"""
Self-Reflection Engine for SE-AGI
Implements continuous self-evaluation and improvement mechanisms
"""

import asyncio
import logging
import numpy as np
from typing import Dict, List, Optional, Any, Tuple, Union
from dataclasses import dataclass
from datetime import datetime, timedelta
from enum import Enum
import json
from abc import ABC, abstractmethod

from ..core.config import ReflectionConfig


class ReflectionType(Enum):
    """Types of self-reflection"""
    PERFORMANCE = "performance"
    STRATEGY = "strategy"
    GOAL = "goal"
    ETHICAL = "ethical"
    METACOGNITIVE = "metacognitive"
    BEHAVIORAL = "behavioral"


class ReflectionLevel(Enum):
    """Levels of reflection depth"""
    SURFACE = "surface"      # What happened?
    DEEP = "deep"           # Why did it happen?
    CRITICAL = "critical"   # How can it be improved?


@dataclass
class ReflectionInsight:
    """Represents an insight from reflection"""
    insight_id: str
    type: ReflectionType
    level: ReflectionLevel
    content: str
    confidence: float
    actionable_items: List[str]
    evidence: Dict[str, Any]
    timestamp: datetime
    priority: int = 1
    
    def __post_init__(self):
        if not self.actionable_items:
            self.actionable_items = []
        if not self.evidence:
            self.evidence = {}


@dataclass
class ReflectionSession:
    """Represents a reflection session"""
    session_id: str
    trigger: str
    start_time: datetime
    end_time: Optional[datetime]
    insights: List[ReflectionInsight]
    metrics_analyzed: Dict[str, Any]
    improvements_identified: List[str]
    actions_planned: List[Dict[str, Any]]
    success: bool = True


class ReflectionTrigger(ABC):
    """Abstract base class for reflection triggers"""
    
    @abstractmethod
    async def should_trigger(self, context: Dict[str, Any]) -> bool:
        """Check if reflection should be triggered"""
        pass
    
    @abstractmethod
    def get_trigger_reason(self) -> str:
        """Get reason for triggering reflection"""
        pass


class PerformanceTrigger(ReflectionTrigger):
    """Triggers reflection based on performance metrics"""
    
    def __init__(self, threshold: float = 0.7):
        self.threshold = threshold
        self.performance_history = []
    
    async def should_trigger(self, context: Dict[str, Any]) -> bool:
        current_performance = context.get("performance", 0.0)
        self.performance_history.append(current_performance)
        
        # Keep only recent history
        if len(self.performance_history) > 10:
            self.performance_history = self.performance_history[-10:]
        
        # Trigger if performance drops below threshold
        if current_performance < self.threshold:
            return True
        
        # Trigger if performance is declining over time
        if len(self.performance_history) >= 3:
            recent_avg = np.mean(self.performance_history[-3:])
            older_avg = np.mean(self.performance_history[:-3])
            if recent_avg < older_avg * 0.9:  # 10% decline
                return True
        
        return False
    
    def get_trigger_reason(self) -> str:
        return "Performance below threshold or declining trend"


class TimeTrigger(ReflectionTrigger):
    """Triggers reflection at regular intervals"""
    
    def __init__(self, interval_seconds: int = 3600):
        self.interval = timedelta(seconds=interval_seconds)
        self.last_reflection = datetime.now()
    
    async def should_trigger(self, context: Dict[str, Any]) -> bool:
        return datetime.now() - self.last_reflection >= self.interval
    
    def get_trigger_reason(self) -> str:
        return "Scheduled reflection interval"


class ErrorTrigger(ReflectionTrigger):
    """Triggers reflection when errors occur"""
    
    def __init__(self, error_threshold: int = 3):
        self.error_threshold = error_threshold
        self.recent_errors = []
    
    async def should_trigger(self, context: Dict[str, Any]) -> bool:
        if context.get("error_occurred", False):
            self.recent_errors.append(datetime.now())
            
