A sophisticated four-layer memory optimization system for AI agents featuring dynamic decay, semantic retrieval, and self-reflection capabilities.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install openclaw-memory-fix
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 openclaw-memory-fix 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 OpenClaw Memory Fix?
OpenClaw Memory Fix is an advanced architectural solution designed to enhance how AI agents handle information retention and retrieval. By moving beyond flat context windows, this skill introduces a hierarchical memory structure that mimics human-like cognitive processing. It is a cornerstone among Openclaw Skills for developers looking to build agents that remember core principles forever while intelligently phasing out temporary session noise.
The system utilizes a tiered approach to separate short-term task states from long-term user personas and semantic knowledge. This ensures that the agent remains performant, avoids context window bloat, and maintains high-quality responses over extended periods of interaction. Through the use of vector indexing and knowledge graphs, it provides a robust framework for persistent agent intelligence.
OpenClaw Memory Fix Use Cases
- Persistent context management for long-term coding projects and complex multi-session workflows.
- Personalizing AI behavior by retaining user-specific principles and persona data over time.
- Reducing token costs and noise by automatically decaying irrelevant or low-confidence information.
- Enhancing research capabilities through tiered semantic memory and knowledge graph integration.
How OpenClaw Memory Fix Works
- The system captures active session data and task states into the L1 Short-term memory layer.
- Upon task completion or user confirmation, relevant data is migrated to the L2 Contextual layer.
- Recurring patterns and scenarios that appear more than three times are distilled into L3 Semantic memory.
- Highly validated knowledge (10+ confirmations) and core identity traits are promoted to the L4 Long-term layer.
- Intelligent retrieval processes use vector indexing and multi-dimensional filtering to pull relevant memories across all layers.
- A dynamic decay algorithm periodically reduces the weight of older or less relevant memories to optimize storage.
OpenClaw Memory Fix Setup
To implement this within your workspace, first create the hierarchical directory structure:
mkdir -p memory/L1 memory/L2 memory/L3 memory/L4
Once the structure is in place, you can manage the memory lifecycle using the provided Node.js scripts. This is a vital part of maintaining your Openclaw Skills setup:
# Check the current status of the memory layers
node scripts/memory.js status
# Manually trigger data migration between layers
node scripts/memory.js migrate
# Preview the impact of dynamic decay on current data
node scripts/memory.js preview-decay
OpenClaw Memory Fix Data Schema & Taxonomy
The memory system organizes data into a tiered file-based structure within the memory/ directory:
| Layer | Memory Type | Storage Content | Retention Policy |
|---|---|---|---|
| L1 | Short-term | Current session and task states | 50% decay per session |
| L2 | Contextual | Scenes, emotions, and confidence levels | 10% decay per month |
| L3 | Semantic | Knowledge base and lessons learned | 5% decay per month |
| L4 | Long-term | Core principles and user persona | Permanent (0-1% decay) |
All configurations and layer-specific parameters are managed via a central config.json file located in the memory root folder.
OpenClaw Memory Fix Advanced Features
- Multi-dimensional filtering allows the agent to retrieve memories based on tags, timestamps, or even emotional context.
- Self-reflection mechanism enables the agent to actively ask clarifying questions when retrieved memory is blurry or low-confidence.
- Integration of vector indexing with knowledge graphs for high-precision semantic retrieval.
- Automated layer migration rules that ensure high-value information naturally ascends to permanent storage.
- Simulation tools to preview memory decay, allowing for fine-tuning of retention parameters within the Openclaw Skills ecosystem.
SKILL.md
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