domains / families / agent-tooling / llamaindex

llamaindex

separate

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-centric LLM applications.

When to use

• 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
• **45,100+ GitHub stars**
• **23,000+ repositories** use LlamaIndex

When not to use

• General-purpose agent building
• Production search pipelines
• Lightweight semantic search
• Vector storage only

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