            # Remove old errors (older than 1 hour)
            cutoff = datetime.now() - timedelta(hours=1)
            self.recent_errors = [e for e in self.recent_errors if e > cutoff]
            
            return len(self.recent_errors) >= self.error_threshold
        
        return False
    
    def get_trigger_reason(self) -> str:
        return f"Multiple errors occurred ({len(self.recent_errors)} in last hour)"


class ReflectionEngine:
    """
    Self-Reflection Engine for SE-AGI
    
    Provides continuous self-evaluation capabilities:
    - Performance reflection
    - Strategy analysis
    - Goal assessment
    - Ethical evaluation
    - Meta-cognitive awareness
    """
    
    def __init__(self, 
                 config: ReflectionConfig,
                 memory_system: Optional[Any] = None):
        self.config = config
        self.memory_system = memory_system
        self.logger = logging.getLogger(__name__)
        
        # Reflection state
        self.reflection_history: List[ReflectionSession] = []
        self.insights_database: Dict[str, ReflectionInsight] = {}
        self.improvement_tracker: Dict[str, List[Any]] = {}
        
        # Triggers
        self.triggers: List[ReflectionTrigger] = []
        self._setup_triggers()
        
        # Reflection strategies
        self.reflection_strategies = {
            ReflectionType.PERFORMANCE: self._reflect_on_performance,
            ReflectionType.STRATEGY: self._reflect_on_strategy,
            ReflectionType.GOAL: self._reflect_on_goals,
            ReflectionType.ETHICAL: self._reflect_on_ethics,
            ReflectionType.METACOGNITIVE: self._reflect_on_metacognition,
            ReflectionType.BEHAVIORAL: self._reflect_on_behavior
        }
        
        # Metrics for self-assessment
        self.performance_metrics = {}
        self.strategy_effectiveness = {}
        self.goal_progress = {}
        
    def _setup_triggers(self) -> None:
        """Setup reflection triggers based on configuration"""
        # Performance-based triggers
        if self.config.performance_reflection:
            self.triggers.append(PerformanceTrigger(
                threshold=self.config.confidence_threshold
            ))
        
        # Time-based triggers
        if self.config.frequency == "continuous":
            self.triggers.append(TimeTrigger(interval_seconds=3600))  # Every hour
        elif self.config.frequency == "hourly":
            self.triggers.append(TimeTrigger(interval_seconds=3600))
        elif self.config.frequency == "daily":
            self.triggers.append(TimeTrigger(interval_seconds=86400))
        
        # Error-based triggers
        self.triggers.append(ErrorTrigger(error_threshold=3))
    
    async def initialize(self) -> None:
        """Initialize reflection engine"""
        self.logger.info("Initializing reflection engine...")
        
        # Load previous insights and sessions
        await self._load_reflection_history()
        
        # Initialize performance baseline
        await self._establish_performance_baseline()
        
        self.logger.info("Reflection engine initialized")
    
    async def reflect(self, context: Optional[Dict[str, Any]] = None) -> ReflectionSession:
        """Perform a reflection session"""
        context = context or {}
        
        # Check if reflection should be triggered
        triggered, trigger_reason = await self._check_triggers(context)
        
        if not triggered and not context.get("force_reflection", False):
            return None
        
        self.logger.info(f"Starting reflection session: {trigger_reason}")
        
        session = ReflectionSession(
            session_id=f"session_{len(self.reflection_history)}",
            trigger=trigger_reason,
            start_time=datetime.now(),
            end_time=None,
            insights=[],
            metrics_analyzed={},
            improvements_identified=[],
            actions_planned=[]
        )
        
        try:
            # Perform different types of reflection
            for reflection_type in ReflectionType:
                if self._should_perform_reflection_type(reflection_type):
                    insights = await self._perform_reflection(reflection_type, context)
                    session.insights.extend(insights)
            
            # Analyze insights and identify improvements
            session.improvements_identified = await self._identify_improvements(session.insights)
            
