How to build persistent memory that survives sessions, compounds over time, and makes your AI smarter with every interaction.
Without persistent memory, every AI session starts from zero. You waste time re-explaining your project, your preferences, and your architecture. With a proper memory system, your AI builds on previous sessions — learning from mistakes, remembering decisions, and improving over time.
Tier 1: Session Memory (context window — gone when session ends). Tier 2: Working Memory (MEMORY.md — curated facts, pruned weekly). Tier 3: Knowledge Base (learned rules, auditor-gated, permanent). Tier 4: Agent Memory (per-agent persistent knowledge). Tier 5: Knowledge Graph (MCP-powered structured entities). Tier 6: Vector Memory (Ruflo HNSW + SQLite semantic search). Tier 7: Daily Notes (chronological session logs).
The most powerful aspect: observations become knowledge nominations, which the Auditor agent reviews before promoting to the permanent knowledge base. This means the system learns from experience but with quality control — not every observation becomes a rule.
Run /sync to refresh working memory mid-day. Run /wrap-up to archive session learnings at end of day. Run /morning to load context at the start of a new day. Keep MEMORY.md under 100 lines — aggressively prune stale items.
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