Prior Labs has released an alpha-version suite of open-source tools designed to standardize and accelerate research in relational learning. The release introduces RelArena-$\alpha$, a unified benchmarking framework modeled after TabArena that provides standardized data loading, evaluation protocols, and tuning regimes for the RelBench v1 dataset. This framework aims to resolve the lack of reproducible comparison methods currently facing the community. Alongside this, the researchers introduced TabPFN-Rel, a specialized relational harness for the TabPFN-3 model. Currently ranked as the top-performing model on the RelArena-$\alpha$ leaderboard, TabPFN-Rel improves upon the RDBLearn architecture and provides empirical evidence that flattening relational databases into single tables remains a competitive strategy against specialized relational architectures. Finally, the suite includes the Relational Predictive Interface (RPI), a model-agnostic interface that allows users to define problems on new databases and apply any model implemented within RelArena-$\alpha$. Together, these tools—RelArena-$\alpha$, TabPFN-Rel, and RPI—aim to bridge the gap between research and industrial application by facilitating the easy deployment and standardized testing of relational learning methods.