`pydantic_ai.ui`

StateDeps `dataclass`

Bases: Generic[StateT]

Dependency type that holds state.

This class is used to manage the state of an agent run. It allows setting the state of the agent run with a specific type of state model, which must be a subclass of BaseModel.

The state is set using the state setter by the Adapter when the run starts.

Implements the StateHandler protocol.

Source code in pydantic_ai_slim/pydantic_ai/ui/_adapter.py

@dataclass class StateDeps(Generic[StateT]):  """Dependency type that holds state.  This class is used to manage the state of an agent run. It allows setting  the state of the agent run with a specific type of state model, which must  be a subclass of `BaseModel`.  The state is set using the `state` setter by the `Adapter` when the run starts.  Implements the `StateHandler` protocol.  """ state: StateT 

StateHandler

Bases: Protocol

Protocol for state handlers in agent runs. Requires the class to be a dataclass with a state field.

Source code in pydantic_ai_slim/pydantic_ai/ui/_adapter.py

@runtime_checkable class StateHandler(Protocol):  """Protocol for state handlers in agent runs. Requires the class to be a dataclass with a `state` field.""" # Has to be a dataclass so we can use `replace` to update the state. # From https://github.com/python/typeshed/blob/9ab7fde0a0cd24ed7a72837fcb21093b811b80d8/stdlib/_typeshed/__init__.pyi#L352 __dataclass_fields__: ClassVar[dict[str, Field[Any]]] @property def state(self) -> Any:  """Get the current state of the agent run.""" ... @state.setter def state(self, state: Any) -> None:  """Set the state of the agent run.  This method is called to update the state of the agent run with the  provided state.  Args:  state: The run state.  """ ... 

state `property` `writable`

state: Any

Get the current state of the agent run.

UIAdapter `dataclass`

Bases: ABC, Generic[RunInputT, MessageT, EventT, AgentDepsT, OutputDataT]

Base class for UI adapters.

This class is responsible for transforming agent run input received from the frontend into arguments for Agent.run_stream_events(), running the agent, and then transforming Pydantic AI events into protocol-specific events.

The event stream transformation is handled by a protocol-specific UIEventStream subclass.

Source code in pydantic_ai_slim/pydantic_ai/ui/_adapter.py

@dataclass class UIAdapter(ABC, Generic[RunInputT, MessageT, EventT, AgentDepsT, OutputDataT]):  """Base class for UI adapters.  This class is responsible for transforming agent run input received from the frontend into arguments for [`Agent.run_stream_events()`][pydantic_ai.Agent.run_stream_events], running the agent, and then transforming Pydantic AI events into protocol-specific events.  The event stream transformation is handled by a protocol-specific [`UIEventStream`][pydantic_ai.ui.UIEventStream] subclass.  """ agent: AbstractAgent[AgentDepsT, OutputDataT]  """The Pydantic AI agent to run.""" run_input: RunInputT  """The protocol-specific run input object.""" _: KW_ONLY accept: str | None = None  """The `Accept` header value of the request, used to determine how to encode the protocol-specific events for the streaming response.""" @classmethod async def from_request( cls, request: Request, *, agent: AbstractAgent[AgentDepsT, OutputDataT] ) -> UIAdapter[RunInputT, MessageT, EventT, AgentDepsT, OutputDataT]:  """Create an adapter from a request.""" return cls( agent=agent, run_input=cls.build_run_input(await request.body()), accept=request.headers.get('accept'), ) @classmethod @abstractmethod def build_run_input(cls, body: bytes) -> RunInputT:  """Build a protocol-specific run input object from the request body.""" raise NotImplementedError @classmethod @abstractmethod def load_messages(cls, messages: Sequence[MessageT]) -> list[ModelMessage]:  """Transform protocol-specific messages into Pydantic AI messages.""" raise NotImplementedError @classmethod def dump_messages(cls, messages: Sequence[ModelMessage]) -> list[MessageT]:  """Transform Pydantic AI messages into protocol-specific messages.""" raise NotImplementedError @abstractmethod def build_event_stream(self) -> UIEventStream[RunInputT, EventT, AgentDepsT, OutputDataT]:  """Build a protocol-specific event stream transformer.""" raise NotImplementedError @cached_property @abstractmethod def messages(self) -> list[ModelMessage]:  """Pydantic AI messages from the protocol-specific run input.""" raise NotImplementedError @cached_property def toolset(self) -> AbstractToolset[AgentDepsT] | None:  """Toolset representing frontend tools from the protocol-specific run input.""" return None @cached_property def state(self) -> dict[str, Any] | None:  """Frontend state from the protocol-specific run input.""" return None def transform_stream( self, stream: AsyncIterator[NativeEvent], on_complete: OnCompleteFunc[EventT] | None = None, ) -> AsyncIterator[EventT]:  """Transform a stream of Pydantic AI events into protocol-specific events.  Args:  stream: The stream of Pydantic AI events to transform.  on_complete: Optional callback function called when the agent run completes successfully.  The callback receives the completed [`AgentRunResult`][pydantic_ai.agent.AgentRunResult] and can optionally yield additional protocol-specific events.  """ return self.build_event_stream().transform_stream(stream, on_complete=on_complete) def encode_stream(self, stream: AsyncIterator[EventT]) -> AsyncIterator[str]:  """Encode a stream of protocol-specific events as strings according to the `Accept` header value.  Args:  stream: The stream of protocol-specific events to encode.  """ return self.build_event_stream().encode_stream(stream) def streaming_response(self, stream: AsyncIterator[EventT]) -> StreamingResponse:  """Generate a streaming response from a stream of protocol-specific events.  Args:  stream: The stream of protocol-specific events to encode.  """ return self.build_event_stream().streaming_response(stream) def run_stream_native( self, *, output_type: OutputSpec[Any] | None = None, message_history: Sequence[ModelMessage] | None = None, deferred_tool_results: DeferredToolResults | None = None, model: Model | KnownModelName | str | None = None, instructions: Instructions[AgentDepsT] = None, deps: AgentDepsT = None, model_settings: ModelSettings | None = None, usage_limits: UsageLimits | None = None, usage: RunUsage | None = None, infer_name: bool = True, toolsets: Sequence[AbstractToolset[AgentDepsT]] | None = None, builtin_tools: Sequence[AbstractBuiltinTool] | None = None, ) -> AsyncIterator[NativeEvent]:  """Run the agent with the protocol-specific run input and stream Pydantic AI events.  Args:  output_type: Custom output type to use for this run, `output_type` may only be used if the agent has no  output validators since output validators would expect an argument that matches the agent's output type.  message_history: History of the conversation so far.  deferred_tool_results: Optional results for deferred tool calls in the message history.  model: Optional model to use for this run, required if `model` was not set when creating the agent.  instructions: Optional additional instructions to use for this run.  deps: Optional dependencies to use for this run.  model_settings: Optional settings to use for this model's request.  usage_limits: Optional limits on model request count or token usage.  usage: Optional usage to start with, useful for resuming a conversation or agents used in tools.  infer_name: Whether to try to infer the agent name from the call frame if it's not set.  toolsets: Optional additional toolsets for this run.  builtin_tools: Optional additional builtin tools to use for this run.  """ message_history = [*(message_history or []), *self.messages] toolset = self.toolset if toolset: output_type = [output_type or self.agent.output_type, DeferredToolRequests] toolsets = [*(toolsets or []), toolset] if isinstance(deps, StateHandler): raw_state = self.state or {} if isinstance(deps.state, BaseModel): state = type(deps.state).model_validate(raw_state) else: state = raw_state deps.state = state elif self.state: warnings.warn( f'State was provided but `deps` of type `{type(deps).__name__}` does not implement the `StateHandler` protocol, so the state was ignored. Use `StateDeps[...]` or implement `StateHandler` to receive AG-UI state.', UserWarning, stacklevel=2, ) return self.agent.run_stream_events( output_type=output_type, message_history=message_history, deferred_tool_results=deferred_tool_results, model=model, deps=deps, model_settings=model_settings, instructions=instructions, usage_limits=usage_limits, usage=usage, infer_name=infer_name, toolsets=toolsets, builtin_tools=builtin_tools, ) def run_stream( self, *, output_type: OutputSpec[Any] | None = None, message_history: Sequence[ModelMessage] | None = None, deferred_tool_results: DeferredToolResults | None = None, model: Model | KnownModelName | str | None = None, instructions: Instructions[AgentDepsT] = None, deps: AgentDepsT = None, model_settings: ModelSettings | None = None, usage_limits: UsageLimits | None = None, usage: RunUsage | None = None, infer_name: bool = True, toolsets: Sequence[AbstractToolset[AgentDepsT]] | None = None, builtin_tools: Sequence[AbstractBuiltinTool] | None = None, on_complete: OnCompleteFunc[EventT] | None = None, ) -> AsyncIterator[EventT]:  """Run the agent with the protocol-specific run input and stream protocol-specific events.  Args:  output_type: Custom output type to use for this run, `output_type` may only be used if the agent has no  output validators since output validators would expect an argument that matches the agent's output type.  message_history: History of the conversation so far.  deferred_tool_results: Optional results for deferred tool calls in the message history.  model: Optional model to use for this run, required if `model` was not set when creating the agent.  instructions: Optional additional instructions to use for this run.  deps: Optional dependencies to use for this run.  model_settings: Optional settings to use for this model's request.  usage_limits: Optional limits on model request count or token usage.  usage: Optional usage to start with, useful for resuming a conversation or agents used in tools.  infer_name: Whether to try to infer the agent name from the call frame if it's not set.  toolsets: Optional additional toolsets for this run.  builtin_tools: Optional additional builtin tools to use for this run.  on_complete: Optional callback function called when the agent run completes successfully.  The callback receives the completed [`AgentRunResult`][pydantic_ai.agent.AgentRunResult] and can optionally yield additional protocol-specific events.  """ return self.transform_stream( self.run_stream_native( output_type=output_type, message_history=message_history, deferred_tool_results=deferred_tool_results, model=model, instructions=instructions, deps=deps, model_settings=model_settings, usage_limits=usage_limits, usage=usage, infer_name=infer_name, toolsets=toolsets, builtin_tools=builtin_tools, ), on_complete=on_complete, ) @classmethod async def dispatch_request( cls, request: Request, *, agent: AbstractAgent[AgentDepsT, OutputDataT], message_history: Sequence[ModelMessage] | None = None, deferred_tool_results: DeferredToolResults | None = None, model: Model | KnownModelName | str | None = None, instructions: Instructions[AgentDepsT] = None, deps: AgentDepsT = None, output_type: OutputSpec[Any] | None = None, model_settings: ModelSettings | None = None, usage_limits: UsageLimits | None = None, usage: RunUsage | None = None, infer_name: bool = True, toolsets: Sequence[AbstractToolset[AgentDepsT]] | None = None, builtin_tools: Sequence[AbstractBuiltinTool] | None = None, on_complete: OnCompleteFunc[EventT] | None = None, ) -> Response:  """Handle a protocol-specific HTTP request by running the agent and returning a streaming response of protocol-specific events.  Args:  request: The incoming Starlette/FastAPI request.  agent: The agent to run.  output_type: Custom output type to use for this run, `output_type` may only be used if the agent has no  output validators since output validators would expect an argument that matches the agent's output type.  message_history: History of the conversation so far.  deferred_tool_results: Optional results for deferred tool calls in the message history.  model: Optional model to use for this run, required if `model` was not set when creating the agent.  instructions: Optional additional instructions to use for this run.  deps: Optional dependencies to use for this run.  model_settings: Optional settings to use for this model's request.  usage_limits: Optional limits on model request count or token usage.  usage: Optional usage to start with, useful for resuming a conversation or agents used in tools.  infer_name: Whether to try to infer the agent name from the call frame if it's not set.  toolsets: Optional additional toolsets for this run.  builtin_tools: Optional additional builtin tools to use for this run.  on_complete: Optional callback function called when the agent run completes successfully.  The callback receives the completed [`AgentRunResult`][pydantic_ai.agent.AgentRunResult] and can optionally yield additional protocol-specific events.  Returns:  A streaming Starlette response with protocol-specific events encoded per the request's `Accept` header value.  """ try: from starlette.responses import Response except ImportError as e: # pragma: no cover raise ImportError( 'Please install the `starlette` package to use `dispatch_request()` method, ' 'you can use the `ui` optional group — `pip install "pydantic-ai-slim[ui]"`' ) from e try: adapter = await cls.from_request(request, agent=agent) except ValidationError as e: # pragma: no cover return Response( content=e.json(), media_type='application/json', status_code=HTTPStatus.UNPROCESSABLE_ENTITY, ) return adapter.streaming_response( adapter.run_stream( message_history=message_history, deferred_tool_results=deferred_tool_results, deps=deps, output_type=output_type, model=model, instructions=instructions, model_settings=model_settings, usage_limits=usage_limits, usage=usage, infer_name=infer_name, toolsets=toolsets, builtin_tools=builtin_tools, on_complete=on_complete, ), ) 

