Memory MCP Visualization Guide

Author: Scot Campbell Date: November 4, 2025 Tool: dzivkovi/mcp-memory-visualizer Installation Location: /Users/sc/GitHub/memory-visualizer

Executive Summary

After comparing two memory visualization tools for Anthropic's memory.json format, I chose dzivkovi/mcp-memory-visualizer because it offers three complementary visualization modes:

Web-based visualizer (zero installation, 100% private)
Python static analysis (statistical + high-res graphs)
Python interactive (browser-based with Python processing)

The tool is installed and tested successfully.

Why This Tool?

Comparison: mjherich vs dzivkovi

Feature	mjherich/memory-visualizer	dzivkovi/mcp-memory-visualizer
Installation	npm + dependencies	None (web) OR pip (Python)
Tech Stack	React + TypeScript + D3.js + Vite	D3.js (web) + NetworkX/PyVis (Python)
Stars	12	0
Maintenance	Active (13 commits, last Aug 2025)	Recent (4 commits, last Jun 2025)
Modes	1 (web app)	3 (web + 2 Python)
Live Demo	memviz.herich.tech	dzivkovi.github.io/mcp-memory-visualizer
Features	Interactive graph, search, filters	Three modes: quick, statistical, deep
Best For	Interactive exploration	Multiple use cases (exploration, analysis, research)

Decision: dzivkovi

Chosen: dzivkovi/mcp-memory-visualizer

Reasons:

Three modes for different workflows: Web (quick), Python static (research), Python interactive (deep analysis)
Easier installation: Web version = zero install, Python version = pip install -r requirements.txt
Statistical analysis: NetworkX provides graph metrics (centrality, clusters, redundancy detection)
Export options: PNG (static), HTML (interactive), screenshots (web)
Simpler codebase: Single HTML file (web) + 2 Python scripts vs full React build system

mjherich's tool is more polished (React + TypeScript, better UI), but dzivkovi's three-mode approach better serves different use cases.

Installation

Method 1: Web Visualizer (Recommended)

Zero installation required!

Visit: https://dzivkovi.github.io/mcp-memory-visualizer/

Features:

Drag & drop memory.json file
100% private (all processing in browser)
Interactive graph with physics simulation
Search entities and observations
Color-coded entity types
Detail panel with relationships

Method 2: Python Tools (For Analysis)

Already installed at: /Users/sc/GitHub/memory-visualizer

# If you need to reinstall:
cd ~/GitHub
git clone https://github.com/dzivkovi/mcp-memory-visualizer.git memory-visualizer
cd memory-visualizer
pip install -r requirements.txt

Dependencies installed:

networkx (graph analysis)
matplotlib (static visualization)
pyvis (interactive HTML output)

Usage

Option 1: Web Visualizer (Quick Exploration)

Navigate to: https://dzivkovi.github.io/mcp-memory-visualizer/
Locate your memory.json file (see "Finding Your Memory File" below)
Drag & drop the file into the browser
Explore:
- Drag nodes to rearrange
- Zoom with mouse wheel
- Click nodes to see observations
- Search for entities
- View relationships in detail panel

Best for: Quick visualization, on-the-fly debugging, sharing with others

Option 2: Python Static Analysis (Research & Reports)

cd /Users/sc/GitHub/memory-visualizer

# Run static analysis (generates PNG + terminal output)
python visualize_memory.py

# Output:
# - memory_graph.png (300 DPI high-res graph)
# - Terminal: statistics, centrality, redundancy detection

Provides:

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

Best for: Research papers, reports, quantitative analysis, finding optimization opportunities

Option 3: Python Interactive (Deep Analysis)

cd /Users/sc/GitHub/memory-visualizer

# Run interactive analysis (generates HTML)
python visualize_memory_interactive.py

# Output:
# - memory_graph_interactive.html (opens in browser)

Provides:

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

Best for: Deep analysis, presentations, sharing with collaborators

Finding Your Memory File

Default Location (Problematic)

