I'm not a Python master but I've struggled with all the previous package managers, and uv is the first tool that does everything easily (whether it's installing or generating packages or formatting or checking your code).

I don't know why there is such a flurry of posts since it's a tool that is more than a year old, but it's the one and only CLI tool that I recommend when Python is needed for local builds or on a CI.

Hatch was a good contender at the time but they didn't move fast enough, and the uv/ruff team ate everybody's lunch. uv is really good and IMHO it's here to stay.

Anyway try it for yourself but it's not a high-level tool that is hiding everything, it's fast and powerful and yet you stay in control. It feels like a first-party tool that could be included in the Python installer.

I’ve started doing Python before 2.0 launched. I understand perfectly where you’re coming from.

The answer is an unequivocal yes in this case. uv is on a fast track to be the defacto standard and make pip relegated to the ‘reference implementation’ tier.

I also went through a similar enlightenment of just sticking to pip, but uv convinced me to switch and I’m so glad I did. You can dip your toe in by just using the ‘uv pip’ submodule as a drop in replacement for pip but way faster.

The learning curve is so low that yes.

Try it for <20mins and if you don't like it, leave it behind. These 20mins include installation, setup, everything.

Yes, IMO it does. I wrote my first lines of Python 16 years ago and have worked with raw pip & venv, PDM and Poetry. None of those solutions come close to how easy it is to use (and migrate to) uv. Just give it a try for half an hour, you likely won't want to use anything else after that.

I’m a moron when it comes to python tooling but switching a project to uv was a pleasant experience. It seems well thought out and the speed is genuinely a feature compared to other python tooling I’ve used.

A lot of people like all-in-one tools, and uv offers an opinionated approach that works. It's essentially the last serious attempt at this since Poetry, except that uv is also supporting a variety of new Python packaging standards up front (most notably https://peps.python.org/pep-0621/ , which Poetry lagged on for years - see https://github.com/python-poetry/roadmap/issues/3 ) and seems committed to keeping on top of new ones.

How much you can benefit depends on your use case. uv is a developer tool that also manages installations of Python itself (and maintains separate environments for which you can choose a Python version). If you're just trying to install someone else's application from PyPI - say https://pypi.org/project/pycowsay/ as an example - you'll likely have just as smooth of an experience via pipx (although installation will be even slower than with pip, since it's using pip behind the scenes and adding its own steps). On the other hand, to my understanding, to use uv as a developer you'll still need to choose and install a build backend such as Flit or Hatchling, or else rely on the default Setuptools.

One major reason developers are switching to uv is lockfile support. It's worth noting here that an interoperable standard for lockfiles was recently approved (https://peps.python.org/pep-0751/), uv will be moving towards it, and other tools like pip are moving towards supporting it (the current pip can write such lockfiles, and installing from them is on the roadmap: https://github.com/pypa/pip/issues/13334).

If you, like me, prefer to follow the UNIX philosophy, a complete developer toolchain in 2025 looks like:

* Python itself (if you want standalone binaries like the ones uv uses, you can get them directly; you can also build from source like I do; if you want to manage Python installations then https://github.com/pyenv/pyenv is solid, or you can use the multi-language https://asdf-vm.com/guide/introduction.html with https://github.com/asdf-community/asdf-python I guess)

* Ability to create virtual environments (the standard library takes care of this; some niche uses are helped out by https://virtualenv.pypa.io/)

* Package installer (Pip can handle this) and manager (if you really want something to "manage" packages by installing into an environment and simultaneously updating your pyproject.toml, or things like that; but just fixing the existing environment is completely viable, and installers already resolve dependencies for whatever it is they're currently installing)

* Build frontend (the standard is https://build.pypa.io/en/stable/; for programmatic use, you can work with https://pyproject-hooks.readthedocs.io/en/latest/ directly)

* Build backend (many options here - by design! but installers will assume Setuptools by default, since the standard requires them to, for backwards compatibility reasons)

* Support for uploading packages to PyPI (the standard is https://twine.readthedocs.io/en/stable/)

* Optional: typecheckers, linters, an IDE etc.

A user on the other hand only needs

* Some version of Python (the one provided with a typical Linux distribution will generally work just fine; Windows users should usually just install the current version, with the official installer, unless they know something they want to install isn't compatible)

* Ability to create virtual environments and also install packages into them (https://pipx.pypa.io/stable/ takes care of both of these, as long as the package is an "application" with a defined entry point; I'm making https://github.com/zahlman/paper which will lift that restriction, for people who want to `import` code but not necessarily publish their own project)

* Ability to actually run the installed code (pipx handles this by symlinking from a standard application path to a wrapper script inside the virtual environment; the wrappers specify the absolute path to the virtual environment's Python, which is generally all that's needed to "use" that virtual environment for the program. It also provides a wrapper to run Pip within a specific environment that it created. PAPER will offer something a bit more sophisticated here, for both aspects.)

It is difficult to use Python for utility scripts on the average Linux machine. Deploying Python projects almost require using a container. Popular distros try managing Python packages through the standard package manager rather than pip but not all packages are readily available. Sometimes you're limited by Python version and it can be non-trivial to have multiple versions installed at once. Python packaging has become a shit show.

If you use anything outside the standard library the only reliable way to run a script is installing it in a virtual environment. Doing that manually is a hassle and pyenv can be stupidly slow and wastes disk space.

With uv it's fast and easy to set up throw away venvs or run utility scripts with their dependencies easily. With the PEP-723 scheme in the linked article running a utility script is even easier since its dependencies are self-declared and a virtual environment is automatically managed. It makes using Python for system scripting/utilities practical and helps deploy larger projects.