A self-driven memory management system for AI agents modeled after the Hermes architecture to ensure long-term memory health and efficiency.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install hermes-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 hermes-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 Hermes Memory System?
The Hermes Memory System is a sophisticated self-improving framework designed to maintain the long-term memory of AI agents. By mimicking the memory auto-check mechanisms of the Hermes Agent, this Openclaw Skills integration periodically monitors the health of stored information, ensuring that data remain concise and actionable. It prevents context window bloat by enforcing soft character limits and suggesting proactive reorganization of files like MEMORY.md and USER.md.
This skill enables a semi-autonomous workflow where the agent recognizes when its memory is reaching capacity and provides actionable prompts to the user for pruning or archiving. By integrating this within the Openclaw Skills ecosystem, developers can ensure their agents remain sharp and context-aware over long-term projects without manual oversight of memory files.
Hermes Memory System Use Cases
- Monitoring AI agent memory usage to prevent token overflow in long-running sessions.
- Automatically archiving outdated long-term memory data to maintain relevance.
- Maintaining a structured and concise user profile for personalized agent interactions.
- Implementing scheduled health checks for self-improving agent systems to ensure data integrity.
How Hermes Memory System Works
- The system monitors the character count of MEMORY.md and USER.md files against predefined recommended thresholds.
- Periodic health checks are executed via a Python script to evaluate memory density and usage rates.
- If memory usage exceeds 70%, the system generates a non-intrusive suggestion for refinement and pruning.
- At 90% usage, the system triggers a mandatory reorganization prompt to ensure the agent does not lose critical context.
- Old or low-priority data is archived, while the active memory file is updated to reflect the most current and vital information.
Hermes Memory System Setup
To perform a manual health check, run the following command:
python3 ~/self-improving/hermes-memory/memory_health.py
To automate the health check (e.g., every weekday at 17:30), add a entry to your crontab:
# Open the crontab editor
crontab -e
# Add the following line
30 17 * * 1-5 /home/linuxbrew/.linuxbrew/bin/python3 /home/jdvrommel/self-improving/hermes-memory/memory_health.py >> /home/jdvrommel/self-improving/hermes-memory/health.log 2>&1
Hermes Memory System Data Schema & Taxonomy
The system organizes data into specific files to maintain a clear separation of concerns:
| File | Purpose | Recommended Size |
|---|---|---|
| MEMORY.md | Agent long-term memory and project context | ≤ 2200 characters |
| USER.md | User profile and preference mapping | ≤ 1375 characters |
| ~/self-improving/ | Directory for skill-specific memory and scripts | Scalable |
Reorganization is triggered based on the following usage thresholds:
- Usage ≥ 70%: Suggests refinement of content.
- Usage ≥ 90%: Requires mandatory cleanup to prevent performance degradation.
Hermes Memory System Advanced Features
- Automated health logging for auditing the evolution of agent memory over time.
- Integration with system-level crontab for headless, scheduled background maintenance.
- Real-time memory updates, offering more flexibility than static snapshot mechanisms.
- Configurable usage thresholds to adapt to different AI model context window requirements.
SKILL.md
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