CrewAI vs AutoGen: 2026 comparison

CrewAI and AutoGen in brief

CrewAI and AutoGen both help you build systems in which multiple agents coordinate their work. They are open source Python projects that support self-hosting and several model families. Their starting points differ: CrewAI structures agents around roles, tasks and workflows, while AutoGen focuses on agents communicating and using tools together. As of October 24, 2026, the supplied figures reveal a notable distinction. AutoGen has a slightly larger GitHub following, whereas CrewAI shows substantially more development and release activity over the measured periods.

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

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

Both meet the same need, which makes the comparison useful. We compare them on 11 verifiable criteria, using the same data as their profiles, collected from official sites and GitHub.

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. 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. CrewAI : 1,269 commits. AutoGen : 4 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. AutoGen : 0. Edge: CrewAI.

Last commit

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

License

A clear open-source license (MIT, Apache…) allows commercial use and modification; without one, usage rights are unclear. CrewAI : 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. CrewAI : 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. CrewAI : 6. AutoGen : 5. Edge: CrewAI.

Interfaces

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

Risk level

Our risk level considers the access granted (terminal, files, network) and default human approval. CrewAI : 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. CrewAI : 8.9K. 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. CrewAI : October 2023. AutoGen : August 2023. Edge: AutoGen.

In charts

Our analysis

CrewAI, published by CrewAI Inc., approaches coordination through assigned responsibilities. You define agents by role, give them tasks and arrange their execution within workflows. This framing suits projects that can be expressed as a division of work. Microsoft’s AutoGen instead foregrounds communication between agents and their shared use of tools. It is relevant when those interactions are the starting point for your application design. Both are Python frameworks, so their open source status should not be confused with an interface requiring no programming. Your team still needs to translate its intended process into an implemented system.

AutoGen has 61,548 GitHub stars and 9,314 forks, compared with CrewAI’s 60,064 stars and 8,863 forks. Recent activity points in the opposite direction: CrewAI records 1,269 commits over twelve months and 28 releases over six months; AutoGen records four commits and no releases. Their latest commits are dated October 24 and October 16, 2026, respectively. These figures indicate visibility and development cadence, rather than reliability. The scores of 88 and 74 out of 100 summarize popularity, activity and framework conditions; they are not quality ratings. The supplied records do not publish pricing.

Both projects can be self-hosted, giving you a way to arrange their execution environment. This does not establish that every deployment keeps all data within your infrastructure, because your model choices also affect data flows. Both records name GPT, Claude, Gemini and local models; CrewAI additionally lists OpenRouter. CrewAI carries an MIT license, while AutoGen is listed under CC-BY-4.0. You should examine the relevant terms against your intended use. The supplied information does not document retention practices or confirm human approval mechanisms. Open source availability therefore answers only part of the questions surrounding control and privacy.

CrewAI offers both an SDK and a command-line interface, while AutoGen’s record lists an SDK alone. That makes CrewAI relevant when your development process includes both application code and terminal operations. AutoGen fits an integration designed around programming agent conversations and tool use. CrewAI’s release cadence deserves a place in your maintenance planning, while AutoGen’s limited recorded activity warrants checking its fit with your present requirements. Neither record supplies comparative performance results or support guarantees. Human approval is also unspecified, so you cannot use these data to establish how either framework handles that requirement.

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 / AutoGen value).

Trying it out quickly

Here, CrewAI has the edge: it wins 2 of the 3 criteria that matter in this situation, namely interfaces (2 / 1), supported models (6 / 5), and popularity (github stars) (60.1K / 61.5K).

Production use

Here, CrewAI has the edge: it wins 3 of the 4 criteria that matter in this situation, namely activity over a year (1,269 commits / 4 commits), last commit (October 24, 2026 / October 16, 2026), license (MIT / CC-BY-4.0), and releases (6 months) (28 / 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 CrewAI if…

• you value activity over a year (1,269 commits)
• you value releases (6 months) (28)
• you value last commit (October 24, 2026)
• you value supported models (6)
• you value interfaces (2)

Choose CrewAI when roles and assigned tasks provide a natural structure for your agent system. Its SDK and CLI are relevant if you need both programming and terminal access, and its recorded development activity may matter to your maintenance planning. The MIT license and self-hosting option also support this profile, although neither establishes the privacy properties of a particular deployment.

Choose AutoGen if…

• you value popularity (github stars) (61.5K)
• you value community (forks) (9.3K)
• you value track record (August 2023)

AutoGen is a logical fit when communication and shared tool use are central to your agent architecture. Its Python SDK, self-hosting capability and listed model families support that programming approach. The GitHub figures show a slightly larger following than CrewAI’s. You should weigh that visibility alongside the limited recent activity and review the listed CC-BY-4.0 license against your intended use.

Strengths and limits of each

CrewAI

• + Widely followed: 60.1K stars
• + Very active development
• + Frequent releases (28 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 Orca, Agno, Paperclip, AgentScope, OpenAI Agents SDK, and Eigent.

Related comparisons:

• CrewAI vs LangGraph
• LangGraph vs AutoGen

Conclusion

Start with the structure your application needs. CrewAI emphasizes roles and tasks alongside a busy recorded development cadence. AutoGen emphasizes agent communication and has more GitHub stars and forks. Neither distinction proves superior output or reliability. Match the framework to your architecture, licensing requirements and maintenance capacity, and separately investigate matters the records leave open. Human approval and deployment-specific data handling remain particularly relevant unanswered questions.