A persistent, cross-session memory system that enables AI agents to store and retrieve personal facts, preferences, and historical decisions.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install managing-actingweb-memory
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 managing-actingweb-memory 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 ActingWeb Personal AI Memory?
ActingWeb Personal AI Memory provides a robust framework for maintaining long-term context across multiple AI interactions. By utilizing this system through Openclaw Skills, developers can ensure their agents remember specific user preferences, past decisions, and unique constraints that typically disappear between sessions. This persistent memory layer is connected via the Model Context Protocol (MCP), allowing for a seamless flow of information between the user and the agent.
This skill transforms a standard AI into a personalized assistant capable of recalling intricate details like dietary restrictions, business logic rationales, or travel preferences. By integrating these Openclaw Skills, you provide your AI with a digital brain that grows more useful over time, significantly reducing the need for repetitive prompting and increasing the accuracy of personalized recommendations.
ActingWeb Personal AI Memory Use Cases
- Retrieving personalized recommendations for restaurants, tools, or products based on historical data.
- Recalling past project decisions and the specific rationale behind them.
- Planning complex itineraries using saved travel constraints and loyalty preferences.
- Managing health and dietary information to ensure safe and relevant advice.
- Summarizing recent activities or recapping long-term progress on specific tasks.
How ActingWeb Personal AI Memory Works
- Discovery: The agent calls the how_to_use tool to understand the user's current memory status and account capabilities.
- Proactive Searching: Before answering a prompt, the agent performs keyword or semantic searches across categories to find relevant background information.
- Contextual Integration: The agent incorporates retrieved facts into its response, attributing the information naturally to build trust.
- Atomic Saving: New insights are saved as atomic memories, ensuring that decision-level information is captured for future use.
- Maintenance: The agent monitors for contradictions and offers to update or delete outdated memories to keep the database accurate.
ActingWeb Personal AI Memory Setup
To integrate this capability into your agent, configure the ActingWeb MCP server. Ensure your environment has the necessary credentials for the ActingWeb service.
# Example of how an agent might initialize the memory search
# This is handled via the MCP tool call interface
search(query="user preference")
For first-time setup or credential recovery, refer to the internal setup guide provided with the Openclaw Skills package.
ActingWeb Personal AI Memory Data Schema & Taxonomy
Memories are organized by unique identifiers and categorized into specific types.
| Field | Description |
|---|---|
| Memory ID | Formatted as memory_type:item_id (e.g., memory_work:123) |
| Category | Defaults include health, travel, work, food, shopping, and more |
| Content | Atomic strings representing specific facts or decisions |
| Dashboard URL | A link to the web interface for manual editing |
| Metadata | Includes recency and source attribution for shared memories |
ActingWeb Personal AI Memory Advanced Features
- Shared Memories: Access memories shared from trusted connections like family members, colleagues, or other AI instances.
- Remote Actions: Control connected IoT devices or trigger external workflows through method execution.
- Context Builder: A guided wizard for complex tasks that require gathering data across multiple memory categories.
- Custom Category Creation: Define new memory types on the fly to handle niche data structures or private workflows.
- Batch Operations: Save, retrieve, or delete multiple memory items in a single tool call for high efficiency.
SKILL.md
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