LTM-RAG

Long-Term Memory for Claude with RAG (Retrieval-Augmented Generation) using Weaviate.

This Model Context Protocol (MCP) server provides Claude with long-term memory capabilities by integrating with a Weaviate-based RAG system. It allows Claude to remember conversations across sessions and retrieve relevant information from past interactions.

• Memory Types: Supports different memory types (procedural, semantic, episodic)
• Configurable Memory Settings:
  • Importance threshold for what to remember
  • Retention period for automatic forgetting
  • Memory strength boosting for frequently accessed information
• Memory Operations:
  • Save conversations to long-term memory
  • Search across all memories semantically
  • Retrieve memories by type
  • Forget old or specific memories

• Source Types:
  • File sources (individual files)
  • Directory sources (folders with multiple files)
  • Web sources (URLs to web pages)
• Source Management:
  • Add, update, and remove knowledge sources
  • Enable/disable sources
  • Index sources into the memory system

• store_message - Stores individual chat messages in temporary memory
• save_chat - Saves the current chat to a file and optionally imports it into the RAG system
• search_rag - Searches the RAG system for relevant information
• get_memory_by_type - Retrieves memories of a specific type (procedural, semantic, episodic)
• get_entity_info - Gets information about a specific entity from the memory system
• get_memory_settings - Retrieves current memory configuration settings
• update_memory_settings - Updates memory configuration (thresholds, retention, etc.)
• forget_old_memories - Forgets memories older than the retention period
• forget_memories - Forgets specific memories by ID
• list_knowledge_sources - Lists all configured external knowledge sources
• add_knowledge_source - Adds a new external knowledge source
• update_knowledge_source - Updates an existing knowledge source
• delete_knowledge_source - Removes a knowledge source
• index_knowledge_source - Indexes a knowledge source into the memory system

• default - Default prompt that introduces the long-term memory capability
• researcher - Researcher assistant with memory integration
• knowledge-explorer - Specialized prompt for exploring external knowledge sources

1. Node.js (v18 or higher)
2. Python 3.8+ with the Weaviate-Chat-RAG system installed
3. Docker for running Weaviate

1. Clone the repository:

   git clone [repository_url] /Users/lorenzo/_pythonScripts/LTM-RAG
   cd /Users/lorenzo/_pythonScripts/LTM-RAG

2. Install dependencies:

   npm install

3. Build the server:

   npm run build

4. Create a symlink to the Claude MCP directory:

   mkdir -p ~/Library/Application\ Support/Claude/MCP/
   ln -s /Users/lorenzo/_pythonScripts/LTM-RAG ~/Library/Application\ Support/Claude/MCP/LTM-RAG

5. Configure Claude Desktop to use the MCP server by editing: ~/Library/Application Support/Claude/claude_desktop_config.json

   Add the following configuration:

   {
     "mcpServers": {
       "ltm-rag": {
         "command": "node",
         "args": [
           "/Users/lorenzo/Library/Application Support/Claude/MCP/LTM-RAG/dist/index.js"
         ],
         "env": {}
       }
     }
   }

6. Or use the provided installation script:

   ./install.sh

1. Start Claude Desktop
2. Click the paperclip icon, then the plug icon
3. Select "default", "researcher", or "knowledge-explorer" under "ltm-rag"
4. Chat with Claude normally

• "Save this conversation to your long-term memory" - Saves the current chat
• "Search your memory for [query]" - Searches the RAG system
• "What do you know about [entity]?" - Retrieves entity information
• "What [memory type] memories do you have?" - Retrieves memories by type
• "Show me your memory settings" - Displays current configuration
• "Update your memory settings" - Changes memory configuration
• "Forget memories older than your retention period" - Removes old memories
• "List your knowledge sources" - Shows configured external sources
• "Add a new knowledge source" - Sets up a new source
• "Index knowledge source [name]" - Imports external content into memory

The system supports integrating with various external knowledge sources:

1. Files:

   • PDF documents, text files, markdown files, etc.
   • Example: "Add a new knowledge source for my research paper at /path/to/paper.pdf"

2. Directories:

   • Folders containing multiple related documents
   • Example: "Add a new knowledge source for my project documentation at /path/to/docs/"

3. Web Pages:

   • URLs to web content that can be scraped and indexed
   • Example: "Add a new knowledge source for the website https://example.com/knowledge-base"

The integration works by:

1. Registering the source in the LTM-RAG configuration
2. When indexed, the content is processed:
   • Extracted and broken into manageable chunks
   • Embedded using the same embedding model as chat memories
   • Stored in the Weaviate vector database with appropriate metadata
   • Made available for retrieval alongside conversation memories

Knowledge sources can be enabled/disabled and managed over time.

The following enhancements are being considered for future development:

1. Scheduled Indexing:

   • Automatic re-indexing of sources on a schedule
   • Support for sources that change or update frequently

2. Specialized Source Types:

   • GitHub repositories integration (code, documentation, issues)
   • Notion workspaces or other note-taking systems
   • SQL databases for structured data access
   • Email archives
   • Calendar data integration

3. Authentication and Access Control:

   • Support for authenticated sources (API keys, OAuth)
   • Permissions and access controls for sensitive information

4. Knowledge Source Processing Plugins:

   • Specialized processors for different file types
   • Custom extractors for domain-specific information
   • Custom classifiers for domain-specific memory types

5. Active Learning for Source Relevance:

   • Track which sources provide useful information
   • Adjust source importance based on user feedback
   • Prioritize sources that consistently provide valuable information

6. Knowledge Graph Building:

   • Automatically extract entities and relationships from sources
   • Build a knowledge graph connecting information across sources
   • Enable more sophisticated reasoning over the combined knowledge

For development with auto-rebuild:

npm run watch

Since MCP servers communicate over stdio, debugging can be challenging. You can add console.log statements that will appear in the Claude Desktop logs.

The LTM-RAG system uses configuration files stored in ~/.ltm-rag/:

• memory_settings.json - Contains memory configuration parameters
• knowledge_sources.json - Lists configured external knowledge sources

MIT