Types

Runtime-validated wrappers used throughout the pipeline and the output type returned by every guardrail.

A machine-readable JSON Schema for GuardrailOutput (generated from these models) is published at https://raw.githubusercontent.com/mozilla-ai/any-guardrail/main/schemas/guardrail_output.schema.json. Pin a release tag in the URL for a specific version.

GuardrailOutput

Represents the output of a guardrail evaluation with runtime validation.

This is the single concrete result shape shared by every guardrail implementation, so application code can swap guardrails without changes.

Example: >>> output = GuardrailOutput(valid=True, explanation="Content is safe", score=0.05) >>> output.valid True

Field	Type	Description
valid	bool
explanation	`str	None`
score	`float	None`
categories	list[CategoryResult]
spans	`list[SpanResult]	None`
modified_text	`str	None`
action	`str	None`
usage	`GuardrailUsage	None`
extra	`dict[str, Any]	None`
raw	`Any	None`

CategoryResult

Result for one taxonomy category evaluated by a guardrail.

Categories give multi-label and taxonomy guardrails (DuoGuard, Llama Guard S-codes, Azure severities, binary classifiers' label distributions) a lossless, uniform home instead of overloading explanation.

Example: >>> CategoryResult(name="S1", description="Violent Crimes", triggered=True)

Field	Type	Description
name	str
description	`str	None`
triggered	`bool	None`
score	`float	None`
severity	`int	None`

SpanResult

A character span flagged by a guardrail.

Reserved for span-producing guardrails (PII/NER detection, span-level toxicity, token classification). Offsets are zero-based character indices into the validated text.

Field	Type	Description
start	int
end	int
text	`str	None`
label	`str	None`
score	`float	None`

GuardrailUsage

Provenance and cost envelope for one validate() call.

Field	Type	Description
model_id	`str	None`
latency_ms	`float	None`
prompt_tokens	`int	None`
completion_tokens	`int	None`

GuardrailPreprocessOutput

Wrapper for preprocessing outputs with runtime validation.

This class wraps the output of the preprocessing stage, providing runtime validation and a consistent interface across all guardrail implementations.

Type Parameters: PreprocessT: The type of the preprocessing result (e.g., tokenized input, API options, chat messages).

Example: >>> output = GuardrailPreprocessOutput(data={"input_ids": tensor, "attention_mask": tensor}) >>> output.data["input_ids"]

Field	Type	Description
data	~PreprocessT

GuardrailInferenceOutput

Wrapper for inference outputs with runtime validation.

This class wraps the output of the inference stage, providing runtime validation and a consistent interface across all guardrail implementations.

Type Parameters: InferenceT: The type of the inference result (e.g., model logits, API response, generated tokens).

Example: >>> output = GuardrailInferenceOutput(data=model_output) >>> logits = output.data["logits"]

Field	Type	Description
data	~InferenceT