> ## Documentation Index
> Fetch the complete documentation index at: https://arizeai-433a7140.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Guardrails AI Tracing

> Instrument LLM applications that use the Guardrails AI framework

<Frame>
  <iframe src="https://cdn.iframe.ly/kZuhMOt" className="w-full h-full aspect-video" />
</Frame>

In this example we will instrument a small program that uses the [Guardrails AI](https://www.guardrailsai.com/) framework to protect their LLM calls.

## Install

```bash theme={null}
pip install openinference-instrumentation-guardrails guardrails-ai
```

## Setup

Connect to your Phoenix instance using the register function.

```python theme={null}
from phoenix.otel import register

# configure the Phoenix tracer
tracer_provider = register(
  project_name="my-llm-app", # Default is 'default'
  auto_instrument=True # Auto-instrument your app based on installed OI dependencies
)
```

## Run Guardrails

From here, you can run Guardrails as normal:

```python theme={null}
from guardrails import Guard
from guardrails.hub import TwoWords
import openai

guard = Guard().use(
    TwoWords(),
)
response = guard(
    llm_api=openai.chat.completions.create,
    prompt="What is another name for America?",
    model="gpt-3.5-turbo",
    max_tokens=1024,
)

print(response)
```

## Observe

Now that you have tracing setup, all invocations of underlying models used by Guardrails (completions, chat completions, embeddings) will be streamed to your running Phoenix for observability and evaluation. Additionally, Guards will be present as a new span kind in Phoenix.

## Resources

* [Example notebook](https://github.com/Arize-ai/dataset-embeddings-guardrails/blob/main/validator/arize_demo_dataset_embeddings_guard.ipynb)

* [OpenInference package](https://github.com/Arize-ai/openinference/blob/main/python/instrumentation/openinference-instrumentation-guardrails)