The only reason it isn't "usable" is because the wrapper script isn't on your system's path. Unless your tools actually depend on venv-related environment variables being set (for example, because they make subprocess calls and assume that `'python'` will use the currently running Python when they should correctly be using `sys.executable`), which IMX is very rare, you don't ever actually have to activate a venv to do anything with it. In particular, you don't need to activate it to use its REPL; you can instead directly specify the path to its Python executable.
The missing affordance basically looks like `ln -s path/to/venv/bin/tool ~/.local/bin`. Which is a big part of what Pipx does for you. (And once I realized that, I started using Pipx's vendored shared copy of pip: https://zahlman.github.io/posts/2025/01/07/python-packaging-...)
You can `uv tool install your_package`, add a dir to your PATH, and then you can launch the tool appropriately, with it installed in its own venv
$ virtualenv ~/venv/yt-dlp
$ . ~/venv/yt-dlp/bin/activate
$ pip install yt-dlp
$ ln -s ~/venv/yt-dlp/bin/yt-dlp ~/bin/yt-dlp
$ deactivate
$ yt-dlp ...(And you didn't handle getting an appropriate version of python installed.)
That's why I use pipx. But the activation and deactivation in that example are completely unnecessary, and the last line is just using the installed package. Installation actually looks like:
$ python -m venv ~/venv/yt-dlp
$ ~/venv/yt-dlp/bin/pip install yt-dlp
$ ln -s ~/venv/yt-dlp/bin/yt-dlp ~/bin/yt-dlp
Pip just happens to be laser focused on the actual installation of packages, so it doesn't provide that wrapper. (With PAPER I'm aiming for slightly broader scope, but still something intended specifically for users that's only one piece of a developer toolchain.)
> (And you didn't handle getting an appropriate version of python installed.)
When was the last time you tried to obtain an application that could be installed as a PyPI package and your system Python wasn't compatible? Everyone knows the CPython release cadence now and is strongly pressured to advance their own support in lock-step with that. Similarly for libraries. There's already a full set of wheels for the latest version of NumPy for 3.14, 22 of them. Even musl-based distributions are provided for. Even 32-bit Windows is provided for, for those few holdouts.
If your target audience doesn't have Python and understand on a basic level what that is, why will they be able to understand using the uv command line and accept using that to install a program?
One thing you can do (I'm not saying it's user friendly) is set up the tool in a virtualenv and then set up an alias like
alias foo-tool=/home/blah blah/tools/something/env/bin/python -m foo-toolCan you give a concrete example of an installation you attempted that failed?
The package versions are however fraught - our machine learning codebase at work only was stuck on the 1.x versions of numpy for more than a year, as scipy and ultralytics both took forever to upgrade, and that prevented us adopting packages that depend on the 2.x numpy.