mem0-fastapi-integration

# Mem0 FastAPI Integration Patterns

**Purpose:** Provide complete Mem0 integration templates, memory service patterns, user tracking implementations, and conversation persistence strategies for building FastAPI applications with intelligent AI memory.

**Activation Triggers:**
- Integrating Mem0 memory layer into FastAPI
- Building chat applications with conversation history
- Implementing user context and personalization
- Adding memory to AI agents
- Creating stateful AI interactions
- User preference management

**Key Resources:**
- `templates/memory_service.py` - Complete Mem0 service implementation
- `templates/memory_middleware.py` - Request-scoped memory middleware
- `templates/memory_client.py` - Mem0 client configuration
- `templates/memory_routes.py` - API routes for memory operations
- `scripts/setup-mem0.sh` - Mem0 installation and configuration
- `scripts/test-memory.sh` - Memory service testing utility
- `examples/chat_with_memory.py` - Complete chat implementation
- `examples/user_preferences.py` - User preference management

## Core Mem0 Integration

### 1. Client Configuration

**Template:** `templates/memory_client.py`

**Workflow:**
```python
from mem0 import Memory, AsyncMemory, MemoryClient
from mem0.configs.base import MemoryConfig

# Hosted Mem0 Platform
client = MemoryClient(api_key=settings.MEM0_API_KEY)

# Self-Hosted Configuration
config = MemoryConfig(
    vector_store={
        "provider": "qdrant",
        "config": {
            "host": settings.QDRANT_HOST,
            "port": settings.QDRANT_PORT,
            "api_key": settings.QDRANT_API_KEY
        }
    },
    llm={
        "provider": "openai",
        "config": {
            "model": "gpt-4",
            "api_key": settings.OPENAI_API_KEY
        }
    },
    embedder={
        "provider": "openai",
        "config": {
            "model": "text-embedding-3-small",
            "api_key": settings.OPENAI_API_KEY
        }
    }
)
memory = AsyncMemory(config)
```

### 2. Memory