README.md

---
title: Long Term Memory MCP Server
emoji: 🧠
colorFrom: blue
colorTo: purple
sdk: gradio
sdk_version: "4.0.0"
app_file: app.py
pinned: false
license: mit
tags:
  - mcp-server-track
  - mcp
  - memory
  - rag
  - llm
  - conversation
short_description: MCP Server providing long-term memory for LLM conversations
---

# 🧠 Long Term Memory MCP Server

**Tags**: mcp-server-track

A Model Context Protocol (MCP) server that provides long-term memory capabilities for LLM conversations. This allows users to save important insights, conclusions, and context from conversations and retrieve them in future interactions.

## 🎯 Problem Solved

Current LLM interactions are stateless - they don't remember previous conversations or insights you've shared. This MCP server solves that by providing:

- **Persistent Memory**: Save important insights and context from conversations
- **Semantic Search**: Find relevant memories using natural language queries
- **Context Continuity**: Build upon previous conversations and learnings
- **Knowledge Accumulation**: Build a personal knowledge base over time

## 🚀 Features

### MCP Server Tools
- `save_memory` - Save insights, conclusions, or context to long-term memory
- `search_memory` - Search through memories using semantic similarity
- `list_memories` - Browse all stored memories
- `delete_memory` - Remove specific memories

### Gradio Demo Interface
- Interactive web interface for testing all MCP tools
- Real-time memory statistics
- Semantic search with adjustable similarity thresholds
- Memory browsing and management

## 🛠️ Technical Architecture

- **MCP Protocol**: Standards-compliant MCP server
- **Vector Storage**: ChromaDB for efficient semantic search
- **Embeddings**: SentenceTransformers (all-MiniLM-L6-v2) for semantic understanding
- **Interface**: Gradio web app for demonstration and testing
- **Storage**: Persistent local database

## 📦 Installation & Usage

### Local Development
```bash
# Clone and install dependencies
pip install -r requirements.txt

# Run the application
python app.py
```

### Hugging Face Spaces
This Space runs both the MCP server and Gradio demo simultaneously.

## 🎮 Demo Video

-

## 💡 Use Cases

### Example Scenario
1. **Initial Conversation**: You discuss quantum consciousness theories with an LLM
2. **Save Insight**: Use `save_memory` to store key conclusions
3. **Future Conversation**: LLM can `search_memory` to find relevant context
4. **Continuity**: Build upon previous insights in new discussions

### Sample Usage with Claude Desktop

**Saving a memory:**
```
User: "Save this insight to memory: 'Consciousness might emerge from quantum processes in microtubules, as proposed by Penrose-Hameroff theory. This could explain the hard problem of consciousness.' Title: 'Quantum Consciousness Theory', Tags: 'consciousness, quantum, penrose, microtubules'"

LLM: *Uses save_memory tool*
✅ Memory saved successfully! ID: abc123...
```

**Searching memories:**
```
User: "What did we previously discuss about consciousness and quantum physics?"

LLM: *Uses search_memory tool*
🔍 Found relevant memory: "Quantum Consciousness Theory" - discusses how consciousness might emerge from quantum processes in microtubules...
```

## 🔧 MCP Client Configuration

### Claude Desktop
Add to your `claude_desktop_config.json`:
```json
{
  "mcpServers": {
    "long-term-memory": {
      "command": "python",
      "args": ["path/to/mcp_server.py"],
      "env": {}
    }
  }
}
```

### Cursor IDE
Configure in your MCP settings to connect to the server.

## 📊 Memory Statistics

The system tracks:
- Total memories stored
- Content length statistics
- Tag usage patterns
- Timestamp-based organization

## 🔐 Privacy & Data

- All data stored locally in ChromaDB
- No external API calls for embeddings (uses local SentenceTransformers)
- Full control over your memory data
- Easy export/import capabilities

## 🚧 Future Enhancements

- [ ] Memory categorization and hierarchical organization
- [ ] Conversation threading and context linking
- [ ] Export/import functionality
- [ ] Advanced search filters (date, tags, content type)
- [ ] Memory summarization and consolidation
- [ ] Integration with external knowledge bases

## 🤝 Contributing

This project was created for the [Hugging Face MCP Hackathon](https://huggingface.co/Agents-MCP-Hackathon). Contributions welcome!

## 📝 License

MIT License - Feel free to use and modify!

## Author
- [Andrei Zagrebin] (@cheeeaaat)
---

*Built with ❤️ for the Hugging Face MCP Hackathon - Track 1: MCP Server/Tool*