What it is
LlamaIndex is an open-source Python framework for building retrieval-augmented generation (RAG) pipelines and agentic applications on top of large language models. It handles the steps between raw data and a model that can query it: loading documents, structuring them into indices, and retrieving relevant chunks at query time.
Interface and dependencies
Install with pip install llama-index, which requires Python 3.10 or higher (per PyPI). The starter package bundles the core with a default set of integrations; llama-index-core gives a bare framework that developers extend with any of the 300+ separately installable integration packages for LLM providers (OpenAI, Anthropic, Cohere, Ollama), vector stores (Pinecone, Weaviate, Chroma, Milvus, Postgres) and embedding models, as listed in the project's own documentation.
What you build with it
A typical use is connecting an LLM to a set of PDFs, APIs or SQL tables: load the data with a connector, index it, then query or chat against it with retrieval back to source documents. The framework also supports event-driven, multi-step workflows for agents that call tools and branch on results.
Limits
The framework itself is free and MIT-licensed. LlamaIndex, Inc. separately sells LlamaCloud/LlamaParse, a hosted document-parsing product, with a free tier plus paid plans starting once the free credit allowance is used up (per llamaindex.ai/pricing). That commercial product is distinct from the open-source library covered here.
