Viewing and deleting traces

View and search

Open the Traces dashboard and select the workspace that owns the data. You can also query with the SDK or CLI.

prime traces list --run-id your-run-id
prime traces get <trace_id> --raw
prime traces search "Traceback" --run-id your-run-id

Search is case-sensitive literal search within one run, with at least three characters. Cross-run search, regular expressions, and arbitrary JSON-path queries are not supported. Results may omit records that have not finished indexing; inspect search coverage rather than treating an empty result as proof that the data is absent.

Download a trace

from prime_traces import TracesClient

with TracesClient() as client:
    client.download_raw("your-trace-id", "trace.json")

This downloads an individual stored trace. It is separate from the legacy archive export; the SDK does not currently provide a working bulk-export method. Prime Traces has no egress fees.

Delete a trace

Download any copy you need to keep before deletion. Check the workspace and trace ID carefully.

from prime_traces import TracesClient

with TracesClient() as client:
    client.delete("your-trace-id")

Deleting a trace deletes every stored copy of that trace. The API returns 202 once it is no longer readable; physical storage is reclaimed in the background. Deleting an already absent trace returns 404 (NotFoundError in the SDK).

Delete a run’s traces

Stop any producer still uploading to the run before deleting its traces.

from prime_traces import TracesClient

with TracesClient() as client:
    client.delete_run("your-run-id")

Deleting a run’s traces removes the traces present when the service looks up the run. Episodes are kept. This is a trace deletion operation, not a request to delete the training or evaluation record. An upload that finishes while deletion is running may or may not be included. An upload that finishes after deletion returns is not included. The API returns 202 once the affected traces are no longer readable. A run with no traces returns 404, including a repeated deletion.

Disabling uploads does not delete existing traces. Deletion acceptance, physical storage reclamation, and billing updates are separate events; a 202 response confirms that the affected traces are no longer readable, not that a billing update has completed.

Troubleshooting

Symptom	What to check
Missing traces	Check the personal/team workspace and run ID. Inspect uploader errors; do not assume a completed training run guarantees every trace uploaded successfully.
Storage limit exceeded	Review the workspace’s Usage Limits. Delete unneeded data or review the configured limit. Physical reclamation runs in the background.
SDK/CLI reads fail after opting out	Account-level opt-out disables those reads; existing data remains viewable in the dashboard.
Validation rejected	Confirm the record format and the API error. Do not retry the same malformed input unchanged.
Permission denied	Check API scopes, team membership, and workspace upload settings.
Search finds no match	Check case, run ID, and indexing coverage. Search is not a full raw-file scan.

If you need help reconciling storage usage or charges after deletion, contact support@primeintellect.ai with the workspace, run or trace ID, and deletion time.

Getting Started

Lab

Inference

Model Factory

Libraries

Compute

View and search

Download a trace

Delete a trace

Delete a run’s traces

Troubleshooting

​View and search

​Download a trace

​Delete a trace

​Delete a run’s traces

​Troubleshooting

View and search

Download a trace

Delete a trace

Delete a run’s traces

Troubleshooting