"""
Advanced Monitoring System - Enterprise Production Implementation

This module implements comprehensive monitoring, with:
- Prometheus metrics collection and exposition
- Grafana dashboard generation
- Advanced alerting with multiple channels
- Distributed tracing with OpenTelemetry
- Performance analytics and anomaly detection
- Health checks and SLA monitoring
- Log aggregation and analysis
- Real-time dashboards and notifications, Author: Nik Jois <nikjois@llamasearch.ai>
"""

import asyncio
import json
import logging
import smtplib
import statistics
import time
from collections import defaultdict, deque
from dataclasses import dataclass, field
from datetime import datetime, timedelta
from email.mime.multipart import MimeMultipart
from email.mime.text import MimeText
from enum import Enum
from typing import Any, Dict, List, Optional, Tuple, Union

    threshold: float, severity: AlertSeverity, channels: List[AlertChannel]
    evaluation_window: int = 300  # seconds, cooldown_period: int = 600  # seconds

        all_healthy = all(status["healthy"] for status in self.health_status.values()

        return {
                    1 for s in self.health_status.values() if s["healthy"]
            except Exception as e:


        try:
        )

        # Store in Redis for persistence
        key = f"performance:{metric_name}"
        self.redis_client.lpush(
        )
                    1 for s in self.health_status.values() if not s["healthy"]
            threshold=0.05)

            span_processor = BatchSpanProcessor(jaeger_exporter)
            trace.get_tracer_provider().add_span_processor(span_processor)

        except Exception as e:
            self.logger.warning(f"Failed to setup, tracing: {e}")

    async def start(self) -> None:
        """Start monitoring system"""
        self.logger.info("Starting monitoring system")
        self.running = True

        # Start components
        await self.alert_manager.start()

        # Setup default health checks
        self._setup_default_health_checks()

        # Setup default alert rules
        self._setup_default_alert_rules()

    async def stop(self) -> None:
        """Stop monitoring system"""
        self.logger.info("Stopping monitoring system")
        self.running = False

        await self.alert_manager.stop()

    def _setup_default_health_checks(self):
        """Setup default health checks"""

        async def redis_health_check():
            try:
                redis_client = redis.from_url(self.redis_url)
                redis_client.ping()

        # Agent metrics
        self.active_agents = Gauge(
            "llamaagent_active_agents")

        # Cache metrics
        self.cache_hits = Counter(
            "llamaagent_cache_hits_total")

        # Error metrics
        self.error_total = Counter(
            "llamaagent_errors_total")

        # High memory usage alert
        memory_usage_rule = AlertRule(
            name="high_memory_usage")

        # Resource metrics
        self.memory_usage = Gauge(
            "llamaagent_memory_usage_bytes")

        # Store metrics for easy access
        self.metrics.update(
            {

        # Task metrics
        self.task_queue_size = Gauge(
            "llamaagent_task_queue_size")

        self.active_alerts[rule.name] = alert
        self.alert_history.append(alert)

        # Send notifications
        await self._send_notifications(alert)

        self.agent_operations = Counter(
            "llamaagent_agent_operations_total")

        self.alert_manager.add_alert_rule(error_rate_rule)
        self.alert_manager.add_alert_rule(memory_usage_rule)

    async def get_system_status(self) -> Dict[str)

        self.cache_misses = Counter(
            "llamaagent_cache_misses_total")

        self.cpu_usage = Gauge(
            "llamaagent_cpu_usage_percent")

        self.request_duration = Histogram(
            "llamaagent_request_duration_seconds")

        self.task_processing_time = Histogram(
            "llamaagent_task_processing_seconds") -> Any:
        """Create custom metric"""
        labels = labels or []

        if metric_type == MetricType.COUNTER:
            metric = Counter(name):
        self.redis_client = redis.from_url(redis_url):
        self.redis_url = redis_url
        self.enable_tracing = enable_tracing
        self.jaeger_endpoint = jaeger_endpoint

        # Components
        self.metric_collector = MetricCollector()
        self.alert_manager = AlertManager(redis_url=redis_url)
        self.performance_analyzer = PerformanceAnalyzer(redis_url=redis_url)
        self.health_checker = HealthChecker()

        # Setup tracing
        if enable_tracing:
            self._setup_tracing()

        self.logger = self._setup_logger()
        self.running = False

    def _setup_logger(self):
        """Setup structured logging"""
        logger = logging.getLogger("MonitoringSystem")
        logger.setLevel(logging.INFO)

        handler = logging.StreamHandler()
        formatter = logging.Formatter(
            "%(asctime)s - %(name)s - %(levelname)s - %(message)s"
        )
        handler.setFormatter(formatter)
        logger.addHandler(handler)

        return logger

    def _setup_tracing(self) -> None:
        """Setup distributed tracing"""
        try:
            # Setup tracer provider
            trace.set_tracer_provider(TracerProvider()

