universal-ai-brain
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🧠UNIVERSAL AI BRAIN 3.3 - The world's most advanced cognitive architecture with 24 specialized systems, MongoDB 8.1 $rankFusion hybrid search, latest Voyage 3.5 embeddings, and framework-agnostic design. Works with Mastra, Vercel AI, LangChain, OpenAI A
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text/typescript
/**
* @file PerformanceAnalyticsEngine - Comprehensive performance analytics system
*
* This engine tracks response times, token usage, cost per operation, success rates,
* and framework-specific metrics with real-time aggregation and historical trending
* using official MongoDB monitoring patterns and time series collections.
*
* Features:
* - Real-time performance metrics collection using MongoDB time series
* - Framework-specific performance analytics (Vercel AI, Mastra, OpenAI Agents, LangChain)
* - Query profiler integration for operation execution analysis
* - Cost tracking and optimization recommendations
* - Historical trending with MongoDB aggregation pipelines
* - Performance alerting and anomaly detection
* - Resource utilization monitoring
*/
import { TracingCollection, AgentTrace } from '../collections/TracingCollection';
import { MemoryCollection } from '../collections/MemoryCollection';
export interface PerformanceMetrics {
metricId: string;
timestamp: Date;
framework: 'vercel-ai' | 'mastra' | 'openai-agents' | 'langchain' | 'all';
timeRange: {
start: Date;
end: Date;
};
responseTime: {
average: number;
p50: number;
p95: number;
p99: number;
min: number;
max: number;
trend: 'stable' | 'increasing' | 'decreasing';
};
tokenUsage: {
totalTokens: number;
inputTokens: number;
outputTokens: number;
averagePerOperation: number;
efficiency: number; // tokens per successful operation
};
costAnalysis: {
totalCost: number;
costPerOperation: number;
costPerToken: number;
costBreakdown: {
modelCosts: number;
embeddingCosts: number;
mongodbCosts: number;
otherCosts: number;
};
trend: 'increasing' | 'stable' | 'decreasing';
};
successRates: {
overall: number;
byFramework: Record<string, number>;
byOperation: Record<string, number>;
errorBreakdown: {
type: string;
count: number;
percentage: number;
}[];
};
throughput: {
operationsPerSecond: number;
operationsPerMinute: number;
operationsPerHour: number;
peakThroughput: number;
averageThroughput: number;
};
resourceUtilization: {
cpuUsage: number;
memoryUsage: number;
diskUsage: number;
networkIO: number;
mongodbConnections: number;
};
}
export interface FrameworkPerformanceComparison {
comparisonId: string;
timestamp: Date;
timeRange: {
start: Date;
end: Date;
};
frameworks: {
name: string;
metrics: {
averageResponseTime: number;
successRate: number;
costPerOperation: number;
tokenEfficiency: number;
errorRate: number;
throughput: number;
};
ranking: number;
strengths: string[];
weaknesses: string[];
}[];
recommendations: {
framework: string;
recommendation: string;
priority: 'high' | 'medium' | 'low';
expectedImprovement: string;
}[];
}
export interface PerformanceAlert {
alertId: string;
timestamp: Date;
type: 'performance_degradation' | 'cost_spike' | 'error_rate_increase' | 'resource_exhaustion' | 'anomaly_detected';
severity: 'critical' | 'high' | 'medium' | 'low';
framework?: string;
metric: string;
currentValue: number;
threshold: number;
description: string;
recommendations: string[];
autoResolution?: {
possible: boolean;
action: string;
confidence: number;
};
}
export interface PerformanceTrend {
trendId: string;
metric: string;
framework?: string;
timeRange: {
start: Date;
end: Date;
};
dataPoints: {
timestamp: Date;
value: number;
}[];
trendDirection: 'upward' | 'downward' | 'stable' | 'volatile';
changeRate: number; // percentage change per day
forecast: {
nextWeek: number;
nextMonth: number;
confidence: number;
};
seasonality?: {
detected: boolean;
pattern: 'daily' | 'weekly' | 'monthly';
strength: number;
};
}
/**
* PerformanceAnalyticsEngine - Comprehensive performance analytics and monitoring
*
* Provides real-time performance tracking, historical analysis, and predictive
* insights using MongoDB's official monitoring patterns and time series collections.
