Install
openclaw skills install memory-distillerOpenClaw's subconscious. Automatically distills conversation insights, corrections, and preferences into durable memory. The agent that learns from every session — so you never have to repeat yourself.
openclaw skills install memory-distillerThe subconscious of your OpenClaw agent. Automatically distills fleeting conversation moments into permanent wisdom.
Most agents wake up blank. memory-distiller changes that — it automatically identifies what's worth remembering during each conversation, writes it to persistent memory files, and creates a true learning loop. Your agent gets smarter every session.
OpenClaw natively has MEMORY.md and memory/YYYY-MM-DD.md, but updating them requires manual effort.
The problem: You correct the agent, it says "got it", and makes the same mistake next session.
memory-distiller closes this loop:
| Skill | Role | Responsibility |
|---|---|---|
| proactive-agent | Butler | Real-time detail capture (WAL), proactive behaviors, Heartbeat |
| memory-distiller | Historian | Post-conversation reflection, distilling lessons into long-term memory |
One line: proactive-agent owns the present. memory-distiller owns the future.
| Type | Signal Words | Example |
|---|---|---|
| 🔴 Correction | "wrong", "not right", "no, I meant", "actually", "stop doing" | "No, that command is wrong" |
| 💚 Preference | "I prefer", "always use", "don't use", "from now on", "by default" | "Always send reports as attachments" |
| 💡 Insight | "the issue was", "turns out", "the key is", "got it", "solved" | "Turns out Feishu doesn't render Markdown" |
| 📌 Explicit | "remember this", "save this", "note that", "write this down" | "Remember this config" |
When the user says any of the following, immediately distill the session:
"remember this" / "save this" / "note that"
"write down what we just learned" / "distill this session"
Before writing anything, check these 4 gates:
MEMORY.md already contain this? (Avoid duplicates; update if stale)Don't record (examples):
Do record (examples):
clawhub install only accepts slugs, not GitHub URLs" → actionable rule| Content Type | Target File |
|---|---|
| Today's new discoveries, lessons | memory/YYYY-MM-DD.md |
| Important rules, persistent preferences | MEMORY.md (relevant section) |
| User personal info / preferences | USER.md |
### 🧠 Auto-Learned [YYYY-MM-DD HH:MM]
- **Type:** Correction / Preference / Insight / Explicit
- **Trigger:** One sentence explaining what triggered this
- **Rule:** Specific, actionable rule that can directly guide future behavior
Example:
### 🧠 Auto-Learned [2026-03-02 00:30]
- **Type:** Correction
- **Trigger:** User corrected the install command format
- **Rule:** `clawhub install` only accepts slugs (e.g. claw-multi-agent), not GitHub URLs
User message arrives
↓
Scan for trigger signals (Correction / Preference / Insight / Explicit)
↓
Signal detected?
├─ No → Reply normally, no action
└─ Yes → Apply quality gate
↓
All 4 gates pass?
├─ No → Discard, reply normally
└─ Yes → Distill into structured memory entry
↓
Write to target file
↓
Reply normally
(silent unless explicitly triggered)
MEMORY.md → don't duplicate; update if staleNever write the following to any memory file, even if the user asks:
npx clawhub@latest install memory-distiller
Zero configuration. Restart your OpenClaw session after installation.
| Skill | Purpose |
|---|---|
| proactive-agent | Real-time WAL protocol + proactive behaviors |
| memory-distiller | Post-conversation automatic experience distillation (this skill) |
Together: never lose present details, never repeat past mistakes.
Inspired by Claudeception (a Claude Code self-learning plugin), redesigned from scratch for OpenClaw's architecture and memory model.
Core belief:
An agent should never be tripped up by the same problem twice.
Every correction is a learning opportunity. memory-distiller ensures none of them go to waste.
Academic foundation: Voyager (2023), CASCADE (2024), SEAgent (2025), Reflexion (2023) — all point to the same conclusion: agents that persist and reflect on their learning dramatically outperform those that start fresh every time.
Designed with 🐝 claw-multi-agent — 3 parallel agents, 28s