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# memU
### 24/7 Always-On Proactive Memory for AI Agents
[](https://badge.fury.io/py/memu-py)
[](https://opensource.org/licenses/Apache-2.0)
[](https://www.python.org/downloads/)
[](https://discord.gg/memu)
[](https://x.com/memU_ai)
<a href="https://trendshift.io/repositories/17374" target="_blank"><img src="https://trendshift.io/api/badge/repositories/17374" alt="NevaMind-AI%2FmemU | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a>
**[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)**
</div>
---
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 (Moltbot, Clawdbot) 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, < 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—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 |
---
## 🔄 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
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*
```python
# 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
```
...
---
## 🗂️ Hierarchical Memory Architecture
MemU's three-layer system enables both **reactive queries** and **proactive context loading**:
<img width="100%" alt="structure" src="assets/structure.png" />
| 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
---
## 🚀 Quick Start
### Option 1: Cloud Version
Experience proactive memory instantly:
👉 **[memu.so](https://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](https://memu.pro/docs#cloud-version)**
---
### Option 2: Self-Hosted
#### Installation
```bash
pip install -e .
```
#### Basic Example
> **Requirements**: Python 3.13+ and an OpenAI API key
**Test Continuous Learning** (in-memory):
```bash
export OPENAI_API_KEY=your_api_key
cd tests
python test_inmemory.py
```
**Test with Persistent Storage** (PostgreSQL):
```bash
# 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`](tests/test_inmemory.py) and [`tests/test_postgres.py`](tests/test_postgres.py) for implementation details.
---
### 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": {
"base_url": "https://dashscope.aliyuncs.com/compatible-mode/v1",
"api_key": "your_api_key",
"chat_model": "qwen3-max",
"client_backend": "sdk" # "sdk" or "http"
},
# Separate profile for embeddings
"embedding": {
"base_url": "https://api.voyageai.com/v1",
"api_key": "your_voyage_api_key",
"embed_model": "voyage-3.5-lite"
}
},
# ... other configuration
)
```
---
### OpenRouter Integration
MemU supports [OpenRouter](https://openrouter.ai) as a model provider, giving you access to multiple LLM providers through a single API.
#### Configuration
```python
from memu import MemoryService
service = MemoryService(
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", # Any OpenRouter model
"embed_model": "openai/text-embedding-3-small", # Embedding model
},
},
database_config={
"metadata_store": {"provider": "inmemory"},
},
)
```
#### Environment Variables
| Variable | Description |
|----------|-------------|
| `OPENROUTER_API_KEY` | Your OpenRouter API key from [openrouter.ai/keys](https://openrouter.ai/keys) |
#### Supported Features
| Feature | Status | Notes |
|---------|--------|-------|
| Chat Completions | Supported | Works with any OpenRouter chat model |
| Embeddings | Supported | Use OpenAI embedding models via OpenRouter |
| Vision | Supported | Use vision-capable models (e.g., `openai/gpt-4o`) |
#### Running OpenRouter Tests
```bash
export OPENROUTER_API_KEY=your_api_key
# Full workflow test (memorize + retrieve)
python tests/test_openrouter.py
# Embedding-specific tests
python tests/test_openrouter_embedding.py
# Vision-specific tests
python tests/test_openrouter_vision.py
```
See [`examples/example_4_openrouter_memory.py`](examples/example_4_openrouter_memory.py) for a complete working example.
