LangChain vs AutoGen

LangChain vs AutoGen compares two open-source stacks for building agentic large-language-model (LLM) applications. LangChain is a modular toolkit—loaders, embeddings, vector stores, chains, agents, memory—designed for fine-grained control and easy model or database swaps. It excels at Retrieval-Augmented Generation (RAG), tool-calling agents, and production observability with callbacks that track tokens, latency, and cost. AutoGen, created by Microsoft, focuses on orchestration of multiple collaborating agents. You define roles (Planner, Coder, Critic) in YAML or Python, wire them to tools, and let agents negotiate via structured messages until a task completes. AutoGen ships evaluators, cost controllers, and self-healing loops ideal for iterative code generation and data analysis notebooks. LangChain shines when you need custom data ingestion, hybrid search, or a single agent with rich memory; AutoGen wins for quick multi-agent workflows and high-level automation. Many teams combine them—AutoGen handles role coordination, while LangChain supplies RAG pipelines and tool wrappers—leveraging the strengths of both ecosystems.