orchestrator.py

"""
Agentic Orchestrator for Advanced AI System
-----------------------------------------
Manages and coordinates multiple agentic components:
1. Task Planning & Decomposition
2. Resource Management
3. Agent Communication
4. State Management
5. Error Recovery
6. Performance Monitoring
"""

import logging
from typing import Dict, Any, List, Optional, Union, TypeVar, Generic
from dataclasses import dataclass, field
from enum import Enum
import json
import asyncio
from datetime import datetime
import uuid
from concurrent.futures import ThreadPoolExecutor
import networkx as nx
from collections import defaultdict
import numpy as np

from reasoning import UnifiedReasoningEngine as ReasoningEngine, StrategyType as ReasoningMode
from reasoning.meta_learning import MetaLearningStrategy

T = TypeVar('T')

class AgentRole(Enum):
    """Different roles an agent can take."""
    PLANNER = "planner"
    EXECUTOR = "executor"
    MONITOR = "monitor"
    COORDINATOR = "coordinator"
    LEARNER = "learner"

class AgentState(Enum):
    """Possible states of an agent."""
    IDLE = "idle"
    BUSY = "busy"
    ERROR = "error"
    LEARNING = "learning"
    TERMINATED = "terminated"

class TaskPriority(Enum):
    """Task priority levels."""
    LOW = 0
    MEDIUM = 1
    HIGH = 2
    CRITICAL = 3

@dataclass
class AgentMetadata:
    """Metadata about an agent."""
    id: str
    role: AgentRole
    capabilities: List[str]
    state: AgentState
    load: float
    last_active: datetime
    metrics: Dict[str, float]

@dataclass
class Task:
    """Represents a task in the system."""
    id: str
    description: str
    priority: TaskPriority
    dependencies: List[str]
    assigned_to: Optional[str]
    state: str
    created_at: datetime
    deadline: Optional[datetime]
    metadata: Dict[str, Any]

class AgentOrchestrator:
    """Advanced orchestrator for managing agentic system."""
    
    def __init__(self, config: Dict[str, Any] = None):
        self.config = config or {}
        
        # Core components
        self.agents: Dict[str, AgentMetadata] = {}
        self.tasks: Dict[str, Task] = {}
        self.task_graph = nx.DiGraph()
        
        # State management
        self.state_history: List[Dict[str, Any]] = []
        self.global_state: Dict[str, Any] = {}
        
        # Resource management
        self.resource_pool: Dict[str, Any] = {}
        self.resource_locks: Dict[str, asyncio.Lock] = {}
        
        # Communication
        self.message_queue = asyncio.Queue()
        self.event_bus = asyncio.Queue()
        
        # Performance monitoring
        self.metrics = defaultdict(list)
        self.performance_log = []
        
        # Error handling
        self.error_handlers: Dict[str, callable] = {}
        self.recovery_strategies: Dict[str, callable] = {}
        
        # Async support
        self.executor = ThreadPoolExecutor(max_workers=4)
        self.lock = asyncio.Lock()
        
        # Logging
        self.logger = logging.getLogger(__name__)
        
        # Initialize components
        self._init_components()

    def _init_components(self):
        """Initialize orchestrator components."""
        # Initialize reasoning engine
        self.reasoning_engine = ReasoningEngine(
            min_confidence=0.7,
            parallel_threshold=5,
            learning_rate=0.1,
            strategy_weights={
                "LOCAL_LLM": 2.0,
                "CHAIN_OF_THOUGHT": 1.0,
                "TREE_OF_THOUGHTS": 1.0,
                "META_LEARNING": 1.5
            }
        )
        
        # Initialize meta-learning
        self.meta_learning = MetaLearningStrategy()
        
        # Register basic error handlers
        self._register_error_handlers()

    async def register_agent(
        self,
        role: AgentRole,
        capabilities: List[str]
    ) -> str:
        """Register a new agent with the orchestrator."""
        agent_id = str(uuid.uuid4())
        
        agent = AgentMetadata(
            id=agent_id,
            role=role,
            capabilities=capabilities,
            state=AgentState.IDLE,
            load=0.0,
            last_active=datetime.now(),
            metrics={}
        )
        
        async with self.lock:
            self.agents[agent_id] = agent
            self.logger.info(f"Registered new agent: {agent_id} with role {role}")
            
        return agent_id

    async def submit_task(
        self,
        description: str,
        priority: TaskPriority = TaskPriority.MEDIUM,
        dependencies: List[str] = None,
        deadline: Optional[datetime] = None,
        metadata: Dict[str, Any] = None
    ) -> str:
        """Submit a new task to the orchestrator."""
        task_id = str(uuid.uuid4())
        
        task = Task(
            id=task_id,
            description=description,
            priority=priority,
            dependencies=dependencies or [],
            assigned_to=None,
            state="pending",
            created_at=datetime.now(),
            deadline=deadline,
            metadata=metadata or {}
        )
        
        async with self.lock:
            self.tasks[task_id] = task
            self._update_task_graph(task)
            
