Agent Wal OpenClaw Plugin & Skill | ClawHub
Looking to integrate Agent Wal into your AI workflows? This free OpenClaw plugin from ClawHub helps you automate notes & pkm tasks instantly, without having to write custom tools from scratch.
What this skill does
Write-Ahead Log protocol for agent state persistence. Prevents losing corrections, decisions, and context during conversation compaction. Use when: (1) receiving a user correction — log it before responding, (2) making an important decision or analysis — log it before continuing, (3) pre-compaction memory flush — flush the working buffer to WAL, (4) session start — replay unapplied WAL entries to restore lost context, (5) any time you want to ensure something survives compaction.
Install
npx clawhub@latest install agent-walFull SKILL.md
Open original| name | description |
|---|---|
| agent-wal | Write-Ahead Log protocol for agent state persistence. Prevents losing corrections, decisions, and context during conversation compaction. Use when: (1) receiving a user correction — log it before responding, (2) making an important decision or analysis — log it before continuing, (3) pre-compaction memory flush — flush the working buffer to WAL, (4) session start — replay unapplied WAL entries to restore lost context, (5) any time you want to ensure something survives compaction. |
SKILL.md content below is scrollable.
Agent WAL (Write-Ahead Log)
Write important state to disk before responding. Prevents the #1 agent failure mode: losing corrections and context during compaction.
Core Rule
Write before you respond. If something is worth remembering, WAL it first.
When to WAL
| Trigger | Action Type | Example |
|---|---|---|
| User corrects you | correction |
"No, use Podman not Docker" |
| You make a key decision | decision |
"Using CogVideoX-2B for text-to-video" |
| Important analysis/conclusion | analysis |
"WAL/VFM patterns should be core infra not skills" |
| State change | state_change |
"GPU server SSH key auth configured" |
| User says "remember this" | correction |
Whatever they said |
Commands
All commands via scripts/wal.py (relative to this skill directory):
# Write before responding
python3 scripts/wal.py append agent1 correction "Use Podman not Docker for all EvoClaw tooling"
python3 scripts/wal.py append agent1 decision "CogVideoX-5B with multi-GPU via accelerate"
python3 scripts/wal.py append agent1 analysis "Signed constraints prevent genome tampering"
# Working buffer (batch writes during conversation, flush before compaction)
python3 scripts/wal.py buffer-add agent1 decision "Some decision"
python3 scripts/wal.py flush-buffer agent1
# Session start: replay lost context
python3 scripts/wal.py replay agent1
# After applying a replayed entry
python3 scripts/wal.py mark-applied agent1 <entry_id>
# Maintenance
python3 scripts/wal.py status agent1
python3 scripts/wal.py prune agent1 --keep 50
Integration Points
On Session Start
- Run
replayto get unapplied entries - Read the summary into your context
- Mark entries as applied after incorporating them
On User Correction
- Run
appendwith action_typecorrectionBEFORE responding - Then respond with the corrected behavior
On Pre-Compaction Flush
- Run
flush-bufferto persist any buffered entries - Then write to daily memory files as usual
During Conversation
For less critical items, use buffer-add to batch writes. Buffer is flushed to WAL on flush-buffer (called during pre-compaction) or manually.
Storage
WAL files: ~/clawd/memory/wal/<agent_id>.wal.jsonl
Buffer files: ~/clawd/memory/wal/<agent_id>.buffer.jsonl
Entries are append-only JSONL. Each entry:
{"id": "abc123", "timestamp": "ISO8601", "agent_id": "agent1", "action_type": "correction", "payload": "Use Podman not Docker", "applied": false}