Setting up a Python script to pull historical interaction data from Genesys Cloud every night. It’s a headless process running on an AWS Lambda. No human logging in.
I’ve been reading the OAuth docs and I’m torn between two approaches.
- Client Credentials Grant: Seems cleaner for a service-to-service thing. I just swap the client ID and secret for a token.
- Authorization Code Grant: Usually for user apps, but maybe I need it if the reporting needs to act ‘as’ a specific admin user to bypass certain scope limits?
Here’s the basic token request I’m testing with Client Credentials:
import requests
url = "https://api.mypurecloud.com/oauth/token"
payload = {
"grant_type": "client_credentials",
"client_id": "my_app_id",
"client_secret": "my_secret"
}
headers = {"Content-Type": "application/x-www-form-urlencoded"}
response = requests.post(url, headers=headers, data=payload)
print(response.json())
This works fine. I get a token back. But when I try to hit /api/v2/analytics/interactions/queries, I get a 403 Forbidden. The error says Insufficient privileges.
If I switch to using a personal access token (which is basically Authorization Code flow with a long-lived refresh), it works instantly.
Is Client Credentials inherently more restricted for analytics endpoints? Or am I missing a scope in the app setup? I’ve added analytics:read to the app, but maybe that’s not enough for a machine account?
Just want to make sure I’m not building this wrong before I deploy it to prod.