from typing import Dict, List, Any, Optional
from dataclasses import dataclass, field
from enum import Enum
from datetime import datetime

class ActionType(Enum):
    SEARCH = "search"
    CALCULATE = "calculate" 
    REASON = "reason"
    FINAL_ANSWER = "final_answer"

@dataclass
class ReActStep:
    """Individual step in ReAct reasoning chain"""
    step_number: int
    thought: str
    action: ActionType
    action_input: str
    observation: str
    confidence: float
    timestamp: datetime = field(default_factory=datetime.now)

class BasicReActAgent:
    """Foundation ReAct agent with transparent reasoning"""

    def __init__(self, llm_client, tools: Dict[str, Any], max_steps: int = 6):
        self.llm = llm_client
        self.tools = tools
        self.max_steps = max_steps
        self.reasoning_history: List[ReActStep] = []

    async def solve(self, problem: str) -> Dict[str, Any]:
        """Main problem-solving method using ReAct pattern"""
        self.reasoning_history = []
        current_step = 1

        while current_step <= self.max_steps:
            # Generate reasoning step
            step = await self._execute_reasoning_step(problem, current_step)
            self.reasoning_history.append(step)

            # Check for completion
            if step.action == ActionType.FINAL_ANSWER:
                break

            current_step += 1

        return self._format_solution()

async def _execute_reasoning_step(
    self, context: str, step_num: int
) -> ReActStep:
    """Execute a single ReAct reasoning step"""

    # Generate thought based on current context
    thought = await self._generate_structured_thought(context)

    # Determine action based on thought
    action_decision = await self._decide_next_action(thought, context)
    action_type = ActionType(action_decision['action'])
    action_input = action_decision['input']

    # Execute action and get observation
    observation = await self._execute_action(action_type, action_input)

    # Calculate confidence in this step
    confidence = await self._calculate_step_confidence(
        thought, action_type, observation
    )

    return ReActStep(
        step_number=step_num,
        thought=thought,
        action=action_type,
        action_input=action_input,
        observation=observation,
        confidence=confidence
    )

def _generate_structured_thought(self, context: str) -> str:
    """Generate systematic thought with reasoning framework"""
    prompt = f"""
    Current context: {context}
    Recent history: {self._get_recent_history_summary()}

    Think systematically:
    1. What do I understand so far?
    2. What information gaps remain?
    3. What's the most productive next action?
    4. What risks should I consider?

    Provide clear reasoning for the next step:
    """
    return await self.llm.generate(prompt)

class MetaReActAnalyzer:
    """Analyzes and improves ReAct reasoning quality"""

    def __init__(self, llm_client):
        self.llm = llm_client

    async def analyze_reasoning_quality(
        self, reasoning_history: List[ReActStep]
    ) -> Dict[str, Any]:
        """Analyze reasoning chain quality"""

        if len(reasoning_history) < 2:
            return {'quality_score': 0.5, 'issues': []}

        # Detect circular reasoning
        circular_analysis = await self._detect_circular_reasoning(reasoning_history)

        # Assess progress quality
        progress_analysis = await self._assess_progress_quality(reasoning_history)

        # Evaluate confidence patterns
        confidence_analysis = await self._analyze_confidence_patterns(reasoning_history)

        return {
            'quality_score': self._calculate_overall_quality(
                circular_analysis, progress_analysis, confidence_analysis
            ),
            'circular_reasoning': circular_analysis,
            'progress_quality': progress_analysis,
            'confidence_patterns': confidence_analysis,
            'recommendations': await self._generate_improvement_recommendations(
                reasoning_history
            )
        }

    async def _detect_circular_reasoning(
        self, history: List[ReActStep]
    ) -> Dict[str, Any]:
        """Detect if agent is stuck in reasoning loops"""
        recent_steps = history[-4:]  # Examine last 4 steps
        action_sequence = [step.action for step in recent_steps]

