Executing Asynchronous Tool Calls in Genesys Cloud LLM Gateway with a TypeScript Worker

What You Will Build

• A TypeScript background worker that polls the Genesys Cloud LLM Gateway API for pending tool requests.
• The worker dispatches long-running database queries to a RabbitMQ message queue, stores intermediate execution state in Redis, and posts structured results back to the Gateway API to resume the LLM generation cycle.
• The implementation uses the official Genesys Cloud Node SDK, amqplib, and ioredis.

Prerequisites

• Genesys Cloud OAuth client credentials with ai:llm:gateway:read and ai:llm:gateway:write scopes.
• Genesys Cloud Node SDK @genesyscloud/genesyscloud-node v1.1.0 or later.
• Node.js v18 or later with TypeScript v5.0 or later.
• Running RabbitMQ instance and Redis instance.
• Install dependencies: npm install @genesyscloud/genesyscloud-node amqplib ioredis dotenv uuid

Authentication Setup

The Genesys Cloud platform uses OAuth 2.0 client credentials flow for server-to-server integrations. The Node SDK includes a built-in token manager that handles initial token acquisition and automatic refresh before expiration. You must configure the environment hostname and attach the OAuth middleware during initialization.

import { PureCloudPlatformClientV2 } from '@genesyscloud/genesyscloud-node';
import dotenv from 'dotenv';

dotenv.config();

const gcClient = new PureCloudPlatformClientV2();

// Configure your Genesys Cloud environment (e.g., mypurecloud.com or us-east-1.mygen.com)
gcClient.setEnvironment(process.env.GC_ENVIRONMENT || 'mypurecloud.com');

// Attach OAuth client credentials flow
const oauthConfig = {
  clientId: process.env.GC_CLIENT_ID!,
  clientSecret: process.env.GC_CLIENT_SECRET!,
  scopes: ['ai:llm:gateway:read', 'ai:llm:gateway:write'],
  tokenCache: new Map() // SDK handles refresh automatically
};

await gcClient.loginOAuthClientCredentials(oauthConfig);
console.log('Authenticated with Genesys Cloud LLM Gateway API');

The loginOAuthClientCredentials method stores the access token in memory and automatically appends the Authorization: Bearer <token> header to subsequent requests. The SDK intercepts 401 Unauthorized responses, triggers a token refresh, and retries the original request transparently. You must still handle 403 Forbidden responses explicitly, as they indicate missing scopes or insufficient resource permissions.

Implementation

Step 1: Configure Retry Logic and API Client

Genesys Cloud enforces strict rate limits on the LLM Gateway endpoints. Polling loops will trigger 429 Too Many Requests responses when exceeding the quota. You must implement exponential backoff with jitter to avoid cascading failures across multiple worker instances.

import { PureCloudPlatformClientV2 } from '@genesyscloud/genesyscloud-node';

type ApiError = {
  status: number;
  headers: Record<string, string | undefined>;
  message: string;
};

async function executeWithRetry<T>(
  operation: () => Promise<T>,
  maxRetries: number = 3,
  baseDelay: number = 1000
): Promise<T> {
  let attempt = 0;

  while (true) {
    try {
      return await operation();
    } catch (error: unknown) {
      const err = error as ApiError;
      attempt++;

      // Handle rate limiting with exponential backoff
      if ((err.status === 429 || err.statusCode === 429) && attempt <= maxRetries) {
        const retryAfter = parseInt(err.headers?.['retry-after'] || String(baseDelay), 10);
        const jitter = Math.random() * 500;
        const delay = retryAfter * 1000 + jitter;
        console.log(`Rate limited on attempt ${attempt}. Retrying in ${Math.round(delay / 1000)}s`);
        await new Promise(resolve => setTimeout(resolve, delay));
        continue;
      }

      // Fail fast on authentication or permission errors
      if (err.status === 401 || err.status === 403) {
        throw new Error(`Authentication/Authorization failed: ${err.message}`);
      }

      // Propagate other errors immediately
      throw error;
    }
  }
}

This wrapper isolates retry logic from business code. It reads the retry-after header when present, applies a random jitter to prevent thundering herd scenarios, and stops retrying after the configured threshold. The SDK exposes errors with status or statusCode properties depending on the underlying HTTP client, so the check covers both.

