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 CrewAI
CrewAI organizes Python agents by roles and processes; open-multi-agent supports dynamic and explicit task DAGs in TypeScript. Language and orchestration model are the main differences.
Pick CrewAI if
You’re in Python and want a batteries-included toolkit; role-based crews, built-in memory and RAG, a large ecosystem.
Pick open-multi-agent if
Your backend is TypeScript and you want a lean core (three dependencies) with goal-driven decomposition and a hard token budget.
01 at a glance
Side by side.
| Dimension | open-multi-agent | CrewAI |
|---|---|---|
| Language / runtime | TypeScript-native; embeds in any Node.js 18+ backend | Python only (3.10+); no official TypeScript port |
| Orchestration model | Three modes: one agent, an explicit task DAG, or a goal decomposed by the coordinator at runtime | Role-based crews under a sequential or hierarchical process |
| Runtime dependencies | 3 direct (Anthropic SDK, OpenAI SDK, Zod); extra providers and MCP are opt-in peers | ~30 direct dependencies, plus many optional extras |
| Mixed-model teams | Yes; each agent can use its own cloud or local model in one team | Yes; per-agent llm= (native SDKs, LiteLLM for the rest) |
| Run-budget control | Token and estimated-USD ceilings through maxTokenBudget, or maxCostBudget with your estimateCost price table | No hard cap; max_rpm / max_iter limits + post-hoc usage metrics |
| Observability | TraceRecord v2 + TraceStore, an optional first-party OTel adapter, and an offline post-run Run Viewer | Native event bus; forward to Langfuse / OpenLIT / MLflow / others |
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.
CrewAI organizes work around role-playing agents grouped into a crew that runs sequentially or hierarchically, with memory and RAG built in. open-multi-agent hands a coordinator a goal and lets it decompose that goal into a task DAG at runtime, running independent tasks in parallel. The orchestration surface is roughly comparable; the decision is mostly the language stack; Python versus TypeScript; and how lean you want the dependency footprint (CrewAI pulls in ~30 direct dependencies; OMA, three).
Where CrewAI fits
CrewAI fits Python projects that want role-based crews, sequential or hierarchical processes, built-in memory and RAG, and its existing integrations in one framework. That bundled surface also brings a larger dependency footprint.
CrewAI on GitHub↗Where open-multi-agent fits
open-multi-agent fits when your backend is TypeScript and you want to stay there; no Python service to stand up beside your Node app. The core is deliberately small (three runtime dependencies; extra providers and MCP load only when you opt in), the coordinator plans the work from a goal, and maxTokenBudget gives you a hard spend ceiling that aborts the run.
// 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 AutoGen → open-multi-agent vs OpenAI Agents SDK → open-multi-agent vs Mastra → 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 →