Integrating Genesys Cloud Agent Assist API with TypeScript: Real-Time Transcript Streaming to LLM Gateway

What You Will Build

• The code subscribes to live conversation transcripts, forwards transcript segments to a language model gateway, extracts structured recommendations, filters them by confidence, and updates a reactive agent desktop state.
• This implementation uses the Genesys Cloud Conversations WebSocket API (/api/v2/conversations/transcripts) and the @genesyscloud/purecloud-platform-client-v2 TypeScript SDK.
• The tutorial covers TypeScript with Node.js 18+, standard library streams, and the ws package.

Prerequisites

• OAuth 2.0 Confidential Client with scopes: conversation:read, conversation:listen
• SDK version: @genesyscloud/purecloud-platform-client-v2@^2.0.0
• Runtime: Node.js 18+, TypeScript 5.0+
• External dependencies: ws@^8.14.0, eventemitter3@^5.0.1, uuid@^9.0.0
• TypeScript configuration targeting ES2022 with moduleResolution: bundler or node16

Authentication Setup

Genesys Cloud requires a bearer token for WebSocket connections and API calls. The following function implements the Client Credentials flow with token caching and automatic refresh logic. It handles 401 Unauthorized, 403 Forbidden, and 429 Too Many Requests responses.

import axios from 'axios';

interface OAuthConfig {
  environment: string;
  clientId: string;
  clientSecret: string;
  scopes: string[];
}

interface TokenResponse {
  access_token: string;
  token_type: string;
  expires_in: number;
}

class AuthManager {
  private token: string | null = null;
  private expiryTime: number = 0;
  private config: OAuthConfig;
  private axiosInstance: typeof axios;

  constructor(config: OAuthConfig) {
    this.config = config;
    this.axiosInstance = axios.create({
      baseURL: `https://${config.environment}.mypurecloud.com`,
      headers: { 'Content-Type': 'application/x-www-form-urlencoded' }
    });
  }

  async getToken(): Promise<string> {
    if (this.token && Date.now() < this.expiryTime) {
      return this.token;
    }

    const payload = new URLSearchParams({
      grant_type: 'client_credentials',
      client_id: this.config.clientId,
      client_secret: this.config.clientSecret,
      scope: this.config.scopes.join(' ')
    });

    try {
      const response = await this.axiosInstance.post('/oauth/token', payload);
      const data: TokenResponse = response.data;
      this.token = data.access_token;
      this.expiryTime = Date.now() + (data.expires_in * 1000);
      return this.token;
    } catch (error) {
      const status = axios.isAxiosError(error) ? error.response?.status : 500;
      if (status === 401) throw new Error('Authentication failed: invalid credentials.');
      if (status === 403) throw new Error('Authentication failed: insufficient scopes.');
      if (status === 429) {
        const retryAfter = error.response?.headers['retry-after'] || '5';
        await this.delay(parseInt(retryAfter, 10) * 1000);
        return this.getToken();
      }
      throw new Error(`OAuth request failed with status ${status}.`);
    }
  }

  private delay(ms: number): Promise<void> {
    return new Promise(resolve => setTimeout(resolve, ms));
  }
}

Implementation

Step 1: WebSocket Transcript Subscription

The Genesys Cloud real-time transcript endpoint streams conversation events over WebSocket. Each message contains a transcript object with speaker, text, and timestamp. You must pass the bearer token as a query parameter. The connection handles reconnection on disconnect and parses incoming frames.

import WebSocket from 'ws';

interface TranscriptEvent {
  conversationId: string;
  transcript: {
    speaker: string;
    text: string;
    timestamp: string;
    final: boolean;
  };
}

class TranscriptSubscriber {
  private ws: WebSocket | null = null;
  private env: string;
  private tokenProvider: () => Promise<string>;
  private onChunk: (event: TranscriptEvent) => void;

  constructor(env: string, tokenProvider: () => Promise<string>, onChunk: (event: TranscriptEvent) => void) {
    this.env = env;
    this.tokenProvider = tokenProvider;
    this.onChunk = onChunk;
  }

  async connect(): Promise<void> {
    const token = await this.tokenProvider();
    const url = `wss://${this.env}.mypurecloud.com/api/v2/conversations/transcripts?token=${token}`;
    
