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

# Ragas

> Evaluate LLM apps with Ragas and TokenLab

## Overview

Ragas can evaluate TokenLab-backed applications by passing an OpenAI-compatible `AsyncOpenAI` client into `llm_factory`.

<Note>
  **Type**: Evaluation framework

  **Primary Path**: OpenAI-compatible Chat Completions

  **Support Confidence**: Supported OpenAI-compatible path
</Note>

## Environment

```bash theme={null}
export TOKENLAB_API_KEY="sk-your-tokenlab-key"
```

## Example Evaluator

```python theme={null}
import os

from openai import AsyncOpenAI
from ragas.llms import llm_factory
from ragas.metrics import DiscreteMetric

client = AsyncOpenAI(
    api_key=os.environ["TOKENLAB_API_KEY"],
    base_url="https://api.tokenlab.sh/v1",
)
llm = llm_factory("claude-sonnet-5", client=client)

metric = DiscreteMetric(
    name="summary_accuracy",
    allowed_values=["accurate", "inaccurate"],
    prompt="Evaluate whether the response is accurate. Answer only accurate or inaccurate.\n\nResponse: {response}",
)
```

Use `llm` in Ragas metrics and testsets the same way you would use an OpenAI SDK-backed model.

## Endpoint Notes

Ragas uses the OpenAI SDK client path here. Native TokenLab Responses, Anthropic Messages, and Gemini routes are best used through evaluation runners that support those request shapes directly.