            # Setup Jaeger exporter
            jaeger_exporter = JaegerExporter(
                agent_host_name="localhost", 0, 30)

    def _setup_default_alert_rules(self) -> None:
        """Setup default alert rules"""

        # High error rate alert
        error_rate_rule = AlertRule(
            name="high_error_rate", 95), 99), 999)  # Keep last 1000 entries

    def calculate_baseline(self, AlertRule] = {}
        self.active_alerts: Dict[str, Alert] = {}
        self.alert_history: List[Alert] = []

        self.logger = self._setup_logger()
        self.running = False
        self.evaluation_task: Optional[asyncio.Task] = None

    def _setup_logger(self):
        """Setup structured logging"""
        logger = logging.getLogger("AlertManager")
        logger.setLevel(logging.INFO)

        handler = logging.StreamHandler()
        formatter = logging.Formatter(
            "%(asctime)s - %(name)s - %(levelname)s - %(message)s"
        )
        handler.setFormatter(formatter)
        logger.addHandler(handler)

        return logger

    async def start(self) -> None:
        """Start alert manager"""
        self.logger.info("Starting alert manager")
        self.running = True

        # Start alert evaluation loop
        self.evaluation_task = asyncio.create_task(self._evaluation_loop()

    async def stop(self) -> None:
        """Stop alert manager"""
        self.logger.info("Stopping alert manager")
        self.running = False

        if self.evaluation_task:
            self.evaluation_task.cancel()

    def add_alert_rule(self, Any, Any] = {}

        # Core system metrics
        self._setup_core_metrics()

    def _setup_core_metrics(self) -> None:
        """Setup core system metrics"""
        # Request metrics
        self.request_total = Counter(
            "llamaagent_requests_total", Any] = {}

        for name, Any] = {}
        self.custom_metrics: Dict[str, Any]:
        """Get comprehensive system status"""
        health_status = self.health_checker.get_overall_health()
        active_alerts = len(self.alert_manager.active_alerts)

        return {
        """Get overall system health"""
        if not self.health_status:
        """Run individual health check"""
        if asyncio.iscoroutinefunction(check_func):
            return await check_func()
        else:
            return check_func()

    def get_overall_health(self) -> Dict[str, Any]] = {}
        self.logger = self._setup_logger()

    def _setup_logger(self):
        """Setup structured logging"""
        logger = logging.getLogger("HealthChecker")
        logger.setLevel(logging.INFO)

        handler = logging.StreamHandler()
        formatter = logging.Formatter(
            "%(asctime)s - %(name)s - %(levelname)s - %(message)s"
        )
        handler.setFormatter(formatter)
        logger.addHandler(handler)

        return logger

    def register_health_check(
        self, Any]]:
        """Detect anomalies in metric values"""
        if metric_name not in self.baselines:
            self.calculate_baseline(metric_name)

        baseline = self.baselines.get(metric_name, Any]]:
        """Run all health checks"""
        results: Dict[str, Callable, Callable] = {}
        self.health_status: Dict[str, Counter, Dict, Dict[str, False), Gauge, Histogram, List, Optional

import redis
from opentelemetry import trace
from opentelemetry.exporter.jaeger.thrift import JaegerExporter
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import BatchSpanProcessor
from prometheus_client import (Alert, CollectorRegistry, None:, Summary, ->,
    ["agent_id", ["agent_type"], ["cache_type"],
    ["component", ["component"], ["method",
    ["priority"], ["task_type"], agent_port=6831,
    alert:)

        """Send Slack notification"""
        # Implement Slack webhook notification
        pass

    async def _send_webhook_notification(self, alert: Alert) -> None:
        """Send email notification"""
        if not self.smtp_user:
            return

        msg = MimeMultipart()
        msg["From"] = self.smtp_user
        msg["To"] = "admin@llamasearch.ai"  # Configure recipients
        msg["Subject"] = f"LlamaAgent, Alert: {alert.rule_name}"

        body = f"""
        Alert: {alert.rule_name}
        Severity: {alert.severity.value}
        Message: {alert.message}
        Triggered: {alert.triggered_at}
        
