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LangGraph vs CrewAI: which agent framework should you use? (October 2026)

LangGraph and CrewAI are both MIT-licensed Python frameworks for building AI agents. LangGraph is a low-level orchestration framework where you define a stateful graph of steps. CrewAI organises role-based agents into Crews and adds event-driven Flows for explicit control. We ran both on 2026-10-25 on the same one-tool task with the same local Qwen2.5-3B-Instruct model (3 of 3 trials each, 3 trials is a smoke test, not a benchmark); the rest of this page compares documented design.

Our pick: Pick LangGraph for developers who want explicit control over agent state and flow; pick CrewAI for developers who want role-based multi-agent flows. No overall winner: we have not benchmarked them against each other. Updated (America/Chicago).

By Agentic Weekly, operated independently from Texas, US · Published · Last updated (America/Chicago) · How we work

Evidence: both are Tested (smoke tests, 2026-10-25). LangGraph had an earlier plain two-node graph run with no LLM, then, like CrewAI, the same one-tool task on a local Qwen2.5-3B-Instruct model: both passed 3 of 3 trials. We did not test speed, cost or output quality beyond that. Everything else is Reported from official docs and GitHub.

LangGraph

LangChain · 1.2.17

Tested (smoke test)

A dependable foundation for stateful agents; this release is a routine fix, so only update if the RemoteGraph or recursion fixes affect you.

CrewAI

CrewAI · 1.15.30

Tested (smoke test)

Reasonable if you like the crew-of-agents mental model; the memory and tool-loop fixes in this release are worth taking.

Side by side, from what we recorded

Each row compares one recorded fact for both tools. Rows are not weighted and nothing is added up into a score; the note under each row says what the marker means.

  • Evidence

    Tested (smoke test) for LangGraph
    Tested (smoke test) for CrewAI

    Tested = we ran it (smoke test); Reported = we read official sources

  • Smoke-test result

    Passed for LangGraph
    Passed for CrewAI

    Each tool's own smoke test on the same CPU-only machine; tasks differ unless the logs say otherwise

  • Editorial rank: agent building

    #3 (LangGraph is ahead on this row) for LangGraph
    #5 for CrewAI

    Lower is higher on our list; an editorial pick, not a benchmark

  • Release date

    Oct 24, 2026 (LangGraph is ahead on this row) for LangGraph
    Oct 23, 2026 for CrewAI

    Newer date only; says nothing about quality

  • Pricing we could verify

    Not verified for LangGraph
    Not verified for CrewAI

    About our evidence only, not about the price

  • Steps in our setup guide

    3 for LangGraph
    3 for CrewAI

    Counts our guide's steps, not difficulty

  • Sources we cite

    2 for LangGraph
    3 for CrewAI

    How much we had to read; more is not better

A list of recorded differences, not a quality score or a ranking. We have not benchmarked these tools against each other; the smoke-test row links each tool's own log. How we label evidence.

What we measured

We ran both LangGraph and CrewAI ourselves; the table links each full test log. These are smoke tests on one CPU-only machine, not benchmarks. Both ran the same task on the same local model, so the two results are comparable on that one task only. Model-driven runs used a small local Qwen2.5-3B-Instruct model.

Our own tests for LangGraph and CrewAI
TestLangGraphCrewAI
Hands-on test log (install and a real task)Tested: LangGraph 1.2.17, Oct 25, 2026: passed. LangGraph 1.2.17 passed our one-tool task on a local 3B model: 3 of 3 trials; cold uv install 2.5 s. Full test logTested: CrewAI 1.15.30, Oct 25, 2026: passed. CrewAI 1.15.30 passed our one-tool task on a local 3B model: 3 of 3 trials; cold uv install 8.4 s. Full test log

How we test and label results: methodology. All test logs: test hub.

LangGraph vs CrewAI at a glance

LangGraph and CrewAI side by side (GitHub and docs read 2026-10-25)
FeatureLangGraphCrewAI
MakerLangChain Inc (docs: built by LangChain Inc, usable without LangChain)CrewAI (GitHub organisation crewAIInc)
What it is"Low-level orchestration framework and runtime for building, managing, and deploying long-running, stateful agents" (docs)"Open-source Python framework with high-level abstractions and low-level APIs for building production-ready multi-agent workflows" (README)
Core modelA graph of nodes and edges over shared stateCrews of role-based agents, plus event-driven Flows
LicenseMITMIT
Latest release we covered1.2.17, published 2026-10-24 (UTC)1.15.30, published 2026-10-24 (UTC)
GitHub repository created2023-08-092023-10-27
Installpip install -U langgraphuv tool install crewai (needs Python 3.10 to 3.13, per README)
Hosted optionLangSmith Deployment (pricing not verified)CrewAI AMP Suite (pricing not verified)
Evidence on this siteTested: smoke tests, no LLM (two-node graph) and local Qwen2.5-3B-Instruct (one-tool task, 3 of 3)Tested: smoke test, local Qwen2.5-3B-Instruct (one-tool task, 3 of 3), not vendor models
Last verified2026-10-252026-10-25

What we ran, and what we did not

We tested each framework on 2026-10-25 on a Linux x86_64 box with 8 CPUs and no GPU. For LangGraph we first built and ran a plain two-node graph with no LLM involved, then ran the one-tool task with a local Qwen2.5-3B-Instruct model. For CrewAI we ran the same one-tool task against the same model (an unpinned install in our shared environment resolved to an older version, 1.6.1, as the CrewAI page notes; the logged runs used 1.15.30). Both passed 3 of 3 trials; the LangGraph log and the CrewAI log give timings, install sizes and what each did not cover.

