A lot of the agent questions on here come down to the same one: which framework should I start with, and how badly will I regret it in six months. This is the order I make that call in, and where the regret usually comes from.
Language Comes First
If your team writes Python, the real shortlist is LangGraph, CrewAI, AutoGen and Phidata. If it writes TypeScript, it is the Vercel AI SDK or Mastra. If you are already deep in one vendor's ecosystem, their SDK is a legitimate answer.
Adding a second runtime to your stack just for agents costs more in deployment, monitoring and on call pain than any framework feature gives back. This is the cheapest decision to get right and the most expensive one to undo.
Architecture Is The Second Filter
Every framework encodes an opinion, and you inherit it whether you like it or not.
LangGraph models an agent as an explicit state machine: nodes, edges, and state that flows through the graph, with checkpointing so a long run survives a process restart. CrewAI models it as a set of roles that collaborate, which fits research, write, review style work and feels forced when the work does not decompose that way. AutoGen models it as agents passing messages, which is strong when the answer improves by having two agents argue toward it.
Pick the opinion that matches the shape of your problem. If you find yourself fighting the abstraction in week one, that is the answer, not a skill issue. There is a framework by framework comparison here covering language support, architecture style, cost behavior and production readiness if you want the long version.
Production Is Where They Separate
Every framework works in a demo. Four things decide how it feels at a thousand runs a day:
Durable execution. Can a run survive a restart, or does a deploy kill everything in flight.
Tracing. When the output is wrong, can you follow the reasoning from input through every tool call, or are you guessing.
The error model. Timeouts, rate limits and malformed tool output are daily events, not edge cases. Retries, fallbacks and circuit breakers decide how many of those reach a user.
Cost. Every framework lets you make LLM calls. Very few help you make fewer. Multi model routing and token level cost tracking matter more than the integration count on the landing page.
Plan For Churn Instead Of Predicting The Winner
This space consolidates every few months. The protection is not picking the eventual winner, it is keeping the framework behind your own interface so a switch means replacing an adapter instead of rewriting the system. Your tools, your prompts and your evaluation set are the assets. The framework is just the runner.
What are you running in production, and what made you pick it over the obvious alternative?
