AutoGen vs CrewAI: Which Agent Framework to Use?

Paradigm

AutoGen treats agents as chat participants: ConversableAgent instances exchange messages via initiate_chat(), with GroupChat and a GroupChatManager picking the next speaker. CrewAI treats agents as roles on a team: Agent(role, goal, backstory) instances execute Task objects under a Crew(process=sequential|hierarchical). AutoGen's primitive is the conversation; CrewAI's primitive is the work item.

Ecosystem

AutoGen is a Microsoft Research project (CC-BY-4.0, 56k+ stars) with a v0.4 rewrite focused on scale, plus a code-execution sandbox baked in. CrewAI is MIT-licensed, founder-led by João Moura with $18M behind an enterprise platform, and ships first-class MCP integration plus three memory tiers (ShortTermMemory, LongTermMemory, EntityMemory) out of the box. Both list MCP support; CrewAI's docs are more opinionated about it.

Use case

Reach for AutoGen when the interaction pattern is the hard part — agents debating, critiquing, or running nested chats via register_nested_chats(), with termination governed by is_termination_msg callbacks. Reach for CrewAI when the role separation is the hard part — a researcher → writer → editor pipeline where you want named agents, typed task outputs, and process=hierarchical doing the routing. AutoGen rewards dynamic, unpredictable speaker selection; CrewAI rewards predictable pipelines where roles and deliverables are known up front.

Pick AutoGen if your project lives or dies on agents actually talking to each other rather than running in a fixed pipeline.

• Dynamic speaker selection: GroupChatManager with LLM-based routing handles workflows where you don't know in advance who should speak next — debate, critique, planner/executor handoffs.
• Nested sub-conversations: register_nested_chats() lets an agent pause the main thread, run a focused sub-chat, and inject the result back. Building this cleanly yourself is real work.
• Code-writing agents: AutoGen's built-in code execution sandbox is the path of least resistance when agents need to write, run, and iterate on code as part of the conversation.

Pick CrewAI if your project lives or dies on clear role separation across a known sequence of tasks.

• Named roles drive prompts: role, goal, and backstory give you a clean vocabulary for shaping per-agent system prompts — useful for researcher → writer → editor style pipelines.
• Typed task outputs: Task objects with expected outputs and Crew(process=sequential|hierarchical) give you predictable orchestration without writing the routing loop yourself.
• Memory tiers built in: ShortTermMemory, LongTermMemory, and EntityMemory are wired up by default, plus delegation guardrails that keep agents from calling outside the crew.

Both frameworks bring a real dependency footprint and a vocabulary you have to learn before you can ship: ConversableAgent, GroupChatManager, is_termination_msg on one side; Agent, Task, Crew, process on the other. Upgrades aren't free either — AutoGen's v0.4 rewrite reshaped the API surface, and CrewAI's enterprise pivot has moved abstractions around.

Both also hide the agent loop. When a tool call misfires or a speaker selection goes sideways, you debug through the framework's abstractions instead of the raw messages array — fine in steady state, painful when something is wrong in production.