            # Plan actions based on insights
            session.actions_planned = await self._plan_actions(session.insights)
            
            # Store reflection session
            session.end_time = datetime.now()
            self.reflection_history.append(session)
            
            # Update insights database
            for insight in session.insights:
                self.insights_database[insight.insight_id] = insight
            
            # Store in memory system
            if self.memory_system:
                await self.memory_system.store_episode({
                    "type": "reflection_session",
                    "session": session,
                    "timestamp": session.end_time
                })
            
            self.logger.info(f"Reflection session complete: {len(session.insights)} insights generated")
            return session
            
        except Exception as e:
            self.logger.error(f"Error in reflection session: {e}")
            session.success = False
            session.end_time = datetime.now()
            return session
    
    async def _check_triggers(self, context: Dict[str, Any]) -> Tuple[bool, str]:
        """Check if any reflection triggers are activated"""
        for trigger in self.triggers:
            if await trigger.should_trigger(context):
                return True, trigger.get_trigger_reason()
        return False, ""
    
    def _should_perform_reflection_type(self, reflection_type: ReflectionType) -> bool:
        """Check if specific reflection type should be performed"""
        type_config = {
            ReflectionType.PERFORMANCE: self.config.performance_reflection,
            ReflectionType.STRATEGY: self.config.strategy_reflection,
            ReflectionType.GOAL: self.config.goal_reflection,
            ReflectionType.ETHICAL: self.config.ethical_reflection,
            ReflectionType.METACOGNITIVE: True,  # Always enabled
            ReflectionType.BEHAVIORAL: True      # Always enabled
        }
        return type_config.get(reflection_type, True)
    
    async def _perform_reflection(self, 
                                reflection_type: ReflectionType,
                                context: Dict[str, Any]) -> List[ReflectionInsight]:
        """Perform specific type of reflection"""
        strategy = self.reflection_strategies.get(reflection_type)
        if strategy:
            return await strategy(context)
        return []
    
    async def _reflect_on_performance(self, context: Dict[str, Any]) -> List[ReflectionInsight]:
        """Reflect on system performance"""
        insights = []
        
        # Analyze recent performance metrics
        recent_performance = self._get_recent_performance_data()
        
        # Surface level: What happened?
        if recent_performance:
            avg_performance = np.mean(list(recent_performance.values()))
            
            insights.append(ReflectionInsight(
                insight_id=f"perf_surface_{datetime.now().timestamp()}",
                type=ReflectionType.PERFORMANCE,
                level=ReflectionLevel.SURFACE,
                content=f"Average performance over recent period: {avg_performance:.3f}",
                confidence=0.9,
                actionable_items=[],
                evidence={"performance_data": recent_performance},
                timestamp=datetime.now()
            ))
            
            # Deep level: Why did this happen?
            performance_factors = await self._analyze_performance_factors(recent_performance)
            
            for factor, impact in performance_factors.items():
                insights.append(ReflectionInsight(
                    insight_id=f"perf_deep_{factor}_{datetime.now().timestamp()}",
                    type=ReflectionType.PERFORMANCE,
                    level=ReflectionLevel.DEEP,
                    content=f"Performance factor '{factor}' has {impact} impact on results",
                    confidence=0.7,
                    actionable_items=[f"Investigate {factor} optimization"],
                    evidence={"factor": factor, "impact": impact},
                    timestamp=datetime.now()
                ))
            
            # Critical level: How can it be improved?
            if avg_performance < self.config.improvement_threshold:
                improvement_strategies = await self._generate_improvement_strategies(recent_performance)
                
                for strategy in improvement_strategies:
                    insights.append(ReflectionInsight(
                        insight_id=f"perf_critical_{strategy['name']}_{datetime.now().timestamp()}",
                        type=ReflectionType.PERFORMANCE,
                        level=ReflectionLevel.CRITICAL,
                        content=f"Improvement strategy: {strategy['description']}",
                        confidence=strategy.get('confidence', 0.6),
                        actionable_items=strategy.get('actions', []),
                        evidence={"strategy": strategy},
                        timestamp=datetime.now(),
                        priority=strategy.get('priority', 2)
                    ))
        
        return insights
    
    async def _reflect_on_strategy(self, context: Dict[str, Any]) -> List[ReflectionInsight]:
        """Reflect on strategic approaches and their effectiveness"""
        insights = []
        
