claude-layered-memory-architecture

Claude Layered Memory Architecture solves AI context saturation by organizing memory into three distinct layers: a Project MD file that acts as a bootstrap config to auto-trigger skill loading, a SKILL.md file containing 900 lines of distilled permanent knowledge, and a rotational RAG layer cleared between sessions to hold only the current working context. This approach was developed after 10+ months of AI-assisted Python learning revealed that accumulating 79,000 lines of documentation in RAG caused 60% retrieval failures and frequent context compaction every 4-5 prompts. By replacing accumulation with hierarchy, the system reduced RAG retrieval failures to 0% and dramatically improved session continuity. It also introduces a Human-as-Firewall principle, where knowledge is manually curated before being uploaded, and supports three-tier sync across local, Claude Code, and Claude Desktop environments. Developers and learners using Claude for long-term, knowledge-intensive workflows are the primary beneficiaries.

Set up a Project MD file as a declarative bootstrap that auto-triggers the SKILL.md on session start. Populate SKILL.md with distilled permanent knowledge rather than raw accumulated documentation. Use RAG only for the current exercise or task, clearing it between sessions to prevent context saturation.