Get Real-Time Queue Metrics via Genesys Cloud Statistics API

What You Will Build

• A Python script that retrieves real-time queue statistics, including the count of waiting interactions and available agents.
• This uses the Genesys Cloud CX Statistics API (/api/v2/analytics/queues/details/query).
• The programming language covered is Python 3.9+ using the requests library.

Prerequisites

• OAuth Client: A Genesys Cloud OAuth client with the contactcenter:queue:read scope.
• API Version: Genesys Cloud CX API v2.
• Language/Runtime: Python 3.9 or higher.
• External Dependencies: requests, python-dotenv (for secure credential management).

Install dependencies:

pip install requests python-dotenv

Authentication Setup

Genesys Cloud uses OAuth 2.0 for authentication. For server-to-server integrations, you will use the Client Credentials flow. You must handle token expiration by implementing a refresh mechanism or caching the token until it expires.

Create a .env file in your project root:

GENESYS_CLOUD_SUBDOMAIN=your-subdomain
GENESYS_CLOUD_CLIENT_ID=your-client-id
GENESYS_CLOUD_CLIENT_SECRET=your-client-secret

The following class handles authentication and token caching. It ensures you do not make unnecessary requests to the token endpoint while the token remains valid.

import os
import time
import requests
from dotenv import load_dotenv

load_dotenv()

class GenesysAuth:
    def __init__(self):
        self.subdomain = os.getenv("GENESYS_CLOUD_SUBDOMAIN")
        self.client_id = os.getenv("GENESYS_CLOUD_CLIENT_ID")
        self.client_secret = os.getenv("GENESYS_CLOUD_CLIENT_SECRET")
        self.token_url = f"https://{self.subdomain}.mypurecloud.com/oauth/token"
        self.access_token = None
        self.token_expiry = 0

    def get_token(self) -> str:
        """
        Returns a valid access token. Refreshes if expired or not present.
        """
        # Check if token is still valid (subtract 60 seconds for safety buffer)
        if self.access_token and time.time() < (self.token_expiry - 60):
            return self.access_token

        payload = {
            "grant_type": "client_credentials",
            "client_id": self.client_id,
            "client_secret": self.client_secret,
            "scope": "contactcenter:queue:read"
        }

        try:
            response = requests.post(self.token_url, data=payload)
            response.raise_for_status()
            data = response.json()
            
            self.access_token = data["access_token"]
            # expires_in is in seconds
            self.token_expiry = time.time() + data["expires_in"]
            
            return self.access_token

        except requests.exceptions.RequestException as e:
            raise Exception(f"Failed to obtain OAuth token: {e}")

# Initialize authentication
auth = GenesysAuth()

Implementation

Step 1: Construct the Queue Details Query

The Statistics API uses a query-based model for real-time data. You do not fetch data for a single queue ID directly in the URL path. Instead, you send a POST request to /api/v2/analytics/queues/details/query with a JSON body containing the filters.

The critical filter is queueIds. You can also filter by timeFrame if you need historical snapshots, but for real-time data, you omit the time frame or use the default current state.

import json
from datetime import datetime, timedelta

def build_queue_query(queue_ids: list) -> dict:
    """
    Constructs the request body for the queue details query.
    """
    # Define the view as "default" to get standard real-time metrics
    # "realtime" view is also available but "default" is sufficient for most counts
    
    query_body = {
        "view": "default",
        "groupBy": [],
        "select": [
            "queueId",
            "queueName",
            "count",
            "waitingCount",
            "availableCount",
            "interactingCount",
            "longestWait",
            "avgWait"
        ],
        "filter": {
            "type": "and",
            "clauses": [
                {
                    "type": "fieldEquals",
                    "field": "queueId",
                    "value": queue_ids,
                    "op": "in"
                }
            ]
        }
    }
    
    return query_body

Step 2: Execute the API Request

This step sends the query to the Genesys Cloud API. You must handle the 429 Too Many Requests error by implementing exponential backoff. Genesys Cloud enforces rate limits per client ID.

import random

def fetch_realtime_queue_stats(auth: GenesysAuth, queue_ids: list, max_retries: int = 3) -> dict:
    """
    Fetches real-time statistics for specified queues.
    Implements exponential backoff for 429 errors.
    """
    subdomain = auth.subdomain
    endpoint = f"https://{subdomain}.mypurecloud.com/api/v2/analytics/queues/details/query"
    headers = {
        "Authorization": f"Bearer {auth.get_token()}",
        "Content-Type": "application/json"
    }
    
    payload = build_queue_query(queue_ids)
    
    for attempt in range(max_retries):
        try:
            response = requests.post(endpoint, json=payload, headers=headers)
            
            # Handle Rate Limiting (429)
            if response.status_code == 429:
                retry_after = int(response.headers.get("Retry-After", 5))
                # Add jitter to prevent thundering herd
                sleep_time = retry_after + random.uniform(0, 1)
                print(f"Rate limited. Retrying in {sleep_time:.2f} seconds...")
                time.sleep(sleep_time)
                continue
            
            # Handle Authentication Errors
            if response.status_code == 401:
                raise Exception("Unauthorized. Check OAuth token and scopes.")
            
