Framework for connecting LLMs to your own documents and data
LlamaIndex is an open-source framework for building retrieval and agent applications on top of language models. It handles the unglamorous parts: loading documents from many sources, splitting them into chunks, creating embeddings, storing them in an index, and retrieving the right context for a query. On top of that sit query engines, chat engines, tool-calling agents and workflow orchestration. The differentiator is the breadth of connectors and index types, which lets a team assemble a working retrieval pipeline in days rather than wiring every piece by hand.
Last updated: 2026-09-20. This site only provides an index; for exact features, pricing, and licensing, see the official website.
rag applications, document question answering, data agents, retrieval evaluation
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It handles the data plumbing behind retrieval applications: loading documents, chunking them, creating embeddings, storing vectors and fetching the right context. That is the work most teams would otherwise write themselves.
Not necessarily to start. LlamaIndex ships with simple in-memory stores for prototyping. For anything at scale you will want a real vector store, and the framework supports most of the popular ones.
No. Retrieval is the core, but the project also covers chat engines, tool-calling agents, multi-step workflows and evaluation, so it can support an agent application as well as a search one.
Developer console and API keys for the Claude model family
Node-based local interface for running image and video diffusion models
Browser playground for prompting and prototyping with Gemini models
Hosted Jupyter notebooks with optional free GPU and TPU runtimes