memU

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# memU

### 24/7 Always-On Proactive Memory for AI Agents

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**[English](readme/README_en.md) | [中文](readme/README_zh.md) | [日本語](readme/README_ja.md) | [한국어](readme/README_ko.md) | [Español](readme/README_es.md) | [Français](readme/README_fr.md)**

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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.

---

## 🤖 [OpenClaw Alternative](https://github.com/NevaMind-AI/memUBot)

<img width="100%" src="https://github.com/NevaMind-AI/memU/blob/main/assets/memUbot.png" />

**[memU Bot](https://github.com/NevaMind-AI/memUBot)** — 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, &lt; 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](https://memu.bot) · Source: [memUBot on GitHub](https://github.com/NevaMind-AI/memUBot)

---

## 🗃️ 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**.

---

## ⭐️ Star the repository

<img width="100%" src="https://github.com/NevaMind-AI/memU/blob/main/assets/star.gif" />
If you find memU useful or interesting, a GitHub Star ⭐️ would be greatly appreciated.

---


## ✨ Core Features

| Capability | Description |
|------------|-------------|
| 🤖 **24/7 Proactive Agent** | Always-on memory agent that works continuously in the background |
| 🎯 **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 |
---

## 🔄 How Proactive Memory Works

```bash

cd examples/proactive
python proactive.py

```

---

### 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 │
                              └──────────────────────────────┘
```

---

## 🎯 Proactive Use Cases

### 1. **Information Recommendation**
*Agent monitors interests and proactively surfaces relevant content*
```python
# 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*
```python
# 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