Because it tries to solve non existent problems (prompt templates for example) and adds complexity, instead of abstracting

Ok I agree with that, I think they had some weird idea of managing templates in LangSmith and then being able to load them dynamically from LangChain.

LangSmith’s prompt engineering workflow is okay-ish but a lot of work and gets quite expensive quite fast, and only works for a specific set of prompts (ie one-turn prompts, multi-turn never works).

PydanticAI seems more lightweight and gets out of the way.

Yes, Pydantic ai successfully abstracts the tool call loop, and makes it easy to test out different models.

Because it's shit.

Have you seen hamilton/burr python packages for building the state machines for llm work?