Head-to-head: LangGraph vs CrewAI

If you're building agents in Python and want an open-source core, two names come up again and again: LangGraph and CrewAI. Both are MIT-licensed, both have a managed tier, and both score well overall — but they embody different philosophies. Here's how they line up. The full side-by-side is at /compare/langgraph-vs-crewai.

The core difference: graph vs crew

• LangGraph is a low-level framework built around a graph mental model. You define stateful, graph-structured workflows and get fine-grained control over every step. It rewards teams that want to engineer the control flow explicitly.
• CrewAI is built around role-playing agents — "crews" and "flows" of autonomous agents that collaborate on a task. It rewards teams that think in terms of a team of specialists dividing work.

Neither is "better"; they suit different ways of decomposing a problem.

Scorecard, side by side

Drawing from our scorecards (each dimension out of 5):

• LangGraph — dx 4, pricing 3, scalability 5, memory 5, integrations 5, MCP 4. Overall it's the highest-scoring platform we track.
• CrewAI — dx 4, pricing 3, scalability 4, memory 4, integrations 4, MCP 5.

Read that as: LangGraph edges ahead on scalability, memory, and integrations, while CrewAI leads on MCP-nativeness (native across Stdio, SSE, and Streamable HTTP, versus LangGraph's separate adapter library).

Memory and production plumbing

LangGraph's managed side leans hard into durability: durable execution across restarts, persistent checkpoints (payloads up to 25 MB), and semantic search for long-term memory. CrewAI offers a unified Memory class where the LLM infers scope and importance on save, with retrieval ranked by similarity, recency, and importance (LanceDB by default). If persistent, resumable long-running workflows are central, LangGraph's checkpointing is the stronger story; see agent long-term memory.

Cost and licensing

Both cores are free and self-hostable (MIT). The watch-items differ:

• LangGraph — managed usage is metered across many dimensions (traces, runs, uptime, LCUs), and self-hosted/hybrid deployment is gated to the Enterprise tier.
• CrewAI — the free plan caps at 50 workflow executions/month, and Enterprise pricing isn't public.

Which to pick

• Choose LangGraph for maximum control over stateful, multi-step workflows, strong memory, and the broadest integrations — if you're comfortable with the graph model and metered managed pricing.
• Choose CrewAI for role-based multi-agent collaboration and best-in-class MCP support, with a gentler on-ramp.

Also worth a look: LangGraph vs the OpenAI Agents SDK and Mastra vs LangGraph.

Key takeaways

• LangGraph = low-level graph control; highest overall score, leads on scalability, memory, and integrations.
• CrewAI = role-playing multi-agent crews; leads on MCP-nativeness (5/5).
• Both are MIT and self-hostable; watch LangGraph's multi-dimension metering and CrewAI's 50-execution free cap.
• Full breakdown at /compare/langgraph-vs-crewai.