pydantic_ai.result

StreamedRunResult dataclass

Bases: Generic[AgentDepsT, OutputDataT]

Result of a streamed run that returns structured data via a tool call.

Source code in pydantic_ai_slim/pydantic_ai/result.py

@dataclass(init=False) class StreamedRunResult(Generic[AgentDepsT, OutputDataT]):  """Result of a streamed run that returns structured data via a tool call.""" _all_messages: list[_messages.ModelMessage] _new_message_index: int _stream_response: AgentStream[AgentDepsT, OutputDataT] | None = None _on_complete: Callable[[], Awaitable[None]] | None = None _run_result: AgentRunResult[OutputDataT] | None = None is_complete: bool = field(default=False, init=False)  """Whether the stream has all been received.  This is set to `True` when one of  [`stream_output`][pydantic_ai.result.StreamedRunResult.stream_output],  [`stream_text`][pydantic_ai.result.StreamedRunResult.stream_text],  [`stream_responses`][pydantic_ai.result.StreamedRunResult.stream_responses] or  [`get_output`][pydantic_ai.result.StreamedRunResult.get_output] completes.  """ @overload def __init__( self, all_messages: list[_messages.ModelMessage], new_message_index: int, stream_response: AgentStream[AgentDepsT, OutputDataT] | None, on_complete: Callable[[], Awaitable[None]] | None, ) -> None: ... @overload def __init__( self, all_messages: list[_messages.ModelMessage], new_message_index: int, *, run_result: AgentRunResult[OutputDataT], ) -> None: ... def __init__( self, all_messages: list[_messages.ModelMessage], new_message_index: int, stream_response: AgentStream[AgentDepsT, OutputDataT] | None = None, on_complete: Callable[[], Awaitable[None]] | None = None, run_result: AgentRunResult[OutputDataT] | None = None, ) -> None: self._all_messages = all_messages self._new_message_index = new_message_index self._stream_response = stream_response self._on_complete = on_complete self._run_result = run_result def all_messages(self, *, output_tool_return_content: str | None = None) -> list[_messages.ModelMessage]:  """Return the history of _messages.  Args:  output_tool_return_content: The return content of the tool call to set in the last message.  This provides a convenient way to modify the content of the output tool call if you want to continue  the conversation and want to set the response to the output tool call. If `None`, the last message will  not be modified.  Returns:  List of messages.  """ # this is a method to be consistent with the other methods if output_tool_return_content is not None: raise NotImplementedError('Setting output tool return content is not supported for this result type.') return self._all_messages def all_messages_json(self, *, output_tool_return_content: str | None = None) -> bytes: # pragma: no cover  """Return all messages from [`all_messages`][pydantic_ai.result.StreamedRunResult.all_messages] as JSON bytes.  Args:  output_tool_return_content: The return content of the tool call to set in the last message.  This provides a convenient way to modify the content of the output tool call if you want to continue  the conversation and want to set the response to the output tool call. If `None`, the last message will  not be modified.  Returns:  JSON bytes representing the messages.  """ return _messages.ModelMessagesTypeAdapter.dump_json( self.all_messages(output_tool_return_content=output_tool_return_content) ) def new_messages(self, *, output_tool_return_content: str | None = None) -> list[_messages.ModelMessage]:  """Return new messages associated with this run.  Messages from older runs are excluded.  Args:  output_tool_return_content: The return content of the tool call to set in the last message.  This provides a convenient way to modify the content of the output tool call if you want to continue  the conversation and want to set the response to the output tool call. If `None`, the last message will  not be modified.  Returns:  List of new messages.  """ return self.all_messages(output_tool_return_content=output_tool_return_content)[self._new_message_index :] def new_messages_json(self, *, output_tool_return_content: str | None = None) -> bytes: # pragma: no cover  """Return new messages from [`new_messages`][pydantic_ai.result.StreamedRunResult.new_messages] as JSON bytes.  Args:  output_tool_return_content: The return content of the tool call to set in the last message.  This provides a convenient way to modify the content of the output tool call if you want to continue  the conversation and want to set the response to the output tool call. If `None`, the last message will  not be modified.  Returns:  JSON bytes representing the new messages.  """ return _messages.ModelMessagesTypeAdapter.dump_json( self.new_messages(output_tool_return_content=output_tool_return_content) ) @deprecated('`StreamedRunResult.stream` is deprecated, use `stream_output` instead.') async def stream(self, *, debounce_by: float | None = 0.1) -> AsyncIterator[OutputDataT]: async for output in self.stream_output(debounce_by=debounce_by): yield output async def stream_output(self, *, debounce_by: float | None = 0.1) -> AsyncIterator[OutputDataT]:  """Stream the output as an async iterable.  The pydantic validator for structured data will be called in  [partial mode](https://docs.pydantic.dev/dev/concepts/experimental/#partial-validation)  on each iteration.  Args:  debounce_by: by how much (if at all) to debounce/group the output chunks by. `None` means no debouncing.  Debouncing is particularly important for long structured outputs to reduce the overhead of  performing validation as each token is received.  Returns:  An async iterable of the response data.  """ if self._run_result is not None: yield self._run_result.output await self._marked_completed() elif self._stream_response is not None: async for output in self._stream_response.stream_output(debounce_by=debounce_by): yield output await self._marked_completed(self.response) else: raise ValueError('No stream response or run result provided') # pragma: no cover async def stream_text(self, *, delta: bool = False, debounce_by: float | None = 0.1) -> AsyncIterator[str]:  """Stream the text result as an async iterable.  !!! note  Result validators will NOT be called on the text result if `delta=True`.  Args:  delta: if `True`, yield each chunk of text as it is received, if `False` (default), yield the full text  up to the current point.  debounce_by: by how much (if at all) to debounce/group the response chunks by. `None` means no debouncing.  Debouncing is particularly important for long structured responses to reduce the overhead of  performing validation as each token is received.  """ if self._run_result is not None: # pragma: no cover # We can't really get here, as `_run_result` is only set in `run_stream` when `CallToolsNode` produces `DeferredToolRequests` output # as a result of a tool function raising `CallDeferred` or `ApprovalRequired`. # That'll change if we ever support something like `raise EndRun(output: OutputT)` where `OutputT` could be `str`. if not isinstance(self._run_result.output, str): raise exceptions.UserError('stream_text() can only be used with text responses') yield self._run_result.output await self._marked_completed() elif self._stream_response is not None: async for text in self._stream_response.stream_text(delta=delta, debounce_by=debounce_by): yield text await self._marked_completed(self.response) else: raise ValueError('No stream response or run result provided') # pragma: no cover @deprecated('`StreamedRunResult.stream_structured` is deprecated, use `stream_responses` instead.') async def stream_structured( self, *, debounce_by: float | None = 0.1 ) -> AsyncIterator[tuple[_messages.ModelResponse, bool]]: async for msg, last in self.stream_responses(debounce_by=debounce_by): yield msg, last async def stream_responses( self, *, debounce_by: float | None = 0.1 ) -> AsyncIterator[tuple[_messages.ModelResponse, bool]]:  """Stream the response as an async iterable of Structured LLM Messages.  Args:  debounce_by: by how much (if at all) to debounce/group the response chunks by. `None` means no debouncing.  Debouncing is particularly important for long structured responses to reduce the overhead of  performing validation as each token is received.  Returns:  An async iterable of the structured response message and whether that is the last message.  """ if self._run_result is not None: yield self.response, True await self._marked_completed() elif self._stream_response is not None: # if the message currently has any parts with content, yield before streaming async for msg in self._stream_response.stream_responses(debounce_by=debounce_by): yield msg, False msg = self.response yield msg, True await self._marked_completed(msg) else: raise ValueError('No stream response or run result provided') # pragma: no cover async def get_output(self) -> OutputDataT:  """Stream the whole response, validate and return it.""" if self._run_result is not None: output = self._run_result.output await self._marked_completed() return output elif self._stream_response is not None: output = await self._stream_response.get_output() await self._marked_completed(self.response) return output else: raise ValueError('No stream response or run result provided') # pragma: no cover @property def response(self) -> _messages.ModelResponse:  """Return the current state of the response.""" if self._run_result is not None: return self._run_result.response elif self._stream_response is not None: return self._stream_response.get() else: raise ValueError('No stream response or run result provided') # pragma: no cover # TODO (v2): Make this a property def usage(self) -> RunUsage:  """Return the usage of the whole run.  !!! note  This won't return the full usage until the stream is finished.  """ if self._run_result is not None: return self._run_result.usage() elif self._stream_response is not None: return self._stream_response.usage() else: raise ValueError('No stream response or run result provided') # pragma: no cover # TODO (v2): Make this a property def timestamp(self) -> datetime:  """Get the timestamp of the response.""" if self._run_result is not None: return self._run_result.timestamp() elif self._stream_response is not None: return self._stream_response.timestamp() else: raise ValueError('No stream response or run result provided') # pragma: no cover @property def run_id(self) -> str:  """The unique identifier for the agent run.""" if self._run_result is not None: return self._run_result.run_id elif self._stream_response is not None: return self._stream_response.run_id else: raise ValueError('No stream response or run result provided') # pragma: no cover @deprecated('`validate_structured_output` is deprecated, use `validate_response_output` instead.') async def validate_structured_output( self, message: _messages.ModelResponse, *, allow_partial: bool = False ) -> OutputDataT: return await self.validate_response_output(message, allow_partial=allow_partial) async def validate_response_output( self, message: _messages.ModelResponse, *, allow_partial: bool = False ) -> OutputDataT:  """Validate a structured result message.""" if self._run_result is not None: return self._run_result.output elif self._stream_response is not None: return await self._stream_response.validate_response_output(message, allow_partial=allow_partial) else: raise ValueError('No stream response or run result provided') # pragma: no cover async def _marked_completed(self, message: _messages.ModelResponse | None = None) -> None: self.is_complete = True if message is not None: if self._stream_response: # pragma: no branch message.run_id = self._stream_response.run_id self._all_messages.append(message) if self._on_complete is not None: await self._on_complete() 

is_complete class-attribute instance-attribute

is_complete: bool = field(default=False, init=False) 

Whether the stream has all been received.

