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. OMA runs a dynamic workflow: the task DAG is built at runtime, so it adapts to the goal instead of being hand-wired for one. The coordinator emits that plan as data for a deterministic scheduler to execute, so the plan is inspectable and replayable.

@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.

Get started

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.

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.

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.

How is this different from X?

Most TypeScript teams choosing a multi-agent layer are weighing OMA against LangGraph JS, Mastra, CrewAI, and the Vercel AI SDK. The short version: OMA is goal-driven. Hand its Coordinator a goal and it builds the task DAG at runtime, instead of making you wire the graph up front.

Full head-to-head on each on the package page: How is this different?

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.
• Plan preview & replay — preview the coordinator's task DAG with planOnly, freeze it with createPlanArtifact, then runFromPlan replays the exact graph without re-invoking the coordinator.

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