Types: Python

Type definitions, Protocols, and result types for Guardrails.

This module provides core types for implementing Guardrails, including:

• The GuardrailResult dataclass, representing the outcome of a guardrail check.
• The CheckFn Protocol, a callable interface for all guardrail functions.

CheckFn module-attribute

CheckFn = Callable[
    [TContext, TIn, TCfg], MaybeAwaitableResult
]

Type alias for a guardrail function.

A guardrail function accepts a context object, input data, and a configuration object, returning either a GuardrailResult or an awaitable resolving to GuardrailResult.

Parameters:

Name	Type	Description	Default
TContext	TypeVar	The context type (often includes resources used by a guardrail).	required
TIn	TypeVar	The input data to validate or check.	required
TCfg	TypeVar	The configuration type, usually a Pydantic model.	required

Returns: GuardrailResult or Awaitable[GuardrailResult]: The outcome of the guardrail check.

GuardrailLLMContextProto

Bases: Protocol

Protocol for context types providing an OpenAI client.

Classes implementing this protocol must expose an OpenAI client via the guardrail_llm attribute. For conversation-aware guardrails (like prompt injection detection), they can also access conversation_history containing the full conversation history.

Attributes:

Name	Type	Description
guardrail_llm	AsyncOpenAI | OpenAI	The OpenAI client used by the guardrail.
conversation_history	list	Full conversation history for conversation-aware guardrails.

Source code in src/guardrails/types.py

@runtime_checkable
class GuardrailLLMContextProto(Protocol):
    """Protocol for context types providing an OpenAI client.

    Classes implementing this protocol must expose an OpenAI client
    via the `guardrail_llm` attribute. For conversation-aware guardrails
    (like prompt injection detection), they can also access `conversation_history`
    containing the full conversation history.

    Attributes:
        guardrail_llm (AsyncOpenAI | OpenAI): The OpenAI client used by the guardrail.
        conversation_history (list, optional): Full conversation history for conversation-aware guardrails.
    """

    guardrail_llm: AsyncOpenAI | OpenAI | AsyncAzureOpenAI | AzureOpenAI

    def get_conversation_history(self) -> list | None:
        """Get conversation history if available, None otherwise."""
        return getattr(self, "conversation_history", None)

get_conversation_history

get_conversation_history() -> list | None

Get conversation history if available, None otherwise.

Source code in src/guardrails/types.py

def get_conversation_history(self) -> list | None:
    """Get conversation history if available, None otherwise."""
    return getattr(self, "conversation_history", None)

GuardrailResult dataclass

Result returned from a guardrail check.

This dataclass encapsulates the outcome of a guardrail function, including whether a tripwire was triggered, execution failure status, and any supplementary metadata.

Attributes:

Name	Type	Description
tripwire_triggered	bool	True if the guardrail identified a critical failure.
execution_failed	bool	True if the guardrail failed to execute properly.
original_exception	Exception | None	The original exception if execution failed.
info	dict[str, Any]	Additional structured data about the check result, such as error details, matched patterns, or diagnostic messages. Implementations may include a 'checked_text' field containing the processed/validated text when applicable. Defaults to an empty dict.

Source code in src/guardrails/types.py

@dataclass(frozen=True, slots=True)
class GuardrailResult:
    """Result returned from a guardrail check.

    This dataclass encapsulates the outcome of a guardrail function,
    including whether a tripwire was triggered, execution failure status,
    and any supplementary metadata.

    Attributes:
        tripwire_triggered (bool): True if the guardrail identified a critical failure.
        execution_failed (bool): True if the guardrail failed to execute properly.
        original_exception (Exception | None): The original exception if execution failed.
        info (dict[str, Any]): Additional structured data about the check result,
            such as error details, matched patterns, or diagnostic messages.
            Implementations may include a 'checked_text' field containing the
            processed/validated text when applicable. Defaults to an empty dict.
    """

    tripwire_triggered: bool
    execution_failed: bool = False
    original_exception: Exception | None = None
    info: dict[str, Any] = field(default_factory=dict)

    def __post_init__(self) -> None:
        """Validate required fields and consistency."""
        # Ensure consistency: if execution_failed=True, original_exception should be present
        if self.execution_failed and self.original_exception is None:
            raise ValueError("When execution_failed=True, original_exception must be provided")

__post_init__

__post_init__() -> None

Validate required fields and consistency.

Source code in src/guardrails/types.py

def __post_init__(self) -> None:
    """Validate required fields and consistency."""
    # Ensure consistency: if execution_failed=True, original_exception should be present
    if self.execution_failed and self.original_exception is None:
        raise ValueError("When execution_failed=True, original_exception must be provided")