        # Analyze strategy effectiveness
        for strategy_name, effectiveness_data in self.strategy_effectiveness.items():
            if effectiveness_data:
                avg_effectiveness = np.mean(effectiveness_data[-10:])  # Last 10 uses
                
                insights.append(ReflectionInsight(
                    insight_id=f"strategy_{strategy_name}_{datetime.now().timestamp()}",
                    type=ReflectionType.STRATEGY,
                    level=ReflectionLevel.DEEP,
                    content=f"Strategy '{strategy_name}' effectiveness: {avg_effectiveness:.3f}",
                    confidence=0.8,
                    actionable_items=[
                        f"Continue using {strategy_name}" if avg_effectiveness > 0.7 
                        else f"Consider alternatives to {strategy_name}"
                    ],
                    evidence={"effectiveness_history": effectiveness_data},
                    timestamp=datetime.now()
                ))
        
        # Identify underutilized strategies
        underutilized = await self._identify_underutilized_strategies()
        for strategy in underutilized:
            insights.append(ReflectionInsight(
                insight_id=f"underutilized_{strategy}_{datetime.now().timestamp()}",
                type=ReflectionType.STRATEGY,
                level=ReflectionLevel.CRITICAL,
                content=f"Strategy '{strategy}' may be underutilized",
                confidence=0.6,
                actionable_items=[f"Experiment with {strategy} in appropriate contexts"],
                evidence={"strategy": strategy},
                timestamp=datetime.now(),
                priority=1
            ))
        
        return insights
    
    async def _reflect_on_goals(self, context: Dict[str, Any]) -> List[ReflectionInsight]:
        """Reflect on goal alignment and progress"""
        insights = []
        
        # Analyze goal progress
        for goal_id, progress_data in self.goal_progress.items():
            current_progress = progress_data.get("current", 0.0)
            target_progress = progress_data.get("target", 1.0)
            deadline = progress_data.get("deadline")
            
            progress_ratio = current_progress / target_progress if target_progress > 0 else 0
            
            insights.append(ReflectionInsight(
                insight_id=f"goal_{goal_id}_{datetime.now().timestamp()}",
                type=ReflectionType.GOAL,
                level=ReflectionLevel.SURFACE,
                content=f"Goal '{goal_id}' progress: {progress_ratio:.1%}",
                confidence=0.9,
                actionable_items=[
                    "Accelerate progress" if progress_ratio < 0.5 else "Maintain current pace"
                ],
                evidence=progress_data,
                timestamp=datetime.now()
            ))
            
            # Check for goal conflicts or misalignment
            if progress_ratio < 0.3 and deadline:
                days_remaining = (deadline - datetime.now()).days
                if days_remaining < 30:  # Less than 30 days
                    insights.append(ReflectionInsight(
                        insight_id=f"goal_risk_{goal_id}_{datetime.now().timestamp()}",
                        type=ReflectionType.GOAL,
                        level=ReflectionLevel.CRITICAL,
                        content=f"Goal '{goal_id}' at risk of not being met",
                        confidence=0.8,
                        actionable_items=[
                            "Reassess goal feasibility",
                            "Allocate additional resources",
                            "Consider goal modification"
                        ],
                        evidence={"progress": progress_ratio, "days_remaining": days_remaining},
                        timestamp=datetime.now(),
                        priority=3
                    ))
        
        return insights
    
    async def _reflect_on_ethics(self, context: Dict[str, Any]) -> List[ReflectionInsight]:
        """Reflect on ethical implications of actions and decisions"""
        insights = []
        
