pydantic_ai.exceptions

ModelRetry

Bases: Exception

Exception to raise when a tool function should be retried.

The agent will return the message to the model and ask it to try calling the function/tool again.

Source code in pydantic_ai_slim/pydantic_ai/exceptions.py

class ModelRetry(Exception):
    """Exception to raise when a tool function should be retried.

    The agent will return the message to the model and ask it to try calling the function/tool again.
    """

    message: str
    """The message to return to the model."""

    def __init__(self, message: str):
        self.message = message
        super().__init__(message)

    def __eq__(self, other: Any) -> bool:
        return isinstance(other, self.__class__) and other.message == self.message

    def __hash__(self) -> int:
        return hash((self.__class__, self.message))

    @classmethod
    def __get_pydantic_core_schema__(cls, _: Any, __: Any) -> core_schema.CoreSchema:
        """Pydantic core schema to allow `ModelRetry` to be (de)serialized."""
        schema = core_schema.typed_dict_schema(
            {
                'message': core_schema.typed_dict_field(core_schema.str_schema()),
                'kind': core_schema.typed_dict_field(core_schema.literal_schema(['model-retry'])),
            }
        )
        return core_schema.no_info_after_validator_function(
            lambda dct: ModelRetry(dct['message']),
            schema,
            serialization=core_schema.plain_serializer_function_ser_schema(
                lambda x: {'message': x.message, 'kind': 'model-retry'},
                return_schema=schema,
            ),
        )

message instance-attribute

message: str = message

The message to return to the model.

__get_pydantic_core_schema__ classmethod

__get_pydantic_core_schema__(_: Any, __: Any) -> CoreSchema

Pydantic core schema to allow ModelRetry to be (de)serialized.

Source code in pydantic_ai_slim/pydantic_ai/exceptions.py

@classmethod
def __get_pydantic_core_schema__(cls, _: Any, __: Any) -> core_schema.CoreSchema:
    """Pydantic core schema to allow `ModelRetry` to be (de)serialized."""
    schema = core_schema.typed_dict_schema(
        {
            'message': core_schema.typed_dict_field(core_schema.str_schema()),
            'kind': core_schema.typed_dict_field(core_schema.literal_schema(['model-retry'])),
        }
    )
    return core_schema.no_info_after_validator_function(
        lambda dct: ModelRetry(dct['message']),
        schema,
        serialization=core_schema.plain_serializer_function_ser_schema(
            lambda x: {'message': x.message, 'kind': 'model-retry'},
            return_schema=schema,
        ),
    )

CallDeferred

Bases: Exception

Exception to raise when a tool call should be deferred.

See tools docs for more information.

Parameters:

Name	Type	Description	Default
metadata	dict[str, Any] | None	Optional dictionary of metadata to attach to the deferred tool call. This metadata will be available in DeferredToolRequests.metadata keyed by tool_call_id.	None

Source code in pydantic_ai_slim/pydantic_ai/exceptions.py

class CallDeferred(Exception):
    """Exception to raise when a tool call should be deferred.

    See [tools docs](../deferred-tools.md#deferred-tools) for more information.

    Args:
        metadata: Optional dictionary of metadata to attach to the deferred tool call.
            This metadata will be available in `DeferredToolRequests.metadata` keyed by `tool_call_id`.
    """

    def __init__(self, metadata: dict[str, Any] | None = None):
        self.metadata = metadata
        super().__init__()

    def __reduce__(self) -> tuple[type, tuple[Any, ...]]:
        return self.__class__, (self.metadata,)

ApprovalRequired

Bases: Exception

Exception to raise when a tool call requires human-in-the-loop approval.

See tools docs for more information.

Parameters:

Name	Type	Description	Default
metadata	dict[str, Any] | None	Optional dictionary of metadata to attach to the deferred tool call. This metadata will be available in DeferredToolRequests.metadata keyed by tool_call_id.	None

Source code in pydantic_ai_slim/pydantic_ai/exceptions.py

class ApprovalRequired(Exception):
    """Exception to raise when a tool call requires human-in-the-loop approval.

    See [tools docs](../deferred-tools.md#human-in-the-loop-tool-approval) for more information.

