If you've tried building an AI agent in the last six months, you've hit the same wall: there are half a dozen frameworks, each with a different philosophy, a different API surface, and a different definition of what an "agent" even is. I spent a weekend writing the same simple agent — "read a GitHub issue, classify it as bug/feature/question, and post a comment" — in six different frameworks. This is what I found. TL;DR Framework Lines of Code* Native Tool System Multi-Agent Learning Curve Best For LangChain / LangGraph ~85 Rich (500+ integrations) Yes (LangGraph) Steep Production pipelines with complex RAG/tool chains CrewAI ~60 Built-in + custom tools Yes (role-based) Moderate Multi-agent roleplay workflows AutoGen (Microsoft) ~55 Custom function tools Yes (conversation-based) Moderate Research experiments, agent-to-agent conversations OpenAI Agents SDK ~40 Built-in (function calling) Yes (handoffs) Low Quick prototyping, simple single-agent tasks Pydantic AI ~45 Structured via Pydantic models Limited Low…