Embedding stores powering retrieval for RAG and agent recall. Ranked by community traction, with live GitHub stars and what each is best at.
Cloud-native vector database built for billion-scale similarity search. Best for billion-scale search.
In-process analytical database whose vss extension adds an HNSW vector index — vector search alongside your columnar analytics. Best for analytical + vector search.
High-performance vector search engine with rich filtering, written in Rust for production-scale retrieval. Best for production RAG.
Open-source embedding database designed for simplicity — the default vector store for many RAG prototypes. Best for RAG.
Vector similarity search inside Postgres — keep embeddings next to your relational data. Best for RAG on existing Postgres.
Open-source vector database with hybrid search and built-in modules for vectorization and RAG. Best for hybrid search.
Embedded, in-process vector database on the columnar Lance format — versioned, updatable, larger-than-RAM retrieval with no server. Best for embedded vector search.
A single-file SQLite extension for vector search — exact brute-force KNN that lives inside the database you already ship. Best for vectors inside SQLite.
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