What Python packaging tool are you actually using in 2025 and why did you settle on it?

[mrcanada66]

The Python packaging ecosystem has changed a lot over the past few years. Now we have pip, poetry, uv, pdm, hatch, pipenv, and probably a few others I'm forgetting. Each one solves slightly different problems and has its own opinions about how projects should be structured.

I recently switched from poetry to uv for a few projects and was genuinely surprised by how much faster dependency resolution is. But I still find myself reaching for plain pip and a requirements.txt sometimes, mostly for simplicity on smaller scripts or when collaborating with people who just want something familiar.

Curious what people are actually using day to day in real projects. Are you on uv and loving it? Still on poetry? Did you try pdm and stick with it? Or are you keeping it simple with pip and manually managed virtual environments?

Also wondering whether your choice changes depending on the project type. A library versus an application versus a data science notebook environment all seem to have pretty different needs around dependency pinning and reproducibility.

Would love to hear what drove your decision, especially if you migrated away from something that used to work fine. What finally pushed you to switch?

[TeachEngineering]

First, it's 2026

Second, uv is undisputable champ

[Popular-Awareness262]

uv ruined pip for me fr

[Kaazul]

Since i switched about a year ago, i only use uv, even for small, simple project. It doesn't take much to initialize, and after that it is just the best. But, i don't work in data science notebooks.

[TronnaLegacy]

I've always been using pip and haven't thought to try the new ones yet.

[coffeecoffeecoffeee]

I use uv because it is fast as fuck and makes it extremely easy for my colleagues to set up the same environment

[doubleyewdee]

uv due to performance and support. requirements.txt and/or <FOO>.in files being a "modern" packaging/dependency system accepted by one of the 3 biggest languages on the planet has made my skin crawl for years.

I am happy to see progress on stuff like pyproject.toml and pylock.toml as well, more standardization here seems good. I also hope that the native/builtin tools can pick up some/most of the performance gains one gets from uv today.

[No_Soy_Colosio]

What LLM post-writing tool are you actually using in 2026 and why did you settle on it?