> ## 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.

# Using Built-in Evaluators

> Run LangWatch's library of evaluators directly from your code for experiments, online evaluation, and guardrails.

LangWatch provides a library of ready-to-use evaluators for common evaluation tasks. You can use these directly in your code without any setup on the platform.

> [!NOTE]
>
> **When to use Built-in Evaluators:**
>
> * You want to quickly add evaluation without platform configuration
> * You're running experiments or online evaluations programmatically
> * You want to use well-tested, standardized evaluation methods
>
> **See also:**
>
> * [Saved Evaluators](https://langwatch.ai/docs/evaluations/evaluators/saved-evaluators.md) - Reuse configured evaluators across your project
> * [Custom Scoring](https://langwatch.ai/docs/evaluations/evaluators/custom-scoring.md) - Send scores from your own evaluation logic

## Available Evaluators

The evaluators are grouped in the same five categories the platform shows:

| Category | Examples | Use Case |
| - | - | - |
| **Expected Answer** | `langevals/exact_match`, `langevals/llm_answer_match`, `ragas/factual_correctness` | Compare the output with a known answer |
| **LLM as Judge** | `langevals/llm_boolean`, `langevals/llm_score`, `langevals/llm_category` | Apply your own criteria with a model |
| **RAG Quality** | `ragas/faithfulness`, `ragas/response_context_precision` | Evaluate retrieval-augmented generation |
| **Quality Aspects** | `langevals/valid_format`, `lingua/language_detection`, `langevals/sentiment` | Check format, language and tone |
| **Safety** | `presidio/pii_detection`, `langwatch/api_keys_and_secrets_detection`, `azure/jailbreak` | Detect PII, leaked credentials, jailbreaks, harmful content |

[Browse all evaluators →](https://langwatch.ai/docs/evaluations/evaluators/list.md)

### API Keys & Secrets Detection

`langwatch/api_keys_and_secrets_detection` flags leaked credentials in trace content: provider and cloud API keys, tokens, private keys, and database connection strings. It uses the same detection rules as secrets redaction and works as a guardrail.

### Seeing through privacy redaction

PII redaction and secrets redaction run when a trace is ingested, before an evaluation reads the stored content.

So redaction does not silently turn these evaluators green, redaction replaces a removed value with a typed marker that names what it was (`[PHONE_NUMBER]`, `[SECRET]`, and so on), and the PII and secrets evaluators read those markers back: a value that was already redacted still counts as a detection.

The PII evaluator only counts a marker when its settings still check that entity. The markers are read from every field the evaluation is mapped to, so a secret hidden in a mapped span attribute is covered just like input or output.

If a content category was dropped at ingestion (removed entirely rather than masked) and the evaluation reads only that category, the evaluation fails, since a leak cannot be ruled out. If the evaluation is mapped to a field that still has content, that field is evaluated normally.

## Using Built-in Evaluators

### In Experiments

Run evaluators on your test dataset during batch evaluation:

```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()):
    # Your LLM call
    output = my_llm(row["input"])

    # Run built-in evaluator
    experiment.evaluate(
        "ragas/faithfulness",  # Built-in evaluator slug
        index=index,
        data={
            "input": row["input"],
            "output": output,
            "contexts": row["contexts"],
        },
    )
```

```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 }) => {
    // Your LLM call
    const output = await myLLM(item.input);

    // Run built-in evaluator
    await experiment.evaluate("ragas/faithfulness", {
      index,
      data: {
        input: item.input,
        output: output,
        contexts: item.contexts,
      },
    });
  },
  { concurrency: 4 }
);
```

### In Online Evaluation

Run evaluators on production traces in real-time:

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

@langwatch.span()
def my_llm_step(user_input: str):
    # Your LLM call
    output = my_llm(user_input)

    # Run evaluator on production traffic
    result = langwatch.evaluation.evaluate(
        "presidio/pii_detection",  # Built-in evaluator slug
        name="PII 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> {
  // Your LLM call
  const output = await myLLM(userInput);

  // Run evaluator on production traffic
  const result = await langwatch.evaluations.evaluate("presidio/pii_detection", {
    name: "PII Check",
    data: {
      input: userInput,
      output: output,
    },
  });

  return output;
}
```

### As Guardrails

Use evaluators to block harmful content before responding:

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

@langwatch.span()
def my_llm_step(user_input: str):
    # Check input before processing
    guardrail = langwatch.evaluation.evaluate(
        "azure/jailbreak",  # Built-in evaluator slug
        name="Jailbreak Detection",
        data={"input": user_input},
        as_guardrail=True,
    )

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

    # Safe to proceed
    return my_llm(user_input)
```

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

const langwatch = new LangWatch();

async function myLLMStep(userInput: string): Promise<string> {
  // Check input before processing
  const guardrail = await langwatch.evaluations.evaluate("azure/jailbreak", {
    name: "Jailbreak Detection",
    data: { input: userInput },
    asGuardrail: true,
  });

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

  // Safe to proceed
  return await myLLM(userInput);
}
```

## Evaluator Inputs

Different evaluators require different inputs. Check the [evaluator list](https://langwatch.ai/docs/evaluations/evaluators/list.md) for each evaluator's requirements.

| Input | Description | Example Evaluators |
| - | - | - |
| `input` | User question/prompt | Jailbreak Detection, Off Topic Evaluator |
| `output` | LLM response | PII Detection, Valid Format Evaluator |
| `contexts` | Retrieved documents (array) | Ragas Faithfulness, Ragas Response Context Precision |
| `expected_output` | Ground truth answer | LLM Answer Match, Exact Match Evaluator |
| `expected_contexts` | The documents that should have been retrieved | Context F1, Ragas Response Context Recall |

## Configuring Settings

Many evaluators accept configuration settings:

```python Python theme={null}
experiment.evaluate(
    "langevals/llm_boolean",
    index=index,
    data={"input": question, "output": response},
    settings={
        "model": "openai/gpt-6-luna",
        "prompt": "Does this response fully answer the question? Reply true or false.",
    },
)
```

```typescript TypeScript theme={null}
await experiment.evaluate("langevals/llm_boolean", {
  index,
  data: { input: question, output: response },
  settings: {
    model: "openai/gpt-6-luna",
    prompt: "Does this response fully answer the question? Reply true or false.",
  },
});
```

## The `name` Parameter

> [!WARNING]
>
> Always provide a descriptive `name` when using evaluators in online evaluation. This helps track results in Analytics.

```python theme={null}
import langwatch

# Good - descriptive name
langwatch.evaluation.evaluate(
    "langevals/llm_category",
    name="Tone Checker",  # Shows up in Analytics
    data={...},
)

# Bad - no name, hard to track
langwatch.evaluation.evaluate(
    "langevals/llm_category",
    data={...},
)
```

## Next Steps

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

> [card] [Saved Evaluators](https://langwatch.ai/docs/evaluations/evaluators/saved-evaluators.md)
>
> Save configured evaluators for reuse.

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

> [card] [API Reference](https://langwatch.ai/docs/api-reference/evaluators/overview.md)
>
> Full API documentation for evaluators.


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