hnsw

MSVBASE is a system that efficiently supports complex queries of both approximate similarity search and relational operators. It integrates high-dimensional vector indices into PostgreSQL, a relational database to facilitate complex approximate similarity queries.

(distributed) vector database

Production-ready KV-backed HNSW implementation in Rust using LMDB

The explainable, local-first memory engine for AI agents. One ~9 MB binary fuses vector + graph + columnar under VelesQL; why() returns the evidence path behind every recall. No cloud, no glue code — runs on server, browser, mobile and desktop.

AI memory system combining vector search with temporal knowledge graph. Built-in cognitive engine for agents. Supports memory decay, contradiction detection, and MCP integration.

hnswlib-wasm attempts to create a browser friendly version of hnswlib

Python SDK for vector search, semantic search, RAG, embeddings, and AI agents on Oracle AI Database

Vector Database implemented in Golang with support for full-text and vector search as well as fault tolerance via Raft.

S3 vector database for LLM Agents and RAG.

Cognitive memory engine for AI agents — temporal decay, contradiction detection, autonomous consolidation, knowledge graph, ANN recall via HNSW. Embeddable Rust library with Python bindings; powers yantrikdb-server (HTTP gateway, MCP server, openraft cluster). Apache-2.0.

NeuronDB PostgreSQL extension: vector similarity search (HNSW, IVFFlat), embeddings, kNN, ML in SQL, and hybrid full-text + vector retrieval.

PostgreSQL-compatible SQL, graph, and vector database built from scratch in Rust.

Pixel-native visual RAG ported to Rust on the ruvector ANN substrate (HNSW + IVF-Flat) — screenshot/document retrieval over visual embeddings, a Rust port of PixelRAG, with a metaharness benchmark CLI: npx rupixel

Embedded on-device vector database for AI agent memory and local RAG — disk-based HNSW in an LSM-tree, C++/Python, optimized for low memory footprint

Versioned, fast and scalable nearest neighbor search.

A hex-native embedded database for Go. Morton keys, native ring scans, seams (contradictions), MVCC, provenance, and optional HNSW embeddings. Built from scratch for lattice-addressed memory.

Fast vector search in PostgreSQL

visualization tool for vector search index

High-performance, Hardware-First Vector Database engine built in Zig and Go. Sub-millisecond HNSW search with 64-bit metadata filtering and 1.8MB binary footprint.

🧠 Predictable, embedded multimodal vector database & offline RAG engine for Edge AI (ARM64 / Zero-OOM). Built in C++20.