How to Turn Code Into Diagrams: Free vs Paid Workflows for GitHub and Cursor

A practical comparison of free and paid tools for turning code into diagrams

You usually know whether a code to diagram workflow is useful in the first few minutes.

If it is simple, you keep going. If it turns into setup, cleanup, and redraw work, most people stop and go back to reading code directly.

That is why the key difference is not only the tool. It is the starting point and the kind of output you need. Sometimes you have a GitHub URL and want a fast overview. Sometimes the code is already open in Cursor and you want a diagram without leaving the editor.

In this guide, I will compare four practical routes I would actually use:

• a free GitHub workflow with GitDiagram
• a paid GitHub workflow with AI Diagram Maker
• a free editor workflow with Mermaid in Cursor
• a paid editor workflow with AI Diagram Maker MCP

The examples are intentionally simple. For the GitHub route, I am using the public [Simple Banking System](https://github.com/erajasekar/Simple-Banking-System) repository. For the editor route, I am using [SpringBlog](https://github.com/Raysmond/SpringBlog), a small Spring Boot application with clear persistence and repository structure.

What “code to diagram” actually means

When developers say they want to turn code into diagrams, they usually mean one of three things:

• they want a fast architecture overview of an unfamiliar codebase
• they want a focused diagram for a specific part of the system
• they want something polished enough to share in docs, onboarding, or a design review

That distinction matters more than the feature list on a pricing page. Some workflows are better for fast orientation. Others are better for editable output or for keeping diagrams close to the codebase.

Workflow 1: Start from a GitHub repository

The repo-first path starts with a repository link rather than a local editor. For this example, the input is a public GitHub repo, which makes it easy to compare a fast exploration tool with a more controlled diagram-generation flow.

Free route: GitDiagram

GitDiagram is the quickest way to get a visual from a public repository with almost no setup. You paste a GitHub URL, or switch a GitHub repo into the GitDiagram view, and it generates an flow diagram of the repository.

The main benefit is speed. It gives you a quick visual view of a public repository without much setup.

The actual workflow is simple:

1. open the public GitHub repository
2. pass the URL to GitDiagram
3. let it generate the repository visualization
4. click through the diagram to understand the structure

The trade-off is control. GitDiagram works on public repositories, the output is mainly for exploration, and you do not get much influence over the result. You cannot meaningfully steer the diagram type, narrow the scope to one module, or treat it as an editable final artifact.

Paid route: AI Diagram Maker from a repository URL

With AI Diagram Maker, the repo-first flow is still straightforward, but you get more control over both the input and the output.

First, choose the repository input type.

Then paste the GitHub repository URL.

From there, select the files you want included.

Then choose the diagram type you want the model to generate.

The important difference is that the output is intended to be worked on. You can change labels, adjust layout, refine the structure, and export something closer to a finished artifact.

It also matters if the repo is private, because that is the point where public-repo tools usually stop being useful.

Workflow 2: Start from code already open in Cursor

The second path is editor-first. Once the repository is open in Cursor, you can ask for a more targeted diagram of a specific layer, class set, or flow instead of generating a generic whole-repo view.

For this example, I am using [SpringBlog](https://github.com/Raysmond/SpringBlog), which is a good candidate for repository and data-layer diagrams because the persistence concepts are easy to recognize.

Quick setup note

The free route does not need extra tooling. You can ask Cursor to generate Mermaid directly from the code you already have open.

The paid route uses MCP, which lets the editor call an external diagram tool directly. If you want the setup path, the official AI Diagram Maker MCP setup guide covers the configuration.

Free route: Mermaid in Cursor

This route is closer to diagram-as-code than to a polished visual workflow. You prompt Cursor to generate Mermaid for the code in front of you. In this case, the prompt is focused on repository classes.

The output stays text-first. Cursor generates the Mermaid syntax, but does not render the diagram visually in the chat. That makes the result easy to keep in Markdown, version in Git, and tweak in a pull request without dealing with a separate binary artifact.

To see it visually, you can paste the generated Mermaid into the Mermaid Live Editor or inspect the Markdown in GitHub, which can render Mermaid blocks natively. In this example, the screenshot below shows the rendered result on GitHub.

The trade-off is polish. You often need a little prompt iteration or cleanup. The result is often a better fit for docs and internal explanations than for presentation-heavy use cases.

Paid route: AI Diagram Maker MCP in Cursor

The paid editor-first route keeps the workflow inside Cursor while producing a more presentation-ready visual than raw Mermaid.

The prompt can stay direct:

Create class diagram for repository classes using ai diagram maker mcp

Here, MCP lets you work from the code in the editor while the output is optimized as a diagram rather than as text syntax. The diagram is rendered directly in Cursor, and the result also includes a link you can open in AI Diagram Maker if you want to edit or refine it afterward.

Compared with Mermaid, the emphasis shifts away from text-first maintainability and toward visual communication.

Free vs paid: what actually changes?

The difference is not only price. It is also the kind of output each workflow supports.

The practical differences are:

• public GitHub exploration versus support for private repositories
• editable text output versus a more polished visual artifact
• lightweight repo inspection versus diagrams meant for sharing and reuse

The better question is what happens after the diagram

Most teams do not struggle to create a first diagram. The harder part is producing one that stays useful after the first pass.

That is why these workflows are easiest to compare by what happens after generation. Some produce a quick visual for exploration. Others produce something you can refine, version, or share more easily.

What do you reach for first when you need to explain a codebase quickly: a repo-level map, a text-based diagram, or a polished visual?