Claude MCP Memory Visualization Tools

Graph visualization utilities for exploring and analyzing Claude's memory data captured by Anthropic's Memory MCP server.

🌐 Try It Now!

Launch Interactive Web Visualizer →

No installation needed! Upload your memory.json file directly in your browser.

• 🔒 100% Private - All processing happens locally in your browser
• 📊 Interactive - Drag, zoom, search, and explore
• 🎨 Beautiful - Color-coded entities with smooth animations
• 📱 Works Everywhere - No Python or dependencies required

Overview

This repository provides three ways to visualize your Claude memory data:

1. 🌐 Web Visualizer - Interactive browser-based visualization (no installation required!)
2. 📊 Python Static Analysis - NetworkX-based statistical analysis and high-res graphs
3. 🔍 Python Interactive - PyVis-powered browser visualization with Python processing

Perfect for:

• Memory Analysis: Understanding what Claude remembers about your conversations
• Knowledge Mapping: Visualizing entity relationships and connections
• Memory Cleanup: Identifying redundant or sparse entities for optimization
• Research: Exploring how AI memory systems organize information

Quick Start

Option 1: Web Visualizer (Easiest!)

Simply visit: https://dzivkovi.github.io/mcp-memory-visualizer/

• No installation required
• Works on any device with a web browser
• Drag & drop your memory.json file
• 100% private - all processing happens in your browser

Option 2: Python Tools

For advanced analysis and batch processing:

# Install dependencies
pip install -r requirements.txt

# Run static analysis
python visualize_memory.py

# Run interactive Python version
python visualize_memory_interactive.py

Memory File Location

Default Location (Problematic)

The Memory MCP server stores memory.json by default in:

C:\Users\[username]\AppData\Local\npm-cache\_npx\[hash]\node_modules\@modelcontextprotocol\server-memory\dist\memory.json

⚠️ Warning: This location is temporary and gets wiped during npm cache clears or package updates.

Recommended Setup

Always configure a persistent location using the MEMORY_FILE_PATH environment variable in your Claude Desktop config:

{
  "mcpServers": {
    "memory": {
      "command": "npx", 
      "args": ["-y", "@modelcontextprotocol/server-memory"],
      "env": {
        "MEMORY_FILE_PATH": "C:\\Users\\[username]\\Documents\\claude-memory\\memory.json"
      }
    }
  }
}

Safe Storage Locations

• C:\Users\[username]\Documents\claude-memory\memory.json
• C:\Users\[username]\AppData\Roaming\claude-memory\memory.json
• C:\claude-memory\memory.json (requires admin rights)

Note: Create the directory first and use double backslashes (\\) in Windows paths for proper JSON escaping.

Tool Comparison

Feature	Web Visualizer	Python Static	Python Interactive
Installation	None	Python + libs	Python + libs
Privacy	100% local	Local	Local
Interactivity	High	None	High
Analysis	Visual	Statistical	Both
Export	Screenshot	PNG + stats	HTML
Best For	Quick exploration	Research/reports	Deep analysis

Demo Data

The repository includes a demo memory.json file with realistic but fictional data showcasing:

• 16 entities across 9 different types (person, technology, project, etc.)
• 25 relationships forming a connected knowledge graph
• Complex connections between AI research, enterprise systems, and academic collaboration
• Varied node sizes from 1 to 10 observations

Features

Web Visualizer

• Drag & Drop file upload
• Search entities and observations
• Interactive Graph with physics simulation
• Detail Panel showing observations and relationships
• Auto-layout with zoom controls
• Privacy-first design with clear messaging

Python Static Analysis (visualize_memory.py)

• Network statistics (nodes, edges, connected components)
• Centrality analysis (most connected entities)
• Redundancy detection (similar entities, sparse nodes)
• High-resolution graph visualization (300 DPI)
• Detailed terminal analysis output

Python Interactive (visualize_memory_interactive.py)

• Browser-based interactive visualization
• Hover tooltips with full entity details
• Physics-based node positioning
• Zoom, pan, and node dragging
• HTML export for sharing

Memory File Format

These tools work with memory.json files in JSONL format (one JSON object per line):

{"type": "entity", "name": "Python", "entityType": "technology", "observations": ["Used for data analysis", "Popular ML language"]}
{"type": "relation", "from": "Python", "to": "Data Science", "relationType": "used_in"}

Technical Details

Web Visualizer

• D3.js for powerful data visualization
• Force-directed graph layout
• Client-side processing for privacy
• Responsive design for all screen sizes

Python Tools

• NetworkX for graph analysis
• Matplotlib for static visualization
• PyVis for interactive HTML output
• Force-directed algorithms for natural clustering

Contributing

Feel free to extend these tools with additional features:

• Export formats (GraphML, GEXF, JSON)
• Filtering options (entity types, date ranges)
• Advanced metrics (betweenness centrality, clustering coefficients)
• Memory editing capabilities

Credits

Built for exploring Claude's memory data from Anthropic's Memory MCP server.

Philosophy: "Perfection is achieved, not when there is nothing more to add, but when there is nothing left to take away." - Antoine de Saint-Exupéry