Agentic AI frameworks compared: LangChain, LangGraph, AutoGen

This article compares three major agentic AI frameworks to help developers choose the right tool for their project. The authors recommend starting with LangChain for most first builds due to its broad ecosystem and gentle learning curve, graduating to LangGraph when workflows require branching, loops, or parallel execution, and reserving AutoGen primarily for multi-agent research rather than production systems. The core thesis emphasizes that the framework chosen shapes debugging, scalability, observability, and how quickly a prototype becomes a production system more significantly than the underlying language model itself.