Show HN: I taught LLMs to play Magic: The Gathering against each other

\u003ch2\u003eShow HN: I taught LLMs to play Magic: The Gathering against each other\u003c/h2\u003e \u003cp\u003eThis Hacker News "Show HN" post presents an innovative project or tool created by developers for the community. The submission represents technical innovation and problem-solving in action.\u003c/p\u003e \u003ch3\u003eProject Highlights\u003c/h3\u003e \u003cp\u003eKey aspects that make this project noteworthy:\u003c/p\u003e \u003cul\u003e \u003cli\u003eOpen-source approach promoting collaboration\u003c/li\u003e \u003cli\u003ePractical solution to real-world problems\u003c/li\u003e \u003cli\u003eTechnical innovation in software development\u003c/li\u003e \u003cli\u003eCommunity engagement and feedback-driven improvement\u003c/li\u003e \u003c/ul\u003e \u003ch3\u003eTechnical Significance\u003c/h3\u003e \u003cp\u003eThis type of project demonstrates the power of community-driven development and the continuous evolution of technical solutions through collaborative efforts.\u003c/p\u003e

Frequently Asked Questions

How do LLMs understand the complex rules of Magic: The Gathering?

LLMs are prompted with structured representations of the game state, including cards in hand, battlefield, graveyard, and available mana. The model reasons through legal actions using its natural language understanding of card text. While LLMs don't inherently "know" MTG rules, carefully engineered prompts and rule summaries guide their decision-making. The result is agents that can navigate card interactions, combat math, and priority windows — though consistency varies significantly between models and deck archetypes.

Which LLM performed best at playing Magic: The Gathering?

Results vary by game phase and deck complexity, but larger reasoning-focused models generally outperform smaller ones in multi-step decision trees like combat. Models with stronger instruction-following tend to make fewer illegal moves. This mirrors findings across complex game AI research — raw capability matters less than structured reasoning. If you're building AI-powered tools like this for your own platform, solutions like Mewayz (207 modules, $19/mo) can accelerate development without starting from scratch.

Can this project be extended to other trading card games like Pokémon or Yu-Gi-Oh?

Yes — the core architecture of encoding game state as structured text and querying an LLM for action selection is game-agnostic. Adapting it requires rewriting the rules layer, card database parsing, and prompt templates for the target game. The open-source nature of this project makes forking and extending it straightforward. Developers looking to build and launch such tools quickly might explore platforms like Mewayz, which offers 207 ready-to-use modules for $19/month to support rapid prototyping and deployment.

What are the main limitations of using LLMs as game-playing agents?

The biggest limitations are latency, cost per inference, and inconsistency — LLMs can make illegal moves or strategically poor choices, especially in long games with large hand sizes. They also lack persistent memory across turns unless the full game log is re-fed each prompt, which increases token usage substantially. These challenges make LLM game agents better suited for research and demos than production competitive play, at least until inference costs and reliability improve significantly.