# AMM - Adaptive Memory Manager

An intelligent memory system that provides continuous learning capabilities for AI conversations.

## Core Features

- **Automatic Memory Injection**: The system automatically retrieves and injects relevant memories without requiring explicit user prompts  
- **Semantic Search**: High‑quality semantic understanding based on Gemini 2.0 Flash embeddings  
- **Continuous Learning**: Learns from every conversation to avoid repeating mistakes  
- **Verifiability**: Tracks memory usage and quantifies system improvements  

## Quick Start

### 1. Install Dependencies

```bash
pip install -r requirements.txt
```

### 2. Configure API Key

Create a `.env` file:

```bash
GEMINI_API_KEY=your_api_key_here
```

### 3. Start the MCP Server

```bash
python src/server.py
```

### 4. Configure in Claude Desktop

Edit `claude_desktop_config.json` (see docs for the location) and add:

```json
{
  "mcpServers": {
    "amm": {
      "command": "python",
      "args": ["C:/Users/notli/Desktop/artificial intelligent/AMM/src/server.py"]
    }
  }
}
```

## Project Structure

```text
AMM/
├── src/
│   ├── server.py           # Main MCP server program
│   ├── memory_store.py     # Memory storage logic
│   ├── embeddings.py       # Gemini embeddings interface
│   └── utils.py            # Utility functions
├── data/
│   └── memories.json       # Memory data storage
├── tests/
│   └── test_basic.py       # Basic tests
├── .env                    # API configuration (not committed to Git)
├── .gitignore
├── requirements.txt
└── README.md
```

## Usage

### MCP Tools

1. **add_memory** - Add a new memory  
2. **search_memory** - Search for relevant memories  
3. **list_memories** - List all memories  
4. **delete_memory** - Delete a memory  
5. **get_stats** - View usage statistics  

### Automatic Injection Mechanism

On each conversation, the system will automatically:

1. Analyze the semantics of the user message  
2. Retrieve the 5 most relevant memories  
3. Inject these memories into the AI’s context  
4. Extract new memories from the conversation  

## Roadmap

- [x] Phase 1: Basic MCP server + JSON storage  
- [ ] Phase 2: Automatic memory extraction and management  
- [ ] Phase 3: Memory lifecycle management  
- [ ] Phase 4: Vector database integration  

## Tech Stack

- **Language**: Python 3.10+  
- **MCP**: Python MCP SDK  
- **Embeddings**: Gemini 2.0 Flash  
- **Storage**: JSON → SQLite → Vector DB  

## License

MIT License