@versatil/sdlc-framework
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🚀 AI-Native SDLC framework with 11-MCP ecosystem, RAG memory, OPERA orchestration, and 6 specialized agents achieving ZERO CONTEXT LOSS. Features complete CI/CD pipeline with 7 GitHub workflows (MCP testing, security scanning, performance benchmarking),
400 lines • 16.7 kB
JavaScript
/**
* VERSATIL SDLC Framework - Adaptive Learning & Auto-Improvement System
*
* This system learns from user interactions and automatically improves
* the Enhanced OPERA agents based on real usage patterns, feedback, and outcomes.
*/
import { EventEmitter } from 'events';
import * as fs from 'fs';
import * as path from 'path';
import { VERSATILLogger } from '../utils/logger.js';
export class AdaptiveLearningEngine extends EventEmitter {
constructor() {
super();
this.interactions = new Map();
this.patterns = new Map();
this.adaptations = new Map();
this.isLearning = false;
this.logger = new VERSATILLogger();
this.dataPath = path.join(process.cwd(), '.versatil', 'learning');
this.learningConfig = {
minInteractionsForPattern: 10,
confidenceThreshold: 0.7,
adaptationInterval: 24 * 60 * 60 * 1000, // 24 hours
maxAdaptationsPerAgent: 5
};
this.initializeLearning();
}
/**
* Start the adaptive learning process
*/
startLearning() {
if (this.isLearning)
return;
this.isLearning = true;
this.logger.info('Adaptive learning engine started', {}, 'adaptive-learning');
// Load historical data
this.loadLearningData();
// Start periodic pattern analysis
setInterval(() => {
this.analyzePatterns();
}, this.learningConfig.adaptationInterval);
// Start real-time adaptation
this.on('interaction', this.handleInteraction.bind(this));
this.on('pattern_discovered', this.handlePatternDiscovery.bind(this));
}
/**
* Stop the learning engine
*/
stopLearning() {
this.isLearning = false;
this.logger.info('Adaptive learning stopped', {}, 'adaptive-learning');
}
/**
* Record a user interaction with an agent
*/
recordInteraction(interaction) {
if (!this.isLearning)
return;
if (!this.interactions.has(interaction.agentId)) {
this.interactions.set(interaction.agentId, []);
}
this.interactions.get(interaction.agentId).push(interaction);
this.logger.info('User interaction recorded', {
agentId: interaction.agentId,
actionType: interaction.actionType,
context: interaction.context
}, 'adaptive-learning');
this.emit('interaction', interaction);
this.saveInteraction(interaction);
}
/**
* Analyze user interactions to discover learning patterns
*/
async analyzePatterns() {
this.logger.info('Analyzing user interaction patterns...', {}, 'adaptive-learning');
for (const [agentId, interactions] of this.interactions) {
if (interactions.length < this.learningConfig.minInteractionsForPattern) {
continue;
}
// Analyze success/failure patterns
const successPatterns = this.findSuccessPatterns(agentId, interactions);
const failurePatterns = this.findFailurePatterns(agentId, interactions);
const userPreferences = this.extractUserPreferences(interactions);
// Create learning patterns
for (const pattern of [...successPatterns, ...failurePatterns]) {
this.patterns.set(pattern.id, pattern);
this.emit('pattern_discovered', pattern);
}
// Generate agent adaptations
const adaptations = await this.generateAdaptations(agentId, interactions, userPreferences);
for (const adaptation of adaptations) {
this.proposeAdaptation(agentId, adaptation);
}
}
}
/**
* Find patterns that lead to successful outcomes
*/
findSuccessPatterns(agentId, interactions) {
const patterns = [];
const successfulInteractions = interactions.filter(i => i.outcome?.problemSolved &&
(i.outcome?.userSatisfaction ?? 0) >= 4);
// Group by file type
const fileTypeGroups = this.groupByFileType(successfulInteractions);
for (const [fileType, groupInteractions] of fileTypeGroups) {
if (groupInteractions.length >= 5) {
const pattern = {
id: `${agentId}_success_${fileType}_${Date.now()}`,
agentId,
pattern: `Successful detection in ${fileType} files`,
confidence: this.calculateConfidence(groupInteractions),
usageCount: groupInteractions.length,
successRate: this.calculateSuccessRate(groupInteractions),
context: {
fileTypes: [fileType],
projectTypes: this.extractProjectTypes(groupInteractions),
commonIssues: this.extractCommonIssues(groupInteractions),
userPreferences: this.extractUserPreferences(groupInteractions)
},
recommendations: {
agentImprovements: this.generateAgentImprovements(groupInteractions),
