RPGAgent: Driving Coherent Story-to-Play Generation with an LLM-Based Multi-Agent System

Abstract

Recent advances in LLMs have enabled new possibilities for creative content generation, yet their use in game design is often limited by poor integration across creative components, particularly for novice designers aiming to rapidly prototype playable concepts. Guided by the Elemental Tetrad framework, we present RPGAgent, an LLM-driven multi-agent system specifically designed to assist novice game creators in transforming a short story outline into a playable game. Specialized agents exchange structured data to generate coherent narrative, scene, and gameplay mechanics, ensuring structural correctness and consistency between story and world. By combining LLM-based generation with procedural content creation, the system offers a controllable and interpretable workflow. In a within-subjects study with 18 participants, RPGAgent outperformed a GPT-assisted baseline in both user experience and creative satisfaction during the prototyping of playable RPGs. These results demonstrate the potential of collaborative multi-agent frameworks for structured, AI-assisted game design.