chroma Skill
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 pr Published by NousResearch in hermes-agent.
What is chroma Skill?
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 pr Published by NousResearch in hermes-agent. This profile combines repository metadata with install, compatibility, and usage signals so developers can quickly decide whether it fits their agent workflow before opening the source repository.
Automated repository signals based on public metadata such as recency, license, installation evidence, and adoption. These are not a security audit or endorsement.
Key capabilities
- Includes SKILL.md support
- Reusable instructions support
- Database workflows
- Deployment
- Research
- Database workflows use cases
- Deployment use cases
Technical details
- Install or run with Copy skill directory
When to use chroma Skill
- Use it for database workflows.
- Use it for deployment.
- Use it for research.
Built with
Editorial notes
Source
- Creator: NousResearch
- Repository: NousResearch/hermes-agent
- Skill file: optional-skills/mlops/chroma/SKILL.md
What it does
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 pr
Skill instructions
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.createcollection(name="mycollection") Add documents collection.add( docume
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Frequently asked questions
What is chroma?
chroma is a open-source AI agent skill with Copy skill directory. 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.
Who is chroma best for?
chroma is best for reusing agent instructions, scripts, and references, database workflows, deployment workflows, research workflows.
How do I install chroma?
Install or run chroma using Copy skill directory. Check chroma for the latest setup command.
Is chroma actively maintained?
chroma may need a closer maintenance check before production use.
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