  Can we avoid wars at the crossroads of history? This question has been
pursued by individuals, scholars, policymakers, and organizations throughout
human history. In this research, we attempt to answer the question based on the
recent advances of Artificial Intelligence (AI) and Large Language Models
(LLMs). We propose \textbf{WarAgent}, an LLM-powered multi-agent AI system, to
simulate the participating countries, their decisions, and the consequences, in
historical international conflicts, including the World War I (WWI), the World
War II (WWII), and the Warring States Period (WSP) in Ancient China. By
evaluating the simulation effectiveness, we examine the advancements and
limitations of cutting-edge AI systems' abilities in studying complex
collective human behaviors such as international conflicts under diverse
settings. In these simulations, the emergent interactions among agents also
offer a novel perspective for examining the triggers and conditions that lead
to war. Our findings offer data-driven and AI-augmented insights that can
redefine how we approach conflict resolution and peacekeeping strategies. The
implications stretch beyond historical analysis, offering a blueprint for using
AI to understand human history and possibly prevent future international
conflicts. Code and data are available at
\url{https://github.com/agiresearch/WarAgent}.
