A memory system for AI agents, modeled on the brain. It keeps what matters across sessions and hands the agent only what each question needs - instead of re-reading everything, every time.
CortexClaw replaces flat memory files with small, searchable memory chunks that are retrieved on demand. The agent asks a question; CortexClaw returns just the relevant pieces.
Think of it as a personal search engine for an AI's memory.
It sits alongside the model as external memory. When the agent needs to recall something, CortexClaw scores every chunk by meaning, recency, past usefulness, and how ideas connect - then returns the best matches.
Afterward, feedback flows back: what helped, what was noise. Over time it learns to retrieve better.
AI agents forget everything between sessions. Loading whole memory files burns tokens and treats every note as equally important - so the useful patterns get lost in the noise.
Model memory like the brain. Recent things stay sharp, important things harden into permanence, related memories link up, and idle time is spent consolidating. You load less and remember more.