> ## 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.

# Guardrails

> Validate TokenLab model outputs with Guardrails

## Overview

Guardrails can wrap any callable compatible with its LLM API interface. For TokenLab, configure the OpenAI SDK with TokenLab's base URL and pass the chat completions callable to your guard.

<Note>
  **Type**: Validation and structured-output 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

```python theme={null}
import os

from guardrails import Guard
from openai import OpenAI
from pydantic import BaseModel, Field


class Pet(BaseModel):
    pet_type: str = Field(description="Species of pet")
    name: str = Field(description="A unique pet name")


guard = Guard.for_pydantic(output_class=Pet)

client = OpenAI(
    api_key=os.environ["TOKENLAB_API_KEY"],
    base_url="https://api.tokenlab.sh/v1",
)

raw_output, validated_output, *rest = guard(
    llm_api=client.chat.completions.create,
    model="claude-sonnet-5",
    messages=[{"role": "user", "content": "What kind of pet should I get?"}],
)
```

## Endpoint Notes

Guardrails focuses on validation around the LLM call. Use the OpenAI-compatible path for chat-completions flows, or pass a custom callable if your application needs a native TokenLab endpoint.