# Deploying MemU on Sealos DevBox

> This guide demonstrates how to build and deploy a Personal AI Assistant with Long-Term Memory using MemU on Sealos DevBox.

- Skill: `tools-only/deploying-memu-on-sealos-devbox` (Agent Skill, multi-file: 3 files)
- Install (CLI): `npx skillmds@latest add tools-only/deploying-memu-on-sealos-devbox`
- Raw SKILL.md: https://api.skillmd.com/api/skills/tools-only/deploying-memu-on-sealos-devbox/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: tools-only (https://skillmd.com/u/tools-only)
- Updated: 2026-09-29
- Page: https://skillmd.com/skills/tools-only/deploying-memu-on-sealos-devbox

---

# Deploying MemU on Sealos DevBox

This guide demonstrates how to build and deploy a **Personal AI Assistant with Long-Term Memory** using MemU on [Sealos DevBox](https://sealos.io/products/devbox).

## Overview

MemU enables AI agents to maintain persistent, structured memory across conversations. Combined with Sealos DevBox's 1-click cloud development environment, you can quickly build and deploy memory-enabled AI applications.

**What we'll build:**
- A FastAPI-based AI assistant that remembers user preferences and past conversations
- Persistent memory storage using MemU's in-memory or PostgreSQL backend
- Simple REST API for chat interactions
- One-click deployment to production

**Time to complete:** ~15 minutes

## Prerequisites

- [Sealos account](https://sealos.io) (free tier available)
- OpenAI API key (or compatible provider like Nebius, Groq)

## Step 1: Create a DevBox Environment

1. Log in to [Sealos Dashboard](https://cloud.sealos.io)
2. Navigate to **DevBox** module
3. Click **Create New Project**
4. Select **Python 3.11+** template
5. Configure resources (recommended: 2 vCPU, 4GB RAM)
6. Click **Create** - your environment will be ready in ~60 seconds

## Step 2: Connect Your IDE

1. In the DevBox project list, click the **VS Code** or **Cursor** button
2. Your local IDE will open with a secure SSH connection to the cloud environment
3. All code runs in the cloud, keeping your local machine free

## Step 3: Set Up the Project

Open the terminal in your connected IDE and run:

```bash
# Clone or create project directory
mkdir memu-assistant && cd memu-assistant

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install memu fastapi uvicorn python-dotenv
```

## Step 4: Create the Application

Create the following files in your project:

### `.env`

```env
# LLM Provider Configuration
OPENAI_API_KEY=your_api_key_here
OPENAI_BASE_URL=https://api.openai.com/v1

# Or use Nebius (OpenAI-compatible)
# OPENAI_API_KEY=your_nebius_key
# OPENAI_BASE_URL=https://api.tokenfactory.nebius.com/v1/

# Model Configuration
CHAT_MODEL=gpt-4o-mini
EMBED_MODEL=text-embedding-3-small

# Server Configuration
HOST=0.0.0.0
PORT=8000
```

