An intelligent memory layer that enables AI agents to semantically store and recall user preferences, patterns, and conversational context.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install mem0
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 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?
Mem0 provides a dynamic, adaptive memory layer for Openclaw Skills, allowing agents to move beyond static fact storage. By integrating semantic search and automatic context extraction, it allows Clawdbot to remember specific user communication styles, work patterns, and evolving interests. This skill bridges the gap between structured static data and fluid conversational context.
Unlike standard file-based memory systems, Mem0 uses vector embeddings to retrieve relevant history in sub-50ms. This significantly improves the personalization and accuracy of agent responses while using up to 90% fewer tokens than full conversation history retrieval. It is a core component for developers building sophisticated agents using Openclaw Skills.
Mem0 Memory Integration Use Cases
- Automatically remembering user preferences for code formatting or communication style
- Recalling historical context from past interactions without re-reading entire log files
- Adapting agent behavior based on observed user patterns and frequent request timings
- Storing non-sensitive personal context to build more helpful and personalized automated workflows
- Learning from user corrections to prevent repeating mistakes in future sessions
How Mem0 Memory Integration Works
- Before generating a response, the agent triggers a semantic search to check for relevant memories associated with the user query.
- The system retrieves the most relevant preferences or patterns based on vector similarity.
- The agent incorporates these retrieved memories into its system prompt to personalize the interaction.
- After the interaction, the agent uses an extraction script to identify new patterns or explicit instructions to store.
- The skill automatically deduplicates and merges new information with existing memories to maintain a clean knowledge base.
Mem0 Memory Integration Setup
Ensure your OPENAI_API_KEY is configured in your environment as the skill uses it for embeddings and extraction. You can test the installation by running a manual search or listing current memories:
# Search for existing user preferences
node scripts/mem0-search.js "communication style"
# List all stored memories for a specific user
node scripts/mem0-list.js --user=username
Mem0 Memory Integration Data Schema & Taxonomy
| Component | Technical Specification |
|---|---|
| Vector Store | Local vector storage for semantic embeddings |
| History DB | SQLite database located at ~/.mem0/history.db |
| Embedding Model | openai/text-embedding-3-small |
| Processing LLM | openai/gpt-4o-mini |
| Metadata | Includes unique memory IDs, user associations, and timestamps |
Mem0 Memory Integration Advanced Features
- Programmatic JSON output mode via environment variables for integration with other Openclaw Skills
- High-performance semantic search with sub-50ms retrieval times
- Intelligent memory merging that uses LLMs to prevent data redundancy
- Support for isolated multi-user memory namespaces
- Explicit vs. Implicit storage modes for fine-grained control over what the agent remembers
SKILL.md
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