backend/services/multi_agent_coordinator.py

#!/usr/bin/env python3
"""
Multi-Agent Coordinator Service for SAAP Platform
Enables autonomous agent-to-agent communication and task delegation
"""

import asyncio
import json
import logging
import time
import uuid
from datetime import datetime
from typing import Dict, List, Optional, Any, Tuple
from enum import Enum
from dataclasses import dataclass

import redis.asyncio as aioredis
from pydantic import BaseModel

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

class TaskStatus(str, Enum):
    CREATED = "created"
    ASSIGNED = "assigned"
    IN_PROGRESS = "in_progress"
    COMPLETED = "completed"
    FAILED = "failed"

class TaskPriority(str, Enum):
    LOW = "low"
    NORMAL = "normal"
    HIGH = "high"
    URGENT = "urgent"

@dataclass
class AgentCapability:
    """Agent capability definition for intelligent task matching"""
    name: str
    description: str
    keywords: List[str]
    complexity_level: int  # 1-10 scale

class TaskRequest(BaseModel):
    task_id: str
    task_type: str
    description: str
    input_data: Dict[str, Any]
    priority: TaskPriority = TaskPriority.NORMAL
    status: TaskStatus = TaskStatus.CREATED
    assigned_agent: Optional[str] = None
    parent_task_id: Optional[str] = None
    max_execution_time: int = 300  # seconds
    
class TaskResult(BaseModel):
    task_id: str
    agent_id: str
    status: TaskStatus
    result: Dict[str, Any]
    execution_time: float
    timestamp: datetime
    error_message: Optional[str] = None

class MultiAgentCoordinator:
    """
    Multi-Agent Coordinator for autonomous task delegation and workflow orchestration
    Jane Alesi acts as the master coordinator with intelligent agent selection
    """
    
    def __init__(self, redis_host: str = "localhost", redis_port: int = 6379):
        self.redis_host = redis_host
        self.redis_port = redis_port
        self.redis_client = None
        
        # Agent capabilities database
        self.agent_capabilities = {
            "jane_alesi": [
                AgentCapability("coordination", "Master coordination and workflow management", 
                               ["coordinate", "manage", "orchestrate", "plan"], 9),
                AgentCapability("architecture", "System architecture and design decisions", 
                               ["architecture", "design", "system", "structure"], 10),
                AgentCapability("integration", "Multi-agent integration and communication", 
                               ["integrate", "communication", "multi-agent"], 10)
            ],
            "john_alesi": [
                AgentCapability("development", "Software development and coding", 
                               ["code", "develop", "program", "software", "implementation"], 9),
                AgentCapability("debugging", "Code debugging and troubleshooting", 
                               ["debug", "fix", "error", "troubleshoot"], 8),
                AgentCapability("optimization", "Performance optimization and refactoring", 
                               ["optimize", "performance", "refactor"], 7),
                AgentCapability("testing", "Unit testing and code quality assurance", 
                               ["test", "quality", "validation"], 8)
            ],
            "lara_alesi": [
                AgentCapability("medical_analysis", "Medical data analysis and diagnosis", 
                               ["medical", "health", "diagnosis", "clinical"], 10),
                AgentCapability("data_analysis", "Statistical data analysis and interpretation", 
                               ["analysis", "statistics", "data", "interpret"], 9),
                AgentCapability("research", "Medical research and literature review", 
                               ["research", "study", "literature", "evidence"], 8)
            ],
            "justus_alesi": [
                AgentCapability("legal_analysis", "Legal compliance and regulatory analysis", 
                               ["legal", "compliance", "regulation", "law"], 10),
                AgentCapability("documentation", "Legal documentation and contract review", 
                               ["document", "contract", "review", "legal"], 9),
                AgentCapability("risk_assessment", "Legal risk assessment and mitigation", 
                               ["risk", "assessment", "legal", "mitigation"], 8)
            ],
            "theo_alesi": [
                AgentCapability("financial_analysis", "Financial analysis and budgeting", 
                               ["finance", "budget", "cost", "investment"], 10),
                AgentCapability("market_analysis", "Market analysis and business intelligence", 
                               ["market", "business", "intelligence", "analysis"], 9),
                AgentCapability("reporting", "Financial reporting and KPI tracking", 
                               ["report", "kpi", "tracking", "metrics"], 8)
            ],
            "leon_alesi": [
                AgentCapability("system_administration", "System administration and deployment", 
                               ["system", "admin", "deploy", "infrastructure"], 10),
                AgentCapability("monitoring", "System monitoring and performance tracking", 
                               ["monitor", "performance", "system", "tracking"], 9),
                AgentCapability("security", "System security and access control", 
                               ["security", "access", "control", "protect"], 9)
            ],
            "luna_alesi": [
                AgentCapability("coaching", "Team coaching and development", 
                               ["coach", "team", "development", "training"], 10),
                AgentCapability("process_improvement", "Process optimization and workflow improvement", 
                               ["process", "improvement", "workflow", "optimize"], 8),
                AgentCapability("communication", "Team communication and collaboration", 
                               ["communication", "collaboration", "team"], 9)
            ]
        }
        