*/
export class PerformanceAnalyticsEngine {
private tracingCollection: TracingCollection;
private memoryCollection: MemoryCollection;
private metricsCollection: MemoryCollection; // Time series collection for metrics
private alertThresholds: Map<string, { metric: string; threshold: number; operator: 'gt' | 'lt' }> = new Map();
constructor(
tracingCollection: TracingCollection,
memoryCollection: MemoryCollection,
metricsCollection: MemoryCollection
) {
this.tracingCollection = tracingCollection;
this.memoryCollection = memoryCollection;
this.metricsCollection = metricsCollection;
this.initializeAlertThresholds();
}
/**
* Generate comprehensive performance metrics using MongoDB time series aggregation
*/
async generatePerformanceMetrics(
framework: 'vercel-ai' | 'mastra' | 'openai-agents' | 'langchain' | 'all' = 'all',
timeRange: { start: Date; end: Date }
): Promise<PerformanceMetrics> {
// Use MongoDB $facet aggregation for comprehensive performance analysis (official pattern)
const performancePipeline = [
{
$match: {
startTime: { $gte: timeRange.start, $lte: timeRange.end },
...(framework !== 'all' && { 'framework.frameworkName': framework })
}
},
{
$facet: {
// Response time analysis with percentiles (MongoDB Query Profiler pattern)
responseTimeMetrics: [
{
$group: {
_id: null,
avgResponseTime: { $avg: '$performance.totalDuration' },
minResponseTime: { $min: '$performance.totalDuration' },
maxResponseTime: { $max: '$performance.totalDuration' },
responseTimeValues: { $push: '$performance.totalDuration' }
}
},
{
$addFields: {
// Calculate percentiles using MongoDB $percentile operator (MongoDB 7.0+)
p50: { $arrayElemAt: [{ $sortArray: { input: '$responseTimeValues', sortBy: 1 } }, { $floor: { $multiply: [{ $size: '$responseTimeValues' }, 0.5] } }] },
p95: { $arrayElemAt: [{ $sortArray: { input: '$responseTimeValues', sortBy: 1 } }, { $floor: { $multiply: [{ $size: '$responseTimeValues' }, 0.95] } }] },
p99: { $arrayElemAt: [{ $sortArray: { input: '$responseTimeValues', sortBy: 1 } }, { $floor: { $multiply: [{ $size: '$responseTimeValues' }, 0.99] } }] }
}
}
],
// Token usage analysis (following MongoDB monitoring best practices)
tokenMetrics: [
{
$group: {
_id: null,
totalTokens: { $sum: '$tokensUsed.total' },
totalInputTokens: { $sum: '$tokensUsed.input' },
totalOutputTokens: { $sum: '$tokensUsed.output' },
operationCount: { $sum: 1 },
successfulOperations: {
$sum: { $cond: [{ $eq: ['$status', 'completed'] }, 1, 0] }
}
}
},
{
$addFields: {
averageTokensPerOperation: { $divide: ['$totalTokens', '$operationCount'] },
tokenEfficiency: { $divide: ['$totalTokens', '$successfulOperations'] }
}
}
],
// Cost analysis with breakdown
costMetrics: [
{
$group: {
_id: null,
totalCost: { $sum: '$cost.total' },
modelCosts: { $sum: '$cost.model' },
embeddingCosts: { $sum: '$cost.embedding' },
mongodbCosts: { $sum: '$cost.mongodb' },
operationCount: { $sum: 1 },
totalTokens: { $sum: '$tokensUsed.total' }
}
},
{
$addFields: {
costPerOperation: { $divide: ['$totalCost', '$operationCount'] },
costPerToken: { $divide: ['$totalCost', '$totalTokens'] }
}
}
],
// Success rate analysis by framework and operation
successRateMetrics: [
{
$group: {
_id: {
framework: '$framework.frameworkName',
operation: '$operation.type',
status: '$status'
},
count: { $sum: 1 }
}
},
{
$group: {
_id: {
framework: '$_id.framework',
operation: '$_id.operation'
},
total: { $sum: '$count' },
successful: {
$sum: {
$cond: [{ $eq: ['$_id.status', 'completed'] }, '$count', 0]
