Memory Management

This implements a three-tier memory system for AI agents: hot memory in MEMORY.md with P0/P1/P2 tags and TTLs, cold searchable storage in JSONL lesson files, and raw daily logs. The hook is the 70-80% token reduction claim, which makes sense since you're only loading ~200 lines of current context instead of everything. Comes with a Python janitor script that auto-archives expired entries based on priority and date tags. The discipline of deciding what's P0 versus P2 is actually the hard part here, not the tooling. If you're running long-lived agents that accumulate context bloat, this gives you a structured way to prune without losing important stuff to semantic search.

skills.sh installs: 229
GitHub stars: 39
last updated: Aug 21, 2026

View on GitHub

Install

$ npx -y skills add jzocb/openclaw-memory-management --skill memory-management --agent claude-code

Installs into .claude/skills of the current project.

Files

Memory Management Skill

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

Quick Setup

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)

Set up auto-archive cron:

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

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"]}