Update existing traces

Sometimes the data you want to monitor isn’t available at inference time. Openlayer lets you update existing traces after they were streamed to the platform. Common use cases:

Adding ground truths that only became available later.
Logging human feedback (e.g., thumbs up/down, ratings).
Attaching business signals such as conversions or revenue impact.

Filter records by feedback

Use the Feedback filter in the records table to find records with Positive, Negative, or No feedback. Records whose feedback has been cleared count as No feedback.

How updates work

Every trace streamed to Openlayer has an inference_id, which is a unique identifier.

If you provide your own inference IDs, you can easily reference and update those traces later.
If you don’t, Openlayer auto-generates them for you.

For maximum flexibility, set custom inference IDs when tracing. This makes it simple to tie traces to feedback, business outcomes, or other systems.

Example: Add a ground truth

Let’s say you want to add a ground_truth column for a previously logged trace.

from openlayer import Openlayer
from openlayer.types.inference_pipelines import row_update_params

row_updates = {
    "ground_truth": "The sun is 94.471 million miles from the earth."
}

config = row_update_params.Config(
    ground_truth_column_name="ground_truth"
)

client = Openlayer()
client.inference_pipelines.rows.update(
    inference_pipeline_id="182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
    inference_id="832y98d3",
    row=row_updates,
    config=config,
)

This updates the trace with inference_id="832y98d3" by attaching a ground truth.

Using custom inference IDs

When tracing with the @trace decorator, you can set your own inference IDs. This makes it easy to correlate requests with later feedback or business signals.

from openlayer.lib import trace, update_current_trace

@trace()
def process_chat_message(user_id: str, message: str, conversation_id: str):
    custom_id = f"chat_{conversation_id}_{user_id}"
    update_current_trace(inference_id=custom_id)

    response = generate_ai_response(message)

    # Store custom_id in your DB for later updates
    store_for_feedback(custom_id, user_id, message, response)

    return response

Later, use that custom ID (chat_{conversation_id}_{user_id}) in your update calls.

Comment on records

Use comments to discuss a monitoring record without changing its data. Each record has one flat thread, available from the records table and record drawer. Choosing Reply on a comment mentions its author automatically. You can mention workspace or project members. Mentioned members receive an in-app notification and, depending on their personal notification setting for being tagged in a comment, an email that links to the record. Mentions also appear in project activity. You are not notified when you mention yourself, and mentions added while editing a comment do not send notifications. Adding a comment requires the same permission as updating a monitoring record. Only the comment’s author can edit or delete it. Comments are deleted with their record and purged according to your data retention policy, like human feedback. They are not included in data exports. If a comment’s author is no longer a member, the author appears as Unknown user.

Comments may not be available for every data source.

Deleting production data

An update rewrites what a record says. Deleting removes the record entirely, which is what you want when the data should not be in Openlayer at all: a load test skewing your metrics, or a trace that captured something it shouldn’t have. Deleting records and sessions requires the delete_monitoring_record permission, which project admins hold. See Roles and permissions for how permissions are granted.

Deletion applies to the records Openlayer stores for you. When a project reads its data from an external table through a connector, such as BigQuery, delete the rows in that table instead.

Delete a single record

Open the record from your project’s data table and choose Delete record. Openlayer asks you to confirm, and warns that the action cannot be undone and that the record leaves the data source forever. Confirm with Delete record.

Delete sessions

On Sessions, select the sessions you want to remove, then choose Batch actions → Delete selected. The Delete sessions dialog states how many records go with them, along with their scores, embeddings, and summaries. Type I am sure to confirm, then click Delete sessions, or Delete session when you selected one. Openlayer accepts up to 500 sessions per delete request. Work through a larger cleanup in batches.

Delete a data source

Deleting a data source is wider than either action above. It removes the data source along with everything attached to it, so reach for it only when you want the whole thing gone. In your project, go to Data sources, then choose Delete from the row’s overflow menu. The Delete data source dialog names the data source and shows how many tests and records it holds, with a View button so you can inspect it first. Confirm by typing the data source name, which is what enables the Delete data source button, or Cancel to back out.

Deleting a data source cannot be undone. Any test, monitoring data, insights, and more that belong to it are permanently lost.

This is an admin action, listed as Delete inference pipelines in the permission matrix.

What deletion removes

When you delete records or sessions, Openlayer registers the deletion immediately, so the data leaves the UI right away, then purges the underlying records in the background: the traces and spans behind each record, the test scores computed on them, embeddings, and OpenTelemetry spans.

Deleting does not stop ingestion. If your application keeps streaming records under a session_id you deleted, the session comes back holding only the records that arrive afterwards. Stop sending that data first when you want it gone for good.

To script deletions instead of clicking through the UI, see Delete record and Delete data source in the REST reference.

Get started

Workspace setup

Governance

Observability

Offline testing

Tests

Gateway

Data quality monitoring

Administration

Notifications

Other resources

Update existing traces

Filter records by feedback

How updates work

Example: Add a ground truth

Using custom inference IDs

Comment on records

Deleting production data

Delete a single record

Delete sessions

Delete a data source

What deletion removes

​Filter records by feedback

​How updates work

​Example: Add a ground truth

​Using custom inference IDs

​Comment on records

​Deleting production data

​Delete a single record

​Delete sessions

​Delete a data source

​What deletion removes

Filter records by feedback

How updates work

Example: Add a ground truth

Using custom inference IDs

Comment on records

Deleting production data

Delete a single record

Delete sessions

Delete a data source

What deletion removes