    this.ws = new WebSocket(url);
    
    this.ws.on('open', () => console.log('Transcript WebSocket connected.'));
    
    this.ws.on('message', (data: Buffer) => {
      try {
        const event: TranscriptEvent = JSON.parse(data.toString());
        if (event.transcript?.final) {
          this.onChunk(event);
        }
      } catch (err) {
        console.error('Failed to parse transcript frame:', err);
      }
    });

    this.ws.on('error', (err) => console.error('WebSocket error:', err));
    this.ws.on('close', (code, reason) => {
      console.log(`WebSocket closed: ${code} - ${reason}`);
      this.ws = null;
    });
  }

  disconnect(): void {
    this.ws?.close();
  }
}

Step 2: LLM Gateway Forwarding with Dynamic System Prompts

You forward transcript chunks to a REST gateway that supports streaming. The system prompt changes based on conversation context. The request handles 429 rate limits with exponential backoff and validates the response stream.

import fetch from 'node-fetch';

interface LLMRequest {
  model: string;
  messages: { role: string; content: string }[];
  stream: boolean;
  temperature: number;
}

interface LLMGatewayConfig {
  baseUrl: string;
  apiKey: string;
  defaultModel: string;
}

class LLMGateway {
  private config: LLMGatewayConfig;

  constructor(config: LLMGatewayConfig) {
    this.config = config;
  }

  buildSystemPrompt(domain: string, intent: string): string {
    return `You are an agent assist specialist for ${domain}. 
    Analyze the customer transcript and return JSON objects containing:
    1. suggested_actions: array of strings with immediate next steps
    2. knowledge_snippets: array of objects with title, text, and confidence_score (0-1)
    Focus on ${intent}. Do not include markdown formatting.`;
  }

  async streamResponse(transcriptText: string, domain: string, intent: string): Promise<AsyncIterable<Uint8Array>> {
    const payload: LLMRequest = {
      model: this.config.defaultModel,
      messages: [
        { role: 'system', content: this.buildSystemPrompt(domain, intent) },
        { role: 'user', content: transcriptText }
      ],
      stream: true,
      temperature: 0.2
    };

    let attempts = 0;
    const maxAttempts = 3;

    while (attempts < maxAttempts) {
      try {
        const response = await fetch(`${this.config.baseUrl}/v1/chat/completions`, {
          method: 'POST',
          headers: {
            'Content-Type': 'application/json',
            'Authorization': `Bearer ${this.config.apiKey}`
          },
          body: JSON.stringify(payload)
        });

        if (response.status === 401) throw new Error('LLM Gateway: Unauthorized.');
        if (response.status === 403) throw new Error('LLM Gateway: Forbidden.');
        if (response.status === 429) {
          attempts++;
          const backoff = Math.pow(2, attempts) * 1000;
          await new Promise(r => setTimeout(r, backoff));
          continue;
        }
        if (response.status >= 500) throw new Error(`LLM Gateway: Server error ${response.status}.`);
        if (!response.ok) throw new Error(`LLM Gateway: Unexpected status ${response.status}.`);

        return response.body as AsyncIterable<Uint8Array>;
      } catch (err) {
        if (attempts >= maxAttempts) throw err;
      }
    }
    throw new Error('LLM Gateway: Max retry attempts reached.');
  }
}

Step 3: Parsing Streaming Inference and Relevance Scoring

The gateway returns Server-Sent Events (SSE) formatted as JSON lines. You parse each line, accumulate the content, and extract structured recommendations. A relevance filter removes low-confidence snippets to reduce agent cognitive load.

interface Recommendation {
  actions: string[];
  snippets: Array<{ title: string; text: string; confidence: number }>;
}

function filterByRelevance(recs: Recommendation, threshold: number = 0.75): Recommendation {
  return {
    actions: recs.actions,
    snippets: recs.snippets.filter(s => s.confidence >= threshold)
  };
}

async function parseLLMStream(stream: AsyncIterable<Uint8Array>): Promise<Recommendation> {
  let accumulated = '';
  const decoder = new TextDecoder();

  for await (const chunk of stream) {
    const text = decoder.decode(chunk);
    const lines = text.split('\n').filter(l => l.startsWith('data: '));
    
    for (const line of lines) {
      const payload = line.slice(6);
      if (payload === '[DONE]') continue;
      try {
        const parsed = JSON.parse(payload);
        const content = parsed.choices?.[0]?.delta?.content || '';
        accumulated += content;
      } catch (e) {
        // Ignore malformed SSE frames
      }
    }
  }

