multi-agent agent-orchestration task-dag dynamic-workflows crewai-alternative langgraph-alternative openai anthropic claude gemini deepseek ollama local-llm opentelemetry acp run-viewer structured-output ai-agents automation nodejs typescript
9 sources
- readme: https://github.com/open-multi-agent/open-multi-agent · fetched 2026-10-04 · 9780bbd0ae1e
- homepage: https://open-multi-agent.com · fetched 2026-08-29 · f90d8c834281
- site_page: https://open-multi-agent.com/getting-started/three-ways-to-run · fetched 2026-08-29 · 2793fe2b274b
- site_page: https://open-multi-agent.com/getting-started/introduction · fetched 2026-08-29 · aecb2e9d9eb0
- site_page: https://open-multi-agent.com/getting-started/quick-start · fetched 2026-08-29 · ff50742644e4
- site_page: https://open-multi-agent.com/about · fetched 2026-08-29 · fcd4bf4befc9
- site_page: https://open-multi-agent.com/integrations/external-agents · fetched 2026-08-29 · bc0139395cd5
- site_page: https://open-multi-agent.com/integrations · fetched 2026-08-29 · d11aaff5964b
- site_page: https://open-multi-agent.com/changelog · fetched 2026-08-29 · b80d25a21d29
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| open-multi-agent/open-multi-agent | main | 81 |
For agents
markdown · JSON · MCP: product_card(name="open-multi-agent/open-multi-agent")
Data as of 2026-10-19T08:05:51.212177+00:00 · Report a problem