This repository provides a modular, open-source framework for LLM-guided symbolic regression applied to interpretable kinetic model discovery. Using ADoK-S as its SR backbone, it enables researchers to automate the process of identifying compact and meaningful kinetic models from experimental data while preserving transparency and scientific interpretability. The project includes structured case studies, data generation and validation pipelines, model evaluation tools, and visualization utilities to support reproducible research in reaction engineering and process modeling. It is designed to accelerate model discovery, reduce the number of wet-lab experiments required for effective model identification, and make the resulting equations easier to understand, validate, and reuse.