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

# Configure graders

Open **Graders**, select **Active graders**, and open the grader's menu. Select
**Edit** to change its project settings.

## Choose the scope

For a grader with editable scope, enable either or both sources:

* **Grades simulations**: choose **All simulations**, or **Selected test
  suites and tests**. A suite selection includes its tests in future runs.
* **Grades production**: set **Production sample** from `1` through `100`
  percent. Use `100` to select every eligible production conversation.

Production sampling selects a stable percentage of eligible traces. Small
groups of conversations can have a different percentage.

Overlapping simulation selections do not run the same project grader twice.
A grader also needs to support the conversation's modality. A trace outside
its scope or modality gets no grade from that grader.

**Expected behaviors** has fixed scope: all simulations and no production
conversations.

## Set the threshold and settings

Set **Pass threshold** between `0` and `1`. The grader passes when its score is
at least that threshold. Keep the threshold at `1` for a binary rule when you
want a failed check to fail the grader.

Some graders have extra settings. For example, **Response latency** has a
maximum response time in milliseconds. These settings control how it computes
the score; the pass threshold controls how that score becomes an individual
result.

For an LLM grader, choose **Language model** from the supported choices.
This includes **Expected behaviors**. Each project saves its own model and
settings. Saving them does not create a grader definition version.

Egma saves defaults when you first use a grader. Compatible updates to an
Egma-provided definition apply to future work and preserve your saved model,
settings, scope, and threshold. A custom clone is independent of its source.

Save your settings, then start a new run. A simulation keeps the grader selection,
definition versions, settings, and pass thresholds captured for its run.
Changes apply to future work.

Production grading saves its selection when the conversation is selected for
grading. Later policy changes do not change that work or its retries.

## Regrade a simulation

Use regrading to run the original grading work again for the same completed
simulation. This can recover from a temporary grading failure or include agent
evidence that arrived after the first grading attempt.

1. Open a completed simulation from **Runs**.
2. Select **Regrade**.
3. Confirm **Regrade simulation** and wait for grading to finish.

Regrading uses the whole original grader selection, with its frozen definitions,
settings, and thresholds. It does not call the agent again and does not apply
your latest project policy. Start a new run to test a changed agent, test,
persona, or grading policy.

Regrading reads the evidence currently stored for that conversation. If agent
spans arrived late, the new attempt can use them. Existing grades do not change
when those spans arrive.

Earlier grades remain in the history. The API returns the latest grade from
each project grader in `grades`, and all attempts in `gradeHistory`.

Each grade's `parameterValues` records the settings used for that attempt,
including its model or numeric settings. Both successful and errored grades
keep these values; they contain no credentials.

## Remove an optional grader

Open the grader's menu in **Active graders** and select **Remove grader**.
This stops selecting it for future project work. It does not remove the grades
or frozen plans from earlier simulations. Expected behaviors cannot be removed.

## Bring your own provider keys

Add your keys in [Settings → Provider API Keys](/configuration/provider-api-keys). Provider keys and model choices are separate settings. Your keys also apply to predefined personas and model graders. Code graders do not make model calls.