LlamaIndex is an open-source data framework for building language model applications, with a particular focus on connecting models to your own data. It specialises in the ingestion, indexing, and querying stages of retrieval-augmented generation, providing tools to load data from many sources, chunk and embed it, store it in a vector database, and query it intelligently.
Where some frameworks aim broadly at orchestrating agents and tools, LlamaIndex concentrates on the data side of the problem: getting your documents into a form a model can retrieve from effectively. It offers a wide range of data connectors, flexible indexing strategies, and advanced query techniques such as multi-step retrieval and result synthesis, making it a strong choice when the core challenge is high-quality retrieval over a large or varied corpus.
Like LangChain, it provides a standard interface to many vector databases and embedding models, so the underlying components can be swapped without rewriting application logic. Teams often choose LlamaIndex when retrieval quality over their own data is the priority, and the two frameworks are sometimes used together, with each handling the part it does best.