LlamaIndex

About

LlamaIndex (formerly GPT Index) is a data framework specifically designed for building LLM applications that connect to, index, and retrieve from your custom data sources. While LangChain provides a general framework for LLM application building, LlamaIndex specializes in the data ingestion and retrieval side — making it the best-in-class tool for retrieval-augmented generation (RAG) systems. It simplifies the complex pipeline of connecting LLMs to your private data: ingesting documents (PDFs, websites, databases, APIs, Notion, Slack), splitting them into optimal chunks, creating searchable embeddings, storing them in vector databases, and retrieving the most relevant context when a user asks a question. LlamaIndex excels at advanced RAG patterns: recursive retrieval (break down complex queries into sub-questions), agent-based retrieval (dynamically decide which data sources to query), metadata filtering (search within date ranges or categories), and multi-modal RAG (retrieve both text and images). The framework supports 40+ vector store integrations (Pinecone, Chroma, Qdrant, Weaviate), 10+ LLM providers, and multiple embedding models. For developers building document Q&A systems, customer support bots, research assistants, or any application where LLM accuracy depends on retrieving the right information from a knowledge base, LlamaIndex provides the most focused and comprehensive toolkit. While there is overlap with LangChain, many production systems use both — LangChain for overall orchestration and LlamaIndex for data ingestion and retrieval. LlamaIndex has a steeper learning curve for simple use cases but becomes indispensable as data complexity grows.

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