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
pip install -r requirements.txt2. Configure API Key
Create a .env file:
GEMINI_API_KEY=your_api_key_here3. Start the MCP Server
python src/server.py4. Configure in Claude Desktop
Edit claude_desktop_config.json (see docs for the location) and add:
{
"mcpServers": {
"amm": {
"command": "python",
"args": ["C:/Users/notli/Desktop/artificial intelligent/AMM/src/server.py"]
}
}
}Project Structure
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.mdUsage
MCP Tools
- add_memory - Add a new memory
- search_memory - Search for relevant memories
- list_memories - List all memories
- delete_memory - Delete a memory
- get_stats - View usage statistics
Automatic Injection Mechanism
On each conversation, the system will automatically:
- Analyze the semantics of the user message
- Retrieve the 5 most relevant memories
- Inject these memories into the AI’s context
- 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