    Args:
        metadata: Optional dictionary of metadata to attach to the deferred tool call.
            This metadata will be available in `DeferredToolRequests.metadata` keyed by `tool_call_id`.
    """

    def __init__(self, metadata: dict[str, Any] | None = None):
        self.metadata = metadata
        super().__init__()

    def __reduce__(self) -> tuple[type, tuple[Any, ...]]:
        return self.__class__, (self.metadata,)

SkipModelRequest

Bases: Exception

Exception to raise in before/wrap model request hooks to skip the model call.

The provided response will be used instead of calling the model.

Note: when raised in before_model_request, any message history modifications made by earlier capabilities in that hook will not be persisted to the agent's message history, since the request preparation is aborted.

Source code in pydantic_ai_slim/pydantic_ai/exceptions.py

class SkipModelRequest(Exception):
    """Exception to raise in before/wrap model request hooks to skip the model call.

    The provided response will be used instead of calling the model.

    Note: when raised in `before_model_request`, any message history modifications
    made by earlier capabilities in that hook will not be persisted to the agent's
    message history, since the request preparation is aborted.
    """

    response: ModelResponse

    def __init__(self, response: ModelResponse):
        self.response = response
        super().__init__()

SkipToolValidation

Bases: Exception

Exception to raise in before/wrap tool validate hooks to skip validation.

The provided args will be used as the validated arguments.

Source code in pydantic_ai_slim/pydantic_ai/exceptions.py

class SkipToolValidation(Exception):
    """Exception to raise in before/wrap tool validate hooks to skip validation.

    The provided args will be used as the validated arguments.
    """

    validated_args: dict[str, Any]

    def __init__(self, validated_args: dict[str, Any]):
        self.validated_args = validated_args
        super().__init__()

SkipToolExecution

Bases: Exception

Exception to raise in before/wrap tool execute hooks to skip execution.

The provided result will be used as the tool result.

Source code in pydantic_ai_slim/pydantic_ai/exceptions.py

class SkipToolExecution(Exception):
    """Exception to raise in before/wrap tool execute hooks to skip execution.

    The provided result will be used as the tool result.
    """

    result: Any

    def __init__(self, result: Any):
        self.result = result
        super().__init__()

UserError

Bases: RuntimeError

Error caused by a usage mistake by the application developer — You!

Source code in pydantic_ai_slim/pydantic_ai/exceptions.py

class UserError(RuntimeError):
    """Error caused by a usage mistake by the application developer — You!"""

    message: str
    """Description of the mistake."""

    def __init__(self, message: str):
        self.message = message
        super().__init__(message)

message instance-attribute

message: str = message

Description of the mistake.

AgentRunError

Bases: RuntimeError

Base class for errors occurring during an agent run.

Source code in pydantic_ai_slim/pydantic_ai/exceptions.py

class AgentRunError(RuntimeError):
    """Base class for errors occurring during an agent run."""

    message: str
    """The error message."""

    def __init__(self, message: str):
        self.message = message
        super().__init__(message)

    def __str__(self) -> str:
        return self.message

message instance-attribute

message: str = message

The error message.

UsageLimitExceeded

Bases: AgentRunError

Error raised when a Model's usage exceeds the specified limits.

Source code in pydantic_ai_slim/pydantic_ai/exceptions.py

class UsageLimitExceeded(AgentRunError):
    """Error raised when a Model's usage exceeds the specified limits."""

ConcurrencyLimitExceeded

Bases: AgentRunError

Error raised when the concurrency queue depth exceeds max_queued.

Source code in pydantic_ai_slim/pydantic_ai/exceptions.py

class ConcurrencyLimitExceeded(AgentRunError):
    """Error raised when the concurrency queue depth exceeds max_queued."""

UnexpectedModelBehavior

Bases: AgentRunError

Error caused by unexpected Model behavior, e.g. an unexpected response code.

Source code in pydantic_ai_slim/pydantic_ai/exceptions.py

class UnexpectedModelBehavior(AgentRunError):
    """Error caused by unexpected Model behavior, e.g. an unexpected response code."""

    message: str
    """Description of the unexpected behavior."""
    body: str | None
    """The body of the response, if available."""

    def __init__(self, message: str, body: str | None = None):
        self.message = message
        if body is None:
            self.body: str | None = None
        else:
            try:
                self.body = json.dumps(json.loads(body), indent=2)
            except ValueError:
                self.body = body
        super().__init__(message)

    def __reduce__(self) -> tuple[type, tuple[Any, ...]]:
        return self.__class__, (self.message, self.body)

    def __str__(self) -> str:
        if self.body:
            return f'{self.message}, body:\n{self.body}'
        else:
            return self.message

message instance-attribute

message: str = message

Description of the unexpected behavior.

body instance-attribute

body: str | None = dumps(loads(body), indent=2)

The body of the response, if available.