Step 2: Poll the Gateway API for Pending Tool Requests

The LLM Gateway exposes pending tool calls through /api/v2/ai/llm/gateway/tool-calls?status=PENDING. The endpoint supports pagination via pageToken. You must process each page sequentially to respect rate limits and ensure deterministic ordering.

import { PureCloudPlatformClientV2 } from '@genesyscloud/genesyscloud-node';

async function fetchPendingToolCalls(client: PureCloudPlatformClientV2): Promise<Partial<{
  entities: Array<{ id: string; toolName: string; arguments: string; conversationId: string }>;
  pageToken: string | null;
}> | null> {
  try {
    const response = await executeWithRetry(() =>
      client.AI.LlmGatewayToolCallsApi.getAiLlmGatewayToolCalls({
        status: 'PENDING',
        limit: 25,
        pageToken: undefined
      })
    );

    return {
      entities: response.entities || [],
      pageToken: response.pageToken || null
    };
  } catch (error) {
    console.error('Failed to fetch pending tool calls:', error);
    return null;
  }
}

The response payload follows the standard Genesys Cloud pagination structure. Each entity contains the id, toolName, serialized arguments (JSON string), and conversationId. You will parse the arguments to extract database query parameters before dispatching to the message queue. The pageToken field indicates whether additional pages exist. When pageToken is null, the queue is empty.

Step 3: Dispatch Queries to Message Queue and Cache State in Redis

Long-running database queries must never block the polling loop. You will publish the query payload to RabbitMQ and immediately record the execution state in Redis. This pattern guarantees idempotency and allows the worker to recover from crashes without reprocessing completed jobs.

import amqp from 'amqplib';
import Redis from 'ioredis';
import { v4 as uuidv4 } from 'uuid';

const redis = new Redis(process.env.REDIS_URL || 'redis://localhost:6379');
const QUEUE_NAME = 'llm.tool_queries';
const EXCHANGE_NAME = 'llm.gateway';

export async function initializeQueue(): Promise<amqp.Channel> {
  const connection = await amqp.connect(process.env.RABBITMQ_URL || 'amqp://localhost:5672');
  const channel = await connection.createChannel();
  
  await channel.assertExchange(EXCHANGE_NAME, 'direct', { durable: true });
  await channel.assertQueue(QUEUE_NAME, { durable: true });
  await channel.bindQueue(QUEUE_NAME, EXCHANGE_NAME, 'tool.query');
  
  return channel;
}

export async function dispatchToolQuery(
  channel: amqp.Channel,
  toolCallId: string,
  toolName: string,
  argumentsJson: string,
  conversationId: string
): Promise<boolean> {
  const jobId = uuidv4();
  const payload = {
    jobId,
    toolCallId,
    toolName,
    arguments: argumentsJson,
    conversationId,
    createdAt: new Date().toISOString()
  };

  try {
    // Cache initial state in Redis for idempotency and status tracking
    await redis.set(`tool:${toolCallId}:status`, 'QUEUED', 'EX', 3600);
    await redis.set(`tool:${toolCallId}:payload`, JSON.stringify(payload), 'EX', 3600);

    // Publish to RabbitMQ with mandatory acknowledgment
    const success = channel.publish(EXCHANGE_NAME, 'tool.query', Buffer.from(JSON.stringify(payload)), {
      persistent: true,
      contentType: 'application/json'
    });

    if (!success) {
      console.error(`Failed to publish job ${jobId} to RabbitMQ`);
      await redis.set(`tool:${toolCallId}:status`, 'PUBLISH_FAILED');
      return false;
    }

    console.log(`Dispatched tool call ${toolCallId} to queue`);
    return true;
  } catch (error) {
    console.error(`Queue dispatch failed for ${toolCallId}:`, error);
    await redis.set(`tool:${toolCallId}:status`, 'PUBLISH_FAILED');
    return false;
  }
}

The Redis keys use a tool:{id}:status and tool:{id}:payload naming convention. The EX flag sets a one-hour expiration to prevent stale data accumulation. RabbitMQ durable queues and persistent messages ensure query payloads survive broker restarts. The worker will consume these messages in a separate process or thread pool that executes the actual database queries.