        # Check for repeated action patterns
        if len(set(action_sequence)) <= 2 and len(action_sequence) >= 3:
            return {
                'has_circular_reasoning': True,
                'pattern': action_sequence,
                'severity': 'high'
            }

        return {'has_circular_reasoning': False}

from typing import Dict, List, Any, Optional, Set
from dataclasses import dataclass, field
from enum import Enum
import asyncio
import uuid
from datetime import datetime

class MessageType(Enum):
    REQUEST = "request"
    RESPONSE = "response"
    PROPOSAL = "proposal"
    VOTE = "vote"
    CONSENSUS = "consensus"
    STATUS_UPDATE = "status_update"

@dataclass
class AgentMessage:
    """Structured message for inter-agent communication"""
    message_id: str = field(default_factory=lambda: str(uuid.uuid4()))
    sender_id: str = ""
    recipient_id: str = ""
    message_type: MessageType = MessageType.REQUEST
    content: Dict[str, Any] = field(default_factory=dict)
    timestamp: datetime = field(default_factory=datetime.now)
    requires_response: bool = True
    conversation_id: Optional[str] = None

class CommunicationHub:
    """Central coordination hub for multi-agent communication"""

    def __init__(self):
        self.agents: Dict[str, 'BaseAgent'] = {}
        self.message_queue: List[AgentMessage] = []
        self.active_conversations: Dict[str, List[AgentMessage]] = {}

    async def register_agent(self, agent: 'BaseAgent'):
        """Register agent with communication hub"""
        self.agents[agent.agent_id] = agent
        await agent.set_communication_hub(self)

    async def send_message(self, message: AgentMessage) -> bool:
        """Send message with delivery confirmation"""

        # Validate recipient exists
        if message.recipient_id not in self.agents:
            return False

        # Add to conversation thread
        if message.conversation_id:
            if message.conversation_id not in self.active_conversations:
                self.active_conversations[message.conversation_id] = []
            self.active_conversations[message.conversation_id].append(message)

        # Deliver message
        recipient = self.agents[message.recipient_id]
        success = await recipient.receive_message(message)

        return success

class SimpleConsensusManager:
    """Basic consensus mechanisms for multi-agent decisions"""

    def __init__(self, agents: List['BaseAgent'], threshold: float = 0.67):
        self.agents = agents
        self.consensus_threshold = threshold
        self.voting_history: List[Dict[str, Any]] = []

    async def simple_majority_consensus(
        self, decision_point: str, context: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Simple majority voting consensus"""

        # Collect proposals from all agents
        proposals = await self._collect_agent_proposals(decision_point, context)

        # Conduct voting round
        votes = await self._conduct_voting_round(proposals, context)

        # Count votes and determine winner
        vote_counts = self._count_votes(votes)
        winner = max(vote_counts.items(), key=lambda x: x[1])

        # Check if threshold met
        total_votes = sum(vote_counts.values())
        if winner[1] / total_votes >= self.consensus_threshold:
            return {
                'consensus_reached': True,
                'decision': winner[0],
                'vote_counts': vote_counts,
                'confidence': winner[1] / total_votes
            }
        else:
            return {
                'consensus_reached': False,
                'vote_counts': vote_counts,
                'reason': 'Threshold not met'
            }

    async def _collect_agent_proposals(
        self, decision_point: str, context: Dict[str, Any]
    ) -> List[Dict[str, Any]]:
        """Collect initial proposals from all agents"""
        proposal_tasks = []
        for agent in self.agents:
            task = self._get_agent_proposal(agent, decision_point, context)
            proposal_tasks.append(task)

        proposals = await asyncio.gather(*proposal_tasks, return_exceptions=True)

        # Filter out failed proposals
        valid_proposals = []
        for i, proposal in enumerate(proposals):
            if not isinstance(proposal, Exception):
                valid_proposals.append({
                    'agent_id': self.agents[i].agent_id,
                    'proposal': proposal,
                    'timestamp': datetime.now()
                })

        return valid_proposals

class HierarchicalCoordinator:
    """Implements hierarchical multi-agent coordination patterns"""

    def __init__(self):
        self.coordinator_agents: Dict[str, 'CoordinatorAgent'] = {}
        self.worker_agents: Dict[str, 'WorkerAgent'] = {}
        self.delegation_rules: Dict[str, List[str]] = {}

    async def create_coordination_hierarchy(
        self, task: str, complexity_analysis: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Create hierarchical coordination structure"""