agent `instance-attribute`

agent: AbstractAgent[AgentDepsT, OutputDataT]

The Pydantic AI agent to run.

run_input `instance-attribute`

run_input: RunInputT

The protocol-specific run input object.

accept `class-attribute` `instance-attribute`

accept: str | None = None

The Accept header value of the request, used to determine how to encode the protocol-specific events for the streaming response.

from_request `async` `classmethod`

from_request( request: Request, *, agent: AbstractAgent[AgentDepsT, OutputDataT] ) -> UIAdapter[ RunInputT, MessageT, EventT, AgentDepsT, OutputDataT ]

Create an adapter from a request.

Source code in pydantic_ai_slim/pydantic_ai/ui/_adapter.py

@classmethod async def from_request( cls, request: Request, *, agent: AbstractAgent[AgentDepsT, OutputDataT] ) -> UIAdapter[RunInputT, MessageT, EventT, AgentDepsT, OutputDataT]:  """Create an adapter from a request.""" return cls( agent=agent, run_input=cls.build_run_input(await request.body()), accept=request.headers.get('accept'), ) 

build_run_input `abstractmethod` `classmethod`

build_run_input(body: bytes) -> RunInputT

Build a protocol-specific run input object from the request body.

Source code in pydantic_ai_slim/pydantic_ai/ui/_adapter.py

@classmethod @abstractmethod def build_run_input(cls, body: bytes) -> RunInputT:  """Build a protocol-specific run input object from the request body.""" raise NotImplementedError 

load_messages `abstractmethod` `classmethod`

load_messages( messages: Sequence[MessageT], ) -> list[ModelMessage]

Transform protocol-specific messages into Pydantic AI messages.

Source code in pydantic_ai_slim/pydantic_ai/ui/_adapter.py

@classmethod @abstractmethod def load_messages(cls, messages: Sequence[MessageT]) -> list[ModelMessage]:  """Transform protocol-specific messages into Pydantic AI messages.""" raise NotImplementedError 

dump_messages `classmethod`

dump_messages( messages: Sequence[ModelMessage], ) -> list[MessageT]

Transform Pydantic AI messages into protocol-specific messages.

Source code in pydantic_ai_slim/pydantic_ai/ui/_adapter.py

@classmethod def dump_messages(cls, messages: Sequence[ModelMessage]) -> list[MessageT]:  """Transform Pydantic AI messages into protocol-specific messages.""" raise NotImplementedError 

build_event_stream `abstractmethod`

build_event_stream() -> ( UIEventStream[ RunInputT, EventT, AgentDepsT, OutputDataT ] )

Build a protocol-specific event stream transformer.

Source code in pydantic_ai_slim/pydantic_ai/ui/_adapter.py

@abstractmethod def build_event_stream(self) -> UIEventStream[RunInputT, EventT, AgentDepsT, OutputDataT]:  """Build a protocol-specific event stream transformer.""" raise NotImplementedError 

messages `abstractmethod` `cached` `property`

messages: list[ModelMessage]

Pydantic AI messages from the protocol-specific run input.

toolset `cached` `property`

toolset: AbstractToolset[AgentDepsT] | None

Toolset representing frontend tools from the protocol-specific run input.

state `cached` `property`

state: dict[str, Any] | None

Frontend state from the protocol-specific run input.

transform_stream

transform_stream( stream: AsyncIterator[NativeEvent], on_complete: OnCompleteFunc[EventT] | None = None, ) -> AsyncIterator[EventT]

Transform a stream of Pydantic AI events into protocol-specific events.

Parameters:

Name	Type	Description	Default
`stream`	`AsyncIterator[NativeEvent]`	The stream of Pydantic AI events to transform.	required
`on_complete`	`OnCompleteFunc[EventT] \| None`	Optional callback function called when the agent run completes successfully. The callback receives the completed `AgentRunResult` and can optionally yield additional protocol-specific events.	`None`

Source code in pydantic_ai_slim/pydantic_ai/ui/_adapter.py

def transform_stream( self, stream: AsyncIterator[NativeEvent], on_complete: OnCompleteFunc[EventT] | None = None, ) -> AsyncIterator[EventT]:  """Transform a stream of Pydantic AI events into protocol-specific events.  Args:  stream: The stream of Pydantic AI events to transform.  on_complete: Optional callback function called when the agent run completes successfully.  The callback receives the completed [`AgentRunResult`][pydantic_ai.agent.AgentRunResult] and can optionally yield additional protocol-specific events.  """ return self.build_event_stream().transform_stream(stream, on_complete=on_complete) 

encode_stream

encode_stream( stream: AsyncIterator[EventT], ) -> AsyncIterator[str]

Encode a stream of protocol-specific events as strings according to the Accept header value.

Parameters:

Name	Type	Description	Default
`stream`	`AsyncIterator[EventT]`	The stream of protocol-specific events to encode.	required

Source code in pydantic_ai_slim/pydantic_ai/ui/_adapter.py

def encode_stream(self, stream: AsyncIterator[EventT]) -> AsyncIterator[str]:  """Encode a stream of protocol-specific events as strings according to the `Accept` header value.  Args:  stream: The stream of protocol-specific events to encode.  """ return self.build_event_stream().encode_stream(stream) 

streaming_response

streaming_response( stream: AsyncIterator[EventT], ) -> StreamingResponse

Generate a streaming response from a stream of protocol-specific events.

Parameters:

Name	Type	Description	Default
`stream`	`AsyncIterator[EventT]`	The stream of protocol-specific events to encode.	required

Source code in pydantic_ai_slim/pydantic_ai/ui/_adapter.py

def streaming_response(self, stream: AsyncIterator[EventT]) -> StreamingResponse:  """Generate a streaming response from a stream of protocol-specific events.  Args:  stream: The stream of protocol-specific events to encode.  """ return self.build_event_stream().streaming_response(stream) 

run_stream_native

run_stream_native( *, output_type: OutputSpec[Any] | None = None, message_history: Sequence[ModelMessage] | None = None, deferred_tool_results: ( DeferredToolResults | None ) = None, model: Model | KnownModelName | str | None = None, instructions: Instructions[AgentDepsT] = None, deps: AgentDepsT = None, model_settings: ModelSettings | None = None, usage_limits: UsageLimits | None = None, usage: RunUsage | None = None, infer_name: bool = True, toolsets: ( Sequence[AbstractToolset[AgentDepsT]] | None ) = None, builtin_tools: ( Sequence[AbstractBuiltinTool] | None ) = None ) -> AsyncIterator[NativeEvent]

Run the agent with the protocol-specific run input and stream Pydantic AI events.