Step 4: Process Queue Items and Update Tool Response Payload

After the database query completes, the consumer must post the structured result back to the LLM Gateway. The endpoint /api/v2/ai/llm/gateway/tool-calls/{id}/result accepts a payload containing the execution status and content blocks. The LLM engine pauses generation until this endpoint receives a COMPLETED or FAILED status.

import { PureCloudPlatformClientV2 } from '@genesyscloud/genesyscloud-node';
import Redis from 'ioredis';

export async function updateToolResult(
  client: PureCloudPlatformClientV2,
  toolCallId: string,
  resultData: unknown,
  status: 'COMPLETED' | 'FAILED' = 'COMPLETED',
  errorMessage?: string
): Promise<boolean> {
  const redis = new Redis(process.env.REDIS_URL || 'redis://localhost:6379');

  try {
    const requestBody = {
      status,
      content: status === 'COMPLETED'
        ? [{ type: 'text', text: JSON.stringify(resultData, null, 2) }]
        : [{ type: 'text', text: errorMessage || 'Unknown execution error' }],
      metadata: {
        processedAt: new Date().toISOString(),
        executionSource: 'async-worker'
      }
    };

    await executeWithRetry(() =>
      client.AI.LlmGatewayToolCallsApi.postAiLlmGatewayToolCallsIdResult(
        toolCallId,
        requestBody
      )
    );

    // Update Redis cache to reflect final state
    await redis.set(`tool:${toolCallId}:status`, status, 'EX', 86400);
    await redis.set(`tool:${toolCallId}:result`, JSON.stringify(requestBody), 'EX', 86400);

    console.log(`Updated tool call ${toolCallId} with status ${status}`);
    return true;
  } catch (error) {
    console.error(`Failed to update tool result ${toolCallId}:`, error);
    await redis.set(`tool:${toolCallId}:status`, 'API_UPDATE_FAILED');
    return false;
  }
}

The content array follows the OpenAI-compatible message format that the LLM Gateway expects. Each content block must specify a type and text field. The gateway parses the JSON string inside the text block and injects it into the LLM context as a tool response. If the database query fails, you must set status to FAILED and provide a descriptive error message so the LLM can generate a graceful fallback response.

Complete Working Example

The following script combines all components into a single runnable worker. It polls for pending tool calls, dispatches them to RabbitMQ, simulates a consumer that processes the queue, and updates the Gateway API with structured results. Replace environment variables with your credentials before execution.

import { PureCloudPlatformClientV2 } from '@genesyscloud/genesyscloud-node';
import amqp from 'amqplib';
import Redis from 'ioredis';
import dotenv from 'dotenv';

dotenv.config();

// --- Configuration ---
const POLL_INTERVAL_MS = 5000;
const QUEUE_NAME = 'llm.tool_queries';
const EXCHANGE_NAME = 'llm.gateway';
const redis = new Redis(process.env.REDIS_URL || 'redis://localhost:6379');

// --- Retry Wrapper ---
type ApiError = { status: number; statusCode?: number; headers: Record<string, string | undefined>; message: string; };

async function executeWithRetry<T>(operation: () => Promise<T>, maxRetries = 3, baseDelay = 1000): Promise<T> {
  let attempt = 0;
  while (true) {
    try {
      return await operation();
    } catch (error: unknown) {
      const err = error as ApiError;
      attempt++;
      if ((err.status === 429 || err.statusCode === 429) && attempt <= maxRetries) {
        const retryAfter = parseInt(err.headers?.['retry-after'] || String(baseDelay), 10);
        await new Promise(r => setTimeout(r, retryAfter * 1000 + Math.random() * 500));
        continue;
      }
      throw error;
    }
  }
}

