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LautaroSanz
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Claude-Mem OpenClaw Plugin

Captures tool usage observations and generates semantic summaries for OpenCLaw agents' persistent memory across sessions.

1|Updated Mar 12, 2026
Search Tags:#context#agent#AI#memory#persistence#OpenClaw
One-click install
npx skills add https://github.com/LautaroSanz/opencode-mem --skill claude-mem-openclaw-plugin-lautarosanz
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: Claude-Mem OpenClaw Plugin
Source: https://github.com/LautaroSanz/opencode-mem/tree/main/openclaw
Command: npx skills add https://github.com/LautaroSanz/opencode-mem --skill claude-mem-openclaw-plugin-lautarosanz

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This plugin provides persistent memory for your OpenClaw agents, allowing them to retain context and learn from past interactions across sessions.

Core Features & Use Cases

  • Persistent Context: Agents remember previous conversations, tool usage, and project details.
  • Automated Summarization: The system automatically summarizes agent work and learning.
  • Real-time Feed: Optionally stream agent observations to a messaging channel for live monitoring.
  • Use Case: Imagine an agent working on a complex coding project. With Claude-Mem, it can recall previous architectural decisions, code snippets, and debugging steps, leading to more efficient and coherent development.

Quick Start

Install the plugin using the provided script to enable persistent memory for your agents.

Frequently Asked Questions about Claude-Mem OpenClaw Plugin

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I add persistent memory to AI agents so they remember context across sessions?▼

You can give agents persistent memory by capturing tool usage observations and generating semantic summaries that integrate with a worker service to store and retrieve context, ensuring knowledge continuity across sessions.

Can I stream AI agent observations to a messaging channel in real time?▼

Yes, you can stream agent observations to a messaging channel for live monitoring. The system supports optional real-time observation feeds to track agent work and automated summarization as it happens.

How does automated summarization work for agent memory and context retention?▼

Automated summarization works by capturing tool usage observations from agent interactions and generating semantic summaries. These summaries are stored via a worker service, allowing agents to recall previous architectural decisions and debugging steps.

Do I need a specific worker service to store and retrieve agent memory context?▼

Yes, the system integrates with a worker service to store and retrieve context across sessions. This worker service is required to handle the semantic summaries and ensure knowledge continuity for your agents.

What's the best way to maintain knowledge continuity for agents working on complex coding projects?▼

The best way to maintain knowledge continuity is using a plugin that provides persistent context by automatically summarizing agent work. This allows agents to recall previous architectural decisions, code snippets, and debugging steps across sessions.

Are there limitations to using persistent context plugins for agent memory management?▼

Limitations of persistent context plugins include the dependency on a worker service for storage and retrieval, and the need to install the plugin via a provided script before agents can begin capturing tool usage observations across sessions.

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