How to Query Real-Time Queue Metrics via Genesys Cloud Statistics API

What You Will Build

• This tutorial builds a script that retrieves real-time queue statistics, specifically waiting call counts and available agent counts, for a specific queue in Genesys Cloud.
• The solution uses the Genesys Cloud Statistics API (/api/v2/analytics/queues/details/query) with the Python SDK.
• The code is written in Python 3.9+ using the genesyscloud package.

Prerequisites

• OAuth Client: A Public or Confidential OAuth client with the analytics:statistics:view scope.
• SDK Version: genesyscloud Python SDK version 160.0.0 or later.
• Runtime: Python 3.9 or higher.
• Dependencies: genesyscloud library installed via pip.

pip install genesyscloud

Authentication Setup

Genesys Cloud uses OAuth 2.0 for authentication. For real-time statistics, a short-lived access token is sufficient, but for production scripts, you must handle token expiration. The Python SDK provides a PureCloudPlatformClientV2 instance that manages the token lifecycle automatically if configured correctly.

You must configure the client with your environment URL, client ID, and client secret. The SDK supports both Public Key (PKCE) and Confidential Client flows. For server-side scripts, the Confidential Client flow is standard.

import os
from purecloudplatformclientv2 import PureCloudPlatformClientV2, Configuration

def init_platform_client() -> PureCloudPlatformClientV2:
    """
    Initializes the Genesys Cloud Platform Client with OAuth2 credentials.
    
    Returns:
        PureCloudPlatformClientV2: The authenticated client instance.
    """
    # Load credentials from environment variables for security
    client_id = os.getenv("GENESYS_CLIENT_ID")
    client_secret = os.getenv("GENESYS_CLIENT_SECRET")
    env_url = os.getenv("GENESYS_ENVIRONMENT", "us-east-1.my.genesys.cloud")

    if not client_id or not client_secret:
        raise ValueError("GENESYS_CLIENT_ID and GENESYS_CLIENT_SECRET must be set.")

    # Create the configuration object
    config = Configuration(
        host=f"https://{env_url}",
        client_id=client_id,
        client_secret=client_secret
    )

    # Initialize the platform client
    # The client will automatically handle token acquisition and refresh
    return PureCloudPlatformClientV2(config)

# Initialize the client at module level or within main
platform_client = init_platform_client()

The Configuration object stores the credentials. When you make an API call, the SDK checks if a valid token exists. If not, it performs the OAuth client credentials grant flow to obtain one. The token is cached in memory for the duration of the session.

Implementation

Step 1: Construct the Query Request Body

The Statistics API does not use simple GET parameters for filtering. Instead, it uses a POST endpoint with a JSON body that defines the query. This body must include:

1. interval: For real-time data, use realtime.
2. dateFrom and dateTo: Even for realtime, these are required. They define the window for aggregated metrics if requested, but for realtime mode, they are largely ignored by the engine, yet mandatory for schema validation.
3. view: The metric set to retrieve. For queues, use queue.
4. entities: A list of queue IDs or names you want to query.
5. groupBy: How to aggregate the data. Usually entity (per queue) or none (total).

The required OAuth scope for this operation is analytics:statistics:view.

from purecloudplatformclientv2.rest import ApiException
import json

def build_query_request(queue_ids: list[str]) -> dict:
    """
    Builds the JSON payload for the /api/v2/analytics/queues/details/query endpoint.
    
    Args:
        queue_ids: List of Queue IDs to query.
        
    Returns:
        dict: The request body for the statistics query.
    """
    # For real-time queries, the interval must be 'realtime'
    # dateFrom and dateTo are required fields even for real-time queries
    # Use ISO 8601 format with timezone offset
    request_body = {
        "interval": "realtime",
        "dateFrom": "2023-01-01T00:00:00.000Z",
        "dateTo": "2023-01-01T01:00:00.000Z",
        "view": "queue",
        "groupBy": ["entity"],
        "entities": [
            {"id": q_id} for q_id in queue_ids
        ],
        # Optional: Filter by specific metric types if you want to reduce payload size
        # However, for 'queue' view, most useful metrics are included by default
    }
    return request_body

Step 2: Execute the API Call

You must use the AnalyticsApi from the SDK. The method is post_analytics_queues_details_query. This method accepts the request body defined in Step 1.

