Operator Memory Stack — Durable Memory for OpenClaw
Your agent forgets people, loses project context, and drifts on preferences. This skill fixes that.
Security reviewed. Manually tested and reviewed by the Remote OpenClaw team before going live.
About
Default OpenClaw memory works until your agent has enough history to trip over itself. After a few weeks, names blur together, old priorities leak into new work, and the same questions get asked twice.
Operator Memory Stack replaces that drift with a durable operating memory built for real operators. People, projects, decisions, preferences, and today's active context all live in the right place — structured, retrievable, and designed to survive long-running work.
What this solves
- Forgetting people — names and relationships mentioned two weeks ago disappear from context.
- Project context drift — older project details get overwritten or blurred by newer conversations.
- Lost preferences — rules you stated clearly get compacted away and the agent starts guessing.
- Repeated questions — the agent asks things you already told it because the facts did not stick.
How it works
The stack splits memory by job. Instead of one flat memory file that decays, you get three purpose-built layers that each handle a different kind of recall.
- Entity Ledger (vault/) — durable record of people, projects, and companies. Each entity gets a fast-read profile and an append-only fact log.
- Daily Stream (journal/) — one file per day captures events, decisions, and extracted facts as they happen.
- Operator Rules (codex.md) — your preferences, boundaries, communication style, and decision patterns. Small, stable, and always consulted first.
What's included
- SKILL.md — the complete memory architecture definition
- vault/ directory scaffold — pre-structured folders for people, projects, companies, and dormant archive
- journal/ directory — daily stream format with summary, events, and extraction tracking
- codex.md template — starter file for operator rules and preferences
- Retrieval order — defined lookup sequence so the agent checks the cheapest artifact first
- Weekly maintenance hook — drop-in block for your HEARTBEAT.md
operator-memory-stack/ ├── SKILL.md — memory architecture definition ├── vault/ — entity ledger scaffold ├── journal/ — daily stream directory └── codex.md — operator rules template
Best for
- Operators running long-term projects where context must survive across weeks and months
- Anyone whose agent forgets people, loses project details, or drifts on preferences
- Power users who need persistent, structured memory instead of flat-file decay
- Teams managing multiple clients or projects where entity recall is critical
Under the hood
- Append-only fact tracking — facts are never deleted. Outdated facts are marked as superseded, preserving full audit history.
- Confidence levels — every fact carries a confidence tag (high, medium, low) so the agent knows how much to trust it.
- Promotion rules — clear triggers for when a journal note earns a permanent spot in the vault.
- Conflict handling — when new facts contradict old ones, the system appends, supersedes, and updates the working profile.
Before vs After
Before
Agent forgets context after every session
After
Persistent 3-layer memory that survives restarts
FAQ
Do I need coding experience?
No. You download the files, drop them into your OpenClaw workspace, and follow the migration steps. Everything is plain Markdown and YAML.
Does this replace my existing MEMORY.md?
It upgrades it. Your current memory gets split into structured layers — operator rules in codex.md, durable entities in the vault, and daily context in the journal. Nothing is lost.
Which AI model works best?
Any model supported by OpenClaw. Claude and GPT-4 both work well. The memory architecture is model-agnostic.
How much maintenance does it need?
A weekly review takes 5-10 minutes. The included maintenance hook handles promotion, compaction, and freshness updates.
Can I customize the vault structure?
Yes. Every file is plain Markdown or JSONL. Add new entity types, change the fact categories, adjust the freshness bands, or modify the retrieval order.