        # Active tasks tracking
        self.active_tasks: Dict[str, TaskRequest] = {}
        self.completed_tasks: Dict[str, TaskResult] = {}
        
        # Agent manager reference (will be injected)
        self.agent_manager = None
        
    async def initialize(self):
        """Initialize Redis connection and coordinator"""
        try:
            self.redis_client = aioredis.from_url(f"redis://{self.redis_host}:{self.redis_port}")
            await self.redis_client.ping()
            logger.info(f"✅ Multi-Agent Coordinator initialized with Redis at {self.redis_host}:{self.redis_port}")
            return True
        except Exception as e:
            logger.error(f"❌ Failed to initialize Multi-Agent Coordinator: {e}")
            return False
    
    def set_agent_manager(self, agent_manager):
        """Inject agent manager dependency"""
        self.agent_manager = agent_manager
    
    async def analyze_intent(self, user_message: str) -> Tuple[str, List[str], str]:
        """
        Analyze user intent and determine if multi-agent coordination is needed
        Returns: (task_type, required_capabilities, primary_agent)
        """
        message_lower = user_message.lower()
        
        # Intent analysis patterns - 🎯 ENHANCED FOR FINANCIAL
        intent_patterns = {
            "development": ["entwicke", "code", "programmier", "implementier", "software", "app"],
            "medical": ["medizinisch", "gesundheit", "diagnose", "patient", "clinical"],
            "legal": ["rechtlich", "legal", "compliance", "vertrag", "regulation"],
            "financial": ["finanzanwendung", "finanz", "finanziell", "budget", "kosten", "investment", "market", "banking", "payment"],  # 🔧 FIXED
            "system": ["system", "deploy", "server", "infrastructure", "admin"],
            "coordination": ["koordinier", "manage", "plan", "orchestrat", "workflow"],
            "analysis": ["analysier", "untersuche", "bewerte", "statistik"]
        }
        
        # Multi-agent patterns (require coordination)
        multi_agent_patterns = [
            "full stack", "complete solution", "end-to-end", "comprehensive",
            "multi", "various", "different aspects", "holistic approach"
        ]
        
        detected_intents = []
        for intent, keywords in intent_patterns.items():
            if any(keyword in message_lower for keyword in keywords):
                detected_intents.append(intent)
        
        # Check if multi-agent coordination is needed
        needs_coordination = any(pattern in message_lower for pattern in multi_agent_patterns) or len(detected_intents) > 1
        
        if needs_coordination or "coordination" in detected_intents:
            return "multi_agent_task", detected_intents, "jane_alesi"
        elif "development" in detected_intents:
            return "development_task", ["development"], "john_alesi" 
        elif "medical" in detected_intents:
            return "medical_task", ["medical_analysis"], "lara_alesi"
        elif "legal" in detected_intents:
            return "legal_task", ["legal_analysis"], "justus_alesi"
        elif "financial" in detected_intents:
            return "financial_task", ["financial_analysis"], "theo_alesi"  # 🔧 THEO HANDLES FINANCE
        elif "system" in detected_intents:
            return "system_task", ["system_administration"], "leon_alesi"
        else:
            return "general_task", ["coordination"], "jane_alesi"
    
    def select_agents_for_capabilities(self, required_capabilities: List[str], exclude_agent: str = None) -> List[str]:
        """
        Select best agents for required capabilities
        """
        agent_scores = {}
        
        for agent_id, capabilities in self.agent_capabilities.items():
            if exclude_agent and agent_id == exclude_agent:
                continue
                
            total_score = 0
            matched_capabilities = 0
            
            for required_cap in required_capabilities:
                best_match_score = 0
                for capability in capabilities:
                    # Check keyword matches
                    keyword_matches = sum(1 for keyword in capability.keywords 
                                        if keyword in required_cap.lower())
                    if keyword_matches > 0:
                        match_score = keyword_matches * capability.complexity_level
                        best_match_score = max(best_match_score, match_score)
                
                if best_match_score > 0:
                    total_score += best_match_score
                    matched_capabilities += 1
            
            if matched_capabilities > 0:
                # Average score weighted by coverage
                agent_scores[agent_id] = (total_score / len(required_capabilities)) * (matched_capabilities / len(required_capabilities))
        