### `main.py`

```python
"""
Personal AI Assistant with Long-Term Memory
Powered by MemU + FastAPI on Sealos DevBox
"""

import os
from contextlib import asynccontextmanager
from dotenv import load_dotenv
from fastapi import FastAPI, HTTPException
from fastapi.middleware.cors import CORSMiddleware
from pydantic import BaseModel

load_dotenv()

# MemU imports
from memu.app import MemoryService

# Global memory service
memory_service: MemoryService | None = None


@asynccontextmanager
async def lifespan(app: FastAPI):
    """Initialize MemU on startup."""
    global memory_service

    llm_profiles = {
        "default": {
            "provider": "openai",
            "base_url": os.getenv("OPENAI_BASE_URL", "https://api.openai.com/v1"),
            "api_key": os.getenv("OPENAI_API_KEY"),
            "chat_model": os.getenv("CHAT_MODEL", "gpt-4o-mini"),
            "client_backend": "sdk",
        },
        "embedding": {
            "provider": "openai",
            "base_url": os.getenv("OPENAI_BASE_URL", "https://api.openai.com/v1"),
            "api_key": os.getenv("OPENAI_API_KEY"),
            "embed_model": os.getenv("EMBED_MODEL", "text-embedding-3-small"),
            "client_backend": "sdk",
        },
    }

    memory_service = MemoryService(llm_profiles=llm_profiles)
    print("✓ MemU Memory Service initialized")
    yield
    print("Shutting down...")


app = FastAPI(
    title="MemU Assistant",
    description="AI Assistant with Long-Term Memory",
    lifespan=lifespan,
)

app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_methods=["*"],
    allow_headers=["*"],
)


class ChatRequest(BaseModel):
    message: str
    user_id: str = "default"


class ChatResponse(BaseModel):
    response: str
    memories_used: int
    memories_stored: int


class MemorizeRequest(BaseModel):
    content: str
    user_id: str = "default"


@app.get("/")
async def root():
    return {
        "service": "MemU Assistant",
        "status": "running",
        "endpoints": ["/chat", "/memorize", "/recall", "/health"],
    }


@app.get("/health")
async def health():
    return {"status": "healthy", "memory_service": memory_service is not None}


@app.post("/chat", response_model=ChatResponse)
async def chat(request: ChatRequest):
    """
    Chat with the AI assistant. The assistant will:
    1. Retrieve relevant memories from past conversations
    2. Generate a response using those memories as context
    3. Store new information from the conversation
    """
    if not memory_service:
        raise HTTPException(status_code=503, detail="Memory service not initialized")

    # Step 1: Retrieve relevant memories
    retrieve_result = await memory_service.retrieve(
        queries=[{"role": "user", "content": request.message}]
    )

    memories = retrieve_result.get("items", [])
    memories_context = ""
    if memories:
        memories_context = "\n\nRelevant memories from past conversations:\n"
        for mem in memories[:5]:  # Limit to top 5 memories
            if isinstance(mem, dict):
                memories_context += f"- {mem.get('summary', str(mem))}\n"

    # Step 2: Generate response (simplified - in production, use full LLM call)
    # For demo, we'll create a simple response acknowledging the memories
    response_text = f"I received your message: '{request.message}'"
    if memories:
        response_text += f"\n\nI found {len(memories)} relevant memories that might help."

    # Step 3: Store the conversation as a new memory
    import tempfile
    with tempfile.NamedTemporaryFile(mode='w', suffix='.txt', delete=False) as f:
        f.write(f"User ({request.user_id}): {request.message}")
        temp_file = f.name

    try:
        memorize_result = await memory_service.memorize(
            resource_url=temp_file,
            modality="text",
        )
        memories_stored = len(memorize_result.get("items", []))
    finally:
        os.unlink(temp_file)

    return ChatResponse(
        response=response_text,
        memories_used=len(memories),
        memories_stored=memories_stored,
    )


@app.post("/memorize")
async def memorize(request: MemorizeRequest):
    """Store information in long-term memory."""
    if not memory_service:
        raise HTTPException(status_code=503, detail="Memory service not initialized")

    import tempfile
    with tempfile.NamedTemporaryFile(mode='w', suffix='.txt', delete=False) as f:
        f.write(request.content)
        temp_file = f.name

    try:
        result = await memory_service.memorize(
            resource_url=temp_file,
            modality="text",
        )
        return {
            "status": "stored",
            "items_created": len(result.get("items", [])),
            "categories": len(result.get("categories", [])),
        }
    finally:
        os.unlink(temp_file)


@app.get("/recall")
async def recall(query: str, limit: int = 5):
    """Recall memories related to a query."""
    if not memory_service:
        raise HTTPException(status_code=503, detail="Memory service not initialized")

    result = await memory_service.retrieve(
        queries=[{"role": "user", "content": query}]
    )

    items = result.get("items", [])[:limit]
    return {
        "query": query,
        "memories_found": len(items),
        "memories": [
            {"summary": item.get("summary", str(item)) if isinstance(item, dict) else str(item)}
            for item in items
        ],
    }


if __name__ == "__main__":
    import uvicorn
    uvicorn.run(
        "main:app",
        host=os.getenv("HOST", "0.0.0.0"),
        port=int(os.getenv("PORT", 8000)),
        reload=True,
    )
```

