Memory System

Layer 5: Context Manager          <- Manages the LLM's context window
  |  Monitors token usage
  |  Proactive flush at 60% capacity
  |  Auto-compacts at 70% capacity
  |  Extracts facts before discarding messages
  |
Layer 4: Learnings                <- Self-improvement through failure tracking
  |  learnings/errors.md         (tool failures with context)
  |  learnings/corrections.md   (user corrections and preferences)
  |  Auto-injected into system prompt each session
  |
Layer 3: Workspace Files          <- Durable, human-readable storage
  |  AGENTS.md, SOUL.md, USER.md  (loaded into system prompt)
  |  MEMORY.md                    (curated long-term facts)
  |  HEARTBEAT.md                 (autonomous monitoring rules)
  |  memory/YYYY-MM-DD.md         (daily session logs)
  |  BM25 search across all files
  |
Layer 2: Structured Memory DB     <- Hierarchical vector database
  |  SQLite + sqlite-vec + FTS5
  |  Facts with embeddings (KNN similarity search)
  |  Auto-categorization with category-scoped search
  |  3-tier retrieval: categories -> scoped facts -> flat fallback
  |  Reinforcement scoring with access-count boost + recency decay
  |
Layer 1: Salience Tracking        <- Prioritizes important facts
     Access count, decay score, last accessed timestamp
     High-salience facts auto-surface in initial context

Session 1: User says "My cat's name is Whiskerino"
           Agent saves to daily log + structured DB

  === Chat Reset ===

Session 2: User asks "What is my cat's name?"
           Agent recalls "Whiskerino" via memory_recall