        # Trigger task planning
        await self._plan_task_execution(task_id)
        
        return task_id

    async def _plan_task_execution(self, task_id: str) -> None:
        """Plan the execution of a task."""
        task = self.tasks[task_id]
        
        # Check dependencies
        if not await self._check_dependencies(task):
            self.logger.info(f"Task {task_id} waiting for dependencies")
            return
        
        # Find suitable agent
        agent_id = await self._find_suitable_agent(task)
        if not agent_id:
            self.logger.warning(f"No suitable agent found for task {task_id}")
            return
        
        # Assign task
        await self._assign_task(task_id, agent_id)

    async def _check_dependencies(self, task: Task) -> bool:
        """Check if all task dependencies are satisfied."""
        for dep_id in task.dependencies:
            if dep_id not in self.tasks:
                return False
            if self.tasks[dep_id].state != "completed":
                return False
        return True

    async def _find_suitable_agent(self, task: Task) -> Optional[str]:
        """Find the most suitable agent for a task."""
        best_agent = None
        best_score = float('-inf')
        
        for agent_id, agent in self.agents.items():
            if agent.state != AgentState.IDLE:
                continue
                
            score = await self._calculate_agent_suitability(agent, task)
            if score > best_score:
                best_score = score
                best_agent = agent_id
                
        return best_agent

    async def _calculate_agent_suitability(
        self,
        agent: AgentMetadata,
        task: Task
    ) -> float:
        """Calculate how suitable an agent is for a task."""
        # Base score on capabilities match
        capability_score = sum(
            1 for cap in task.metadata.get("required_capabilities", [])
            if cap in agent.capabilities
        )
        
        # Consider agent load
        load_score = 1 - agent.load
        
        # Consider agent's recent performance
        performance_score = sum(agent.metrics.values()) / len(agent.metrics) if agent.metrics else 0.5
        
        # Weighted combination
        weights = self.config.get("agent_selection_weights", {
            "capabilities": 0.5,
            "load": 0.3,
            "performance": 0.2
        })
        
        return (
            weights["capabilities"] * capability_score +
            weights["load"] * load_score +
            weights["performance"] * performance_score
        )

    async def _assign_task(self, task_id: str, agent_id: str) -> None:
        """Assign a task to an agent."""
        async with self.lock:
            task = self.tasks[task_id]
            agent = self.agents[agent_id]
            
            task.assigned_to = agent_id
            task.state = "assigned"
            agent.state = AgentState.BUSY
            agent.load += 1
            agent.last_active = datetime.now()
            
            self.logger.info(f"Assigned task {task_id} to agent {agent_id}")
            
            # Notify agent
            await self.message_queue.put({
                "type": "task_assignment",
                "task_id": task_id,
                "agent_id": agent_id,
                "timestamp": datetime.now()
            })

    def _update_task_graph(self, task: Task) -> None:
        """Update the task dependency graph."""
        self.task_graph.add_node(task.id, task=task)
        for dep_id in task.dependencies:
            self.task_graph.add_edge(dep_id, task.id)

    async def _monitor_system_state(self):
        """Monitor overall system state."""
        while True:
            try:
                # Collect agent states
                agent_states = {
                    agent_id: {
                        "state": agent.state,
                        "load": agent.load,
                        "metrics": agent.metrics
                    }
                    for agent_id, agent in self.agents.items()
                }
                
                # Collect task states
                task_states = {
                    task_id: {
                        "state": task.state,
                        "assigned_to": task.assigned_to,
                        "deadline": task.deadline
                    }
                    for task_id, task in self.tasks.items()
                }
                
                # Update global state
                self.global_state = {
                    "timestamp": datetime.now(),
                    "agents": agent_states,
                    "tasks": task_states,
                    "resource_usage": self._get_resource_usage(),
                    "performance_metrics": self._calculate_performance_metrics()
                }
                
