openclaw-backups/archive/inactive-skills/memu/SKILL.md

name, version, description, homepage, repository, metadata

name	version	description	homepage	repository	metadata
memu	1.0.10	24/7 Always-On Proactive Memory for AI Agents. Built for long-running use and reduces LLM token cost of keeping agents always online.	https://memu.bot	https://github.com/NevaMind-AI/memU	emoji category 🧠 memory

MemU 🧠

24/7 always-on proactive memory for AI agents.

Overview

MemU continuously captures and understands user intent without needing explicit commands. It transforms raw interactions into structured, searchable, proactive intelligence.

Key Features:

• 🤖 24/7 proactive agent - continuous background monitoring
• 🎯 User intention capture - remembers goals, preferences, context
• 💰 Cost efficient - caches insights, avoids redundant LLM calls
• 🗂️ File-system-like memory - hierarchical, instantly accessible
• 🔄 Cross-references - memories link to each other
• 📥 Persistent & portable - export, backup, transfer like files

Memory Structure

memory/
├── preferences/
│   ├── communication_style.md
│   └── topic_interests.md
├── relationships/
│   ├── contacts/
│   └── interaction_history/
├── knowledge/
│   ├── domain_expertise/
│   └── learned_skills/
└── context/
    ├── recent_conversations/
    └── pending_tasks/

Installation Options

Option 1: Cloud Version (Recommended for Quick Start)

Use hosted service at https://memu.so - no installation needed!

API Base URL: https://api.memu.so Auth: Authorization: Bearer YOUR_API_KEY

Option 2: Self-Hosted

Requires Python 3.13+

# Install from source
git clone https://github.com/NevaMind-AI/memU.git
cd memU
pip install -e .

# Quick test (in-memory)
export OPENAI_API_KEY=your_api_key
cd tests
python test_inmemory.py

# With PostgreSQL (persistent storage)
docker run -d \
  --name memu-postgres \
  -e POSTGRES_USER=postgres \
  -e POSTGRES_PASSWORD=postgres \
  -e POSTGRES_DB=memu \
  -p 5432:5432 \
  pgvector/pgvector:pg16
export OPENAI_API_KEY=your_api_key
cd tests
python test_postgres.py

Core APIs

memorize() - Continuous Learning

Processes inputs in real-time and immediately updates memory:

result = await service.memorize(
    resource_url="path/to/file.json",  # File path or URL
    modality="conversation",            # conversation | document | image | video | audio
    user={"user_id": "123"}             # Optional: scope to a user
)

retrieve() - Dual-Mode Intelligence

MemU supports both proactive context loading and reactive querying:

# RAG-based retrieval (fast, proactive context)
result = await service.retrieve(
    queries=[
        {"role": "user", "content": {"text": "What are their preferences?"}},
        {"role": "user", "content": {"text": "Tell me about work habits"}}
    ],
    where={"user_id": "123"},  # Optional: scope filter
    method="rag"  # or "llm" for deeper reasoning
)

# Returns context-aware results:
{
    "categories": [...],     # Relevant topic areas (auto-prioritized)
    "items": [...],          # Specific memory facts
    "resources": [...],      # Original sources for traceability
    "next_step_query": "..." # Predicted follow-up context
}

Proactive Use Cases

1. Information Recommendation

Agent monitors interests and proactively surfaces relevant content based on:

• Reading history
• Saved articles
• Search queries

2. Email Management

Learns communication patterns and handles routine correspondence:

• Response templates
• Priority contacts
• Scheduling preferences
• Categorization and prioritization

3. Trading & Financial Monitoring

Tracks market context and user investment behavior:

• Risk tolerance
• Preferred sectors
• Response patterns to market events
• Portfolio rebalancing triggers

Custom LLM and Embedding Providers

MemU supports custom providers beyond OpenAI:

from memu import MemUService

service = MemUService(
    llm_profiles={
        "default": {
            "base_url": "https://dashscope.aliyuncs.com/compatible-mode/v1",
            "api_key": "your_api_key",
            "chat_model": "qwen3-max",
        },
        "embedding": {
            "base_url": "https://api.voyageai.com/v1",
            "api_key": "your_voyage_api_key",
            "embed_model": "voyage-3.5-lite"
        }
    }
)

OpenRouter Integration

from memu import MemUService

service = MemUService(
    llm_profiles={
        "default": {
            "provider": "openrouter",
            "client_backend": "httpx",
            "base_url": "https://openrouter.ai",
            "api_key": "your_openrouter_api_key",
            "chat_model": "anthropic/claude-3.5-sonnet",
            "embed_model": "openai/text-embedding-3-small",
        }
    }
)

Environment Variables

Variable	Description
OPENAI_API_KEY	OpenAI API key
OPENROUTER_API_KEY	OpenRouter API key

Performance

MemU achieves 92.09% average accuracy on Locomo benchmark.

Resources

• API Documentation: https://memu.pro/docs
• Cloud Version: https://app.memu.so
• GitHub: https://github.com/NevaMind-AI/memU
• Discord: https://discord.gg/memu