LlamaIndex
Python framework for building LLM apps that query your own documents and data.
- Stars
- 52.8k
- Forks
- 8.5k
- Licence
- MIT
- Language
- Python
- Last update
- 10 days ago
- Type
- Library
What it is
LlamaIndex is an open-source Python framework for connecting large language models to your own data, such as PDFs, documents, APIs and SQL databases. It provides connectors to load data, ways to structure it into indexes, and retrieval tools that feed relevant context to an LLM. The README also notes that the company's main focus has shifted toward its commercial LlamaParse document platform, with the open-source framework still available.
- Build a vector store index over a folder of documents and ask questions about it
- Connect a chosen LLM provider such as OpenAI or Ollama to your own data
- Combine core with integration packages for different embedding models or vector databases
- Build document agents with its Workflows abstraction
Great for
- Python developers building retrieval-augmented generation (RAG) apps over private documents
- Teams wanting a modular toolkit where they can swap LLM, embedding and vector store providers
- Developers building agent workflows that query their own data
- Beginners who want a short path to a working document question-answering prototype
Not ideal for
- Non-programmers, since it is a Python library that requires writing code
- Projects that need a fully managed hosted service, because the README points to LlamaParse for the hosted offering and the framework is a toolkit you run yourself
- Users who only need simple one-off document parsing, where a standalone parser may be simpler
How to get it
- 1
Install with a package manager
You'll need: Python
Install core and selected integrations
pip install llama-index-coreAdd an LLM integration
pip install llama-index-llms-openai - 2
Run it
- Install llama-index-core and an LLM integration package with pip
- Set your OPENAI_API_KEY environment variable, or use another provider's key
- Load documents with SimpleDirectoryReader from a folder of your files
- Build a VectorStoreIndex from the documents and query it
- 3
Get the most out of it
- Use the integrations page to pick embedding, LLM and vector store packages that match your stack
- Use the lower-level APIs to customize retrievers, query engines and reranking modules
- Follow the official documentation for current tutorials and how-to guides
- Install the starter llama-index package if you want core plus a default selection of integrations
Downloads
Strengths
- Large ecosystem of over 300 integration packages, per the README
- Modular design lets you customize or replace components like retrievers and query engines
- Offers both a high-level API for quick starts and lower-level APIs for advanced control
Trade-offs
- The README says the company's primary focus has shifted to LlamaParse, so the open-source framework is secondary to its commercial product
- The README is not updated as often as the documentation, so you should check the docs for current details
- Many separate integration packages means you must choose and install the ones you need
How it compares
| Project | Stars | Type | Level | Updated | Best for |
|---|---|---|---|---|---|
| llama_index (this) | 52.8k | Library | intermediate | 10 days ago | Python developers building retrieval-augmented generation (RAG) apps over private documents |
| langchain | 149k | Framework | intermediate | 4 days ago | Python developers building LLM-powered applications |
| langgraph | 44.3k | Library | intermediate | 4 days ago | Python developers building multi-step AI agents that must survive failures or long waits |
| PageIndex | 40k | Library | intermediate | 7 days ago | Developers building question-answering over long PDFs such as annual reports or manuals |
| openai-agents-python | 30.5k | Library | intermediate | 5 days ago | Python developers building multi-step AI workflows |
| DB-GPT | 20.2k | App | intermediate | 4 days ago | Analysts who want to ask questions of CSV or Excel files in plain language |
Stats synced from GitHub. The assessment above was drafted with AI from the project's README on 2026-10-29 and may miss recent changes — check the README before relying on a command. MIT licence: free to use, modify and distribute, including commercially, as long as you keep the copyright notice.