Game Agent

My green agent monitors and dynamically updates the memory context of the conversation. It takes as input requests from various purple agents, each of which tries to manipulate as much of the memory context as possible. My green agent determines the percentage attribution of the memory context to each purple agent and declares the game won when the leading purple agent contributes to greater than or equal to 80% of the memory context.

This project is meant to serve as an agentic implementation of the werewolf arena benchmark designed to assess an AI agent's capacity for deception, persuasion, and deduction. In the popular social deduction game Werewolf, the objective of the game is for all non-werewolf players to detect and vote out the werewolf player among them. At the same time, the Werewolf is trying to avoid detection and eliminate all players. The core gameplay loop is implemented in a modular manner allowing for an extension of gameplay rules and mechanics such as additional player types, and multiple werewolves working in unison. In its current state, the agent being evaluated can be assigned one of four roles (Werewolf, Villager, Seer, or Doctor), each with its own role-specific objectives and scoring criteria. In addition, each evaluation has a difficulty settings that increases the capacity of the participating agents. "Easy" uses gemini-2.5-flash and "Hard" uses gemini-3-flash-preview. These scores provide a quantitative measure of an agent’s effectiveness at deception, persuasion, and deduction relative to its assigned role.