Brain Memory

Recall and store persistent memories in the user's brain (~/.brain) with the brain CLI: deterministic recall with spreading activation, spaced reinforcement after presenting results, and classified model-driven memorization across life domains (personal, family, social, professional).

The user has a persistent, neuroscience-inspired memory system — a single global ~/.brain/ directory shared across all their AI agents. Memories are Markdown files with YAML frontmatter (type, cognitive type, strength, decay, salience, confidence, tags, associations). Recalled memories get stronger; ignored ones fade. Use the brain CLI for every operation — never compute scores or write memory files by hand.

Recall (when asked to "remember", or when past context would help)

1. Run the deterministic recall engine:

   bash

   brain recall "<query>" --project <project> --task <task_type> --top 10
   

   It returns a scored JSON array (id, title, path, type, score, relevance, decayed_strength, context_match, spreading_bonus, confidence, tags). Scoring combines TF-IDF relevance, decayed strength, spreading activation, context match, and salience — the same ranking on every agent.

2. Read the top-scoring memory bodies from ~/.brain/<path> (score > 0.3).

3. Decide how to respond:

   • One dominant match (score > 0.7, 2x the runner-up) → present it fully.
   • Several related matches (scores > 0.4) → synthesize a consolidated answer and cite the contributing memories by title and path.
   • Only weak matches → list the top 5-7 titles and ask which to explore.
   • No matches → check ~/.brain/_archived/, then suggest other keywords.

4. After presenting results, always reinforce what you showed:

   bash

   brain reinforce <id1> <id2> ...
   

   This applies spaced reinforcement (longer gap → bigger boost), improves decay resistance, and strengthens Hebbian links between co-recalled memories.

5. Flag low-confidence memories (confidence < 0.5) as unverified.

Memorize (when durable decisions, learnings, insights, preferences emerge)

Default: store immediately, report after — do not ask for confirmation when the user asked to memorize. Classify each memory yourself:

• type (sets strength/decay): decision 0.85 · insight 0.90 · goal 0.80 · experience 0.75 · learning 0.70 · relationship 0.70 · preference 0.60 · observation 0.40
• cognitive_type: episodic (events), semantic (facts), procedural (skills/workflows)
• path: life-domain hierarchy under ~/.brain — personal/, family/, social/, professional/ with kebab-case subdirectories, e.g. personal/health/sleep-routine.md, family/events/2026-summer-trip.md, social/friends/marta-preferences.md, professional/projects/foo/api-decision.md
• salience and confidence (0.0–1.0), tags, related memory IDs

Pipe the classified memories to the CLI in one call (add --sync to push to Brain Cloud / git afterwards):

bash

brain memorize <<'EOF'
{
  "memories": [
    {
      "title": "Prefers morning workouts before 8am",
      "type": "preference",
      "cognitive_type": "semantic",
      "path": "personal/health/workout-preference.md",
      "tags": ["health", "routine"],
      "salience": 0.6,
      "confidence": 0.9,
      "source": "Conversation about scheduling",
      "encoding_context": {
        "project": "openclaw",
        "topics": ["fitness", "scheduling"],
        "task_type": "conversation"
      },
      "content": "# Morning Workouts\n\nPrefers to train before 8am; avoid booking anything earlier than 9am.\n"
    }
  ]
}
EOF

The CLI handles IDs, strength/decay computation, directories, index updates, association edges, and the search index.

Guidelines:

• Set BRAIN_AGENT=openclaw in the environment when invoking the CLI so memories record their host agent.
• Only propose "pinned": true (always-injected, decay-exempt) for durable conventions — and only with the user's agreement.
• Never store secrets, credentials, or trivia.
• If the CLI reports potential_conflicts with a pinned/stable memory, surface the contradiction and let the user decide (supersede, scope, or reject) — never silently keep both.

Session awareness

• brain session-start --project <project> returns the budget-bounded session payload (pinned facts, relevant memories, skills index) — internalize it silently; do not dump it.
• On session boundaries, append a summary entry to ~/.brain/contexts.json (keep only the last 20) so future sessions get context-dependent recall.