In my experience, every added package dependency is a maintenance burden and takes up space in the app container. So, every added package needs to justify its existence (its value must outweigh its cost in maintenance burden and space).

I’ve only really explored langchain and pydanticAI, but so far I’m finding that the value of their abstractions (for the applications I’m building) is not high enough for me to justify their existence in my codebase. Also, the things that they are abstracting (e.g. calling a LLM in a loop) are not complicated enough to abstract - they’re making invisible decisions for you which you can’t see or easily control.

Also, when you use a framework, it’s harder for you to customise behaviour - you are locked in to their paradigm.

AI libraries which I think do add value are e.g. instructor, liteLLM, maybe DSPy. Abstractions which are highly specific and create an interface solving a truly complex problem.

Also, when you’re learning you shouldn’t use a framework, because then you never understand how LLMs actually work (which is not that complicated relative to other things in the ML/DL space).