detectionRules: this.generateDetectionRules(groupInteractions),
suggestionTypes: this.generateSuggestionTypes(groupInteractions)
}
};
patterns.push(pattern);
}
}
return patterns;
}
/**
* Find patterns that lead to failed outcomes
*/
findFailurePatterns(agentId, interactions) {
const patterns = [];
const failedInteractions = interactions.filter(i => !i.outcome?.problemSolved ||
(i.outcome?.userSatisfaction ?? 5) < 3 ||
i.context.issue?.wasAccurate === false);
// Analyze false positives
const falsePositives = failedInteractions.filter(i => i.context.issue?.wasAccurate === false);
if (falsePositives.length >= 3) {
const pattern = {
id: `${agentId}_false_positive_${Date.now()}`,
agentId,
pattern: 'False positive detection pattern',
confidence: this.calculateConfidence(falsePositives),
usageCount: falsePositives.length,
successRate: 0,
context: {
fileTypes: this.extractFileTypes(falsePositives),
projectTypes: this.extractProjectTypes(falsePositives),
commonIssues: this.extractCommonIssues(falsePositives),
userPreferences: {}
},
recommendations: {
agentImprovements: ['Reduce false positive rate for these patterns'],
detectionRules: this.generateAntiPatterns(falsePositives),
suggestionTypes: ['Add confidence scoring to suggestions']
}
};
patterns.push(pattern);
}
return patterns;
}
/**
* Generate adaptive improvements for agents
*/
async generateAdaptations(agentId, interactions, userPreferences) {
const adaptations = [];
// Analyze suggestion follow-through rates
const suggestionAnalysis = this.analyzeSuggestionEffectiveness(interactions);
if (suggestionAnalysis.lowFollowThroughSuggestions.length > 0) {
adaptations.push({
agentId,
adaptationType: 'suggestion_algorithm',
changes: {
deprioritizeSuggestions: suggestionAnalysis.lowFollowThroughSuggestions,
prioritizeSuggestions: suggestionAnalysis.highFollowThroughSuggestions
},
confidence: 0.8,
expectedImprovement: 0.15
});
}
// Analyze detection accuracy
const detectionAnalysis = this.analyzeDetectionAccuracy(interactions);
if (detectionAnalysis.falsePositiveRate > 0.2) {
adaptations.push({
agentId,
adaptationType: 'detection_rule',
changes: {
addExclusions: detectionAnalysis.falsePositivePatterns,
increaseConfidenceThreshold: true
},
confidence: 0.7,
expectedImprovement: 0.25
});
}
// Adapt to user preferences
if (userPreferences['preferredSeverityLevel']) {
adaptations.push({
agentId,
adaptationType: 'priority_weighting',
changes: {
adjustSeverityWeights: userPreferences['preferredSeverityLevel'],
personalizeAlerts: userPreferences['alertPreferences']
},
confidence: 0.9,
expectedImprovement: 0.1
});
}
return adaptations;
}
/**
* Propose an adaptation to an agent
*/
proposeAdaptation(agentId, adaptation) {
if (!this.adaptations.has(agentId)) {
this.adaptations.set(agentId, []);
}
const agentAdaptations = this.adaptations.get(agentId);
// Don't exceed max adaptations per agent
if (agentAdaptations.length >= this.learningConfig.maxAdaptationsPerAgent) {
// Remove least confident adaptation
const leastConfident = agentAdaptations.reduce((min, curr) => curr.confidence < min.confidence ? curr : min);
const index = agentAdaptations.indexOf(leastConfident);
agentAdaptations.splice(index, 1);
}
agentAdaptations.push(adaptation);
this.logger.info('Agent adaptation proposed', {
agentId,
adaptationType: adaptation.adaptationType,
confidence: adaptation.confidence,
expectedImprovement: adaptation.expectedImprovement
}, 'adaptive-learning');
this.emit('adaptation_proposed', { agentId, adaptation });
}
/**
* Apply approved adaptations to agents
*/
async applyAdaptation(agentId, adaptationId) {
const agentAdaptations = this.adaptations.get(agentId);
if (!agentAdaptations)
return false;
const adaptation = agentAdaptations.find(a => `${a.agentId}_${a.adaptationType}_${a.confidence}` === adaptationId);
if (!adaptation)
return false;
try {
// Store rollback data
adaptation.rollbackData = await this.createRollbackData(agentId);
// Apply the adaptation
await this.applyAdaptationChanges(agentId, adaptation);
this.logger.info('Agent adaptation applied successfully', {
agentId,
adaptationType: adaptation.adaptationType
}, 'adaptive-learning');
this.emit('adaptation_applied', { agentId, adaptation });
return true;
}
catch (error) {
this.logger.error('Failed to apply agent adaptation', {
agentId,
adaptationType: adaptation.adaptationType,
error: error instanceof Error ? error.message : String(error)
}, 'adaptive-learning');
