The mem0 skill provides an intelligent, adaptive memory layer that learns and recalls user preferences, patterns, and context across conversations.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install mem0-1-0-0
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 mem0-1-0-0 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 mem0 Memory Integration?
The mem0 integration serves as the dynamic long-term memory layer for your AI agent, moving beyond static facts into the realm of behavioral intelligence. Unlike traditional structured storage, this Openclaw Skills component uses semantic search to understand the nuances of how a user interacts, allowing the agent to adapt its tone, detail level, and suggestions based on historical data.
By implementing this skill, developers can reduce token usage by 90% compared to full-context retrieval while increasing response accuracy by up to 26%. It acts as a companion to standard documentation, focusing on how the user likes things done rather than just what the facts are, creating a truly personalized AI experience that evolves over time.
mem0 Memory Integration Use Cases
- Explicitly remembering specific user requests or 'remember this' prompts during a chat.
- Learning communication style preferences, such as a preference for concise updates or direct answers.
- Recalling daily patterns, such as specific times a user typically requests transit or weather information.
- Adapting to evolving user interests and current project contexts without requiring manual updates to configuration files.
- Tracking user corrections to ensure the agent does not repeat the same mistakes in future sessions.
How mem0 Memory Integration Works
- Search: Before responding to a user, the agent executes a semantic search using mem0-search.js to find contextually relevant past interactions.
- Retrieve: The system identifies relevant memories ranked by semantic similarity and pulls them into the current reasoning loop.
- Adapt: The agent incorporates these retrieved memories to personalize the response, matching the user's known preferences and patterns.
- Store: After the interaction, the agent uses mem0-add.js to either store explicit user requests or automatically extract new patterns from the conversation history.
- Manage: The underlying LLM automatically extracts, deduplicates, and merges related memories to maintain a clean and efficient vector store.
mem0 Memory Integration Setup
To integrate this component into your Openclaw Skills library, ensure your OPENAI_API_KEY is configured in your environment. The system uses local storage by default, which can be configured in the scripts directory.
# Install dependencies and add your first memory
node scripts/mem0-add.js "The user prefers Python for all data science tasks"
# Search your memories semantically
node scripts/mem0-search.js "coding preferences"
mem0 Memory Integration Data Schema & Taxonomy
The skill organizes data using a combination of a local history database and a semantic vector store. This allows for both precise ID-based management and fluid natural language retrieval.
| Component | Description | Format |
|---|---|---|
| Memory ID | Unique identifier for deleting or updating specific entries | UUID |
| Content | The semantic text of the learned preference or pattern | String |
| Metadata | Timestamps and user associations for context filtering | JSON |
| Vector Store | Local storage for embedding-based similarity searching | Vector DB |
mem0 Memory Integration Advanced Features
- Programmatic JSON output mode via environment variables for seamless integration into larger automated workflows.
- Automatic memory deduplication and merging to prevent redundant data entries across multiple sessions.
- Support for bulk conversation analysis to extract multiple behavioral patterns from long chat histories.
- High-performance semantic retrieval with sub-50ms search times for real-time responsiveness.
- Cross-conversation context retention that allows the agent to maintain a consistent persona across different platforms.
SKILL.md
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