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

# llama-index-core — Interface between LLMs and your data

License: permissive · Maintenance: active · Downloads: 16.4M/mo

## What it is and what it does

LlamaIndex Core is the foundational library for building LLM-powered applications, particularly those using retrieval-augmented generation (RAG). It provides core abstractions—interfaces for LLMs, vector stores, embeddings, and storage backends—that let you connect language models to your data. The library is designed to be extended through subclasses, so you build applications by combining LlamaIndex Core with integrations for specific providers that your application needs.

The package handles the plumbing between your data and language models: document loading, chunking, embedding, indexing, and retrieval. It abstracts away provider-specific details so you can swap vector stores, embedding models, or LLM backends without rewriting application logic. With 29 runtime dependencies covering async I/O, data serialization, and common utilities, it trades some installation weight for broad compatibility and reduced boilerplate in downstream code.

Use it for:

- Build a document Q&A system that retrieves relevant passages from your knowledge base before sending queries to an LLM.
- Create a multi-step RAG pipeline that chains document retrieval, reranking, and LLM synthesis for complex queries.
- Prototype LLM applications quickly by swapping vector stores or embedding providers without changing core application code.
- Index and query structured data alongside unstructured documents in a unified interface.
- Develop agent systems where the LLM can retrieve context from multiple data sources before deciding on actions.

## Worth the install?

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

LlamaIndex Core provides foundational abstractions and classes for building LLM applications, particularly retrieval-augmented generation (RAG) systems, with extensible interfaces for LLMs, vector stores, embeddings, and storage.

Yes. LlamaIndex Core is actively maintained, permissively licensed, and widely adopted (top 5000 PyPI packages by downloads). It has no known vulnerabilities and provides genuine abstraction value for RAG and LLM application development. The 29 dependencies are a real cost, but they enable broad integration support. Install it if you're building LLM applications; skip it only if you need a minimal LLM wrapper without data integration.

## Install

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

## Installing llama-index-core

Before you install: Low friction installation with a pure Python wheel. Active maintenance with recent commits and a large repository footprint (51641 stars). Requires Python 3.10 or later and brings in 29 runtime dependencies including common data-handling and async libraries.

License in practice: MIT license permits commercial and private use with minimal restrictions, making it suitable for most production and research contexts.

Quickstart:

pip install llama-index-core

from llama_index_core import VectorStoreIndex

index = VectorStoreIndex.from_documents(documents)

Requires Python 3.10 or later. Most use cases will also require external LLM and embedding provider credentials or local model setup.

Verify before relying:

- Whether the 29 runtime dependencies are all strictly necessary for basic usage or if many are optional for specific integrations.
- Performance characteristics and memory footprint when indexing large document collections.
- Specific version compatibility constraints with popular LLM providers and vector databases.
- Whether SimpleDirectoryReader is available in core or requires a separate integration package.

## Package facts

- License: MIT (permissive)
- Python support: supports_current
- Install friction: low
- Maintenance: active
- Downloads: 16.4M/month (top 5,000 on PyPI)
- Known vulnerabilities: none known

## Tags

RAG framework python, LLM application building blocks, vector store abstraction, retrieval augmented generation, LLM orchestration library, embedding and storage interface, LLM data integration, rag, llm-framework, data-integration

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