pydantic_ai.output

OutputDataT module-attribute

OutputDataT = TypeVar( "OutputDataT", default=str, covariant=True ) 

Covariant type variable for the output data type of a run.

ToolOutput dataclass

Bases: Generic[OutputDataT]

Marker class to use a tool for output and optionally customize the tool.

Example:

tool_output.py

from pydantic import BaseModel from pydantic_ai import Agent, ToolOutput class Fruit(BaseModel): name: str color: str class Vehicle(BaseModel): name: str wheels: int agent = Agent( 'openai:gpt-4o', output_type=[ ToolOutput(Fruit, name='return_fruit'), ToolOutput(Vehicle, name='return_vehicle'), ], ) result = agent.run_sync('What is a banana?') print(repr(result.output)) #> Fruit(name='banana', color='yellow') 

Source code in pydantic_ai_slim/pydantic_ai/output.py

@dataclass(init=False) class ToolOutput(Generic[OutputDataT]):  """Marker class to use a tool for output and optionally customize the tool.  Example:  ```python {title="tool_output.py"}  from pydantic import BaseModel  from pydantic_ai import Agent, ToolOutput  class Fruit(BaseModel):  name: str  color: str  class Vehicle(BaseModel):  name: str  wheels: int  agent = Agent(  'openai:gpt-4o',  output_type=[  ToolOutput(Fruit, name='return_fruit'),  ToolOutput(Vehicle, name='return_vehicle'),  ],  )  result = agent.run_sync('What is a banana?')  print(repr(result.output))  #> Fruit(name='banana', color='yellow')  ```  """ output: OutputTypeOrFunction[OutputDataT]  """An output type or function.""" name: str | None  """The name of the tool that will be passed to the model. If not specified and only one output is provided, `final_result` will be used. If multiple outputs are provided, the name of the output type or function will be added to the tool name.""" description: str | None  """The description of the tool that will be passed to the model. If not specified, the docstring of the output type or function will be used.""" max_retries: int | None  """The maximum number of retries for the tool.""" strict: bool | None  """Whether to use strict mode for the tool.""" def __init__( self, type_: OutputTypeOrFunction[OutputDataT], *, name: str | None = None, description: str | None = None, max_retries: int | None = None, strict: bool | None = None, ): self.output = type_ self.name = name self.description = description self.max_retries = max_retries self.strict = strict 

output instance-attribute

output: OutputTypeOrFunction[OutputDataT] = type_ 

An output type or function.

name instance-attribute

name: str | None = name 

The name of the tool that will be passed to the model. If not specified and only one output is provided, final_result will be used. If multiple outputs are provided, the name of the output type or function will be added to the tool name.

description instance-attribute

description: str | None = description 

The description of the tool that will be passed to the model. If not specified, the docstring of the output type or function will be used.

max_retries instance-attribute

max_retries: int | None = max_retries 

The maximum number of retries for the tool.

strict instance-attribute

strict: bool | None = strict 

Whether to use strict mode for the tool.

NativeOutput dataclass

Bases: Generic[OutputDataT]

Marker class to use the model's native structured outputs functionality for outputs and optionally customize the name and description.

Example:

native_output.py

from pydantic_ai import Agent, NativeOutput from tool_output import Fruit, Vehicle agent = Agent( 'openai:gpt-4o', output_type=NativeOutput( [Fruit, Vehicle], name='Fruit or vehicle', description='Return a fruit or vehicle.' ), ) result = agent.run_sync('What is a Ford Explorer?') print(repr(result.output)) #> Vehicle(name='Ford Explorer', wheels=4) 