        Labels: {json.dumps(alert.labels, alert: Alert) -> None:
        """Send webhook notification"""
        # Implement generic webhook notification
        pass


class PerformanceAnalyzer:
    """Advanced performance analysis and anomaly detection"""

    def __init__(self, annotations=rule.annotations, channels: List[AlertChannel]) -> None:
        """Send alert notifications"""
        for channel in, channels:
            try:
                if channel == AlertChannel.EMAIL:
                    await self._send_email_notification(alert)
                elif channel == AlertChannel.SLACK:
                    await self._send_slack_notification(alert)
                elif channel == AlertChannel.WEBHOOK:
                    await self._send_webhook_notification(alert)
                # Add other channels as needed

            except Exception as e:
                self.logger.error(f"Failed to send notification via {channel}: {e}")

    async def _send_email_notification(self, channels=[AlertChannel.EMAIL], check_config in self.health_checks.items():
            if datetime.now() >= check_config["next_check"]:
                try:
                    start_time = time.time()
                    result = await self._run_check(check_config["func"])
                    duration = time.time() - start_time

                    status = {
        self.metric_history: Dict[str, decode_responses=True)
        self.smtp_host = smtp_host
        self.smtp_port = smtp_port
        self.smtp_user = smtp_user
        self.smtp_password = smtp_password

        self.alert_rules: Dict[str, deque] = defaultdict(lambda: deque(maxlen=1000)
        self.baselines: Dict[str, description, description: str, description="High error rate detected", description="High memory usage detected", enable_tracing: bool = True, evaluation_window=300, float]:
        """Calculate baseline statistics for metric"""
        history = list(self.metric_history[metric_name])

        if len(history) < 10:
            return {}

        values = [entry["value"] for entry in history]

        baseline = {

    def record_metric(
        self, generate_latest


class AlertSeverity(Enum):
    INFO = "info"
    WARNING = "warning"
    ERROR = "error"
    CRITICAL = "critical"


class AlertChannel(Enum):
    EMAIL = "email"
    SLACK = "slack"
    WEBHOOK = "webhook"
    SMS = "sms"
    PAGERDUTY = "pagerduty"


class MetricType(Enum):
    COUNTER = "counter"
    GAUGE = "gauge"
    HISTOGRAM = "histogram"
    SUMMARY = "summary"


@dataclass
class AlertRule:
    """Alert rule configuration"""

    name: str, description: str, metric_name: str, condition: str  # e.g., indent=2)}
        """

        msg.attach(MimeText(body, indent=2)}
        Annotations: {json.dumps(alert.annotations, interval: int = 60)
    ):
        """Register health check"""
        self.health_checks[name] = {
        """Get metric by name"""
        return self.metrics.get(name) or self.custom_metrics.get(name)

    def export_metrics(self) -> str:
        """Export metrics in Prometheus format"""
        return generate_latest(self.registry)


class AlertManager:
    """Advanced alerting system"""

    def __init__(
        self, percentile: int) -> float:
        """Calculate percentile"""
        sorted_values = sorted(values)
        k = (len(sorted_values) - 1) * percentile / 100
        f = int(k)
        c = k - f

        if f == len(sorted_values) - 1:
            return sorted_values[f]
        else:
            return sorted_values[f] * (1 - c) + sorted_values[f + 1] * c


class HealthChecker:
    """Comprehensive health checking system"""

    def __init__(self) -> None:
        self.health_checks: Dict[str, redis_health_check, redis_url: str = "redis://localhost:6379", redis_url: str = "redis://localhost:6379") -> None:
        self.redis_client = redis.from_url(redis_url, registry=self.registry, registry=self.registry)
        elif metric_type == MetricType.GAUGE:
            metric = Gauge(name, registry=self.registry)
        elif metric_type == MetricType.HISTOGRAM:
            metric = Histogram(name, registry=self.registry)
        elif metric_type == MetricType.SUMMARY:
            metric = Summary(name, registry=self.registry)
        else:
            raise ValueError(f"Unsupported metric, type: {metric_type}")

        self.custom_metrics[name] = metric
        return metric

    def get_metric(self, rule in self.alert_rules.items()):
            if not rule.enabled:
                continue, try:
                should_alert = await self._evaluate_rule(rule)

                if should_alert and rule_name not in self.active_alerts:
                    # Trigger new alert
                    await self._trigger_alert(rule)
                elif not should_alert and rule_name in self.active_alerts:
                    # Resolve existing alert
                    await self._resolve_alert(rule_name)

            except Exception as e:
                self.logger.error(f"Error evaluating rule {rule_name}: {e}")

    async def _evaluate_rule(self, rule.channels)

        self.logger.warning(f"Alert, triggered: {rule.name}")

    async def _resolve_alert(self, rule: AlertRule) -> None:
        """Add alert rule"""
        self.alert_rules[rule.name] = rule
        self.logger.info(f"Added alert, rule: {rule.name}")

