Multi-Agent Guide

LangGraph vs CrewAI

LangGraph is a low-level graph runtime: you define typed state, nodes and edges, and get checkpoints, interrupts and time travel. CrewAI supplies the agent abstractions for you: roles, goals, tasks, sequential or hierarchical crews, plus Flows for deterministic steps. Both are MIT-licensed Python libraries with a paid hosted platform from the vendor.

Facts side by side

LangGraph vs CrewAI. Data as of 2026-10-01.
Fact LangGraph CrewAI
Type Framework Framework
Languages / SDKs Python Python
License MIT MIT
Pricing model Open core Open core
Orchestration pattern Graph Crew / roles
GitHub stars 42,532 (as of 2026-10-01) 59,239 (as of 2026-10-01)
GitHub forks 7,208 8,618
Last push 2026-10-01 2026-10-01
Latest release cli==0.4.32.dev0 1.15.23
Repository langchain-ai/langgraph crewAIInc/crewAI
Website www.langchain.com crewai.com
Documentation docs.langchain.com docs.crewai.com
Last verified 2026-09-30 2026-09-30
MCP support Yes (checked 2026-09-30) Yes (checked 2026-09-30)
A2A support Yes (checked 2026-09-30) Yes (checked 2026-09-30)
AG-UI support Partial (checked 2026-09-30) Partial (checked 2026-09-30)
Install verified 2026-09-30 2026-09-30

Choose LangGraph if

  • You want explicit control of state and control flow (StateGraph, reducers, conditional edges) rather than role and task abstractions.
  • You need per-step checkpoints, interrupt() anywhere in a node, and replay or fork from an earlier checkpoint.
  • You are fine assembling multi-agent patterns yourself from subgraphs; LangGraph has no built-in agent-role concept.

Choose CrewAI if

  • You want agents described by role, goal and backstory, with a sequential or manager-led (hierarchical) process out of the box.
  • You want A2A client and server support in the library itself (crewai[a2a]); LangGraph's A2A endpoint comes from Agent Server, not the langgraph package.
  • You want one package that covers both autonomous crews and event-driven Flows with @start, @listen and @router.

Migration notes

There is no official converter in either direction. Moving from CrewAI to LangGraph means turning each task into a node, replacing the crew's process with explicit edges, and defining the shared state schema that CrewAI kept implicit in task outputs. Moving from LangGraph to CrewAI means mapping nodes to agents and tasks; deterministic branches fit CrewAI Flows better than crews. Human review differs: LangGraph resumes an interrupt() with Command(resume=...) on the same thread_id (the node re-executes from its start), while CrewAI uses human_input=True on tasks or @human_feedback in Flows (1.8.0+), and documents webhook-based human input as an AMP (paid) feature. Python ranges differ too: CrewAI requires Python >=3.10,<3.14.

FAQ

Is LangGraph or CrewAI easier to start with?

CrewAI provides role, task and process abstractions, so a first multi-agent run needs less structure. LangGraph's own overview calls it low-level and points to LangChain's prebuilt agents for a quicker start.

Do both support MCP and A2A?

Both have MCP support with evidence on their tool pages. CrewAI documents A2A client and server in the library. For LangGraph, MCP and A2A endpoints are served by Agent Server (the runtime behind langgraph dev and LangSmith deployments).

Can they be used together?

Yes, at the process level: a CrewAI Flow step or a LangGraph node can call any Python code, including the other framework. Neither project documents an official integration, so state and error handling across the boundary are yours to write.

Are both free?

Both libraries are MIT-licensed. LangChain sells LangSmith (deployment from the Plus plan) and CrewAI sells AMP; see each tool page for the pricing link.