task-master-neo-sdlc
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Enhanced task management system with Neo SDLC agents and MCP tools for comprehensive, AI-driven software development lifecycle management.
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JavaScript
import { KnowledgeGraph } from '../knowledge-graph';
import { AgentWorkflowSystem } from '../agent-workflow';
import { MonitoringSystem } from '../monitoring'; // Assuming monitoring data is needed
export class PerformanceAnalystAgent {
constructor(knowledgeGraph, workflow, monitoringSystem) {
this.knowledgeGraph = knowledgeGraph;
this.workflow = workflow;
this.monitoringSystem = monitoringSystem;
}
/**
* Analyzes performance data from the monitoring system or knowledge graph.
* @param {object} scope - Defines the scope of analysis (e.g., time range, components).
* @returns {Promise<object>} An analysis report object.
*/
async analyzePerformanceData(scope) {
console.log('Analyzing performance data with scope:', scope);
// In a real implementation, this would fetch data from monitoringSystem or knowledgeGraph
// based on the scope and perform analysis (e.g., calculate average response times, identify bottlenecks).
const analysisId = `perfAnalysis_${Date.now()}`;
const report = {
id: analysisId,
timestamp: Date.now(),
scope,
metrics: {
avgResponseTime: Math.random() * 100 + 50, // Placeholder
errorRate: Math.random() * 0.05, // Placeholder
resourceUtilization: {
cpu: Math.random() * 80 + 10, // Placeholder
memory: Math.random() * 70 + 20 // Placeholder
}
},
bottlenecks: [], // Placeholder for identified bottlenecks
recommendations: [] // Placeholder for optimization recommendations
};
// Add analysis report to knowledge graph
await this.knowledgeGraph.addNode({
id: `performanceAnalysis:${analysisId}`,
type: 'performance_analysis',
data: report
});
console.log(`Performance analysis ${analysisId} completed.`);
return report;
}
/**
* Generates optimization recommendations based on performance analysis.
* @param {string} analysisId - The ID of the performance analysis report.
* @returns {Promise<string[]>} A list of optimization recommendations.
*/
async generateOptimizationRecommendations(analysisId) {
console.log(`Generating recommendations for analysis: ${analysisId}`);
const analysisNode = await this.knowledgeGraph.findNodes({
type: 'performance_analysis',
id: `performanceAnalysis:${analysisId}`
})[0];
if (!analysisNode) {
throw new Error(`Performance analysis ${analysisId} not found`);
}
const analysisData = analysisNode.data;
const recommendations = [];
// Example recommendation logic (placeholders)
if (analysisData.metrics.avgResponseTime > 100) {
recommendations.push('Optimize database queries affecting high response times.');
}
if (analysisData.metrics.errorRate > 0.02) {
recommendations.push('Investigate components with high error rates.');
}
if (analysisData.metrics.resourceUtilization.cpu > 80) {
recommendations.push('Consider scaling CPU resources or optimizing CPU-intensive tasks.');
}
// Update the analysis node with recommendations
analysisData.recommendations = recommendations;
await this.knowledgeGraph.updateContext({
id: analysisNode.id,
data: analysisData
});
console.log(`Recommendations generated for ${analysisId}:`, recommendations);
return recommendations;
}
/**
* Creates a performance benchmark task.
* @param {string} name - Name of the benchmark.
* @param {object} configuration - Configuration for the benchmark run.
* @returns {Promise<object>} The created benchmark task object.
*/
async createBenchmark(name, configuration) {
console.log(`Creating benchmark: ${name}`);
const benchmarkId = `benchmark_${Date.now()}`;
const benchmark = {
id: benchmarkId,
name,
configuration,
status: 'pending', // pending, running, completed, failed
results: null
};
// Add benchmark task to knowledge graph
await this.knowledgeGraph.addNode({
id: `benchmark:${benchmarkId}`,
type: 'performance_benchmark',
data: benchmark
});
// Potentially trigger the benchmark run via the workflow system
// await this.workflow.startTask('runPerformanceBenchmark', { benchmarkId });
console.log(`Benchmark ${benchmarkId} created.`);
return benchmark;
}
/**
* Suggests caching strategies based on performance data or system design.
* @param {string} targetId - ID of the component/system/API to analyze for caching.
* @returns {Promise<object>} An object containing suggested caching strategies.
*/
async suggestCacheStrategy(targetId) {
console.log(`Suggesting cache strategy for target: ${targetId}`);
const targetNode = await this.knowledgeGraph.findNodes({ id: targetId }).then(n => n[0]);
if (!targetNode) {
console.warn(`Target ${targetId} not found for cache strategy suggestion.`);
return { suggestions: [], rationale: 'Target not found' };
}
// Placeholder logic: Suggest caching if response times are high or data is static.