        # Analyze task decomposition requirements
        decomposition = await self._analyze_task_decomposition(task, complexity_analysis)

        # Create coordinator for high-level planning
        coordinator = await self._create_task_coordinator(decomposition)

        # Create workers for execution
        workers = await self._create_worker_agents(decomposition)

        # Establish delegation relationships
        delegation_map = await self._establish_delegation_hierarchy(
            coordinator, workers, decomposition
        )

        return {
            'coordinator': coordinator,
            'workers': workers,
            'delegation_map': delegation_map,
            'hierarchy_depth': decomposition['required_levels']
        }

    async def execute_hierarchical_task(
        self, task: str, hierarchy: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Execute task using hierarchical coordination"""

        # Phase 1: High-level planning
        high_level_plan = await self._create_high_level_plan(
            task, hierarchy['coordinator']
        )

        # Phase 2: Task delegation and parallel execution
        delegation_results = await self._execute_delegated_tasks(
            high_level_plan, hierarchy['delegation_map']
        )

        # Phase 3: Result aggregation
        final_result = await self._aggregate_hierarchical_results(
            delegation_results, hierarchy['coordinator']
        )

        return {
            'task': task,
            'result': final_result,
            'execution_success': True
        }

class SimpleAuctionCoordinator:
    """Basic auction-based task allocation"""

    def __init__(self, agents: List['BaseAgent']):
        self.agents = agents
        self.auction_history: List[Dict[str, Any]] = []

    async def conduct_simple_auction(
        self, task: str, requirements: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Conduct sealed-bid auction for task allocation"""

        # Phase 1: Assess agent capabilities
        capability_assessments = await self._assess_agent_capabilities(
            task, requirements
        )

        # Filter eligible agents
        eligible_agents = [
            agent for agent, assessment in capability_assessments.items()
            if assessment['meets_requirements']
        ]

        if not eligible_agents:
            return {'success': False, 'reason': 'No eligible agents'}

        # Phase 2: Collect bids
        bids = await self._collect_sealed_bids(task, eligible_agents, requirements)

        # Phase 3: Select winner (lowest cost, highest capability)
        winner = await self._select_auction_winner(bids, requirements)

        if winner:
            return {
                'success': True,
                'winner': winner['agent_id'],
                'winning_bid': winner['bid'],
                'task': task
            }
        else:
            return {'success': False, 'reason': 'No valid bids received'}

from typing import Dict, List, Any, Optional, Tuple
from dataclasses import dataclass, field
from enum import Enum
from datetime import datetime, timedelta

class TaskType(Enum):
    PRIMITIVE = "primitive"      # Directly executable
    COMPOUND = "compound"        # Requires decomposition
    ABSTRACT = "abstract"        # High-level goal

@dataclass
class Task:
    """Represents a task in the HTN hierarchy"""
    task_id: str
    name: str
    task_type: TaskType
    parameters: Dict[str, Any] = field(default_factory=dict)
    preconditions: List[str] = field(default_factory=list)
    effects: List[str] = field(default_factory=list)
    estimated_duration: Optional[timedelta] = None
    priority: int = 1

@dataclass
class TaskDecomposition:
    """Represents a way to decompose a compound task"""
    decomposition_id: str
    subtasks: List[Task]
    ordering_constraints: List[Tuple[str, str]] = field(default_factory=list)
    success_probability: float = 1.0

class HTNPlanner:
    """Hierarchical Task Network planner"""

    def __init__(self, agent, domain_knowledge: Dict[str, Any]):
        self.agent = agent
        self.domain = domain_knowledge
        self.current_plan: Optional[List[Task]] = None
        self.planning_history: List[Dict[str, Any]] = []

    async def create_hierarchical_plan(
        self, goal: str, initial_state: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Create hierarchical plan using HTN methodology"""