This is set to True when one of stream_output, stream_text, stream_responses or get_output completes.

all_messages

all_messages( *, output_tool_return_content: str | None = None ) -> list[ModelMessage] 

Return the history of _messages.

Parameters:

Name	Type	Description	Default
output_tool_return_content	str | None	The return content of the tool call to set in the last message. This provides a convenient way to modify the content of the output tool call if you want to continue the conversation and want to set the response to the output tool call. If None, the last message will not be modified.	None

Returns:

Type	Description
list[ModelMessage]	List of messages.

Source code in pydantic_ai_slim/pydantic_ai/result.py

def all_messages(self, *, output_tool_return_content: str | None = None) -> list[_messages.ModelMessage]:  """Return the history of _messages.  Args:  output_tool_return_content: The return content of the tool call to set in the last message.  This provides a convenient way to modify the content of the output tool call if you want to continue  the conversation and want to set the response to the output tool call. If `None`, the last message will  not be modified.  Returns:  List of messages.  """ # this is a method to be consistent with the other methods if output_tool_return_content is not None: raise NotImplementedError('Setting output tool return content is not supported for this result type.') return self._all_messages 

all_messages_json

all_messages_json( *, output_tool_return_content: str | None = None ) -> bytes 

Return all messages from all_messages as JSON bytes.

Parameters:

Name	Type	Description	Default
output_tool_return_content	str | None	The return content of the tool call to set in the last message. This provides a convenient way to modify the content of the output tool call if you want to continue the conversation and want to set the response to the output tool call. If None, the last message will not be modified.	None

Returns:

Type	Description
bytes	JSON bytes representing the messages.

Source code in pydantic_ai_slim/pydantic_ai/result.py

def all_messages_json(self, *, output_tool_return_content: str | None = None) -> bytes: # pragma: no cover  """Return all messages from [`all_messages`][pydantic_ai.result.StreamedRunResult.all_messages] as JSON bytes.  Args:  output_tool_return_content: The return content of the tool call to set in the last message.  This provides a convenient way to modify the content of the output tool call if you want to continue  the conversation and want to set the response to the output tool call. If `None`, the last message will  not be modified.  Returns:  JSON bytes representing the messages.  """ return _messages.ModelMessagesTypeAdapter.dump_json( self.all_messages(output_tool_return_content=output_tool_return_content) ) 

new_messages

new_messages( *, output_tool_return_content: str | None = None ) -> list[ModelMessage] 

Return new messages associated with this run.

Messages from older runs are excluded.

Parameters:

Name	Type	Description	Default
output_tool_return_content	str | None	The return content of the tool call to set in the last message. This provides a convenient way to modify the content of the output tool call if you want to continue the conversation and want to set the response to the output tool call. If None, the last message will not be modified.	None

Returns:

Type	Description
list[ModelMessage]	List of new messages.

Source code in pydantic_ai_slim/pydantic_ai/result.py

def new_messages(self, *, output_tool_return_content: str | None = None) -> list[_messages.ModelMessage]:  """Return new messages associated with this run.  Messages from older runs are excluded.  Args:  output_tool_return_content: The return content of the tool call to set in the last message.  This provides a convenient way to modify the content of the output tool call if you want to continue  the conversation and want to set the response to the output tool call. If `None`, the last message will  not be modified.  Returns:  List of new messages.  """ return self.all_messages(output_tool_return_content=output_tool_return_content)[self._new_message_index :] 

new_messages_json

new_messages_json( *, output_tool_return_content: str | None = None ) -> bytes 

Return new messages from new_messages as JSON bytes.

Parameters:

Name	Type	Description	Default
output_tool_return_content	str | None	The return content of the tool call to set in the last message. This provides a convenient way to modify the content of the output tool call if you want to continue the conversation and want to set the response to the output tool call. If None, the last message will not be modified.	None

Returns:

Type	Description
bytes	JSON bytes representing the new messages.

Source code in pydantic_ai_slim/pydantic_ai/result.py

def new_messages_json(self, *, output_tool_return_content: str | None = None) -> bytes: # pragma: no cover  """Return new messages from [`new_messages`][pydantic_ai.result.StreamedRunResult.new_messages] as JSON bytes.  Args:  output_tool_return_content: The return content of the tool call to set in the last message.  This provides a convenient way to modify the content of the output tool call if you want to continue  the conversation and want to set the response to the output tool call. If `None`, the last message will  not be modified.  Returns:  JSON bytes representing the new messages.  """ return _messages.ModelMessagesTypeAdapter.dump_json( self.new_messages(output_tool_return_content=output_tool_return_content) ) 

stream async deprecated

stream( *, debounce_by: float | None = 0.1 ) -> AsyncIterator[OutputDataT] 

Deprecated

StreamedRunResult.stream is deprecated, use stream_output instead.

