Chroma

If you're building RAG apps or anything that needs semantic search over documents, this gives you a local vector database with a clean Python API. Four core functions (add, query, get, delete), built-in embedding support for OpenAI and HuggingFace, and metadata filtering that actually works. It's legitimately popular at 24k+ GitHub stars and integrates cleanly with LangChain and LlamaIndex. The persistent client mode means your embeddings survive restarts, which is table stakes but worth noting. Best for prototyping and self-hosted setups. If you need managed infrastructure or are already at scale, look at Pinecone or Weaviate instead.

skills.sh installs: 1K
GitHub stars: 14.4K
last updated: Aug 21, 2026

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Install

$ npx -y skills add orchestra-research/ai-research-skills --skill chroma --agent claude-code

Installs into .claude/skills of the current project.

Files

Chroma - Open-Source Embedding Database

The AI-native database for building LLM applications with memory.

When to use Chroma

Use Chroma when:

Building RAG (retrieval-augmented generation) applications
Need local/self-hosted vector database
Want open-source solution (Apache 2.0)
Prototyping in notebooks
Semantic search over documents
Storing embeddings with metadata

Metrics:

24,300+ GitHub stars
1,900+ forks
v1.3.3 (stable, weekly releases)
Apache 2.0 license

Use alternatives instead:

Pinecone: Managed cloud, auto-scaling
FAISS: Pure similarity search, no metadata
Weaviate: Production ML-native database
Qdrant: High performance, Rust-based

Quick start

Installation

# Python
pip install chromadb

# JavaScript/TypeScript
npm install chromadb @chroma-core/default-embed

Basic usage (Python)

import chromadb

# Create client
client = chromadb.Client()

# Create collection
collection = client.create_collection(name="my_collection")

SKILL.md on GitHub

Details

Repository: github.com/orchestra-research/ai-research-skills
Categories: Backend & APIs, AI & Agent Building, Data Science & ML, Databases
First seen: Jun 3, 2026