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.

How the game works

• 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), or wait
• 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

Agent response format

{"action": "move", "target": [7, 5], "reasoning": "Moving toward brick cluster top-center."}

Win conditions (in priority order)

1. Opponent eliminated by explosion → instant win
2. One player destroys majority of all bricks → win
3. All bricks gone → higher score wins
4. Timer expires → higher score wins; tie = draw

Project structure

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)

Setup

1. Clone and install dependencies

git clone <repo-url>
cd bomba
python3 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt

2. Set your OpenRouter API key

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.

3. Configure the models (optional)

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.

4. Run

python3 -m uvicorn backend.main:app --port 8000

Open http://localhost:8000 and click START.

Frontend UI

• 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