NeuralMemory — Associative Memory for AI Agents

A biologically-inspired memory system that uses spreading activation instead of keyword/vector search. Memories form a neural graph where neurons connect via 20 typed synapses. Frequently co-accessed memories strengthen their connections (Hebbian learning). Stale memories decay naturally. Contradictions are auto-detected.

Why not just vector search? Vector search finds documents similar to your query. NeuralMemory finds conceptually related memories through graph traversal — even when there's no keyword or embedding overlap. "What decision did we make about auth?" activates time + entity + concept neurons simultaneously and finds the intersection.

Setup

1. Install NeuralMemory

pip install neural-memory

The brain and config at ~/.neuralmemory/ are auto-created on first use.

2. Install the OpenClaw Plugin (Recommended)

The plugin occupies the exclusive memory slot — auto-injects context before each agent run and auto-captures memories after.