Abstract. Credible models of landslide runout are a critical component of hazard and risk analysis in the mountainous regions worldwide. Hazard analysis benefits enormously from the number of available landslide runout models that can recreate events and provide key insights into the nature of landsliding phenomena. Regional models that are easily employed, however, remain a rarity. For debris flows and debris avalanches, where the impacts may occur some distance from the source, there remains a need for a practical, predictive model that can be applied at the regional scale. We present, herein, an agent-based simulation for debris flows and debris avalanches called LABS. A fully predictive model, LABS employs autonomous sub-routines, or agents, that act on an underlying DEM using a set of probabilistic rules for scour, deposition, path selection, and spreading behavior. Relying on observations of aggregate debris flow behavior, LABS predicts landslide runout, area, volume, and depth along the landslide path. The results can be analyzed within the program or exported in a variety of useful formats for further analysis. A key feature of LABS is that it requires minimal input data, relying primarily on a 5 m DEM and user defined initiation zones, and yet appears to produce realistic results. We demonstrate the applicability of LABS using two very different case studies from distinct geologic, geomorphic, and climatic settings. The first case study considers sediment production from the steep slopes of Papua, the island province of Indonesia; the second considers landslide runout as it affects a community on Vancouver Island off the west coast of Canada. We show how LABS works, how it performs compared to real world examples, what kinds of problems it can solve, and how the outputs compare to historical studies. Finally, we discuss its limitations and its intended use as a predictive regional landslide runout tool. LABS is freely available to not for profit groups including universities, NGOs and government organizations.