Keras is built with a Functional API at its core. How it works is that it infer the whole pipeline specification (the tensor shapes) and construct a DAG (directed acyclic graph) of trainable layers only by knowing the inputs and outputs, in our case the tensor are replaced with a JsonDataModel with a JSON schema for specification. In this version, each layer (called Module) is a trainable element that can be optimized using In-Context Reinforcement Learning (and other RL algorithms in the near future). Because there is no gradient descent but only a RL-like back-propagation we can have inactive branches and build logical flows, with python operators like `|` (or) and `&` (and), on top on them. Which make easier to build any workflow or Agent with it. Synalinks also optimize with asyncio each module call thanks to the DAG. We use constrained structured output by default, and combined with in-context RL we can ensure a correct syntax, and content at the same time. In addition to that, the application is serializable in JSON and the best performing program (with associated prompts) is automatically saved and can be versioned with git. You can try it online, you will find more explanation and diagrams: https://huggingface.co/spaces/YoanSallami/synalinks-notebooo...