Source code in pydantic_ai_slim/pydantic_ai/result.py

@deprecated('`StreamedRunResult.stream` is deprecated, use `stream_output` instead.') async def stream(self, *, debounce_by: float | None = 0.1) -> AsyncIterator[OutputDataT]: async for output in self.stream_output(debounce_by=debounce_by): yield output 

stream_output async

stream_output( *, debounce_by: float | None = 0.1 ) -> AsyncIterator[OutputDataT] 

Stream the output as an async iterable.

The pydantic validator for structured data will be called in partial mode on each iteration.

Parameters:

Name	Type	Description	Default
debounce_by	float | None	by how much (if at all) to debounce/group the output chunks by. None means no debouncing. Debouncing is particularly important for long structured outputs to reduce the overhead of performing validation as each token is received.	0.1

Returns:

Type	Description
AsyncIterator[OutputDataT]	An async iterable of the response data.

Source code in pydantic_ai_slim/pydantic_ai/result.py

async def stream_output(self, *, debounce_by: float | None = 0.1) -> AsyncIterator[OutputDataT]:  """Stream the output as an async iterable.  The pydantic validator for structured data will be called in  [partial mode](https://docs.pydantic.dev/dev/concepts/experimental/#partial-validation)  on each iteration.  Args:  debounce_by: by how much (if at all) to debounce/group the output chunks by. `None` means no debouncing.  Debouncing is particularly important for long structured outputs to reduce the overhead of  performing validation as each token is received.  Returns:  An async iterable of the response data.  """ if self._run_result is not None: yield self._run_result.output await self._marked_completed() elif self._stream_response is not None: async for output in self._stream_response.stream_output(debounce_by=debounce_by): yield output await self._marked_completed(self.response) else: raise ValueError('No stream response or run result provided') # pragma: no cover 

stream_text async

stream_text( *, delta: bool = False, debounce_by: float | None = 0.1 ) -> AsyncIterator[str] 

Stream the text result as an async iterable.

Note

Result validators will NOT be called on the text result if delta=True.

Parameters:

Name	Type	Description	Default
delta	bool	if True, yield each chunk of text as it is received, if False (default), yield the full text up to the current point.	False
debounce_by	float | None	by how much (if at all) to debounce/group the response chunks by. None means no debouncing. Debouncing is particularly important for long structured responses to reduce the overhead of performing validation as each token is received.	0.1

Source code in pydantic_ai_slim/pydantic_ai/result.py

async def stream_text(self, *, delta: bool = False, debounce_by: float | None = 0.1) -> AsyncIterator[str]:  """Stream the text result as an async iterable.  !!! note  Result validators will NOT be called on the text result if `delta=True`.  Args:  delta: if `True`, yield each chunk of text as it is received, if `False` (default), yield the full text  up to the current point.  debounce_by: by how much (if at all) to debounce/group the response chunks by. `None` means no debouncing.  Debouncing is particularly important for long structured responses to reduce the overhead of  performing validation as each token is received.  """ if self._run_result is not None: # pragma: no cover # We can't really get here, as `_run_result` is only set in `run_stream` when `CallToolsNode` produces `DeferredToolRequests` output # as a result of a tool function raising `CallDeferred` or `ApprovalRequired`. # That'll change if we ever support something like `raise EndRun(output: OutputT)` where `OutputT` could be `str`. if not isinstance(self._run_result.output, str): raise exceptions.UserError('stream_text() can only be used with text responses') yield self._run_result.output await self._marked_completed() elif self._stream_response is not None: async for text in self._stream_response.stream_text(delta=delta, debounce_by=debounce_by): yield text await self._marked_completed(self.response) else: raise ValueError('No stream response or run result provided') # pragma: no cover 

stream_structured async deprecated

stream_structured( *, debounce_by: float | None = 0.1 ) -> AsyncIterator[tuple[ModelResponse, bool]] 

Deprecated

StreamedRunResult.stream_structured is deprecated, use stream_responses instead.

Source code in pydantic_ai_slim/pydantic_ai/result.py

@deprecated('`StreamedRunResult.stream_structured` is deprecated, use `stream_responses` instead.') async def stream_structured( self, *, debounce_by: float | None = 0.1 ) -> AsyncIterator[tuple[_messages.ModelResponse, bool]]: async for msg, last in self.stream_responses(debounce_by=debounce_by): yield msg, last 

stream_responses async

stream_responses( *, debounce_by: float | None = 0.1 ) -> AsyncIterator[tuple[ModelResponse, bool]] 

Stream the response as an async iterable of Structured LLM Messages.