Parameters:

Name	Type	Description	Default
`output_type`	`OutputSpec[Any] \| None`	Custom output type to use for this run, `output_type` may only be used if the agent has no output validators since output validators would expect an argument that matches the agent's output type.	`None`
`message_history`	`Sequence[ModelMessage] \| None`	History of the conversation so far.	`None`
`deferred_tool_results`	`DeferredToolResults \| None`	Optional results for deferred tool calls in the message history.	`None`
`model`	`Model \| KnownModelName \| str \| None`	Optional model to use for this run, required if `model` was not set when creating the agent.	`None`
`instructions`	`Instructions[AgentDepsT]`	Optional additional instructions to use for this run.	`None`
`deps`	`AgentDepsT`	Optional dependencies to use for this run.	`None`
`model_settings`	`ModelSettings \| None`	Optional settings to use for this model's request.	`None`
`usage_limits`	`UsageLimits \| None`	Optional limits on model request count or token usage.	`None`
`usage`	`RunUsage \| None`	Optional usage to start with, useful for resuming a conversation or agents used in tools.	`None`
`infer_name`	`bool`	Whether to try to infer the agent name from the call frame if it's not set.	`True`
`toolsets`	`Sequence[AbstractToolset[AgentDepsT]] \| None`	Optional additional toolsets for this run.	`None`
`builtin_tools`	`Sequence[AbstractBuiltinTool] \| None`	Optional additional builtin tools to use for this run.	`None`

Source code in pydantic_ai_slim/pydantic_ai/ui/_adapter.py

def run_stream_native( self, *, output_type: OutputSpec[Any] | None = None, message_history: Sequence[ModelMessage] | None = None, deferred_tool_results: DeferredToolResults | None = None, model: Model | KnownModelName | str | None = None, instructions: Instructions[AgentDepsT] = None, deps: AgentDepsT = None, model_settings: ModelSettings | None = None, usage_limits: UsageLimits | None = None, usage: RunUsage | None = None, infer_name: bool = True, toolsets: Sequence[AbstractToolset[AgentDepsT]] | None = None, builtin_tools: Sequence[AbstractBuiltinTool] | None = None, ) -> AsyncIterator[NativeEvent]:  """Run the agent with the protocol-specific run input and stream Pydantic AI events.  Args:  output_type: Custom output type to use for this run, `output_type` may only be used if the agent has no  output validators since output validators would expect an argument that matches the agent's output type.  message_history: History of the conversation so far.  deferred_tool_results: Optional results for deferred tool calls in the message history.  model: Optional model to use for this run, required if `model` was not set when creating the agent.  instructions: Optional additional instructions to use for this run.  deps: Optional dependencies to use for this run.  model_settings: Optional settings to use for this model's request.  usage_limits: Optional limits on model request count or token usage.  usage: Optional usage to start with, useful for resuming a conversation or agents used in tools.  infer_name: Whether to try to infer the agent name from the call frame if it's not set.  toolsets: Optional additional toolsets for this run.  builtin_tools: Optional additional builtin tools to use for this run.  """ message_history = [*(message_history or []), *self.messages] toolset = self.toolset if toolset: output_type = [output_type or self.agent.output_type, DeferredToolRequests] toolsets = [*(toolsets or []), toolset] if isinstance(deps, StateHandler): raw_state = self.state or {} if isinstance(deps.state, BaseModel): state = type(deps.state).model_validate(raw_state) else: state = raw_state deps.state = state elif self.state: warnings.warn( f'State was provided but `deps` of type `{type(deps).__name__}` does not implement the `StateHandler` protocol, so the state was ignored. Use `StateDeps[...]` or implement `StateHandler` to receive AG-UI state.', UserWarning, stacklevel=2, ) return self.agent.run_stream_events( output_type=output_type, message_history=message_history, deferred_tool_results=deferred_tool_results, model=model, deps=deps, model_settings=model_settings, instructions=instructions, usage_limits=usage_limits, usage=usage, infer_name=infer_name, toolsets=toolsets, builtin_tools=builtin_tools, ) 

run_stream

run_stream( *, output_type: OutputSpec[Any] | None = None, message_history: Sequence[ModelMessage] | None = None, deferred_tool_results: ( DeferredToolResults | None ) = None, model: Model | KnownModelName | str | None = None, instructions: Instructions[AgentDepsT] = None, deps: AgentDepsT = None, model_settings: ModelSettings | None = None, usage_limits: UsageLimits | None = None, usage: RunUsage | None = None, infer_name: bool = True, toolsets: ( Sequence[AbstractToolset[AgentDepsT]] | None ) = None, builtin_tools: ( Sequence[AbstractBuiltinTool] | None ) = None, on_complete: OnCompleteFunc[EventT] | None = None ) -> AsyncIterator[EventT]

Run the agent with the protocol-specific run input and stream protocol-specific events.

Parameters:

Name	Type	Description	Default
`output_type`	`OutputSpec[Any] \| None`	Custom output type to use for this run, `output_type` may only be used if the agent has no output validators since output validators would expect an argument that matches the agent's output type.	`None`
`message_history`	`Sequence[ModelMessage] \| None`	History of the conversation so far.	`None`
`deferred_tool_results`	`DeferredToolResults \| None`	Optional results for deferred tool calls in the message history.	`None`
`model`	`Model \| KnownModelName \| str \| None`	Optional model to use for this run, required if `model` was not set when creating the agent.	`None`
`instructions`	`Instructions[AgentDepsT]`	Optional additional instructions to use for this run.	`None`
`deps`	`AgentDepsT`	Optional dependencies to use for this run.	`None`
`model_settings`	`ModelSettings \| None`	Optional settings to use for this model's request.	`None`
`usage_limits`	`UsageLimits \| None`	Optional limits on model request count or token usage.	`None`
`usage`	`RunUsage \| None`	Optional usage to start with, useful for resuming a conversation or agents used in tools.	`None`
`infer_name`	`bool`	Whether to try to infer the agent name from the call frame if it's not set.	`True`
`toolsets`	`Sequence[AbstractToolset[AgentDepsT]] \| None`	Optional additional toolsets for this run.	`None`
`builtin_tools`	`Sequence[AbstractBuiltinTool] \| None`	Optional additional builtin tools to use for this run.	`None`
`on_complete`	`OnCompleteFunc[EventT] \| None`	Optional callback function called when the agent run completes successfully. The callback receives the completed `AgentRunResult` and can optionally yield additional protocol-specific events.	`None`

Source code in pydantic_ai_slim/pydantic_ai/ui/_adapter.py

def run_stream( self, *, output_type: OutputSpec[Any] | None = None, message_history: Sequence[ModelMessage] | None = None, deferred_tool_results: DeferredToolResults | None = None, model: Model | KnownModelName | str | None = None, instructions: Instructions[AgentDepsT] = None, deps: AgentDepsT = None, model_settings: ModelSettings | None = None, usage_limits: UsageLimits | None = None, usage: RunUsage | None = None, infer_name: bool = True, toolsets: Sequence[AbstractToolset[AgentDepsT]] | None = None, builtin_tools: Sequence[AbstractBuiltinTool] | None = None, on_complete: OnCompleteFunc[EventT] | None = None, ) -> AsyncIterator[EventT]:  """Run the agent with the protocol-specific run input and stream protocol-specific events.  Args:  output_type: Custom output type to use for this run, `output_type` may only be used if the agent has no  output validators since output validators would expect an argument that matches the agent's output type.  message_history: History of the conversation so far.  deferred_tool_results: Optional results for deferred tool calls in the message history.  model: Optional model to use for this run, required if `model` was not set when creating the agent.  instructions: Optional additional instructions to use for this run.  deps: Optional dependencies to use for this run.  model_settings: Optional settings to use for this model's request.  usage_limits: Optional limits on model request count or token usage.  usage: Optional usage to start with, useful for resuming a conversation or agents used in tools.  infer_name: Whether to try to infer the agent name from the call frame if it's not set.  toolsets: Optional additional toolsets for this run.  builtin_tools: Optional additional builtin tools to use for this run.  on_complete: Optional callback function called when the agent run completes successfully.  The callback receives the completed [`AgentRunResult`][pydantic_ai.agent.AgentRunResult] and can optionally yield additional protocol-specific events.  """ return self.transform_stream( self.run_stream_native( output_type=output_type, message_history=message_history, deferred_tool_results=deferred_tool_results, model=model, instructions=instructions, deps=deps, model_settings=model_settings, usage_limits=usage_limits, usage=usage, infer_name=infer_name, toolsets=toolsets, builtin_tools=builtin_tools, ), on_complete=on_complete, ) 

dispatch_request `async` `classmethod`

dispatch_request( request: Request, *, agent: AbstractAgent[AgentDepsT, OutputDataT], message_history: Sequence[ModelMessage] | None = None, deferred_tool_results: ( DeferredToolResults | None ) = None, model: Model | KnownModelName | str | None = None, instructions: Instructions[AgentDepsT] = None, deps: AgentDepsT = None, output_type: OutputSpec[Any] | None = None, model_settings: ModelSettings | None = None, usage_limits: UsageLimits | None = None, usage: RunUsage | None = None, infer_name: bool = True, toolsets: ( Sequence[AbstractToolset[AgentDepsT]] | None ) = None, builtin_tools: ( Sequence[AbstractBuiltinTool] | None ) = None, on_complete: OnCompleteFunc[EventT] | None = None ) -> Response

Handle a protocol-specific HTTP request by running the agent and returning a streaming response of protocol-specific events.

Parameters:

Name	Type	Description	Default
`request`	`Request`	The incoming Starlette/FastAPI request.	required
`agent`	`AbstractAgent[AgentDepsT, OutputDataT]`	The agent to run.	required
`output_type`	`OutputSpec[Any] \| None`	Custom output type to use for this run, `output_type` may only be used if the agent has no output validators since output validators would expect an argument that matches the agent's output type.	`None`
`message_history`	`Sequence[ModelMessage] \| None`	History of the conversation so far.	`None`
`deferred_tool_results`	`DeferredToolResults \| None`	Optional results for deferred tool calls in the message history.	`None`
`model`	`Model \| KnownModelName \| str \| None`	Optional model to use for this run, required if `model` was not set when creating the agent.	`None`
`instructions`	`Instructions[AgentDepsT]`	Optional additional instructions to use for this run.	`None`
`deps`	`AgentDepsT`	Optional dependencies to use for this run.	`None`
`model_settings`	`ModelSettings \| None`	Optional settings to use for this model's request.	`None`
`usage_limits`	`UsageLimits \| None`	Optional limits on model request count or token usage.	`None`
`usage`	`RunUsage \| None`	Optional usage to start with, useful for resuming a conversation or agents used in tools.	`None`
`infer_name`	`bool`	Whether to try to infer the agent name from the call frame if it's not set.	`True`
`toolsets`	`Sequence[AbstractToolset[AgentDepsT]] \| None`	Optional additional toolsets for this run.	`None`
`builtin_tools`	`Sequence[AbstractBuiltinTool] \| None`	Optional additional builtin tools to use for this run.	`None`
`on_complete`	`OnCompleteFunc[EventT] \| None`	Optional callback function called when the agent run completes successfully. The callback receives the completed `AgentRunResult` and can optionally yield additional protocol-specific events.	`None`

Returns:

Type	Description
`Response`	A streaming Starlette response with protocol-specific events encoded per the request's `Accept` header value.