            # Handle Not Found or Bad Request
            if response.status_code in [400, 404]:
                raise Exception(f"API Error {response.status_code}: {response.text}")
            
            response.raise_for_status()
            return response.json()
            
        except requests.exceptions.RequestException as e:
            print(f"Request failed on attempt {attempt + 1}: {e}")
            if attempt == max_retries - 1:
                raise
            time.sleep(2 ** attempt) # Exponential backoff for network errors

    return {}

Step 3: Process and Interpret Results

The response from the Statistics API contains an array of entities. Each entity represents a queue (or a specific time slice if grouping by time). You must parse this array to extract the metrics for each queue.

Key fields:

• waitingCount: The number of interactions currently waiting in the queue.
• availableCount: The number of agents logged in and available to take interactions.
• interactingCount: The number of agents currently on a call/chat.
• count: The total number of agents in the queue (available + interacting + offline).

def parse_queue_stats(api_response: dict) -> list:
    """
    Parses the API response into a list of readable queue metrics.
    """
    if not api_response.get("entities"):
        return []
    
    queue_metrics = []
    
    for entity in api_response["entities"]:
        # Extract metrics safely with defaults
        waiting = entity.get("waitingCount", 0)
        available = entity.get("availableCount", 0)
        interacting = entity.get("interactingCount", 0)
        total_agents = entity.get("count", 0)
        longest_wait_seconds = entity.get("longestWait", 0)
        avg_wait_seconds = entity.get("avgWait", 0)
        
        # Convert seconds to minutes for readability
        longest_wait_min = round(longest_wait_seconds / 60, 2)
        avg_wait_min = round(avg_wait_seconds / 60, 2)
        
        metric = {
            "queue_id": entity.get("queueId"),
            "queue_name": entity.get("queueName"),
            "waiting_count": waiting,
            "available_agents": available,
            "interacting_agents": interacting,
            "total_agents": total_agents,
            "longest_wait_minutes": longest_wait_min,
            "avg_wait_minutes": avg_wait_min
        }
        queue_metrics.append(metric)
        
    return queue_metrics

Complete Working Example

This script combines authentication, query construction, API execution, and result parsing. It monitors a list of queue IDs and prints the real-time status.

import os
import time
import requests
import random
import json
from dotenv import load_dotenv

load_dotenv()

class GenesysAuth:
    def __init__(self):
        self.subdomain = os.getenv("GENESYS_CLOUD_SUBDOMAIN")
        self.client_id = os.getenv("GENESYS_CLOUD_CLIENT_ID")
        self.client_secret = os.getenv("GENESYS_CLOUD_CLIENT_SECRET")
        self.token_url = f"https://{self.subdomain}.mypurecloud.com/oauth/token"
        self.access_token = None
        self.token_expiry = 0

    def get_token(self) -> str:
        if self.access_token and time.time() < (self.token_expiry - 60):
            return self.access_token

        payload = {
            "grant_type": "client_credentials",
            "client_id": self.client_id,
            "client_secret": self.client_secret,
            "scope": "contactcenter:queue:read"
        }

        try:
            response = requests.post(self.token_url, data=payload)
            response.raise_for_status()
            data = response.json()
            self.access_token = data["access_token"]
            self.token_expiry = time.time() + data["expires_in"]
            return self.access_token
        except requests.exceptions.RequestException as e:
            raise Exception(f"Failed to obtain OAuth token: {e}")

def build_queue_query(queue_ids: list) -> dict:
    return {
        "view": "default",
        "groupBy": [],
        "select": [
            "queueId",
            "queueName",
            "count",
            "waitingCount",
            "availableCount",
            "interactingCount",
            "longestWait",
            "avgWait"
        ],
        "filter": {
            "type": "and",
            "clauses": [
                {
                    "type": "fieldEquals",
                    "field": "queueId",
                    "value": queue_ids,
                    "op": "in"
                }
            ]
        }
    }

def fetch_realtime_queue_stats(auth: GenesysAuth, queue_ids: list, max_retries: int = 3) -> dict:
    subdomain = auth.subdomain
    endpoint = f"https://{subdomain}.mypurecloud.com/api/v2/analytics/queues/details/query"
    headers = {
        "Authorization": f"Bearer {auth.get_token()}",
        "Content-Type": "application/json"
    }
    
    payload = build_queue_query(queue_ids)
    
    for attempt in range(max_retries):
        try:
            response = requests.post(endpoint, json=payload, headers=headers)
            
            if response.status_code == 429:
                retry_after = int(response.headers.get("Retry-After", 5))
                sleep_time = retry_after + random.uniform(0, 1)
                print(f"Rate limited. Retrying in {sleep_time:.2f} seconds...")
                time.sleep(sleep_time)
                continue
            
            if response.status_code == 401:
                raise Exception("Unauthorized. Check OAuth token and scopes.")
            
