Build Long-Running Fault-Tolerant Maintenance Agent Workflows with Temporal and LangGraph

"""
Production implementation for building long-running fault-tolerant maintenance agent workflows.
Provides secure, scalable operations using Temporal and LangGraph.
"""
from typing import Dict, Any, List
import os
import logging
import time
import random
from temporalio import workflow
from temporalio.client import Client
from temporalio.worker import Worker

# Setup logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class Config:
    """
    Configuration class for environment variables.
    """
    database_url: str = os.getenv('DATABASE_URL', 'sqlite:///:memory:')  # Default to in-memory SQLite
    temporal_service: str = os.getenv('TEMPORAL_SERVICE', 'localhost:7233')

async def validate_input(data: Dict[str, Any]) -> bool:
    """Validate request data.
    
    Args:
        data: Input to validate
    Returns:
        True if valid
    Raises:
        ValueError: If validation fails
    """
    if 'task_id' not in data or 'task_data' not in data:
        raise ValueError('Missing task_id or task_data')  # Ensure required keys are present
    return True

async def sanitize_fields(data: Dict[str, Any]) -> Dict[str, Any]:
    """Sanitize input fields to prevent security issues.
    
    Args:
        data: Input data to sanitize
    Returns:
        Sanitized data
    """  
    return {key: str(value).strip() for key, value in data.items()}  # Strip whitespace from all fields

async def fetch_data(task_id: str) -> Dict[str, Any]:
    """Fetch task data from the database.
    
    Args:
        task_id: The ID of the task to fetch
    Returns:
        Task data dictionary
    Raises:
        ValueError: If task not found
    """
    logger.info(f'Fetching data for task {task_id}')
    # Simulate data fetching and return mock data
    return {'task_id': task_id, 'task_data': 'Sample data'}

async def save_to_db(data: Dict[str, Any]) -> None:
    """Save processed data to the database.
    
    Args:
        data: Data to save
    """
    logger.info(f'Saving data: {data}')  # Log the data being saved
    # Simulate database save with a sleep
    time.sleep(1)

async def call_api(task_data: Dict[str, Any]) -> Dict[str, Any]:
    """Call an external API with task data.
    
    Args:
        task_data: Data to send to the API
    Returns:
        Response data from the API
    """  
    logger.info(f'Calling API with data: {task_data}')
    # Simulate API call with random success/failure
    if random.choice([True, False]):
        raise ConnectionError('API call failed')
    return {'status': 'success'}

async def process_batch(data: List[Dict[str, Any]]) -> None:
    """Process a batch of tasks.
    
    Args:
        data: List of task data dictionaries
    """
    for task in data:
        try:
            logger.info(f'Processing task: {task}')
            await validate_input(task)  # Validate input data
            sanitized_data = await sanitize_fields(task)  # Sanitize fields
            await save_to_db(sanitized_data)  # Save to DB
        except Exception as e:
            logger.error(f'Error processing task {task}: {e}')  # Log the error

@workflow.defn
class MaintenanceWorkflow:
    """Workflow definition for maintenance tasks.
    """
    @workflow.run
    async def run(self, task_id: str) -> None:
        """Main workflow logic.
        
        Args:
            task_id: The ID of the task to process
        """
        try:
            task_data = await fetch_data(task_id)  # Fetch data
            response = await call_api(task_data)  # Call external API
            if response['status'] == 'success':
                logger.info(f'Task {task_id} processed successfully')
            else:
                logger.warning(f'Task {task_id} failed with response: {response}')
        except Exception as e:
            logger.error(f'Workflow failed for task {task_id}: {e}')  # Log workflow failure
            raise  # Rethrow to indicate failure

async def main() -> None:
    """Main entry point for the application.
    """
    config = Config()  # Load configuration
    client = Client.connect(config.temporal_service)  # Connect to Temporal service
    worker = Worker(client, task_queue='maintenance_tasks', workflows=[MaintenanceWorkflow])  # Setup worker
    await worker.run()  # Start the worker

if __name__ == '__main__':
    # Run the main function
    import asyncio
    asyncio.run(main())  # Execute the application loop