Source code in pydantic_ai_slim/pydantic_ai/ui/_adapter.py

@classmethod async def dispatch_request( cls, request: Request, *, agent: AbstractAgent[AgentDepsT, OutputDataT], message_history: Sequence[ModelMessage] | None = None, deferred_tool_results: DeferredToolResults | None = None, model: Model | KnownModelName | str | None = None, instructions: Instructions[AgentDepsT] = None, deps: AgentDepsT = None, output_type: OutputSpec[Any] | None = None, model_settings: ModelSettings | None = None, usage_limits: UsageLimits | None = None, usage: RunUsage | None = None, infer_name: bool = True, toolsets: Sequence[AbstractToolset[AgentDepsT]] | None = None, builtin_tools: Sequence[AbstractBuiltinTool] | None = None, on_complete: OnCompleteFunc[EventT] | None = None, ) -> Response:  """Handle a protocol-specific HTTP request by running the agent and returning a streaming response of protocol-specific events.  Args:  request: The incoming Starlette/FastAPI request.  agent: The agent to run.  output_type: Custom output type to use for this run, `output_type` may only be used if the agent has no  output validators since output validators would expect an argument that matches the agent's output type.  message_history: History of the conversation so far.  deferred_tool_results: Optional results for deferred tool calls in the message history.  model: Optional model to use for this run, required if `model` was not set when creating the agent.  instructions: Optional additional instructions to use for this run.  deps: Optional dependencies to use for this run.  model_settings: Optional settings to use for this model's request.  usage_limits: Optional limits on model request count or token usage.  usage: Optional usage to start with, useful for resuming a conversation or agents used in tools.  infer_name: Whether to try to infer the agent name from the call frame if it's not set.  toolsets: Optional additional toolsets for this run.  builtin_tools: Optional additional builtin tools to use for this run.  on_complete: Optional callback function called when the agent run completes successfully.  The callback receives the completed [`AgentRunResult`][pydantic_ai.agent.AgentRunResult] and can optionally yield additional protocol-specific events.  Returns:  A streaming Starlette response with protocol-specific events encoded per the request's `Accept` header value.  """ try: from starlette.responses import Response except ImportError as e: # pragma: no cover raise ImportError( 'Please install the `starlette` package to use `dispatch_request()` method, ' 'you can use the `ui` optional group — `pip install "pydantic-ai-slim[ui]"`' ) from e try: adapter = await cls.from_request(request, agent=agent) except ValidationError as e: # pragma: no cover return Response( content=e.json(), media_type='application/json', status_code=HTTPStatus.UNPROCESSABLE_ENTITY, ) return adapter.streaming_response( adapter.run_stream( message_history=message_history, deferred_tool_results=deferred_tool_results, deps=deps, output_type=output_type, model=model, instructions=instructions, model_settings=model_settings, usage_limits=usage_limits, usage=usage, infer_name=infer_name, toolsets=toolsets, builtin_tools=builtin_tools, on_complete=on_complete, ), ) 

SSE_CONTENT_TYPE `module-attribute`

SSE_CONTENT_TYPE = 'text/event-stream'

Content type header value for Server-Sent Events (SSE).

NativeEvent `module-attribute`

NativeEvent: TypeAlias = ( AgentStreamEvent | AgentRunResultEvent[Any] )

Type alias for the native event type, which is either an AgentStreamEvent or an AgentRunResultEvent.

OnCompleteFunc `module-attribute`

OnCompleteFunc: TypeAlias = ( Callable[[AgentRunResult[Any]], None] | Callable[[AgentRunResult[Any]], Awaitable[None]] | Callable[[AgentRunResult[Any]], AsyncIterator[EventT]] )

Callback function type that receives the AgentRunResult of the completed run. Can be sync, async, or an async generator of protocol-specific events.

UIEventStream `dataclass`

Bases: ABC, Generic[RunInputT, EventT, AgentDepsT, OutputDataT]

Base class for UI event stream transformers.

This class is responsible for transforming Pydantic AI events into protocol-specific events.

