#!/usr/bin/env python3 """ Multi-Agent Communication API Endpoints for SAAP Platform Provides REST API interface for multi-agent coordination and task delegation """ from fastapi import APIRouter, HTTPException, Depends from typing import Dict, Any, Optional, List import logging from datetime import datetime from pydantic import BaseModel from services.multi_agent_coordinator import MultiAgentCoordinator, TaskPriority, get_coordinator # Configure logging logging.basicConfig(level=logging.INFO) logger = logging.getLogger(__name__) # Create router for multi-agent endpoints multi_agent_router = APIRouter(prefix="/api/v1/multi-agent", tags=["Multi-Agent Communication"]) class MultiAgentChatRequest(BaseModel): user_message: str user_context: Optional[Dict[str, Any]] = None preferred_agent: Optional[str] = None task_priority: TaskPriority = TaskPriority.NORMAL class MultiAgentChatResponse(BaseModel): success: bool coordinator_response: str delegated_agent: Optional[str] = None specialist_response: Optional[str] = None coordination_chain: List[str] = [] processing_time: float = 0.0 workflow_type: str = "single_agent" task_id: Optional[str] = None cost_info: Optional[Dict[str, Any]] = None error: Optional[str] = None @multi_agent_router.post("/chat", response_model=MultiAgentChatResponse) async def multi_agent_chat( request: MultiAgentChatRequest, coordinator: MultiAgentCoordinator = Depends(get_coordinator) ): """ πŸ€– Multi-Agent Chat Endpoint - Jane Alesi Master Coordinator Automatically analyzes user intent and either: 1. Handles request directly (Jane as coordinator) 2. Delegates to appropriate specialist agent 3. Orchestrates multi-agent workflow for complex tasks Examples: - "Entwickle eine Python App" β†’ Jane delegates to John Alesi (Development) - "Medizinische Beratung fΓΌr Diabetes" β†’ Jane delegates to Lara Alesi (Medical) - "Legal Compliance Check" β†’ Jane delegates to Justus Alesi (Legal) - "SAAP Platform Status" β†’ Jane handles directly as Coordinator """ start_time = datetime.now() try: logger.info(f"πŸ€– Multi-Agent Chat Request: {request.user_message[:100]}...") # Execute multi-agent coordination coordination_result = await coordinator.coordinate_multi_agent_task( user_message=request.user_message, user_context=request.user_context or {} ) processing_time = (datetime.now() - start_time).total_seconds() if coordination_result.get("success", False): # Successful coordination workflow_type = coordination_result.get("workflow_type", "single_agent") if workflow_type == "multi_agent": # Complex multi-agent workflow specialists = coordination_result.get("specialists", []) workflow_steps = coordination_result.get("workflow_steps", []) # Build coordination chain coordination_chain = ["jane_alesi"] # Jane always starts coordination_chain.extend(specialists) # Get final response from synthesis step coordinator_response = coordination_result.get("final_response", "Multi-agent workflow completed successfully.") # Get specialist response (first specialist for simplicity) specialist_response = None delegated_agent = None if workflow_steps: for step in workflow_steps: if step.get("step") == "specialist_analysis": delegated_agent = step.get("agent") specialist_response = step.get("result", {}).get("response", "Specialist analysis completed.") break logger.info(f"βœ… Multi-Agent Workflow: {len(workflow_steps)} steps, {len(specialists)} specialists") return MultiAgentChatResponse( success=True, coordinator_response=coordinator_response, delegated_agent=delegated_agent, specialist_response=specialist_response, coordination_chain=coordination_chain, processing_time=processing_time, workflow_type="multi_agent", task_id=coordination_result.get("task_id"), cost_info={ "total_cost": 0.0, # Multi-agent coordination is free "task_count": coordination_result.get("task_count", 1), "agents_involved": len(coordination_chain) } ) else: # Single agent delegation primary_agent = coordination_result.get("primary_agent", "jane_alesi") response_text = coordination_result.get("response", "Task completed successfully.") # Determine coordination chain coordination_chain = ["jane_alesi"] # Jane analyzes intent if primary_agent != "jane_alesi": coordination_chain.append(primary_agent) # Delegate to specialist coordination_chain.append("jane_alesi") # Jane provides final coordination logger.info(f"βœ… Single Agent Delegation: jane_alesi β†’ {primary_agent}") return MultiAgentChatResponse( success=True, coordinator_response=f"Als