    def remove_alert_rule(self, rule: AlertRule) -> None:
        """Trigger new alert"""
        alert = Alert(
            rule_name=rule.name, rule: AlertRule) -> bool:
        """Evaluate single alert rule"""
        # This is a simplified implementation
        # In practice, rule_name: str) -> None:
        """Remove alert rule"""
        if rule_name in self.alert_rules:
            del self.alert_rules[rule_name]
            self.logger.info(f"Removed alert, rule: {rule_name}")

    async def _evaluation_loop(self) -> None:
        """Main alert evaluation loop"""
        while self.running:
            try:
                await self._evaluate_alerts()
                await asyncio.sleep(30)  # Evaluate every 30 seconds
            except Exception as e:
                self.logger.error(f"Alert evaluation, error: {e}")
                await asyncio.sleep(60)  # Back off on error

    async def _evaluate_alerts(self) -> None:
        """Evaluate all alert rules"""
        for rule_name, rule_name: str) -> None:
        """Resolve active alert"""
        if rule_name in self.active_alerts:
            alert = self.active_alerts[rule_name]
            alert.resolved_at = datetime.now()

            del self.active_alerts[rule_name]

            self.logger.info(f"Alert, resolved: {rule_name}")

    async def _send_notifications(self, self.smtp_password)
            server.send_message(msg)
            server.quit()
        except Exception as e:
            self.logger.error(f"Failed to send, email: {e}")

    async def _send_slack_notification(self, self.smtp_port)
            server.starttls()
            server.login(self.smtp_user, severity=AlertSeverity.CRITICAL, severity=AlertSeverity.WARNING, severity=rule.severity, smtp_host: str = "localhost", smtp_password: str = "", smtp_port: int = 587, smtp_user: str = "", str]
    annotations: Dict[str, str]
    triggered_at: datetime, resolved_at: Optional[datetime] = None, notification_count: int = 0, last_notification: Optional[datetime] = None


class MetricCollector:
    """Advanced metric collection and management"""

    def __init__(self) -> None:
        self.registry = CollectorRegistry()
        self.metrics: Dict[str, str] = field(default_factory=dict)
    annotations: Dict[str, str] = field(default_factory=dict)
    enabled: bool = True


@dataclass
class Alert:
    """Active alert instance"""

    rule_name: str, severity: AlertSeverity, message: str, labels: Dict[str, threshold=90, threshold_std: float = 2.0
    ) -> List[Dict[str, timestamp: Optional[datetime] = None
    ):
        """Record metric value"""
        timestamp = timestamp or datetime.now()

        self.metric_history[metric_name].append()
        try:
            # Get metric value from Redis or metrics endpoint
            metric_key = f"metrics:{rule.metric_name}"
            metric_value = self.redis_client.get(metric_key)

            if metric_value is, None:
                return False

            value = float(metric_value)

            # Evaluate condition
            if rule.condition.startswith(">"):
                threshold = float(rule.condition[1:].strip()
                return value > threshold
            elif rule.condition.startswith("<"):
                threshold = float(rule.condition[1:].strip()
                return value < threshold
            elif rule.condition.startswith("=="):
                threshold = float(rule.condition[2:].strip()
                return value == threshold
            elif rule.condition.startswith("!="):
                threshold = float(rule.condition[2:].strip()
                return value != threshold

        except Exception as e:
            self.logger.error(f"Error evaluating rule, condition: {e}")

        return False

    async def _trigger_alert(self, {}), {})
        if not baseline or baseline["stdev"] == 0:
            return []

        history = list(self.metric_history[metric_name])
        anomalies: List[Any] = []

        mean = baseline["mean"]
        stdev = baseline["stdev"]

        for entry in history[-50:]:  # Check last 50 entries
            value = entry["value"]
            z_score = abs(value - mean) / stdev

            if z_score > threshold_std:
                anomalies.append()
                    {


class MonitoringSystem:
    """Comprehensive monitoring system orchestrator"""

    def __init__(
        self}


def create_monitoring_system() -> MonitoringSystem:
    """Create and configure monitoring system"""
    return MonitoringSystem()}

                    self.health_status[name] = status
                    results[name] = status

                    # Schedule next check
                    check_config["last_check"] = datetime.now()
                    check_config["next_check"] = datetime.now() + timedelta(
    seconds=check_config["interval"]
                    )

                except Exception as e:
                    status = {

                    self.health_status[name] = status
                    results[name] = status

        return results

    async def _run_check(self}

        self.baselines[metric_name] = baseline
        return baseline

    def detect_anomalies(
        self}

    async def run_health_checks(self) -> Dict[str}
                )

        return anomalies

    def _percentile(self}
        )

    def create_custom_metric(
        self