Side-by-Side Comparison
When to choose CrewAI
- ▸Your use case maps cleanly to a team of specialists — a researcher agent, analyst agent, and writer agent each with defined roles and goals.
- ▸You want a clean, readable YAML or Python DSL to define crews that non-engineers on your team can understand.
- ▸You're building pipelines that run sequentially through defined tasks with clear handoffs between agents.
- ▸You need a framework that integrates naturally with LangChain tools and the broader Python ecosystem.
- ▸You want hierarchical crews where a manager agent automatically delegates subtasks to the right specialist.
When to choose AutoGen
- ▸Your workflow involves agents that need to converse back and forth — debugging code, iterating on a document, or doing research through dialogue.
- ▸You need robust code execution agents with a sandbox — AutoGen's code interpreter integration is excellent for data analysis and software engineering tasks.
- ▸You're in an enterprise environment and want the backing, support, and roadmap confidence of a Microsoft-maintained project.
- ▸You want a visual interface: AutoGen Studio lets non-developers design and test agent workflows without writing code.
- ▸Your agents need to dynamically decide their own conversation patterns rather than follow a predefined task sequence.
Find CrewAI and AutoGen Agencies
When evaluating a CrewAI vs AutoGen AI agent agency, describe your workflow in one sentence and ask them to design the agent architecture out loud. A CrewAI answer will focus on agent roles and task sequencing; an AutoGen answer will focus on conversation termination logic and agent pairing. Neither is wrong — but the answer should match your use case's natural structure.
Which has more agencies?
In our directory, there are currently 28 CrewAI agencies and 16 AutoGen agencies. CrewAI leads the directory — reflecting its longer history and broader ecosystem adoption. However, AutoGen agency numbers are growing as the framework matures.
Bottom line
CrewAI wins on structured, predictable workflows with clear agent roles — it's easier to reason about what will happen. AutoGen excels when agents need to converse dynamically, especially for code-heavy or research tasks where back-and-forth iteration between agents produces better results. AutoGen's Microsoft backing also makes it a safer enterprise bet for long-term support.