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tool

Chroma

Stores embeddings, documents, and metadata behind a developer-friendly retrieval API for AI applications.

Install in ToolsView on GitHub ↗

What it provides

EmbeddingsRetrievalDeveloper API

Connect an HTTPS data-service API. Storage remains external; use a tenant-scoped token and namespace on the target service.

Connection requirements

A Chroma server endpoint

Protocol
REST / OpenAPI
Endpoint
https://chroma.example.com
Authentication
Choose the mode required by your service
Open official setup guide ↗

Where to add it

Open Dashboard Tools, select a running OpenClaw or Hermes instance, then use the card’s Install or Connect action. External services may require an HTTPS endpoint and credentials; the dashboard shows those fields before anything is saved.

Related Vector & data stores

QdrantA high-performance vector search engine with filtering, payloads, distributed deployment, and client libraries.MilvusScalable vector similarity search for large embedding collections, hybrid retrieval, and production RAG.WeaviateCombines vector, keyword, and structured filtering over stored objects in a cloud-native database.