Agreed. It seems like this has nothing to do with agents. In particular because this is well trodden territory that frameworks like Airflow have been tackling since _well_ before the recent deep learning craze.
I would say that unless agent based examples are highly compelling, it makes more sense to simply remove the agent stuff completely from the pitch, lest you be inevitably accused of taking advantage of the current AI hype for an otherwise unrelated piece of technology (that happens to be a good fit for agent based workflows - something which I haven't observed to even work very well with the best models).
For me Agent is essentially a decision making machine. I had many iterations on the software around building agents and Flow is the culmination of all of the learnings.
For some reason some LLM specific examples just slipped of my mind because I really wanted to show the barebone nature of this engine and how powerful it is despite its simplicity.
But you also right, it's general enough that you can build any task based system or rebuild complex system with task architecture with Flow.
Signal is clear, add more agent specific examples.
It supports tasks, dynamic routes and parallel execution in pure Python build-ins(zero deps). But just a side project so no persistent stuff and just a easy tool.
Totally makes, don't know why I missed agent specific examples... will add them asap.
I focused on the Agent because it was initially built for that internally. But you are right, for the release I made it extremely general and to be used as a foundation for the complex systems.
Moreover, there isn't an example how it `could` work for inference or function_calls/tools/agents
But looks simple to get started and feels like it has the right foundation for good things to come. Kudos!