OpenGame: Open Agentic Coding for Games
Yilei Jiang, Jinyuan Hu, Qianyin Xiao, Yaozhi Zheng, Ruize Ma + 6 more
TLDR
OpenGame is an open-source agentic framework using a specialized LLM and skills to generate fully playable web games, achieving SOTA.
Key contributions
- Introduces OpenGame, the first open-source agentic framework for end-to-end web game creation.
- Features "Game Skill" with Template Skill for project skeletons and Debug Skill for systematic error repair.
- Powered by GameCoder-27B, an LLM specialized for game engines via a three-stage training pipeline.
- Evaluates game generation with OpenGame-Bench, scoring Build Health, Usability, and Intent Alignment.
Why it matters
This paper addresses the significant challenge of LLMs failing to create complete, playable games. OpenGame provides a novel, open-source solution by integrating specialized LLMs and agentic skills. It pushes the boundaries of code agents beyond isolated tasks, enabling them to build complex, interactive real-world applications.
Original Abstract
Game development sits at the intersection of creative design and intricate software engineering, demanding the joint orchestration of game engines, real-time loops, and tightly coupled state across many files. While Large Language Models (LLMs) and code agents now solve isolated programming tasks with ease, they consistently stumble when asked to produce a fully playable game from a high-level design, collapsing under cross-file inconsistencies, broken scene wiring, and logical incoherence. We bridge this gap with OpenGame, the first open-source agentic framework explicitly designed for end-to-end web game creation. At its core lies Game Skill, a reusable, evolving capability composed of a Template Skill that grows a library of project skeletons from experience and a Debug Skill that maintains a living protocol of verified fixes - together enabling the agent to scaffold stable architectures and systematically repair integration errors rather than patch isolated syntax bugs. Powering this framework is GameCoder-27B, a code LLM specialized for game engine mastery through a three-stage pipeline of continual pre-training, supervised fine-tuning, and execution-grounded reinforcement learning. Since verifying interactive playability is fundamentally harder than checking static code, we further introduce OpenGame-Bench, an evaluation pipeline that scores agentic game generation along Build Health, Visual Usability, and Intent Alignment via headless browser execution and VLM judging. Across 150 diverse game prompts, OpenGame establishes a new state-of-the-art. We hope OpenGame pushes code agents beyond discrete software engineering problems and toward building complex, interactive real-world applications. Our framework will be fully open-sourced.
📬 Weekly AI Paper Digest
Get the top 10 AI/ML arXiv papers from the week — summarized, scored, and delivered to your inbox every Monday.