llama-index-vector-stores-chroma
llama-index vector_stores chroma integration
Decision gist · record as of 2026-08-14
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.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later (supports current versions, <4.0)
- 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 · maintenance · safety
MIT (permissive) — MIT license permits unrestricted use, modification, and distribution in both open and proprietary projects with minimal obligations.
last release 2025-12-30 (227 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 252,014 downloads/mo, #8,562 on PyPI
Alternatives
Verify before relying
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)- 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
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 on this page. Verify before relying on it.
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
llama-index-vector-stores-chroma on PyPI
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.
Requires Python 3.10 or later (supports current versions, <4.0)
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)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 the current Python release <4.0,>=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 2 packageschromadbllama-index-core |
| Maintenance | Aging 227 days since the last release |
| First released | |
| Downloads | 252,014 / month, #8,562 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
Evidence: llama_index_vector_stores_chroma-0.5.5-py3-none-any.whl
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See also llama-index-utils-workflow · llama-index-vector-stores-pinecone · llama-index-vector-stores-qdrant · llama-index-retrievers-bm25 · llama-index-vector-stores-faiss · llama-index-vector-stores-milvus · llama-index-embeddings-langchain · llama-index-vector-stores-redis · llama-index-vector-stores-postgres · llama-index-vector-stores-lancedb