Parameters:

Name	Type	Description	Default
debounce_by	float | None	by how much (if at all) to debounce/group the response chunks by. None means no debouncing. Debouncing is particularly important for long structured responses to reduce the overhead of performing validation as each token is received.	0.1

Returns:

Type	Description
AsyncIterator[tuple[ModelResponse, bool]]	An async iterable of the structured response message and whether that is the last message.

Source code in pydantic_ai_slim/pydantic_ai/result.py

async def stream_responses( self, *, debounce_by: float | None = 0.1 ) -> AsyncIterator[tuple[_messages.ModelResponse, bool]]:  """Stream the response as an async iterable of Structured LLM Messages.  Args:  debounce_by: by how much (if at all) to debounce/group the response chunks by. `None` means no debouncing.  Debouncing is particularly important for long structured responses to reduce the overhead of  performing validation as each token is received.  Returns:  An async iterable of the structured response message and whether that is the last message.  """ if self._run_result is not None: yield self.response, True await self._marked_completed() elif self._stream_response is not None: # if the message currently has any parts with content, yield before streaming async for msg in self._stream_response.stream_responses(debounce_by=debounce_by): yield msg, False msg = self.response yield msg, True await self._marked_completed(msg) else: raise ValueError('No stream response or run result provided') # pragma: no cover 

get_output async

get_output() -> OutputDataT 

Stream the whole response, validate and return it.

Source code in pydantic_ai_slim/pydantic_ai/result.py

async def get_output(self) -> OutputDataT:  """Stream the whole response, validate and return it.""" if self._run_result is not None: output = self._run_result.output await self._marked_completed() return output elif self._stream_response is not None: output = await self._stream_response.get_output() await self._marked_completed(self.response) return output else: raise ValueError('No stream response or run result provided') # pragma: no cover 

response property

response: ModelResponse 

Return the current state of the response.

usage

usage() -> RunUsage 

Return the usage of the whole run.

Note

This won't return the full usage until the stream is finished.

Source code in pydantic_ai_slim/pydantic_ai/result.py

def usage(self) -> RunUsage:  """Return the usage of the whole run.  !!! note  This won't return the full usage until the stream is finished.  """ if self._run_result is not None: return self._run_result.usage() elif self._stream_response is not None: return self._stream_response.usage() else: raise ValueError('No stream response or run result provided') # pragma: no cover 

timestamp

timestamp() -> datetime 

Get the timestamp of the response.

Source code in pydantic_ai_slim/pydantic_ai/result.py

def timestamp(self) -> datetime:  """Get the timestamp of the response.""" if self._run_result is not None: return self._run_result.timestamp() elif self._stream_response is not None: return self._stream_response.timestamp() else: raise ValueError('No stream response or run result provided') # pragma: no cover 

run_id property

run_id: str 

The unique identifier for the agent run.

validate_structured_output async deprecated

validate_structured_output( message: ModelResponse, *, allow_partial: bool = False ) -> OutputDataT 

Deprecated

validate_structured_output is deprecated, use validate_response_output instead.

Source code in pydantic_ai_slim/pydantic_ai/result.py

@deprecated('`validate_structured_output` is deprecated, use `validate_response_output` instead.') async def validate_structured_output( self, message: _messages.ModelResponse, *, allow_partial: bool = False ) -> OutputDataT: return await self.validate_response_output(message, allow_partial=allow_partial) 

validate_response_output async

validate_response_output( message: ModelResponse, *, allow_partial: bool = False ) -> OutputDataT 

Validate a structured result message.

Source code in pydantic_ai_slim/pydantic_ai/result.py

async def validate_response_output( self, message: _messages.ModelResponse, *, allow_partial: bool = False ) -> OutputDataT:  """Validate a structured result message.""" if self._run_result is not None: return self._run_result.output elif self._stream_response is not None: return await self._stream_response.validate_response_output(message, allow_partial=allow_partial) else: raise ValueError('No stream response or run result provided') # pragma: no cover 

StreamedRunResultSync dataclass

Bases: Generic[AgentDepsT, OutputDataT]

Synchronous wrapper for StreamedRunResult that only exposes sync methods.

