Honest comparisons of the frameworks that actually ship
The agent framework ecosystem moves fast, and the differences between tools matter more than marketing pages suggest. This category gives you honest, feature-by-feature breakdowns: which frameworks handle stateful multi-step workflows well, which ones are easier to debug, where each one falls apart under production load, and when a lighter-weight tool beats the full-featured alternatives. We update these comparisons as frameworks evolve so you are not reading a review written against a version from six months ago.
LangGraph wins on control and debuggability. CrewAI wins on team abstractions. AutoGen wins on conversational multi-agent patterns. No single framework is best — the right choice depends on your task structure, team size, and tolerance for complexity.
LangGraph makes agents explicit: nodes are functions, edges are routing logic, and state is a typed dict that persists across every step. In 30 minutes you can build a stateful research agent that searches the web and synthesizes a summary.
CrewAI wins when your multi-agent workflow maps naturally onto human team roles. AutoGen wins when agents need to deliberate, debate, and build on each other's work conversationally. Both are mature, well-supported, and genuinely different in how they model coordination.