Export annotations
Download a queue's results as a file, or write them into a Future AGI dataset
Support quality review, the queue from Create a queue, now has completed items worth keeping. This guide walks through both routes, starting with the quick download and ending with a write into a dataset.
Download as JSON or CSV
Open Support quality review and click Export > Download in the header. Every item in the queue, any status, downloads immediately as a JSON file: one entry per item, carrying its annotations, review status, and the source’s own content resolved onto it.
Tip
The endpoint behind Download also accepts a CSV format, which flattens item, review, and annotation fields to one row per label value. It drops source, evals, source_id, item_notes, and annotation_metrics. There’s no format picker in the UI for it yet, so pull CSV directly through the API if you need rows instead of nested JSON; see SDK & API.
Note
Download tops out at 1,000 items. Push past that and it returns an error instead of a file, since there’s no status filter on this button to narrow the set first. For a queue that big, use Export to Dataset instead, which carries no such cap, or filter by status through the API. Self-hosted deployments can raise the ceiling with the ANNOTATION_EXPORT_SYNC_MAX Django setting.
What’s in an export
| Field | What it holds |
|---|---|
item_id | The queue item’s ID |
source_type | trace, observation_span, trace_session, prototype_run, call_execution, or dataset_row |
source_id | ID of the annotated source |
status | pending, in_progress, completed, or skipped |
order | The item’s position in the queue |
review | Review status and reviewer, filled in once the item’s been reviewed |
item_notes | The latest note left on the item |
annotations | Every label value submitted, with the annotator and score source |
annotation_metrics | The item’s annotations keyed by label name |
evals | Eval scores already attached to the item’s source, if any |
source | The resolved content of the source itself |
Export to Dataset
Click Export > Export to Dataset in the header to open the export drawer.
- Choose Create new dataset and name it, or Add to existing dataset and search for one
- Set Items to export: it defaults to Completed only, and can widen to All items, or switch to Pending only or In Progress only
- Review the column mapping: each source field, label, and review detail maps to a dataset column, and you can rename, add, or drop columns before running
- Click Export
Unlike Download, Export to Dataset has no item cap, so a queue past 1,000 items still exports in full.
What you do with it
- Fine-tuning: the annotated examples become training data for a model update
- Eval datasets: completed items become a golden set you run other evals against
Dive deeper
Questions & Discussion