            if response.status_code in [400, 404]:
                raise Exception(f"API Error {response.status_code}: {response.text}")
            
            response.raise_for_status()
            return response.json()
            
        except requests.exceptions.RequestException as e:
            print(f"Request failed on attempt {attempt + 1}: {e}")
            if attempt == max_retries - 1:
                raise
            time.sleep(2 ** attempt)

    return {}

def parse_queue_stats(api_response: dict) -> list:
    if not api_response.get("entities"):
        return []
    
    queue_metrics = []
    for entity in api_response["entities"]:
        metric = {
            "queue_id": entity.get("queueId"),
            "queue_name": entity.get("queueName"),
            "waiting_count": entity.get("waitingCount", 0),
            "available_agents": entity.get("availableCount", 0),
            "interacting_agents": entity.get("interactingCount", 0),
            "total_agents": entity.get("count", 0),
            "longest_wait_minutes": round(entity.get("longestWait", 0) / 60, 2),
            "avg_wait_minutes": round(entity.get("avgWait", 0) / 60, 2)
        }
        queue_metrics.append(metric)
    return queue_metrics

def main():
    # Replace with actual Queue IDs from your Genesys Cloud instance
    TARGET_QUEUE_IDS = ["queue-id-1", "queue-id-2"]
    
    if not TARGET_QUEUE_IDS:
        print("Please provide valid Queue IDs in TARGET_QUEUE_IDS.")
        return

    auth = GenesysAuth()
    
    print(f"Fetching real-time stats for {len(TARGET_QUEUE_IDS)} queues...")
    try:
        raw_response = fetch_realtime_queue_stats(auth, TARGET_QUEUE_IDS)
        metrics = parse_queue_stats(raw_response)
        
        if not metrics:
            print("No data returned. Check Queue IDs and permissions.")
            return

        print("\n--- Real-Time Queue Status ---")
        for m in metrics:
            print(f"Queue: {m['queue_name']}")
            print(f"  Waiting: {m['waiting_count']}")
            print(f"  Available Agents: {m['available_agents']}")
            print(f"  Interacting Agents: {m['interacting_agents']}")
            print(f"  Total Agents: {m['total_agents']}")
            print(f"  Avg Wait: {m['avg_wait_minutes']} min")
            print(f"  Longest Wait: {m['longest_wait_minutes']} min")
            print("-" * 30)
            
    except Exception as e:
        print(f"Error: {e}")

if __name__ == "__main__":
    main()

Common Errors & Debugging

Error: 401 Unauthorized

Cause: The OAuth token is expired, invalid, or the client credentials are incorrect.
Fix:

1. Verify GENESYS_CLOUD_CLIENT_ID and GENESYS_CLOUD_CLIENT_SECRET in your .env file.
2. Ensure the OAuth client has the contactcenter:queue:read scope assigned in the Genesys Cloud Admin Console.
3. Check that your subdomain matches the one used in the token URL.

Error: 403 Forbidden

Cause: The OAuth client has the correct token but lacks the specific permission to read queue statistics.
Fix:

1. Go to Admin > Applications > OAuth Clients.
2. Select your client.
3. Under “Scopes”, ensure contactcenter:queue:read is checked.
4. Save and regenerate the token.

Error: 429 Too Many Requests

Cause: You have exceeded the rate limit for the Statistics API. The limit is typically per client ID.
Fix:

1. Implement exponential backoff as shown in the fetch_realtime_queue_stats function.
2. Respect the Retry-After header in the response.
3. Avoid polling too frequently. For real-time dashboards, consider using Websockets (/api/v2/analytics/conversations/details/query is not websocket-based, but for pure real-time streaming, look into the Events API or Websockets for interactions). However, for queue stats, polling every 10-30 seconds is usually safe.

Error: Empty Entities Array

Cause: The queueIds filter returned no results.
Fix:

1. Verify that the queueIds in TARGET_QUEUE_IDS are valid UUIDs from your Genesys Cloud instance.
2. Ensure the queues exist and are not deleted.
3. Check that the OAuth client has access to the specific routing configuration if using specific org permissions.

Official References

• Genesys Cloud Statistics API Documentation
• Queue Details Query Endpoint
• OAuth 2.0 Client Credentials Flow