[P] zer0dex - dual-layer memory for local LLM agents: 91.2% recall vs 80.3% R.A.G. across 97 benchmarked cases

The problem: Persistent agent memory is a solved problem in theory but not in practice for local/offline deployments. Flat file context is too noisy, full RAG loses cross-referential structure, and most memory frameworks assume cloud infrastructure. The approach: Two-layer architecture separating what the agent knows it knows from what it retrieves on demand. Layer 1: Compressed markdown semantic index (~800 tokens, always in context). Functions as a cognitive map: the agent can navigate its own knowledge without loading it. Layer 2: ChromaDB vector with a pre-message HTTP hook.