Anthropic's Memory MCP server stores memory.json by default in:

# macOS
~/.cache/npm/_npx/[hash]/node_modules/@modelcontextprotocol/server-memory/dist/memory.json

# Windows
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: Configure Persistent Location

Edit Claude Desktop config to use a persistent location:

macOS: ~/Library/Application Support/Claude/claude_desktop_config.json Windows: %APPDATA%\Claude\claude_desktop_config.json

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

Recommended locations (macOS):

~/Documents/claude-memory/memory.json
~/Library/Application Support/claude-memory/memory.json
~/Dropbox/claude-memory/memory.json (if you want cloud backup)

Note: Create the directory first!

mkdir -p ~/Documents/claude-memory

Memory File Format

Anthropic's memory.json uses 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"}

Entity types: person, technology, project, event, organization, product, concept, research, feature, etc.

Relation types: leads, organizes, collaborates_with, uses, powers, built_on, implements, analyzed_in, supports, etc.

Demo Data

The repo includes demo data at /Users/sc/GitHub/memory-visualizer/memory.json:

16 entities across 9 types
25 relationships
Complex connections (AI research, enterprise systems, academic collaboration)
Varied node sizes (1-10 observations)

Use this to test the tools before using your own data.

Technical Details

Web Visualizer

D3.js for force-directed graph layout
Client-side processing (privacy-first)
Responsive design (works on mobile)
No backend required

Python Static Analysis

NetworkX for graph analysis
Matplotlib for high-res visualization (300 DPI)
Metrics: degree centrality, betweenness, clustering
Output: PNG + terminal statistics

Python Interactive

PyVis for interactive HTML output
NetworkX for graph processing
Physics simulation for natural layout
HTML export for sharing

Workflow Examples

Workflow 1: Quick Check (Web)

Open https://dzivkovi.github.io/mcp-memory-visualizer/
Drop your memory.json
Visually scan for:
- Disconnected entities (isolated nodes)
- Overly connected entities (hubs)
- Clusters (related concepts)

Workflow 2: Research Analysis (Python Static)

cd /Users/sc/GitHub/memory-visualizer
python visualize_memory.py > analysis.txt
Review terminal output for:
- Network statistics
- Centrality rankings
- Redundancy candidates
Include memory_graph.png in your paper/report

Workflow 3: Deep Dive (Python Interactive)

cd /Users/sc/GitHub/memory-visualizer
python visualize_memory_interactive.py
Open generated HTML in browser
Explore with hover tooltips
Share HTML file with collaborators

Workflow 4: Memory Cleanup

Run static analysis: python visualize_memory.py
Identify:
- Sparse nodes: Entities with <2 observations (candidates for deletion)
- Redundant entities: Similar names (e.g., "AI Project" vs "AI_Project")
- Disconnected nodes: Entities with no relations (orphans)
Manually edit memory.json or use Claude Desktop to refine memories

Comparison to mjherich's Tool

If you want to try the React-based alternative:

cd ~/GitHub
git clone https://github.com/mjherich/memory-visualizer.git mjherich-visualizer
cd mjherich-visualizer
npm install
npm run dev

Live demo: https://memviz.herich.tech

Pros:

More polished UI (React + TypeScript + TailwindCSS)
Better keyboard shortcuts (documented in CLAUDE.md)
Theme system (light/dark modes)
More active maintenance (12 stars, recent commits)

Cons:

Requires Node.js build setup (more complex)
Only one mode (interactive web)
No statistical analysis features
No export to PNG/HTML

When to use mjherich instead:

You prefer modern React UI
You want theme support
You don't need statistical analysis
You're already familiar with npm/Vite workflows

Troubleshooting

Issue: "File not found" when running Python scripts

Solution: Ensure you're in the correct directory:

cd /Users/sc/GitHub/memory-visualizer
ls  # Should show: visualize_memory.py, memory.json, etc.

