llamaindex

Load documents with SimpleDirectoryReader, create a VectorStoreIndex from them, and call index.as_query_engine() to answer questions. The minimal pipeline takes about five lines of Python and can be persisted to disk for reuse.

LlamaIndex is purpose-built for RAG with 300+ data connectors and a simpler document Q&A API, while LangChain is more general-purpose and better suited for complex agent workflows. Choose LlamaIndex when retrieval over private data is the primary use case.

LlamaIndex integrates with Chroma for local storage, Pinecone for cloud deployments, and FAISS for fast in-memory search, among others. You configure them through a StorageContext passed when creating the index.

Use index.as_chat_engine() with chat_mode set to condense_plus_context, which condenses chat history and retrieves relevant context each turn. The chat engine maintains conversation state automatically and can be reset with chat_engine.reset().

Avoid LlamaIndex when building complex multi-step agents requiring general-purpose tooling, where LangChain fits better, or when you only need vector storage, where Chroma alone suffices. It is optimized for data-centric RAG rather than general LLM orchestration.