Neuroscience-grounded memory for AI agents — ACT-R activation, Ebbinghaus forgetting, Hebbian learning, cognitive consolidation. Rust core + Python/TS legacy. Published on crates.io as engramai.
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Updated
Jun 13, 2026 - Rust
Neuroscience-grounded memory for AI agents — ACT-R activation, Ebbinghaus forgetting, Hebbian learning, cognitive consolidation. Rust core + Python/TS legacy. Published on crates.io as engramai.
🧠 A tool for creating & running basic ACT-R models on multiple implementations using a single declarative file format
Predictive human performance modeling for UI/UX design
Mirror of the official ACT-R Lisp implementation
A Julia Package for the ACT-R Cognitive Architecture
A cognitive memory architecture for agents: ACT-R activation, association, consolidation, and forgetting. sqlite, zero dependencies.
Generate synthetic eye-tracking data using deep learning and ACT-R cognitive model
A set of tutorials for building likelihood based models in ACT-R
Brain-inspired persistent memory for AI coding assistants. MAGMA multi-graph, 3-stage retrieval, ACT-R + surprise scoring, constitutional governance, sleep consolidation, self-linking Zettelkasten. Successor to memory-v2.
A Julia package for the ACT-R cognitive architecture
Multi-user multi-agent memory system for OpenClaw. Replaces the default file-backed memory with an intelligent, layered architecture featuring ACT-R activation scoring, Ebbinghaus forgetting curves, Zettelkasten-style note linking, and automatic consolidation.
Agent memory + every context-engineering primitive (write / select / compress / isolate) in one importable library. Ranked recall with activation decay over SQLite/FTS5. One production dependency (better-sqlite3).
Brain-inspired persistent memory for AI coding assistants. Hybrid BM25+vector search, ACT-R activation scoring, FadeMem decay, knowledge graphs, multi-agent coordination. Successor to claude-memory.
A Python implementation of the ACT-R cognitive Architecture
Visual Studio Code proof of concept plugin for the ACT-R project http://act-r.psy.cmu.edu/
Cognitive architecture for emergent AI identity — blank slate to selfhood through lived experience
A model of how reinforcement learning parameters affect performance on Raven's matrices
A module to replace ACT-R's utility module with classic RL theory algorithms
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