Agents That Never Forget — Complete Memory Architecture From Session Storage to Permanent Memory

The problem: Context windows fill up. Sessions end. Restarts wipe everything. Building a memory system that actually persists meaningful information — not just dumps raw conversation history — requires an architecture, not a feature flag.

The solution: Three memory frameworks that cover the full stack: how to tier your memory (hot/warm/cold), how to decide what gets kept, and how to optimise your context window so the agent stays coherent across long sessions and cold starts.

What you get (3 SKILL.md frameworks):

• Agent Memory Architecture — three-tier memory design from session to permanent storage
• Agent Memory Decision Tree — the decision logic for what to retain, compress, or discard
• Context Window Optimisation — patterns for maximising useful context before the window fills

Who it's for: Any OpenClaw operator whose agents have a memory problem — including the ones who haven't hit it yet.

Time to value: One afternoon to read and implement the architecture. Your agents remember everything from the next session.