Description
LlamaIndex is an open source data framework for building applications on top of large language models. Its purpose is to solve a specific problem: an LLM is trained on public data, so connecting it to private or proprietary information requires a separate layer for ingesting, structuring, and retrieving that data. LlamaIndex provides that layer through data connectors for sources such as APIs, PDFs, other documents, and SQL databases, ways to structure the ingested data into indices or graphs, and a retrieval and query interface that feeds relevant context into an LLM prompt.
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There are two ways to start building with it in Python. The starter package, llama-index, bundles the core library with a selection of integrations for a quick setup. A customized build instead installs llama-index-core plus specific integration packages, of which there are more than 300 covering different LLM, embedding, and vector store providers. A high-level API lets a beginner ingest and query data in a handful of lines of code, while lower-level APIs expose the data connectors, indices, retrievers, query engines, and reranking modules for advanced customization. LlamaIndex also integrates with outside frameworks such as LangChain, Flask, or Docker rather than requiring everything to run through it alone.
The company behind LlamaIndex has shifted its primary focus since the framework's 2023 launch. Its main current product is LlamaParse, a separate enterprise platform for agentic document parsing, OCR, and structured extraction, alongside a free, lightweight parser called LiteParse and open benchmarking efforts (ParseBench and ExtractBench) for parsing and extraction quality. LlamaParse can be used together with the LlamaIndex framework or on its own. The open source framework itself remains available and maintained as a toolkit for developers building retrieval and agent applications.
The repository is released under the MIT license and has drawn more than 52,000 GitHub stars and over 8,000 forks, making it one of the more widely used open source tools for connecting LLMs to external data.
- CategoryMachine Learning & Data Science
- Pricing ModelOpen source
- DeploymentCloud
- PlatformsWeb
LlamaIndex's Core Features
Data connectors for ingesting APIs, PDFs, other documents, and SQL databases
Indices and graph structures for organizing ingested data for LLM use
A retrieval and query interface that feeds relevant context into an LLM prompt
A high-level API for a basic ingest-and-query pipeline in a few lines of code
Lower-level APIs for customizing data connectors, indices, retrievers, query engines, and reranking modules
More than 300 integration packages for different LLM, embedding, and vector store providers
Interoperates with outside frameworks such as LangChain, Flask, and Docker
Optional pairing with LlamaParse, the company's separate agentic document-parsing and extraction platform
MIT licensed, with more than 52,000 GitHub stars and over 8,000 forks
Getting Started with LlamaIndex
Install it: run pip install llama-index for the starter bundle, or llama-index-core plus chosen integrations for a custom setup.
Connect your data: use a data connector to load documents, APIs, or a database.
Build an index: structure the ingested data into an index or graph.
Query it: send a prompt through the retrieval interface to get an answer grounded in that data.
LlamaIndex's Use Cases
- Retrieval-augmented generation
- Custom data pipelines
- Agent applications
- Document parsing and extraction
