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name: m2wise description: Memory-to-Wisdom Engine for AI agents. Use this skill to give yourself long-term memory, extract user preferences/facts from conversations, and track wisdom evolution. emoji: 🧠 requires: bins: [python3, pip, m2wise-mcp] env: [OPENAI_API_KEY, M2WISE_SILICONFLOW_API_KEY]

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M2Wise - Memory-to-Wisdom Engine

This is a skill for the OpenClaw AI Agent to enable long-term memory and self-evolving wisdom.

🎯 Purpose

As an AI agent, you inherently lack persistent memory across distinct sessions. The M2Wise skill bridges this gap. It allows you to:

• Extract and store memories (facts, preferences, commitments) from user conversations.

• Retrieve contextual memories and aggregated wisdom before answering.

• Consolidate raw memories into overarching "Wisdom" (rules, schemas, and skills) during idle time.

🚦 When to Use This Skill

You should proactively invoke this skill in the following scenarios:

• User expresses a preference: e.g., "I prefer short answers", "Don't use emojis."

• User shares a personal fact: e.g., "I work as a Python developer", "I live in Beijing."

• User asks you to remember: e.g., "Remember that I hate spam."

• Before answering complex questions: To recall the user's past preferences or facts to personalize your response.

🛠️ How to Use (Agent Instructions)

You can interact with M2Wise via its Python SDK. Use your shell/execution environment to run these scripts snippet by snippet.

1. Installation Requirements

Make sure the environment has M2Wise installed before calling its Python API: ``bash pip install m2wise[all] `

2. Saving Memories (Online Phase)

When you detect a fact or preference in the conversation, run a quick python script to save it: `python from m2wise_sdk import M2WiseSDK sdk = M2WiseSDK()

Extract and save the user's message

sdk.add_message("current_user_id", "I prefer concise Chinese answers for technical questions") `

3. Retrieving Context (Online Phase)

Before fulfilling a user's request, fetch their relevant memories: `python from m2wise_sdk import M2WiseSDK sdk = M2WiseSDK() context = sdk.get_context("current_user_id", "How should I answer this technical question?") print("Retrieved Context:", context) ` Action: Read the output of this script and adapt your final response to the user based on the retrieved context.

4. Background Processing (Sleep & Dream)

It is a good practice to trigger memory consolidation periodically (e.g., at the end of a long task). `python from m2wise_sdk import M2WiseSDK sdk = M2WiseSDK()

Sleep: Extracts memories and groups them into Wisdom Drafts

sdk.trigger_sleep("current_user_id")

Dream: Verifies drafts against counterexamples and publishes them

sdk.trigger_dream("current_user_id") `

🧩 MCP Server Alternative

If your OpenClaw runtime supports MCP (Model Context Protocol), you can start the M2Wise MCP server and use its native tools instead of writing Python scripts: `bash

Start the MCP server

m2wise-mcp --data-dir ./data ` Available MCP Tools:

• m2wise_add: Add memory from conversation.

• m2wise_search: Search memories and wisdom.

• m2wise_sleep: Generate wisdom drafts.

• m2wise_dream: Verify and publish wisdom.

🧠 Memory and Wisdom Types You Will Encounter

• Memories: preference (likes/dislikes), fact (states/attributes), commitment (future actions).

• Wisdoms: principle (interaction guidelines), schema (behavioral patterns), skill (operational tactics).

🚀 Best Practices

• Be Proactive: Don't wait for the user to explicitly say "remember this". If they state a strong preference, save it using sdk.add_message().

• Context First: For ambiguous requests, always query the memory bank first.

• Consolidate Often: Run trigger_sleep() and trigger_dream() after completing a major task to ensure your wisdom evolves and stays clean.

🔗 Resources

• GitHub Repository: https://github.com/zengyi-thinking/M2Wise.git

• Installation via OpenClaw (ClawHub):

`bash npx clawdhub@latest install m2wise ``