Source code in pydantic_ai_slim/pydantic_ai/output.py

@dataclass(init=False) class NativeOutput(Generic[OutputDataT]):  """Marker class to use the model's native structured outputs functionality for outputs and optionally customize the name and description.  Example:  ```python {title="native_output.py" requires="tool_output.py"}  from pydantic_ai import Agent, NativeOutput  from tool_output import Fruit, Vehicle  agent = Agent(  'openai:gpt-4o',  output_type=NativeOutput(  [Fruit, Vehicle],  name='Fruit or vehicle',  description='Return a fruit or vehicle.'  ),  )  result = agent.run_sync('What is a Ford Explorer?')  print(repr(result.output))  #> Vehicle(name='Ford Explorer', wheels=4)  ```  """ outputs: OutputTypeOrFunction[OutputDataT] | Sequence[OutputTypeOrFunction[OutputDataT]]  """The output types or functions.""" name: str | None  """The name of the structured output that will be passed to the model. If not specified and only one output is provided, the name of the output type or function will be used.""" description: str | None  """The description of the structured output that will be passed to the model. If not specified and only one output is provided, the docstring of the output type or function will be used.""" strict: bool | None  """Whether to use strict mode for the output, if the model supports it.""" def __init__( self, outputs: OutputTypeOrFunction[OutputDataT] | Sequence[OutputTypeOrFunction[OutputDataT]], *, name: str | None = None, description: str | None = None, strict: bool | None = None, ): self.outputs = outputs self.name = name self.description = description self.strict = strict 

outputs instance-attribute

outputs: ( OutputTypeOrFunction[OutputDataT] | Sequence[OutputTypeOrFunction[OutputDataT]] ) = outputs 

The output types or functions.

name instance-attribute

name: str | None = name 

The name of the structured output that will be passed to the model. If not specified and only one output is provided, the name of the output type or function will be used.

description instance-attribute

description: str | None = description 

The description of the structured output that will be passed to the model. If not specified and only one output is provided, the docstring of the output type or function will be used.

strict instance-attribute

strict: bool | None = strict 

Whether to use strict mode for the output, if the model supports it.

PromptedOutput dataclass

Bases: Generic[OutputDataT]

Marker class to use a prompt to tell the model what to output and optionally customize the prompt.

Example:

prompted_output.py

from pydantic import BaseModel from pydantic_ai import Agent, PromptedOutput from tool_output import Vehicle class Device(BaseModel): name: str kind: str agent = Agent( 'openai:gpt-4o', output_type=PromptedOutput( [Vehicle, Device], name='Vehicle or device', description='Return a vehicle or device.' ), ) result = agent.run_sync('What is a MacBook?') print(repr(result.output)) #> Device(name='MacBook', kind='laptop') agent = Agent( 'openai:gpt-4o', output_type=PromptedOutput( [Vehicle, Device], template='Gimme some JSON: {schema}' ), ) result = agent.run_sync('What is a Ford Explorer?') print(repr(result.output)) #> Vehicle(name='Ford Explorer', wheels=4) 

Source code in pydantic_ai_slim/pydantic_ai/output.py

@dataclass(init=False) class PromptedOutput(Generic[OutputDataT]):  """Marker class to use a prompt to tell the model what to output and optionally customize the prompt.  Example:  ```python {title="prompted_output.py" requires="tool_output.py"}  from pydantic import BaseModel  from pydantic_ai import Agent, PromptedOutput  from tool_output import Vehicle  class Device(BaseModel):  name: str  kind: str  agent = Agent(  'openai:gpt-4o',  output_type=PromptedOutput(  [Vehicle, Device],  name='Vehicle or device',  description='Return a vehicle or device.'  ),  )  result = agent.run_sync('What is a MacBook?')  print(repr(result.output))  #> Device(name='MacBook', kind='laptop')  agent = Agent(  'openai:gpt-4o',  output_type=PromptedOutput(  [Vehicle, Device],  template='Gimme some JSON: {schema}'  ),  )  result = agent.run_sync('What is a Ford Explorer?')  print(repr(result.output))  #> Vehicle(name='Ford Explorer', wheels=4)  ```  """ outputs: OutputTypeOrFunction[OutputDataT] | Sequence[OutputTypeOrFunction[OutputDataT]]  """The output types or functions.""" name: str | None  """The name of the structured output that will be passed to the model. If not specified and only one output is provided, the name of the output type or function will be used.""" description: str | None  """The description that will be passed to the model. If not specified and only one output is provided, the docstring of the output type or function will be used.""" template: str | None  """Template for the prompt passed to the model.  The '{schema}' placeholder will be replaced with the output JSON schema.  If not specified, the default template specified on the model's profile will be used.  """ def __init__( self, outputs: OutputTypeOrFunction[OutputDataT] | Sequence[OutputTypeOrFunction[OutputDataT]], *, name: str | None = None, description: str | None = None, template: str | None = None, ): self.outputs = outputs self.name = name self.description = description self.template = template 

