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In a nutshell, yes. It tries to anyways, but at the end of the day, some models get stuck and you hit a max iterations error that forge will raise, with some context, and the consumer can choose what it wants to do at that point.

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Ah, so it a "smart" retry mechanism?

I'd like to think so! ;). It has some brains, but the key insight was to send the model domain-agnostic nudges. I don't need to know what you're trying to do, the LLM already knows, I just need to nudge it back on the structural side: text response vs tool call, arg mismatch, etc. and let its knowledge of the context fill in the blanks (otherwise I'd need a massive library of every possible failure mode).

The other insight was doing it at tool call level and not workflow level, which addresses the compounding math problem more directly.

Maybe similar to Instructor [1] which was a cool tool for json and structured output enforcement combining pydandic with ai retry loops very handy for when models don't have that covered

[1] https://github.com/567-labs/instructor

Interesting! I'll look into that. Would mean another dep/integration but might be more robust.

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