You must handle potential errors:

• 401 Unauthorized: Token is invalid or expired. The SDK usually retries, but if it fails, check your client secrets.
• 403 Forbidden: The OAuth client lacks the analytics:statistics:view scope.
• 429 Too Many Requests: You have exceeded the rate limit. Implement exponential backoff.
• 400 Bad Request: The JSON body is malformed or invalid.

from purecloudplatformclientv2 import AnalyticsApi
import time

def get_realtime_queue_stats(queue_ids: list[str], retries: int = 3) -> dict:
    """
    Fetches real-time statistics for a list of queues.
    
    Args:
        queue_ids: List of Queue IDs.
        retries: Number of retry attempts for 429 errors.
        
    Returns:
        dict: The response body containing queue metrics.
    """
    analytics_api = AnalyticsApi(platform_client)
    request_body = build_query_request(queue_ids)

    attempt = 0
    while attempt < retries:
        try:
            # Post the query to the Analytics API
            response = analytics_api.post_analytics_queues_details_query(body=request_body)
            
            # Log success
            print(f"Successfully retrieved data for {len(queue_ids)} queues.")
            return response
            
        except ApiException as e:
            print(f"API Call failed with status: {e.status}")
            print(f"Reason: {e.reason}")
            print(f"Error body: {e.body}")
            
            # Handle Rate Limiting (429)
            if e.status == 429:
                wait_time = 2 ** attempt  # Exponential backoff: 1s, 2s, 4s
                print(f"Rate limited. Retrying in {wait_time} seconds...")
                time.sleep(wait_time)
                attempt += 1
            else:
                # For other errors, re-raise immediately
                raise
                
    raise Exception("Max retries exceeded for 429 Too Many Requests.")

Step 3: Parse the Response for Waiting Count and Available Agents

The response from /api/v2/analytics/queues/details/query is a complex nested object. It contains an entities array, where each item represents a queue. Inside each queue entity, there is a metrics object.

The key metrics you need are:

• waitingCount: The number of interactions currently waiting in the queue.
• availableCount: The number of agents logged in and available to take calls for this queue.
• smalltalkCount: Agents on hold after a call.
• wrapupCount: Agents in wrap-up state.

The metrics object contains keys for different interaction types (e.g., call, callback, chat). For voice queues, you typically look at call.

def extract_queue_metrics(response: dict) -> dict:
    """
    Parses the API response to extract waiting count and available agents.
    
    Args:
        response: The raw response from post_analytics_queues_details_query.
        
    Returns:
        dict: A simplified dictionary with queue IDs as keys and metrics as values.
    """
    results = {}
    
    # Check if response has entities
    if not hasattr(response, 'entities') or response.entities is None:
        print("No entities found in response.")
        return results

    for entity in response.entities:
        # entity.id is the Queue ID
        queue_id = entity.id
        queue_name = entity.name
        
        # Initialize metrics for this queue
        queue_metrics = {
            "queueId": queue_id,
            "queueName": queue_name,
            "waitingCount": 0,
            "availableCount": 0,
            "wrapupCount": 0,
            "smalltalkCount": 0
        }
        
        # Check if metrics exist for this entity
        if hasattr(entity, 'metrics') and entity.metrics:
            metrics = entity.metrics
            
            # We focus on 'call' metrics for voice queues
            if 'call' in metrics:
                call_metrics = metrics['call']
                
                # Extract waiting count
                if 'waitingCount' in call_metrics:
                    queue_metrics['waitingCount'] = call_metrics['waitingCount']
                
                # Extract available agent count
                if 'availableCount' in call_metrics:
                    queue_metrics['availableCount'] = call_metrics['availableCount']
                    
                # Extract wrapup count
                if 'wrapupCount' in call_metrics:
                    queue_metrics['wrapupCount'] = call_metrics['wrapupCount']
                    
                # Extract smalltalk count
                if 'smalltalkCount' in call_metrics:
                    queue_metrics['smalltalkCount'] = call_metrics['smalltalkCount']
        
        results[queue_id] = queue_metrics

    return results

Complete Working Example

This script combines authentication, query construction, API execution, and response parsing into a single runnable module. Replace YOUR_CLIENT_ID and YOUR_CLIENT_SECRET with actual values or set the environment variables.

import os
import sys
import time
from purecloudplatformclientv2 import PureCloudPlatformClientV2, Configuration, AnalyticsApi
from purecloudplatformclientv2.rest import ApiException

def init_platform_client() -> PureCloudPlatformClientV2:
    """Initializes the Genesys Cloud Platform Client."""
    client_id = os.getenv("GENESYS_CLIENT_ID")
    client_secret = os.getenv("GENESYS_CLIENT_SECRET")
    env_url = os.getenv("GENESYS_ENVIRONMENT", "us-east-1.my.genesys.cloud")

    if not client_id or not client_secret:
        raise ValueError("GENESYS_CLIENT_ID and GENESYS_CLIENT_SECRET must be set.")

