The Fastest Distributed Database for Transactional, Analytical, and AI Workloads.

Database for AI. Store Vectors, Images, Texts, Videos, etc. Use with LLMs/LangChain. Store, query, version, & visualize any AI data. Stream data in real-time to PyTorch/TensorFlow. https://activeloop.ai

MariaDB server is a community developed fork of MySQL server. Started by core members of the original MySQL team, MariaDB actively works with outside developers to deliver the most featureful, stable, and sanely licensed open SQL server in the industry.

The AI-native database built for LLM applications, providing incredibly fast hybrid search of dense vector, sparse vector, tensor (multi-vector), and full-text.

The AI-Native Search Database. Unifies vector, text, structured and semi-structured data in a single engine, enabling hybrid search and in-database AI workflows.

Epsilla is a high performance Vector Database Management System

A Vector Database Tutorial (over CMU-DB's BusTub system)

Fast, SQL powered, in-process vector search for any language with an SQLite driver

Vector search engine inside Milvus, integrating FAISS, HNSW, DiskANN.

AlayaLite – A Fast, Flexible Vector Database for Everyone.

A distributed Key-Value Storage using Raft

A lightweight library for the RaBitQ algorithm and its applications in vector search.

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.

A simple and fast chat records searching bot for Telegram. Supports OCR and sematic vector search.

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

[SIGMOD 2025] Practical and Asymptotically Optimal Quantization of High-Dimensional Vectors in Euclidean Space for Approximate Nearest Neighbor Search

Faiss-based library for efficient similarity search

Vector Database with support for late interaction and token level embeddings.

Approximate Nearest Neighbor search using reduced-rank regression, with extremely fast queries, tiny memory usage, and rapid indexing on modern vector embeddings.

[SIGMOD 2026] DARTH: Declarative Recall Through Early Termination for Approximate Nearest Neighbor Search.