Thanks for your comments! I fully agree that the Python ecosystem is (overly) complex, and Conda is generally not necessary for Python tools.
I made this library for a workflow management system, which can use any tool packaged with Conda, not just Python tools. The tools can be binaries made in C++, Java programs, or anything Conda can containerize. Note that Docker is not an option because it cannot be installed automatically on all platforms (and because of performances on non-Linux OS).
My users do not have to worry about command lines to install tools since Wetlands is installed in the workflow management system. Each tool is installed when the user executes a workflow using it.
In the bio-image analysis and medical imaging communities —as well as many others— scientists are often unfamiliar with the Python ecosystem and the concept of virtual environments. However, they rely heavily on a wide range of tools, each with numerous dependencies written in various languages. Applications with a built-in package management system like Wetlands greatly simplify their workflow by handling the complex task of setting up environments for these tools behind the scenes.
For example, Napari is an excellent viewer for multi-dimensional images written in Python which can be easily extended via plugins. There are hundreds of plugins, to do things like image denoising, registration, segmentation, particle tracking, etc. Plugins depend on tools (like Segment-Anything-Model, Cellpose, Stardist, etc.) which cannot be installed in the same environment. Wetlands can come to the rescue and isolate each plugin in its own environment.