One shared memory for every AI you use, in plain Markdown files you own.
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.
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.
AI-CONTEXT turns user-controlled sources such as AI exports, repositories, notes, documents, Drive exports, and email archives into reviewed canonical memory that can be selectively disclosed to a model or agent between sessions. AI-CONTEXT works by itself. QSOL-SUBSTRATE is optional.
Context hub
Optional skills for Hermes Agent — memory discipline, audit, and self-improvement loop. Decision trees, source-based docs, and tools for rigorous LLM memory management.
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