Data framework for building LLM applications with RAG. Specializes in document ingestion (300+ connectors), indexing, and querying. Features vector indices, query engines, agents, and multi-modal support. Use for document Q&A, chatbots, knowledge retrieval, or building RAG pipelines. Best for data-c

LlamaIndex Data Framework for LLM Applications The leading framework for connecting LLMs with your data. When to use LlamaIndex Use LlamaIndex when: Building RAG (retrieval augmented generation) applications Need document question answering over private data Ingesting data from multiple sources (300+ connectors) Creating knowledge bases for LLMs Building chatbots with enterprise data Need structured data extraction from documents Metrics : 45,100+ GitHub stars 23,000+ repositories use LlamaIndex 300+ data connectors (LlamaHub) 1,715+ contributors v0.14.7 (stable) Use alternatives instead : LangChain : More general purpose, better for agents Haystack : Production search pipelines txtai : Lightweight semantic search Chroma : Just need vector storage Quick start Installation 5 line RAG example Core concepts 1. Data connectors Load documents 2. Indices Structure data 3. Query engines Ask questions 4. Retrievers Find relevant chunks Agents with tools Basic agent RAG agent (document search + tools) Advanced RAG patterns Chat engine (conversational) Metadata filtering Structured output Data ingestion patterns Multiple file types Web scraping Database API endpoints Vector store integrations Chroma (local) Pinecone (cloud) FAISS (fast) Customization Custom LLM Custom embeddings Custom prompt templates Multi modal RAG Image + text Evaluation Response quality Best practices 1. Use vector indices for most cases Best performance 2. Save indices to disk Avoid re indexing 3. Chunk documents properly 512 1024 tokens optimal 4. Add metadata Enables filtering and tracking 5. Use streaming Better UX for long responses 6. Enable verbose during dev See retrieval process 7. Evaluate responses Check relevance and faithfulness 8. Use chat engine for conversations Built in memory 9. Persist storage Don't lose your index 10. Monitor costs Track embedding and LLM usage Common patterns Document Q&A system Chatbot with memory Performance benchmarks Operation Latency Notes Index 100 docs ~10 30s One time, can persist Query (vector) ~0.5 2s Retrieval + LLM Streaming query ~0.5s first token Better UX Agent with tools ~3 8s Multiple tool calls LlamaIndex vs LangChain Feature LlamaIndex LangChain Best for RAG, document Q&A Agents, general LLM apps Data connectors 300+ (LlamaHub) 100+ RAG focus Core feature One of many Learning curve Easier for RAG Steeper Customization High Very high Documentation Excellent Good Use LlamaIndex when: Your primary use case is RAG Need many data connectors Want simpler API for document Q&A Building knowledge retrieval system Use LangChain when: Building complex agents Need more general purpose tools Want more flexibility Complex multi step workflows References [Query Engines Guide](references/query engines.md) Query modes, customization, streaming [Agents Guide](references/agents.md) Tool creation, RAG agents, multi step reasoning [Data Connectors Guide](references/data connectors.md) 300+ connectors, custom loaders Resources GitHub : https://github.com/run llama/llama index ⭐ 45,100+ Docs : https://developers.llamaindex.ai/python/framework/ LlamaHub : https://llamahub.ai (data connectors) LlamaCloud : https://cloud.llamaindex.ai (enterprise) Discord : https://discord.gg/dGcwcsnxhU Version : 0.14.7+ License : MIT