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Krilly
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skills/memu/SKILL.md
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skills/memu/SKILL.md
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name: memu
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version: 1.0.10
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description: 24/7 Always-On Proactive Memory for AI Agents. Built for long-running use and reduces LLM token cost of keeping agents always online.
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homepage: https://memu.bot
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repository: https://github.com/NevaMind-AI/memU
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metadata: {"emoji":"🧠","category":"memory"}
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---
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# MemU 🧠
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24/7 always-on proactive memory for AI agents.
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## Overview
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MemU continuously captures and understands user intent without needing explicit commands. It transforms raw interactions into structured, searchable, proactive intelligence.
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**Key Features:**
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- 🤖 24/7 proactive agent - continuous background monitoring
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- 🎯 User intention capture - remembers goals, preferences, context
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- 💰 Cost efficient - caches insights, avoids redundant LLM calls
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- 🗂️ File-system-like memory - hierarchical, instantly accessible
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- 🔄 Cross-references - memories link to each other
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- 📥 Persistent & portable - export, backup, transfer like files
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## Memory Structure
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```
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memory/
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├── preferences/
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│ ├── communication_style.md
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│ └── topic_interests.md
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├── relationships/
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│ ├── contacts/
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│ └── interaction_history/
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├── knowledge/
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│ ├── domain_expertise/
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│ └── learned_skills/
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└── context/
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├── recent_conversations/
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└── pending_tasks/
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```
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## Installation Options
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### Option 1: Cloud Version (Recommended for Quick Start)
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Use hosted service at https://memu.so - no installation needed!
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**API Base URL:** `https://api.memu.so`
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**Auth:** `Authorization: Bearer YOUR_API_KEY`
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### Option 2: Self-Hosted
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Requires Python 3.13+
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```bash
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# Install from source
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git clone https://github.com/NevaMind-AI/memU.git
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cd memU
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pip install -e .
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# Quick test (in-memory)
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export OPENAI_API_KEY=your_api_key
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cd tests
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python test_inmemory.py
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# With PostgreSQL (persistent storage)
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docker run -d \
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--name memu-postgres \
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-e POSTGRES_USER=postgres \
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-e POSTGRES_PASSWORD=postgres \
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-e POSTGRES_DB=memu \
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-p 5432:5432 \
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pgvector/pgvector:pg16
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export OPENAI_API_KEY=your_api_key
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cd tests
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python test_postgres.py
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```
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## Core APIs
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### `memorize()` - Continuous Learning
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Processes inputs in real-time and immediately updates memory:
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```python
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result = await service.memorize(
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resource_url="path/to/file.json", # File path or URL
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modality="conversation", # conversation | document | image | video | audio
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user={"user_id": "123"} # Optional: scope to a user
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)
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```
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### `retrieve()` - Dual-Mode Intelligence
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MemU supports both **proactive context loading** and **reactive querying**:
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```python
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# RAG-based retrieval (fast, proactive context)
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result = await service.retrieve(
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queries=[
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{"role": "user", "content": {"text": "What are their preferences?"}},
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{"role": "user", "content": {"text": "Tell me about work habits"}}
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],
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where={"user_id": "123"}, # Optional: scope filter
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method="rag" # or "llm" for deeper reasoning
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)
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# Returns context-aware results:
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{
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"categories": [...], # Relevant topic areas (auto-prioritized)
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"items": [...], # Specific memory facts
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"resources": [...], # Original sources for traceability
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"next_step_query": "..." # Predicted follow-up context
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}
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```
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## Proactive Use Cases
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### 1. Information Recommendation
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Agent monitors interests and proactively surfaces relevant content based on:
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- Reading history
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- Saved articles
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- Search queries
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### 2. Email Management
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Learns communication patterns and handles routine correspondence:
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- Response templates
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- Priority contacts
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- Scheduling preferences
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- Categorization and prioritization
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### 3. Trading & Financial Monitoring
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Tracks market context and user investment behavior:
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- Risk tolerance
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- Preferred sectors
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- Response patterns to market events
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- Portfolio rebalancing triggers
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## Custom LLM and Embedding Providers
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MemU supports custom providers beyond OpenAI:
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```python
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from memu import MemUService
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service = MemUService(
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llm_profiles={
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"default": {
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"base_url": "https://dashscope.aliyuncs.com/compatible-mode/v1",
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"api_key": "your_api_key",
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"chat_model": "qwen3-max",
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},
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"embedding": {
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"base_url": "https://api.voyageai.com/v1",
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"api_key": "your_voyage_api_key",
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"embed_model": "voyage-3.5-lite"
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}
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}
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)
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```
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## OpenRouter Integration
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```python
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from memu import MemUService
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service = MemUService(
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llm_profiles={
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"default": {
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"provider": "openrouter",
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"client_backend": "httpx",
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"base_url": "https://openrouter.ai",
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"api_key": "your_openrouter_api_key",
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"chat_model": "anthropic/claude-3.5-sonnet",
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"embed_model": "openai/text-embedding-3-small",
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}
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}
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)
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```
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## Environment Variables
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| Variable | Description |
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|----------|-------------|
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| `OPENAI_API_KEY` | OpenAI API key |
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| `OPENROUTER_API_KEY` | OpenRouter API key |
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## Performance
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MemU achieves **92.09% average accuracy** on Locomo benchmark.
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## Resources
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- **API Documentation:** https://memu.pro/docs
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- **Cloud Version:** https://app.memu.so
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- **GitHub:** https://github.com/NevaMind-AI/memU
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- **Discord:** https://discord.gg/memu
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