A sophisticated four-tier memory architecture designed to manage AI persona, task context, and long-term knowledge without overwhelming context windows.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install four-layer-memory
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install four-layer-memory using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Four-Layer Memory?
Four-Layer Memory is a specialized framework for AI agents to manage information across distinct temporal and functional layers. By categorizing data into Identity, Working Memory, Short-Term Logs, and Long-Term Storage, it ensures that an agent maintains a consistent persona and retains critical knowledge while keeping the active context window lean and relevant.
This architecture is a cornerstone for developers building persistent agents within Openclaw Skills. It solves the common 'context bloat' problem by providing a clear hierarchy for where information should live, ensuring that daily interactions are logged and significant insights are promoted to long-term storage, while transient task data is cleared appropriately.
Four-Layer Memory Use Cases
- Maintaining a consistent AI personality and core user preferences across multiple sessions.
- Managing complex, multi-step development tasks that require a dedicated working memory buffer.
- Archiving daily logs for auditability without degrading model performance over time.
- Building personalized AI assistants that grow smarter by distilling short-term logs into long-term knowledge bases.
How Four-Layer Memory Works
- The Identity Layer serves as the persona anchor, containing core identity and long-term preferences that never change.
- The Working Memory Layer holds data related to the current task and immediate focal points for real-time processing.
- The Short-Term Log Layer records daily interactions and recent session history for full traceability.
- The Long-Term Storage Layer stores validated knowledge, relationships, and distilled experiences derived from shorter-term layers.
- Automated scripts facilitate the promotion of data between layers and the archiving of expired logs to keep the system performant.
Four-Layer Memory Setup
To initialize and manage the memory architecture within your Openclaw Skills environment, use the provided utility scripts:
# Bootstrap the directory structure and initial memory files
python3 scripts/personal_ai_memory.py bootstrap
# Check the current status and health of the memory layers
python3 scripts/personal_ai_memory.py status
# Cleanup and archive short-term logs older than 14 days
python3 scripts/personal_ai_memory.py archive --keep-days 14
Four-Layer Memory Data Schema & Taxonomy
The skill organizes data into a strictly partitioned directory structure to ensure modularity and ease of access for Openclaw Skills agents:
| Layer | Directory Path | Data Type |
|---|---|---|
| Identity | identity/ |
Personality anchors, core preferences |
| Working | working-memory/ |
Current task state, active context |
| Short-Term | short-term-logs/ |
Daily conversation logs, session history |
| Long-Term | long-term-memory/ |
Verified facts, distilled experiences |
| Archive | archive/ |
Historical logs moved out of active context |
Four-Layer Memory Advanced Features
- Intelligent Context Pruning: Automatically moves expired short-term logs to the archive to maintain high token efficiency.
- Persona Anchoring: Prevents 'identity drift' in LLMs by keeping core identity traits in a dedicated, high-priority memory layer.
- Knowledge Distillation: Framework-ready for workflows that summarize logs into permanent long-term storage entries.
- Multi-Layer Retrieval: Compatible with RAG (Retrieval-Augmented Generation) patterns to pull specifically from the most relevant tier.
SKILL.md
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