- 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]
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:
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:
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:
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).
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:
# Start the MCP server
m2wise-mcp --data-dir ./dataAvailable 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()andtrigger_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):
npx clawdhub@latest install m2wise