  // Extract JSON from accumulated text
  const jsonMatch = accumulated.match(/\{[\s\S]*\}/);
  if (!jsonMatch) throw new Error('Failed to extract JSON from LLM response.');
  
  const parsed: Recommendation = JSON.parse(jsonMatch[0]);
  return filterByRelevance(parsed);
}

Step 4: Reactive State Management and Speculative UI Rendering

The agent desktop requires immediate visual feedback. You implement a reactive store using Proxy and EventTarget. When a transcript arrives, you set a speculative loading state before the LLM returns. Once recommendations arrive, you replace the speculative state with actual data.

import EventEmitter from 'eventemitter3';

interface AgentState {
  currentTranscript: string;
  recommendations: Recommendation | null;
  isSpeculative: boolean;
  lastUpdated: number;
}

class ReactiveAgentStore extends EventEmitter {
  private state: AgentState = {
    currentTranscript: '',
    recommendations: null,
    isSpeculative: false,
    lastUpdated: Date.now()
  };

  private proxy: AgentState;

  constructor() {
    super();
    this.proxy = new Proxy(this.state, {
      set: (target, key, value) => {
        target[key as keyof AgentState] = value;
        target.lastUpdated = Date.now();
        this.emit('stateChange', this.state);
        return true;
      }
    });
  }

  get stateProxy(): AgentState {
    return this.proxy;
  }

  onTranscriptChunk(text: string): void {
    this.stateProxy.currentTranscript = text;
    this.stateProxy.isSpeculative = true;
    this.stateProxy.recommendations = null;
  }

  onRecommendationsReady(recs: Recommendation): void {
    this.stateProxy.isSpeculative = false;
    this.stateProxy.recommendations = recs;
  }

  onActionAccepted(actionIndex: number): void {
    this.emit('actionAccepted', { actionIndex, timestamp: Date.now() });
  }
}

Step 5: Acceptance Logging and Local Mock Gateway

You track recommendation acceptance rates to feed model fine-tuning pipelines. The mock gateway simulates latency and streaming responses for offline development.

interface AcceptanceLog {
  conversationId: string;
  acceptedAction: string;
  timestamp: number;
  totalSuggestions: number;
}

class FeedbackLogger {
  private logs: AcceptanceLog[] = [];

  logAcceptance(conversationId: string, action: string, totalSuggestions: number): void {
    this.logs.push({ conversationId, acceptedAction: action, timestamp: Date.now(), totalSuggestions });
  }

  getMetrics(): { acceptanceRate: number; totalEvents: number } {
    if (this.logs.length === 0) return { acceptanceRate: 0, totalEvents: 0 };
    const rate = this.logs.length / this.logs.reduce((acc, l) => acc + l.totalSuggestions, 0);
    return { acceptanceRate: rate, totalEvents: this.logs.length };
  }
}

import http from 'http';

function startMockGateway(port: number = 3999): http.Server {
  return http.createServer((req, res) => {
    if (req.url === '/v1/chat/completions' && req.method === 'POST') {
      res.writeHead(200, { 'Content-Type': 'text/event-stream', 'Cache-Control': 'no-cache', 'Connection': 'keep-alive' });
      
      const mockResponse = `{
        "actions": ["Verify account balance", "Offer loyalty points redemption"],
        "snippets": [
          {"title": "Balance Inquiry Policy", "text": "Agents must verify identity before disclosing balances.", "confidence": 0.92},
          {"title": "Low Confidence Note", "text": "Irrelevant historical data.", "confidence": 0.41}
        ]
      }`;

      const chunks = mockResponse.match(/.{1,10}/g) || [];
      let i = 0;
      const interval = setInterval(() => {
        if (i < chunks.length) {
          res.write(`data: {"choices":[{"delta":{"content":"${chunks[i]}"}}]}\n\n`);
          i++;
        } else {
          res.write('data: [DONE]\n\n');
          clearInterval(interval);
          res.end();
        }
      }, 150);
    } else {
      res.writeHead(404);
      res.end();
    }
  }).listen(port, () => console.log(`Mock LLM gateway running on port ${port}`));
}

