Smart Warehouse MCP Agent

""" Coordinator Agent for the warehouse simulation. """ from typing import Dict, List, Any, Optional import logging import json from datetime import datetime from simulation.warehouse import Warehouse from agents.inventory_manager import InventoryManagerAgent from agents.agv_planner import AGVPlannerAgent from agents.restock_agent import RestockAgent class CoordinatorAgent: """ Coordinator agent that orchestrates the actions of other agents. Acts as the central decision-making component that delegates tasks to specialized agents. """ def __init__( self, warehouse: Warehouse, log_file: Optional[str] = None, logger: Optional[logging.Logger] = None ): self.warehouse = warehouse self.logger = logger or logging.getLogger(__name__) self.log_file = log_file # Initialize specialized agents self.inventory_agent = InventoryManagerAgent(warehouse, self.logger) self.agv_agent = AGVPlannerAgent(warehouse, self.logger) self.restock_agent = RestockAgent(warehouse, self.logger) # Action history self.action_history = [] def process_query(self, query: str, state: Dict[str, Any]) -> Dict[str, Any]: """ Process a natural language query and return a response. This is a placeholder for the Claude integration. """ self.logger.info(f"Received query: {query}") # In a real implementation, this would call Claude # For now, we'll just return a simple response return { "query": query, "response": "This is a placeholder response. In the real implementation, this would be processed by Claude.", "timestamp": datetime.now().isoformat() } def execute_action(self, action: Dict[str, Any]) -> Dict[str, Any]: """Execute an action by delegating to the appropriate agent.""" action_type = action.get("type") agent_type = action.get("agent") if not action_type: self.logger.warning("No action type specified") return { "success": False, "message": "No action type specified" } # Log the action self._log_action(action) # Route to the appropriate agent if agent_type == "inventory": result = self.inventory_agent.execute_action(action) elif agent_type == "agv": result = self.agv_agent.execute_action(action) elif agent_type == "restock": result = self.restock_agent.execute_action(action) elif agent_type == "warehouse": # Direct warehouse actions result = self.warehouse.process_action(action) else: self.logger.warning(f"Unknown agent type: {agent_type}") result = { "success": False, "message": f"Unknown agent type: {agent_type}" } # Log the result self._log_result(action, result) return result def execute_plan(self, plan: List[Dict[str, Any]]) -> List[Dict[str, Any]]: """Execute a sequence of actions as a plan.""" results = [] for action in plan: result = self.execute_action(action) results.append(result) # Stop execution if an action fails if not result.get("success", False): self.logger.warning(f"Plan execution stopped due to failed action: {action}") break return results def get_warehouse_state(self) -> Dict[str, Any]: """Get the current state of the warehouse.""" return self.warehouse.get_warehouse_state() def get_action_history(self, limit: int = 10) -> List[Dict[str, Any]]: """Get the recent action history.""" return self.action_history[-limit:] if limit > 0 else self.action_history def _log_action(self, action: Dict[str, Any]) -> None: """Log an action to the action history and log file.""" timestamp = datetime.now().isoformat() log_entry = { "timestamp": timestamp, "type": "action", "action": action } self.action_history.append(log_entry) if self.log_file: try: with open(self.log_file, 'a') as f: f.write(json.dumps(log_entry) + '\n') except Exception as e: self.logger.error(f"Failed to write to log file: {str(e)}") def _log_result(self, action: Dict[str, Any], result: Dict[str, Any]) -> None: """Log an action result to the action history and log file.""" timestamp = datetime.now().isoformat() log_entry = { "timestamp": timestamp, "type": "result", "action": action, "result": result } self.action_history.append(log_entry) if self.log_file: try: with open(self.log_file, 'a') as f: f.write(json.dumps(log_entry) + '\n') except Exception as e: self.logger.error(f"Failed to write to log file: {str(e)}") def analyze_warehouse_state(self) -> Dict[str, Any]: """ Analyze the current warehouse state and provide recommendations. This would typically be done by Claude in the real implementation. """ state = self.get_warehouse_state() # Get items that need restocking items_below_threshold = [ item for item_id, item in state["inventory"].items() if item["needs_restock"] ] # Get available AGVs available_agvs = [ agv for agv_id, agv in state["agvs"].items() if agv["is_available"] ] # Get pending orders pending_orders = [ order for order_id, order in state.get("orders", {}).items() if order["status"] == "pending" ] # Generate recommendations recommendations = [] if items_below_threshold: recommendations.append({ "type": "restock", "message": f"{len(items_below_threshold)} items need restocking", "items": items_below_threshold }) if not available_agvs: recommendations.append({ "type": "agv_availability", "message": "No AGVs are currently available", "suggestion": "Consider freeing up AGVs or adding more to the fleet" }) if pending_orders: recommendations.append({ "type": "order_processing", "message": f"{len(pending_orders)} orders are pending", "orders": pending_orders }) return { "timestamp": datetime.now().isoformat(), "recommendations": recommendations, "summary": { "inventory_items": len(state["inventory"]), "items_below_threshold": len(items_below_threshold), "agvs": len(state["agvs"]), "available_agvs": len(available_agvs), "pending_orders": len(pending_orders) } }