[SkyPuncher]

I'm working on something similar as a side project. I'm finding frustration with a lack of repeatability in my LLM flows. 90% of my code is AI written, but most of my guidance to LLMs is not particularly specific. It's "make sure you've read this file", "how does that match against existing patterns", "what's the performance like".

I've ended up building my workflow engine directly in Python, despite YAML being the default choice for LLMs.

I found that YAML had some drawbacks:

* LLMs don't have an inherent understanding of YAML conventions. They tend to be overly verbose. Python code solved this because "good" code is generally as short as you need.

* YAML isn't really composable. Yes, you can technically compose it, but you'll be fighting the LLM the entire time. Python solved this because the LLM knows how to decouple code.

* I want _some_ things to be programatic still. Having Python solves that

* Pretty much any programming language would do. Python just feels like the default for LLM-centric code.