GameHatch3D: An End-to-End Agentic Framework for 3D Game Generation

Traditional 3D game development requires substantial human effort in asset creation, programming, and playtesting. Recent agentic frameworks have shown promise in automating the planning, execution, and revision of complex systems. However, end-to-end automated 3D game generation remains at an early stage and presents additional challenges. It requires agents to jointly construct complex scenes and characters, model physical behavior, and implement gameplay logic. The resulting combinatorial complexity increases reasoning overhead and reduces generation reliability. To address these challenges, we propose GameHatch3D, an end-to-end agentic framework for generating 3D games from a text prompt. GameHatch3D first extracts game-design cues from the input prompt and consolidates them into a structured game specification. Guided by this specification, HatchCraft, our game-construction harness, coordinates reusable Environment, Character, Physics, and Rule Skills to construct the core components of a functional game. These skills encode domain-specific construction knowledge, enabling more reliable and efficient game construction. To support automated refinement, we further equip the agent with an engine-guided playtesting mechanism that enables dual-path self-correction. Engine-provided state information guides navigation planning and action generation, while playtesting observations and failures are used to diagnose visual and gameplay defects. For evaluation, we establish a benchmark comprising diverse gameplay demonstrations and curated high-level game descriptions, together with a comprehensive evaluation protocol. Experiments show that GameHatch3D generates higher-quality and more playable games than general-purpose agentic baseline while consuming fewer tokens.