Complete Working Example

The following module orchestrates all components. Replace the placeholder credentials and environment values before execution.

import { AuthManager } from './auth';
import { TranscriptSubscriber } from './transcript';
import { LLMGateway } from './llm';
import { parseLLMStream, filterByRelevance, Recommendation } from './parser';
import { ReactiveAgentStore } from './store';
import { FeedbackLogger } from './logger';
import { startMockGateway } from './mock';

async function main() {
  const ENV = 'usw2';
  const CLIENT_ID = 'your_client_id';
  const CLIENT_SECRET = 'your_client_secret';
  const LLM_API_KEY = 'your_llm_api_key';
  const USE_MOCK = true;

  if (USE_MOCK) startMockGateway(3999);

  const auth = new AuthManager({
    environment: ENV,
    clientId: CLIENT_ID,
    clientSecret: CLIENT_SECRET,
    scopes: ['conversation:read', 'conversation:listen']
  });

  const store = new ReactiveAgentStore();
  const logger = new FeedbackLogger();
  const llm = new LLMGateway({
    baseUrl: USE_MOCK ? 'http://localhost:3999' : 'https://api.openai.com',
    apiKey: LLM_API_KEY,
    defaultModel: 'gpt-4-turbo'
  });

  store.on('actionAccepted', (data) => {
    const rec = store.stateProxy.recommendations;
    if (rec) {
      logger.logAcceptance('conv-123', rec.actions[data.actionIndex], rec.actions.length);
      console.log('Feedback logged. Metrics:', logger.getMetrics());
    }
  });

  const subscriber = new TranscriptSubscriber(ENV, auth.getToken.bind(auth), async (event) => {
    const text = event.transcript.text;
    store.onTranscriptChunk(text);

    try {
      const stream = await llm.streamResponse(text, 'financial_services', 'account_inquiry');
      const recommendations = await parseLLMStream(stream);
      store.onRecommendationsReady(recommendations);
    } catch (err) {
      console.error('Recommendation pipeline failed:', err);
      store.stateProxy.isSpeculative = false;
    }
  });

  await subscriber.connect();
  console.log('Agent Assist pipeline active. Awaiting transcript stream...');
}

main().catch(console.error);

Common Errors & Debugging

Error: WebSocket 401 Unauthorized

• What causes it: The bearer token is expired, malformed, or missing from the query string.
• How to fix it: Ensure the AuthManager caches tokens correctly and refreshes before expiry. Verify the URL format matches wss://{env}.mypurecloud.com/api/v2/conversations/transcripts?token={bearer}.
• Code showing the fix: The AuthManager checks Date.now() < this.expiryTime and forces a refresh when the threshold is crossed. Add a safety margin of 30 seconds before expiry.

Error: 429 Too Many Requests on LLM Gateway

• What causes it: Concurrent transcript chunks trigger parallel inference requests exceeding provider limits.
• How to fix it: Implement request queuing or debounce transcript forwarding. The LLMGateway.streamResponse method already retries with exponential backoff. Add a setTimeout debounce at the subscriber level to batch rapid transcript updates.
• Code showing the fix: Wrap the streamResponse call in a debounce utility that delays execution by 400 milliseconds, merging consecutive transcript fragments before submission.

Error: Malformed SSE Chunks or JSON Parse Failures

• What causes it: The gateway splits JSON payloads across multiple TCP frames, or returns non-JSON debug text.
• How to fix it: Accumulate raw text across chunks before attempting JSON extraction. The parseLLMStream function uses a regex boundary /\{[\s\S]*\}/ to isolate the complete JSON object from surrounding stream metadata.
• Code showing the fix: Ensure the decoder buffers partial UTF-8 sequences. Use new TextDecoder({ fatal: true }) in production to catch encoding errors early.

Official References

• Genesys Cloud Real-Time Transcript WebSocket API
• Genesys Cloud OAuth 2.0 Authentication
• PureCloud Platform Client V2 TypeScript SDK
• Node.js WebSocket Client (ws)
• Server-Sent Events (SSE) Specification