// 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.

Enterprise support

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 run-level token circuit breaker.

01 at a glance

Side by side.

Dimension	open-multi-agent	CrewAI
Language / runtime	TypeScript-native; embeds in any Node.js 20+ backend	Python only (3.10+); no official TypeScript port
Orchestration model	One agent, an explicit task DAG, or a goal the coordinator decomposes at runtime; explicit mode, governance policy, or an ExecutionRouter selects the topology, and a run can revise its not-yet-executed tasks	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 runs its own cloud or local model in one team; model routing can send planning to a flagship model and leaves to a cheap one	Yes; per-agent llm= (native SDKs, LiteLLM for the rest)
Run-budget control	Run-level token and estimated-USD ceilings — maxTokenBudget, or maxCostBudget with your estimateCost table — checked between model calls and task dispatches; one in-flight model turn can cross the ceiling	No hard cap; max_rpm / max_iter limits + post-hoc usage metrics
Observability	TraceRecord v2 + TraceStore, stable run identity, an optional first-party OTel adapter, and an offline post-run Run Viewer — no hosted service required	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.

Dynamic, explicit, and routed orchestration

runTeam() builds a task DAG from a goal, runTasks() runs a graph you define, and runAgent() covers one agent. Explicit mode, governance declarations, or a custom ExecutionRouter choose Single or Team execution and expose a routingDecision. Opt-in hybrid routing adds one semantic assessment where the deterministic result would be Single — the policy that decides stays deterministic. Plans remain previewable, reviewable, and replayable as data.

Governance and approvals at distinct boundaries

Declare required or preferred roles, ordered review paths, and budget-aware degradation. Gate the plan with onPlanReady, one ready task with onTaskDispatch, one consequential tool call with onToolCall, and any mid-run plan revision with onPlanPatch; then inspect governanceConclusion.

Event-driven scheduling and task evidence

Ready dependents start as soon as prerequisites complete. Hard task requirements are enforced across every assignment strategy, so an unsatisfiable task is rejected instead of dispatched to an ineligible agent; taskResults preserves unmerged task outputs and structured dependency payloads carry bounded provenance. Retries and checkpoints resume from completed task boundaries, and opt-in repairable recovery can append replacement work at an outcome barrier before any original dependent starts.

Production controls

Bound each run with turn, token, estimated-cost, timeout, context, and loop limits. maxTokenBudget and maxCostBudget stop further calls after a boundary check; one in-flight model turn can cross the ceiling. Model routes support ordered fallbacks. Built-in tools are default-deny, and trace payloads redact detected secrets on a best-effort basis.

Your environment and your models

Run in your own Node.js backend — locally, offline, or air-gapped, on your own credentials, with no hosted service. Mix cloud and local models in one team, connect MCP tools, and bring external agents in through ACP or process backends.

Inspect, trace, and evaluate

Stable run identity, routing decisions, privacy-preserving execution receipts, TraceStore, and the offline Run Viewer work with no hosted service. Score quality with versioned EvalSets and GateVerdict, including a routing-stability gate, then connect runs to production telemetry through the optional OTel adapter.

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 stops further calls after a run-boundary check.

Quick Start→

// 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.

Enterprise support

// Compare with another framework

open-multi-agent vs AutoGen→open-multi-agent vs LangGraph→open-multi-agent vs Google ADK→