Agent Framework Tradeoffs — LangGraph vs CrewAI vs AutoGen vs Agno
Every framework sells the same demo (research agent builds a report) and hides the same bug (state schema fights with the orchestration layer). Pick the framework whose abstractions match the shape of your problem; everything else is glue you write twice.
You have a task that needs more than one LLM call. Maybe it is a research workflow (plan, search, summarize, cite). Maybe it is a code-review pipeline (parse diff, critique, patch, validate). Maybe it is a multi-turn assistant that books flights, writes emails, and files expense reports. You pick a framework.
Three days later, you discover the framework's abstractions leak. CrewAI gives you roles but fights you when the "researcher" needs to hand a structured plan to the "writer." AutoGen gives you chat between agents but has no first-class state so your checkpoint is a pickle of a conversation log. LangGraph gives you a state graph but forces you to name every transition before you know what the agent will do. Agno gives you a single-agent abstraction that screams when you try to fan out to three concurrent workers.
The fix is not "pick the best framework." It is to match the framework's core abstraction to the shape of your problem. This lesson draws that map.
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