In this paper, we explore the potential of Large Language Models (LLMs) Agents in playing the strategic social deduction game, \textbf{Resistance Avalon}. Players in Avalon are challenged not only to make informed decisions based on dynamically evolving game phases, but also to engage in discussions where they must deceive, deduce, and negotiate with other players. These characteristics make Avalon a compelling test-bed to study the decision-making and language-processing capabilities of LLM Agents.To facilitate research in this line, we introduce \textsc{AvalonBench} - a comprehensive game environment tailored for evaluating multi-agent LLM Agents. This benchmark incorporates: (1) a game environment for Avalon, (2) rule-based bots as baseline opponents, and (3) ReAct-style LLM agents with tailored prompts for each role. Notably, our evaluations based on \textsc{AvalonBench} highlight a clear capability gap. For instance, models like ChatGPT playing good-role got a win rate of 22.2\% against rule-based bots playing evil, while good-role bot achieves 38.2\% win rate in the same setting.We envision \textsc{AvalonBench} could be a good test-bed for developing more advanced LLMs (with self-playing) and agent frameworks that can effectively model the layered complexities of such game environments.