                # Archive state
                self.state_history.append(self.global_state.copy())
                
                # Trim history if too long
                if len(self.state_history) > 1000:
                    self.state_history = self.state_history[-1000:]
                
                # Check for anomalies
                await self._check_anomalies()
                
                await asyncio.sleep(1)  # Monitor frequency
                
            except Exception as e:
                self.logger.error(f"Error in system monitoring: {e}")
                await self._handle_error("monitoring_error", e)

    def _get_resource_usage(self) -> Dict[str, float]:
        """Get current resource usage statistics."""
        return {
            "cpu_usage": sum(agent.load for agent in self.agents.values()) / len(self.agents),
            "memory_usage": len(self.state_history) * 1000,  # Rough estimate
            "queue_size": self.message_queue.qsize()
        }

    def _calculate_performance_metrics(self) -> Dict[str, float]:
        """Calculate current performance metrics."""
        metrics = {}
        
        # Task completion rate
        completed_tasks = sum(1 for task in self.tasks.values() if task.state == "completed")
        total_tasks = len(self.tasks)
        metrics["task_completion_rate"] = completed_tasks / max(1, total_tasks)
        
        # Average task duration
        durations = []
        for task in self.tasks.values():
            if task.state == "completed" and "completion_time" in task.metadata:
                duration = (task.metadata["completion_time"] - task.created_at).total_seconds()
                durations.append(duration)
        metrics["avg_task_duration"] = sum(durations) / len(durations) if durations else 0
        
        # Agent utilization
        metrics["agent_utilization"] = sum(agent.load for agent in self.agents.values()) / len(self.agents)
        
        return metrics

    async def _check_anomalies(self):
        """Check for system anomalies."""
        # Check for overloaded agents
        for agent_id, agent in self.agents.items():
            if agent.load > 0.9:  # 90% load threshold
                await self._handle_overload(agent_id)
        
        # Check for stalled tasks
        now = datetime.now()
        for task_id, task in self.tasks.items():
            if task.state == "assigned":
                duration = (now - task.created_at).total_seconds()
                if duration > 3600:  # 1 hour threshold
                    await self._handle_stalled_task(task_id)
        
        # Check for missed deadlines
        for task_id, task in self.tasks.items():
            if task.deadline and now > task.deadline and task.state != "completed":
                await self._handle_missed_deadline(task_id)

    async def _handle_overload(self, agent_id: str):
        """Handle an overloaded agent."""
        agent = self.agents[agent_id]
        
        # Try to redistribute tasks
        assigned_tasks = [
            task_id for task_id, task in self.tasks.items()
            if task.assigned_to == agent_id and task.state == "assigned"
        ]
        
        for task_id in assigned_tasks:
            # Find another suitable agent
            new_agent_id = await self._find_suitable_agent(self.tasks[task_id])
            if new_agent_id:
                await self._reassign_task(task_id, new_agent_id)

    async def _handle_stalled_task(self, task_id: str):
        """Handle a stalled task."""
        task = self.tasks[task_id]
        
        # First, try to ping the assigned agent
        if task.assigned_to:
            agent = self.agents[task.assigned_to]
            if agent.state == AgentState.ERROR:
                # Agent is in error state, reassign task
                await self._reassign_task(task_id, None)
            else:
                # Request status update from agent
                await self.message_queue.put({
                    "type": "status_request",
                    "task_id": task_id,
                    "agent_id": task.assigned_to,
                    "timestamp": datetime.now()
                })

    async def _handle_missed_deadline(self, task_id: str):
        """Handle a missed deadline."""
        task = self.tasks[task_id]
        
        # Log the incident
        self.logger.warning(f"Task {task_id} missed deadline: {task.deadline}")
        
        # Update task priority to CRITICAL
        task.priority = TaskPriority.CRITICAL
        
        # If task is assigned, try to speed it up
        if task.assigned_to:
            await self.message_queue.put({
                "type": "expedite_request",
                "task_id": task_id,
                "agent_id": task.assigned_to,
                "timestamp": datetime.now()
            })
        else:
            # If not assigned, try to assign to fastest available agent
            await self._plan_task_execution(task_id)

    async def _reassign_task(self, task_id: str, new_agent_id: Optional[str] = None):
        """Reassign a task to a new agent."""
        task = self.tasks[task_id]
        old_agent_id = task.assigned_to
        