Source code in pydantic_ai_slim/pydantic_ai/ui/_event_stream.py

@dataclass class UIEventStream(ABC, Generic[RunInputT, EventT, AgentDepsT, OutputDataT]):  """Base class for UI event stream transformers.  This class is responsible for transforming Pydantic AI events into protocol-specific events.  """ run_input: RunInputT accept: str | None = None  """The `Accept` header value of the request, used to determine how to encode the protocol-specific events for the streaming response.""" message_id: str = field(default_factory=lambda: str(uuid4()))  """The message ID to use for the next event.""" _turn: Literal['request', 'response'] | None = None _result: AgentRunResult[OutputDataT] | None = None _final_result_event: FinalResultEvent | None = None def new_message_id(self) -> str:  """Generate and store a new message ID.""" self.message_id = str(uuid4()) return self.message_id @property def response_headers(self) -> Mapping[str, str] | None:  """Response headers to return to the frontend.""" return None @property def content_type(self) -> str:  """Get the content type for the event stream, compatible with the `Accept` header value.  By default, this returns the Server-Sent Events content type (`text/event-stream`).  If a subclass supports other types as well, it should consider `self.accept` in [`encode_event()`][pydantic_ai.ui.UIEventStream.encode_event] and return the resulting content type.  """ return SSE_CONTENT_TYPE @abstractmethod def encode_event(self, event: EventT) -> str:  """Encode a protocol-specific event as a string.""" raise NotImplementedError async def encode_stream(self, stream: AsyncIterator[EventT]) -> AsyncIterator[str]:  """Encode a stream of protocol-specific events as strings according to the `Accept` header value.""" async for event in stream: yield self.encode_event(event) def streaming_response(self, stream: AsyncIterator[EventT]) -> StreamingResponse:  """Generate a streaming response from a stream of protocol-specific events.""" try: from starlette.responses import StreamingResponse except ImportError as e: # pragma: no cover raise ImportError( 'Please install the `starlette` package to use the `streaming_response()` method, ' 'you can use the `ui` optional group — `pip install "pydantic-ai-slim[ui]"`' ) from e return StreamingResponse( self.encode_stream(stream), headers=self.response_headers, media_type=self.content_type, ) async def transform_stream( # noqa: C901 self, stream: AsyncIterator[NativeEvent], on_complete: OnCompleteFunc[EventT] | None = None ) -> AsyncIterator[EventT]:  """Transform a stream of Pydantic AI events into protocol-specific events.  This method dispatches to specific hooks and `handle_*` methods that subclasses can override:  - [`before_stream()`][pydantic_ai.ui.UIEventStream.before_stream]  - [`after_stream()`][pydantic_ai.ui.UIEventStream.after_stream]  - [`on_error()`][pydantic_ai.ui.UIEventStream.on_error]  - [`before_request()`][pydantic_ai.ui.UIEventStream.before_request]  - [`after_request()`][pydantic_ai.ui.UIEventStream.after_request]  - [`before_response()`][pydantic_ai.ui.UIEventStream.before_response]  - [`after_response()`][pydantic_ai.ui.UIEventStream.after_response]  - [`handle_event()`][pydantic_ai.ui.UIEventStream.handle_event]  Args:  stream: The stream of Pydantic AI events to transform.  on_complete: Optional callback function called when the agent run completes successfully.  The callback receives the completed [`AgentRunResult`][pydantic_ai.agent.AgentRunResult] and can optionally yield additional protocol-specific events.  """ async for e in self.before_stream(): yield e try: async for event in stream: if isinstance(event, PartStartEvent): async for e in self._turn_to('response'): yield e elif isinstance(event, FunctionToolCallEvent): async for e in self._turn_to('request'): yield e elif isinstance(event, AgentRunResultEvent): if ( self._final_result_event and (tool_call_id := self._final_result_event.tool_call_id) and (tool_name := self._final_result_event.tool_name) ): async for e in self._turn_to('request'): yield e self._final_result_event = None # Ensure the stream does not end on a dangling tool call without a result. output_tool_result_event = FunctionToolResultEvent( result=ToolReturnPart( tool_call_id=tool_call_id, tool_name=tool_name, content='Final result processed.', ) ) async for e in self.handle_function_tool_result(output_tool_result_event): yield e result = cast(AgentRunResult[OutputDataT], event.result) self._result = result async for e in self._turn_to(None): yield e if on_complete is not None: if inspect.isasyncgenfunction(on_complete): async for e in on_complete(result): yield e elif _utils.is_async_callable(on_complete): await on_complete(result) else: await _utils.run_in_executor(on_complete, result) elif isinstance(event, FinalResultEvent): self._final_result_event = event if isinstance(event, BuiltinToolCallEvent | BuiltinToolResultEvent): # pyright: ignore[reportDeprecated] # These events were deprecated before this feature was introduced continue async for e in self.handle_event(event): yield e except Exception as e: async for e in self.on_error(e): yield e finally: async for e in self._turn_to(None): yield e async for e in self.after_stream(): yield e async def _turn_to(self, to_turn: Literal['request', 'response'] | None) -> AsyncIterator[EventT]:  """Fire hooks when turning from request to response or vice versa.""" if to_turn == self._turn: return if self._turn == 'request': async for e in self.after_request(): yield e elif self._turn == 'response': async for e in self.after_response(): yield e self._turn = to_turn if to_turn == 'request': async for e in self.before_request(): yield e elif to_turn == 'response': async for e in self.before_response(): yield e async def handle_event(self, event: NativeEvent) -> AsyncIterator[EventT]:  """Transform a Pydantic AI event into one or more protocol-specific events.  This method dispatches to specific `handle_*` methods based on event type:  - [`PartStartEvent`][pydantic_ai.messages.PartStartEvent] -> [`handle_part_start()`][pydantic_ai.ui.UIEventStream.handle_part_start]  - [`PartDeltaEvent`][pydantic_ai.messages.PartDeltaEvent] -> `handle_part_delta`  - [`PartEndEvent`][pydantic_ai.messages.PartEndEvent] -> `handle_part_end`  - [`FinalResultEvent`][pydantic_ai.messages.FinalResultEvent] -> `handle_final_result`  - [`FunctionToolCallEvent`][pydantic_ai.messages.FunctionToolCallEvent] -> `handle_function_tool_call`  - [`FunctionToolResultEvent`][pydantic_ai.messages.FunctionToolResultEvent] -> `handle_function_tool_result`  - [`AgentRunResultEvent`][pydantic_ai.run.AgentRunResultEvent] -> `handle_run_result`  Subclasses are encouraged to override the individual `handle_*` methods rather than this one.  If you need specific behavior for all events, make sure you call the super method.  """ match event: case PartStartEvent(): async for e in self.handle_part_start(event): yield e case PartDeltaEvent(): async for e in self.handle_part_delta(event): yield e case PartEndEvent(): async for e in self.handle_part_end(event): yield e case FinalResultEvent(): async for e in self.handle_final_result(event): yield e case FunctionToolCallEvent(): async for e in self.handle_function_tool_call(event): yield e case FunctionToolResultEvent(): async for e in self.handle_function_tool_result(event): yield e case AgentRunResultEvent(): async for e in self.handle_run_result(event): yield e case _: pass async def handle_part_start(self, event: PartStartEvent) -> AsyncIterator[EventT]:  """Handle a `PartStartEvent`.  This method dispatches to specific `handle_*` methods based on part type:  - [`TextPart`][pydantic_ai.messages.TextPart] -> [`handle_text_start()`][pydantic_ai.ui.UIEventStream.handle_text_start]  - [`ThinkingPart`][pydantic_ai.messages.ThinkingPart] -> [`handle_thinking_start()`][pydantic_ai.ui.UIEventStream.handle_thinking_start]  - [`ToolCallPart`][pydantic_ai.messages.ToolCallPart] -> [`handle_tool_call_start()`][pydantic_ai.ui.UIEventStream.handle_tool_call_start]  - [`BuiltinToolCallPart`][pydantic_ai.messages.BuiltinToolCallPart] -> [`handle_builtin_tool_call_start()`][pydantic_ai.ui.UIEventStream.handle_builtin_tool_call_start]  - [`BuiltinToolReturnPart`][pydantic_ai.messages.BuiltinToolReturnPart] -> [`handle_builtin_tool_return()`][pydantic_ai.ui.UIEventStream.handle_builtin_tool_return]  - [`FilePart`][pydantic_ai.messages.FilePart] -> [`handle_file()`][pydantic_ai.ui.UIEventStream.handle_file]  Subclasses are encouraged to override the individual `handle_*` methods rather than this one.  If you need specific behavior for all part start events, make sure you call the super method.  Args:  event: The part start event.  """ part = event.part previous_part_kind = event.previous_part_kind match part: case TextPart(): async for e in self.handle_text_start(part, follows_text=previous_part_kind == 'text'): yield e case ThinkingPart(): async for e in self.handle_thinking_start(part, follows_thinking=previous_part_kind == 'thinking'): yield e case ToolCallPart(): async for e in self.handle_tool_call_start(part): yield e case BuiltinToolCallPart(): async for e in self.handle_builtin_tool_call_start(part): yield e case BuiltinToolReturnPart(): async for e in self.handle_builtin_tool_return(part): yield e case FilePart(): # pragma: no branch async for e in self.handle_file(part): yield e async def handle_part_delta(self, event: PartDeltaEvent) -> AsyncIterator[EventT]:  """Handle a PartDeltaEvent.  This method dispatches to specific `handle_*_delta` methods based on part delta type:  - [`TextPartDelta`][pydantic_ai.messages.TextPartDelta] -> [`handle_text_delta()`][pydantic_ai.ui.UIEventStream.handle_text_delta]  - [`ThinkingPartDelta`][pydantic_ai.messages.ThinkingPartDelta] -> [`handle_thinking_delta()`][pydantic_ai.ui.UIEventStream.handle_thinking_delta]  - [`ToolCallPartDelta`][pydantic_ai.messages.ToolCallPartDelta] -> [`handle_tool_call_delta()`][pydantic_ai.ui.UIEventStream.handle_tool_call_delta]  Subclasses are encouraged to override the individual `handle_*_delta` methods rather than this one.  If you need specific behavior for all part delta events, make sure you call the super method.  Args:  event: The PartDeltaEvent.  """ delta = event.delta match delta: case TextPartDelta(): async for e in self.handle_text_delta(delta): yield e case ThinkingPartDelta(): async for e in self.handle_thinking_delta(delta): yield e case ToolCallPartDelta(): # pragma: no branch async for e in self.handle_tool_call_delta(delta): yield e async def handle_part_end(self, event: PartEndEvent) -> AsyncIterator[EventT]:  """Handle a `PartEndEvent`.  This method dispatches to specific `handle_*_end` methods based on part type:  - [`TextPart`][pydantic_ai.messages.TextPart] -> [`handle_text_end()`][pydantic_ai.ui.UIEventStream.handle_text_end]  - [`ThinkingPart`][pydantic_ai.messages.ThinkingPart] -> [`handle_thinking_end()`][pydantic_ai.ui.UIEventStream.handle_thinking_end]  - [`ToolCallPart`][pydantic_ai.messages.ToolCallPart] -> [`handle_tool_call_end()`][pydantic_ai.ui.UIEventStream.handle_tool_call_end]  - [`BuiltinToolCallPart`][pydantic_ai.messages.BuiltinToolCallPart] -> [`handle_builtin_tool_call_end()`][pydantic_ai.ui.UIEventStream.handle_builtin_tool_call_end]  Subclasses are encouraged to override the individual `handle_*_end` methods rather than this one.  If you need specific behavior for all part end events, make sure you call the super method.  Args:  event: The part end event.  """ part = event.part next_part_kind = event.next_part_kind match part: case TextPart(): async for e in self.handle_text_end(part, followed_by_text=next_part_kind == 'text'): yield e case ThinkingPart(): async for e in self.handle_thinking_end(part, followed_by_thinking=next_part_kind == 'thinking'): yield e case ToolCallPart(): async for e in self.handle_tool_call_end(part): yield e case BuiltinToolCallPart(): async for e in self.handle_builtin_tool_call_end(part): yield e case BuiltinToolReturnPart() | FilePart(): # pragma: no cover # These don't have deltas, so they don't need to be ended. pass async def before_stream(self) -> AsyncIterator[EventT]:  """Yield events before agent streaming starts.  This hook is called before any agent events are processed.  Override this to inject custom events at the start of the stream.  """ return # pragma: no cover yield # Make this an async generator async def after_stream(self) -> AsyncIterator[EventT]:  """Yield events after agent streaming completes.  This hook is called after all agent events have been processed.  Override this to inject custom events at the end of the stream.  """ return # pragma: no cover yield # Make this an async generator async def on_error(self, error: Exception) -> AsyncIterator[EventT]:  """Handle errors that occur during streaming.  Args:  error: The error that occurred during streaming.  """ return # pragma: no cover yield # Make this an async generator async def before_request(self) -> AsyncIterator[EventT]:  """Yield events before a model request is processed.  Override this to inject custom events at the start of the request.  """ return # pragma: lax no cover yield # Make this an async generator async def after_request(self) -> AsyncIterator[EventT]:  """Yield events after a model request is processed.  Override this to inject custom events at the end of the request.  """ return # pragma: lax no cover yield # Make this an async generator async def before_response(self) -> AsyncIterator[EventT]:  """Yield events before a model response is processed.  Override this to inject custom events at the start of the response.  """ return # pragma: no cover yield # Make this an async generator async def after_response(self) -> AsyncIterator[EventT]:  """Yield events after a model response is processed.  Override this to inject custom events at the end of the response.  """ return # pragma: lax no cover yield # Make this an async generator async def handle_text_start(self, part: TextPart, follows_text: bool = False) -> AsyncIterator[EventT]:  """Handle the start of a `TextPart`.  Args:  part: The text part.  follows_text: Whether the part is directly preceded by another text part. In this case, you may want to yield a "text-delta" event instead of a "text-start" event.  """ return # pragma: no cover yield # Make this an async generator async def handle_text_delta(self, delta: TextPartDelta) -> AsyncIterator[EventT]:  """Handle a `TextPartDelta`.  Args:  delta: The text part delta.  """ return # pragma: no cover yield # Make this an async generator async def handle_text_end(self, part: TextPart, followed_by_text: bool = False) -> AsyncIterator[EventT]:  """Handle the end of a `TextPart`.  Args:  part: The text part.  followed_by_text: Whether the part is directly followed by another text part. In this case, you may not want to yield a "text-end" event yet.  """ return # pragma: no cover yield # Make this an async generator async def handle_thinking_start(self, part: ThinkingPart, follows_thinking: bool = False) -> AsyncIterator[EventT]:  """Handle the start of a `ThinkingPart`.  Args:  part: The thinking part.  follows_thinking: Whether the part is directly preceded by another thinking part. In this case, you may want to yield a "thinking-delta" event instead of a "thinking-start" event.  """ return # pragma: no cover yield # Make this an async generator async def handle_thinking_delta(self, delta: ThinkingPartDelta) -> AsyncIterator[EventT]:  """Handle a `ThinkingPartDelta`.  Args:  delta: The thinking part delta.  """ return # pragma: no cover yield # Make this an async generator async def handle_thinking_end( self, part: ThinkingPart, followed_by_thinking: bool = False ) -> AsyncIterator[EventT]:  """Handle the end of a `ThinkingPart`.  Args:  part: The thinking part.  followed_by_thinking: Whether the part is directly followed by another thinking part. In this case, you may not want to yield a "thinking-end" event yet.  """ return # pragma: no cover yield # Make this an async generator async def handle_tool_call_start(self, part: ToolCallPart) -> AsyncIterator[EventT]:  """Handle the start of a `ToolCallPart`.  Args:  part: The tool call part.  """ return # pragma: no cover yield # Make this an async generator async def handle_tool_call_delta(self, delta: ToolCallPartDelta) -> AsyncIterator[EventT]:  """Handle a `ToolCallPartDelta`.  Args:  delta: The tool call part delta.  """ return # pragma: no cover yield # Make this an async generator async def handle_tool_call_end(self, part: ToolCallPart) -> AsyncIterator[EventT]:  """Handle the end of a `ToolCallPart`.  Args:  part: The tool call part.  """ return # pragma: no cover yield # Make this an async generator async def handle_builtin_tool_call_start(self, part: BuiltinToolCallPart) -> AsyncIterator[EventT]:  """Handle a `BuiltinToolCallPart` at start.  Args:  part: The builtin tool call part.  """ return # pragma: no cover yield # Make this an async generator async def handle_builtin_tool_call_end(self, part: BuiltinToolCallPart) -> AsyncIterator[EventT]:  """Handle the end of a `BuiltinToolCallPart`.  Args:  part: The builtin tool call part.  """ return # pragma: no cover yield # Make this an async generator async def handle_builtin_tool_return(self, part: BuiltinToolReturnPart) -> AsyncIterator[EventT]:  """Handle a `BuiltinToolReturnPart`.  Args:  part: The builtin tool return part.  """ return # pragma: no cover yield # Make this an async generator async def handle_file(self, part: FilePart) -> AsyncIterator[EventT]:  """Handle a `FilePart`.  Args:  part: The file part.  """ return # pragma: no cover yield # Make this an async generator async def handle_final_result(self, event: FinalResultEvent) -> AsyncIterator[EventT]:  """Handle a `FinalResultEvent`.  Args:  event: The final result event.  """ return yield # Make this an async generator async def handle_function_tool_call(self, event: FunctionToolCallEvent) -> AsyncIterator[EventT]:  """Handle a `FunctionToolCallEvent`.  Args:  event: The function tool call event.  """ return yield # Make this an async generator async def handle_function_tool_result(self, event: FunctionToolResultEvent) -> AsyncIterator[EventT]:  """Handle a `FunctionToolResultEvent`.  Args:  event: The function tool result event.  """ return # pragma: no cover yield # Make this an async generator async def handle_run_result(self, event: AgentRunResultEvent) -> AsyncIterator[EventT]:  """Handle an `AgentRunResultEvent`.  Args:  event: The agent run result event.  """ return yield # Make this an async generator 

accept `class-attribute` `instance-attribute`

accept: str | None = None

The Accept header value of the request, used to determine how to encode the protocol-specific events for the streaming response.

message_id `class-attribute` `instance-attribute`

message_id: str = field( default_factory=lambda: str(uuid4()) )

The message ID to use for the next event.

new_message_id

new_message_id() -> str

Generate and store a new message ID.

