The rule we apply: Use neither at scale; pick LangGraph. If forced to choose, CrewAI for prototyping speed, AutoGen for research-shaped multi-agent problems where the conversation pattern is the natural fit.
Where AutoGen wins
- Expressive multi-agent shapes. Conversation models problems that are awkward in role or graph forms.
- Code execution agents are competent and configurable.
- Microsoft research provenance for academic-shaped problems.
Where CrewAI wins
- Faster prototyping. Roles plus tasks plus crew is a smaller mental model than conversational coordination.
- Cheaper per task on workloads where the role abstraction is sufficient.
- Friendlier on-ramp for engineers new to multi-agent.
Cost comparison
AutoGen's conversational shape often produces 20+ LLM calls per task; CrewAI's coordination overhead grows past 5 agents. Both have economic ceilings. LangGraph beats both at scale because the graph is explicit and the call count is bounded by the graph shape.
Three scenarios, three decisions
- Research a multi-agent debate prototype: AutoGen.
- Ship a 3-agent pipeline this week: CrewAI.
- Production at 10+ agents: Neither; LangGraph.
Read next
Oliver runs Digital Signet, a research and product studio that operates ~500 production sites with AI agents as the engineering layer. The Digital Signet portfolio is built using a continuous AI-agent build pipeline, one of the largest agent-operated publishing operations on the open web. The handbook draws directly from those deployments: real cost data, real failure modes, real recovery patterns.