        # Review recent decisions for ethical considerations
        recent_decisions = context.get("recent_decisions", [])
        
        for decision in recent_decisions:
            ethical_score = await self._evaluate_ethical_implications(decision)
            
            if ethical_score < 0.8:  # Potential ethical concern
                insights.append(ReflectionInsight(
                    insight_id=f"ethics_{decision.get('id', 'unknown')}_{datetime.now().timestamp()}",
                    type=ReflectionType.ETHICAL,
                    level=ReflectionLevel.CRITICAL,
                    content=f"Decision may have ethical implications: {decision.get('description', 'Unknown decision')}",
                    confidence=0.7,
                    actionable_items=[
                        "Review decision against ethical guidelines",
                        "Consult ethical framework",
                        "Consider alternative approaches"
                    ],
                    evidence={"decision": decision, "ethical_score": ethical_score},
                    timestamp=datetime.now(),
                    priority=3
                ))
        
        # Check alignment with constitutional rules
        constitutional_violations = await self._check_constitutional_alignment(context)
        for violation in constitutional_violations:
            insights.append(ReflectionInsight(
                insight_id=f"constitutional_{violation['rule_id']}_{datetime.now().timestamp()}",
                type=ReflectionType.ETHICAL,
                level=ReflectionLevel.CRITICAL,
                content=f"Potential violation of constitutional rule: {violation['rule']}",
                confidence=violation.get('confidence', 0.8),
                actionable_items=[
                    "Immediate review required",
                    "Implement corrective measures"
                ],
                evidence=violation,
                timestamp=datetime.now(),
                priority=4
            ))
        
        return insights
    
    async def _reflect_on_metacognition(self, context: Dict[str, Any]) -> List[ReflectionInsight]:
        """Reflect on thinking processes and meta-cognitive awareness"""
        insights = []
        
        # Analyze reasoning patterns
        reasoning_quality = await self._assess_reasoning_quality(context)
        
        insights.append(ReflectionInsight(
            insight_id=f"metacog_reasoning_{datetime.now().timestamp()}",
            type=ReflectionType.METACOGNITIVE,
            level=ReflectionLevel.DEEP,
            content=f"Current reasoning quality assessment: {reasoning_quality:.3f}",
            confidence=0.7,
            actionable_items=[
                "Enhance reasoning depth" if reasoning_quality < 0.6 else "Maintain reasoning quality"
            ],
            evidence={"reasoning_quality": reasoning_quality},
            timestamp=datetime.now()
        ))
        
        # Assess learning effectiveness
        learning_rate = await self._assess_learning_rate()
        
        insights.append(ReflectionInsight(
            insight_id=f"metacog_learning_{datetime.now().timestamp()}",
            type=ReflectionType.METACOGNITIVE,
            level=ReflectionLevel.DEEP,
            content=f"Learning rate assessment: {learning_rate:.3f}",
            confidence=0.6,
            actionable_items=[
                "Investigate learning bottlenecks" if learning_rate < 0.5 
                else "Continue current learning approach"
            ],
            evidence={"learning_rate": learning_rate},
            timestamp=datetime.now()
        ))
        
        return insights
    
    async def _reflect_on_behavior(self, context: Dict[str, Any]) -> List[ReflectionInsight]:
        """Reflect on behavioral patterns and adaptations"""
        insights = []
        
        # Analyze behavioral consistency
        consistency_score = await self._assess_behavioral_consistency(context)
        
        insights.append(ReflectionInsight(
            insight_id=f"behavior_consistency_{datetime.now().timestamp()}",
            type=ReflectionType.BEHAVIORAL,
            level=ReflectionLevel.SURFACE,
            content=f"Behavioral consistency score: {consistency_score:.3f}",
            confidence=0.8,
            actionable_items=[
                "Improve behavioral consistency" if consistency_score < 0.7 else "Maintain consistency"
            ],
            evidence={"consistency_score": consistency_score},
            timestamp=datetime.now()
        ))
        