outputs instance-attribute

outputs: ( OutputTypeOrFunction[OutputDataT] | Sequence[OutputTypeOrFunction[OutputDataT]] ) = outputs 

The output types or functions.

name instance-attribute

name: str | None = name 

The name of the structured output that will be passed to the model. If not specified and only one output is provided, the name of the output type or function will be used.

description instance-attribute

description: str | None = description 

The description that will be passed to the model. If not specified and only one output is provided, the docstring of the output type or function will be used.

template instance-attribute

template: str | None = template 

Template for the prompt passed to the model. The '{schema}' placeholder will be replaced with the output JSON schema. If not specified, the default template specified on the model's profile will be used.

TextOutput dataclass

Bases: Generic[OutputDataT]

Marker class to use text output for an output function taking a string argument.

Example:

from pydantic_ai import Agent, TextOutput def split_into_words(text: str) -> list[str]: return text.split() agent = Agent( 'openai:gpt-4o', output_type=TextOutput(split_into_words), ) result = agent.run_sync('Who was Albert Einstein?') print(result.output) #> ['Albert', 'Einstein', 'was', 'a', 'German-born', 'theoretical', 'physicist.'] 

Source code in pydantic_ai_slim/pydantic_ai/output.py

@dataclass class TextOutput(Generic[OutputDataT]):  """Marker class to use text output for an output function taking a string argument.  Example:  ```python  from pydantic_ai import Agent, TextOutput  def split_into_words(text: str) -> list[str]:  return text.split()  agent = Agent(  'openai:gpt-4o',  output_type=TextOutput(split_into_words),  )  result = agent.run_sync('Who was Albert Einstein?')  print(result.output)  #> ['Albert', 'Einstein', 'was', 'a', 'German-born', 'theoretical', 'physicist.']  ```  """ output_function: TextOutputFunc[OutputDataT]  """The function that will be called to process the model's plain text output. The function must take a single string argument.""" 

output_function instance-attribute

output_function: TextOutputFunc[OutputDataT] 

The function that will be called to process the model's plain text output. The function must take a single string argument.

StructuredDict

StructuredDict( json_schema: JsonSchemaValue, name: str | None = None, description: str | None = None, ) -> type[JsonSchemaValue] 

Returns a dict[str, Any] subclass with a JSON schema attached that will be used for structured output.

Parameters:

Name	Type	Description	Default
json_schema	JsonSchemaValue	A JSON schema of type object defining the structure of the dictionary content.	required
name	str | None	Optional name of the structured output. If not provided, the title field of the JSON schema will be used if it's present.	None
description	str | None	Optional description of the structured output. If not provided, the description field of the JSON schema will be used if it's present.	None

Example:

structured_dict.py

from pydantic_ai import Agent, StructuredDict schema = { 'type': 'object', 'properties': { 'name': {'type': 'string'}, 'age': {'type': 'integer'} }, 'required': ['name', 'age'] } agent = Agent('openai:gpt-4o', output_type=StructuredDict(schema)) result = agent.run_sync('Create a person') print(result.output) #> {'name': 'John Doe', 'age': 30} 