// In reality, this would analyze access patterns, data volatility, etc.
const suggestions = [];
let rationale = 'Based on analysis of target properties and potential performance bottlenecks. ';
// Example: Check linked performance reports
const perfEdges = await this.knowledgeGraph.findEdges({ target: targetId, relationship: 'analyzes' }); // Assuming relationship exists
const perfReports = await this.knowledgeGraph.findNodes({ ids: perfEdges.map(e => e.source), type: 'performance_analysis' });
let highResponseTime = false;
for(const report of perfReports) {
if(report.data?.metrics?.avgResponseTime > 150) { // Example threshold
highResponseTime = true;
rationale += `High average response time (${report.data.metrics.avgResponseTime}ms) detected in report ${report.id}. `;
break;
}
}
if (highResponseTime) {
suggestions.push({ type: 'server_side', strategy: 'redis', ttl: '1h', details: 'Consider Redis for caching frequently accessed data related to high response times.' });
}
// Example: Check data properties (if available)
if (targetNode.data?.dataType === 'static_config' || targetNode.data?.updateFrequency === 'daily') {
suggestions.push({ type: 'client_side', strategy: 'localStorage', ttl: '24h', details: 'Data appears static or updates infrequently, consider client-side caching.' });
rationale += 'Target data appears static or infrequently updated. ';
}
if(suggestions.length === 0) {
rationale = 'No obvious caching opportunities identified based on available data.';
}
// Optionally add suggestion to KG
const suggestionId = `cacheSuggestion_${Date.now()}`;
await this.knowledgeGraph.addNode({
id: `cacheSuggestion:${suggestionId}`,
type: 'performance_suggestion',
data: { targetId, suggestionType: 'caching', suggestions, rationale },
edges: [{ target: targetId, relationship: 'suggests_optimisation_for' }]
});
console.log(`Cache suggestions generated for ${targetId}:`, suggestions);
return { suggestions, rationale };
}
/**
* Suggests resource allocation adjustments based on performance analysis.
* @param {string} analysisId - The ID of the performance analysis report.
* @returns {Promise<object>} An object containing suggested resource adjustments.
*/
async suggestResourceAllocation(analysisId) {
console.log(`Suggesting resource allocation based on analysis: ${analysisId}`);
const analysisNode = await this.knowledgeGraph.findNodes({
type: 'performance_analysis',
id: `performanceAnalysis:${analysisId}`
})[0];
if (!analysisNode) {
throw new Error(`Performance analysis ${analysisId} not found`);
}
const analysisData = analysisNode.data;
const suggestions = [];
let rationale = 'Based on resource utilization metrics in the analysis report. ';
// Example logic based on CPU/Memory utilization
const cpuUsage = analysisData.metrics?.resourceUtilization?.cpu;
const memoryUsage = analysisData.metrics?.resourceUtilization?.memory;
if (cpuUsage > 85) { // Example threshold for scaling up CPU
suggestions.push({ resource: 'CPU', action: 'increase', details: `CPU utilization high (${cpuUsage.toFixed(1)}%). Consider increasing CPU allocation or optimizing CPU-bound tasks.` });
rationale += `High CPU utilization (${cpuUsage.toFixed(1)}%) detected. `;
} else if (cpuUsage < 20) { // Example threshold for scaling down CPU
suggestions.push({ resource: 'CPU', action: 'decrease', details: `CPU utilization low (${cpuUsage.toFixed(1)}%). Consider decreasing CPU allocation if consistently low.` });
rationale += `Low CPU utilization (${cpuUsage.toFixed(1)}%) detected. `;
}
if (memoryUsage > 90) { // Example threshold for scaling up Memory
suggestions.push({ resource: 'Memory', action: 'increase', details: `Memory utilization high (${memoryUsage.toFixed(1)}%). Consider increasing memory allocation or investigating memory leaks.` });
rationale += `High Memory utilization (${memoryUsage.toFixed(1)}%) detected. `;
} else if (memoryUsage < 30) { // Example threshold for scaling down Memory
suggestions.push({ resource: 'Memory', action: 'decrease', details: `Memory utilization low (${memoryUsage.toFixed(1)}%). Consider decreasing memory allocation if consistently low.` });
rationale += `Low Memory utilization (${memoryUsage.toFixed(1)}%) detected. `;
}
if(suggestions.length === 0) {
rationale = 'Resource utilization within normal parameters based on this report.';
}
// Optionally add suggestion to KG
const suggestionId = `resourceSuggestion_${Date.now()}`;
await this.knowledgeGraph.addNode({
id: `resourceSuggestion:${suggestionId}`,
type: 'performance_suggestion',
data: { targetId: analysisData.scope?.component || 'system', suggestionType: 'resource_allocation', suggestions, rationale, sourceAnalysis: analysisId },
edges: [{ target: `performanceAnalysis:${analysisId}`, relationship: 'derived_from' }]
});
console.log(`Resource allocation suggestions generated for analysis ${analysisId}:`, suggestions);
return { suggestions, rationale };
}
}