        if old_agent_id:
            # Update old agent
            old_agent = self.agents[old_agent_id]
            old_agent.load -= 1
            if old_agent.load <= 0:
                old_agent.state = AgentState.IDLE
        
        if new_agent_id is None:
            # Find new suitable agent
            new_agent_id = await self._find_suitable_agent(task)
        
        if new_agent_id:
            # Assign to new agent
            await self._assign_task(task_id, new_agent_id)
        else:
            # No suitable agent found, mark task as pending
            task.state = "pending"
            task.assigned_to = None

    def _register_error_handlers(self):
        """Register basic error handlers."""
        self.error_handlers.update({
            "monitoring_error": self._handle_monitoring_error,
            "agent_error": self._handle_agent_error,
            "task_error": self._handle_task_error,
            "resource_error": self._handle_resource_error
        })
        
        self.recovery_strategies.update({
            "agent_recovery": self._recover_agent,
            "task_recovery": self._recover_task,
            "resource_recovery": self._recover_resource
        })

    async def _handle_error(self, error_type: str, error: Exception):
        """Handle an error using registered handlers."""
        handler = self.error_handlers.get(error_type)
        if handler:
            try:
                await handler(error)
            except Exception as e:
                self.logger.error(f"Error in error handler: {e}")
        else:
            self.logger.error(f"No handler for error type: {error_type}")
            self.logger.error(f"Error: {error}")

    async def _handle_monitoring_error(self, error: Exception):
        """Handle monitoring system errors."""
        self.logger.error(f"Monitoring error: {error}")
        # Implement recovery logic
        pass

    async def _handle_agent_error(self, error: Exception):
        """Handle agent-related errors."""
        self.logger.error(f"Agent error: {error}")
        # Implement recovery logic
        pass

    async def _handle_task_error(self, error: Exception):
        """Handle task-related errors."""
        self.logger.error(f"Task error: {error}")
        # Implement recovery logic
        pass

    async def _handle_resource_error(self, error: Exception):
        """Handle resource-related errors."""
        self.logger.error(f"Resource error: {error}")
        # Implement recovery logic
        pass

    async def _recover_agent(self, agent_id: str):
        """Recover a failed agent."""
        try:
            agent = self.agents[agent_id]
            
            # Log recovery attempt
            self.logger.info(f"Attempting to recover agent {agent_id}")
            
            # Reset agent state
            agent.state = AgentState.IDLE
            agent.load = 0
            agent.last_active = datetime.now()
            
            # Reassign any tasks that were assigned to this agent
            for task_id, task in self.tasks.items():
                if task.assigned_to == agent_id:
                    await self._reassign_task(task_id)
            
            # Update metrics
            agent.metrics["recovery_attempts"] = agent.metrics.get("recovery_attempts", 0) + 1
            
            self.logger.info(f"Successfully recovered agent {agent_id}")
            return True
            
        except Exception as e:
            self.logger.error(f"Failed to recover agent {agent_id}: {e}")
            return False

    async def _recover_task(self, task_id: str):
        """Recover a failed task."""
        try:
            task = self.tasks[task_id]
            
            # Log recovery attempt
            self.logger.info(f"Attempting to recover task {task_id}")
            
            # Reset task state
            task.state = "pending"
            task.assigned_to = None
            
            # Try to reassign the task
            await self._reassign_task(task_id)
            
            self.logger.info(f"Successfully recovered task {task_id}")
            return True
            
        except Exception as e:
            self.logger.error(f"Failed to recover task {task_id}: {e}")
            return False

    async def _recover_resource(self, resource_id: str):
        """Recover a failed resource."""
        try:
            # Log recovery attempt
            self.logger.info(f"Attempting to recover resource {resource_id}")
            
            # Release any locks on the resource
            if resource_id in self.resource_locks:
                lock = self.resource_locks[resource_id]
                if lock.locked():
                    lock.release()
            
            # Reset resource state
            if resource_id in self.resource_pool:
                self.resource_pool[resource_id] = {
                    "state": "available",
                    "last_error": None,
                    "last_recovery": datetime.now()
                }
            
            self.logger.info(f"Successfully recovered resource {resource_id}")
            return True
            
        except Exception as e:
            self.logger.error(f"Failed to recover resource {resource_id}: {e}")
            return False