A mob is an event organized via digital communication technologies where people assemble (online, offline, or both) to exert a collective force, ranging from benign gatherings such as dance performances at a shopping mall to deviant/malicious actions like coordinated cyber-attacks, organized crimes, and street takeovers. While previous research has established theoretical frameworks and data-driven approaches to understand mob dynamics and mobbers' behavior, these models often remain inaccessible to practitioners and researchers. Building upon the collective action theory-based model developed by Al-khateeb et al. (2024) and the multi-theoretical framework by Murray et al. (2024), we implement a stochastic agent-based modeling (ABM) simulation tool mainly using the Python programming language to understand mob phenomenon better. Our model incorporates key behavioral scenarios driven by 5 social science theories where agents can: act (participate), withdraw, perform power exchange (to gain utility), or act against the mob. The simulation takes three primary inputs: the number of invited participants, the number of known powerful participants/agents (e.g., mob organizers), and a threshold for mob success. We randomly assign each participating agent/mobber 18 traits/social science factors, then we determine the agent's decision/action based on multi-theoretical constraints. Then the model uses various theoretical scenarios (relating to each agent decision) and calculates a participation rate to determine mob success or failure. All of this is then wrapped into a web application to create an accessible and interactive tool for studying collective behavior, which is particularly valuable for understanding cyber-social group formation and its potential societal impacts. Our work bridges the gap between complex theoretical models and practical applications, offering insights into related phenomena such as social movements, organized protests, the diffusion of innovation, and the spread of online rumors.