I've encountered the same problem with Python codebases in the LLM / machine learning space. The requirements.txt files for those projects are full of unversioned dependencies, including Git repositories at some floating ref (such as master/HEAD).
In the easy cases, digging through the PyPI version history to identify the latest version as of some date is enough to get a working install (as far as I can tell -- maybe it's half-broken and I only use the working half?). In the hard cases, it may take an entire day to locate a CI log or contemporary bug report or something that lists out all the installed package versions.
It doesn't help that every Python-based project seems to have its own bespoke packaging system. It's never just pip + requirements.txt, it'll have a Dockerfile with `apt update`, or some weird meta-packaging thing like Conda that adds it own layers of non-determinism. Overall the feeling is that it was only barely holding together on the author's original machine, and getting it to build anywhere else is pure luck.
For example: https://github.com/AUTOMATIC1111/stable-diffusion-webui/blob... (with some discussion at https://github.com/AUTOMATIC1111/stable-diffusion-webui/disc...)