Large language model agents have enabled software development through natural-language interaction, but extending this paradigm to end-to-end game development remains challenging. Building a playable game requires coordinating code, scenes, assets, animations, physics, and configurations, while allowing developers to validate their integrated behavior through interactive testing. We introduce VibeGame, a framework for creating complete 2D game projects from natural-language prompts and optional reference images. Its AI-native game engine represents project state as schema-validated structured text and provides frame-synchronous runtime control, enabling agents to inspect game states, execute actions, and verify behavior at their own pace. Built upon this interface, we design an Adversarial Agent Team of eight specialized agents to separate implementation from review, so that problems are found and fixed through multi-round loops. For each conducted and accepted project, we further introduce a training-free self-evolution mechanism to distill it into reusable skeletons, modules, and collaboration contracts for future development. We evaluate prompt-assets-to-game (PA2G), where agents are given an asset library, and prompt-to-game (P2G), where agents generate their own art. On a GameCraft-Bench subset, VibeGame in P2G consistently outperforms a range of agent harnesses in either P2G or PA2G with the same underlying model, including GPT-6.1-Sol.
Figures & tables
Figure 2: Games created by VibeGame from prompts across diverse 2D game genres.
Figure 3: System overview of VibeGame. (a) The Adversarial Agent Team turns a user request into an accepted game through intention alignment, parallel development, and adversarial correction. (b) The AI-native game engine exposes a structured project representation and frame-synchronous runtime control for implementation and play-testing. (c) Training-free self-evolution extracts skeletons, modules, and collaboration contracts from accepted projects and returns them to future development as project priors.
Harness
Model
Input
M
D
V
A
Overall
OpenGame [ 9 ]
GPT-5.6-Luna
P2G
36.78
25.57
23.30
44.57
33.58
Qwen Code [ 28 ]
GPT-5.6-Luna
PA2G
46.38
32.18
27.88
37.15
35.45
Kimi Code [ 29 ]
GPT-5.6-Luna
P2G
45.26
32.13
31.00
38.34
36.14
Kimi Code
GPT-5.6-Luna
PA2G
47.54
31.86
29.67
38.46
36.22
Codex [ 7 ]
GPT-5.6-Luna
PA2G
50.72
33.32
28.33
36.87
36.47
Codex
GPT-5.6-Luna
P2G
45.22
34.47
31.54
37.65
36.77
Table 1: Results on the GameCraft-Bench subset. DSH denotes DeepSeek Harness. M, D, V, and A denote Core Mechanics, Content Depth, Functional Visuals, and Art & Presentation, respectively; Overall is the weighted score defined in Appendix 6 . Bold numbers in green cells and underlined numbers in amber cells denote the best and second-best scores per column within each model, respectively.
Figure 4: Results by game type and pairwise win rates. (a) Gain of VibeGame over the strongest baseline of each model, Codex (P2G) for GPT-6.1-Sol and Qwen Code (P2G) for GPT-5.6-Luna, on each game type. (b) Win rate of VibeGame against each GPT-6.1-Sol baseline in the user study (top) and under the GPT-6.1-Sol judge (bottom); each bar stacks the win rates in the four rubric categories (M, D, V, A), and the number at its end is the overall win rate.
Figure 5: Qualitative comparison between VibeGame, Codex (PA2G), and Codex (P2G), all using GPT-6.1-Sol. Left: Sports Skateboard Park. Right: Sports Boxing Gym. Red boxes mark the defects.
Variant
M
D
V
A
Overall
VibeGame
60.73
48.58
43.10
59.26
53.45
w/o Engine
52.16
32.95
32.98
53.22
43.12
w/o AAT
57.79
41.94
34.43
58.09
48.82
w/ Experience
68.76
47.47
40.42
66.39
56.37
Table 2: Ablation on the ablation set (GPT-5.6-Luna). Columns and marks as in Table 1 .
