A high-performance multimodal memory framework for AI agents featuring granular project isolation and natural language feedback processing.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install openclaw-memory-system
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-system 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 System Skill?
The OpenClaw Memory System Skill is a robust, developer-centric memory architecture designed to elevate the persistence and reliability of AI agents. By integrating this skill into your ecosystem of Openclaw Skills, you enable agents to remember not just text, but also images and complex tool execution histories across multiple sessions.
This system provides a standardized way to manage long-term intelligence, ensuring that agents can learn from past interactions while adhering to strict security protocols. It handles the heavy lifting of data categorization, permission management, and natural language intent parsing for memory updates, allowing developers to focus on building more capable agentic workflows.
OpenClaw Memory System Skill Use Cases
- Persistent image recognition and metadata retrieval for visual-heavy agent workflows.
- Auditing and optimizing agent performance through comprehensive tool call history analysis.
- Implementing multi-tenant environments with strict project and user memory isolation.
- Iterative knowledge refinement using natural language feedback and version-controlled rollbacks.
How OpenClaw Memory System Skill Works
- The agent captures multimodal inputs like images or tool results and routes them through the storage engine.
- The system assigns data to specific namespaces based on the active project, agent, or user context defined in the configuration.
- Security layers validate access paths and permissions before executing atomic write operations to prevent data corruption.
- When users provide natural language corrections, the feedback parser identifies the intent to modify, delete, or confirm specific knowledge entries.
- The agent utilizes a unified API to query historical context, which is then fed back into the LLM prompt to inform future decision-making.
OpenClaw Memory System Skill Setup
Method 1: ClawHub Installation (Recommended)
clawhub install openclaw-memory-system
Method 2: Manual Installation
# Copy skill files to the OpenClaw workspace
cp -r /path/to/openclaw-memory-system/skills/* ~/.openclaw/workspace/skills/
# Copy configuration files
cp -r /path/to/openclaw-memory-system/configs/* ~/.openclaw/workspace/configs/
# Verify the installation
node ~/.openclaw/workspace/skills/multimodal-memory/test-multimodal.js
OpenClaw Memory System Skill Data Schema & Taxonomy
The OpenClaw Memory System organizes data through a dual-configuration approach to ensure scalability for diverse Openclaw Skills.
| File | Role | Key Components |
|---|---|---|
multimodal-config.json |
Storage Logic | Defines directories for images, captions, tool-calls, and cache TTL settings. |
memory-namespaces.json |
Access Logic | Manages project-level isolation, agent inheritance, and granular user permissions. |
Directory Structure:
memory/multimodal/images: Stores image assets with associated metadata.memory/multimodal/tool-calls: Logs execution success, duration, and parameters.users/user_[id]: Isolated storage path for individual user memory and admin permissions.
OpenClaw Memory System Skill Advanced Features
- Multi-level permission hierarchy supporting read, write, delete, and admin roles for secure environments.
- Version control system for memory entries with full support for historical comparison and rollbacks.
- Natural language intent parsing that converts user feedback directly into memory updates.
- Lark (Feishu) interaction card integration for real-time memory management notifications.
- Cross-modal retrieval capabilities allowing agents to associate visual data with specific tool execution logs.
SKILL.md
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