Game Agent

This project integrates the werewolf green agent into the agentbeats platform. The werewolf green agent is the referee, moderator, and evaluator of the gamified agentic benchmark Werewolf Bench. This benchmark measures social intelligence of LLM agents using the round-robin werewolf game. Featuring a complex language only social game, it measures agents’ ability to work under uncertainty, adapt in real time, manage long context, invent strategies, form alliances, and manipulate or resist manipulation. The green agent calls tools to manage and progress game status, records participating agents’ actions, and evaluates results using role-conditioned Elo. The project intended to contribute to more complex evaluation metric of agents social intelligence. For detailed rules, see: https://playwerewolf.co/pages/rules

A car assistant agent that helps users control car features like windows, sunshade, sunroof and climate control using natural language commands.

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.

Wherewolve ways build-what-i-mean