A comprehensive memory management system for OpenClaw that enables AI agents to remember preferences, learn from errors, and maintain long-term context.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install openclaw-memory-fix-skill
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-skill 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 Skill?
The OpenClaw Memory Fix Skill is a specialized solution designed to overcome the common limitation of context loss in AI agents. By providing a structured, four-layer memory architecture, this skill allows an AI to think and grow more like a human. It transforms the agent from a stateless chat bot into a persistent assistant that understands its identity, recognizes user habits, and continuously evolves through self-reflection.
Utilizing this skill within the ecosystem of Openclaw Skills ensures that every interaction contributes to a cumulative knowledge base. It effectively bridges the gap between short-term task execution and long-term intelligence, making the agent significantly more reliable for complex, multi-session workflows.
OpenClaw Memory Fix Skill Use Cases
- Persistent user personalization across different chat sessions using the USER.md configuration.
- Error prevention by logging past mistakes in a feedback loop to avoid repeating them.
- Long-term project tracking where the agent maintains a current task board via NOW.md.
- Building a local knowledge graph that grows automatically as the agent completes tasks.
How OpenClaw Memory Fix Skill Works
- Initialization: Upon startup, the skill loads the SOUL.md and USER.md files to establish the agent's persona and understand the user's environment.
- Memory Triggering: The system monitors interactions for specific triggers, such as the first conversation of the day, task completion, or explicit user requests to remember data.
- Tiered Storage: Information is routed through a four-tier architecture: Short-term (current session), Context (daily logs), Semantic (knowledge graph), and Long-term (curated memory).
- Confidence Assessment: The agent evaluates its own memory retrieval accuracy using a percentage-based confidence scale to determine how to present information to the user.
- Consolidation: A daily heartbeat process or script organizes daily logs into permanent entries in the MEMORY.md file, ensuring Openclaw Skills stay optimized.
OpenClaw Memory Fix Skill Setup
To deploy the memory system, you need to migrate the configuration templates into your active workspace:
# 1. Copy the configuration files into your OpenClaw workspace directory
cp -r openclaw-memory-fix/config-files/* ~/.openclaw/workspace/
# 2. Restart your OpenClaw instance to initialize the memory layers
OpenClaw Memory Fix Skill Data Schema & Taxonomy
The skill organizes data across several Markdown-based files to maintain a human-readable yet machine-parseable taxonomy:
| File | Purpose |
|---|---|
SOUL.md |
Defines the AI's identity, personality, and behavioral guidelines. |
USER.md |
Stores user profiles, frequent preferences, and environmental constants. |
MEMORY.md |
The master file for curated, long-term historical memory. |
NOW.md |
A real-time task board for tracking the current operational focus. |
FEEDBACK-LOG.md |
A specialized log for recording errors and planning self-improvements. |
AGENTS.md |
Contains rules for multi-agent collaboration and memory access permissions. |
OpenClaw Memory Fix Skill Advanced Features
- Multi-Tiered Architecture: Separates ephemeral data from permanent knowledge for better token efficiency.
- Self-Reflection Mechanism: Enables the agent to analyze its own performance and update its TRAINING.md logic.
- Semantic Knowledge Integration: Supports dynamic knowledge graph construction for complex information retrieval.
- Context Compression: Automatically optimizes long-term storage to keep Openclaw Skills performing at peak speed.
- Confidence-Based Reasoning: Implements a 50-100% confidence scale to manage uncertainty in retrieved memories.
SKILL.md
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