        # Check for adaptive behaviors
        adaptive_behaviors = await self._identify_adaptive_behaviors(context)
        for behavior in adaptive_behaviors:
            insights.append(ReflectionInsight(
                insight_id=f"behavior_adaptive_{behavior['name']}_{datetime.now().timestamp()}",
                type=ReflectionType.BEHAVIORAL,
                level=ReflectionLevel.DEEP,
                content=f"Adaptive behavior identified: {behavior['description']}",
                confidence=behavior.get('confidence', 0.6),
                actionable_items=[f"Reinforce {behavior['name']} behavior"],
                evidence=behavior,
                timestamp=datetime.now()
            ))
        
        return insights
    
    # Helper methods for analysis
    
    def _get_recent_performance_data(self) -> Dict[str, float]:
        """Get recent performance metrics"""
        # This would integrate with actual performance tracking
        return self.performance_metrics
    
    async def _analyze_performance_factors(self, performance_data: Dict[str, float]) -> Dict[str, str]:
        """Analyze factors affecting performance"""
        factors = {}
        
        # Simple analysis - would be more sophisticated in practice
        if performance_data:
            values = list(performance_data.values())
            if max(values) - min(values) > 0.3:
                factors["variance"] = "high"
            if np.mean(values) < 0.5:
                factors["overall_quality"] = "low"
            if len(values) > 5 and np.mean(values[-3:]) < np.mean(values[:-3]):
                factors["trend"] = "declining"
        
        return factors
    
    async def _generate_improvement_strategies(self, performance_data: Dict[str, float]) -> List[Dict[str, Any]]:
        """Generate strategies for performance improvement"""
        strategies = []
        
        if performance_data:
            avg_perf = np.mean(list(performance_data.values()))
            
            if avg_perf < 0.3:
                strategies.append({
                    "name": "fundamental_review",
                    "description": "Conduct fundamental review of approaches and methods",
                    "confidence": 0.8,
                    "priority": 3,
                    "actions": [
                        "Review core algorithms",
                        "Analyze failure patterns",
                        "Explore alternative approaches"
                    ]
                })
            elif avg_perf < 0.6:
                strategies.append({
                    "name": "incremental_improvement",
                    "description": "Apply incremental improvements to existing methods",
                    "confidence": 0.7,
                    "priority": 2,
                    "actions": [
                        "Fine-tune parameters",
                        "Optimize workflows",
                        "Enhance data quality"
                    ]
                })
        
        return strategies
    
    async def _identify_underutilized_strategies(self) -> List[str]:
        """Identify strategies that might be underutilized"""
        # This would analyze strategy usage patterns
        underutilized = []
        
        all_strategies = ["creative_thinking", "analytical_reasoning", "collaborative_approach"]
        
        for strategy in all_strategies:
            if strategy not in self.strategy_effectiveness or not self.strategy_effectiveness[strategy]:
                underutilized.append(strategy)
        
        return underutilized
    
    async def _evaluate_ethical_implications(self, decision: Dict[str, Any]) -> float:
        """Evaluate ethical implications of a decision"""
        # Simplified ethical evaluation
        score = 0.8  # Base score
        
        # Check for potential harm
        if "harm" in decision.get("description", "").lower():
            score -= 0.3
        
        # Check for fairness
        if "unfair" in decision.get("description", "").lower():
            score -= 0.2
        
        # Check for transparency
        if decision.get("transparent", True):
            score += 0.1
        
        return max(0.0, min(1.0, score))
    
    async def _check_constitutional_alignment(self, context: Dict[str, Any]) -> List[Dict[str, Any]]:
        """Check alignment with constitutional rules"""
        violations = []
        
