Torchhd is a Python library for Hyperdimensional Computing and Vector Symbolic Architectures

The Fuzzy-Pattern Tsetlin Machine library, with zero external dependencies, performs blazingly fast.

Local, gradient-free neuro-symbolic memory engine combining Hyperdimensional Computing (HDC/VSA), Hebbian plasticity, and graph triples for offline AI.

Cognitive Computing with Associative Memory

Hyperdimensional Computing Library for building Vector-Symbolic Architectures in Python 3

Accelerator for Hyperdimensional Computing (HDC)

Boolean Hypervectors with various operators for experiments in hyperdimensional computing (HDC).

Hyperdimensional computing in JAX, with statistical guarantees.

A computational foundation for AGI based on hyperdimensional computing and set theory.

An automated HDC platform

A collection of Hyperdimensional Computing (HDC) models implemented in C++

High-performance Holographic Memory System (HMS) for Node.js, powered by Rust. This library implements Vector Symbolic Architectures (VSA) using Binary Spatter Code (BSC) to enable semantic search, analogical reasoning, and associative memory at scale.

A Rust library for hyperdimensional computing (HDC)

HD3C: A lightweight classification framework designed for low-power devices.

A chef's palate for AI agent memory; Un-mix any day's work into its exact projects, and detect workstreams nobody has named yet. Hyperdimensional fingerprints, zero dependencies.

Holographic vectors you can compute with. Bind structure, bundle sets, unbind components cross NumPy, PyTorch, and JAX.

A cognitive substrate for AI agents — hyperdimensional memory with first-class goals, beliefs, sensory, self-model, and a non-destructive unlearn primitive. Rust top to bottom, embedded-first, no LLM in the read path. One binary, no query language.

A library for training and running HDC models on embedded devices.