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https://github.com/ruvnet/RuView
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feat: vendor midstream and sublinear-time-solver libraries
Add ruvnet/midstream (AIMDS real-time inference) and ruvnet/sublinear-time-solver (sublinear optimization algorithms) as vendored dependencies under vendor/. Co-Authored-By: claude-flow <ruv@ruv.net>
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ruv
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/**
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* Example: Agentic Flow Proxy Integration
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*
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* Demonstrates using OpenAI Realtime API through agentic-flow proxy
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* with workflow orchestration and MidStream analysis
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*/
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import { AgenticFlowProxyClient, OpenAIRealtimeClient } from '../src/openai-realtime.js';
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import * as dotenv from 'dotenv';
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dotenv.config();
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async function main() {
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console.log('🔄 Agentic Flow Proxy + OpenAI Realtime + MidStream');
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console.log('═══════════════════════════════════════════════════\n');
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// Create agentic-flow proxy client
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const proxyClient = new AgenticFlowProxyClient({
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baseUrl: process.env.AGENTIC_FLOW_PROXY_URL || 'https://api.agenticflow.com/v1',
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apiKey: process.env.AGENTIC_FLOW_API_KEY!,
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openAiApiKey: process.env.OPENAI_API_KEY!,
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});
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console.log('✓ Agentic Flow Proxy client created');
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// Create realtime session through proxy
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const realtimeClient = await proxyClient.createRealtimeSession({
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apiKey: process.env.OPENAI_API_KEY!,
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model: process.env.OPENAI_REALTIME_MODEL,
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voice: 'nova',
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temperature: 0.7,
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});
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console.log('✓ Realtime session created through proxy\n');
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// Set up event listeners
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realtimeClient.on('session.created', (session) => {
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console.log('📡 Session ID:', session.id);
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// Configure session
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realtimeClient.updateSession({
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modalities: ['text'],
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instructions: `You are an AI assistant integrated with agentic-flow for workflow orchestration.
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You can analyze conversations, detect patterns, and coordinate multi-agent workflows.`,
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temperature: 0.7,
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});
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});
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realtimeClient.on('response.text.delta', (delta) => {
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process.stdout.write(delta);
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});
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realtimeClient.on('response.done', () => {
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console.log('\n');
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});
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realtimeClient.on('midstream.analysis', (status) => {
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console.log('🧠 MidStream:', {
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messages: status.conversationHistorySize,
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avgReward: status.averageReward.toFixed(2),
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});
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});
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try {
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// Scenario 1: Simple conversation through proxy
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console.log('💬 Scenario 1: Proxied Conversation\n');
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console.log('User: Hello! I need help analyzing customer support patterns.\n');
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realtimeClient.sendText('Hello! I need help analyzing customer support patterns.');
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await new Promise(resolve => {
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realtimeClient.once('response.done', resolve);
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});
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await new Promise(resolve => setTimeout(resolve, 1000));
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// Scenario 2: Pattern analysis
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console.log('\nUser: Can you detect patterns in this conversation flow?\n');
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realtimeClient.sendText(`Can you analyze this conversation pattern:
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1. Customer: "I have a problem"
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2. Agent: "What's the issue?"
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3. Customer: "Can't login"
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4. Agent: "Let me help you reset your password"
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5. Customer: "Thank you, it works now"`);
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await new Promise(resolve => {
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realtimeClient.once('response.done', resolve);
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});
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await new Promise(resolve => setTimeout(resolve, 1000));
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// Get MidStream's pattern analysis
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console.log('\n📊 MidStream Pattern Analysis:');
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const agent = realtimeClient.getAgent();
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const testSequence = [
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'problem_report',
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'info_request',
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'problem_description',
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'solution_offer',
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'gratitude',
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];
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const commonPattern = ['problem_report', 'info_request', 'problem_description'];
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const positions = agent.detectPattern(testSequence, commonPattern);
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console.log(' Pattern detected at positions:', positions);
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// Compare with another sequence
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const similarSequence = [
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'problem_report',
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'info_request',
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'problem_description',
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'solution_offer',
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'confirmation',
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];
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const similarity = agent.compareSequences(testSequence, similarSequence, 'dtw');
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console.log(' Similarity to variant pattern:', similarity.toFixed(3));
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// Scenario 3: Workflow execution (if agentic-flow is configured)
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if (process.env.AGENTIC_FLOW_API_KEY) {
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console.log('\n🔄 Scenario 3: Workflow Orchestration\n');
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try {
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// Example workflow execution
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// In production, you'd have pre-configured workflows in agentic-flow
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const workflowResult = await proxyClient.executeWorkflow('conversation-analyzer', {
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conversation: realtimeClient.getConversation(),
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analysisType: 'pattern_detection',
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});
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console.log('Workflow result:', workflowResult);
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} catch (error: any) {
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console.log(' (Workflow not configured - this is expected in demo)');
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}
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}
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// Final comprehensive analysis
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console.log('\n═══════════════════════════════════════');
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console.log('📈 Final Comprehensive Analysis');
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console.log('═══════════════════════════════════════\n');
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const finalAnalysis = realtimeClient.getMidStreamAnalysis();
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console.log('MidStream Analysis:', JSON.stringify(finalAnalysis, null, 2));
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const status = agent.getStatus();
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console.log('\nAgent Status:');
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console.log(' - Conversation size:', status.conversationHistorySize);
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console.log(' - Average reward:', status.averageReward.toFixed(3));
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console.log(' - Meta-learning level:', status.metaLearning.currentLevel);
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// Behavior analysis
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if (status.rewardHistorySize > 5) {
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const behaviorAnalysis = agent.analyzeBehavior(
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Array(status.rewardHistorySize).fill(0.8)
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);
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console.log('\nBehavior Analysis:');
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console.log(' - Is stable:', behaviorAnalysis.isStable);
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console.log(' - Is chaotic:', behaviorAnalysis.isChaotic);
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}
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// Conversation insights
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const conversation = realtimeClient.getConversation();
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console.log('\nConversation Insights:');
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console.log(' - Total items:', conversation.length);
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console.log(' - User messages:', conversation.filter(i => i.role === 'user').length);
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console.log(' - Assistant messages:', conversation.filter(i => i.role === 'assistant').length);
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// Cleanup
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realtimeClient.disconnect();
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console.log('\n✓ Session ended gracefully');
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process.exit(0);
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} catch (error) {
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console.error('\n❌ Error:', error);
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realtimeClient.disconnect();
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process.exit(1);
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}
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}
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// Handle graceful shutdown
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process.on('SIGINT', () => {
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console.log('\n\nShutting down...');
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process.exit(0);
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});
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main();
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