chroma
Open-source embedding database for AI applications. Store embeddings and metadata, perform vector and full-text search, filter by metadata. Simple 4-function API. Scales from notebooks to production clusters. Use for semantic search, RAG applications, or document retrieval. Best for local development and open-source projects.
npx skills add Orchestra-Research/AI-Research-SKILLs --skill chroma --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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 ```bash # Python pip install chromadb # JavaScript/TypeScript npm install chromadb @chroma-core/default-embed ``` ### Basic usage (Python) ```python import chromadb # Create client client = chromadb.Client() # Create collection collection = client.create_collection(name="my_collection") # Add documents collection.add( documents=["This is document 1", "This is document 2"], metadatas=[{"source": "doc1"}, {"source": "doc2"}], ids=["id1", "id2
- When to use Chroma
- Quick start
- Installation
- Basic usage (Python)
- Core operations
- 1. Create collection
- 2. Add documents
- 3. Query (similarity search)
- 4. Get documents
- 5. Update documents
- 6. Delete documents
- Persistent storage
- Embedding functions
- Default (Sentence Transformers)
Python pip install chromadb JavaScript/TypeScript npm install chromadb @chroma-core/default-embed
What does the chroma skill do?
Open-source embedding database for AI applications. Store embeddings and metadata, perform vector and full-text search, filter by metadata. Simple 4-function API. Scales from notebooks to production clusters. Use for semantic search, RAG applications, or document retrieval. Best for local development and open-source projects.
How do I install it?
Run `npx skills add Orchestra-Research/AI-Research-SKILLs --skill chroma --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From Orchestra-Research/AI-Research-SKILLs, a repository with 11,391 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.