README.md

metadata

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

# 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:

{
  "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. Contributions welcome!

📝 License

MIT License - Feel free to use and modify!

Author

• [Andrei Zagrebin] (@cheeeaaat)

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