        # Return top agents sorted by score
        sorted_agents = sorted(agent_scores.items(), key=lambda x: x[1], reverse=True)
        return [agent_id for agent_id, score in sorted_agents[:3]]  # Top 3 agents
    
    async def create_task(self, task_type: str, description: str, input_data: Dict[str, Any], 
                         priority: TaskPriority = TaskPriority.NORMAL, 
                         parent_task_id: str = None) -> str:
        """Create a new task in the coordination system"""
        task_id = str(uuid.uuid4())
        
        task = TaskRequest(
            task_id=task_id,
            task_type=task_type,
            description=description,
            input_data=input_data,
            priority=priority,
            status=TaskStatus.CREATED,
            parent_task_id=parent_task_id
        )
        
        self.active_tasks[task_id] = task
        
        # Store in Redis for persistence
        if self.redis_client:
            try:
                await self.redis_client.hset("saap:tasks", task_id, task.json())
            except Exception as e:
                logger.warning(f"⚠️ Failed to store task in Redis: {e}")
        
        logger.info(f"📋 Created task {task_id}: {task_type} - {description}")
        return task_id
    
    async def delegate_task(self, task_id: str, agent_id: str) -> bool:
        """Delegate a task to a specific agent"""
        if task_id not in self.active_tasks:
            logger.error(f"❌ Task {task_id} not found")
            return False
        
        task = self.active_tasks[task_id]
        task.assigned_agent = agent_id
        task.status = TaskStatus.ASSIGNED
        
        # Update Redis
        if self.redis_client:
            try:
                await self.redis_client.hset("saap:tasks", task_id, task.json())
            except Exception as e:
                logger.warning(f"⚠️ Failed to update task in Redis: {e}")
        
        logger.info(f"👤 Delegated task {task_id} to {agent_id}")
        return True
    
    async def execute_task(self, task_id: str) -> TaskResult:
        """Execute a delegated task through the assigned agent"""
        if task_id not in self.active_tasks:
            raise ValueError(f"Task {task_id} not found")
        
        task = self.active_tasks[task_id]
        if not task.assigned_agent:
            raise ValueError(f"Task {task_id} has no assigned agent")
        
        start_time = time.time()
        task.status = TaskStatus.IN_PROGRESS
        
        try:
            # Get agent and execute task
            if not self.agent_manager:
                raise ValueError("Agent manager not available")
            
            # 🔧 FIX: Don't await synchronous methods
            agent = self.agent_manager.get_agent(task.assigned_agent)
            if not agent:
                raise ValueError(f"Agent {task.assigned_agent} not found")
            
            # Create task-specific prompt
            task_prompt = self._create_task_prompt(task)
            
            # 🔧 FIX: Check if send_message is async or sync
            if hasattr(self.agent_manager, 'send_message'):
                # Try to determine if it's async
                send_method = getattr(self.agent_manager, 'send_message')
                if asyncio.iscoroutinefunction(send_method):
                    response = await self.agent_manager.send_message(task.assigned_agent, task_prompt)
                else:
                    response = self.agent_manager.send_message(task.assigned_agent, task_prompt)
            else:
                # Fallback: Use a generic message
                response = f"Als Master Coordinator habe ich deine Anfrage analysiert und das beste Ergebnis koordiniert."
            
            execution_time = time.time() - start_time
            
            result = TaskResult(
                task_id=task_id,
                agent_id=task.assigned_agent,
                status=TaskStatus.COMPLETED,
                result={"response": response, "execution_time": execution_time},
                execution_time=execution_time,
                timestamp=datetime.now()
            )
            
            # Move to completed tasks
            self.completed_tasks[task_id] = result
            del self.active_tasks[task_id]
            
            # Update Redis
            if self.redis_client:
                try:
                    await self.redis_client.hset("saap:completed_tasks", task_id, result.json())
                    await self.redis_client.hdel("saap:tasks", task_id)
                except Exception as e:
                    logger.warning(f"⚠️ Failed to update Redis: {e}")
            
            logger.info(f"✅ Completed task {task_id} in {execution_time:.2f}s")
            return result
            
        except Exception as e:
            execution_time = time.time() - start_time
            error_result = TaskResult(
                task_id=task_id,
                agent_id=task.assigned_agent,
                status=TaskStatus.FAILED,
                result={"error": str(e)},
                execution_time=execution_time,
                timestamp=datetime.now(),
                error_message=str(e)
            )
            
            self.completed_tasks[task_id] = error_result
            del self.active_tasks[task_id]
            
            logger.error(f"❌ Task {task_id} failed: {e}")
            return error_result
    
    def _create_task_prompt(self, task: TaskRequest) -> str:
        """Create agent-specific prompt for task execution"""
        prompt = f"""
Task ID: {task.task_id}
Task Type: {task.task_type}
Priority: {task.priority}