That is a pass or fail on one tiny task, not a ranking. We have no data on speed under load, cost, reliability across tasks or output quality, and nothing here claims one is better. The comparison below is built from each project's documentation and GitHub pages, and is Reported.

How each one models an agent

LangGraph's docs describe a framework you wire yourself. You define nodes and edges over shared state, and it "does not abstract prompts or architecture". A core idea is mixing deterministic, hand-coded steps with LLM-driven steps in one graph, so some parts of a workflow are predictable and auditable while others are model-driven. The docs recommend LangChain's higher-level agents if you are just starting or want an abstraction, and say you do not need LangChain to use LangGraph.

CrewAI's README describes two building blocks. Crews optimise for autonomy and collaboration between role-based agents. Flows are event-driven automations that combine workflow control, single LLM calls and native support for Crews. Its "When to Use CrewAI" section lists coordinating multiple agents with clear roles and tasks, wrapping agent work in deterministic event-driven workflows, and keeping application logic in regular Python.

State, human review and observability

Features each project documents (Reported, not tested by us)
TopicLangGraphCrewAI
Durable execution and resumePersistence so agents survive failures and resume where they left offREADME lists checkpointing among things you add as a system grows
Human in the loopInspect and modify agent state at any pointREADME lists human review among workflow needs
MemoryShort-term working memory and long-term memory across sessionsMemory support; the 1.15.29 release notes include a fix for private memories leaking into every recall
Tracing and debuggingLangSmith tracing, switched on with LANGSMITH_TRACING=true plus an API keyCrewAI Control Plane for tracing and observability; the README also describes anonymous telemetry you can disable with OTEL_SDK_DISABLED=true
EvaluationLangSmith evaluation toolingcrewai eval CLI; the 1.15.29 release notes add features that run the crew itself and export evaluations

Note the telemetry difference as a documented default, not a finding: CrewAI's README says it collects anonymous usage telemetry unless disabled, while LangGraph's docs describe tracing as something you turn on. Check each project's current docs for exact behaviour before deploying.

How to choose

These conclusions follow from the documented designs, not from a performance test.

  • Choose LangGraph if you want explicit control of state and flow, long-running or resumable agents, and you are comfortable wiring a graph yourself or already use LangChain tooling.
  • Choose CrewAI if your problem maps naturally onto a team of role-based agents with tasks, and you want Flows for the deterministic parts and a built-in eval command.
  • Think twice if you need the shortest path to a simple single agent. Both vendors describe more advanced abstractions than that; see the OpenAI Agents SDK and Google ADK entries below.
  • Mind the release pace. Both ship often. CrewAI published several versions within a few days in the notes we read, and our CrewAI page flags that as a reason to pin versions.

Other frameworks we track

Three-way comparisons are not what most people search for, so we keep this page to two frameworks. If you are weighing others we have notes on, see OpenAI Agents SDK, Google ADK and Mastra, or the full best AI agent tools list.

Licenses, hosting and cost

Both libraries are MIT-licensed on GitHub, so the open-source packages themselves are free to use. Each vendor also sells a hosted platform: LangSmith Deployment for LangGraph and CrewAI AMP Suite for CrewAI. We did not verify the prices of either, so treat them as unknown. Your model costs are separate from either framework.

FAQ

What is the difference between LangGraph and CrewAI?

LangGraph is a low-level framework where you define a stateful graph of nodes and edges, mixing deterministic and LLM-driven steps. CrewAI organises role-based agents into Crews and adds event-driven Flows. Both are MIT-licensed Python projects. We compared documented design, not performance.

Is LangGraph or CrewAI better?

We cannot say. Both passed our one-tool task 3 of 3 times on the same local Qwen2.5-3B-Instruct model on 2026-10-25, which is a smoke test and does not separate them. Choose on how you want to model your agents.

Are LangGraph and CrewAI free?

Both libraries are MIT-licensed on GitHub. LangChain sells LangSmith Deployment and CrewAI sells CrewAI AMP Suite as hosted options; we did not verify their pricing. Model usage is billed separately by your model provider.

Does LangGraph require LangChain?

No. LangGraph's docs say you do not need LangChain to use LangGraph, though the docs use LangChain components in many examples.

Which Python versions does CrewAI support?

CrewAI's README says it requires Python 3.10 or newer and below 3.14. We did not verify LangGraph's supported Python versions.

Have you tested LangGraph and CrewAI?

Yes, on 2026-10-25. LangGraph: a two-node graph with no LLM, then the one-tool task on a local Qwen2.5-3B-Instruct model. CrewAI: the same one-tool task on the same model. Both passed 3 of 3 trials. We did not test persistence, hosted platforms, vendor-hosted models or performance beyond that.

Sources

Page history

  • : First published. LangGraph docs and README, CrewAI README and GitHub release data read on this date.

Markdown version for agents: /compare/langgraph-vs-crewai.md