---
id: llama-index
version: "0.14.23"
license: MIT
license_treatment: permissive
maintenance: active
---

# llama-index — Interface between LLMs and your data

License: permissive · Maintenance: active · Downloads: 7.4M/mo

## What it is and what it does

LlamaIndex is a data framework designed to bridge the gap between large language models and private or custom data sources. It provides data connectors to ingest various formats (APIs, PDFs, SQL databases, etc.), structures that data into indices and graphs, and retrieval interfaces that augment LLM prompts with relevant context. The framework supports both high-level APIs for quick prototyping and lower-level customization for advanced use cases.

The package ships with integrations for llama-index-embeddings-openai and llama-index-llms-openai out of the box, and can be extended with additional integration packages for different LLM providers, embedding models, and vector stores. It handles the full pipeline from document ingestion through indexing to query execution, with built-in support for persistence and reloading from disk.

Use it for:

- Build a chatbot that answers questions about your company's internal documentation or knowledge base.
- Create a semantic search system over a large collection of PDFs, research papers, or legal documents.
- Develop an agentic application that retrieves and reasons over structured and unstructured data.
- Index and query data from multiple sources (APIs, databases, files) with a unified interface.
- Prototype a retrieval-augmented generation (RAG) pipeline without managing vector databases directly.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

LlamaIndex is a data framework that connects large language models to your own data sources, enabling retrieval-augmented generation (RAG) and agentic applications through data connectors, indexing, and query interfaces.

Yes. LlamaIndex is actively maintained with low install friction, permissive MIT licensing, and no known vulnerabilities. It is well-suited for developers building LLM applications that need to augment models with private data. Start here if you want a batteries-included RAG framework; use llama-index-core if you prefer to assemble integrations à la carte.

## Install

pip install llama-index
uv add llama-index
poetry add llama-index

## Installing llama-index

Before you install: Low install friction with a pure-Python wheel. Actively maintained with recent releases; last commit 2026-08-14 indicates a mature, well-supported project.

License in practice: MIT license permits unrestricted use, modification, and distribution in commercial and private projects with minimal obligations.

Quickstart:

pip install llama-index

from llama_index.core import VectorStoreIndex, SimpleDirectoryReader

documents = SimpleDirectoryReader("YOUR_DATA_DIRECTORY").load_data()
index = VectorStoreIndex.from_documents(documents)
query_engine = index.as_query_engine()
query_engine.query("YOUR_QUESTION")

Requires Python 3.10 or later. OpenAI API key or alternative LLM provider needed for actual queries; default uses in-memory storage.

Verify before relying:

- Performance characteristics and scalability limits for large document collections.
- Specific vector store backends supported beyond basic examples.
- Latency and throughput benchmarks for typical RAG query patterns.
- Whether over 300 integration packages claim is current and verifiable.

## Package facts

- License: MIT (permissive)
- Python support: supports_current
- Install friction: low
- Maintenance: active
- Downloads: 7.4M/month (top 5,000 on PyPI)
- Known vulnerabilities: none known

## Tags

RAG framework for LLMs, LLM data augmentation, document indexing and retrieval, agentic application framework, LLM integration with private data, vector store indexing, semantic search over documents, rag-framework, llm-integration, data-indexing

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