#37 · Primary category: Vector Databases
chromem-go
Embeddable vector database for Go with Chroma-like interface and zero third-party dependencies. In-memory with optional persistence.
Project last updated:05/17/26
GitHub Stars
1.1K
Forks
75
Contributors
9
License
MPL-2.0
Why we included this project
Go teams that want retrieval-augmented generation, semantic search, or similar embedding-based features in their app, without standing up a separate database service, are the natural audience here. The library runs in-process the way SQLite does for relational data, and it has no third-party dependencies at all. If you already know Chroma, the API will look familiar, though it also adds more idiomatic Go methods. It supports several embedding providers, metadata filtering, and optional disk persistence, and within its scope the performance is strong: roughly 40 milliseconds to query 100,000 documents on a mid-range 2020 laptop. It is still in beta, with breaking changes possible before v1.0.0, so expect a little maintenance, but for single-process Go services that need lightweight semantic recall it avoids the operational overhead of a clustered deployment.
Articles for this project
No articles for this project yet.
To suggest a topic or contribute an article, contact us.
Related projects in this category
supabase
The Postgres development platform. Supabase gives you a dedicated Postgres database to build your web, mobile, and AI applications.
tidb
TiDB is built for agentic workloads that grow unpredictably, with ACID guarantees and native support for transactions, analytics, and vector search. No data silos. No noisy neighbors. No infrastructure ceiling.
milvus
Milvus is a high-performance, cloud-native vector database built for scalable vector ANN search
qdrant
Qdrant - High-performance, massive-scale Vector Database and Vector Search Engine for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/
chroma
Search infrastructure for AI