    config = Configuration(
        host=f"https://{env_url}",
        client_id=client_id,
        client_secret=client_secret
    )
    return PureCloudPlatformClientV2(config)

def get_realtime_queue_stats(queue_ids: list[str], platform_client: PureCloudPlatformClientV2, retries: int = 3) -> dict:
    """Fetches real-time statistics for a list of queues."""
    analytics_api = AnalyticsApi(platform_client)
    
    # Build the request body
    request_body = {
        "interval": "realtime",
        "dateFrom": "2023-01-01T00:00:00.000Z",
        "dateTo": "2023-01-01T01:00:00.000Z",
        "view": "queue",
        "groupBy": ["entity"],
        "entities": [{"id": q_id} for q_id in queue_ids]
    }

    attempt = 0
    while attempt < retries:
        try:
            response = analytics_api.post_analytics_queues_details_query(body=request_body)
            return response
        except ApiException as e:
            if e.status == 429:
                wait_time = 2 ** attempt
                print(f"Rate limited. Retrying in {wait_time} seconds...")
                time.sleep(wait_time)
                attempt += 1
            else:
                raise

def extract_queue_metrics(response: dict) -> dict:
    """Parses the API response to extract waiting count and available agents."""
    results = {}
    if not hasattr(response, 'entities') or response.entities is None:
        return results

    for entity in response.entities:
        queue_id = entity.id
        queue_name = entity.name
        
        queue_metrics = {
            "queueId": queue_id,
            "queueName": queue_name,
            "waitingCount": 0,
            "availableCount": 0
        }
        
        if hasattr(entity, 'metrics') and entity.metrics and 'call' in entity.metrics:
            call_metrics = entity.metrics['call']
            queue_metrics['waitingCount'] = call_metrics.get('waitingCount', 0)
            queue_metrics['availableCount'] = call_metrics.get('availableCount', 0)
        
        results[queue_id] = queue_metrics

    return results

def main():
    # 1. Initialize Client
    try:
        client = init_platform_client()
    except Exception as e:
        print(f"Failed to initialize client: {e}")
        sys.exit(1)

    # 2. Define Queue IDs (Replace with actual IDs from your environment)
    # You can get these via the Platform API: GET /api/v2/queues
    target_queue_ids = [
        "12345678-1234-1234-1234-123456789012", 
        "87654321-4321-4321-4321-210987654321"
    ]

    # 3. Fetch Data
    try:
        response = get_realtime_queue_stats(target_queue_ids, client)
    except ApiException as e:
        print(f"API Error: {e}")
        sys.exit(1)

    # 4. Parse and Display Results
    metrics = extract_queue_metrics(response)
    
    print("\n--- Real-Time Queue Statistics ---")
    for q_id, data in metrics.items():
        print(f"Queue: {data['queueName']} (ID: {data['queueId']})")
        print(f"  Waiting Calls: {data['waitingCount']}")
        print(f"  Available Agents: {data['availableCount']}")
        print("-" * 30)

if __name__ == "__main__":
    main()

Common Errors & Debugging

Error: 403 Forbidden

Cause: The OAuth client does not have the analytics:statistics:view scope.
Fix: Go to the Genesys Cloud Admin Console → Settings → Integrations → OAuth Clients. Select your client, edit it, and ensure analytics:statistics:view is checked under the Scopes section. Save and re-generate the token.

Error: 429 Too Many Requests

Cause: The Analytics API has strict rate limits, especially for realtime queries which hit the live data store.
Fix: Implement exponential backoff as shown in the get_realtime_queue_stats function. Do not poll faster than once every 10-15 seconds for real-time data unless you have negotiated higher limits with Genesys Support.

Error: Empty Entities List

Cause: The queue IDs provided do not exist, or the OAuth client does not have access to those queues (security profiles).
Fix: Verify the queue IDs using GET /api/v2/queues/{id}. Ensure the OAuth client’s associated security profile has Queue: View permissions.

Error: Metrics Missing call Key

Cause: The queue is not configured for voice calls, or no call metrics are available in the current real-time snapshot.
Fix: Check the view parameter. If you are querying a chat queue, look for chat in the metrics object instead of call. Ensure the queue actually has interactions routed to it.

Official References

• Genesys Cloud Statistics API Documentation
• Python SDK Reference: PureCloudPlatformClientV2
• OAuth 2.0 Client Credentials Grant