I agree with the debugging part - we need a new approach to handle these abstractions, and a regular code-stepping or reading logs isn't enough in the case of agents. It's all about managing state, and most ppl here say to avoid it. What I say is that if you go all-in and unify the architecture from the bottom layer, it's actually easier in the long run, but you'll need dedicated devtools for that.

This is precisely why I've created AI-gent Workflows (launched on HN today [0]), which comes with a purpose-built state machine and devtools. Unlike LangGraph, it starts already in the lowest layer and everything is state-based. You can time travel and even modify states of a live agent.

[0] https://news.ycombinator.com/item?id=44386314

OpenAI SDK is much simpler to use and understand than langchain etc. I havent deployed really complicated agents, but I have a number of simple use cases that work just fine

you're better off just implementing the logic yourself as it is more flexible.