ai-memory-management

One shared memory for every AI you use, in plain Markdown files you own.

mRAG (micro-RAG) is an agnostic backend memory encoder and retrieval system designed to segment memories into basic short form belief statements that can be retrieved and injected directly into an Agent's context using multi-head queries to inject only the most highly relevant beliefs with minimal excess.

The context quality layer for AI agents — memory that checks itself: lifecycle governance, calibrated confidence, and staged claim gates. Local-first, MCP 19 tools, DeepSeek Harness plugin.

Optional skills for Hermes Agent — memory discipline, audit, and self-improvement loop. Decision trees, source-based docs, and tools for rigorous LLM memory management.

Public AI Private Memory Framework For Use For Private AI Context Memory Storage Between Sessions

Context hub