mnemos-memory

What This Skill Does

The mnemos-memory skill provides an end-to-end interface for Mnemos, a local-first memory layer designed specifically for autonomous coding agents like OpenClaw and ClawHub. This skill enables agents to store, retrieve, consolidate, and troubleshoot persistent, scoped memory across coding sessions. By utilizing Mnemos, your agent can move beyond ephemeral context windows, maintaining durable knowledge about project architecture, user preferences, and historical task decisions. The skill handles the complexity of installing MCP (Model Context Protocol) bridges, configuring storage backends, and validating the health of the memory layer using built-in diagnostic tools.

Installation

To integrate this capability into your OpenClaw agent, execute the following command: clawhub install openclaw/skills/skills/anthony-maio/mnemos-memory.

Once installed, verify the environment by running the default installation path: pip install "mnemos-memory[mcp]" followed by mnemos ui to initialize your local memory store. For OpenClaw automation, ensure the agent self-bootstraps by wiring the mnemos-mcp module to your MNEMOS_CONFIG_PATH before attempting memory-heavy tasks.

Use Cases

• Long-term Context Retention: Remembering project-specific conventions, code style, or architectural decisions across multiple coding sessions.
• Agent Self-Bootstrapping: Configuring OpenClaw to set up its own memory infrastructure in new repositories.
• Troubleshooting Persistence: Using mnemos-cli doctor or mnemos_inspect to diagnose why a specific fact or instruction was not retrieved correctly.
• Knowledge Consolidation: Running mnemos_consolidate after completing major feature branches to ensure the memory store remains clean and relevant.

Example Prompts

• "OpenClaw, please install the Mnemos memory system for this repository and set it up with SQLite storage."
• "I need you to check the memory logs—it seems like you forgot the folder structure we established last week; use mnemos_inspect to verify what is currently stored."
• "We are finishing this PR; please trigger mnemos_consolidate to ensure all architectural decisions are saved to the memory layer before we switch tasks."

Tips & Limitations

For production quality retrieval, always pair this skill with a robust embedding provider like openclaw, openai, or ollama. Avoid using the SimpleEmbeddingProvider for mission-critical tasks. While the skill supports advanced backends like Qdrant or Neo4j, these should only be used if you have a genuine scale requirement; otherwise, stick to the robust SQLite profile. Remember that for hosts like Cursor or generic MCP environments, memory capture is not always deterministic; ensure you trigger mnemos_store manually for essential facts you want to persist permanently.