philippgille/chromem-go

Health v2 · maintenance only

Adoption not part of the score

1048 stars · 75 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

chromem-go is an embeddable vector database for Go with a Chroma-like interface and zero third-party dependencies. It runs in-memory with optional persistence, enabling RAG and embeddings-based features without running a separate database server.

Use cases

• add rag to my go app without running a separate vector database
• store and search document embeddings in-process
• build a question answering system over private documents
• implement semantic text or code search
• build a recommendation system with embeddings
• classify or cluster documents by similarity

When to choose

• you want an embedded vector store like SQLite instead of a standalone database
• your dataset is small to medium (up to ~100k documents) and query latency matters
• you want zero third-party dependencies in your Go project
• you want a Chroma-like API in pure Go

When to avoid

• you need to scale to millions of documents or distributed deployment
• you need advanced database features like filtering, sharding, or replication
• you need a stable API before v1.0.0 since breaking changes may occur
• you need a client library to connect to an actual Chroma server

Facets

library · maturity experimental

vector-database search-engine rag databases large-language-models machine-learning go cross-platform embeddings vector-search in-memory chroma-compatible embeddable cosine-similarity nearest-neighbor persistence search retrieval-augmented-generation

Member repositories

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markdown · JSON · MCP: product_card(name="philippgille/chromem-go")