return false;
}
}
/**
* Get learning insights for dashboard
*/
getLearningInsights() {
const totalInteractions = Array.from(this.interactions.values())
.reduce((sum, interactions) => sum + interactions.length, 0);
const patternsDiscovered = this.patterns.size;
const adaptationsProposed = Array.from(this.adaptations.values())
.reduce((sum, adaptations) => sum + adaptations.length, 0);
const topPerformingAgents = this.calculateTopPerformingAgents();
const recentLearnings = Array.from(this.patterns.values())
.sort((a, b) => parseInt(b.id.split('_').pop()) - parseInt(a.id.split('_').pop()))
.slice(0, 5);
return {
totalInteractions,
patternsDiscovered,
adaptationsProposed,
adaptationsApplied: 0, // Would track from applied adaptations log
topPerformingAgents,
recentLearnings
};
}
// Helper methods
initializeLearning() {
if (!fs.existsSync(this.dataPath)) {
fs.mkdirSync(this.dataPath, { recursive: true });
}
}
loadLearningData() {
try {
const interactionsFile = path.join(this.dataPath, 'interactions.json');
const patternsFile = path.join(this.dataPath, 'patterns.json');
if (fs.existsSync(interactionsFile)) {
const data = JSON.parse(fs.readFileSync(interactionsFile, 'utf8'));
this.interactions = new Map(Object.entries(data));
}
if (fs.existsSync(patternsFile)) {
const data = JSON.parse(fs.readFileSync(patternsFile, 'utf8'));
this.patterns = new Map(Object.entries(data));
}
}
catch (error) {
this.logger.error('Failed to load learning data', { error: error instanceof Error ? error.message : String(error) }, 'adaptive-learning');
}
}
saveInteraction(interaction) {
// Save to persistent storage for learning
const interactionsFile = path.join(this.dataPath, 'interactions.json');
const data = Object.fromEntries(this.interactions);
fs.writeFileSync(interactionsFile, JSON.stringify(data, null, 2));
}
handleInteraction(interaction) {
// Real-time learning from interactions
if (interaction.outcome?.problemSolved && interaction.outcome.userSatisfaction && interaction.outcome.userSatisfaction >= 4) {
this.reinforceSuccessfulBehavior(interaction);
}
else if (interaction.context.issue?.wasAccurate === false) {
this.adjustForFalsePositive(interaction);
}
}
handlePatternDiscovery(pattern) {
this.logger.info('New learning pattern discovered', {
patternId: pattern.id,
agentId: pattern.agentId,
confidence: pattern.confidence,
successRate: pattern.successRate
}, 'adaptive-learning');
}
// Additional helper methods would be implemented here...
groupByFileType(interactions) {
const groups = new Map();
// Implementation...
return groups;
}
calculateConfidence(interactions) {
// Implementation based on interaction quality and outcomes
return 0.8;
}
calculateSuccessRate(interactions) {
const successful = interactions.filter(i => i.outcome?.problemSolved);
return successful.length / interactions.length;
}
extractProjectTypes(interactions) {
return interactions.map(i => i.context.projectType).filter(Boolean);
}
extractCommonIssues(interactions) {
return interactions.map(i => i.context.issue?.type).filter(Boolean);
}
extractUserPreferences(interactions) {
// Analyze user behavior patterns to extract preferences
return {};
}
generateAgentImprovements(interactions) {
return ['Improve detection accuracy for this file type'];
}
generateDetectionRules(interactions) {
return ['Add specialized rules for successful patterns'];
}
generateSuggestionTypes(interactions) {
return ['Prioritize suggestion types that users follow'];
}
extractFileTypes(interactions) {
return interactions.map(i => i.context.fileType).filter(Boolean);
}
generateAntiPatterns(interactions) {
return ['Exclude patterns that cause false positives'];
}
analyzeSuggestionEffectiveness(interactions) {
return { lowFollowThroughSuggestions: [], highFollowThroughSuggestions: [] };
}
analyzeDetectionAccuracy(interactions) {
return { falsePositiveRate: 0.1, falsePositivePatterns: [] };
}
calculateTopPerformingAgents() {
return [];
}
reinforceSuccessfulBehavior(interaction) {
// Implement reinforcement learning
}
adjustForFalsePositive(interaction) {
// Implement negative feedback learning
}
async createRollbackData(agentId) {
return {};
}
async applyAdaptationChanges(agentId, adaptation) {
// Apply changes to the agent's configuration/behavior
}
}
// Export singleton instance
export const adaptiveLearning = new AdaptiveLearningEngine();
export default adaptiveLearning;
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