BrainBox equips AI coding agents with a unique form of muscle memory, learning passively from interactions to recall files and tool sequences instantly. Unlike traditional vector databases or RAG systems, it implements neuroscience-inspired Hebbian learning, forming 'synapses' between co-accessed 'neurons' (files, tools, errors). This allows agents to skip costly search operations, predict next steps, and even remember solutions for recurring errors, significantly saving tokens and accelerating development workflows.
Key Features
01Anti-Recall Escalation for unused suggestions
02SNAP Plasticity for balanced learning
03Error-Fix Immune System for bug resolution
04Tool Sequence Prediction
059 GitHub stars
06Hebbian Learning for co-access patterns