### `requirements.txt`

```
memu>=0.1.0
fastapi>=0.100.0
uvicorn[standard]>=0.23.0
python-dotenv>=1.0.0
```

### `entrypoint.sh`

```bash
#!/bin/bash
source venv/bin/activate
uvicorn main:app --host 0.0.0.0 --port 8000
```

## Step 5: Test Locally in DevBox

```bash
# Run the application
python main.py
```

Use the DevBox preview feature to access your running application, or test with curl:

```bash
# Health check
curl http://localhost:8000/health

# Store a memory
curl -X POST http://localhost:8000/memorize \
  -H "Content-Type: application/json" \
  -d '{"content": "User prefers dark mode and uses Python for AI development"}'

# Chat with memory
curl -X POST http://localhost:8000/chat \
  -H "Content-Type: application/json" \
  -d '{"message": "What programming language do I use?"}'

# Recall memories
curl "http://localhost:8000/recall?query=programming%20preferences"
```

## Step 6: Deploy to Production

1. In the Sealos Dashboard, go to your DevBox project
2. Click **Create Release** to package your application
3. Click **Deploy** next to your release
4. Configure environment variables (OPENAI_API_KEY, etc.)
5. Click **Deploy** - your app will be live in minutes!

Your application will receive a public URL like: `https://your-app.cloud.sealos.io`

## Using with PostgreSQL (Optional)

For production deployments with persistent storage:

1. In Sealos Dashboard, go to **Database** module
2. Create a PostgreSQL instance
3. Update your `.env` with the connection string:

```env
DATABASE_URL=postgresql://user:password@host:5432/memu
```

4. Update `main.py` to use PostgreSQL backend (see MemU documentation)

## API Reference

| Endpoint | Method | Description |
|----------|--------|-------------|
| `/` | GET | Service info |
| `/health` | GET | Health check |
| `/chat` | POST | Chat with memory-aware AI |
| `/memorize` | POST | Store information in memory |
| `/recall` | GET | Query stored memories |

## Architecture

```
┌─────────────────────────────────────────────────────────┐
│                    Sealos DevBox                        │
│  ┌─────────────────────────────────────────────────┐   │
│  │              FastAPI Application                 │   │
│  │  ┌─────────┐  ┌─────────┐  ┌─────────────────┐  │   │
│  │  │  /chat  │  │/memorize│  │    /recall      │  │   │
│  │  └────┬────┘  └────┬────┘  └────────┬────────┘  │   │
│  │       │            │                │           │   │
│  │       └────────────┼────────────────┘           │   │
│  │                    │                            │   │
│  │            ┌───────▼───────┐                    │   │
│  │            │  MemU Service │                    │   │
│  │            │  (Memory Mgmt)│                    │   │
│  │            └───────┬───────┘                    │   │
│  │                    │                            │   │
│  │       ┌────────────┼────────────┐               │   │
│  │       │            │            │               │   │
│  │  ┌────▼────┐  ┌────▼────┐  ┌───▼────┐          │   │
│  │  │ Vector  │  │   LLM   │  │Postgres│          │   │
│  │  │ Store   │  │   API   │  │(opt.)  │          │   │
│  │  └─────────┘  └─────────┘  └────────┘          │   │
│  └─────────────────────────────────────────────────┘   │
└─────────────────────────────────────────────────────────┘
```

## Benefits of This Setup

- **Zero Infrastructure Management**: Sealos handles Kubernetes complexity
- **Instant Environment**: Ready-to-code in 60 seconds
- **Persistent Memory**: MemU maintains context across sessions
- **Scalable**: Easily scale resources as needed
- **Cost-Effective**: Pay only for what you use

## Next Steps

- Add authentication for multi-user support
- Integrate with Slack, Discord, or other platforms
- Use PostgreSQL for production-grade persistence
- Add conversation history UI

## Resources

- [MemU Documentation](https://github.com/NevaMind-AI/memU)
- [Sealos DevBox Guide](https://sealos.io/blog/how-to-setup-devbox)
- [FastAPI Documentation](https://fastapi.tiangolo.com)

---

*This guide was created for the MemU PR Hackathon - 2026 New Year Challenge (Issue #228)*