Source code in pydantic_ai_slim/pydantic_ai/result.py

@dataclass(init=False) class StreamedRunResultSync(Generic[AgentDepsT, OutputDataT]):  """Synchronous wrapper for [`StreamedRunResult`][pydantic_ai.result.StreamedRunResult] that only exposes sync methods.""" _streamed_run_result: StreamedRunResult[AgentDepsT, OutputDataT] def __init__(self, streamed_run_result: StreamedRunResult[AgentDepsT, OutputDataT]) -> None: self._streamed_run_result = streamed_run_result def all_messages(self, *, output_tool_return_content: str | None = None) -> list[_messages.ModelMessage]:  """Return the history of messages.  Args:  output_tool_return_content: The return content of the tool call to set in the last message.  This provides a convenient way to modify the content of the output tool call if you want to continue  the conversation and want to set the response to the output tool call. If `None`, the last message will  not be modified.  Returns:  List of messages.  """ return self._streamed_run_result.all_messages(output_tool_return_content=output_tool_return_content) def all_messages_json(self, *, output_tool_return_content: str | None = None) -> bytes: # pragma: no cover  """Return all messages from [`all_messages`][pydantic_ai.result.StreamedRunResultSync.all_messages] as JSON bytes.  Args:  output_tool_return_content: The return content of the tool call to set in the last message.  This provides a convenient way to modify the content of the output tool call if you want to continue  the conversation and want to set the response to the output tool call. If `None`, the last message will  not be modified.  Returns:  JSON bytes representing the messages.  """ return self._streamed_run_result.all_messages_json(output_tool_return_content=output_tool_return_content) def new_messages(self, *, output_tool_return_content: str | None = None) -> list[_messages.ModelMessage]:  """Return new messages associated with this run.  Messages from older runs are excluded.  Args:  output_tool_return_content: The return content of the tool call to set in the last message.  This provides a convenient way to modify the content of the output tool call if you want to continue  the conversation and want to set the response to the output tool call. If `None`, the last message will  not be modified.  Returns:  List of new messages.  """ return self._streamed_run_result.new_messages(output_tool_return_content=output_tool_return_content) def new_messages_json(self, *, output_tool_return_content: str | None = None) -> bytes: # pragma: no cover  """Return new messages from [`new_messages`][pydantic_ai.result.StreamedRunResultSync.new_messages] as JSON bytes.  Args:  output_tool_return_content: The return content of the tool call to set in the last message.  This provides a convenient way to modify the content of the output tool call if you want to continue  the conversation and want to set the response to the output tool call. If `None`, the last message will  not be modified.  Returns:  JSON bytes representing the new messages.  """ return self._streamed_run_result.new_messages_json(output_tool_return_content=output_tool_return_content) def stream_output(self, *, debounce_by: float | None = 0.1) -> Iterator[OutputDataT]:  """Stream the output as an iterable.  The pydantic validator for structured data will be called in  [partial mode](https://docs.pydantic.dev/dev/concepts/experimental/#partial-validation)  on each iteration.  Args:  debounce_by: by how much (if at all) to debounce/group the output chunks by. `None` means no debouncing.  Debouncing is particularly important for long structured outputs to reduce the overhead of  performing validation as each token is received.  Returns:  An iterable of the response data.  """ return _utils.sync_async_iterator(self._streamed_run_result.stream_output(debounce_by=debounce_by)) def stream_text(self, *, delta: bool = False, debounce_by: float | None = 0.1) -> Iterator[str]:  """Stream the text result as an iterable.  !!! note  Result validators will NOT be called on the text result if `delta=True`.  Args:  delta: if `True`, yield each chunk of text as it is received, if `False` (default), yield the full text  up to the current point.  debounce_by: by how much (if at all) to debounce/group the response chunks by. `None` means no debouncing.  Debouncing is particularly important for long structured responses to reduce the overhead of  performing validation as each token is received.  """ return _utils.sync_async_iterator(self._streamed_run_result.stream_text(delta=delta, debounce_by=debounce_by)) def stream_responses(self, *, debounce_by: float | None = 0.1) -> Iterator[tuple[_messages.ModelResponse, bool]]:  """Stream the response as an iterable of Structured LLM Messages.  Args:  debounce_by: by how much (if at all) to debounce/group the response chunks by. `None` means no debouncing.  Debouncing is particularly important for long structured responses to reduce the overhead of  performing validation as each token is received.  Returns:  An iterable of the structured response message and whether that is the last message.  """ return _utils.sync_async_iterator(self._streamed_run_result.stream_responses(debounce_by=debounce_by)) def get_output(self) -> OutputDataT:  """Stream the whole response, validate and return it.""" return _utils.get_event_loop().run_until_complete(self._streamed_run_result.get_output()) @property def response(self) -> _messages.ModelResponse:  """Return the current state of the response.""" return self._streamed_run_result.response def usage(self) -> RunUsage:  """Return the usage of the whole run.  !!! note  This won't return the full usage until the stream is finished.  """ return self._streamed_run_result.usage() def timestamp(self) -> datetime:  """Get the timestamp of the response.""" return self._streamed_run_result.timestamp() @property def run_id(self) -> str:  """The unique identifier for the agent run.""" return self._streamed_run_result.run_id def validate_response_output(self, message: _messages.ModelResponse, *, allow_partial: bool = False) -> OutputDataT:  """Validate a structured result message.""" return _utils.get_event_loop().run_until_complete( self._streamed_run_result.validate_response_output(message, allow_partial=allow_partial) ) @property def is_complete(self) -> bool:  """Whether the stream has all been received.  This is set to `True` when one of  [`stream_output`][pydantic_ai.result.StreamedRunResultSync.stream_output],  [`stream_text`][pydantic_ai.result.StreamedRunResultSync.stream_text],  [`stream_responses`][pydantic_ai.result.StreamedRunResultSync.stream_responses] or  [`get_output`][pydantic_ai.result.StreamedRunResultSync.get_output] completes.  """ return self._streamed_run_result.is_complete 

all_messages

all_messages( *, output_tool_return_content: str | None = None ) -> list[ModelMessage] 

Return the history of messages.