---
## 📖 Core APIs
### `memorize()` - Continuous Learning Pipeline
Processes inputs in real-time and immediately updates memory:
<img width="100%" alt="memorize" src="assets/memorize.png" />
```python
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
)
# Returns immediately with extracted memory:
{
"resource": {...}, # Stored resource metadata
"items": [...], # Extracted memory items (available instantly)
"categories": [...] # Auto-updated category structure
}
```
**Proactive Features:**
- Zero-delay processing—memories available immediately
- Automatic categorization without manual tagging
- Cross-reference with existing memories for pattern detection
### `retrieve()` - Dual-Mode Intelligence
MemU supports both **proactive context loading** and **reactive querying**:
<img width="100%" alt="retrieve" src="assets/retrieve.png" />
#### RAG-based Retrieval (`method="rag"`)
Fast **proactive context assembly** using embeddings:
- ✅ **Instant context**: Sub-second memory surfacing
- ✅ **Background monitoring**: Can run continuously without LLM costs
- ✅ **Similarity scoring**: Identifies most relevant memories automatically
#### LLM-based Retrieval (`method="llm"`)
Deep **anticipatory reasoning** for complex contexts:
- ✅ **Intent prediction**: LLM infers what user needs before they ask
- ✅ **Query evolution**: Automatically refines search as context develops
- ✅ **Early termination**: Stops when sufficient context is gathered
#### Comparison
| Aspect | RAG (Fast Context) | LLM (Deep Reasoning) |
|--------|-------------------|---------------------|
| **Speed** | ⚡ Milliseconds | 🐢 Seconds |
| **Cost** | 💰 Embedding only | 💰💰 LLM inference |
| **Proactive use** | Continuous monitoring | Triggered context loading |
| **Best for** | Real-time suggestions | Complex anticipation |
#### Usage
```python
# Proactive retrieval with context history
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 Filtering**: Use `where` to scope continuous monitoring:
- `where={"user_id": "123"}` - User-specific context
- `where={"agent_id__in": ["1", "2"]}` - Multi-agent coordination
- Omit `where` for global context awareness
---
## 💡 Proactive Scenarios
### Example 1: Always-Learning Assistant
Continuously learns from every interaction without explicit memory commands:
```bash
export OPENAI_API_KEY=your_api_key
python examples/example_1_conversation_memory.py
```
**Proactive Behavior:**
- Automatically extracts preferences from casual mentions
- Builds relationship models from interaction patterns
- Surfaces relevant context in future conversations
- Adapts communication style based on learned preferences
**Best for:** Personal AI assistants, customer support that remembers, social chatbots
---
### Example 2: Self-Improving Agent
Learns from execution logs and proactively suggests optimizations:
```bash
export OPENAI_API_KEY=your_api_key
python examples/example_2_skill_extraction.py
```
**Proactive Behavior:**
- Monitors agent actions and outcomes continuously
- Identifies patterns in successes and failures
- Auto-generates skill guides from experience
- Proactively suggests strategies for similar future tasks
**Best for:** DevOps automation, agent self-improvement, knowledge capture
---
### Example 3: Multimodal Context Builder
Unifies memory across different input types for comprehensive context:
```bash
export OPENAI_API_KEY=your_api_key
python examples/example_3_multimodal_memory.py
```
**Proactive Behavior:**
- Cross-references text, images, and documents automatically
- Builds unified understanding across modalities
- Surfaces visual context when discussing related topics
- Anticipates information needs by combining multiple sources
**Best for:** Documentation systems, learning platforms, research assistants
---
## 📊 Performance
MemU achieves **92.09% average accuracy** on the Locomo benchmark across all reasoning tasks, demonstrating reliable proactive memory operations.
