Orchestrate Parallel Factory Diagnostics Agents with Claude Agent SDK and LangGraph

"""
Production implementation for orchestrating parallel factory diagnostics agents.
Provides secure, scalable operations using Claude Agent SDK and LangGraph.
"""
from typing import Dict, Any, List, Optional
import os
import logging
import asyncio
import aiohttp
from tenacity import retry, stop_after_attempt, wait_exponential

# Logger setup for monitoring
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class Config:
    """
    Configuration class for environment variables.
    """
    database_url: str = os.getenv('DATABASE_URL', 'sqlite:///default.db')
    api_key: str = os.getenv('CLAUDE_API_KEY', 'your_api_key')

async def validate_input(data: Dict[str, Any]) -> bool:
    """
    Validate request data for diagnostics.
    
    Args:
        data: Input to validate
    Returns:
        True if valid
    Raises:
        ValueError: If validation fails
    """
    if 'agent_id' not in data:
        raise ValueError('Missing agent_id')  # Validate essential fields
    return True

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

async def fetch_data(url: str) -> Dict[str, Any]:
    """
    Fetch data from the external Diagnostics API.
    
    Args:
        url: API endpoint to call
    Returns:
        Parsed JSON response
    Raises:
        RuntimeError: If the fetch fails
    """
    async with aiohttp.ClientSession() as session:
        async with session.get(url) as response:
            if response.status != 200:
                raise RuntimeError(f'Failed to fetch data: {response.status}')  # Handle HTTP errors
            return await response.json()

@retry(wait=wait_exponential(multiplier=1, min=4, max=10),
       stop=stop_after_attempt(5))  # Retry logic with exponential backoff
async def save_to_db(data: Dict[str, Any]) -> None:
    """
    Save diagnostics data to the database.
    
    Args:
        data: Data to save
    Raises:
        Exception: If database operation fails
    """
    logger.info('Saving data to database.')  # Log the saving process
    # Simulating database save operation
    await asyncio.sleep(1)  # Simulate I/O operation
    logger.info('Data saved successfully.')  # Log success

async def process_batch(batch: List[Dict[str, Any]]) -> None:
    """
    Process a batch of diagnostics data.
    
    Args:
        batch: List of data dictionaries
    Raises:
        Exception: If processing fails
    """
    for item in batch:
        await validate_input(item)  # Validate each item
        sanitized_item = await sanitize_fields(item)  # Sanitize item
        await save_to_db(sanitized_item)  # Save to database

async def aggregate_metrics(data: List[Dict[str, Any]]) -> Dict[str, Any]:
    """
    Aggregate metrics from diagnostics data.
    
    Args:
        data: List of processed data dictionaries
    Returns:
        Aggregated metrics
    """
    # Dummy aggregation logic: Count items
    return {'total_agents': len(data)}  # Simple aggregation

class DiagnosticsAgent:
    """
    Main class to orchestrate diagnostics agents.
    """
    def __init__(self, agent_id: str):
        self.agent_id = agent_id
        self.config = Config()  # Load configuration

    async def run_diagnostics(self) -> None:
        """
        Main workflow to execute diagnostics.
        """
        try:
            logger.info(f'Starting diagnostics for agent: {self.agent_id}')  # Log start
            data = await fetch_data(f'https://api.diagnostics.com/{self.agent_id}')  # Fetch data
            await validate_input(data)  # Validate fetched data
            sanitized_data = await sanitize_fields(data)  # Sanitize fetched data
            await save_to_db(sanitized_data)  # Save sanitized data
            logger.info('Diagnostics completed successfully.')  # Log success
        except Exception as e:
            logger.error(f'Error during diagnostics: {e}')  # Log any errors

async def main(agent_ids: List[str]) -> None:
    """
    Main entry point for running diagnostics on multiple agents.
    
    Args:
        agent_ids: List of agent IDs
    """
    agents = [DiagnosticsAgent(agent_id) for agent_id in agent_ids]  # Create agents
    await asyncio.gather(*(agent.run_diagnostics() for agent in agents))  # Run diagnostics in parallel

if __name__ == '__main__':
    # Example usage
    agent_ids = ['agent1', 'agent2', 'agent3']  # Sample agent IDs
    asyncio.run(main(agent_ids))  # Run the main function