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LangGraph vs CrewAI. Rows marked Differs have different values.

Open source

LangGraph

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Open source

CrewAI

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TypeOpen sourceOpen source
Categories
In one lineLow-level orchestration for controllable, stateful agents.Role-based multi-agent teams with a simple mental model.
Stage & funding——
GitHub starsDiffers≈44k≈60k
LicenseMITMIT
LanguagePythonPython
Last pushOct 28, 2026Oct 28, 2026
Works with——
Built on——
Built on itDiffers—
Similar toDiffers—
People linkedDiffers30
EventsDiffers0
Pros
  • User reportsUsers running it in production cite checkpointing, pausing mid-graph for human input and time-travel over state as the main reasons to use it.[1][2][3]
  • User reportsUsers find the explicit graph/state-machine model a useful way to control agent flow, even when they dislike the wider LangChain abstractions.[1][2]
  • User reportsThe Postgres checkpointer lets stateless backends resume sessions without session affinity.[1]
  • MetadataMIT licensed, actively released (1.2.18 on 2026-10-26), about 44k stars.[1]
  • User reportsUsers find it fast for prototyping, with role-based agents that are intuitive to set up.[1]
  • User reportsSeparate agents, tasks and tools, plus YAML config for roles and models, keep behaviour changes out of code.[1]
  • MetadataMIT licensed and released very often (1.15.33 on Oct 29, 2026).[1]
Cons
  • User reportsUsers describe a steep learning curve for people not used to graphs, and say tracing a mid-graph production failure means building your own observability.[1][2]
  • User reportsDurability edge cases: long tool calls (about 180s or more) silently re-execute from a checkpoint on LangGraph Cloud, and sync durability doesn't enforce write ordering.[1][2]
  • User reportsCancelling a run loses streamed state that hasn't been checkpointed yet, and the Postgres checkpointer has a long-open SSL error.[1][2]
  • User reportsDocs lag the fast-moving API, and breaking changes are a recurring complaint.[1][2]
  • User reportsSeveral teams outgrow it and write their own loop, keeping only the graph idea.[1]
  • User reportsUsers report the whole crew hanging when one agent or API call times out; one moved the pipeline to LangGraph.[1]
  • User reportsThe early simplicity becomes a constraint: users report hard-to-reproduce drift on long tasks and a 'loose and underspecified' abstraction.[1][2]
  • User reportsForum users report poor visibility into agent decisions, roughly 10-minute crew runs, and a virtualenv near 1 GB.[1]
Best forLong-running, stateful, resumable workflows with human approval steps where you want explicit control flow.Demos and internal multi-agent prototypes where speed to a first result matters more than determinism.
Watch out forMake tool calls idempotent: a checkpoint replay can run them again.Timeouts, hangs and limited observability; plan your own monitoring before production.
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