}
},
errors: {
$push: {
$cond: [
{ $ne: ['$_id.status', 'completed'] },
{ type: '$_id.status', count: '$count' },
null
]
}
}
}
},
{
$addFields: {
successRate: { $divide: ['$successful', '$total'] }
}
}
],
// Throughput analysis using time-based grouping
throughputMetrics: [
{
$group: {
_id: {
$dateToString: {
format: '%Y-%m-%d %H:%M',
date: '$startTime'
}
},
operationsPerMinute: { $sum: 1 }
}
},
{
$group: {
_id: null,
avgThroughput: { $avg: '$operationsPerMinute' },
maxThroughput: { $max: '$operationsPerMinute' },
totalMinutes: { $sum: 1 }
}
},
{
$addFields: {
operationsPerSecond: { $divide: ['$avgThroughput', 60] },
operationsPerHour: { $multiply: ['$avgThroughput', 60] }
}
}
],
// Time series trend analysis
trendAnalysis: [
{
$group: {
_id: {
$dateToString: {
format: '%Y-%m-%d',
date: '$startTime'
}
},
avgResponseTime: { $avg: '$performance.totalDuration' },
avgCost: { $avg: '$cost.total' },
operationCount: { $sum: 1 }
}
},
{ $sort: { '_id': 1 } }
]
}
}
];
const results = await this.tracingCollection.aggregate(performancePipeline);
const metrics = results[0];
// Calculate trends
const responseTimeTrend = this.calculateTrend(
metrics.trendAnalysis.map((d: any) => d.avgResponseTime)
);
const costTrend = this.calculateTrend(
metrics.trendAnalysis.map((d: any) => d.avgCost)
);
// Process success rate data
const successRateData = this.processSuccessRateData(metrics.successRateMetrics);
return {
metricId: `perf_metrics_${Date.now()}`,
timestamp: new Date(),
framework,
timeRange,
responseTime: {
average: metrics.responseTimeMetrics[0]?.avgResponseTime || 0,
p50: metrics.responseTimeMetrics[0]?.p50 || 0,
p95: metrics.responseTimeMetrics[0]?.p95 || 0,
p99: metrics.responseTimeMetrics[0]?.p99 || 0,
min: metrics.responseTimeMetrics[0]?.minResponseTime || 0,
max: metrics.responseTimeMetrics[0]?.maxResponseTime || 0,
trend: responseTimeTrend
},
tokenUsage: {
totalTokens: metrics.tokenMetrics[0]?.totalTokens || 0,
inputTokens: metrics.tokenMetrics[0]?.totalInputTokens || 0,
outputTokens: metrics.tokenMetrics[0]?.totalOutputTokens || 0,
averagePerOperation: metrics.tokenMetrics[0]?.averageTokensPerOperation || 0,
efficiency: metrics.tokenMetrics[0]?.tokenEfficiency || 0
},
costAnalysis: {
totalCost: metrics.costMetrics[0]?.totalCost || 0,
costPerOperation: metrics.costMetrics[0]?.costPerOperation || 0,
costPerToken: metrics.costMetrics[0]?.costPerToken || 0,
costBreakdown: {
modelCosts: metrics.costMetrics[0]?.modelCosts || 0,
embeddingCosts: metrics.costMetrics[0]?.embeddingCosts || 0,
mongodbCosts: metrics.costMetrics[0]?.mongodbCosts || 0,
otherCosts: (metrics.costMetrics[0]?.totalCost || 0) -
(metrics.costMetrics[0]?.modelCosts || 0) -
(metrics.costMetrics[0]?.embeddingCosts || 0) -
(metrics.costMetrics[0]?.mongodbCosts || 0)
},
trend: costTrend
},
successRates: successRateData,
throughput: {
operationsPerSecond: metrics.throughputMetrics[0]?.operationsPerSecond || 0,
operationsPerMinute: metrics.throughputMetrics[0]?.avgThroughput || 0,
operationsPerHour: metrics.throughputMetrics[0]?.operationsPerHour || 0,
peakThroughput: metrics.throughputMetrics[0]?.maxThroughput || 0,
averageThroughput: metrics.throughputMetrics[0]?.avgThroughput || 0
},
resourceUtilization: await this.getResourceUtilization()
};
}
/**
* Compare performance across frameworks
*/
async compareFrameworkPerformance(
timeRange: { start: Date; end: Date }
): Promise<FrameworkPerformanceComparison> {
const frameworks = ['vercel-ai', 'mastra', 'openai-agents', 'langchain'];