Parameters:

Name	Type	Description	Default
output_tool_return_content	str | None	The return content of the tool call to set in the last message. This provides a convenient way to modify the content of the output tool call if you want to continue the conversation and want to set the response to the output tool call. If None, the last message will not be modified.	None

Returns:

Type	Description
list[ModelMessage]	List of messages.

Source code in pydantic_ai_slim/pydantic_ai/result.py

def all_messages(self, *, output_tool_return_content: str | None = None) -> list[_messages.ModelMessage]:  """Return the history of messages.  Args:  output_tool_return_content: The return content of the tool call to set in the last message.  This provides a convenient way to modify the content of the output tool call if you want to continue  the conversation and want to set the response to the output tool call. If `None`, the last message will  not be modified.  Returns:  List of messages.  """ return self._streamed_run_result.all_messages(output_tool_return_content=output_tool_return_content) 

all_messages_json

all_messages_json( *, output_tool_return_content: str | None = None ) -> bytes 

Return all messages from all_messages as JSON bytes.

Parameters:

Name	Type	Description	Default
output_tool_return_content	str | None	The return content of the tool call to set in the last message. This provides a convenient way to modify the content of the output tool call if you want to continue the conversation and want to set the response to the output tool call. If None, the last message will not be modified.	None

Returns:

Type	Description
bytes	JSON bytes representing the messages.

Source code in pydantic_ai_slim/pydantic_ai/result.py

def all_messages_json(self, *, output_tool_return_content: str | None = None) -> bytes: # pragma: no cover  """Return all messages from [`all_messages`][pydantic_ai.result.StreamedRunResultSync.all_messages] as JSON bytes.  Args:  output_tool_return_content: The return content of the tool call to set in the last message.  This provides a convenient way to modify the content of the output tool call if you want to continue  the conversation and want to set the response to the output tool call. If `None`, the last message will  not be modified.  Returns:  JSON bytes representing the messages.  """ return self._streamed_run_result.all_messages_json(output_tool_return_content=output_tool_return_content) 

new_messages

new_messages( *, output_tool_return_content: str | None = None ) -> list[ModelMessage] 

Return new messages associated with this run.

Messages from older runs are excluded.

Parameters:

Name	Type	Description	Default
output_tool_return_content	str | None	The return content of the tool call to set in the last message. This provides a convenient way to modify the content of the output tool call if you want to continue the conversation and want to set the response to the output tool call. If None, the last message will not be modified.	None

Returns:

Type	Description
list[ModelMessage]	List of new messages.

Source code in pydantic_ai_slim/pydantic_ai/result.py

def new_messages(self, *, output_tool_return_content: str | None = None) -> list[_messages.ModelMessage]:  """Return new messages associated with this run.  Messages from older runs are excluded.  Args:  output_tool_return_content: The return content of the tool call to set in the last message.  This provides a convenient way to modify the content of the output tool call if you want to continue  the conversation and want to set the response to the output tool call. If `None`, the last message will  not be modified.  Returns:  List of new messages.  """ return self._streamed_run_result.new_messages(output_tool_return_content=output_tool_return_content) 

new_messages_json

new_messages_json( *, output_tool_return_content: str | None = None ) -> bytes 

Return new messages from new_messages as JSON bytes.

Parameters:

Name	Type	Description	Default
output_tool_return_content	str | None	The return content of the tool call to set in the last message. This provides a convenient way to modify the content of the output tool call if you want to continue the conversation and want to set the response to the output tool call. If None, the last message will not be modified.	None

Returns:

Type	Description
bytes	JSON bytes representing the new messages.

Source code in pydantic_ai_slim/pydantic_ai/result.py

def new_messages_json(self, *, output_tool_return_content: str | None = None) -> bytes: # pragma: no cover  """Return new messages from [`new_messages`][pydantic_ai.result.StreamedRunResultSync.new_messages] as JSON bytes.  Args:  output_tool_return_content: The return content of the tool call to set in the last message.  This provides a convenient way to modify the content of the output tool call if you want to continue  the conversation and want to set the response to the output tool call. If `None`, the last message will  not be modified.  Returns:  JSON bytes representing the new messages.  """ return self._streamed_run_result.new_messages_json(output_tool_return_content=output_tool_return_content) 

stream_output

stream_output( *, debounce_by: float | None = 0.1 ) -> Iterator[OutputDataT] 

Stream the output as an iterable.

The pydantic validator for structured data will be called in partial mode on each iteration.

Parameters:

Name	Type	Description	Default
debounce_by	float | None	by how much (if at all) to debounce/group the output chunks by. None means no debouncing. Debouncing is particularly important for long structured outputs to reduce the overhead of performing validation as each token is received.	0.1

Returns:

Type	Description
Iterator[OutputDataT]	An iterable of the response data.