ContentFilterError

Bases: UnexpectedModelBehavior

Raised when content filtering is triggered by the model provider resulting in an empty response.

Source code in pydantic_ai_slim/pydantic_ai/exceptions.py

class ContentFilterError(UnexpectedModelBehavior):
    """Raised when content filtering is triggered by the model provider resulting in an empty response."""

ModelAPIError

Bases: AgentRunError

Raised when a model provider API request fails.

Source code in pydantic_ai_slim/pydantic_ai/exceptions.py

class ModelAPIError(AgentRunError):
    """Raised when a model provider API request fails."""

    model_name: str
    """The name of the model associated with the error."""

    def __init__(self, model_name: str, message: str):
        self.model_name = model_name
        super().__init__(message)

    def __reduce__(self) -> tuple[type, tuple[Any, ...]]:
        return self.__class__, (self.model_name, self.message)

model_name instance-attribute

model_name: str = model_name

The name of the model associated with the error.

ModelHTTPError

Bases: ModelAPIError

Raised when an model provider response has a status code of 4xx or 5xx.

Source code in pydantic_ai_slim/pydantic_ai/exceptions.py

class ModelHTTPError(ModelAPIError):
    """Raised when an model provider response has a status code of 4xx or 5xx."""

    status_code: int
    """The HTTP status code returned by the API."""

    body: object | None
    """The body of the response, if available."""

    def __init__(self, status_code: int, model_name: str, body: object | None = None):
        self.status_code = status_code
        self.body = body
        message = f'status_code: {status_code}, model_name: {model_name}, body: {body}'
        super().__init__(model_name=model_name, message=message)

    def __reduce__(self) -> tuple[type, tuple[Any, ...]]:
        return self.__class__, (self.status_code, self.model_name, self.body)

status_code instance-attribute

status_code: int = status_code

The HTTP status code returned by the API.

body instance-attribute

body: object | None = body

The body of the response, if available.

FallbackExceptionGroup

Bases: ExceptionGroup[Any]

A group of exceptions that can be raised when all fallback models fail.

Source code in pydantic_ai_slim/pydantic_ai/exceptions.py

class FallbackExceptionGroup(ExceptionGroup[Any]):
    """A group of exceptions that can be raised when all fallback models fail."""

ToolRetryError

Bases: Exception

Exception used to signal a ToolRetry message should be returned to the LLM.

Source code in pydantic_ai_slim/pydantic_ai/exceptions.py

class ToolRetryError(Exception):
    """Exception used to signal a `ToolRetry` message should be returned to the LLM."""

    def __init__(self, tool_retry: RetryPromptPart):
        self.tool_retry = tool_retry
        message = (
            tool_retry.content
            if isinstance(tool_retry.content, str)
            else self._format_error_details(tool_retry.content, tool_retry.tool_name)
        )
        super().__init__(message)

    def __reduce__(self) -> tuple[type, tuple[Any, ...]]:
        return self.__class__, (self.tool_retry,)

    @staticmethod
    def _format_error_details(errors: list[pydantic_core.ErrorDetails], tool_name: str | None) -> str:
        """Format ErrorDetails as a human-readable message.

        We format manually rather than using ValidationError.from_exception_data because
        some error types (value_error, assertion_error, etc.) require an 'error' key in ctx,
        but when ErrorDetails are serialized, exception objects are stripped from ctx.
        The 'msg' field already contains the human-readable message, so we use that directly.
        """
        error_count = len(errors)
        lines = [
            f'{error_count} validation error{"" if error_count == 1 else "s"}{f" for {tool_name!r}" if tool_name else ""}'
        ]
        for e in errors:
            loc = '.'.join(str(x) for x in e['loc']) if e['loc'] else '__root__'
            lines.append(loc)
            lines.append(f'  {e["msg"]} [type={e["type"]}, input_value={e["input"]!r}]')
        return '\n'.join(lines)

IncompleteToolCall

Bases: UnexpectedModelBehavior

Error raised when a model stops due to token limit while emitting a tool call.

Source code in pydantic_ai_slim/pydantic_ai/exceptions.py

class IncompleteToolCall(UnexpectedModelBehavior):
    """Error raised when a model stops due to token limit while emitting a tool call."""