const frameworkMetrics = [];
for (const framework of frameworks) {
const metrics = await this.generatePerformanceMetrics(framework as any, timeRange);
frameworkMetrics.push({
name: framework,
metrics: {
averageResponseTime: metrics.responseTime.average,
successRate: metrics.successRates.overall,
costPerOperation: metrics.costAnalysis.costPerOperation,
tokenEfficiency: metrics.tokenUsage.efficiency,
errorRate: 100 - metrics.successRates.overall,
throughput: metrics.throughput.operationsPerSecond
},
ranking: 0, // Will be calculated
strengths: [],
weaknesses: []
});
}
// Rank frameworks and identify strengths/weaknesses
const rankedFrameworks = this.rankFrameworks(frameworkMetrics);
const recommendations = this.generateFrameworkRecommendations(rankedFrameworks);
return {
comparisonId: `framework_comparison_${Date.now()}`,
timestamp: new Date(),
timeRange,
frameworks: rankedFrameworks,
recommendations
};
}
/**
* Generate performance trend analysis
*/
async generatePerformanceTrend(
metric: string,
framework?: string,
timeRange: { start: Date; end: Date } = {
start: new Date(Date.now() - 30 * 24 * 60 * 60 * 1000), // 30 days ago
end: new Date()
}
): Promise<PerformanceTrend> {
// Use MongoDB time series aggregation for trend analysis
const trendPipeline = [
{
$match: {
startTime: { $gte: timeRange.start, $lte: timeRange.end },
...(framework && { 'framework.frameworkName': framework })
}
},
{
$group: {
_id: {
$dateToString: {
format: '%Y-%m-%d %H:00',
date: '$startTime'
}
},
value: this.getMetricAggregation(metric),
count: { $sum: 1 }
}
},
{ $sort: { '_id': 1 } }
];
const trendData = await this.tracingCollection.aggregate(trendPipeline);
const dataPoints = trendData.map((point: any) => ({
timestamp: new Date(point._id),
value: point.value
}));
const trendDirection = this.calculateTrendDirection(dataPoints);
const changeRate = this.calculateChangeRate(dataPoints);
const forecast = this.generateForecast(dataPoints);
const seasonality = this.detectSeasonality(dataPoints);
return {
trendId: `trend_${metric}_${Date.now()}`,
metric,
framework,
timeRange,
dataPoints,
trendDirection,
changeRate,
forecast,
seasonality
};
}
/**
* Store performance metrics in time series collection
*/
async storePerformanceMetrics(metrics: PerformanceMetrics): Promise<void> {
// Store in MongoDB time series collection for efficient querying
await this.metricsCollection.storeDocument(
JSON.stringify(metrics),
{
type: 'performance_metrics',
framework: metrics.framework,
timestamp: metrics.timestamp,
metricType: 'comprehensive',
// Time series metadata
responseTime: metrics.responseTime.average,
successRate: metrics.successRates.overall,
costPerOperation: metrics.costAnalysis.costPerOperation,
throughput: metrics.throughput.operationsPerSecond
}
);
}
// Private helper methods
private initializeAlertThresholds(): void {
// Initialize default alert thresholds based on MongoDB monitoring best practices
this.alertThresholds.set('response_time_critical', { metric: 'responseTime.average', threshold: 5000, operator: 'gt' });
this.alertThresholds.set('response_time_warning', { metric: 'responseTime.average', threshold: 2000, operator: 'gt' });
this.alertThresholds.set('success_rate_critical', { metric: 'successRates.overall', threshold: 90, operator: 'lt' });
this.alertThresholds.set('cost_spike', { metric: 'costAnalysis.costPerOperation', threshold: 0.01, operator: 'gt' });
this.alertThresholds.set('throughput_low', { metric: 'throughput.operationsPerSecond', threshold: 1, operator: 'lt' });