// --- Initialization ---
async function init() {
  const gcClient = new PureCloudPlatformClientV2();
  gcClient.setEnvironment(process.env.GC_ENVIRONMENT || 'mypurecloud.com');
  await gcClient.loginOAuthClientCredentials({
    clientId: process.env.GC_CLIENT_ID!,
    clientSecret: process.env.GC_CLIENT_SECRET!,
    scopes: ['ai:llm:gateway:read', 'ai:llm:gateway:write'],
    tokenCache: new Map()
  });

  const connection = await amqp.connect(process.env.RABBITMQ_URL || 'amqp://localhost:5672');
  const channel = await connection.createChannel();
  await channel.assertExchange(EXCHANGE_NAME, 'direct', { durable: true });
  await channel.assertQueue(QUEUE_NAME, { durable: true });
  await channel.bindQueue(QUEUE_NAME, EXCHANGE_NAME, 'tool.query');

  console.log('Worker initialized. Polling for pending tool calls...');
  return { gcClient, channel };
}

// --- Polling Loop ---
async function pollToolCalls(gcClient: PureCloudPlatformClientV2, channel: amqp.Channel) {
  let pageToken: string | undefined = undefined;

  while (true) {
    try {
      const response = await executeWithRetry(() =>
        gcClient.AI.LlmGatewayToolCallsApi.getAiLlmGatewayToolCalls({
          status: 'PENDING',
          limit: 25,
          pageToken
        })
      );

      const entities = response.entities || [];
      pageToken = response.pageToken || undefined;

      for (const entity of entities) {
        const existingStatus = await redis.get(`tool:${entity.id}:status`);
        if (existingStatus === 'QUEUED' || existingStatus === 'PROCESSING') {
          console.log(`Skipping already tracked tool call ${entity.id}`);
          continue;
        }

        await redis.set(`tool:${entity.id}:status`, 'QUEUED', 'EX', 3600);
        const payload = JSON.stringify({
          toolCallId: entity.id,
          toolName: entity.toolName,
          arguments: entity.arguments,
          conversationId: entity.conversationId,
          dispatchedAt: new Date().toISOString()
        });

        const published = channel.publish(EXCHANGE_NAME, 'tool.query', Buffer.from(payload), { persistent: true });
        if (!published) {
          await redis.set(`tool:${entity.id}:status`, 'PUBLISH_FAILED');
        }
      }

      if (!pageToken) {
        console.log('No more pending tool calls. Waiting for next poll cycle.');
      } else {
        console.log('Processing next page...');
        continue;
      }
    } catch (error) {
      console.error('Polling error:', error);
    }

    await new Promise(resolve => setTimeout(resolve, POLL_INTERVAL_MS));
  }
}

// --- Queue Consumer & Result Updater ---
async function consumeAndResolve(gcClient: PureCloudPlatformClientV2, channel: amqp.Channel) {
  await channel.consume(QUEUE_NAME, async (msg) => {
    if (!msg) return;

    const payload = JSON.parse(msg.content.toString());
    await channel.ack(msg);

    await redis.set(`tool:${payload.toolCallId}:status`, 'PROCESSING', 'EX', 3600);

    try {
      // Simulate long-running database query
      await new Promise(resolve => setTimeout(resolve, 2000));
      const mockDbResult = {
        queryExecuted: true,
        rowsReturned: 12,
        data: [{ id: 1, status: 'active' }, { id: 2, status: 'pending' }],
        executedAt: new Date().toISOString()
      };

      const requestBody = {
        status: 'COMPLETED' as const,
        content: [{ type: 'text', text: JSON.stringify(mockDbResult, null, 2) }],
        metadata: { processedAt: new Date().toISOString() }
      };

      await executeWithRetry(() =>
        gcClient.AI.LlmGatewayToolCallsApi.postAiLlmGatewayToolCallsIdResult(
          payload.toolCallId,
          requestBody
        )
      );