Harness
Model
Input
M
D
V
A
Overall
OpenGame
GPT-5.6-Luna
P2G
46.04
33.90
33.17
47.22
40.29
Codex
GPT-5.6-Luna
P2G
58.51
45.23
48.69
47.24
48.46
Kimi Code
GPT-5.6-Luna
P2G
61.04
43.42
52.14
47.67
48.89
Qwen Code
GPT-5.6-Luna
PA2G
66.67
46.47
51.61
49.93
51.51
Kimi Code
GPT-5.6-Luna
PA2G
65.79
44.68
50.50
52.73
51.57
Qwen Code
GPT-5.6-Luna
P2G
64.56
47.71
53.30
50.17
51.97
Table 3: Results on the GameCraft-Bench subset scored by the GPT-5.5 judge (mean of its passes). Rows, columns, and marks as in Table 1 .
Figure 6: Judge error case 1: Kimi Code + GPT-5.6-Luna (P2G) on Racing Drift Circuit. The judged track-select frame: its twelve thumbnails reuse four layouts, one per biome (e.g., the three Canyon tracks share one layout).
Figure 7: Judge error case 2: OpenGame + GPT-5.6-Luna on Cardgame Autobattler. The board has a front and a back row (BACK 2–4) and no bench; the board heading and row labels are covered by cards.
Figure 8: Judge error case 3: Kimi Code + GPT-5.6-Luna (P2G) on Idle Factory Planet. Three consecutive judged frames, cropped to one production chain and shown at the source recording’s 1280 × 720 resolution (the judges received them downscaled to 854 × 480): conveyors connect the four machines and the resource particles move along them.
Game type
Tasks
Card Game
Autobattler † , Poker Roguelike †
Horror
Lighthouse † , Tape Archive
Idle
Dungeon Guild † , Factory Planet
Open World
Beast Tamer, Sky Islands
Platformer
Shape Shift, Thunder Valkyrie
Puzzle
Gravity Shift † , Pipe Crisis †
Table 4: The 30 benchmark tasks, two per game type. † marks the 10 tasks of the ablation set.
Figure 9: Game creation demonstrations. Top: Pixel Sekiro, a pixel-art boss fight created from a text prompt. Bottom: Hollow Knight boss slice, a side-scrolling boss fight created from a gameplay prompt and a concept image.
Figure 10: Game edit demonstrations. Top: intellectual property (IP) transfer, replacing the visual theme while preserving gameplay logic. Middle: genre transfer, converting a turn-based card game into a real-time boss fight while preserving its visual identity. Bottom: rule transfer, swapping the player and boss roles while preserving their abilities and animations.
Figure 11: A three-fruit combo sliced mid-air with split halves and juice effects.
Figure 12: Two fighters trade blows with an impact spark on the neon stage.
Figure 13: The player fires a spread of projectiles at an enemy inside a sealed combat room.
Figure 14: The protagonist floats above a balloon, with the goal flag on its platform.
Figure 15: A selected warrior unit and its stats beside a city on the hex map.
Figure 16: The round-table layout with AI player portraits and live speech text during the sheriff election.
Figure 17: Tanjiro faces Upper Rank Three under the moon on the Mugen Train night field.
Figure 18: Auditor case, before (top) and after (bottom). After the repair, each band is placed and sized by its tapping window on one time axis. Placeholder art; the bottom image shows the physics debug overlay.
Figure 19: Player case, before (top) and after (bottom). After the repair, every entry shows its full name, type, and status.
Figure 20: Reviewer case, before (top) and after (bottom). After the repair, the companion stands beside the keeper.
Harness
M
D
V
A
Overall
Codex
80.21
58.42
57.51
66.52
64.44
w/ Engine
82.41
62.37
58.94
68.75
67.13
Table 5: Codex with and without the AI-native engine of VibeGame (GPT-6.1-Sol, PA2G, all 30 tasks). Columns as in Table 1 .