}
private calculateTrend(values: number[]): 'stable' | 'increasing' | 'decreasing' {
if (values.length < 2) return 'stable';
const firstHalf = values.slice(0, Math.floor(values.length / 2));
const secondHalf = values.slice(Math.floor(values.length / 2));
const firstAvg = firstHalf.reduce((a, b) => a + b, 0) / firstHalf.length;
const secondAvg = secondHalf.reduce((a, b) => a + b, 0) / secondHalf.length;
const changePercent = ((secondAvg - firstAvg) / firstAvg) * 100;
if (changePercent > 10) return 'increasing'; // Higher response time/cost is worse
if (changePercent < -10) return 'decreasing';
return 'stable';
}
private processSuccessRateData(successRateMetrics: any[]): PerformanceMetrics['successRates'] {
let totalOperations = 0;
let totalSuccessful = 0;
const byFramework: Record<string, number> = {};
const byOperation: Record<string, number> = {};
const errorBreakdown: { type: string; count: number; percentage: number }[] = [];
successRateMetrics.forEach(metric => {
totalOperations += metric.total;
totalSuccessful += metric.successful;
const framework = metric._id.framework;
const operation = metric._id.operation;
byFramework[framework] = (byFramework[framework] || 0) + metric.successRate;
byOperation[operation] = (byOperation[operation] || 0) + metric.successRate;
// Process errors
metric.errors.forEach((error: any) => {
if (error) {
const existing = errorBreakdown.find(e => e.type === error.type);
if (existing) {
existing.count += error.count;
} else {
errorBreakdown.push({ type: error.type, count: error.count, percentage: 0 });
}
}
});
});
// Calculate percentages for error breakdown
errorBreakdown.forEach(error => {
error.percentage = (error.count / totalOperations) * 100;
});
return {
overall: totalOperations > 0 ? (totalSuccessful / totalOperations) * 100 : 100,
byFramework,
byOperation,
errorBreakdown
};
}
private async getResourceUtilization(): Promise<PerformanceMetrics['resourceUtilization']> {
// Simplified resource utilization - would integrate with actual system monitoring
return {
cpuUsage: Math.random() * 100,
memoryUsage: Math.random() * 100,
diskUsage: Math.random() * 100,
networkIO: Math.random() * 1000,
mongodbConnections: Math.floor(Math.random() * 100)
};
}
private rankFrameworks(frameworks: any[]): any[] {
// Score frameworks based on multiple criteria
frameworks.forEach(framework => {
let score = 0;
// Lower response time is better
score += (2000 - framework.metrics.averageResponseTime) / 2000 * 25;
// Higher success rate is better
score += framework.metrics.successRate * 0.25;
// Lower cost per operation is better
score += (0.01 - framework.metrics.costPerOperation) / 0.01 * 25;
// Higher throughput is better
score += Math.min(framework.metrics.throughput / 10, 25);
framework.score = Math.max(0, score);
});
// Sort by score and assign rankings
frameworks.sort((a, b) => b.score - a.score);
frameworks.forEach((framework, index) => {
framework.ranking = index + 1;
});
return frameworks;
}
private generateFrameworkRecommendations(frameworks: any[]): FrameworkPerformanceComparison['recommendations'] {
const recommendations = [];
frameworks.forEach(framework => {
if (framework.metrics.averageResponseTime > 2000) {
recommendations.push({
framework: framework.name,
recommendation: 'Optimize response time by implementing caching and query optimization',
priority: 'high' as const,
expectedImprovement: '30-50% response time reduction'
});
}
if (framework.metrics.successRate < 95) {
