MeshMorize is a robust multi-layer memory system designed for LLM agents to manage daily notes, graph-based knowledge, and interaction compliance.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install meshmorize
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 meshmorize 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 MeshMorize?
MeshMorize provides a comprehensive memory framework tailored for AI coding agents within the ecosystem of Openclaw Skills. By decoupling knowledge into specialized contextual layers, it enables agents running Python to persist and retrieve information with extreme efficiency. It serves as a structural foundation for long-term intelligence, transforming fleeting LLM context windows into durable, searchable data assets.
The system bridges short-term activity with structured, relational knowledge networks. With native workflows for session initiation, structured communication logging, and automated safety reviews, it ensures that your autonomous workflows remain deeply informed by history while adhering strictly to operational guardrails.
MeshMorize Use Cases
- Creating an autonomous agent with a rolling 5-day operational memory for daily tasks.
- Indexing complex interaction histories into a structured graph database for deep relationship modeling.
- Enforcing data compliance and performing multi-point safety checks across all historical agent communications.
- Enhancing Openclaw Skills deployment strategies with cross-layer retrieval-augmented generation (RAG).
How MeshMorize Works
- Initialization: The system initializes the workspace and rotates the short-term storage layer, creating a fresh environment for the current operational session.
- Interaction Logging: As the agent communicates, the interaction logger captures data inputs and outputs, saving them instantly into chronological logs.
- Graph Indexing: Chronological data points are processed and integrated into a central JSON-based mesh graph to build relational search indexes.
- Cross-Layer Querying: When requested, a specialized search engine traverses all tiers—from the freshest daily updates to historical long-term repositories—to surface relevant context.
- Compliance Verification: An automated auditing process runs a strict 10-point compliance check across stored data files to ensure integrity and alignment.
MeshMorize Setup
To deploy MeshMorize in your workspace, place the core Python module into your memory directory and the executable scripts into your scripts directory. Run the following commands to add them to your environment PATH and kick off your initial session:
# Symlink the operational scripts to your local bin
ln -s $(pwd)/scripts/* ~/.local/bin/
# Symlink the core bridge utility
ln -s $(pwd)/memory/bridge.py ~/.local/bin/mem-bridge
# Initialize the memory layer for your session
mem-bridge init
MeshMorize Data Schema & Taxonomy
MeshMorize splits data storage across several discrete files and directories within your agent's workspace to maintain separation of concerns:
| Storage Target | Format / Path | Description |
|---|---|---|
| Fresh Layer | memory/fresh/today.md |
Holds ephemeral notes with a rolling 5-day rotation pattern. |
| Mesh Layer | memory/mesh.json |
Stores the primary graph node structures and multi-layer search indexes. |
| Log Layer | scripts/auto_log |
Appends every captured conversation interaction chronologically. |
| Search Tool | scripts/memory_search |
Executes retrieval across fresh, daily, mesh, raw, and long-term storage. |
| Check Tool | scripts/memory_check |
Runs the specialized 10-point validation routine known as memcheck. |
MeshMorize Advanced Features
- 5-Day Fresh Rotation: Automatically cycles and archives daily markdown notes to prevent context bloat while retaining critical short-term data.
- Cross-Layer Search Graph: Executes comprehensive multi-tier lookup queries navigating seamlessly from raw text logs up to structured graph schemas.
- 10-Point
memcheckCompliance: Features a built-in verification suite that systematically scans active knowledge repositories for structural and behavioral conformity. - Extensible Python Bridge: Seamlessly hooks into any custom Python-based AI agent setup, empowering developers to expand standard Openclaw Skills capabilities.
SKILL.md
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