Paper 2604.18394
OpenGame: Open Agentic Coding for Games
- Published
- Apr 2026
- Research lab
- Independent
- Citations
- 5
- GitHub
- 2.8K stars
01 In brief
Summary
OpenGame is an open-source agentic framework for end-to-end web game creation from natural-language specifications.
It introduces Game Skill, comprising a Template Skill that evolves a library of project skeletons and a Debug Skill that maintains a living protocol of verified fixes, to address cross-file inconsistencies and integration errors.
The framework is powered by GameCoder-27B, a code LLM trained via continual pre-training, supervised fine-tuning, and execution-grounded reinforcement learning.
OpenGame-Bench evaluates generated games on Build Health, Visual Usability, and Intent Alignment using headless browser execution and VLM judging.
Across 150 diverse game prompts, OpenGame with Claude Sonnet 4.6 achieves state-of-the-art scores (BH=72.4, VU=67.2, IA=65.1), outperforming baselines like Cursor.
Ablations show that the Template Method Pattern, Three-Layer Reading Strategy, and evolved Game Skills are critical.
The framework is fully open-sourced.
02 From the paper
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.