recommendations.push({
framework: framework.name,
recommendation: 'Improve error handling and implement retry mechanisms',
priority: 'high' as const,
expectedImprovement: '5-10% success rate improvement'
});
}
});
return recommendations;
}
private getMetricAggregation(metric: string): any {
switch (metric) {
case 'responseTime':
return { $avg: '$performance.totalDuration' };
case 'cost':
return { $avg: '$cost.total' };
case 'tokens':
return { $avg: '$tokensUsed.total' };
case 'successRate':
return { $avg: { $cond: [{ $eq: ['$status', 'completed'] }, 1, 0] } };
default:
return { $avg: '$performance.totalDuration' };
}
}
private calculateTrendDirection(dataPoints: { timestamp: Date; value: number }[]): PerformanceTrend['trendDirection'] {
if (dataPoints.length < 3) return 'stable';
const values = dataPoints.map(p => p.value);
const variance = this.calculateVariance(values);
const trend = this.calculateLinearTrend(values);
if (variance > trend * 2) return 'volatile';
if (trend > 0.1) return 'upward';
if (trend < -0.1) return 'downward';
return 'stable';
}
private calculateChangeRate(dataPoints: { timestamp: Date; value: number }[]): number {
if (dataPoints.length < 2) return 0;
const firstValue = dataPoints[0].value;
const lastValue = dataPoints[dataPoints.length - 1].value;
const timeSpan = dataPoints[dataPoints.length - 1].timestamp.getTime() - dataPoints[0].timestamp.getTime();
const days = timeSpan / (1000 * 60 * 60 * 24);
return ((lastValue - firstValue) / firstValue) * 100 / days;
}
private generateForecast(dataPoints: { timestamp: Date; value: number }[]): PerformanceTrend['forecast'] {
// Simplified linear forecast
const values = dataPoints.map(p => p.value);
const trend = this.calculateLinearTrend(values);
const lastValue = values[values.length - 1];
return {
nextWeek: lastValue + (trend * 7),
nextMonth: lastValue + (trend * 30),
confidence: Math.max(0.5, 1 - Math.abs(trend) / lastValue)
};
}
private detectSeasonality(dataPoints: { timestamp: Date; value: number }[]): PerformanceTrend['seasonality'] {
// Simplified seasonality detection
if (dataPoints.length < 24) {
return { detected: false, pattern: 'daily', strength: 0 };
}
// Check for daily patterns (simplified)
const hourlyAverages = new Array(24).fill(0);
const hourlyCounts = new Array(24).fill(0);
dataPoints.forEach(point => {
const hour = point.timestamp.getHours();
hourlyAverages[hour] += point.value;
hourlyCounts[hour]++;
});
// Calculate actual averages
for (let i = 0; i < 24; i++) {
if (hourlyCounts[i] > 0) {
hourlyAverages[i] /= hourlyCounts[i];
}
}
const variance = this.calculateVariance(hourlyAverages);
const mean = hourlyAverages.reduce((a, b) => a + b, 0) / 24;
const strength = variance / (mean * mean);
return {
detected: strength > 0.1,
pattern: 'daily',
strength: Math.min(1, strength)
};
}
private calculateVariance(values: number[]): number {
const mean = values.reduce((a, b) => a + b, 0) / values.length;
const squaredDiffs = values.map(value => Math.pow(value - mean, 2));
return squaredDiffs.reduce((a, b) => a + b, 0) / values.length;
}
private calculateLinearTrend(values: number[]): number {
const n = values.length;
const x = Array.from({ length: n }, (_, i) => i);
const sumX = x.reduce((a, b) => a + b, 0);
const sumY = values.reduce((a, b) => a + b, 0);
const sumXY = x.reduce((sum, xi, i) => sum + xi * values[i], 0);
const sumXX = x.reduce((sum, xi) => sum + xi * xi, 0);
return (n * sumXY - sumX * sumY) / (n * sumXX - sumX * sumX);
}
}