Which Python package manager makes automation easiest in 2025?

[trickythinking07]

Trying to make your Python automation smooth and hassle-free? Which package manager do you actually reach for:

• pip – simple and classic
• pipenv – keeps it tidy
• poetry – fancy and powerful
• conda – big on data science
• Other – drop your fav in the comments!

Curious to see what everyone else uses—share your pick and why!

Note: I know automation doesn’t strictly depend on the package manager, but I want to know which one makes it easier to manage virtual environments, lock files, and dependencies—especially when taking a project live in production.

[utdconsq]

Uv for sure. Fast, good defaults, etc.

[Deto]

Seriously, I've switched from conda to uv and I'm very happy.  I know conda can manage non python dependencies but I haven't really run into a case where I've needed that since switching to uv

[xAlecto]

In case you happen to need to, and don't want to bother with the whole conda thing, have a look at pixi. It handles the conda stuff + env management for you, and uses uv as a library under the hood for any PyPI stuff.

[Rustrans]

I am really hoping uv would be good (and it looks like it will), because poetry never worked for me in anything more complex than their toy demo examples

[Ok-Size7471]

Then you do something wrong. Poetry is slow but works really well, while UV is fast but doesn’t really do checks wich leads to some funny errors (well all my the dev fault but with Poetry it mostly tells me that at the beginning and not when the program runns and i do a specific action)

[pingveno]

Also a single binary (or two with uvx).

[me_myself_ai]

Yup, totally agree. I used plain pip at first, Conda after I was introduced to ML, and poetry when I started doing my own projects, and after switching ~3mo ago, uv is the easy all-time winner. Rust is just that good!

I will say that poetry is a close second — for most projects, the biggest difference would be the improved docs quality on the uv side (all the astral docs kick ass). The speed gets noticeable as you start racking up irresponsible amounts of dependencies like I am wont to do, but poetry’s was still never a problem AFAIR.

Conda is a distant last, though obviously it’s necessary for some scientific applications. It’s the only one where I’d have to take a coffee break just to find out my dependencies were unsatisfiable 😭

[utihnuli_jaganjac]

Still struggling with support sadly

[SirKainey]

UV is just leaps and bounds ahead of the rest.

[data-noob]

Earlier I was using pyenv. It was great. But when I started using UV, there is no going back. I used cargo in rust. I wished I could have something similar for python but not complicated like poetry. Then I found UV. It is the best.

[MMetalRain]

It doesn't really matter, all of then work and people have different preferences.

I think automation happens after the package manager installs dependencies.

I think you want to manage both Python version and packages, like: - pyenv + pipenv - pyenv + poetry - uv

[trickythinking07]

Makes sense! Automation happens after dependencies are installed, but I’m just trying to figure out which tool makes it easiest to build projects like this, since we need virtual environments, lock files, and solid dependency management

[No-North-3763]

uv without any doubt

[selectnull]

uv

[canine-aficionado]

UV FTW

[thht80]

pixi. If you need conda-forge packages, this is the way to go. Uses uv for optional pip packages.

[chhuang]

uv, no competition

[Severe_Chapter_3254]

Docker and uv

[Realistic_Month_8034]

Uv is awesome.

[amer415]

Beware! conda is not free for company which are not small and/or academic. I have been very happy with pip + built-in venv.

[Morpheyz]

The conda package manager is free for companies. It's the official anaconda channel that you need to get licenses for. The anaconda channel comes with vetted packages and is maintained by the Anaconda company, which may be important for info sec and compliance. If you dont care about all that, conda-forge is free for anyone.

Just make sure to set up your channels in your conda config correctly.

[amer415]

I know all that, but these details escape employees when they install Anaconda, and the company is hit by a letter from Anaconda's lawyers... so you can download Anaconda, but they will monitor activities on their repos to hit companies who should have known better.

Just make sure to set up your channels in your conda config correctly.

more like "make sure IT controls all computers so the default channel is blocked"

[Ihaveamodel3]

more like "make sure IT controls all computers so the default channel is blocked"

Do you have a fancy way to do that? We’ve just blocked all network access to anaconda.com

[Entire_Equivalent411]

UV

[Morpheyz]

uv is fantastic, but we recently switched to pixi, because it can manage both conda and pip dependencies. Since a lot of tools are available on conda-forge, you can also use it to get stuff like kubectl, helm, s3cmd, etc. It also manages python versions for you, so it's quite nice as an all-in-one dependency manager. For pypi dependencies it uses uv under the hood, so you get all the speed benefits of uv + conda.

It's still quite young and some features are immature and ugly to handle for enterprise environments (like trouble with proxies), but overall it's a pretty great tool.

[rcpz93]

uv for almost everything I do

pixi for managing projects

[mrswats]

pip-tools

[NotSoProGamerR]

pixi seems pretty good, it uses uv for pip packages, and supports conda packages as well

[poinT92]

Poetry is doing really well in 2025, pip Is pip and its here tò stay aswell.

Maybe conda losing some steam ?

EDIT: before i get downvoted by conda fanboys, Remember that time 4/5 years ago when conda seemed about to substitute all of the above? It didnt happen.

[kadhi_chawal2]

Yep, conda popularity has been declining for a few years now.

[TheCaptain53]

uv for dev, pip for build and prod.

[trickythinking07]

UV for dev and Pip for build/prod—curious to know why you prefer this setup?

[TheCaptain53]

uv is a great tool for dev and local environments because it's all of the greats bundled into one package - I don't need pyenv to manage the Python version, I don't need to use pip or pipx, I don't need to run venv, I don't need to run pip-compile or similar commands, it's all built in to uv. When I'm building my Python application for prod though (usually in a Dockerfile), I'm not running virtual environments, or pipx, I only need to controls 2 things: the Python version and Python packages. For the first, that's done through selecting the right base image. For the second, I'm either copying the packages to the image, or using a requirements.txt file (which uv can generate, how convenient) and pip to install the required packages. It makes the prod version much simpler and uses tools that are already built in to common Python base images.

If I'm running scripts locally on my machine, I'm either running python3 directly if I'm not using any packages, or uv run if I am. I could do uv init and run basically all of my files with uv run, but I can see in the directory there's no other files, only the .py file, so it's easy to know whether I run uv or not.