The article introduces ancestral recombination graphs (ARGs) and their significance in empirical evolutionary genomics. ARGs are graphical structures that describe the complex interwoven collection of genealogies among genomes, encoding valuable information about their ancestry. Despite their potential, ARGs have been underutilized due to technological and methodological limitations, as well as a lack of accessible literature. Recent advancements in ARG reconstruction and simulation have made ARG-based approaches feasible for a wide range of questions and systems. The authors provide an accessible introduction to ARGs, survey recent methodological breakthroughs, and discuss the potential of ARGs to advance evolutionary genomics research. They highlight the importance of ARGs in understanding genetic transmission, selection, and demographic processes, and emphasize the need for further development and adoption of ARG-based inference methods. The article also includes a primer on ARGs, explaining their structure, properties, and applications, and explores the use of ARGs in various empirical and simulation contexts.The article introduces ancestral recombination graphs (ARGs) and their significance in empirical evolutionary genomics. ARGs are graphical structures that describe the complex interwoven collection of genealogies among genomes, encoding valuable information about their ancestry. Despite their potential, ARGs have been underutilized due to technological and methodological limitations, as well as a lack of accessible literature. Recent advancements in ARG reconstruction and simulation have made ARG-based approaches feasible for a wide range of questions and systems. The authors provide an accessible introduction to ARGs, survey recent methodological breakthroughs, and discuss the potential of ARGs to advance evolutionary genomics research. They highlight the importance of ARGs in understanding genetic transmission, selection, and demographic processes, and emphasize the need for further development and adoption of ARG-based inference methods. The article also includes a primer on ARGs, explaining their structure, properties, and applications, and explores the use of ARGs in various empirical and simulation contexts.