LangGraph vs AutoGen: 2026 comparison

LangGraph and AutoGen in brief

LangGraph and AutoGen both provide a programming foundation for agent systems, but they emphasize different aspects of coordination. LangGraph highlights state, execution loops and interruptions for human intervention. AutoGen focuses on agents communicating and using tools together. Both are open source Python projects that you can self-host. As of October 24, 2026, AutoGen has more GitHub stars and forks, while LangGraph records substantially more development and release activity. The useful distinction is therefore both architectural and operational, rather than a simple comparison of audience size.

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

AutoGen : A programming framework for building agent systems that communicate, coordinate tasks, and use tools together.

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. LangGraph : 43.8K. AutoGen : 61.5K. Edge: AutoGen.

Activity over a year

Commits over 52 weeks show whether the project moves fast, fixes bugs and keeps up with new models. LangGraph : 871 commits. AutoGen : 4 commits. Edge: LangGraph.

Releases (6 months)

Regular releases signal a project that ships; very frequent releases can also mean frequent updates on your side. LangGraph : 30. AutoGen : 0. Edge: LangGraph.

Last commit

A repository with no commit for months may fall behind new APIs and security fixes. LangGraph : October 24, 2026. AutoGen : October 16, 2026. Edge: LangGraph.

License

A clear open-source license (MIT, Apache…) allows commercial use and modification; without one, usage rights are unclear. LangGraph : MIT. AutoGen : CC-BY-4.0. A tie on this point.

Self-hosting

Running the agent on your machine or server keeps your files and keys with you. LangGraph : yes. AutoGen : 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. LangGraph : 1. AutoGen : 5. Edge: AutoGen.

Interfaces

Terminal, code editor, web app or chat: more interfaces mean more ways to fit it into your work. LangGraph : 1. AutoGen : 1. A tie on this point.

Risk level

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

Community (forks)

Forks count developers who copied the project to adapt it: a sign of a living ecosystem. LangGraph : 7.5K. AutoGen : 9.3K. Edge: AutoGen.

Track record

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

In charts

Our analysis

LangGraph, published by LangChain, is designed for agents whose execution involves state, loops and opportunities for human intervention. This makes its stated approach relevant when managing the course of a process is central to your application. Microsoft’s AutoGen frames the problem around agents that exchange messages and use tools together. That orientation suits designs in which cooperation is expressed primarily through agent interactions. Both provide Python SDKs, so choosing between them means matching these concepts to a system you will implement. Neither the publisher’s name nor the GitHub audience establishes which architecture will suit your intended process.

AutoGen records 61,548 GitHub stars and 9,314 forks, versus LangGraph’s 43,830 stars and 7,526 forks. LangGraph nevertheless shows 871 commits over twelve months and 30 releases over six months. AutoGen shows four commits and no releases over those periods. Their latest commits are dated October 24 and October 16, 2026, respectively. These indicators separate audience size from recent project activity; they do not provide comparative performance evidence. Scores of 87 for LangGraph and 74 for AutoGen summarize popularity, activity and framework conditions. They should not be interpreted as quality ratings. Pricing is not published in the supplied records.

Self-hosting is available for both frameworks, but it does not independently explain where every part of your data processing occurs. LangGraph’s record lists support for multiple models without naming individual families. AutoGen’s record explicitly includes GPT, Claude, Gemini and local models, alongside multiple-model support. LangGraph is listed under MIT and AutoGen under CC-BY-4.0, creating distinct licensing considerations for your intended use. Neither record details data retention or location policies. LangGraph’s description includes interruptions for human intervention, but its human-validation field remains unspecified. That distinction prevents treating the description as confirmation of a complete approval mechanism.

In practical development, LangGraph is relevant when state, repeated execution and interruption points organize your process. AutoGen is relevant when agent communication and shared tool use define the application’s structure. Both records list an SDK, with no additional interface specified. LangGraph’s active release cadence belongs in your version-maintenance planning, while AutoGen’s limited measured activity calls for assessing its fit with current requirements. The supplied information does not quantify implementation complexity or compare runtime performance. It also offers no support commitments, so those questions cannot be resolved from audience figures, recent commits or the descriptions alone.

Depending on your situation

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

Trying it out quickly

Here, AutoGen has the edge: it wins 2 of the 3 criteria that matter in this situation, namely interfaces (1 / 1), supported models (1 / 5), and popularity (github stars) (43.8K / 61.5K).

Production use

Here, LangGraph has the edge: it wins 3 of the 4 criteria that matter in this situation, namely activity over a year (871 commits / 4 commits), last commit (October 24, 2026 / October 16, 2026), license (MIT / CC-BY-4.0), and releases (6 months) (30 / 0).

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 LangGraph if…

• you value activity over a year (871 commits)
• you value releases (6 months) (30)
• you value last commit (October 24, 2026)
• you value track record (August 2023)

LangGraph fits projects where you need stateful execution, loops or explicit opportunities for human intervention. Its recorded development activity may also matter if you prioritize a project with frequent recent releases. The Python SDK, MIT license and self-hosting option support this profile. You should still establish the precise approval behavior and data handling required for your own implementation.

Choose AutoGen if…

• you value popularity (github stars) (61.5K)
• you value supported models (5)
• you value community (forks) (9.3K)

AutoGen is a logical choice when agent conversations and shared use of tools provide the natural structure for your application. Its record names several model families, including local models, and provides a Python SDK with self-hosting. It also has a larger GitHub following in the supplied figures. Consider those characteristics alongside its limited recent activity and listed CC-BY-4.0 license.

Strengths and limits of each

LangGraph

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

AutoGen

• + Widely followed: 61.5K stars
• + Very active development
• + Can run on your side: data under control
• − 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 LangChain, Haystack, DSPy, Agent Development Kit, Pydantic AI, and Semantic Kernel.

Related comparisons:

• CrewAI vs LangGraph
• CrewAI vs AutoGen

Conclusion

Choose according to the control structure your project needs. LangGraph emphasizes state, loops and interruptions; AutoGen emphasizes communication between agents. LangGraph’s stronger recent activity is relevant to maintenance planning, while AutoGen’s larger audience describes a different dimension. Neither establishes output quality. Assess the architectural fit first, then review licensing, actual data flows and the specific human-intervention behavior your application requires. The supplied records leave those deployment details partly unresolved.