Description: {task.description}

Input Data: {json.dumps(task.input_data, indent=2)}

Please process this task according to your role and capabilities. 
Provide a detailed response with actionable results.
"""
        return prompt
    
    async def coordinate_multi_agent_task(self, user_message: str, user_context: Dict[str, Any] = None, provider: str = "auto") -> Dict[str, Any]:
        """
        Main coordination method for multi-agent tasks
        This is the entry point for complex multi-agent workflows
        
        Args:
            user_message: User's message/request
            user_context: Additional context data
            provider: LLM provider selection ("auto", "colossus", "openrouter")
        """
        start_time = time.time()
        
        try:
            # Step 1: Analyze intent
            task_type, required_capabilities, primary_agent = await self.analyze_intent(user_message)
            
            logger.info(f"🎯 Intent Analysis: {task_type} → {primary_agent} (capabilities: {required_capabilities})")
            logger.info(f"🔒 Provider: {provider}")
            
            # Step 2: Determine if multi-agent coordination is needed
            if task_type == "multi_agent_task" or len(required_capabilities) > 1:
                return await self._handle_multi_agent_workflow(user_message, required_capabilities, user_context, provider)
            else:
                return await self._handle_single_agent_task(user_message, primary_agent, user_context, provider)
                
        except Exception as e:
            logger.error(f"❌ Coordination error: {e}")
            return {
                "success": False,
                "error": str(e),
                "execution_time": time.time() - start_time
            }
    
    async def _handle_single_agent_task(self, message: str, agent_id: str, context: Dict[str, Any], provider: str = "auto") -> Dict[str, Any]:
        """Handle simple single-agent task with provider selection"""
        start_time = time.time()
        
        try:
            # Create and execute task
            task_id = await self.create_task(
                task_type="single_agent",
                description=message,
                input_data={"message": message, "context": context or {}, "provider": provider}
            )
            
            await self.delegate_task(task_id, agent_id)
            result = await self.execute_task(task_id)
            
            return {
                "success": result.status == TaskStatus.COMPLETED,
                "task_id": task_id,
                "coordinator": agent_id,
                "coordinator_response": result.result.get("response", "Als Master Coordinator habe ich deine Anfrage analysiert und das beste Ergebnis koordiniert."),
                "specialist_response": result.result.get("response", ""),
                "execution_time": time.time() - start_time,
                "workflow_type": "single_agent",
                "coordination_chain": [agent_id],
                "processing_time": result.execution_time,
                "timestamp": result.timestamp.isoformat()
            }
        except Exception as e:
            logger.error(f"❌ Single agent task failed: {e}")
            return {
                "success": False,
                "coordinator": agent_id,
                "coordinator_response": "Als Master Coordinator habe ich deine Anfrage analysiert und das beste Ergebnis koordiniert.",
                "specialist_response": "",
                "error": str(e),
                "execution_time": time.time() - start_time,
                "workflow_type": "single_agent",
                "coordination_chain": [agent_id],
                "processing_time": 0,
                "timestamp": datetime.now().isoformat()
            }
    
    async def _handle_multi_agent_workflow(self, message: str, capabilities: List[str], context: Dict[str, Any], provider: str = "auto") -> Dict[str, Any]:
        """Handle complex multi-agent workflow with Jane as coordinator and provider selection"""
        start_time = time.time()
        workflow_steps = []
        
        try:
            # Step 1: Jane analyzes and creates coordination plan
            coordination_task_id = await self.create_task(
                task_type="coordination_analysis",
                description=f"Analyze the following request and create a multi-agent coordination plan: {message}",
                input_data={
                    "original_message": message,
                    "detected_capabilities": capabilities,
                    "context": context or {},
                    "provider": provider
                },
                priority=TaskPriority.HIGH
            )
            
            await self.delegate_task(coordination_task_id, "jane_alesi")
            coordination_result = await self.execute_task(coordination_task_id)
            
            workflow_steps.append({
                "step": "coordination_analysis",
                "agent": "jane_alesi",
                "result": coordination_result.result,
                "execution_time": coordination_result.execution_time
            })
            
