---
id: llama-index-vector-stores-chroma
version: "0.5.5"
license: MIT
license_treatment: permissive
maintenance: aging
---

# llama-index-vector-stores-chroma — llama-index vector_stores chroma integration

License: permissive · Maintenance: aging · Downloads: 252.0K/mo

## What it is and what it does

This package provides a LlamaIndex integration layer for Chroma, a vector database designed for storing and querying embeddings. It acts as a bridge between LlamaIndex's retrieval-augmented generation (RAG) framework and Chroma's vector storage backend, allowing developers to persist embeddings and perform semantic similarity searches within LlamaIndex workflows.

The integration depends on chromadb for the actual vector storage operations and llama-index-core for the framework integration. It is positioned as a specialized connector rather than a standalone tool—you install it when you've already chosen LlamaIndex as your RAG framework and Chroma as your vector store, and you need them to work together seamlessly.

Use it for:

- Build RAG pipelines with LlamaIndex that persist embeddings in Chroma for retrieval across sessions
- Implement semantic search over document collections using LlamaIndex queries backed by Chroma storage
- Integrate Chroma as a vector store option in LlamaIndex applications without writing custom adapter code
- Store and query embeddings from multiple data sources in a unified vector database within LlamaIndex workflows

## Worth the install?

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

Integrates Chroma vector database with LlamaIndex for storing and retrieving embeddings in RAG and semantic search applications.

Yes, if you are already using LlamaIndex and have chosen Chroma as your vector store. The low install friction, permissive MIT license, and lack of known vulnerabilities make it a straightforward choice. The aging maintenance status warrants checking that your versions of chromadb and llama-index-core remain compatible, but the package itself carries no security risk.

## Install

pip install llama-index-vector-stores-chroma
uv add llama-index-vector-stores-chroma
poetry add llama-index-vector-stores-chroma

## Installing llama-index-vector-stores-chroma

Before you install: Low install friction with only two runtime dependencies (chromadb and llama-index-core). Package is aging at 227 days since last release, which may indicate stable maintenance or reduced active development.

License in practice: MIT license permits unrestricted use, modification, and distribution in both open and proprietary projects with minimal obligations.

Quickstart:

pip install llama-index-vector-stores-chroma

from llama_index.vector_stores.chroma import ChromaVectorStore
from chromadb import Client

client = Client()
vector_store = ChromaVectorStore(chroma_client=client)

Requires Python 3.10 or later (supports current versions, <4.0)

Verify before relying:

- Whether the package receives active maintenance or security updates despite aging status
- Compatibility guarantees with recent versions of chromadb and llama-index-core
- Performance characteristics or limitations when handling large-scale embeddings

## Package facts

- License: MIT (permissive)
- Python support: supports_current
- Install friction: low
- Maintenance: aging
- Downloads: 252.0K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

chroma vector store integration, llama index chroma connector, embedding storage and retrieval, semantic search with chroma, rag vector database adapter, chroma llama-index plugin, rag, vector-database, embeddings

[View on SkillFed](https://skillfed.io/packages/llama-index-vector-stores-chroma) · [View on PyPI](https://pypi.org/project/llama-index-vector-stores-chroma/)