Show HN: LLM이 실시간으로 경쟁하는 Bomberman 스타일의 1v1 게임

A 1v1 Bomberman game where two LLM agents play autonomously against each other. No human plays — you watch the AIs fight. Each agent receives a text description of the board state, reasons about it, and outputs a move as JSON. The game engine executes it. Built with FastAPI + asyncio on the backend, vanilla JS + Canvas on the frontend. Agents are powered by any model available on OpenRouter. - Board: 15×13 grid with indestructible walls, destructible bricks, and two spawn corners - Goal: Score points by blowing up bricks. Win by having the highest score when all bricks are gone, killing your opponent, or having more points when the 3-minute timer expires - Actions: Each agent can move to a tile,move_and_bomb (move then plant a bomb),bomb_here (plant immediately), orwait - Bombs: Explode after ~6 seconds in a cross pattern. Chain reactions are supported. Killing your opponent is an instant win regardless of score - Agents: Each LLM gets the full board as text with coordinates, active threats, bomb timers, and their own position. They respond with a single JSON line {"action": "move", "target": [7, 5], "reasoning": "Moving toward brick cluster top-center."} - Opponent eliminated by explosion → instant win - One player destroys majority of all bricks → win - All bricks gone → higher score wins - Timer expires → higher score wins; tie = draw bomba/ ├── backend/ │ ├── main.py # FastAPI app, WebSocket broadcast, game manager │ ├── game/ │ │ ├── state.py # Dataclasses: GameState, Player, Bomb, Explosion │ │ ├── engine.py # Tick loop, move execution, win condition checks │ │ ├── pathfinder.py # BFS pathfinding for agent moves │ │ └── serializer.py # Converts game state to text prompt for LLMs │ └── agents/ │ └── llm_agent.py # Async LLM agent — prompts model, queues actions ├── frontend/ │ ├── index.html # Layout, HUD, side output panels │ └── game.js # Canvas renderer, WebSocket client, interpolation ├── requirements.txt └── .env # Your API key (not committed) git clone cd bomba python3 -m venv .venv source .venv/bin/activate pip install -r requirements.txt Create a .env file in the project root: OPENROUTER_API_KEY=your_key_here Get a key at openrouter.ai/keys. The game routes all LLM calls through OpenRouter, so you can use any supported model. Edit the model names in backend/main.py : p1_model = "openai/gpt-5.4" p2_model = "openai/gpt-5.4-mini" Any model slug from openrouter.ai/models works. Faster models (sub-1s) give snappier gameplay; slower models still work but may take a few seconds per move. python3 -m uvicorn backend.main:app --port 8000 Open http://localhost:8000 and click START. - Side panels: Show each agent's raw LLM output — the parsed action badge, target coordinates, and reasoning text - ⚠ badges: If an agent makes an illegal move (unreachable tile, bomb already active, response too slow), the reason is shown in the output panel - Intent lines: Dashed lines on the canvas show where each agent is currently headed - Scoreboard: Live score, bricks remaining, and countdown timer - Death log: On player death, shows their last 5 actions with positions and reasoning - Prompt panels: Collapsible panels showing the full text prompt each agent received