Source code in pydantic_ai_slim/pydantic_ai/ui/_event_stream.py

def new_message_id(self) -> str:  """Generate and store a new message ID.""" self.message_id = str(uuid4()) return self.message_id 

response_headers `property`

response_headers: Mapping[str, str] | None

Response headers to return to the frontend.

content_type `property`

content_type: str

Get the content type for the event stream, compatible with the Accept header value.

By default, this returns the Server-Sent Events content type (text/event-stream). If a subclass supports other types as well, it should consider self.accept in encode_event() and return the resulting content type.

encode_event `abstractmethod`

encode_event(event: EventT) -> str

Encode a protocol-specific event as a string.

Source code in pydantic_ai_slim/pydantic_ai/ui/_event_stream.py

@abstractmethod def encode_event(self, event: EventT) -> str:  """Encode a protocol-specific event as a string.""" raise NotImplementedError 

encode_stream `async`

encode_stream( stream: AsyncIterator[EventT], ) -> AsyncIterator[str]

Encode a stream of protocol-specific events as strings according to the Accept header value.

Source code in pydantic_ai_slim/pydantic_ai/ui/_event_stream.py

async def encode_stream(self, stream: AsyncIterator[EventT]) -> AsyncIterator[str]:  """Encode a stream of protocol-specific events as strings according to the `Accept` header value.""" async for event in stream: yield self.encode_event(event) 

streaming_response

streaming_response( stream: AsyncIterator[EventT], ) -> StreamingResponse

Generate a streaming response from a stream of protocol-specific events.

Source code in pydantic_ai_slim/pydantic_ai/ui/_event_stream.py

def streaming_response(self, stream: AsyncIterator[EventT]) -> StreamingResponse:  """Generate a streaming response from a stream of protocol-specific events.""" try: from starlette.responses import StreamingResponse except ImportError as e: # pragma: no cover raise ImportError( 'Please install the `starlette` package to use the `streaming_response()` method, ' 'you can use the `ui` optional group — `pip install "pydantic-ai-slim[ui]"`' ) from e return StreamingResponse( self.encode_stream(stream), headers=self.response_headers, media_type=self.content_type, ) 

transform_stream `async`

transform_stream( stream: AsyncIterator[NativeEvent], on_complete: OnCompleteFunc[EventT] | None = None, ) -> AsyncIterator[EventT]

Transform a stream of Pydantic AI events into protocol-specific events.

This method dispatches to specific hooks and handle_* methods that subclasses can override: - before_stream() - after_stream() - on_error() - before_request() - after_request() - before_response() - after_response() - handle_event()

Parameters:

Name	Type	Description	Default
`stream`	`AsyncIterator[NativeEvent]`	The stream of Pydantic AI events to transform.	required
`on_complete`	`OnCompleteFunc[EventT] \| None`	Optional callback function called when the agent run completes successfully. The callback receives the completed `AgentRunResult` and can optionally yield additional protocol-specific events.	`None`

Source code in pydantic_ai_slim/pydantic_ai/ui/_event_stream.py

async def transform_stream( # noqa: C901 self, stream: AsyncIterator[NativeEvent], on_complete: OnCompleteFunc[EventT] | None = None ) -> AsyncIterator[EventT]:  """Transform a stream of Pydantic AI events into protocol-specific events.  This method dispatches to specific hooks and `handle_*` methods that subclasses can override:  - [`before_stream()`][pydantic_ai.ui.UIEventStream.before_stream]  - [`after_stream()`][pydantic_ai.ui.UIEventStream.after_stream]  - [`on_error()`][pydantic_ai.ui.UIEventStream.on_error]  - [`before_request()`][pydantic_ai.ui.UIEventStream.before_request]  - [`after_request()`][pydantic_ai.ui.UIEventStream.after_request]  - [`before_response()`][pydantic_ai.ui.UIEventStream.before_response]  - [`after_response()`][pydantic_ai.ui.UIEventStream.after_response]  - [`handle_event()`][pydantic_ai.ui.UIEventStream.handle_event]  Args:  stream: The stream of Pydantic AI events to transform.  on_complete: Optional callback function called when the agent run completes successfully.  The callback receives the completed [`AgentRunResult`][pydantic_ai.agent.AgentRunResult] and can optionally yield additional protocol-specific events.  """ async for e in self.before_stream(): yield e try: async for event in stream: if isinstance(event, PartStartEvent): async for e in self._turn_to('response'): yield e elif isinstance(event, FunctionToolCallEvent): async for e in self._turn_to('request'): yield e elif isinstance(event, AgentRunResultEvent): if ( self._final_result_event and (tool_call_id := self._final_result_event.tool_call_id) and (tool_name := self._final_result_event.tool_name) ): async for e in self._turn_to('request'): yield e self._final_result_event = None # Ensure the stream does not end on a dangling tool call without a result. output_tool_result_event = FunctionToolResultEvent( result=ToolReturnPart( tool_call_id=tool_call_id, tool_name=tool_name, content='Final result processed.', ) ) async for e in self.handle_function_tool_result(output_tool_result_event): yield e result = cast(AgentRunResult[OutputDataT], event.result) self._result = result async for e in self._turn_to(None): yield e if on_complete is not None: if inspect.isasyncgenfunction(on_complete): async for e in on_complete(result): yield e elif _utils.is_async_callable(on_complete): await on_complete(result) else: await _utils.run_in_executor(on_complete, result) elif isinstance(event, FinalResultEvent): self._final_result_event = event if isinstance(event, BuiltinToolCallEvent | BuiltinToolResultEvent): # pyright: ignore[reportDeprecated] # These events were deprecated before this feature was introduced continue async for e in self.handle_event(event): yield e except Exception as e: async for e in self.on_error(e): yield e finally: async for e in self._turn_to(None): yield e async for e in self.after_stream(): yield e 

handle_event `async`

handle_event(event: NativeEvent) -> AsyncIterator[EventT]

Transform a Pydantic AI event into one or more protocol-specific events.

This method dispatches to specific handle_* methods based on event type:

PartStartEvent -> handle_part_start()
PartDeltaEvent -> handle_part_delta
PartEndEvent -> handle_part_end
FinalResultEvent -> handle_final_result
FunctionToolCallEvent -> handle_function_tool_call
FunctionToolResultEvent -> handle_function_tool_result
AgentRunResultEvent -> handle_run_result

Subclasses are encouraged to override the individual handle_* methods rather than this one. If you need specific behavior for all events, make sure you call the super method.

Source code in pydantic_ai_slim/pydantic_ai/ui/_event_stream.py

async def handle_event(self, event: NativeEvent) -> AsyncIterator[EventT]:  """Transform a Pydantic AI event into one or more protocol-specific events.  This method dispatches to specific `handle_*` methods based on event type:  - [`PartStartEvent`][pydantic_ai.messages.PartStartEvent] -> [`handle_part_start()`][pydantic_ai.ui.UIEventStream.handle_part_start]  - [`PartDeltaEvent`][pydantic_ai.messages.PartDeltaEvent] -> `handle_part_delta`  - [`PartEndEvent`][pydantic_ai.messages.PartEndEvent] -> `handle_part_end`  - [`FinalResultEvent`][pydantic_ai.messages.FinalResultEvent] -> `handle_final_result`  - [`FunctionToolCallEvent`][pydantic_ai.messages.FunctionToolCallEvent] -> `handle_function_tool_call`  - [`FunctionToolResultEvent`][pydantic_ai.messages.FunctionToolResultEvent] -> `handle_function_tool_result`  - [`AgentRunResultEvent`][pydantic_ai.run.AgentRunResultEvent] -> `handle_run_result`  Subclasses are encouraged to override the individual `handle_*` methods rather than this one.  If you need specific behavior for all events, make sure you call the super method.  """ match event: case PartStartEvent(): async for e in self.handle_part_start(event): yield e case PartDeltaEvent(): async for e in self.handle_part_delta(event): yield e case PartEndEvent(): async for e in self.handle_part_end(event): yield e case FinalResultEvent(): async for e in self.handle_final_result(event): yield e case FunctionToolCallEvent(): async for e in self.handle_function_tool_call(event): yield e case FunctionToolResultEvent(): async for e in self.handle_function_tool_result(event): yield e case AgentRunResultEvent(): async for e in self.handle_run_result(event): yield e case _: pass 

handle_part_start `async`

handle_part_start( event: PartStartEvent, ) -> AsyncIterator[EventT]

Handle a PartStartEvent.

This method dispatches to specific handle_* methods based on part type:

Subclasses are encouraged to override the individual handle_* methods rather than this one. If you need specific behavior for all part start events, make sure you call the super method.

