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VisualTorch

by willyfhversion 1.4.1i★ –iRuns on your computer
Registry nameio.github.willyfh/visualtorch

Install

Overview

Description from the maker (original English)

Render PyTorch architecture diagrams and animated GIF reveals from trusted model source.

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

  • Connection: Runs on your computer
  • Transport: stdio
  • Category: Other

Install

Pick the AI app you use, then copy the settings.

Before you install

  • Basic checks: 1 of 1 passed
  • Runs on your computer with your user’s permissions (files, network, apps).
  • The registry lists no key to fill in.

You need

  • PyPIPython’s uv installed (for the uvx command)

Which app do you use?

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

{
  "mcpServers": {
    "visualtorch": {
      "command": "uvx",
      "args": [
        "visualtorch",
        "--from",
        "visualtorch[mcp]",
        "visualtorch-mcp"
      ]
    }
  }
}

Run this in your terminal

claude mcp add visualtorch -- uvx visualtorch --from 'visualtorch[mcp]' visualtorch-mcp

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

{
  "mcpServers": {
    "visualtorch": {
      "command": "uvx",
      "args": [
        "visualtorch",
        "--from",
        "visualtorch[mcp]",
        "visualtorch-mcp"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "visualtorch": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "visualtorch",
        "--from",
        "visualtorch[mcp]",
        "visualtorch-mcp"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 6, 2026

  • ✓ Pass

    Maker matches the repo

    The registry name was verified with the GitHub account willyfh, which owns the repo

  • … Not checked yet

    Still maintained

    We have not read this repo from GitHub yet

  • … Not checked yet

    Has a licence

    We have not read this repo from GitHub yet

  • – Not applicable

    Endpoint responds

    This one runs on your computer; there is no internet endpoint

  • – Not applicable

    Sign-in with OAuth

    Runs on your computer and uses local settings instead

  • … Not checked yet

    No hidden instructions in tool descriptions

    Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Otheri

  • More stars than 0% of the other 32451 servers in this category
  • Published to the registry more recently than 20% of the other 32451 servers in this category
  • Basic checks equal to or better than 100% of the other 32451 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is VisualTorch safe?

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

The registry does not list a required key, though some servers ask you to sign in on first use.

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (1.4.1) reached the registry 3 months ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Python (uv) on your computer.

What can I use instead?

In the same category: Spec Driven Development, Remote Desktop Commander, Recta

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README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

VisualTorch aims to help visualize Torch-based neural network architectures. It currently supports generating flow-style, graph-style, and LeNet-style architectures for PyTorch Sequential and Custom models. Its original visual styles were inspired by visualkeras, pytorchviz, pytorch-summary, and torchview; since then, it has grown its own unified tracing backend and architecture-handling logic well beyond its origins.

Note: 1.0+ is a major release with breaking API changes, but with significantly better features and algorithms - upgrading is recommended. For the old API, use 0.2.5 or older.

Limitation: VisualTorch traces a real forward pass to build the diagram, which has an inherent limitation shared by any tracing-based approach (not a bug, and not fixable without full symbolic execution): models with data-dependent control flow (e.g. a branch only taken if a tensor value crosses some threshold) only show whichever branch the traced dummy input happened to take. Separately, a layer that returns multiple meaningful output tensors (e.g. a custom multi-task head, or nn.LSTM's (output, (h_n, c_n))) still has its node's size based on only its first tensor; with show_dimension=True, every output tensor's shape is shown in the label, not just the first. Downstream connections are correct either way. Contributions are welcome!

Animated Reveal

Every style can also render as an animated GIF, revealing the model one layer/column at a time, via visualtorch.animate(model, input_shape, style=...) - see it in action for flow, graph, and lenet styles.

Documentation

Online documentation is available at visualtorch.readthedocs.io.

The docs include usage examples, API references, and other useful information.

Installation

See the Installation page.

MCP integration

VisualTorch includes an optional, client-neutral stdio MCP server for generating static PNG diagrams and animated GIF reveals from PyTorch model source. It exposes capability discovery, structured output metadata, documentation resources, subprocess timeouts, and all three canonical styles (graph, flow, and lenet). Install it with pip install "visualtorch[mcp]" and see the MCP integration guide for the tool schemas, generic stdio configuration, examples, and trusted-code security boundary. No client-specific plugin or extension is required.

Used in Research

VisualTorch has been used in published research, including works published in Nature, IEEE, and MDPI.

See the Research Showcase page for the full list.

Used VisualTorch in your research, built something with it, or found a paper that cites it? Tell us about it or open a pull request to add it directly - we'd love to hear.

Examples

See the Usage Examples page.

Contributing

Please feel free to send a pull request to contribute to this project by following this guideline.

Releases

See GOVERNANCE.md for release methodology and cadence, and the PyPI release history for past releases.

License

This poject is available as open source under the terms of the MIT License.

Originally, this project was based on the visualkeras (under the MIT license), with additional modifications inspired by pytorchviz, pytorch-summary, and torchview, all of which are also licensed under the MIT license.

Citation

Please cite this project in your publications if it helps your research.

Note: the paper below describes VisualTorch as of its publication date (2024). The project has since been substantially refactored, including breaking API changes (see the documentation for the current API) - the DOI always resolves to what was actually reviewed and published.

@article{Hendria2024,

doi = {10.21105/joss.06678}, url = {https://doi.org/10.21105/joss.06678}, year = {2024}, publisher = {The Open Journal}, volume = {9}, number = {102}, pages = {6678}, author = {Willy Fitra Hendria and Paul Gavrikov}, title = {VisualTorch: Streamlining Visualization for PyTorch Neural Network Architectures}, journal = {Journal of Open Source Software} }

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