        # This would check against actual constitutional rules
        # Simplified implementation
        recent_actions = context.get("recent_actions", [])
        
        for action in recent_actions:
            if "harmful" in action.get("description", "").lower():
                violations.append({
                    "rule_id": "no_harm",
                    "rule": "Do not cause harm to humans",
                    "action": action,
                    "confidence": 0.8
                })
        
        return violations
    
    async def _assess_reasoning_quality(self, context: Dict[str, Any]) -> float:
        """Assess quality of reasoning processes"""
        # Simplified reasoning quality assessment
        recent_reasoning = context.get("recent_reasoning", [])
        
        if not recent_reasoning:
            return 0.5  # Default
        
        quality_scores = []
        for reasoning in recent_reasoning:
            # Simple heuristics for reasoning quality
            score = 0.5
            
            if reasoning.get("evidence_based", False):
                score += 0.2
            if reasoning.get("logical_structure", False):
                score += 0.2
            if reasoning.get("considers_alternatives", False):
                score += 0.1
            
            quality_scores.append(score)
        
        return np.mean(quality_scores) if quality_scores else 0.5
    
    async def _assess_learning_rate(self) -> float:
        """Assess how quickly the system is learning"""
        # Analyze improvement over time
        if len(self.reflection_history) < 2:
            return 0.5
        
        # Simple learning rate based on insight generation
        recent_sessions = self.reflection_history[-5:]
        insight_counts = [len(session.insights) for session in recent_sessions]
        
        if len(insight_counts) >= 2:
            if insight_counts[-1] > insight_counts[0]:
                return 0.7  # Improving
            elif insight_counts[-1] < insight_counts[0]:
                return 0.3  # Declining
        
        return 0.5  # Stable
    
    async def _assess_behavioral_consistency(self, context: Dict[str, Any]) -> float:
        """Assess consistency of behavior patterns"""
        # Simplified behavioral consistency assessment
        recent_behaviors = context.get("recent_behaviors", [])
        
        if len(recent_behaviors) < 2:
            return 0.5
        
        # Check for consistency in response patterns
        response_types = [b.get("type", "unknown") for b in recent_behaviors]
        unique_types = set(response_types)
        
        # Higher consistency if fewer unique response types
        consistency = 1.0 - (len(unique_types) / len(response_types))
        return max(0.0, min(1.0, consistency))
    
    async def _identify_adaptive_behaviors(self, context: Dict[str, Any]) -> List[Dict[str, Any]]:
        """Identify adaptive behavioral patterns"""
        adaptive_behaviors = []
        
        # Look for context-appropriate responses
        recent_contexts = context.get("recent_contexts", [])
        recent_responses = context.get("recent_responses", [])
        
        if len(recent_contexts) == len(recent_responses):
            for ctx, resp in zip(recent_contexts, recent_responses):
                if self._is_adaptive_response(ctx, resp):
                    adaptive_behaviors.append({
                        "name": f"context_adaptation_{ctx.get('type', 'unknown')}",
                        "description": f"Adapted response style for {ctx.get('type', 'unknown')} context",
                        "confidence": 0.6,
                        "context": ctx,
                        "response": resp
                    })
        
        return adaptive_behaviors
    
    def _is_adaptive_response(self, context: Dict[str, Any], response: Dict[str, Any]) -> bool:
        """Check if response is adaptive to context"""
        # Simplified adaptive response check
        context_type = context.get("type", "")
        response_style = response.get("style", "")
        
        # Some simple adaptation patterns
        adaptive_patterns = {
            "technical": ["detailed", "precise", "analytical"],
            "creative": ["imaginative", "open", "exploratory"],
            "social": ["empathetic", "collaborative", "supportive"]
        }
        
        return response_style in adaptive_patterns.get(context_type, [])
    
    async def _identify_improvements(self, insights: List[ReflectionInsight]) -> List[str]:
        """Identify concrete improvements from insights"""
        improvements = []
        
        # Extract actionable items from high-priority insights
        high_priority_insights = [i for i in insights if i.priority >= 3]
        
        for insight in high_priority_insights:
            improvements.extend(insight.actionable_items)
        
