guardrails

---

News and Updates

• [Feb 12, 2025] We just launched Guardrails Index -- the first of its kind benchmark comparing the performance and latency of 24 guardrails across 6 most common categories! Check out the index at index.guardrailsai.com

What is Guardrails?

Guardrails is a Python framework that helps build reliable AI applications by performing two key functions:

1. Guardrails runs Input/Output Guards in your application that detect, quantify and mitigate the presence of specific types of risks. To look at the full suite of risks, check out Guardrails Hub.
2. Guardrails help you generate structured data from LLMs.

Guardrails Hub

Guardrails Hub is a collection of pre-built measures of specific types of risks (called 'validators'). Multiple validators can be combined together into Input and Output Guards that intercept the inputs and outputs of LLMs. Visit Guardrails Hub to see the full list of validators and their documentation.

Installation

Getting Started

Create Input and Output Guards for LLM Validation

1. Download and configure the Guardrails Hub CLI.

2. Install a guardrail from Guardrails Hub.

3. Create a Guard from the installed guardrail.

   Output:

4. Run multiple guardrails within a Guard. First, install the necessary guardrails from Guardrails Hub.

   Then, create a Guard from the installed guardrails.

   Output:

Use Guardrails to generate structured data from LLMs

Let's go through an example where we ask an LLM to generate fake pet names. To do this, we'll create a Pydantic BaseModel that represents the structure of the output we want.

Now, create a Guard from the

1. Function calling: For LLMs that support function calling, we generate structured data using the function call syntax.
2. Prompt optimization: For LLMs that don't support function calling, we add the schema of the expected output to the prompt so that the LLM can generate structured data.

This prints:

{
    "pet_type": "dog",
    "name": "Buddy
}

Guardrails Server

Guardrails can be set up as a standalone service served by Flask with

1. Install:
   pip install "guardrails-ai"
2. Configure:
   guardrails configure
3. Create a config:
   guardrails create --validators=hub://guardrails/two_words --guard-name=two-word-guard
4. Start the dev server:
   guardrails start --config=./config.py
5. Interact with the dev server via the snippets below

# with the guardrails client
import guardrails as gr

gr.settings.use_server = True
guard = gr.Guard(name='two-word-guard')
guard.validate('this is more than two words')

# or with the openai sdk
import openai
openai.base_url = "http://localhost:8000/guards/two-word-guard/openai/v1/"
os.environ["OPENAI_API_KEY"] = "youropenaikey"

messages = [
        {
            "role": "user",
            "content": "tell me about an apple with 3 words exactly",
        },
    ]

completion = openai.chat.completions.create(
    model="gpt-4o-mini",
    messages=messages,
)

For production deployments, we recommend using Docker with Gunicorn as the WSGI server for improved performance and scalability.

FAQ

I'm running into issues with Guardrails. Where can I get help?

You can reach out to us on Discord or Twitter.

Can I use Guardrails with any LLM?

Yes, Guardrails can be used with proprietary and open-source LLMs. Check out this guide on how to use Guardrails with any LLM.

Can I create my own validators?

Yes, you can create your own validators and contribute them to Guardrails Hub. Check out this guide on how to create your own validators.

Does Guardrails support other languages?

Guardrails can be used with Python and JavaScript. Check out the docs on how to use Guardrails from JavaScript. We are working on adding support for other languages. If you would like to contribute to Guardrails, please reach out to us on Discord or Twitter.

Contributing

We welcome contributions to Guardrails!

Get started by checking out Github issues and check out the Contributing Guide. Feel free to open an issue, or reach out if you would like to add to the project!