Memory Hygiene
Audit, clean, and optimize Clawdbot's vector memory (LanceDB). Use when memory is bloated with junk, token usage is high from irrelevant auto-recalls, or setting up memory maintena…
dylanbaker24
@dylanbaker24
Install
$ openclaw skills install @dylanbaker24/memory-hygieneMemory Hygiene
Keep vector memory lean. Prevent token waste from junk memories.
Quick Commands
Audit: Check what's in memory
memory_recall query="*" limit=50
Wipe: Clear all vector memory
rm -rf ~/.clawdbot/memory/lancedb/
Then restart gateway: clawdbot gateway restart
Reseed: After wipe, store key facts from MEMORY.md
memory_store text="<fact>" category="preference|fact|decision" importance=0.9
Config: Disable Auto-Capture
The main source of junk is autoCapture: true. Disable it:
{
"plugins": {
"entries": {
"memory-lancedb": {
"config": {
"autoCapture": false,
"autoRecall": true
}
}
}
}
}
Use gateway action=config.patch to apply.
What to Store (Intentionally)
✅ Store:
- User preferences (tools, workflows, communication style)
- Key decisions (project choices, architecture)
- Important facts (accounts, credentials locations, contacts)
- Lessons learned
❌ Never store:
- Heartbeat status ("HEARTBEAT_OK", "No new messages")
- Transient info (current time, temp states)
- Raw message logs (already in files)
- OAuth URLs or tokens
Monthly Maintenance Cron
Set up a monthly wipe + reseed:
cron action=add job={
"name": "memory-maintenance",
"schedule": "0 4 1 * *",
"text": "Monthly memory maintenance: 1) Wipe ~/.clawdbot/memory/lancedb/ 2) Parse MEMORY.md 3) Store key facts to fresh LanceDB 4) Report completion"
}
Storage Guidelines
When using memory_store:
- Keep text concise (<100 words)
- Use appropriate category
- Set importance 0.7-1.0 for valuable info
- One concept per memory entry
Related skills
Memory Setup
@jrbobbyhansen-pixelEnable and configure Moltbot/Clawdbot memory search for persistent context. Use when setting up memory, fixing "goldfish brain," or helping users configure memorySearch in their config. Covers MEMORY.md, daily logs, and vector search setup.
Context Budgeting
@sarielwang93Manage and optimize OpenClaw context window usage via partitioning, pre-compression checkpointing, and information lifecycle management. Use when the session context is near its limit (>80%), when the agent experiences "memory loss" after compaction, or when aiming to reduce token costs and latency for long-running tasks.
Triple Memory
@ktpriyathamComplete memory system combining LanceDB auto-recall, Git-Notes structured memory, and file-based workspace search. Use when setting up comprehensive agent memory, when you need persistent context across sessions, or when managing decisions/preferences/tasks with multiple memory backends working together.
Triple Memory
@ktpriyathamComplete memory system combining LanceDB auto-recall, Git-Notes structured memory, and file-based workspace search. Use when setting up comprehensive agent memory, when you need persistent context across sessions, or when managing decisions/preferences/tasks with multiple memory backends working together.
Memory Manager
@marmikcfcLocal memory management for agents. Compression detection, auto-snapshots, and semantic search. Use when agents need to detect compression risk before memory loss, save context snapshots, search historical memories, or track memory usage patterns. Never lose context again.
Cognitive Memory
@icemilo414Intelligent multi-store memory system with human-like encoding, consolidation, decay, and recall. Use when setting up agent memory, configuring remember/forget triggers, enabling sleep-time reflection, building knowledge graphs, or adding audit trails. Replaces basic flat-file memory with a cognitive architecture featuring episodic, semantic, procedural, and core memory stores. Supports multi-agent systems with shared read, gated write access model. Includes philosophical meta-reflection that deepens understanding over time. Covers MEMORY.md, episode logging, entity graphs, decay scoring, reflection cycles, evolution tracking, and system-wide audit.