agent-memory-tiers
Installation
SKILL.md
Agent Memory Tiers
Stop your agents from forgetting everything between runs.
OpenClaw agents start every activation with zero memory of what they did last time. They waste hundreds or thousands of tokens re-reading old files, parsing chat history, and reconstructing context. This skill fixes that.
Agent Memory Tiers is a structured, self-updating memory system that gives agents instant context on startup. Two files, updated automatically at the end of every run, so the next activation starts warm.
Tested in production across a 20-agent swarm running daily for 3+ weeks.
How It Works
Two memory layers sit in each agent's workspace:
| Layer | Purpose | Size | Loaded |
|---|---|---|---|
| L0 | Instant state snapshot | 4 lines | Every activation |
| L1 | 7-day rolling context | ~20-40 lines | Every activation |
| L2+ | Historical memory | Varies | Only when needed |