Multi-agent orchestration: OpenAI Agents SDK vs CrewAI vs AutoGen vs LangGraph
Four very different answers to "how should multiple agents coordinate?" — and the differences are architectural, not cosmetic. Picking wrong here is expensive: the coordination model shapes your whole codebase.
| openai-agents-python | CrewAI | AutoGen | LangGraph | |
|---|---|---|---|---|
| ⭐ Stars | 27.3k | 54.1k | 59.1k | 35.3k |
| Coordination model | Handoffs — agents transfer control like a call-center escalation | Roles — agents with goals/backstories collaborate in Crews; Flows for control | Conversation — agents talk in group chats until done | Graph — explicit state machine; agents are nodes |
| Adoption surface (list tier) | mostly simple | complex (product suite) | complex (product suite) | slightly complex |
| Control flow visibility | Medium — emergent from handoff rules | Low-medium — declarative, framework decides | Low — emergent from dialogue | High — you drew the graph |
| Production posture | Guardrails, tracing; you own the loop | Flows, hierarchical control | Code execution, human-in-the-loop | Checkpointing, durable execution, human-in-the-loop |
| Autonomy (list axis) | bounded | bounded | bounded | headless |
| Recovery (list axis) | resumable | resumable | resumable | durable |
Stars as captured for the main list (see README for the capture date).
Pick by situation
- You want the least framework between you and the model → OpenAI Agents SDK. Handoffs + guardrails and almost nothing else; the list rates it the smallest adoption surface of the four. Multi-LLM routing means it's not OpenAI-only in practice. Start here if you're unsure — it's the cheapest to walk away from.
- You think in org charts → CrewAI. Role-based crews are the fastest path to a working multi-agent demo and the most readable to non-engineers. The trade: it's a product suite — you adopt its worldview, and stepping outside the declarative model means fighting it. Flows mitigate this for production control flow.
- Your problem is genuinely conversational → AutoGen. Group chat is a great fit when agents should debate (review panels, negotiation sims, brainstorming) and an awkward fit when you wanted a pipeline. One watch item: Microsoft has been converging its agent efforts into the Microsoft Agent Framework — check the roadmap before betting a new production system on AutoGen specifically.
- It's going to production and must survive restarts → LangGraph. Explicit graphs, checkpointing, and durable execution make it the infrastructure-grade choice; the same explicitness makes it the most up-front design work of the four. If your "multi-agent system" is honestly a workflow with LLM steps, this is the right honesty.
The unfashionable default
A majority of "multi-agent" use cases in the wild are one orchestrator delegating to stateless sub-tasks. All four frameworks can express that — and so can a for loop over your provider's SDK. Reach for orchestration frameworks when agents need to interact, not just fan out.
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