LangChain and LlamaIndex are the two frameworks everyone considers when building AI applications. After building production systems with both, I can tell you they're optimized for different use cases despite overlapping features.
LangChain excels at complex multi-step workflows. Its agent system is powerful for building AI that can use tools, make decisions, and chain operations together. If you're building something that needs to do things—browse the web, call APIs, execute code—LangChain's abstractions help.
LlamaIndex is purpose-built for RAG applications. Document loading, chunking, embedding, retrieval, and query engines are all first-class citizens with sensible defaults. If your primary use case is "chat with data," LlamaIndex gets you there faster with less code.
My rule: start with LlamaIndex for data-centric apps, LangChain for action-centric apps. Both frameworks are evolving rapidly; check documentation rather than outdated tutorials.
Jake Morrison
Contributing writer at MoltBotSupport, covering AI productivity, automation, and the future of work.