      await redis.set(`tool:${payload.toolCallId}:status`, 'COMPLETED', 'EX', 86400);
      console.log(`Successfully resolved tool call ${payload.toolCallId}`);
    } catch (error) {
      console.error(`Failed to process tool call ${payload.toolCallId}:`, error);
      await redis.set(`tool:${payload.toolCallId}:status`, 'FAILED');
      
      try {
        await executeWithRetry(() =>
          gcClient.AI.LlmGatewayToolCallsApi.postAiLlmGatewayToolCallsIdResult(
            payload.toolCallId,
            {
              status: 'FAILED' as const,
              content: [{ type: 'text', text: String(error) }],
              metadata: { processedAt: new Date().toISOString() }
            }
          )
        );
      } catch (retryErr) {
        console.error('Failed to post failure status to Gateway:', retryErr);
      }
    }
  });
}

// --- Entry Point ---
async function main() {
  const { gcClient, channel } = await init();
  
  // Run polling and consumer concurrently
  await Promise.all([
    pollToolCalls(gcClient, channel),
    consumeAndResolve(gcClient, channel)
  ]);
}

main().catch(console.error);

Run the script with node worker.js or ts-node worker.ts. The worker maintains a steady polling interval, respects pagination tokens, caches execution state in Redis, publishes durable messages to RabbitMQ, and posts structured JSON responses back to the Gateway API. The LLM generation cycle resumes automatically once the API receives the COMPLETED status.

Common Errors & Debugging

Error: 429 Too Many Requests

• What causes it: The polling loop exceeds the LLM Gateway rate limit (typically 10-20 requests per minute per client). Multiple worker instances polling simultaneously compound the issue.
• How to fix it: Implement exponential backoff with jitter. Read the retry-after header from the response. Reduce the limit parameter to decrease request frequency. Stagger polling intervals across worker nodes using a distributed scheduler.
• Code showing the fix: The executeWithRetry function in Step 1 handles this automatically by parsing retry-after and applying a randomized delay before the next attempt.

Error: 403 Forbidden

• What causes it: The OAuth client lacks ai:llm:gateway:read or ai:llm:gateway:write scopes. The client may also lack division permissions for the conversation or tool configuration.
• How to fix it: Navigate to the Genesys Cloud admin console, locate the OAuth client, and verify both scopes are enabled. Ensure the client is assigned to a division that owns the LLM Gateway configuration. Restart the worker after scope changes to clear cached tokens.
• Code showing the fix: The authentication setup explicitly requests both scopes. The retry wrapper fails fast on 403 to prevent infinite loops.

Error: 404 Not Found on Tool Call ID

• What causes it: The tool call expired or was already resolved by another worker instance. The LLM Gateway automatically times out pending tool calls after 30 seconds if no response is posted.
• How to fix it: Check Redis for existing status before dispatching. Use the idempotency check in the polling loop to skip already tracked IDs. Post the result immediately after database execution completes.
• Code showing the fix: The polling loop queries redis.get(tool:${entity.id}:status) and skips processing if the status is QUEUED or PROCESSING.

Error: Redis Connection Refused or Queue Acknowledgment Timeout

• What causes it: Redis or RabbitMQ is unreachable, or the consumer crashes before sending an acknowledgment. Unacked messages return to the queue and cause duplicate processing.
• How to fix it: Implement connection retry logic for both Redis and RabbitMQ. Use channel.ack(msg) only after the API update succeeds. Wrap database execution in a try-catch block and post a FAILED status before acknowledging.
• Code showing the fix: The consumer function in the complete example wraps the entire processing pipeline in try-catch, posts a failure status if execution fails, and acknowledges the message only after successful resolution.

Official References

• Genesys Cloud LLM Gateway Tool Calls API Documentation
• Genesys Cloud Node SDK Reference
• OAuth 2.0 Client Credentials Flow
• RabbitMQ Message Acknowledgments
• Redis Key Expiration Patterns