memU

24/7 Always-On Proactive Memory for AI Agents

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memU is a memory framework built for 24/7 proactive agents. It is designed for long-running use and greatly reduces the LLM token cost of keeping agents always online, making always-on, evolving agents practical in production systems. memU continuously captures and understands user intent. Even without a command, the agent can tell what you are about to do and act on it by itself.

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🤖 OpenClaw (Moltbot, Clawdbot) Alternative

memU Bot — Now open source. The enterprise-ready OpenClaw. Your proactive AI assistant that remembers everything.

• Download-and-use and simple to get started (one-click install, < 3 min).
• Builds long-term memory to understand user intent and act proactively (24/7).
• Cuts LLM token cost with smaller context (~1/10 of comparable usage).

Try now: memu.bot · Source: memUBot on GitHub

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🗃️ Memory as File System, File System as Memory

memU treats memory like a file system—structured, hierarchical, and instantly accessible.

File System	memU Memory
📁 Folders	🏷️ Categories (auto-organized topics)
📄 Files	🧠 Memory Items (extracted facts, preferences, skills)
🔗 Symlinks	🔄 Cross-references (related memories linked)
📂 Mount points	📥 Resources (conversations, documents, images)

Why this matters: - Navigate memories like browsing directories—drill down from broad categories to specific facts - Mount new knowledge instantly—conversations and documents become queryable memory - Cross-link everything—memories reference each other, building a connected knowledge graph - Persistent & portable—export, backup, and transfer memory like files

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

Just as a file system turns raw bytes into organized data, memU transforms raw interactions into structured, searchable, proactive intelligence.

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⭐️ Star the repository

If you find memU useful or interesting, a GitHub Star ⭐️ would be greatly appreciated.

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✨ Core Features

Capability	Description
🤖 24/7 Proactive Agent	Always-on memory agent that works continuously in the background—never sleeps, never forgets
🎯 User Intention Capture	Understands and remembers user goals, preferences, and context across sessions automatically
💰 Cost Efficient	Reduces long-running token costs by caching insights and avoiding redundant LLM calls
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🔄 How Proactive Memory Works

cd examples/proactive
python proactive.py

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Proactive Memory Lifecycle

┌──────────────────────────────────────────────────────────────────────────────────────────────────┐
│                                         USER QUERY                                               │
└──────────────────────────────────────────────────────────────────────────────────────────────────┘
                 │                                                           │
                 ▼                                                           ▼
┌────────────────────────────────────────┐         ┌────────────────────────────────────────────────┐
│         🤖 MAIN AGENT                  │         │              🧠 MEMU BOT                       │
│                                        │         │                                                │
│  Handle user queries & execute tasks   │  ◄───►  │  Monitor, memorize & proactive intelligence   │
├────────────────────────────────────────┤         ├────────────────────────────────────────────────┤
│                                        │         │                                                │
│  ┌──────────────────────────────────┐  │         │  ┌──────────────────────────────────────────┐  │
│  │  1. RECEIVE USER INPUT           │  │         │  │  1. MONITOR INPUT/OUTPUT                 │  │
│  │     Parse query, understand      │  │   ───►  │  │     Observe agent interactions           │  │
│  │     context and intent           │  │         │  │     Track conversation flow              │  │
│  └──────────────────────────────────┘  │         │  └──────────────────────────────────────────┘  │
│                 │                      │         │                    │                           │
│                 ▼                      │         │                    ▼                           │
│  ┌──────────────────────────────────┐  │         │  ┌──────────────────────────────────────────┐  │
│  │  2. PLAN & EXECUTE               │  │         │  │  2. MEMORIZE & EXTRACT                   │  │
│  │     Break down tasks             │  │   ◄───  │  │     Store insights, facts, preferences   │  │
│  │     Call tools, retrieve data    │  │  inject │  │     Extract skills & knowledge           │  │
│  │     Generate responses           │  │  memory │  │     Update user profile                  │  │
│  └──────────────────────────────────┘  │         │  └──────────────────────────────────────────┘  │
│                 │                      │         │                    │                           │
│                 ▼                      │         │                    ▼                           │
│  ┌──────────────────────────────────┐  │         │  ┌──────────────────────────────────────────┐  │
│  │  3. RESPOND TO USER              │  │         │  │  3. PREDICT USER INTENT                  │  │
│  │     Deliver answer/result        │  │   ───►  │  │     Anticipate next steps                │  │
│  │     Continue conversation        │  │         │  │     Identify upcoming needs              │  │
│  └──────────────────────────────────┘  │         │  └──────────────────────────────────────────┘  │
│                 │                      │         │                    │                           │
│                 ▼                      │         │                    ▼                           │
│  ┌──────────────────────────────────┐  │         │  ┌──────────────────────────────────────────┐  │
│  │  4. LOOP                         │  │         │  │  4. RUN PROACTIVE TASKS                  │  │
│  │     Wait for next user input     │  │   ◄───  │  │     Pre-fetch relevant context           │  │
│  │     or proactive suggestions     │  │  suggest│  │     Prepare recommendations              │  │
│  └──────────────────────────────────┘  │         │  │     Update todolist autonomously         │  │
│                                        │         │  └──────────────────────────────────────────┘  │
└────────────────────────────────────────┘         └────────────────────────────────────────────────┘
                 │                                                           │
                 └───────────────────────────┬───────────────────────────────┘
                                             ▼
                              ┌──────────────────────────────┐
                              │     CONTINUOUS SYNC LOOP     │
                              │  Agent ◄──► MemU Bot ◄──► DB │
                              └──────────────────────────────┘

