MCP Self-Learning Server

/** * Integration with Claudio Multi-Agent Orchestration System * Provides learning capabilities to Claudio agents */ import SelfLearningClient from '../lib/self-learning-client.js'; import logger from '../lib/logger.js'; export class ClaudioLearningAgent { constructor(options = {}) { this.agentId = 'learning-agent'; this.agentName = 'MCP Self-Learning Agent'; this.version = '1.0.0'; this.capabilities = [ 'pattern-analysis', 'performance-optimization', 'predictive-suggestions', 'knowledge-management' ]; this.client = new SelfLearningClient({ baseUrl: options.mcpServerUrl || 'http://localhost:8765' }); this.isConnected = false; this.sessionId = null; } async initialize() { try { await this.client.connect(); this.isConnected = true; logger.info('Claudio learning agent initialized', { agentId: this.agentId, capabilities: this.capabilities }); return { success: true, agentId: this.agentId, capabilities: this.capabilities }; } catch (error) { logger.error('Failed to initialize Claudio learning agent', { error: error.message }); throw error; } } async handleRequest(request) { if (!this.isConnected) { throw new Error('Learning agent not connected'); } const { action, data, context } = request; try { switch (action) { case 'analyze-interaction': return await this.analyzeInteraction(data, context); case 'get-insights': return await this.getInsights(context); case 'optimize-workflow': return await this.optimizeWorkflow(data, context); case 'predict-next-action': return await this.predictNextAction(data, context); case 'learn-from-outcome': return await this.learnFromOutcome(data, context); default: throw new Error(`Unknown action: ${action}`); } } catch (error) { logger.error('Learning agent request failed', { action, error: error.message }); throw error; } } async analyzeInteraction(data, context) { const interaction = { type: 'claudio-interaction', input: data.input || '', output: data.output || '', success: data.success !== false, context: { ...context, agentId: context.sourceAgent || 'unknown', workflowId: context.workflowId, orchestrator: 'claudio' }, performance: { duration: data.duration || 0, resourceUsage: data.resourceUsage || {} } }; const result = await this.client.analyzePattern(interaction); logger.debug('Analyzed Claudio interaction', { patternId: result.patternId, sourceAgent: context.sourceAgent }); return { success: true, patternId: result.patternId, features: result.features, recommendations: result.recommendations, learningImpact: this.assessLearningImpact(result) }; } async getInsights(context) { const insights = await this.client.getInsights(); // Filter insights relevant to Claudio const claudioInsights = this.filterClaudioInsights(insights); return { success: true, insights: claudioInsights, recommendations: this.generateClaudioRecommendations(claudioInsights), metadata: { retrievedAt: new Date().toISOString(), context } }; } async optimizeWorkflow(data, context) { const workflowContext = { workflow: data.workflowId, agents: data.involvedAgents || [], currentStep: data.currentStep, performance: data.performance || {} }; const optimizations = await this.client.getOptimizations('claudio-workflow'); const predictions = await this.client.predictNextAction(workflowContext); return { success: true, optimizations: this.adaptOptimizationsForClaudio(optimizations), nextStepSuggestions: predictions.suggestions || [], performanceImprovements: this.calculatePerformanceImprovements(data, optimizations), confidence: predictions.confidence || 0.5 }; } async predictNextAction(data, context) { const predictionContext = { currentAgent: context.sourceAgent, workflowState: data.workflowState, userIntent: data.userIntent, availableAgents: data.availableAgents || [], history: data.recentActions || [] }; const predictions = await this.client.predictNextAction(predictionContext); return { success: true, predictions: predictions.suggestions?.map(suggestion => ({ action: suggestion.action, confidence: suggestion.confidence, reasoning: suggestion.reasoning, suggestedAgent: this.mapActionToAgent(suggestion.action, data.availableAgents) })) || [], confidence: predictions.confidence || 0.5, metadata: predictions.metadata }; } async learnFromOutcome(data, context) { const outcomeInteraction = { type: 'claudio-outcome', input: JSON.stringify(data.initialRequest), output: JSON.stringify(data.finalResult), success: data.success, context: { ...context, workflowId: data.workflowId, involvedAgents: data.involvedAgents, totalDuration: data.totalDuration, userSatisfaction: data.userSatisfaction }, performance: { duration: data.totalDuration, efficiency: data.efficiency, accuracy: data.accuracy } }; const result = await this.client.analyzePattern(outcomeInteraction); // Trigger learning cycle if enough outcomes collected const insights = await this.client.getInsights(); const totalInteractions = insights.insights?.metrics?.totalInteractions || 0; if (totalInteractions > 0 && totalInteractions % 10 === 0) { await this.client.triggerLearning(); } return { success: true, learned: true, patternId: result.patternId, impact: this.assessLearningImpact(result), nextRecommendations: result.recommendations }; } filterClaudioInsights(insights) { const claudioPatterns = insights.insights.topPatterns?.filter(pattern => pattern.type?.includes('claudio') || pattern.features?.contextualCues?.orchestrator === 'claudio' ) || []; return { ...insights.insights, topPatterns: claudioPatterns, claudioSpecific: { totalClaudioInteractions: claudioPatterns.reduce((sum, p) => sum + (p.count || 0), 0), mostUsedAgents: this.extractAgentUsage(claudioPatterns), workflowEfficiency: this.calculateWorkflowEfficiency(claudioPatterns) } }; } generateClaudioRecommendations(insights) { const recommendations = []; // Agent usage recommendations if (insights.claudioSpecific?.mostUsedAgents) { const agents = Object.entries(insights.claudioSpecific.mostUsedAgents) .sort(([,a], [,b]) => b - a); if (agents.length > 3) { recommendations.push({ type: 'agent-optimization', message: `Consider optimizing frequently used agents: ${agents.slice(0, 3).map(([name]) => name).join(', ')}`, priority: 'medium', actionable: true }); } } // Workflow efficiency recommendations if (insights.claudioSpecific?.workflowEfficiency < 0.7) { recommendations.push({ type: 'workflow-optimization', message: 'Workflow efficiency below optimal threshold. Consider reviewing agent coordination patterns.', priority: 'high', actionable: true }); } // Performance recommendations if (insights.performanceMetrics?.averageResponseTime > 5000) { recommendations.push({ type: 'performance-optimization', message: 'High average response time detected. Consider agent parallelization or caching.', priority: 'high', actionable: true }); } return recommendations; } adaptOptimizationsForClaudio(optimizations) { return optimizations.optimizations?.map(opt => ({ ...opt, claudioSpecific: true, implementation: this.generateClaudioImplementation(opt), estimatedImpact: this.estimateImpactForClaudio(opt) })) || []; } calculatePerformanceImprovements(workflowData, optimizations) { const basePerformance = workflowData.performance || {}; const improvements = {}; optimizations.optimizations?.forEach(opt => { if (opt.type === 'response-time') { improvements.responseTime = { current: basePerformance.averageResponseTime || 0, potential: (basePerformance.averageResponseTime || 0) * 0.7, // 30% improvement estimate improvement: '30%' }; } if (opt.type === 'resource-usage') { improvements.resourceUsage = { current: basePerformance.memoryUsage || 0, potential: (basePerformance.memoryUsage || 0) * 0.8, // 20% improvement estimate improvement: '20%' }; } }); return improvements; } mapActionToAgent(action, availableAgents) { // Simple mapping logic - in practice this would be more sophisticated const actionAgentMap = { 'analyze': 'analysis-agent', 'generate': 'generation-agent', 'validate': 'validation-agent', 'execute': 'execution-agent', 'monitor': 'monitoring-agent' }; const suggestedAgentType = actionAgentMap[action.split('-')[0]] || 'general-agent'; return availableAgents.find(agent => agent.type === suggestedAgentType || agent.capabilities?.includes(action) ) || availableAgents[0]; } assessLearningImpact(result) { const features = result.features || {}; const recommendations = result.recommendations || []; let impact = 'low'; if (recommendations.length > 2) impact = 'medium'; if (features.confidence > 0.8) impact = 'high'; if (features.novelty && features.novelty > 0.7) impact = 'high'; return { level: impact, factors: { recommendationCount: recommendations.length, confidence: features.confidence || 0, novelty: features.novelty || 0 } }; } extractAgentUsage(patterns) { const agentUsage = {}; patterns.forEach(pattern => { const agentId = pattern.context?.agentId || 'unknown'; agentUsage[agentId] = (agentUsage[agentId] || 0) + (pattern.count || 1); }); return agentUsage; } calculateWorkflowEfficiency(patterns) { if (!patterns.length) return 1.0; const successfulPatterns = patterns.filter(p => p.features?.success !== false); return successfulPatterns.length / patterns.length; } generateClaudioImplementation(optimization) { return { description: `Claudio implementation for: ${optimization.description}`, steps: [ 'Update agent configuration', 'Modify workflow orchestration', 'Deploy optimized agents', 'Monitor performance improvements' ], estimatedEffort: 'medium', dependencies: optimization.dependencies || [] }; } estimateImpactForClaudio(optimization) { // Simplified impact estimation const impactMap = { 'performance': 0.25, 'accuracy': 0.15, 'efficiency': 0.20, 'resource-usage': 0.10 }; return impactMap[optimization.type] || 0.10; } async cleanup() { if (this.client) { // Cleanup client connections (Node.js client disconnects WebSocket automatically) } this.isConnected = false; logger.info('Claudio learning agent cleaned up'); } } export default ClaudioLearningAgent;