Autogen vs LangChain

Autogen vs LangChain pits two open-source stacks for agentic AI against each other. Autogen, by Microsoft, focuses on multi-agent orchestration: you declare roles (Planner, Coder, Critic) in YAML, wire them to tools, and let them negotiate via LLM messages until a task closes. It shines in code generation, data analysis notebooks, and self-healing loops, leveraging built-in evaluators and cost controllers. LangChain centers on composability: loaders, embeddings, vector stores, chains, and agents share unified APIs, letting you swap GPT-4 for Llama 3 or Chroma for Qdrant with one line of Python. LangChain excels at Retrieval-Augmented Generation (RAG), prompt engineering, and single-agent tool calling, backed by a vast plugin ecosystem and granular callbacks for tracing. Autogen offers higher-level automation—fewer lines to spawn collaborating agents—but is less flexible for custom data ingestion. LangChain provides a Lego-like toolkit that scales fromtoy scripts to microservice clusters yet requires more assembly. Teams choose Autogen for rapid, multi-agent prototypes; LangChain for fine-tuned, data-grounded applications—or combine them by calling LangChain chains inside Autogen workflows.