The rule we apply: We use LangGraph in production. We tried CrewAI for two months. We moved off CrewAI because, around five concurrent agents, the role-based coordination overhead becomes the bottleneck. Pick LangGraph if you expect to scale; pick CrewAI if you expect to ship fast.
Where LangGraph wins
- Linear scaling. Concurrency adds latency, not coordination overhead, up to LLM-provider rate limits.
- State checkpointing. Built-in, persistent, debuggable.
- Type-driven graph. Verifiable; debugging is the same activity as reading the code.
Where CrewAI wins
- Prototyping speed. The role-based abstraction matches how engineers think about teams; the on-ramp is fast.
- Documentation approachability. Easier first day than LangGraph.
- Small-crew production. 2-3 agent setups are stable.
Cost comparison
Both open source; cost is model passthrough. CrewAI's coordination overhead at scale shows up as additional model calls, which shows up as cost. The cost difference is small at small scale and material at large scale.
Three scenarios, three decisions
- Prototype a 3-agent research workflow this week: CrewAI.
- Run a 12-agent build pipeline in production: LangGraph.
- Migrate a CrewAI prototype that hit the 5-agent ceiling: 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.