In this contribution, we highlighted the newly developed functionalities of LinChemIn, an open-source Python toolkit for editing and analyzing synthetic routes. These functionalities aim to bridge the gap between digital resources (data, models, predictions) in synthetic chemistry and end-users, enabling. Through a case study, we showcased the key features offered by LinChemIn: the identification of unique routes, their chemical-aware manipulation, the possibility of merging routes into a synthetic tree, and mining synthetic routes from a tree. This work is a step toward a data-driven and model-enabled route design and selection. We aimed at lowering the barrier for a scientist to interact with model output, taking inspiration from them and editing whenever necessary or appropriate. A streamlined program interface will support software developers in using the toolkit to develop custom workflows. In the future releases of the toolkit, we will enhance the analytical capabilities of the toolkit to yield quantitative assessment of or route properties through the calculation of descriptors and metrics.