        # Phase 1: Goal analysis and task creation
        root_task = await self._create_root_task(goal, initial_state)

        # Phase 2: Hierarchical decomposition
        decomposition_result = await self._decompose_task_hierarchy(
            root_task, initial_state
        )

        # Phase 3: Plan optimization
        optimized_plan = await self._optimize_plan(
            decomposition_result['plan'], initial_state
        )

        # Phase 4: Risk assessment
        risk_analysis = await self._analyze_plan_risks(
            optimized_plan, initial_state
        )

        return {
            'plan': optimized_plan,
            'risk_analysis': risk_analysis,
            'confidence': decomposition_result['confidence'],
            'estimated_duration': sum(
                t.estimated_duration or timedelta(0) for t in optimized_plan
            )
        }

class DynamicReplanner:
    """Handles dynamic replanning during plan execution"""

    def __init__(self, htn_planner: HTNPlanner):
        self.planner = htn_planner
        self.monitoring_active = False
        self.replanning_history: List[Dict[str, Any]] = []

    async def execute_with_replanning(
        self, plan: List[Task], initial_state: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Execute plan with continuous monitoring and replanning"""

        current_state = initial_state.copy()
        remaining_tasks = plan.copy()
        completed_tasks = []
        execution_trace = []

        self.monitoring_active = True

        while remaining_tasks and self.monitoring_active:
            current_task = remaining_tasks[0]

            # Pre-execution validation
            validation_result = await self._validate_task_execution(
                current_task, current_state
            )

            if not validation_result['can_execute']:
                # Trigger replanning
                replanning_result = await self._trigger_replanning(
                    current_task, remaining_tasks, current_state,
                    validation_result['reason']
                )

                if replanning_result['success']:
                    remaining_tasks = replanning_result['new_plan']
                    execution_trace.append(('replan', replanning_result))
                    continue
                else:
                    execution_trace.append(('failure', replanning_result))
                    break

            # Execute task
            execution_result = await self._execute_monitored_task(
                current_task, current_state
            )

            execution_trace.append(('execute', execution_result))

            if execution_result['success']:
                # Update state and continue
                current_state = self._apply_task_effects(
                    current_task, current_state, execution_result
                )
                completed_tasks.append(current_task)
                remaining_tasks.pop(0)
            else:
                # Handle execution failure
                failure_analysis = await self._analyze_execution_failure(
                    current_task, execution_result
                )

                if failure_analysis['should_replan']:
                    replanning_result = await self._trigger_replanning(
                        current_task, remaining_tasks, current_state,
                        execution_result['error']
                    )

                    if replanning_result['success']:
                        remaining_tasks = replanning_result['new_plan']
                        continue

                execution_trace.append(('abort', failure_analysis))
                break

        return {
            'completed_tasks': completed_tasks,
            'remaining_tasks': remaining_tasks,
            'final_state': current_state,
            'execution_trace': execution_trace,
            'success': len(remaining_tasks) == 0
        }

class ReflectionEngine:
    """Implements reflection patterns for continuous improvement"""

    def __init__(self, agent):
        self.agent = agent
        self.experience_buffer: List[Dict[str, Any]] = []
        self.learned_patterns: Dict[str, Any] = {}
        self.performance_metrics: Dict[str, List[float]] = {}

    async def reflect_on_execution(
        self, execution_result: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Conduct comprehensive reflection on execution experience"""

        # Phase 1: Experience analysis
        experience_analysis = await self._analyze_execution_experience(
            execution_result
        )

        # Phase 2: Pattern identification
        patterns = await self._identify_learning_patterns(
            experience_analysis, self.experience_buffer
        )

        # Phase 3: Performance assessment
        performance_assessment = await self._assess_performance_trends(
            execution_result, patterns
        )

        # Phase 4: Strategy adaptation
        adaptations = await self._generate_strategy_adaptations(
            patterns, performance_assessment
        )

