Open Multi-Agent: TypeScript Multi-Agent Orchestration Framework
Open Multi-Agent is an open-source TypeScript framework for building multi-agent AI orchestration with goal-driven task DAGs, parallel LLM calls, and mixed-model teams. It provides a coordinator agent that decomposes goals at runtime, runs independent nodes in parallel, and synthesizes typed results, supporting cloud, local, or air-gapped models.
About this tool
What is Open Multi-Agent?
Open Multi-Agent is an open-source TypeScript framework for multi-agent AI orchestration, designed for AI engineers and TypeScript developers. It enables you to build goal-driven task DAGs, parallel LLM calls, and mixed-model teams.
Core Capabilities
- Runtime Task DAGs: A coordinator agent decomposes a goal into a task DAG at runtime, runs independent nodes in parallel, and synthesizes a typed result.
- Mixed-Model Teams: Mix any model in one agent team—use a strong model where it earns its cost and a cheap one everywhere else, in the same team, in one run.
- Local & Private Deployment: Runs against a local Ollama endpoint just like a cloud provider, allowing the whole team, coordinator included, to run on a small local model at zero API cost.
- Vercel AI SDK Integration: Acts as a coordination layer above a Vercel AI SDK app, keeping the AI SDK for streaming and model calls.
- Flexible Configuration: Give the planner its own model, adapter, or extra instructions without touching the worker agents.
Use Cases
- Turn a goal into a task DAG at runtime
- Adaptive customer support with coordinator and specialists
- Add multi-agent orchestration to a Vercel AI SDK app
- Run a local, private multi-agent team with Ollama
- Mix any model in one agent team
Technical Details
- MIT licensed, open-source.
- Public API available.
- Self-hosted.
- Medium learning curve.
- Monetization includes free and freemium.
- Three runtime dependencies.
- Supports cloud, local, or air-gapped models on your credentials.
Considerations
- Bash stays unsandboxed once granted, and every tool result is sent to your model provider—so grant read/exec access on purpose.
- Pricing model is unknown.
Features & use cases
' }, }) Fan-out without dependencies Section titled “Fan-out without dependencies” For MapReduce-style parallelism, use AgentPool// Blog Which workflows are worth a team of agents← All use cases// use case Add multi-agent orchestration to a Vercel AI SDK app Keep the AI SDK for streaming and model calls← All use cases// use case Mix any model in one agent team Use a strong model where it earns its cost and a cheap one everywhere else — in the same team, in one run← All use cases// use case Run a local, private multi-agent team Keep the data on your machine: open-multi-agent runs against a local Ollama endpoint just like a cloud provider← All use cases// use case Turn a goal into a task DAG — at runtime In most agent frameworks you draw the task graph by hand0 latest release MIT license temodar-agent by xeloxa WordPress security analysis platform02 the approach How open-multi-agent does it13: Route, Govern, Schedule, and Prove the Run Jul 24, 2026 · 2 min readReleaseOpen Multi-Agent v114: Repair the Plan, Keep the Record Aug 2, 2026 · 4 min readReleaseOpen Multi-Agent v1A coordinator agent decomposes it into a task DAG at runtime, runs the independent nodes in parallel, and synthesizes a typed resultA coordinator plus scoped reviewer agents, with custom tools and token-aware diff compression
Quick facts
Open-source license: MIT