Vector Databases & RAG AI Tools

Turns documents and past sessions into a graph-plus-vector memory that AI agents can query across runs.

Open-source search engine with vector storage and hybrid keyword-plus-semantic retrieval, self-hosted or run as a cloud service

Postgres backend bundled with a RAG pipeline, agent runtime and a visual workflow builder

Open-source vector search engine written in Rust, available self-hosted or as a managed cloud for RAG and recommendation apps.

Builds an open-source, Postgres-based memory engine that gives AI agents relational, vector, and graph retrieval.

Open-source search and vector engine that combines text, vectors, ranking and model inference for RAG, recommendations and agents.

Open-source backend platform (auth, database, storage, functions) increasingly pitched as the data layer under AI agents and RAG apps.

Managed MongoDB cloud database with built-in vector search, hybrid search and Voyage AI embeddings for RAG and agent apps.

Source-available multimodal retrieval engine that embeds whole document pages, so RAG works over diagrams and datasheets.

Managed vector database for similarity search over billions of embeddings, with serverless and BYOC options