Memory Management Skill

Manage agent memory efficiently using a three-layer architecture with automatic archival.

Quick Setup

1. Format MEMORY.md with priority tags:

- [P0] Core identity item (never expires)
- [P1][2026-02-10] Active project (90-day TTL)
- [P2][2026-02-10] Temporary item (30-day TTL)

2. Set up auto-archive cron:

0 4 * * * python3 ~/.openclaw/workspace/scripts/memory-janitor.py

3. Store lessons in memory/lessons/*.jsonl:

{"id": "lesson-001", "date": "2026-02-10", "category": "infra", "title": "Problem title", "problem": "What happened", "solution": "How to fix", "tags": ["tag1"]}

Three-Layer Architecture

Layer 1: Hot Memory (MEMORY.md)

• Always loaded, ≤200 lines
• P0: Core identity, never expires
• P1: Active projects, 90-day TTL
• P2: Temporary, 30-day TTL

Layer 2: Cold Memory (searchable)

• memory/lessons/*.jsonl — structured lessons
• memory/archive/ — expired content
• Use memory_search to recall

Layer 3: Raw Logs

• memory/YYYY-MM-DD.md — daily logs
• Not loaded automatically

Priority Guidelines

Core Principles (max 5 in AGENTS.md)

Keep only essential rules in AGENTS.md. Other lessons go to lessons/*.jsonl.

Example 5 rules:

1. Real money = correctness > speed
2. External actions require confirmation
3. Check both cron systems (system + OpenClaw)
4. Long-running processes need setsid isolation
5. Read platform rules before trading

When Memory Gets Too Large

Run janitor with stats:

python3 scripts/memory-janitor.py --stats

If >200 lines:

1. Review P0 entries — are they all truly permanent?
2. Add dates to P1/P2 entries missing them
3. Move detailed content to lessons/*.jsonl
4. Run --dry-run then archive