alt Hacker NewsDspys advertising aside, imho it is a library only for optimizing an existing workflow/ prompt and not for the use cases described there. Similar to how I would not write "production" code with sklearn :)
They themselves are turning into wrapper code for other libraries (e.g. the LLM abstraction which litellm handles for them).
Can also add:
Option 3: Use instructor + litellm (probabyly pydantic AI, but have not tried that yet)
Edit: As others pointed out their optimizing algorithms are very good (GEPA is great and let's you easily visualize / track the changes it makes to the prompt)
The sklearn to me is (and mirrors) the insane amount of engineering that exists/existed to bring Jupyter notebooks to something more prod-worthy and reproducible. There’s always going to be re-engineering of these things, you don’t need to use the same tools for all use cases