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Guardrails in the AI Gateway are LLM-as-a-Judge and Python evaluators that validate requests and responses passing through the gateway. Once created, they can be attached to Guardrail Rules and Policies to block non-compliant generations before they reach the caller.

LLM Guardrail

Use a model to judge outputs against any criteria defined in a prompt.

Python Guardrail

Write custom Python code for full flexibility: regex checks, length validation, HTTP calls, or JSON schema validation.

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LLM Guardrail

LLM Guardrails use a model to judge requests or responses against criteria defined in a prompt. Navigate to Guardrails in the AI Gateway sidebar, click + Guardrail, and select LLM. Fill in the following fields:
FieldDescription
KeyUnique identifier for the guardrail
DescriptionOptional context
ModelThe model used as judge. Any model enabled in the AI Gateway is available

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Configure Prompt

The prompt has access to the following string variables:
  • {{log.input}}: the last message sent to the model
  • {{log.output}}: the output response generated by the evaluated model
  • {{log.messages}}: all messages sent to the model, excluding the last message
  • {{log.retrievals}}: Knowledge Base retrievals
  • {{log.reference}}: the reference used to compare output

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Output and Guardrail Configuration

Select the output type and set the pass condition. The Guardrail configuration panel is visible directly in the settings.
The model returns a True or False response. Use for binary pass/fail checks.Pass condition: Select True or False. The guardrail passes when the model returns the selected value.

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Testing

The Playground panel provides an Editor for testing. Fill the payload manually: 
{
  "messages": [],
  "input": "",
  "retrievals": [],
  "output": "",
  "reference": ""
}
Click Run test to execute the guardrail. The result appears in the Response field.
The Dataset tab is not available for Guardrails.

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Python Guardrail

Python Guardrails accept custom Python code for full evaluation flexibility. The UI has three panels: Settings, Code, and Playground. Navigate to Guardrails in the AI Gateway sidebar, click + Guardrail, and select Python. The evaluation function receives a log object with the following fields:
  • log["input"] <str>: the last message sent to generate the output
  • log["output"] <str>: the generated response from the model
  • log["reference"] <str>: the reference used to compare the output
  • log["messages"] list<str>: all previous messages sent to the model
  • log["retrievals"] list<str>: all Knowledge Base retrievals
The function must return a Boolean or Number: 
def evaluate(log):
    return True
Define multiple helper functions if needed. The last defined function is the entry point when the guardrail runs.

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Environment and Libraries

The Python Guardrail runs in Python 3.12 with the following preloaded libraries: 
numpy==1.26.4
nltk==3.9.1
requests
pydantic
json
re

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Guardrail Configuration

Set the pass condition based on the return type:
  • Boolean: select True or False. The guardrail passes when the function returns the selected value.
  • Number: enter a score threshold. The guardrail passes when the return value is greater than or equal to the threshold.

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Testing

The Playground panel provides an Editor for testing. Fill the payload manually and click Run test to execute the guardrail.
The Dataset tab is not available for Guardrails.

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Versions

Click Publish to save changes. Choose a version bump:
  • Patch (e.g. v1.0.0 → v1.0.1): small fixes, no behavior change
  • Minor (e.g. v1.0.0 → v1.1.0): new functionality, backwards compatible
  • Major (e.g. v1.0.0 → v2.0.0): breaking change or significant rework
The Versions tab shows the full history with author and publish timestamp for each version.

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Using Guardrails

Guardrails created here are available for selection when configuring Guardrail Rules and Policies.