- uv is aware of your dependencies, you can add/remove development dependencies, create group of development dependencies (test, lint, dev, etc) and add or remove those and only those at will. You can add dependencies and optional dependencies for a project as well, think my_app[cli,standard]. You don't need to have different requirements.txt for each case nor do you need to remove things by hand as you'd do in pip, since it doesn't remove deps when you remove a package for example. As a result, you can remove {conda,poetry,...} from your workflows.

- uv can install python and a virtualenv for you. Any command you run with `uv run` from the root of a repo will be aware of its environment, you don't even need to activate a virtualenv anymore. This replaces {pyenv, pyenv-virtualenv, virtualenvwrapper,...}.

- uv follows the PEPs for project config (dependencies, optional dependencies, tool configs) in the pyproject.toml so in case uv dies, it's possible to migrate away for the features are defined in the PEPs. Which is not the case for say, poetry.

- uv has a lock file and it's possible to make deps platform specific (Windows, Linux, MacOS, etc). This is in compliance with a PEP but not supported by all tools.

- uv supports custom indexes for packages so you can prefer a certain index, for example your company package index or pytorch's own index (for ML work).

- very fast, makes local dev very seamless and is really helpful in CI/CD where you might just setup and tear down python envs a lot.

Also, the team is responsive on Github so it's easy to get help.

Not only it's faster, it also provides a lock file, `uvx tool_name` just like `npx`, and a comprehensive set of tools to manage your Python version, your venv and your project.

You don't need `pyenv`, `poetry` and `pipx` anymore, `uv` does all of that for you.

> over pip

It's a much more complete tool than pip. If you've used poetry, or (in other languages) cargo, bundler, maven, then it's like that (and faster than poetry).

If you haven't, in addition to installing dependencies it will manage and lock their versions (no requirements.txt, and much more robust), look after the environment (no venv step), hold your hand creating projects, and probably other things.

Edit to add: the one thing it won't do is replace conda et al, nor is it intended to.

The problems start as soon as your scripts should run on more than your own computer.

If you pip install something, you install it on the system python (the python binary located at sys.executable). This can break systems if the wrong combination of dependencies comes together. This is why you should never install things via pip for other people, unless you asked them first.

Now how else would you install them? There is a thing called virtual environments, which basically allows you to install pip dependencies in such way, they are only there within the context of the virtual environment. This is what you should do when you distribute python programs.

Now the problem is how do you ensure that this install to the virtual environment uses specific versions? What happens when one library depends on package A with version 1.0 and another library depends on a package with version 2.0? Now what happens if you deploy that to an old debian with an older python version.. Before uv I had to spend literal days to resolve such conflicts.

uv solves most of these problems in one unified place, is extremely performant, just works and when it does not, it tells you precisely why.

The whole explaination is here: https://www.bitecode.dev/p/a-year-of-uv-pros-cons-and-should

The td;rd is that is has a lot less modes of failure.

It brings way more to the table than just being fast, like people are commenting. E.g. it manages Python for your projects, so if you say you want Python 3.12 in your project, and then you do 'uv run python my script.py', it will fetch and run the version of Python you specified, which pip can't do. It also creates lock files, so you know the exact set of Python package dependencies that worked, while you specify them more loosely. Plus a bunch of other stuff..

The only advantage over pip is it's faster. But the downside is it's not written in Python.

The real point of uv is to be more than pip, though. It can manage projects, so basically CLI commands to edit your `pyproject.toml`, update a lockfile, and your venv all in one go. Unlike earlier tools it implements a pretty natural workflow on top of existing standards where possible, but for some things there are no standards, the most obvious being lockfiles. Earlier tools used "requirements.txt" for this which was quite lacking. uv's lockfile is cross-platform, although, admittedly does produce noisier diffs than requirements.txt, which is a shame.

Faster.