Parameters:

Name	Type	Description	Default
`event`	`PartStartEvent`	The part start event.	required

Source code in pydantic_ai_slim/pydantic_ai/ui/_event_stream.py

async def handle_part_start(self, event: PartStartEvent) -> AsyncIterator[EventT]:  """Handle a `PartStartEvent`.  This method dispatches to specific `handle_*` methods based on part type:  - [`TextPart`][pydantic_ai.messages.TextPart] -> [`handle_text_start()`][pydantic_ai.ui.UIEventStream.handle_text_start]  - [`ThinkingPart`][pydantic_ai.messages.ThinkingPart] -> [`handle_thinking_start()`][pydantic_ai.ui.UIEventStream.handle_thinking_start]  - [`ToolCallPart`][pydantic_ai.messages.ToolCallPart] -> [`handle_tool_call_start()`][pydantic_ai.ui.UIEventStream.handle_tool_call_start]  - [`BuiltinToolCallPart`][pydantic_ai.messages.BuiltinToolCallPart] -> [`handle_builtin_tool_call_start()`][pydantic_ai.ui.UIEventStream.handle_builtin_tool_call_start]  - [`BuiltinToolReturnPart`][pydantic_ai.messages.BuiltinToolReturnPart] -> [`handle_builtin_tool_return()`][pydantic_ai.ui.UIEventStream.handle_builtin_tool_return]  - [`FilePart`][pydantic_ai.messages.FilePart] -> [`handle_file()`][pydantic_ai.ui.UIEventStream.handle_file]  Subclasses are encouraged to override the individual `handle_*` methods rather than this one.  If you need specific behavior for all part start events, make sure you call the super method.  Args:  event: The part start event.  """ part = event.part previous_part_kind = event.previous_part_kind match part: case TextPart(): async for e in self.handle_text_start(part, follows_text=previous_part_kind == 'text'): yield e case ThinkingPart(): async for e in self.handle_thinking_start(part, follows_thinking=previous_part_kind == 'thinking'): yield e case ToolCallPart(): async for e in self.handle_tool_call_start(part): yield e case BuiltinToolCallPart(): async for e in self.handle_builtin_tool_call_start(part): yield e case BuiltinToolReturnPart(): async for e in self.handle_builtin_tool_return(part): yield e case FilePart(): # pragma: no branch async for e in self.handle_file(part): yield e 

handle_part_delta `async`

handle_part_delta( event: PartDeltaEvent, ) -> AsyncIterator[EventT]

Handle a PartDeltaEvent.

This method dispatches to specific handle_*_delta methods based on part delta type:

Subclasses are encouraged to override the individual handle_*_delta methods rather than this one. If you need specific behavior for all part delta events, make sure you call the super method.

Parameters:

Name	Type	Description	Default
`event`	`PartDeltaEvent`	The PartDeltaEvent.	required

Source code in pydantic_ai_slim/pydantic_ai/ui/_event_stream.py

async def handle_part_delta(self, event: PartDeltaEvent) -> AsyncIterator[EventT]:  """Handle a PartDeltaEvent.  This method dispatches to specific `handle_*_delta` methods based on part delta type:  - [`TextPartDelta`][pydantic_ai.messages.TextPartDelta] -> [`handle_text_delta()`][pydantic_ai.ui.UIEventStream.handle_text_delta]  - [`ThinkingPartDelta`][pydantic_ai.messages.ThinkingPartDelta] -> [`handle_thinking_delta()`][pydantic_ai.ui.UIEventStream.handle_thinking_delta]  - [`ToolCallPartDelta`][pydantic_ai.messages.ToolCallPartDelta] -> [`handle_tool_call_delta()`][pydantic_ai.ui.UIEventStream.handle_tool_call_delta]  Subclasses are encouraged to override the individual `handle_*_delta` methods rather than this one.  If you need specific behavior for all part delta events, make sure you call the super method.  Args:  event: The PartDeltaEvent.  """ delta = event.delta match delta: case TextPartDelta(): async for e in self.handle_text_delta(delta): yield e case ThinkingPartDelta(): async for e in self.handle_thinking_delta(delta): yield e case ToolCallPartDelta(): # pragma: no branch async for e in self.handle_tool_call_delta(delta): yield e 

handle_part_end `async`

handle_part_end( event: PartEndEvent, ) -> AsyncIterator[EventT]

Handle a PartEndEvent.

This method dispatches to specific handle_*_end methods based on part type:

Subclasses are encouraged to override the individual handle_*_end methods rather than this one. If you need specific behavior for all part end events, make sure you call the super method.

Parameters:

Name	Type	Description	Default
`event`	`PartEndEvent`	The part end event.	required

Source code in pydantic_ai_slim/pydantic_ai/ui/_event_stream.py

async def handle_part_end(self, event: PartEndEvent) -> AsyncIterator[EventT]:  """Handle a `PartEndEvent`.  This method dispatches to specific `handle_*_end` methods based on part type:  - [`TextPart`][pydantic_ai.messages.TextPart] -> [`handle_text_end()`][pydantic_ai.ui.UIEventStream.handle_text_end]  - [`ThinkingPart`][pydantic_ai.messages.ThinkingPart] -> [`handle_thinking_end()`][pydantic_ai.ui.UIEventStream.handle_thinking_end]  - [`ToolCallPart`][pydantic_ai.messages.ToolCallPart] -> [`handle_tool_call_end()`][pydantic_ai.ui.UIEventStream.handle_tool_call_end]  - [`BuiltinToolCallPart`][pydantic_ai.messages.BuiltinToolCallPart] -> [`handle_builtin_tool_call_end()`][pydantic_ai.ui.UIEventStream.handle_builtin_tool_call_end]  Subclasses are encouraged to override the individual `handle_*_end` methods rather than this one.  If you need specific behavior for all part end events, make sure you call the super method.  Args:  event: The part end event.  """ part = event.part next_part_kind = event.next_part_kind match part: case TextPart(): async for e in self.handle_text_end(part, followed_by_text=next_part_kind == 'text'): yield e case ThinkingPart(): async for e in self.handle_thinking_end(part, followed_by_thinking=next_part_kind == 'thinking'): yield e case ToolCallPart(): async for e in self.handle_tool_call_end(part): yield e case BuiltinToolCallPart(): async for e in self.handle_builtin_tool_call_end(part): yield e case BuiltinToolReturnPart() | FilePart(): # pragma: no cover # These don't have deltas, so they don't need to be ended. pass 

before_stream `async`

before_stream() -> AsyncIterator[EventT]

Yield events before agent streaming starts.

This hook is called before any agent events are processed. Override this to inject custom events at the start of the stream.

Source code in pydantic_ai_slim/pydantic_ai/ui/_event_stream.py

async def before_stream(self) -> AsyncIterator[EventT]:  """Yield events before agent streaming starts.  This hook is called before any agent events are processed.  Override this to inject custom events at the start of the stream.  """ return # pragma: no cover yield # Make this an async generator 

after_stream `async`

after_stream() -> AsyncIterator[EventT]

Yield events after agent streaming completes.

This hook is called after all agent events have been processed. Override this to inject custom events at the end of the stream.

Source code in pydantic_ai_slim/pydantic_ai/ui/_event_stream.py

async def after_stream(self) -> AsyncIterator[EventT]:  """Yield events after agent streaming completes.  This hook is called after all agent events have been processed.  Override this to inject custom events at the end of the stream.  """ return # pragma: no cover yield # Make this an async generator 

on_error `async`

on_error(error: Exception) -> AsyncIterator[EventT]

Handle errors that occur during streaming.

Parameters:

Name	Type	Description	Default
`error`	`Exception`	The error that occurred during streaming.	required

Source code in pydantic_ai_slim/pydantic_ai/ui/_event_stream.py

async def on_error(self, error: Exception) -> AsyncIterator[EventT]:  """Handle errors that occur during streaming.  Args:  error: The error that occurred during streaming.  """ return # pragma: no cover yield # Make this an async generator 

before_request `async`

before_request() -> AsyncIterator[EventT]

Yield events before a model request is processed.

Override this to inject custom events at the start of the request.

Source code in pydantic_ai_slim/pydantic_ai/ui/_event_stream.py

async def before_request(self) -> AsyncIterator[EventT]:  """Yield events before a model request is processed.  Override this to inject custom events at the start of the request.  """ return # pragma: lax no cover yield # Make this an async generator 

after_request `async`

after_request() -> AsyncIterator[EventT]

Yield events after a model request is processed.

Override this to inject custom events at the end of the request.

Source code in pydantic_ai_slim/pydantic_ai/ui/_event_stream.py

async def after_request(self) -> AsyncIterator[EventT]:  """Yield events after a model request is processed.  Override this to inject custom events at the end of the request.  """ return # pragma: lax no cover yield # Make this an async generator 

before_response `async`

before_response() -> AsyncIterator[EventT]

Yield events before a model response is processed.

Override this to inject custom events at the start of the response.

Source code in pydantic_ai_slim/pydantic_ai/ui/_event_stream.py

async def before_response(self) -> AsyncIterator[EventT]:  """Yield events before a model response is processed.  Override this to inject custom events at the start of the response.  """ return # pragma: no cover yield # Make this an async generator 

after_response `async`

after_response() -> AsyncIterator[EventT]

Yield events after a model response is processed.

Override this to inject custom events at the end of the response.

Source code in pydantic_ai_slim/pydantic_ai/ui/_event_stream.py

async def after_response(self) -> AsyncIterator[EventT]:  """Yield events after a model response is processed.  Override this to inject custom events at the end of the response.  """ return # pragma: lax no cover yield # Make this an async generator 

handle_text_start `async`

handle_text_start( part: TextPart, follows_text: bool = False ) -> AsyncIterator[EventT]

Handle the start of a TextPart.

Parameters:

Name	Type	Description	Default
`part`	`TextPart`	The text part.	required
`follows_text`	`bool`	Whether the part is directly preceded by another text part. In this case, you may want to yield a "text-delta" event instead of a "text-start" event.	`False`

Source code in pydantic_ai_slim/pydantic_ai/ui/_event_stream.py

async def handle_text_start(self, part: TextPart, follows_text: bool = False) -> AsyncIterator[EventT]:  """Handle the start of a `TextPart`.  Args:  part: The text part.  follows_text: Whether the part is directly preceded by another text part. In this case, you may want to yield a "text-delta" event instead of a "text-start" event.  """ return # pragma: no cover yield # Make this an async generator 

handle_text_delta `async`

handle_text_delta( delta: TextPartDelta, ) -> AsyncIterator[EventT]

Handle a TextPartDelta.

