Ludo Game Automation — Scheduling & Batch Scripts
Move beyond single-game bots. Learn how to schedule Ludo game sessions with cron, run batch bot operations for tournament qualification, and manage multiple concurrent game instances at scale.
Why Automate Multiple Ludo Game Sessions?
Single-game bot execution is a proof of concept. Tournament qualification, leaderboard farming, matchmaking stress testing, and AI model training all require running hundreds or thousands of game sessions in a coordinated way. Ludo game automation handles this by wrapping the core bot loop in scheduling primitives, worker queues, and result aggregation pipelines.
The Ludo API realtime feed provides structured game state that feeds directly into automation scripts. For tournament use cases, the tournament API handles match scheduling and result reporting automatically.
Bot Game Loop — The Core Scheduler
The fundamental building block is a GameRunner class that manages a single bot session:
authenticates with the API, fetches the current game state, calls the AI decision logic, submits the move, and
repeats until the game ends or a timeout is reached.
import asyncio, logging, time, aiohttp
from dataclasses import dataclass, field
from typing import Optional
logger = logging.getLogger(__name__)
@dataclass
class GameResult:
game_id: str
winner: int
turns: int
duration_seconds: float
errors: list = field(default_factory=list)
class GameRunner:
BASE_URL = "https://api.ludokingapi.site/v1"
def __init__(self, api_key: str, bot_id: str, max_turns: int = 200):
self.api_key = api_key
self.bot_id = bot_id
self.max_turns = max_turns
self.session: Optional[aiohttp.ClientSession] = None
self.headers = {"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"}
async def start_session(self) -> None:
self.session = aiohttp.ClientSession(headers=self.headers)
async def run_game(self, game_id: str) -> GameResult:
start_time = time.time()
turn_count = 0
errors = []
try:
async with self.session.get(f"{self.BASE_URL}/games/{game_id}/state") as resp:
state = await resp.json()
while not state["game_over"] and turn_count < self.max_turns:
my_turn = state["current_player"] == self.bot_id
if my_turn:
move = self._compute_best_move(state)
async with self.session.post(
f"{self.BASE_URL}/games/{game_id}/move",
json={"token_index": move["token"], "dice": state["dice"]}) as r:
if r.status != 200: errors.append(await r.text())
await asyncio.sleep(0.3)
async with self.session.get(f"{self.BASE_URL}/games/{game_id}/state") as resp:
state = await resp.json()
turn_count += 1
except Exception as e:
errors.append(str(e))
logger.error(f"Game {game_id} error: {e}")
return GameResult(
game_id=game_id,
winner=state.get("winner", -1),
turns=turn_count,
duration_seconds=time.time() - start_time,
errors=errors
)
def _compute_best_move(self, state: dict) -> dict:
tokens = state["tokens"]
my_tokens = [t for t in tokens if t["player"] == self.bot_id]
return {"token": my_tokens[0]["index"]} if my_tokens else {}Running Multiple Games with asyncio.gather
For batch execution, launch multiple GameRunner instances concurrently using
asyncio.gather. This saturates your API rate limit efficiently without blocking. A
batch_run function collects all results and writes them to a CSV for later analysis.
# Crontab entry to run batch automation every 6 hours
0 */6 * * * cd /opt/ludo-bot && /usr/bin/python3 batch_runner.py >> /var/log/ludo-batch.log 2&1
# batch_runner.py — run 20 concurrent game sessions
import asyncio, csv, os
from game_runner import GameRunner
API_KEY = os.getenv("LUDO_API_KEY")
GAME_IDS = [f"game_{i:04d}" for i in range(1, 21)] # 20 concurrent sessions
async def batch_run():
runner = GameRunner(api_key=API_KEY, bot_id="bot_001")
await runner.start_session()
tasks = [runner.run_game(gid) for gid GAME_IDS]
results = await asyncio.gather(*tasks)
with open("/var/log/ludo_results.csv", "a", newline="") as f:
writer = csv.DictWriter(f, fieldnames=["game_id","winner","turns","duration"])
for r in results:
writer.writerow({"game_id": r.game_id, "winner": r.winner,
"turns": r.turns, "duration": round(r.duration_seconds, 2)})
await runner.session.close()
if __name__ == "__main__":
asyncio.run(batch_run())Production Considerations
For production batch automation, respect the API rate limits and implement exponential backoff on 429 responses. Use a Redis queue to coordinate workers across multiple processes. Containerise the runner using the Docker setup guide so you can spin up hundreds of concurrent game sessions across a Kubernetes cluster.
Frequently Asked Questions
0 */6 * * * python3 batch_runner.py runs
the batch every 6 hours. On containerised deployments, use a Kubernetes CronJob or a Celery beat
schedule.max_turns limit in GameRunner (default
200 turns). If a game exceeds this limit, the runner logs it, records it as an error in the results CSV,
and moves to the next game. Adjust the limit based on your average game length.Scale Your Ludo Game Automation
Run hundreds of concurrent bot sessions with LudoKingAPI's high-throughput game endpoints and realtime state feeds.