Coordinate Industrial AI Agents Across Heterogeneous Frameworks with BeeAI and LangGraph

"""
Production implementation for coordinating industrial AI agents across heterogeneous frameworks using BeeAI and LangGraph.
Provides secure, scalable operations for data processing and integration.
"""

from typing import Dict, Any, List, Tuple
import os
import logging
import requests
import asyncio
from aiohttp import ClientSession
from sqlalchemy import create_engine, Column, Integer, String
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy.orm import sessionmaker, Session

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

# SQLAlchemy setup for database interaction
Base = declarative_base()

class Config:
    database_url: str = os.getenv('DATABASE_URL', 'sqlite:///./test.db')
    max_retries: int = int(os.getenv('MAX_RETRIES', 3))

class Agent(Base):
    __tablename__ = 'agents'
    id = Column(Integer, primary_key=True)
    name = Column(String)
    status = Column(String)

# Create a connection pool
engine = create_engine(Config.database_url)
Base.metadata.create_all(engine)
SessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=engine)

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 'id' not in data:
        raise ValueError('Missing id')
    if not isinstance(data['id'], int):
        raise ValueError('Invalid id type; must be an integer')
    return True

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

async def fetch_data(agent_id: int) -> Dict[str, Any]:
    """Fetch data for a specific agent.
    
    Args:
        agent_id: ID of the agent to fetch data for
    Returns:
        Agent data
    Raises:
        ConnectionError: If fetching data fails
    """
    url = f'https://api.beeai.com/agents/{agent_id}'
    try:
        async with ClientSession() as session:
            async with session.get(url) as response:
                if response.status != 200:
                    raise ConnectionError('Failed to fetch data')
                return await response.json()
    except Exception as e:
        logger.error(f'Error fetching data: {e}')
        raise

async def save_to_db(data: Dict[str, Any]) -> None:
    """Save agent data to the database.
    
    Args:
        data: Data to save
    Raises:
        Exception: If saving data fails
    """
    db: Session = SessionLocal()
    try:
        agent = Agent(**data)
        db.add(agent)
        db.commit()
    except Exception as e:
        db.rollback()
        logger.error(f'Error saving to DB: {e}')
        raise
    finally:
        db.close()

async def process_batch(agent_ids: List[int]) -> None:
    """Process a batch of agents.
    
    Args:
        agent_ids: List of agent IDs to process
    """
    for agent_id in agent_ids:
        try:
            data = await fetch_data(agent_id)
            sanitized_data = await sanitize_fields(data)
            await save_to_db(sanitized_data)
            logger.info(f'Processed agent {agent_id}')
        except Exception as e:
            logger.error(f'Error processing agent {agent_id}: {e}')

async def call_api(agent_id: int) -> None:
    """Integrate with external API to synchronize agent data.
    
    Args:
        agent_id: ID of the agent to synchronize
    """
    url = f'https://api.langgraph.com/sync/{agent_id}'
    try:
        response = requests.post(url)
        response.raise_for_status()
        logger.info(f'Successfully synced agent {agent_id}')
    except requests.HTTPError as e:
        logger.error(f'HTTP error during sync: {e}')
    except Exception as e:
        logger.error(f'Error during sync: {e}')

async def aggregate_metrics() -> Dict[str, Any]:
    """Aggregate metrics from the database.
    
    Returns:
        Aggregated metrics
    """
    return {'total_agents': 100}  # Placeholder

async def handle_errors(e: Exception) -> None:
    """Handle errors gracefully with retries and logging.
    
    Args:
        e: Exception to handle
    """
    logger.error(f'Handling error: {e}')
    # Retry logic can be implemented here

class AgentCoordinator:
    """Main orchestrator class for coordinating agents."""
    def __init__(self, agent_ids: List[int]):
        self.agent_ids = agent_ids

    async def orchestrate(self) -> None:
        """Main workflow to coordinate agents."""
        await process_batch(self.agent_ids)
        metrics = await aggregate_metrics()
        logger.info(f'Aggregated metrics: {metrics}')

if __name__ == '__main__':
    # Example usage
    agent_ids = [1, 2, 3]
    coordinator = AgentCoordinator(agent_ids)
    asyncio.run(coordinator.orchestrate())