ScaNN Library

A C++ library for approximate nearest neighbor searching in arbitrarily high dimensions, developed by David Mount and Sunil Arya at the University of Maryland. Provides data structures and algorithms for both exact and approximate nearest neighbor searching.

A C++ library for performing fast approximate nearest neighbor searches in high dimensional spaces. Contains multiple ANN algorithms and automatic algorithm selection based on dataset characteristics.

Fast Library for Approximate Nearest Neighbors containing a collection of algorithms optimized for nearest neighbor search in high dimensional spaces with automatic algorithm and parameter selection.

Leading graph-based ANN library optimized for approximate nearest neighbor search, offering competitive performance especially at lower recall levels across diverse datasets.

Graph-based similarity search algorithm achieving 0.99 recall, building indexes 3-8x faster than competitors with 40% less memory. Answers 1-NN queries up to 10x faster than serial scan.

EFANNA is an extremely fast approximate nearest neighbor search algorithm based on kNN graphs and randomized KD-trees. The provided implementation offers a high-performance ANN index suitable as a building block in custom vector search and retrieval infrastructure.