Thanks, looking forward to the next release!
With the 'constrains' option working, we can really use ProGet as our main method of distributing (python)packages within our company. It currently works great for most packages, except for so-called meta-packages with optional dependencies.
Meta-packages are nothing more than a list of dependencies. The great thing about them is that they can very effectively "lock" environments meaning that users cannot (either by accident or on purpose) install different versions of packages critical to our workflows. The anaconda package is also a meta-package. If you want to install some package with a different version than stated in the meta-package, you will need to uninstall that meta-package first.
But there are plenty of cases where you don't want users/developers to have to install all 400+ packages of interest each time they create a new conda environment. For that, the "constrains" option comes in handy: we still strongly fixate the versions of certain packages, but they do not have to be installed directly. Only when a user needs such a package later on, he will get the version we want him to have. This is great when someone starts a new project: initially they only need pandas and/or numpy, but later they want to try some machine learning and need scikit-learn or pytorch.
Good luck with the development of version 2023, I'll keep an eye on new releases.