Source code in pydantic_ai_slim/pydantic_ai/output.py

def StructuredDict( json_schema: JsonSchemaValue, name: str | None = None, description: str | None = None ) -> type[JsonSchemaValue]:  """Returns a `dict[str, Any]` subclass with a JSON schema attached that will be used for structured output.  Args:  json_schema: A JSON schema of type `object` defining the structure of the dictionary content.  name: Optional name of the structured output. If not provided, the `title` field of the JSON schema will be used if it's present.  description: Optional description of the structured output. If not provided, the `description` field of the JSON schema will be used if it's present.  Example:  ```python {title="structured_dict.py"}  from pydantic_ai import Agent, StructuredDict  schema = {  'type': 'object',  'properties': {  'name': {'type': 'string'},  'age': {'type': 'integer'}  },  'required': ['name', 'age']  }  agent = Agent('openai:gpt-4o', output_type=StructuredDict(schema))  result = agent.run_sync('Create a person')  print(result.output)  #> {'name': 'John Doe', 'age': 30}  ```  """ json_schema = _utils.check_object_json_schema(json_schema) # Pydantic `TypeAdapter` fails when `object.__get_pydantic_json_schema__` has `$defs`, so we inline them # See https://github.com/pydantic/pydantic/issues/12145 if '$defs' in json_schema: json_schema = InlineDefsJsonSchemaTransformer(json_schema).walk() if '$defs' in json_schema: raise exceptions.UserError( '`StructuredDict` does not currently support recursive `$ref`s and `$defs`. See https://github.com/pydantic/pydantic/issues/12145 for more information.' ) if name: json_schema['title'] = name if description: json_schema['description'] = description class _StructuredDict(JsonSchemaValue): __is_model_like__ = True @classmethod def __get_pydantic_core_schema__( cls, source_type: Any, handler: GetCoreSchemaHandler ) -> core_schema.CoreSchema: return core_schema.dict_schema( keys_schema=core_schema.str_schema(), values_schema=core_schema.any_schema(), ) @classmethod def __get_pydantic_json_schema__( cls, core_schema: core_schema.CoreSchema, handler: GetJsonSchemaHandler ) -> JsonSchemaValue: return json_schema return _StructuredDict 

DeferredToolRequests dataclass

Tool calls that require approval or external execution.

This can be used as an agent's output_type and will be used as the output of the agent run if the model called any deferred tools.

Results can be passed to the next agent run using a DeferredToolResults object with the same tool call IDs.

See deferred tools docs for more information.

Source code in pydantic_ai_slim/pydantic_ai/tools.py

@dataclass(kw_only=True) class DeferredToolRequests:  """Tool calls that require approval or external execution.  This can be used as an agent's `output_type` and will be used as the output of the agent run if the model called any deferred tools.  Results can be passed to the next agent run using a [`DeferredToolResults`][pydantic_ai.tools.DeferredToolResults] object with the same tool call IDs.  See [deferred tools docs](../deferred-tools.md#deferred-tools) for more information.  """ calls: list[ToolCallPart] = field(default_factory=list)  """Tool calls that require external execution.""" approvals: list[ToolCallPart] = field(default_factory=list)  """Tool calls that require human-in-the-loop approval.""" metadata: dict[str, dict[str, Any]] = field(default_factory=dict)  """Metadata for deferred tool calls, keyed by `tool_call_id`.""" 

calls class-attribute instance-attribute

calls: list[ToolCallPart] = field(default_factory=list) 

Tool calls that require external execution.

approvals class-attribute instance-attribute

approvals: list[ToolCallPart] = field(default_factory=list) 

Tool calls that require human-in-the-loop approval.

metadata class-attribute instance-attribute

metadata: dict[str, dict[str, Any]] = field( default_factory=dict ) 

Metadata for deferred tool calls, keyed by tool_call_id.