> For the complete documentation index, see [llms.txt](https://docs.layerlens.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.layerlens.ai/more-in-this-section-9/scorers-2.md).

# Scorers

client.scorers — LLM-backed scorers (model + prompt). CRUD reference.

In Stratix, custom scorers are **LLM-backed**: a scorer wraps a model + prompt. Use scorers when you need a custom scoring rule that doesn't fit the built-in benchmark metrics or judge surfaces.

## Create

```python
scorer = client.scorers.create(
 name="response-rubric",
 description="Rate response on factual accuracy",
 model_id=model.id,
 prompt="Rate the response on a 1-5 scale based on factual accuracy...",
)
```

## Get

```python
scorer = client.scorers.get(scorer_id)
# Returns Scorer with: id, organization_id, project_id, name, description,
# model_id, model_name, model_key, model_company, prompt, created_at, updated_at
```

## List

```python
response = client.scorers.get_many(page=1, page_size=20)
```

## Update

```python
client.scorers.update(
 scorer_id,
 name="...", # any subset of fields
 description="...",
 model_id="...",
 prompt="...",
)
```

## Delete

```python
client.scorers.delete(scorer_id)
```

## Where scorers fit

* **Custom benchmark** evaluation — pass `custom_scorer_ids=[...]` to `client.benchmarks.create_custom(...)` to apply scorers to that benchmark.
* **Trace evaluation** — judges are typically the path; scorers are used when the rubric is reusable across many benchmarks.

## Async

All methods have `AsyncStratix` equivalents.

## See also

* [Concept: Scorers](/more-in-this-section-9/scorers-2.md)
* [Stratix Premium — Scorers](/more-in-this-section-9/scorers-2.md)
* [Judges](/more-in-this-section-9/judges-2.md) — LLM-as-a-judge for trace evaluations