> ## Documentation Index
> Fetch the complete documentation index at: https://www.dataframer.ai/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# API guardrails

> Call a judge from your own code to grade content the moment it is produced, and act on the verdict.

A **guardrail** is a call to one of your judges from your own code. You send one piece of content, the judge grades it against its rubric, and the call blocks until the verdict comes back, so your system can act on it at once: hold the content back, hand it to a person, or record the score. Use a judge you have [calibrated](/docs/judges/overview) first, so its verdicts match your reviewers.

## Making a call

```python theme={null}
from dataframer import Dataframer

client = Dataframer()  # uses DATAFRAMER_API_KEY
evaluation = client.dataframer.judges.evaluations.create(
    judge_id=JUDGE_ID,
    input="Summarize the attached policy in two sentences.",
    output="The policy covers water damage from burst pipes but excludes flooding.",
)
```

Send the content as a plain `input`/`output` pair, or as a chat transcript in `messages` with the candidate `output`.

## Reading the verdict

The verdict carries one result per rubric dimension, scored from 0 to 1. It also carries the judge's confidence, its reasoning, and an aggregated overall score.

Every call is stored. In the app, open **Evaluations → Guardrail API calls** to read them back, or list them through the API.

## Next steps

<Card title="API reference" icon="code" href="/docs/api-reference/judges/evaluate">
  Full payload, file attachments, and error codes
</Card>