Genesys Cloud Python SDK automatic token refresh implementation

General Discussion
1

Does anyone know how to properly configure the Genesys Cloud Python SDK to handle OAuth token refresh automatically without manual intervention in my PagerDuty integration loop? I am building an SLA breach monitor that polls /api/v2/analytics/conversations/details/query every 30 seconds, and my current implementation relies on a static access token obtained via post_oauth2_client_credentials. After approximately one hour, the SDK stops refreshing the token internally, resulting in 401 Unauthorized errors that break my webhook payload construction. I have initialized the ApiClient with configuration = Configuration() configuration.access_token = initial_token and then instantiated api_instance = AnalyticsApi(ApiClient(configuration)). The documentation suggests the SDK should handle refresh tokens, but I am only using client credentials flow which typically does not issue a refresh token. I need to understand if I must implement a custom background thread to call post_oauth2_client_credentials periodically or if there is a specific SDK method like ApiClient.refresh_token() that I am missing. My current error log shows purecloud_platform_client.rest.ApiException: (401) Reason: Unauthorized when the token expires. I want to avoid hardcoding a retry logic block around every API call. Is there a recommended pattern for long-running Python scripts using the client credentials grant type to maintain a valid session?

2

TL;DR: Configure the refresh callback.

Make sure you pass a custom refresh_token callback to PureCloudPlatformClientV2 initialization so the SDK persists the new token instead of relying on the default in-memory store that expires with the process.

3

I think the issue isn’t just about persisting the token, but ensuring the trace context survives the refresh cycle. When you poll /api/v2/analytics/conversations/details/query every 30 seconds, you are creating a high-frequency distributed tracing scenario. The standard PureCloudPlatformClientV2 refresh mechanism often drops the active span context during the token exchange, leading to orphaned spans in Jaeger.

You need to inject a custom refresh callback that explicitly propagates the current OTel context. Here is how I structure it in my data action wrappers:

from purecloudplatformclientv2 import PureCloudPlatformClientV2
from opentelemetry import trace

def custom_refresh(client, refresh_token):
 # Capture current span context before refresh
 current_span = trace.get_current_span()
 
 # Perform refresh
 client.set_access_token(refresh_token)
 
 # Re-inject context if needed for subsequent calls
 # This ensures the trace ID remains consistent across the token boundary
 return True

client = PureCloudPlatformClientV2()
client.set_refresh_token_callback(custom_refresh)

401 Unauthorized: Token expired during high-frequency polling loop. 
Trace context lost after refresh. Span ID mismatch detected in Jaeger backend.

The key is that the custom_refresh function must not just update the token, but also verify that the underlying HTTP client maintains the correlation ID for the analytics query. If you are using a loop, ensure you are not instantiating a new PlatformClient object every iteration, as that breaks the connection pool and the trace propagation. Stick to a single client instance with the callback bound. This approach keeps the SLA monitor’s trace lineage intact even when the token rotates.

4

Depends on your setup, but generally the SDK’s in-memory refresh is sufficient if you avoid process restarts.

  • Instantiate PureCloudPlatformClientV2 once.
  • Pass client_credentials_flow during initialization.
  • Let the SDK handle the background refresh automatically.
5

To fix this easily, this is to bypass the SDK’s local clock validation by forcing a refresh in your Zapier custom action before the API call. Just implement a simple try/except block that catches 401 and triggers an immediate oauth2_client_credentials fetch, ensuring your polling trigger never hits the token expiry window during high-frequency analytics queries.