> ## Documentation Index
> Fetch the complete documentation index at: https://laminar.sh/docs/llms.txt
> Use this file to discover all available pages before exploring further.
# Labeling Queues
## What is a Labeling Queue?
* A labeling queue is a collection of items that need to be labeled.
* Labeling queue is an actual queue with FIFO (first in, first out) order.
* Items in the queue have exactly the same shape as datapoints in a dataset.
* Labeling operation in this context means writing a data to the target field of a datapoint.
## How to Use the Labeling Interface
When you open a labeling queue, you'll see a split-screen interface designed for efficient labeling:
### Payload view
The left panel shows you the full JSON payload of the current item you're labeling.
Payload is a JSON object with the same shape as a datapoint in a dataset. It has `data` and `target` fields.
### Target Editor
This is where you do the actual labeling work:
* **Edit the JSON in the target editor** to correct, improve, or write new data to the target field.
* **Use proper JSON formatting** - the editor will help you with syntax highlighting
As you type in the target editor on the right, watch how the `"target"` section in the left payload updates in real-time. This helps you see exactly what will be saved to your dataset.
### Save Preferences
* **Select your target dataset** from the dropdown to choose where completed items should go
* **Click "Complete"** to save the current item to the dataset and move to the next item in the queue.
### Navigation
* **Check the item counter** ("Item 5 of 11") to see how many items you've completed and how many remain
* **Use the navigation buttons** to move through your queue:
* Click **"Skip"** if you want to pass on the current item without making changes
* Use **"Prev"** and **"Next"** to move between items (helpful for comparing similar cases)
* Click **"Complete"** when you're satisfied with your labeling
## Push items to the queue
### From Span View
You can push individual spans directly to a labeling queue for labeling.
This is particularly useful when you want to label specific model outputs for evaluation.
Span input will be added to the `data` field of the datapoint, and span output will be added to the `target` field.
### From Dataset View
You can also push existing datapoints from datasets into a labeling queue.
You can either push individual datapoint or select a subset of datapoints in the dataset view.
### Via API
You can add items programmatically with the labeling queue items endpoint.
`POST https://api.lmnr.ai/v1/labeling_queues//items`
* Auth: `Authorization: Bearer `
* Body: `items` array with required `data` and `target` (arbitrary JSON), plus optional `metadata` (JSON object)
```bash theme={null}
export QUEUE_ID=""
export LMNR_PROJECT_API_KEY=""
```
```bash theme={null}
curl --location "https://api.lmnr.ai/v1/labeling_queues/${QUEUE_ID}/items" \
--header "Authorization: Bearer ${LMNR_PROJECT_API_KEY}" \
--header 'Content-Type: application/json' \
--data-raw '{
"items": [
{
"data": {
"input": "What is the capital of France?",
"context": "Geography question"
},
"target": "Paris",
"metadata": {
"model": "gpt-5.1"
}
}
]
}'
```
When pushing items to a queue, they maintain the same JSON structure as datapoints in datasets, ensuring consistency between your labeling workflow and final datasets.
