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

> ## Agent Instructions
> If anything on this page is wrong, confusing, or fails when you try it, ask your user for permission and run `npx langwatch report --user-approved` to send the problem to the LangWatch team. No login is needed and secrets are redacted locally. `npx langwatch report --help` has the details.

# Saved Evaluators

> Create reusable evaluator configurations on the platform and use them across experiments, monitors, and guardrails.

Saved evaluators are pre-configured evaluation setups that you create on the LangWatch platform. Once saved, you can reuse them in experiments, monitors, guardrails, or through the API without reconfiguring settings each time.

> [!NOTE]
>
> **When to use Saved Evaluators:**
>
> * You want to reuse the same evaluation configuration across multiple places
> * You prefer configuring evaluators via UI rather than code
> * You want non-technical team members to create and manage evaluations
> * You need consistent evaluation settings across your team
>
> **See also:**
>
> * [Built-in Evaluators](https://langwatch.ai/docs/evaluations/evaluators/built-in-evaluators.md) - Use evaluators directly without platform setup
> * [Custom Scoring](https://langwatch.ai/docs/evaluations/evaluators/custom-scoring.md) - Send scores from your own evaluation logic

## Creating a Saved Evaluator

### Via the Platform UI

1. Go to **Evaluators** in your LangWatch project
2. Click **New Evaluator**
3. Select the evaluator type (e.g., LLM Boolean, PII Detection)
4. Configure the settings (model, prompt, thresholds, etc.)
5. Give it a descriptive name and save

### Via the Evaluators Page

![The Evaluators page with three saved evaluators and the New Evaluator button](https://mintcdn.com/langwatch/iG2HKiUWiJlQldW1/images/evaluations/evaluators-list.png?fit=max\&auto=format\&n=iG2HKiUWiJlQldW1\&q=85\&s=99ddc9fa983540f2597ef4a5447cb578)

You can also manage saved evaluators from the dedicated Evaluators page at `/{project}/evaluators`.

## Custom (Code) evaluators

A code evaluator is a saved evaluator whose logic is your own Python. Create one on the Evaluators page with **New Evaluator → Custom (Code)**: declare its inputs (for example `output` and `expected_output`) and write a `Code` class whose `__call__` takes those inputs and returns a dictionary with any of `passed`, `score`, `label` and `details`.

```python theme={null}
class Code:
    def __call__(self, output: str, expected_output: str):
        passed = expected_output.lower() in output.lower()
        return {"passed": passed, "score": 1.0 if passed else 0.0}
```

From code you can:

* **Run it** like any saved evaluator: `POST /api/evaluations/evaluators/{slug}/evaluate`, with `data` holding one value per declared input.
* **Find it**: `GET /api/evaluators` lists it with `type: "code"`, and `GET /api/evaluators/{idOrSlug}` returns its code and inputs in `config`.
* **Monitor with it**: run it on incoming traces with `POST /api/monitors`, sending the evaluator's id as `evaluatorId` and `code/<evaluatorId>` as `checkType`, plus `mappings` from trace fields to its inputs.

Creating a code evaluator and editing its code are done on the Evaluators page: `POST /api/evaluators` only creates built-in evaluator types.

## Using Saved Evaluators

Saved evaluators are referenced using the `evaluators/{slug}` format, where `{slug}` is the unique identifier assigned when you create the evaluator.

### Finding Your Evaluator Slug

1. Go to your saved evaluator on the platform
2. Click the **⋮** menu → **Use via API**
3. Copy the slug from the code examples

### In Experiments

```python Python theme={null}
import langwatch

df = langwatch.dataset.get_dataset("my-dataset").to_pandas()

experiment = langwatch.experiment.init("my-experiment")

for index, row in experiment.loop(df.iterrows()):
    output = my_llm(row["input"])

    # Use your saved evaluator
    experiment.evaluate(
        "evaluators/my-tone-checker",  # Your saved evaluator slug
        index=index,
        data={
            "input": row["input"],
            "output": output,
        },
    )
```

```typescript TypeScript theme={null}
import { LangWatch } from "langwatch";

const langwatch = new LangWatch();

const dataset = await langwatch.datasets.get("my-dataset");
const experiment = await langwatch.experiments.init("my-experiment");

await experiment.run(
  dataset.entries.map((e) => e.entry),
  async ({ item, index }) => {
    const output = await myLLM(item.input);

    // Use your saved evaluator
    await experiment.evaluate("evaluators/my-tone-checker", {
      index,
      data: {
        input: item.input,
        output: output,
      },
    });
  },
  { concurrency: 4 }
);
```