        # Remove duplicates while preserving order
        seen = set()
        unique_improvements = []
        for item in improvements:
            if item not in seen:
                seen.add(item)
                unique_improvements.append(item)
        
        return unique_improvements
    
    async def _plan_actions(self, insights: List[ReflectionInsight]) -> List[Dict[str, Any]]:
        """Plan concrete actions based on insights"""
        actions = []
        
        # Group insights by type and priority
        insight_groups = {}
        for insight in insights:
            key = f"{insight.type.value}_{insight.priority}"
            if key not in insight_groups:
                insight_groups[key] = []
            insight_groups[key].append(insight)
        
        # Create action plans for each group
        for group_key, group_insights in insight_groups.items():
            if group_insights[0].priority >= 2:  # Only plan for medium+ priority
                action = {
                    "action_id": f"action_{group_key}_{datetime.now().timestamp()}",
                    "type": group_insights[0].type.value,
                    "priority": group_insights[0].priority,
                    "description": f"Address {len(group_insights)} insights in {group_insights[0].type.value}",
                    "timeline": "immediate" if group_insights[0].priority >= 3 else "near_term",
                    "insights_addressed": [i.insight_id for i in group_insights],
                    "specific_actions": []
                }
                
                # Collect specific actions
                for insight in group_insights:
                    action["specific_actions"].extend(insight.actionable_items)
                
                actions.append(action)
        
        return actions
    
    async def _load_reflection_history(self) -> None:
        """Load previous reflection sessions"""
        try:
            # This would load from persistent storage
            pass
        except Exception as e:
            self.logger.warning(f"Could not load reflection history: {e}")
    
    async def _establish_performance_baseline(self) -> None:
        """Establish baseline performance metrics"""
        # Initialize with default metrics
        self.performance_metrics = {
            "response_quality": 0.7,
            "response_time": 0.8,
            "user_satisfaction": 0.6,
            "task_completion": 0.75
        }
    
    # Public API methods
    
    def get_reflection_summary(self) -> Dict[str, Any]:
        """Get summary of reflection activities"""
        if not self.reflection_history:
            return {"status": "no_reflections_yet"}
        
        recent_session = self.reflection_history[-1]
        
        return {
            "total_sessions": len(self.reflection_history),
            "recent_session": {
                "timestamp": recent_session.start_time,
                "insights_count": len(recent_session.insights),
                "improvements_identified": len(recent_session.improvements_identified),
                "actions_planned": len(recent_session.actions_planned)
            },
            "total_insights": len(self.insights_database),
            "insight_types": {
                rtype.value: len([i for i in self.insights_database.values() if i.type == rtype])
                for rtype in ReflectionType
            }
        }
    
    def get_insights_by_type(self, reflection_type: ReflectionType) -> List[ReflectionInsight]:
        """Get insights of a specific type"""
        return [i for i in self.insights_database.values() if i.type == reflection_type]
    
    def get_high_priority_insights(self) -> List[ReflectionInsight]:
        """Get high-priority insights requiring attention"""
        return sorted(
            [i for i in self.insights_database.values() if i.priority >= 3],
            key=lambda x: x.priority,
            reverse=True
        )
    
    async def force_reflection(self, reflection_types: Optional[List[ReflectionType]] = None) -> ReflectionSession:
        """Force a reflection session"""
        context = {"force_reflection": True}
        if reflection_types:
            context["specific_types"] = reflection_types
        
        return await self.reflect(context)
    
    async def shutdown(self) -> None:
        """Shutdown reflection engine"""
        self.logger.info("Shutting down reflection engine...")
        
        # Save reflection state
        await self._save_reflection_state()
        
        self.logger.info("Reflection engine shutdown complete")
    
    async def _save_reflection_state(self) -> None:
        """Save reflection state to persistent storage"""
        try:
            # This would save to persistent storage
            pass
        except Exception as e:
            self.logger.error(f"Could not save reflection state: {e}")
