A persistent memory skill that enables AI agents to query and recall past interactions and user preferences from MemOS Cloud.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install huamu668-memos-cloud
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 huamu668-memos-cloud 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 MemOS Memory Recall?
MemOS Memory Recall is a sophisticated bridge between your AI coding agent and your personal knowledge base stored in MemOS Cloud. By utilizing this skill, your agent gains the ability to "remember" specific details from previous sessions, such as project requirements, architectural decisions, and personal coding preferences. This continuity transforms the AI from a stateless assistant into a long-term collaborator that understands the evolution of your work.
As part of the broader Openclaw Skills ecosystem, MemOS Recall focuses on context-aware retrieval. When a user asks about something discussed "last time" or refers to a previous decision, the agent proactively searches your cloud memories to provide an informed response, significantly reducing the need for repetitive explanations and manual context-setting.
MemOS Memory Recall Use Cases
- Recalling specific technical decisions made in previous development sessions.
- Retrieving user-specific coding style preferences or architectural patterns.
- Responding to queries like "What did we decide on the database schema last time?"
- Providing continuity across long-term projects where context spans multiple days or weeks.
How MemOS Memory Recall Works
- The AI agent monitors user input for triggers such as "previously," "before," or direct questions about past interactions.
- Upon detection, the skill triggers a search query processed by a local Node.js bridge.
- The bridge communicates with the MemOS Cloud API to find relevant memory fragments based on the user's current query.
- If relevant data is returned, the agent integrates this historical context into its current reasoning process.
- The agent generates a final response that cites the retrieved memory to provide a seamless and personalized experience.
MemOS Memory Recall Setup
To get started with this skill within the Openclaw Skills framework, ensure the MemOS plugin is correctly placed in your agent's directory.
- Deploy the MemOS bridge script to your local plugin folder:
# Ensure the script is located at:
~/.claude/plugins/memos-cloud/memos-api.js
- Configure your MemOS Cloud API credentials as required by the script environment.
- Test the retrieval functionality manually via the CLI:
node ~/.claude/plugins/memos-cloud/memos-api.js search "your search term"
MemOS Memory Recall Data Schema & Taxonomy
The skill manages memory retrieval through a structured command-line interface, processing data as follows:
| Attribute | Description |
|---|---|
user_query |
The search term or extracted intent used to query MemOS Cloud. |
memory_content |
The raw text or summarized record retrieved from the cloud storage. |
search_results |
A collection of matches integrated into the AI's prompt context. |
error_handling |
Silent failure mode ensures the agent continues responding even if the API is unreachable. |
MemOS Memory Recall Advanced Features
- Intelligent query refinement to improve search accuracy based on conversation intent.
- Silent failover logic that prevents API errors from disrupting the user experience.
- Seamless integration with MemOS Cloud tagging for structured data retrieval.
- Support for multi-session context persistence across different Openclaw Skills implementations.
SKILL.md
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