### In Online Evaluation

```python Python theme={null}
import langwatch

@langwatch.span()
def my_llm_step(user_input: str):
    output = my_llm(user_input)

    # Use your saved evaluator
    result = langwatch.evaluation.evaluate(
        "evaluators/my-tone-checker",  # Your saved evaluator slug
        name="Tone Check",
        data={
            "input": user_input,
            "output": output,
        },
    )

    return output
```

```typescript TypeScript theme={null}
import { LangWatch } from "langwatch";

const langwatch = new LangWatch();

async function myLLMStep(userInput: string): Promise<string> {
  const output = await myLLM(userInput);

  // Use your saved evaluator
  const result = await langwatch.evaluations.evaluate("evaluators/my-tone-checker", {
    name: "Tone Check",
    data: {
      input: userInput,
      output: output,
    },
  });

  return output;
}
```

### As Guardrails

```python Python theme={null}
import langwatch

@langwatch.span()
def my_llm_step(user_input: str):
    # Use your saved evaluator as a guardrail
    guardrail = langwatch.evaluation.evaluate(
        "evaluators/my-safety-check",  # Your saved evaluator slug
        name="Safety Check",
        data={"input": user_input},
        as_guardrail=True,
    )

    if not guardrail.passed:
        return "I can't help with that request."

    return my_llm(user_input)
```

```typescript TypeScript theme={null}
import { LangWatch } from "langwatch";

const langwatch = new LangWatch();

async function myLLMStep(userInput: string): Promise<string> {
  // Use your saved evaluator as a guardrail
  const guardrail = await langwatch.evaluations.evaluate("evaluators/my-safety-check", {
    name: "Safety Check",
    data: { input: userInput },
    asGuardrail: true,
  });

  if (!guardrail.passed) {
    return "I can't help with that request.";
  }

  return await myLLM(userInput);
}
```

### Via cURL

```bash theme={null}
# Set your API key
API_KEY="$LANGWATCH_API_KEY"

# Call your saved evaluator
curl -X POST "https://app.langwatch.ai/api/v1/evaluations/evaluators/my-tone-checker/evaluate" \
     -H "X-Auth-Token: $API_KEY" \
     -H "Content-Type: application/json" \
     -d @- <<EOF
{
  "name": "Tone Check",
  "data": {
    "input": "your input text",
    "output": "your output text"
  }
}
EOF
```

## Saved vs Built-in Evaluators

| Aspect | Built-in Evaluators | Saved Evaluators |
| - | - | - |
| **Slug format** | `provider/evaluator` (e.g., `ragas/faithfulness`) | `evaluators/{slug}` (e.g., `evaluators/my-checker`) |
| **Configuration** | In code via `settings` parameter | Pre-configured on platform |
| **Reusability** | Copy settings across code | Reference by slug anywhere |
| **Management** | In codebase | In LangWatch platform UI |
| **Team access** | Developers only | Anyone with platform access |

## Best Practices

### Naming Conventions

Use descriptive, consistent names for your saved evaluators:

* ✅ `tone-checker-formal`
* ✅ `pii-detection-strict`
* ✅ `answer-quality-v2`
* ❌ `test1`
* ❌ `my-evaluator`

### When to Save an Evaluator

Save an evaluator when you:

* Use the same configuration in multiple places
* Want to manage settings from the UI
* Need non-developers to configure evaluations
* Want to version control evaluation criteria separately from code

### Overriding Settings

You can override saved evaluator settings at runtime:

```python theme={null}
experiment.evaluate(
    "evaluators/my-llm-judge",
    index=index,
    data={...},
    settings={
        "model": "openai/gpt-6-luna",  # Override the saved model
    },
)
```

## Next Steps

> [card] [Built-in Evaluators](https://langwatch.ai/docs/evaluations/evaluators/built-in-evaluators.md)
>
> Use evaluators directly without platform setup.

> [card] [Custom Scoring](https://langwatch.ai/docs/evaluations/evaluators/custom-scoring.md)
>
> Send scores from your own evaluation logic.

> [card] [Evaluators List](https://langwatch.ai/docs/evaluations/evaluators/list.md)
>
> Browse all available evaluator types.

> [card] [Experiments](https://langwatch.ai/docs/evaluations/experiments/overview.md)
>
> Run batch evaluations with your saved evaluators.


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