CrewAI vs LangGraph: 2026 comparison

CrewAI and LangGraph in brief

CrewAI and LangGraph help you build agent systems, with Python, MIT licensing and self-hosting listed for both. Their immediate distinction is how they describe the work. CrewAI organizes agents by roles, assigns tasks and coordinates execution within workflows. LangGraph emphasizes stateful agents, execution loops and interruptions that allow human intervention. The useful starting point is therefore how you want to represent responsibilities, progress and control points, rather than treating their close project scores as a measure of agent quality.

CrewAI : A framework for organizing agents into roles, assigning tasks, and coordinating their execution within workflows.

LangGraph : LangGraph provides stateful agent execution with graph workflows, persistent checkpoints, and support for human intervention.

They are often weighed against each other by people who hesitate. This page lines up their features point by point (11 criteria), with no partnership with either publisher.

Comparison table

Criterion by criterion

For each criterion: why it matters, each one's value and who has the edge. A criterion without data on both sides is not decided.

Popularity (GitHub stars)

Stars measure developer interest; a widely followed project has more tutorials, integrations and answers. CrewAI : 60.1K. LangGraph : 43.8K. Edge: CrewAI.

Activity over a year

Commits over 52 weeks show whether the project moves fast, fixes bugs and keeps up with new models. CrewAI : 1,269 commits. LangGraph : 871 commits. Edge: CrewAI.

Releases (6 months)

Regular releases signal a project that ships; very frequent releases can also mean frequent updates on your side. CrewAI : 28. LangGraph : 30. Edge: LangGraph.

Last commit

A repository with no commit for months may fall behind new APIs and security fixes. CrewAI : October 24, 2026. LangGraph : October 24, 2026. A tie on this point.

License

A clear open-source license (MIT, Apache…) allows commercial use and modification; without one, usage rights are unclear. CrewAI : MIT. LangGraph : MIT. A tie on this point.

Self-hosting

Running the agent on your machine or server keeps your files and keys with you. CrewAI : yes. LangGraph : yes. A tie on this point.

Supported models

An agent that accepts several model families lets you pick the best value, or a local model. CrewAI : 6. LangGraph : 1. Edge: CrewAI.

Interfaces

Terminal, code editor, web app or chat: more interfaces mean more ways to fit it into your work. CrewAI : 2. LangGraph : 1. Edge: CrewAI.

Risk level

Our risk level considers the access granted (terminal, files, network) and default human approval. CrewAI : low. LangGraph : low. A tie on this point.

Community (forks)

Forks count developers who copied the project to adapt it: a sign of a living ecosystem. CrewAI : 8.9K. LangGraph : 7.5K. Edge: CrewAI.

Track record

An older project has had time to fix early flaws; a recent one often brings the latest ideas. CrewAI : October 2023. LangGraph : August 2023. Edge: LangGraph.

In charts

Our analysis

CrewAI structures agent work around roles, assigned tasks and coordination. That representation may suit a problem you naturally describe as distributing responsibilities among participants. LangGraph instead highlights stateful agents, execution loops and interruptions for human intervention. It may align with work where representing progress and pauses is central to the design. Both projects use Python and expose an SDK; CrewAI also lists a CLI. These differences concern how you approach building an agent system. They do not establish that one framework produces better results, and the supplied descriptions do not define every capability or limitation of either approach in a deployed application.

CrewAI records 60,064 GitHub stars and 8,863 forks, compared with 43,830 stars and 7,526 forks for LangGraph. Over twelve months, the supplied totals are 1,269 commits for CrewAI and 871 for LangGraph. Over six months, CrewAI has 28 releases and LangGraph has 30. Both latest commits are dated October 24, 2026. These figures show recent activity in both repositories, with higher star and commit totals for CrewAI but slightly more recorded releases for LangGraph. Their scores of 88 and 87 concern popularity, activity and project conditions. They do not measure the effectiveness, correctness or reliability of agents built with either framework.

Both projects publish an MIT license and are listed as self-hostable. That provides an explicit reuse framework, but does not determine how your chosen models process data. CrewAI lists GPT, Claude, Gemini, local models, OpenRouter and multi-model use. LangGraph records multi-model support without a detailed family list in this snapshot. Neither record lists sensitive access, and both have a low risk classification. Those facts describe the provided framework records, not every permission a future application might receive. Human validation is unspecified for both, even though LangGraph's description includes interruptions for human intervention. An intervention mechanism does not establish universal review of every action.

For day-to-day development, CrewAI offers a role-and-task structure with SDK and CLI interfaces. LangGraph offers an SDK with an emphasis on state, loops and interruptions. You can start by deciding whether your process is more clearly expressed through coordinated responsibilities or explicit progression and stopping points. The descriptions emphasize different elements without proving that the approaches are mutually exclusive. Prices are not supplied for either project. Model costs, production performance and maintenance effort are also absent from the data. Consequently, neither the licensing information nor repository activity can establish the total operating cost or practical performance of the system you intend to build.

Depending on your situation

The right choice depends on use. For a few common situations, here are the criteria that matter and who wins (CrewAI value / LangGraph value).

Trying it out quickly

Here, CrewAI has the edge: it wins 3 of the 3 criteria that matter in this situation, namely interfaces (2 / 1), supported models (6 / 1), and popularity (github stars) (60.1K / 43.8K).

Production use

Neither stands out on the criteria for this situation (activity over a year (1,269 commits / 871 commits), last commit (October 24, 2026 / October 24, 2026), license (MIT / MIT), and releases (6 months) (28 / 30)): decide with a trial.

Security and control

Neither stands out on the criteria for this situation (risk level (low / low) and self-hosting (yes / yes)): decide with a trial.

Which one to choose?

Choose CrewAI if…

• you value popularity (github stars) (60.1K)
• you value activity over a year (1,269 commits)
• you value supported models (6)
• you value interfaces (2)
• you value community (forks) (8.9K)

CrewAI fits a design based on agent roles, assigned tasks and coordinated execution. Its SDK and CLI provide two documented interfaces, and its model list names several families alongside local options. Its GitHub indicators can help you assess project visibility and activity, but they do not guarantee that your workflow will be effective or straightforward to maintain.

Choose LangGraph if…

• you value releases (6 months) (30)
• you value track record (August 2023)

LangGraph fits needs expressed through state, execution loops and interruptions for human intervention. Its Python SDK, MIT license and self-hosting are documented integration characteristics. The interruption mechanism is relevant when designing control points, but the separate human-validation field remains unspecified. You should not interpret the description as proof that every action automatically receives human approval.

Strengths and limits of each

CrewAI

• + Widely followed: 60.1K stars
• + Very active development
• + Frequent releases (28 in a year)
• − Requires programming skills

LangGraph

• + Widely followed: 43.8K stars
• + Very active development
• + Frequent releases (30 in a year)
• − Requires programming skills

The best test is still yours: take three real tasks from your day and give them to both for a week. Our “Which AI agent?” quiz can also guide you.

What about the others?

If neither suits you, look at Orca, Agno, Paperclip, AgentScope, OpenAI Agents SDK, and Eigent.

Related comparisons:

• CrewAI vs AutoGen
• LangGraph vs AutoGen

Conclusion

Start with the structure your work requires. CrewAI emphasizes roles, tasks and coordination; LangGraph emphasizes state, loops and interruptions. Both provide MIT licensing, self-hosting and recent repository activity. Their nearly identical scores concern popularity, activity and project conditions, rather than output quality. The supplied data does not establish operating costs or production performance, so those close scores cannot decide which framework will serve your intended system better.