Issue: Matplotlib "building font cache"

Solution: This is normal on first run. Wait 30-60 seconds. Subsequent runs will be faster.

Issue: "ModuleNotFoundError"

Solution: Reinstall dependencies:

cd /Users/sc/GitHub/memory-visualizer
pip install -r requirements.txt

Issue: Web visualizer not loading memory.json

Solution: Check file format. Must be JSONL (one JSON object per line), not pretty-printed JSON array.

Wrong:

[
  {"type": "entity", ...},
  {"type": "relation", ...}
]

Correct:

{"type":"entity",...}
{"type":"relation",...}

Issue: Memory file in temporary npm cache location

Solution: Configure persistent location (see "Finding Your Memory File" section above).

Future Enhancements

Potential extensions (mentioned in repo):

Export formats (GraphML, GEXF, JSON)
Filtering options (entity types, date ranges)
Advanced metrics (betweenness centrality, clustering coefficients)
Memory editing capabilities (add/remove entities, relations)

Related Tools

Anthropic Memory MCP Server: https://github.com/modelcontextprotocol/servers/tree/main/src/memory
mjherich's visualizer: https://github.com/mjherich/memory-visualizer
cortexgraph (your temporal memory system): https://github.com/cortexgraphai/cortexgraph

Credits

Tool: dzivkovi/mcp-memory-visualizer GitHub: https://github.com/dzivkovi/mcp-memory-visualizer Live Demo: https://dzivkovi.github.io/mcp-memory-visualizer/ 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

Installation verified: November 4, 2025 Tool version: Latest commit 7a0bb1e (Jun 20, 2025) Python dependencies: networkx, matplotlib, pyvis (installed) Status: Ready to use

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Memory MCP Visualization Guide

Executive Summary

Why This Tool?

Comparison: mjherich vs dzivkovi

Decision: dzivkovi

Installation

Method 1: Web Visualizer (Recommended)

Method 2: Python Tools (For Analysis)

Usage

Option 1: Web Visualizer (Quick Exploration)

Option 2: Python Static Analysis (Research & Reports)

Option 3: Python Interactive (Deep Analysis)

Finding Your Memory File

Default Location (Problematic)

Recommended: Configure Persistent Location

Memory File Format

Demo Data

Technical Details

Web Visualizer

Python Static Analysis

Python Interactive

Workflow Examples

Workflow 1: Quick Check (Web)

Workflow 2: Research Analysis (Python Static)

Workflow 3: Deep Dive (Python Interactive)

Workflow 4: Memory Cleanup

Comparison to mjherich's Tool

Troubleshooting

Issue: "File not found" when running Python scripts

Issue: Matplotlib "building font cache"

Issue: "ModuleNotFoundError"

Issue: Web visualizer not loading memory.json

Issue: Memory file in temporary npm cache location

Future Enhancements

Related Tools

Credits

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Memory MCP Visualization Guide

Executive Summary

Why This Tool?

Comparison: mjherich vs dzivkovi

Decision: dzivkovi

Installation

Method 1: Web Visualizer (Recommended)

Method 2: Python Tools (For Analysis)

Usage

Option 1: Web Visualizer (Quick Exploration)

Option 2: Python Static Analysis (Research & Reports)

Option 3: Python Interactive (Deep Analysis)

Finding Your Memory File

Default Location (Problematic)

Recommended: Configure Persistent Location

Memory File Format

Demo Data

Technical Details

Web Visualizer

Python Static Analysis

Python Interactive

Workflow Examples

Workflow 1: Quick Check (Web)

Workflow 2: Research Analysis (Python Static)

Workflow 3: Deep Dive (Python Interactive)

Workflow 4: Memory Cleanup

Comparison to mjherich's Tool

Troubleshooting

Issue: "File not found" when running Python scripts

Issue: Matplotlib "building font cache"

Issue: "ModuleNotFoundError"

Issue: Web visualizer not loading memory.json

Issue: Memory file in temporary npm cache location

Future Enhancements

Related Tools

Credits