Hey, I think I am not using this framework to its full potential, but I have been using it as my main framework for 2 years now. Advantages for me are, that I can easily develop integrations myself. I like the concept of contracts, that I can first make a detailed output model using their LLMDataModel class (an extension of pydantic), I can validate every field both syntactically and semantically using their semantic operations and raise exceptions in case of failures with meaningful messages (and those will be considered via the LLM for retries). So I like this clean separation of actual business logic and all things validation, hallucinations checking and I don’t have to worry about retries and error handling, because that is done by framework as long as I provide a good output data model. It makes my code much more clean and secure and it also makes it easier for me to analyse by just looking at the output model how secure it is. Also I liked that I can basically inject any python logic,be multimodal without overcomplicating everything with DAGs etc. I like having low level control of what is going on. Also once I started out building my main issue with most frameworks was speed. I wanted to create complex multistep workflows using LLMs and still be very fast and I can’t really stand when my code looks messy. I had a data set of 50 million news and a very small compute budget. After some experiments, decided to go with symbolicai, because it was easier for me to speed it up with batching my agent workflows.