Open Multi-Agent

From a goal to a task DAG, automatically.
TypeScript-native multi-agent orchestration.

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open-multi-agent is a multi-agent orchestration framework for TypeScript backends. Give it a goal; a coordinator agent decomposes it into a task DAG, parallelizes independents, and synthesizes the result. Drops into any Node.js backend.

Your engineers describe the goal, not the graph.

Graph-first frameworks make you enumerate every node and edge up front. Here the task DAG is built at runtime, so it adapts to the goal instead of being hand-wired for one.

@open-multi-agent/core keeps a lightweight core. The orchestration engine plus the mainstream model providers (Anthropic, OpenAI, and any OpenAI-compatible endpoint) work out of the box; additional providers (Gemini, Bedrock), MCP, and the Vercel AI SDK bridge are opt-in peer dependencies you install only when you use them.

One command scaffolds a project and starts a multi-agent DAG:

npm create oma-app@latest

Answer one prompt; the first run shows the coordinator turn one goal into a multi-agent DAG and opens a dashboard of the run (OpenAI or any OpenAI-compatible provider). To add the library to your own project:

npm install @open-multi-agent/core

The full quickstart, the three ways to run, provider setup, the production checklist, and the complete API reference live on the package page:

→ packages/core/README.md

Other ways to run: clone the repo and run any example with npx tsx packages/core/examples/basics/team-collaboration.ts, or embed OMA in a real backend with the Express and Next.js apps. To skip local setup, the Next.js starter deploys to Vercel in one click; local models via Ollama need no API key.

How is this different from X?

Most TypeScript teams picking a multi-agent layer are really choosing between OMA, LangGraph JS, and Mastra. The mechanism is what differs.

vs. LangGraph JS. LangGraph has you design a declarative graph (nodes, edges, conditional routing) up front, then compiles it into an invokable; OMA's Coordinator decomposes the goal into a task DAG at runtime and auto-parallelizes independents. Both checkpoint and resume, though LangGraph's persistence ecosystem runs deeper. Reach for OMA when the plan should adapt to the goal instead of being wired ahead of time.

vs. Mastra. Both are TypeScript-native; the difference is who drives orchestration. Mastra has you wire the workflow by hand. OMA is goal-driven: hand its Coordinator a goal and it builds the task DAG at runtime. runTeam(team, goal) in one call.

vs. CrewAI. CrewAI is the established multi-agent option in Python. OMA brings goal-driven decomposition to TypeScript backends with a lean runtime (three core dependencies, plus opt-in peers you install only when you use them) and direct Node.js embedding, with no separate Python service to stand up alongside your stack.

vs. Vercel AI SDK. AI SDK is the LLM-call layer (provider abstraction, streaming, tool calls, and structured outputs), not a multi-agent orchestrator. Use it alone for single-agent calls; reach for OMA the moment you need a coordinated team. OMA even ships an optional AI SDK bridge.

Ecosystem

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

Built with OMA

• temodar-agent (~60 stars). WordPress security analysis platform by Ali Sünbül. Uses our built-in tools (bash, file_*, grep) directly 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, across compaction, sampling, and tool-call parsing.
• PR-Copilot. AI pull-request review assistant by kidoom. Runs an OMA review team (coordinator + scoped reviewer agents), defines repo-context tools with defineTool, and adds a custom ContextStrategy for token-aware PR-diff compression. Public code on @open-multi-agent/core.
• StuFlow by znc15. Terminal AI coding assistant on OMA's orchestration core: builds a team and drives it through runAgent / runTasks / runTeam with a custom RunTeamOptions coordinator, paired with DeepSeek. Public code on @open-multi-agent/core.

Integrations

• Engram — "Git for AI memory." Syncs knowledge across agents instantly and flags conflicts. (repo)
• @agentsonar/oma — Sidecar detecting cross-run delegation cycles, repetition, and rate bursts.
• CodingScaffold — Agentic-coding scaffold that lists OMA as an optional orchestration backend, with a runTeam workflow template.

Provider community offers — limited-time, not paid endorsements.

• MiniMax — Use MiniMax M3 in OMA's TypeScript multi-agent workflows. OMA users get 12% off the MiniMax Token Plan until 2026-06-30. See the MiniMax setup guide.

Using open-multi-agent in production or a side project? Open a discussion and we will list it here. For a deep integration, see the Featured partner program.

Repository

This is a monorepo. The published package is @open-multi-agent/core, and it lives in packages/core/ — the source of truth for the library, its tests, examples, and the npm package page.

open-multi-agent/
├── packages/
│   └── core/          # @open-multi-agent/core — the published library
│       ├── src/       # framework source
│       ├── tests/     # vitest suite
│       └── examples/  # runnable examples (npx tsx packages/core/examples/<path>.ts)
└── docs/              # subsystem documentation

Build, lint, and test orchestrate across the workspace from the repo root:

npm install            # install all workspaces
npm run build          # compile packages/core
npm run lint           # type-check
npm test               # run the test suite

Documentation

• Providers — env vars, model examples, local tool-calling, timeouts, troubleshooting.
• Tool configuration — tool presets, custom tools, the filesystem sandbox, and MCP.
• Observability — onProgress events, onTrace spans, and the post-run dashboard.
• Shared memory — the default store and custom MemoryStore backends.
• Checkpoint & resume — opt-in per-run snapshot/resume over any MemoryStore; survive crashes and restarts.
• Context management — sliding window, summarization, compaction, and custom compressors.
• CLI — the JSON-first oma binary for shell and CI.
• Consensus — the runConsensus proposer→judge primitive, the per-task verify hook, and the budget invariant.
• Model routing — the opt-in modelRouting policy: match by phase / agent / role / priority / leaf, first match wins.

Contributing

Issues, feature requests, and PRs are welcome. Some areas where contributions would be especially valuable:

• Production examples. Real-world end-to-end workflows. See packages/core/examples/production/README.md for the acceptance criteria and submission format.
• Documentation. Guides, tutorials, and API docs.
• Translations. Help translate the docs into other languages. Open a PR.

Contributors

Full credits by area are on the package page.

License

MIT