            # Step 2: Select and coordinate specialist agents
            selected_agents = self.select_agents_for_capabilities(capabilities, exclude_agent="jane_alesi")
            specialist_results = []
            
            for agent_id in selected_agents[:2]:  # Limit to 2 specialists for now
                specialist_task_id = await self.create_task(
                    task_type="specialist_analysis",
                    description=f"Provide specialist analysis for: {message}",
                    input_data={
                        "original_message": message,
                        "coordination_plan": coordination_result.result,
                        "context": context or {},
                        "provider": provider
                    },
                    parent_task_id=coordination_task_id
                )
                
                await self.delegate_task(specialist_task_id, agent_id)
                specialist_result = await self.execute_task(specialist_task_id)
                
                specialist_results.append(specialist_result)
                workflow_steps.append({
                    "step": "specialist_analysis",
                    "agent": agent_id,
                    "result": specialist_result.result,
                    "execution_time": specialist_result.execution_time
                })
            
            # Step 3: Jane synthesizes all results
            synthesis_task_id = await self.create_task(
                task_type="result_synthesis",
                description="Synthesize specialist results into comprehensive response",
                input_data={
                    "original_message": message,
                    "coordination_result": coordination_result.result,
                    "specialist_results": [r.result for r in specialist_results],
                    "context": context or {},
                    "provider": provider
                },
                priority=TaskPriority.HIGH,
                parent_task_id=coordination_task_id
            )
            
            await self.delegate_task(synthesis_task_id, "jane_alesi")
            synthesis_result = await self.execute_task(synthesis_task_id)
            
            workflow_steps.append({
                "step": "result_synthesis",
                "agent": "jane_alesi",
                "result": synthesis_result.result,
                "execution_time": synthesis_result.execution_time
            })
            
            total_execution_time = time.time() - start_time
            
            return {
                "success": True,
                "workflow_type": "multi_agent",
                "coordinator": "jane_alesi",
                "specialists": selected_agents[:2],
                "workflow_steps": workflow_steps,
                "coordinator_response": "Als Master Coordinator habe ich deine Anfrage analysiert und das beste Ergebnis koordiniert.",
                "specialist_response": synthesis_result.result.get("response", ""),
                "final_response": synthesis_result.result.get("response", ""),
                "coordination_chain": ["jane_alesi"] + selected_agents[:2],
                "processing_time": total_execution_time,
                "timestamp": datetime.now().isoformat(),
                "task_count": len(workflow_steps)
            }
            
        except Exception as e:
            logger.error(f"❌ Multi-agent workflow failed: {e}")
            return {
                "success": False,
                "workflow_type": "multi_agent",
                "coordinator": "jane_alesi",
                "coordinator_response": "Als Master Coordinator habe ich deine Anfrage analysiert und das beste Ergebnis koordiniert.",
                "specialist_response": "",
                "error": str(e),
                "coordination_chain": ["jane_alesi"],
                "processing_time": time.time() - start_time,
                "timestamp": datetime.now().isoformat()
            }
    
    async def get_agent_workload(self, agent_id: str) -> Dict[str, Any]:
        """Get current workload statistics for an agent"""
        active_count = sum(1 for task in self.active_tasks.values() 
                          if task.assigned_agent == agent_id)
        completed_count = sum(1 for result in self.completed_tasks.values() 
                             if result.agent_id == agent_id)
        
        return {
            "agent_id": agent_id,
            "active_tasks": active_count,
            "completed_tasks": completed_count,
            "capabilities": [cap.name for cap in self.agent_capabilities.get(agent_id, [])]
        }
    
    async def get_coordination_stats(self) -> Dict[str, Any]:
        """Get overall coordination statistics"""
        return {
            "active_tasks": len(self.active_tasks),
            "completed_tasks": len(self.completed_tasks),
            "available_agents": len(self.agent_capabilities),
            "agent_workloads": {
                agent_id: await self.get_agent_workload(agent_id)
                for agent_id in self.agent_capabilities.keys()
            }
        }
    
    async def cleanup(self):
        """Cleanup Redis connections"""
        if self.redis_client:
            await self.redis_client.close()

# Global coordinator instance
coordinator_instance = None

async def get_coordinator() -> MultiAgentCoordinator:
    """Get global coordinator instance"""
    global coordinator_instance
    if coordinator_instance is None:
        coordinator_instance = MultiAgentCoordinator()
        await coordinator_instance.initialize()
    return coordinator_instance