Source code in pydantic_ai_slim/pydantic_ai/result.py

def stream_output(self, *, debounce_by: float | None = 0.1) -> Iterator[OutputDataT]:  """Stream the output as an iterable.  The pydantic validator for structured data will be called in  [partial mode](https://docs.pydantic.dev/dev/concepts/experimental/#partial-validation)  on each iteration.  Args:  debounce_by: by how much (if at all) to debounce/group the output chunks by. `None` means no debouncing.  Debouncing is particularly important for long structured outputs to reduce the overhead of  performing validation as each token is received.  Returns:  An iterable of the response data.  """ return _utils.sync_async_iterator(self._streamed_run_result.stream_output(debounce_by=debounce_by)) 

stream_text

stream_text( *, delta: bool = False, debounce_by: float | None = 0.1 ) -> Iterator[str] 

Stream the text result as an iterable.

Note

Result validators will NOT be called on the text result if delta=True.

Parameters:

Name	Type	Description	Default
delta	bool	if True, yield each chunk of text as it is received, if False (default), yield the full text up to the current point.	False
debounce_by	float | None	by how much (if at all) to debounce/group the response chunks by. None means no debouncing. Debouncing is particularly important for long structured responses to reduce the overhead of performing validation as each token is received.	0.1

Source code in pydantic_ai_slim/pydantic_ai/result.py

def stream_text(self, *, delta: bool = False, debounce_by: float | None = 0.1) -> Iterator[str]:  """Stream the text result as an iterable.  !!! note  Result validators will NOT be called on the text result if `delta=True`.  Args:  delta: if `True`, yield each chunk of text as it is received, if `False` (default), yield the full text  up to the current point.  debounce_by: by how much (if at all) to debounce/group the response chunks by. `None` means no debouncing.  Debouncing is particularly important for long structured responses to reduce the overhead of  performing validation as each token is received.  """ return _utils.sync_async_iterator(self._streamed_run_result.stream_text(delta=delta, debounce_by=debounce_by)) 

stream_responses

stream_responses( *, debounce_by: float | None = 0.1 ) -> Iterator[tuple[ModelResponse, bool]] 

Stream the response as an iterable of Structured LLM Messages.

Parameters:

Name	Type	Description	Default
debounce_by	float | None	by how much (if at all) to debounce/group the response chunks by. None means no debouncing. Debouncing is particularly important for long structured responses to reduce the overhead of performing validation as each token is received.	0.1

Returns:

Type	Description
Iterator[tuple[ModelResponse, bool]]	An iterable of the structured response message and whether that is the last message.

Source code in pydantic_ai_slim/pydantic_ai/result.py

def stream_responses(self, *, debounce_by: float | None = 0.1) -> Iterator[tuple[_messages.ModelResponse, bool]]:  """Stream the response as an iterable of Structured LLM Messages.  Args:  debounce_by: by how much (if at all) to debounce/group the response chunks by. `None` means no debouncing.  Debouncing is particularly important for long structured responses to reduce the overhead of  performing validation as each token is received.  Returns:  An iterable of the structured response message and whether that is the last message.  """ return _utils.sync_async_iterator(self._streamed_run_result.stream_responses(debounce_by=debounce_by)) 

get_output

get_output() -> OutputDataT 

Stream the whole response, validate and return it.

Source code in pydantic_ai_slim/pydantic_ai/result.py

def get_output(self) -> OutputDataT:  """Stream the whole response, validate and return it.""" return _utils.get_event_loop().run_until_complete(self._streamed_run_result.get_output()) 

response property

response: ModelResponse 

Return the current state of the response.

usage

usage() -> RunUsage 

Return the usage of the whole run.

Note

This won't return the full usage until the stream is finished.

Source code in pydantic_ai_slim/pydantic_ai/result.py

def usage(self) -> RunUsage:  """Return the usage of the whole run.  !!! note  This won't return the full usage until the stream is finished.  """ return self._streamed_run_result.usage() 

timestamp

timestamp() -> datetime 

Get the timestamp of the response.

Source code in pydantic_ai_slim/pydantic_ai/result.py

def timestamp(self) -> datetime:  """Get the timestamp of the response.""" return self._streamed_run_result.timestamp() 

run_id property

run_id: str 

The unique identifier for the agent run.

validate_response_output

validate_response_output( message: ModelResponse, *, allow_partial: bool = False ) -> OutputDataT 

Validate a structured result message.

Source code in pydantic_ai_slim/pydantic_ai/result.py

def validate_response_output(self, message: _messages.ModelResponse, *, allow_partial: bool = False) -> OutputDataT:  """Validate a structured result message.""" return _utils.get_event_loop().run_until_complete( self._streamed_run_result.validate_response_output(message, allow_partial=allow_partial) ) 

is_complete property

is_complete: bool 

Whether the stream has all been received.

This is set to True when one of stream_output, stream_text, stream_responses or get_output completes.