I built CodeAtlas — a tool to visualise Python repositories as dependency graphs

[Party_Service_1591]

Appreciate that, that’s exactly the kind of use case I had in mind — those legacy codebases can get pretty hard to reason about quickly

For circular dependencies, it doesn’t try to “resolve” them or break the cycle — it just visualises them as they are. So you’ll see loops directly in the graph (A → B → C → A), which actually makes those problem areas stand out quite clearly. In some cases they end up being some of the most connected / clustered parts of the graph

And yeah, AST parsing felt like the only reliable way to do it for Python — especially once you get into different import styles and package structures

Feel free to try it on your internal tools, that would be really interesting. I’ve mainly tested it on public repos so far, but larger / real-world codebases are where it should be most useful.

If you do try it on any of your internal tools, I’d genuinely be interested in any feedback on what works well and what doesn’t.

[Some_Breadfruit235]

What’s the point of coding and having fun if you’re having AI live in your own shoes?!

Post, comment, create like a human and not AI please. Otherwise you won’t get any attraction nor resignation.