        # Phase 5: Knowledge integration
        integration_result = await self._integrate_learned_knowledge(
            patterns, adaptations
        )

        # Store experience for future learning
        self.experience_buffer.append({
            'execution_result': execution_result,
            'reflection': {
                'analysis': experience_analysis,
                'patterns': patterns,
                'performance': performance_assessment,
                'adaptations': adaptations
            },
            'timestamp': datetime.now()
        })

        # Prune old experiences if buffer is too large
        if len(self.experience_buffer) > 500:
            self.experience_buffer = self.experience_buffer[-400:]

        return {
            'reflection_summary': experience_analysis['summary'],
            'identified_patterns': patterns,
            'performance_insights': performance_assessment,
            'recommended_adaptations': adaptations,
            'integration_success': integration_result
        }

from dataclasses import dataclass
from typing import Dict, List, Any
from datetime import timedelta
import logging

@dataclass
class BasicProductionConfig:
    """Basic configuration for production multi-agent systems"""
    max_agents: int = 20
    consensus_timeout: timedelta = timedelta(seconds=15)
    health_check_interval: timedelta = timedelta(seconds=5)
    enable_monitoring: bool = True
    log_level: str = "INFO"

class BasicProductionSystem:
    """Basic production multi-agent system"""

    def __init__(self, config: BasicProductionConfig):
        self.config = config
        self.agents: Dict[str, 'BaseAgent'] = {}
        self._setup_logging()

    def _setup_logging(self):
        """Setup production logging"""
        logging.basicConfig(
            level=getattr(logging, self.config.log_level),
            format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
        )

    async def deploy_agent(self, agent: 'BaseAgent') -> Dict[str, Any]:
        """Deploy agent with basic validation"""

        # Basic validation
        if len(self.agents) >= self.config.max_agents:
            return {'success': False, 'error': 'Maximum agents reached'}

        if agent.agent_id in self.agents:
            return {'success': False, 'error': 'Agent ID already exists'}

        # Register agent
        self.agents[agent.agent_id] = agent

        # Basic health check
        health = await self._basic_health_check(agent)
        if not health['healthy']:
            del self.agents[agent.agent_id]
            return {'success': False, 'error': 'Agent failed health check'}

        logging.info(f"Agent {agent.agent_id} deployed successfully")

        return {
            'success': True,
            'agent_id': agent.agent_id,
            'deployment_time': datetime.now()
        }

    async def _basic_health_check(self, agent: 'BaseAgent') -> Dict[str, Any]:
        """Perform basic agent health check"""
        try:
            # Test basic functionality
            test_response = await agent.process_simple_request("health check")

            return {
                'healthy': bool(test_response),
                'response_time': 'measured_time_here'
            }
        except Exception as e:
            return {
                'healthy': False,
                'error': str(e)
            }

class BasicSystemMonitor:
    """Basic monitoring for multi-agent systems"""

    def __init__(self, system: BasicProductionSystem):
        self.system = system
        self.metrics: Dict[str, List[Any]] = {
            'agent_health': [],
            'message_count': [],
            'error_count': []
        }

    async def collect_basic_metrics(self) -> Dict[str, Any]:
        """Collect basic system metrics"""

        # Agent health metrics
        healthy_agents = 0
        for agent_id, agent in self.system.agents.items():
            health = await self.system._basic_health_check(agent)
            if health['healthy']:
                healthy_agents += 1

        return {
            'timestamp': datetime.now(),
            'total_agents': len(self.system.agents),
            'healthy_agents': healthy_agents,
            'system_health': healthy_agents / len(self.system.agents) if self.system.agents else 0
        }

    async def generate_basic_report(self) -> str:
        """Generate basic system status report"""
        metrics = await self.collect_basic_metrics()

        return f"""
Basic Multi-Agent System Report
================================
Time: {metrics['timestamp']}
Total Agents: {metrics['total_agents']}
Healthy Agents: {metrics['healthy_agents']}
System Health: {metrics['system_health']:.2%}
"""