Master Coordinatorin habe ich deinen Request analysiert und {'direkt bearbeitet' if primary_agent == 'jane_alesi' else f'an {primary_agent} delegiert'}.", delegated_agent=primary_agent if primary_agent != "jane_alesi" else None, specialist_response=response_text if primary_agent != "jane_alesi" else None, coordination_chain=coordination_chain, processing_time=processing_time, workflow_type="single_agent", task_id=coordination_result.get("task_id"), cost_info={ "total_cost": 0.0, "agents_involved": len(coordination_chain) } ) else: # Coordination failed error_msg = coordination_result.get("error", "Unknown coordination error") logger.error(f"❌ Multi-Agent Coordination failed: {error_msg}") return MultiAgentChatResponse( success=False, coordinator_response="Als Master Coordinatorin konnte ich deinen Request leider nicht erfolgreich bearbeiten.", processing_time=processing_time, error=error_msg ) except Exception as e: processing_time = (datetime.now() - start_time).total_seconds() logger.error(f"❌ Multi-Agent Chat API Error: {e}") return MultiAgentChatResponse( success=False, coordinator_response="Entschuldigung, es ist ein technischer Fehler im Multi-Agent System aufgetreten.", processing_time=processing_time, error=str(e) ) @multi_agent_router.get("/status") async def get_multi_agent_status( coordinator: MultiAgentCoordinator = Depends(get_coordinator) ): """ Get current multi-agent coordination status and statistics """ try: stats = await coordinator.get_coordination_stats() return { "status": "active", "coordinator": "jane_alesi", "available_specialists": [ {"id": "john_alesi", "name": "John Alesi", "specialization": "Development"}, {"id": "lara_alesi", "name": "Lara Alesi", "specialization": "Medical"}, {"id": "justus_alesi", "name": "Justus Alesi", "specialization": "Legal"}, {"id": "theo_alesi", "name": "Theo Alesi", "specialization": "Finance"}, {"id": "leon_alesi", "name": "Leon Alesi", "specialization": "System"}, {"id": "luna_alesi", "name": "Luna Alesi", "specialization": "Coaching"} ], "coordination_stats": stats, "features": { "intent_analysis": True, "automatic_delegation": True, "multi_agent_workflows": True, "real_time_coordination": True, "task_orchestration": True }, "timestamp": datetime.now().isoformat() } except Exception as e: logger.error(f"❌ Multi-Agent Status Error: {e}") raise HTTPException(status_code=500, detail=f"Status check failed: {str(e)}") @multi_agent_router.get("/capabilities") async def get_agent_capabilities( coordinator: MultiAgentCoordinator = Depends(get_coordinator) ): """ Get detailed agent capabilities for intelligent task delegation """ try: capabilities = {} for agent_id, agent_caps in coordinator.agent_capabilities.items(): capabilities[agent_id] = { "agent_name": { "jane_alesi": "Jane Alesi - Master Coordinator", "john_alesi": "John Alesi - Software Developer", "lara_alesi": "Lara Alesi - Medical Expert", "justus_alesi": "Justus Alesi - Legal Expert", "theo_alesi": "Theo Alesi - Financial Analyst", "leon_alesi": "Leon Alesi - System Administrator", "luna_alesi": "Luna Alesi - Coaching Specialist" }.get(agent_id, agent_id), "capabilities": [ { "name": cap.name, "description": cap.description, "keywords": cap.keywords, "complexity_level": cap.complexity_level } for cap in agent_caps ], "specialization": { "jane_alesi": "Coordination & Architecture", "john_alesi": "Software Development", "lara_alesi": "Medical Analysis", "justus_alesi": "Legal Compliance", "theo_alesi": "Financial Analysis", "leon_alesi": "System Administration", "luna_alesi": "Coaching & Process" }.get(agent_id, "General") } return { "total_agents": len(capabilities), "coordinator": "jane_alesi", "specialists_count": len(capabilities) - 1, "capabilities": capabilities, "timestamp": datetime.now().isoformat() } except Exception as e: logger.error(f"❌ Agent Capabilities Error: {e}") raise HTTPException(status_code=500, detail=f"Capabilities retrieval failed: {str(e)}") @multi_agent_router.get("/workload/{agent_id}") async def get_agent_workload( agent_id: str, coordinator: MultiAgentCoordinator = Depends(get_coordinator) ): """ Get current workload and task statistics for a specific agent """ try: workload = await coordinator.get_agent_workload(agent_id) return workload except Exception as e: logger.error(f"❌ Agent Workload Error for {agent_id}: {e}") raise HTTPException(status_code=500, detail=f"Workload check failed: {str(e)}")