<img width="100%" alt="benchmark" src="https://github.com/user-attachments/assets/6fec4884-94e5-4058-ad5c-baac3d7e76d9" />
View detailed experimental data: [memU-experiment](https://github.com/NevaMind-AI/memU-experiment)
---
## 🧩 Ecosystem
| Repository | Description | Proactive Features |
|------------|-------------|-------------------|
| **[memU](https://github.com/NevaMind-AI/memU)** | Core proactive memory engine | 7×24 learning pipeline, auto-categorization |
| **[memU-server](https://github.com/NevaMind-AI/memU-server)** | Backend with continuous sync | Real-time memory updates, webhook triggers |
| **[memU-ui](https://github.com/NevaMind-AI/memU-ui)** | Visual memory dashboard | Live memory evolution monitoring |
**Quick Links:**
- 🚀 [Try MemU Cloud](https://app.memu.so/quick-start)
- 📚 [API Documentation](https://memu.pro/docs)
- 💬 [Discord Community](https://discord.gg/memu)
---
## 🤝 Partners
<div align="center">
<a href="https://github.com/TEN-framework/ten-framework"><img src="https://avatars.githubusercontent.com/u/113095513?s=200&v=4" alt="Ten" height="40" style="margin: 10px;"></a>
<a href="https://openagents.org"><img src="assets/partners/openagents.png" alt="OpenAgents" height="40" style="margin: 10px;"></a>
<a href="https://github.com/milvus-io/milvus"><img src="https://miro.medium.com/v2/resize:fit:2400/1*-VEGyAgcIBD62XtZWavy8w.png" alt="Milvus" height="40" style="margin: 10px;"></a>
<a href="https://xroute.ai/"><img src="assets/partners/xroute.png" alt="xRoute" height="40" style="margin: 10px;"></a>
<a href="https://jaaz.app/"><img src="assets/partners/jazz.png" alt="Jazz" height="40" style="margin: 10px;"></a>
<a href="https://github.com/Buddie-AI/Buddie"><img src="assets/partners/buddie.png" alt="Buddie" height="40" style="margin: 10px;"></a>
<a href="https://github.com/bytebase/bytebase"><img src="assets/partners/bytebase.png" alt="Bytebase" height="40" style="margin: 10px;"></a>
<a href="https://github.com/LazyAGI/LazyLLM"><img src="assets/partners/LazyLLM.png" alt="LazyLLM" height="40" style="margin: 10px;"></a>
<a href="https://clawdchat.ai/"><img src="assets/partners/Clawdchat.png" alt="Clawdchat" height="40" style="margin: 10px;"></a>
</div>
---
## 🤝 How to Contribute
We welcome contributions from the community! Whether you're fixing bugs, adding features, or improving documentation, your help is appreciated.
### Getting Started
To start contributing to MemU, you'll need to set up your development environment:
#### Prerequisites
- Python 3.13+
- [uv](https://github.com/astral-sh/uv) (Python package manager)
- Git
#### Setup Development Environment
```bash
# 1. Fork and clone the repository
git clone https://github.com/YOUR_USERNAME/memU.git
cd memU
# 2. Install development dependencies
make install
```
The `make install` command will:
- Create a virtual environment using `uv`
- Install all project dependencies
- Set up pre-commit hooks for code quality checks
#### Running Quality Checks
Before submitting your contribution, ensure your code passes all quality checks:
```bash
make check
```
The `make check` command runs:
- **Lock file verification**: Ensures `pyproject.toml` consistency
- **Pre-commit hooks**: Lints code with Ruff, formats with Black
- **Type checking**: Runs `mypy` for static type analysis
- **Dependency analysis**: Uses `deptry` to find obsolete dependencies
### Contributing Guidelines
For detailed contribution guidelines, code standards, and development practices, please see [CONTRIBUTING.md](CONTRIBUTING.md).
**Quick tips:**
- Create a new branch for each feature or bug fix
- Write clear commit messages
- Add tests for new functionality
- Update documentation as needed
- Run `make check` before pushing
---
## 📄 License
[Apache License 2.0](LICENSE.txt)
---
## 🌍 Community
- **GitHub Issues**: [Report bugs & request features](https://github.com/NevaMind-AI/memU/issues)
- **Discord**: [Join the community](https://discord.com/invite/hQZntfGsbJ)
- **X (Twitter)**: [Follow @memU_ai](https://x.com/memU_ai)
- **Contact**: info@nevamind.ai
---
<div align="center">
⭐ **Star us on GitHub** to get notified about new releases!
</div>
