Stuck on a 500 Internal Server Error hitting /api/v2/analytics/users/summary. The request fails immediately when from and to parameters span more than 90 days, despite pagination being configured correctly in the CLI wrapper.
Environment is Genesys Cloud v23.4.0, running via GitHub Actions on ubuntu-latest. The payload is valid JSON, verified against the schema. No 400 or 422 errors, just a hard 500 from the platform.
Has the date range limit been tightened recently? Need this for quarterly reporting automation. Any workaround or flag to bypass this?
This looks like a constraint imposed by the underlying data warehouse architecture rather than a simple API timeout. The users/summary endpoint aggregates high-volume interaction data, and Genesys Cloud enforces a strict 90-day window for single-request aggregation to maintain query performance and system stability. Attempting to exceed this window in a single call triggers the 500 error because the backend query optimizer rejects the unbounded range.
The solution is to implement a segmented date-range strategy. You must break the total period into chunks that do not exceed 90 days, execute separate requests for each chunk, and then aggregate the results client-side.
Here is a pseudo-code example of the required logic:
def fetch_user_summary(start_date, end_date):
chunk_size_days = 89 # Stay safely under the 90-day limit
all_data = []
current_start = start_date
while current_start < end_date:
current_end = min(current_start + days(chunk_size_days), end_date)
# Execute API call for the specific chunk
response = api.call("/api/v2/analytics/users/summary",
params={"from": current_start, "to": current_end})
all_data.extend(response.data)
current_start = current_end + 1 day # Move to next chunk
return aggregate_metrics(all_data)
In the Performance Dashboard, this segmentation is handled automatically when viewing historical trends, but direct API access requires manual implementation. Ensure that your client-side aggregation function handles overlapping metrics correctly, particularly for real-time versus historical data boundaries. This approach aligns with the platform’s documented best practices for large-scale data extraction and prevents resource exhaustion errors.