  We introduce rLLM (relationLLM), a PyTorch library designed for Relational
Table Learning (RTL) with Large Language Models (LLMs). The core idea is to
decompose state-of-the-art Graph Neural Networks, LLMs, and Table Neural
Networks into standardized modules, to enable the fast construction of novel
RTL-type models in a simple "combine, align, and co-train" manner. To
illustrate the usage of rLLM, we introduce a simple RTL method named
\textbf{BRIDGE}. Additionally, we present three novel relational tabular
datasets (TML1M, TLF2K, and TACM12K) by enhancing classic datasets. We hope
rLLM can serve as a useful and easy-to-use development framework for
RTL-related tasks. Our code is available at:
https://github.com/rllm-project/rllm.
