roadmap

for building projects if it had supported PEPS 517/518/621 promptly. 621 came later but Poetry's workaround was nicer than Setuptools' to me. And it was easier to use with the new pyproject.toml setup, and I really wanted to get away from the expectation of using setup.py even for pure-Python projects. But that was really it. The main selling point point of Poetry was (and is) that they offered a lockfile and dependency resolution, but these weren't really things I personally needed. So there was nothing really to outweigh the downsides: * The way Poetry does the actual installation is, as far as I can tell, not much different from what pip does. And there are a ton of problems with that model. * The early days of Poetry were very inconsistent in terms of installation and upgrade procedures. There was at least once that it seemed that the only thing that would work was a complete manual uninstall and reinstall, and I had to do research to figure out what I had to remove for the uninstallation as there was nothing provided to automate that. * In the end, Poetry didn't have PEP 621 support for about four years (https://github.com/python-poetry/roadmap/issues/3 ; the OP was already almost a year after PEP acceptance in https://discuss.python.org/t/_/5472/109); there was this whole thing about how you were supposed to use pyproject.toml to describe the basic metadata of your project for packaging purposes, but if you used Poetry then you used Masonry to build, and that meant using a whole separate metadata configuration. Setuptools was slow in getting PEP 621 support off the ground (and really, PEP 621 itself was slow! It's hard to justify expecting anyone to edit pyproject.toml manually without PEP 621!), but Poetry was far slower still. I had already long given up on it at that point. So for me, Poetry was basically there to provide Masonry, and Masonry was still sub-par. I was still creating venvs manually, using `twine` to upload to PyPI etc. because that's just how I think. Writing something like `poetry shell` (or `uv run`) makes about as much sense to me as `git run-unit-tests` would.

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.)