# OpenGame: Open Agentic Coding for Games arXiv: https://arxiv.org/abs/2604.18394 ยท HTML: https://mlsanity.com/paper/2604.18394/ ## Key facts - Cited by 18 papers in the map (data through 14 Oct 2026). - Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use. - Builds on 13 of its 34 references that are in the map. - First posted on arXiv 20 Apr 2026. ## Placed in - LLM agents and tool use / [Software engineering coding agents](https://mlsanity.com/agents/software-engineering-coding-agents/) ## Builds on (most cited first, 13 of 13) - [Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities](https://mlsanity.com/paper/2507.06261/) (2025) - [SWE-bench: Can Language Models Resolve Real-World GitHub Issues?](https://mlsanity.com/paper/2310.06770/) (2023) - [SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering](https://mlsanity.com/paper/2405.15793/) (2024) - [Genie: Generative Interactive Environments](https://mlsanity.com/paper/2402.15391/) (2024) - [Terminal-Bench: Benchmarking Agents on Hard, Realistic Tasks in Command Line Interfaces](https://mlsanity.com/paper/2601.11868/) (2026) - [Procedural Content Generation via Machine Learning (PCGML)](https://mlsanity.com/paper/1702.00539/) (2017) - [Generative agent-based modeling with actions grounded in physical, social, or digital space using Concordia](https://mlsanity.com/paper/2312.03664/) (2023) - [Large Language Models and Games: A Survey and Roadmap](https://mlsanity.com/paper/2402.18659/) (2024) - [SIMA 2: A Generalist Embodied Agent for Virtual Worlds](https://mlsanity.com/paper/2512.04797/) (2025) - [FrontendBench: A Benchmark for Evaluating LLMs on Front-End Development via Automatic Evaluation](https://mlsanity.com/paper/2506.13832/) (2025) - [Pok'eChamp: an Expert-level Minimax Language Agent](https://mlsanity.com/paper/2503.04094/) (2025) - [The PokeAgent Challenge: Competitive and Long-Context Learning at Scale](https://mlsanity.com/paper/2603.15563/) (2026) - [Multi-Actor Generative Artificial Intelligence as a Game Engine](https://mlsanity.com/paper/2507.08892/) (2025) ## Built on by (most cited first, 18 of 18) - [GameCraft-Bench: Can Agents Build Playable Games End-to-End in a Real Game Engine?](https://mlsanity.com/paper/2606.17861/) (2026) - [GUI Agents for Continual Game Generation](https://mlsanity.com/paper/2605.28258/) (2026) - [Code World Model: Coding Agent as World Brain](https://mlsanity.com/paper/2608.25927/) (2026) - [GameXpert-Bench: How Far Are Coding Agents from Expert Game Development?](https://mlsanity.com/paper/2608.21833/) (2026) - [The Verifier is the Curriculum: Precision Sets the Return on Search in Code Self-Distillation](https://mlsanity.com/paper/2607.09709/) (2026) - [AI for Games in the Foundation Model Era](https://mlsanity.com/paper/2609.16679/) (2026) - [UniCoder: Unified Visual-to-Code Generation via Symbolic Rewards and Reference-Guided Code Optimization](https://mlsanity.com/paper/2606.31732/) (2026) - [MAGIC: Transition-Aware Generation of Navigable Multi-Scene Game Worlds with Large Language Models](https://mlsanity.com/paper/2607.11594/) (2026) - [GameASG-Bench: Benchmarking Autonomous Software Generation for Game Development](https://mlsanity.com/paper/2609.21293/) (2026) - [From Generation to Simulation: How Far Are World Models from Being True Simulators?](https://mlsanity.com/paper/2608.23070/) (2026) - [SWE-Game: Can Coding Agents Build the Games We Want?](https://mlsanity.com/paper/2609.33678/) (2026) - [A2Z GameSpec-Bench: How Faithfully Can Coding Agents Generate Games from Game Design Specifications?](https://mlsanity.com/paper/2609.39564/) (2026) - [Spec2Game: Can LLMs Generate Complete Playable Games from Detailed Specifications?](https://mlsanity.com/paper/2610.04253/) (2026) - [Code2Games: Enabling Coding Agents for Gaming World Generation](https://mlsanity.com/paper/2610.05033/) (2026) - [GAMEGO: Training Game-Dev Agents with Synthetic Trajectories Anchored in Real-World Assets](https://mlsanity.com/paper/2610.06910/) (2026) - [Recursive Game Creator: An Agentic Product-Level Experience-Oriented Game Harness](https://mlsanity.com/paper/2610.08621/) (2026) - [WorldSolver: Can LLM Agents Simulate the Physical Dynamics via Solver Generation?](https://mlsanity.com/paper/2610.08720/) (2026) - [Game-Creation-Agents: Autonomous Game Production](https://mlsanity.com/paper/2610.16776/) (2026) ## Abstract (arXiv) 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.