Pawz Engram biologically-inspired memory architecture for persistent AI agents

What is Engram? Most “agent memory” today is basically: Project Engram is our attempt to treat memory like a cognitive system instead of a dumping ground. It’s a three-tier architecture inspired by how humans handle information: Sensory Buffer (Tier 0) : short-lived, raw input for a single turn Working Memory (Tier 1) : what the agent is “currently aware of” under a strict token budget Long-Term Memory Graph (Tier 2) : persistent episodic + semantic + procedural memory with typed edges Engram is implemented in OpenPawz , an open-source Tauri v2 desktop AI platform. Everything runs local-first . Read the full whitepaper: ENGRAM.md (Project Engram) Why build this? Flat memory stores tend to fail the same way: everything competes equally (no prioritization) nothing fades (stale facts stick around forever) “facts”, “events”, and “how-to” get mixed into one blob pile retrieval runs even when it shouldn’t (latency + context pollution) security is often an afterthought Engram’s core bet is si