Dynamic and explicit orchestration
runTeam() builds a task DAG from a goal. runTasks() runs a graph you define, and runAgent() covers the single-agent case.
← All comparisons Enterprise support
// comparison
open-multi-agent vs Mastra
Both are TypeScript-native and actively developed; the real difference is surface area. Mastra is a batteries-included framework; open-multi-agent is a lean, goal-driven core.
Pick Mastra if
You want an all-in-one TypeScript framework; graph-based workflows, built-in memory and RAG, evals, a dev playground; in one package.
Pick open-multi-agent if
You want a small core (three dependencies), goal-driven decomposition instead of hand-built workflow graphs, and a hard token-budget cap.
01 at a glance
Side by side.
| Dimension | open-multi-agent | Mastra |
|---|---|---|
| Language / runtime | TypeScript-native; embeds in any Node.js 18+ backend | TypeScript-native; runs on Node.js |
| Orchestration model | Three modes: one agent, an explicit task DAG, or a goal decomposed by the coordinator at runtime | Agents plus graph-based workflows (.then / .branch / suspend), with built-in memory, RAG, and evals |
| Runtime dependencies | 3 direct (Anthropic SDK, OpenAI SDK, Zod); extra providers and MCP are opt-in peers | ~32 direct in @mastra/core; built on the Vercel AI SDK provider layer |
| Mixed-model teams | Yes; each agent can use its own cloud or local model in one team | Yes; per-agent model via the AI SDK model interface |
| Run-budget control | Token and estimated-USD ceilings through maxTokenBudget, or maxCostBudget with your estimateCost price table | No hard token cap; maxSteps limits agent steps |
| Observability | TraceRecord v2 + TraceStore, an optional first-party OTel adapter, and an offline post-run Run Viewer | OpenTelemetry tracing, plus a local dev playground for inspecting runs |
02 actual capabilities
What open-multi-agent includes.
OMA is more than goal decomposition and a small dependency count. These are current framework capabilities documented in the project README.
Deterministic control around agents
Inspect and approve plans, freeze and replay them as data, validate outputs with Zod, stream per agent, cancel runs, or add a proposer and judge consensus loop.
Dependency scheduling and recovery
The scheduler runs independent branches in parallel. Retries and checkpoints let an interrupted run resume without repeating completed tasks.
Production controls
Bound work with turn, token, estimated-cost, timeout, context, and loop controls. Tools are default-deny, and trace payloads redact secrets by default.
Your environment and your models
Run in your Node.js backend, locally, offline, or air-gapped. Mix cloud and local models, connect MCP tools, and bring external agents through ACP or process backends.
Inspect, trace, and evaluate
Stable run identity, TraceStore, and the offline DAG and Waterfall Viewer work without a hosted service. An optional OTel adapter and EvalSets connect runs to production telemetry and CI gates.
03 mechanism
How they differ.
Mastra bundles the whole design surface; agents, graph-based workflows you compose with .then()/.branch(), plus memory, RAG, and evals; into one framework. open-multi-agent keeps the core small and hands a coordinator a goal, which it decomposes into a task DAG at runtime and auto-parallelizes. Mastra is built on the Vercel AI SDK provider layer and carries ~32 direct dependencies in its core; OMA carries three, with extra providers and MCP loaded only when you opt in.
Where Mastra fits
Mastra fits when you want one TypeScript stack that includes graph-based workflows with suspend and resume, human-in-the-loop controls, memory, RAG, evals, and a development playground. You author the workflow steps explicitly.
Mastra on GitHub↗Where open-multi-agent fits
open-multi-agent fits when you want to stay lean and let the plan be built for you. The coordinator decomposes a goal into a task DAG at runtime, so you describe the outcome instead of wiring a workflow graph; the core is three dependencies; and maxTokenBudget gives a hard spend ceiling that aborts the run; a guardrail Mastra doesn’t offer at the token level.
// Enterprise
Taking this to production?
open-multi-agent is MIT-licensed and free to run yourself. When you need it delivered, integrated, or supported on a deadline, 元定义科技 (YuanASI) offers commercial delivery and support.
// Compare with another framework
open-multi-agent vs LangGraph → open-multi-agent vs CrewAI → open-multi-agent vs AutoGen → open-multi-agent vs OpenAI Agents SDK → open-multi-agent vs Vercel AI SDK → open-multi-agent vs VoltAgent → open-multi-agent vs Inngest AgentKit → open-multi-agent vs LangChain → open-multi-agent vs LlamaIndex → open-multi-agent vs Pydantic AI → open-multi-agent vs Google ADK → open-multi-agent vs Semantic Kernel →