Zettelkasten knowledge management MCP server for Claude Desktop with knowledge graphs, imoort/export tools, and Claude Skills integration"
git clone https://github.com/sandraschi/advanced-memory-mcp.gitAdvanced Memory (Memops) is a FastMCP 3.2 GA server written in Python that gives Claude and MCP-capable assistants a durable place to store notes, research, and retrieve information. The server exposes 79 tools across 12 functional categories including zettelkasten management, search, knowledge graphs, and RAG capabilities. Instead of losing context at the end of each session, you can search and perform retrieval-augmented generation over your own content, maintaining continuity across conversations. The skill integrates with Claude Desktop and Cursor, supporting both direct MCP client connections and an optional webapp for browser-based access to the same backend.
Clone the repository and run `just` to open an interactive dashboard with available commands. Run `just bootstrap` to install dependencies (requires Python 3.12+), then `just serve` or `just dev` to start the MCP server. Connect it from Claude Desktop or your MCP-capable client using the configuration in the installation guide.
Maintain persistent research notes and knowledge bases across Claude sessions
Perform RAG-based retrieval over personal documents and notes
Build and navigate knowledge graphs of connected ideas using zettelkasten methodology
Search and organize project-related information without losing context between conversations
No install command available. Check the GitHub repository for manual installation instructions.
git clone https://github.com/sandraschi/advanced-memory-mcpCopy the install command above and run it in your terminal.
Launch Claude Code, Cursor, or your preferred AI coding agent.
Use the prompt template or examples below to test the skill.
Adapt the skill to your specific use case and workflow.
Create a new Zettelkasten note in my advanced-memory-mcp server about [TOPIC]. Ensure it's connected to at least 3 related notes in my knowledge graph. Include a summary, key points, and any relevant context from my existing notes. Also, suggest 2 follow-up notes that should be created based on this topic.
I've created a new Zettelkasten note titled 'Quantum Computing Applications in Drug Discovery' in your advanced-memory-mcp server. Here's a summary of the note and its connections: Summary: This note explores how quantum computing can accelerate drug discovery by simulating molecular interactions at quantum levels, potentially reducing drug development timelines from years to months. Key Points: 1. Quantum computers can model molecular structures with far greater accuracy than classical computers 2. Potential to identify new drug candidates by simulating protein folding and molecular docking 3. Current limitations include qubit stability and error correction challenges Connected Notes: 1. 'Quantum Computing Basics' - Provides foundational knowledge about quantum computing principles 2. 'Molecular Simulation Techniques' - Discusses classical methods for molecular modeling 3. 'Drug Discovery Process' - Outlines the traditional pharmaceutical development pipeline Suggested Follow-up Notes: 1. 'Quantum Machine Learning for Drug Discovery' - Explore how quantum algorithms could enhance machine learning in pharmaceutical research 2. 'Current Quantum Computing Hardware' - Investigate the state of quantum hardware development and its implications for drug discovery applications
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