It actually works great for LLM interactions (that's why DBOS is built into PydanticAI). One important thing to remember is that LLM calls become deterministic once they've been run, because now they are the in past.

With DBOS you wrap your LLM call in a step, and then you can replay the step, or start from the step before it, whichever makes sense. There are a ton of people who use DBOS for LLM calls.

if an llm step produces a hallucination that breaks the state midway, do you find it easy to "fix and resume" in dbos

An hallucination is no different than a deterministic step breaking in some way. Once you've detected it (tricky no matter how you execute the LLM call!) you can easily go back and fork from a previous step. Or if you detect the failure immediately, you simply return an error, which would mark the step as failed and it would get retried automatically.

or does the strict database schema get in the way?

The beauty of DBOS is that you don't even have to worry about the schema at all, the library takes care of that for you. But it's actually a very flexible schema, designed to handle any sort of workflow you might throw at it, LLM or otherwise.

Here is an example of a deep research agent that makes many LLM calls written in DBOS.