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🎯 Proactive Use Cases

1. Information Recommendation

Agent monitors interests and proactively surfaces relevant content

# User has been researching AI topics
MemU tracks: reading history, saved articles, search queries

# When new content arrives:
Agent: "I found 3 new papers on RAG optimization that align with
        your recent research on retrieval systems. One author
        (Dr. Chen) you've cited before published yesterday."

# Proactive behaviors:
- Learns topic preferences from browsing patterns
- Tracks author/source credibility preferences
- Filters noise based on engagement history
- Times recommendations for optimal attention

2. Email Management

Agent learns communication patterns and handles routine correspondence

# MemU observes email patterns over time:
- Response templates for common scenarios
- Priority contacts and urgent keywords
- Scheduling preferences and availability
- Writing style and tone variations

# Proactive email assistance:
Agent: "You have 12 new emails. I've drafted responses for 3 routine
        requests and flagged 2 urgent items from your priority contacts.
        Should I also reschedule tomorrow's meeting based on the
        conflict John mentioned?"

# Autonomous actions:
✓ Draft context-aware replies
✓ Categorize and prioritize inbox
✓ Detect scheduling conflicts
✓ Summarize long threads with key decisions

3. Trading & Financial Monitoring

Agent tracks market context and user investment behavior

# MemU learns trading preferences:
- Risk tolerance from historical decisions
- Preferred sectors and asset classes
- Response patterns to market events
- Portfolio rebalancing triggers

# Proactive alerts:
Agent: "NVDA dropped 5% in after-hours trading. Based on your past
        behavior, you typically buy tech dips above 3%. Your current
        allocation allows for $2,000 additional exposure while
        maintaining your 70/30 equity-bond target."

# Continuous monitoring:
- Track price alerts tied to user-defined thresholds
- Correlate news events with portfolio impact
- Learn from executed vs. ignored recommendations
- Anticipate tax-loss harvesting opportunities

...

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🗂️ Hierarchical Memory Architecture

MemU's three-layer system enables both reactive queries and proactive context loading:

Layer	Reactive Use	Proactive Use
Resource	Direct access to original data	Background monitoring for new patterns
Item	Targeted fact retrieval	Real-time extraction from ongoing interactions
Category	Summary-level overview	Automatic context assembly for anticipation

Proactive Benefits: - Auto-categorization: New memories self-organize into topics - Pattern Detection: System identifies recurring themes - Context Prediction: Anticipates what information will be needed next

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🚀 Quick Start

Option 1: Cloud Version

Experience proactive memory instantly:

👉 memu.so - Hosted service with 7×24 continuous learning

For enterprise deployment with custom proactive workflows, contact info@nevamind.ai

Cloud API (v3)

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

Method	Endpoint	Description
POST	/api/v3/memory/memorize	Register continuous learning task
GET	/api/v3/memory/memorize/status/{task_id}	Check real-time processing status
POST	/api/v3/memory/categories	List auto-generated categories
POST	/api/v3/memory/retrieve	Query memory (supports proactive context loading)

📚 Full API Documentation

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Option 2: Self-Hosted

Installation

pip install -e .

Basic Example

Requirements: Python 3.13+ and an OpenAI API key

Test Continuous Learning (in-memory):

export OPENAI_API_KEY=your_api_key
cd tests
python test_inmemory.py

Test with Persistent Storage (PostgreSQL):

# Start PostgreSQL with pgvector
docker run -d \
  --name memu-postgres \
  -e POSTGRES_USER=postgres \
  -e POSTGRES_PASSWORD=postgres \
  -e POSTGRES_DB=memu \
  -p 5432:5432 \
  pgvector/pgvector:pg16

# Run continuous learning test
export OPENAI_API_KEY=your_api_key
cd tests
python test_postgres.py

Both examples demonstrate proactive memory workflows: 1. Continuous Ingestion: Process multiple files sequentially 2. Auto-Extraction: Immediate memory creation 3. Proactive Retrieval: Context-aware memory surfacing

See tests/test_inmemory.py and tests/test_postgres.py for implementation details.

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Custom LLM and Embedding Providers

MemU supports custom LLM and embedding providers beyond OpenAI. Configure them via llm_profiles: ```python from memu import MemUService

service = MemUService( llm_profiles={ # Default profile for LLM operations "default": {