Parameters:

Name	Type	Description	Default
`delta`	`TextPartDelta`	The text part delta.	required

Source code in pydantic_ai_slim/pydantic_ai/ui/_event_stream.py

async def handle_text_delta(self, delta: TextPartDelta) -> AsyncIterator[EventT]:  """Handle a `TextPartDelta`.  Args:  delta: The text part delta.  """ return # pragma: no cover yield # Make this an async generator 

handle_text_end `async`

handle_text_end( part: TextPart, followed_by_text: bool = False ) -> AsyncIterator[EventT]

Handle the end of a TextPart.

Parameters:

Name	Type	Description	Default
`part`	`TextPart`	The text part.	required
`followed_by_text`	`bool`	Whether the part is directly followed by another text part. In this case, you may not want to yield a "text-end" event yet.	`False`

Source code in pydantic_ai_slim/pydantic_ai/ui/_event_stream.py

async def handle_text_end(self, part: TextPart, followed_by_text: bool = False) -> AsyncIterator[EventT]:  """Handle the end of a `TextPart`.  Args:  part: The text part.  followed_by_text: Whether the part is directly followed by another text part. In this case, you may not want to yield a "text-end" event yet.  """ return # pragma: no cover yield # Make this an async generator 

handle_thinking_start `async`

handle_thinking_start( part: ThinkingPart, follows_thinking: bool = False ) -> AsyncIterator[EventT]

Handle the start of a ThinkingPart.

Parameters:

Name	Type	Description	Default
`part`	`ThinkingPart`	The thinking part.	required
`follows_thinking`	`bool`	Whether the part is directly preceded by another thinking part. In this case, you may want to yield a "thinking-delta" event instead of a "thinking-start" event.	`False`

Source code in pydantic_ai_slim/pydantic_ai/ui/_event_stream.py

async def handle_thinking_start(self, part: ThinkingPart, follows_thinking: bool = False) -> AsyncIterator[EventT]:  """Handle the start of a `ThinkingPart`.  Args:  part: The thinking part.  follows_thinking: Whether the part is directly preceded by another thinking part. In this case, you may want to yield a "thinking-delta" event instead of a "thinking-start" event.  """ return # pragma: no cover yield # Make this an async generator 

handle_thinking_delta `async`

handle_thinking_delta( delta: ThinkingPartDelta, ) -> AsyncIterator[EventT]

Handle a ThinkingPartDelta.

Parameters:

Name	Type	Description	Default
`delta`	`ThinkingPartDelta`	The thinking part delta.	required

Source code in pydantic_ai_slim/pydantic_ai/ui/_event_stream.py

async def handle_thinking_delta(self, delta: ThinkingPartDelta) -> AsyncIterator[EventT]:  """Handle a `ThinkingPartDelta`.  Args:  delta: The thinking part delta.  """ return # pragma: no cover yield # Make this an async generator 

handle_thinking_end `async`

handle_thinking_end( part: ThinkingPart, followed_by_thinking: bool = False ) -> AsyncIterator[EventT]

Handle the end of a ThinkingPart.

Parameters:

Name	Type	Description	Default
`part`	`ThinkingPart`	The thinking part.	required
`followed_by_thinking`	`bool`	Whether the part is directly followed by another thinking part. In this case, you may not want to yield a "thinking-end" event yet.	`False`

Source code in pydantic_ai_slim/pydantic_ai/ui/_event_stream.py

async def handle_thinking_end( self, part: ThinkingPart, followed_by_thinking: bool = False ) -> AsyncIterator[EventT]:  """Handle the end of a `ThinkingPart`.  Args:  part: The thinking part.  followed_by_thinking: Whether the part is directly followed by another thinking part. In this case, you may not want to yield a "thinking-end" event yet.  """ return # pragma: no cover yield # Make this an async generator 

handle_tool_call_start `async`

handle_tool_call_start( part: ToolCallPart, ) -> AsyncIterator[EventT]

Handle the start of a ToolCallPart.

Parameters:

Name	Type	Description	Default
`part`	`ToolCallPart`	The tool call part.	required

Source code in pydantic_ai_slim/pydantic_ai/ui/_event_stream.py

async def handle_tool_call_start(self, part: ToolCallPart) -> AsyncIterator[EventT]:  """Handle the start of a `ToolCallPart`.  Args:  part: The tool call part.  """ return # pragma: no cover yield # Make this an async generator 

handle_tool_call_delta `async`

handle_tool_call_delta( delta: ToolCallPartDelta, ) -> AsyncIterator[EventT]

Handle a ToolCallPartDelta.

Parameters:

Name	Type	Description	Default
`delta`	`ToolCallPartDelta`	The tool call part delta.	required

Source code in pydantic_ai_slim/pydantic_ai/ui/_event_stream.py

async def handle_tool_call_delta(self, delta: ToolCallPartDelta) -> AsyncIterator[EventT]:  """Handle a `ToolCallPartDelta`.  Args:  delta: The tool call part delta.  """ return # pragma: no cover yield # Make this an async generator 

handle_tool_call_end `async`

handle_tool_call_end( part: ToolCallPart, ) -> AsyncIterator[EventT]

Handle the end of a ToolCallPart.

Parameters:

Name	Type	Description	Default
`part`	`ToolCallPart`	The tool call part.	required

Source code in pydantic_ai_slim/pydantic_ai/ui/_event_stream.py

async def handle_tool_call_end(self, part: ToolCallPart) -> AsyncIterator[EventT]:  """Handle the end of a `ToolCallPart`.  Args:  part: The tool call part.  """ return # pragma: no cover yield # Make this an async generator 

handle_builtin_tool_call_start `async`

handle_builtin_tool_call_start( part: BuiltinToolCallPart, ) -> AsyncIterator[EventT]

Handle a BuiltinToolCallPart at start.

Parameters:

Name	Type	Description	Default
`part`	`BuiltinToolCallPart`	The builtin tool call part.	required

Source code in pydantic_ai_slim/pydantic_ai/ui/_event_stream.py

async def handle_builtin_tool_call_start(self, part: BuiltinToolCallPart) -> AsyncIterator[EventT]:  """Handle a `BuiltinToolCallPart` at start.  Args:  part: The builtin tool call part.  """ return # pragma: no cover yield # Make this an async generator 

handle_builtin_tool_call_end `async`

handle_builtin_tool_call_end( part: BuiltinToolCallPart, ) -> AsyncIterator[EventT]

Handle the end of a BuiltinToolCallPart.

Parameters:

Name	Type	Description	Default
`part`	`BuiltinToolCallPart`	The builtin tool call part.	required

Source code in pydantic_ai_slim/pydantic_ai/ui/_event_stream.py

async def handle_builtin_tool_call_end(self, part: BuiltinToolCallPart) -> AsyncIterator[EventT]:  """Handle the end of a `BuiltinToolCallPart`.  Args:  part: The builtin tool call part.  """ return # pragma: no cover yield # Make this an async generator 

handle_builtin_tool_return `async`

handle_builtin_tool_return( part: BuiltinToolReturnPart, ) -> AsyncIterator[EventT]

Handle a BuiltinToolReturnPart.

Parameters:

Name	Type	Description	Default
`part`	`BuiltinToolReturnPart`	The builtin tool return part.	required

Source code in pydantic_ai_slim/pydantic_ai/ui/_event_stream.py

async def handle_builtin_tool_return(self, part: BuiltinToolReturnPart) -> AsyncIterator[EventT]:  """Handle a `BuiltinToolReturnPart`.  Args:  part: The builtin tool return part.  """ return # pragma: no cover yield # Make this an async generator 

handle_file `async`

handle_file(part: FilePart) -> AsyncIterator[EventT]

Handle a FilePart.

Parameters:

Name	Type	Description	Default
`part`	`FilePart`	The file part.	required

Source code in pydantic_ai_slim/pydantic_ai/ui/_event_stream.py

async def handle_file(self, part: FilePart) -> AsyncIterator[EventT]:  """Handle a `FilePart`.  Args:  part: The file part.  """ return # pragma: no cover yield # Make this an async generator 

handle_final_result `async`

handle_final_result( event: FinalResultEvent, ) -> AsyncIterator[EventT]

Handle a FinalResultEvent.

Parameters:

Name	Type	Description	Default
`event`	`FinalResultEvent`	The final result event.	required

Source code in pydantic_ai_slim/pydantic_ai/ui/_event_stream.py

async def handle_final_result(self, event: FinalResultEvent) -> AsyncIterator[EventT]:  """Handle a `FinalResultEvent`.  Args:  event: The final result event.  """ return yield # Make this an async generator 

handle_function_tool_call `async`

handle_function_tool_call( event: FunctionToolCallEvent, ) -> AsyncIterator[EventT]

Handle a FunctionToolCallEvent.

Parameters:

Name	Type	Description	Default
`event`	`FunctionToolCallEvent`	The function tool call event.	required

Source code in pydantic_ai_slim/pydantic_ai/ui/_event_stream.py

async def handle_function_tool_call(self, event: FunctionToolCallEvent) -> AsyncIterator[EventT]:  """Handle a `FunctionToolCallEvent`.  Args:  event: The function tool call event.  """ return yield # Make this an async generator 

handle_function_tool_result `async`

handle_function_tool_result( event: FunctionToolResultEvent, ) -> AsyncIterator[EventT]

Handle a FunctionToolResultEvent.

Parameters:

Name	Type	Description	Default
`event`	`FunctionToolResultEvent`	The function tool result event.	required

Source code in pydantic_ai_slim/pydantic_ai/ui/_event_stream.py

async def handle_function_tool_result(self, event: FunctionToolResultEvent) -> AsyncIterator[EventT]:  """Handle a `FunctionToolResultEvent`.  Args:  event: The function tool result event.  """ return # pragma: no cover yield # Make this an async generator 

handle_run_result `async`

handle_run_result( event: AgentRunResultEvent, ) -> AsyncIterator[EventT]

Handle an AgentRunResultEvent.

Parameters:

Name	Type	Description	Default
`event`	`AgentRunResultEvent`	The agent run result event.	required

Source code in pydantic_ai_slim/pydantic_ai/ui/_event_stream.py

async def handle_run_result(self, event: AgentRunResultEvent) -> AsyncIterator[EventT]:  """Handle an `AgentRunResultEvent`.  Args:  event: The agent run result event.  """ return yield # Make this an async generator 

MessagesBuilder `dataclass`

Helper class to build Pydantic AI messages from request/response parts.

Source code in pydantic_ai_slim/pydantic_ai/ui/_messages_builder.py

@dataclass class MessagesBuilder:  """Helper class to build Pydantic AI messages from request/response parts.""" messages: list[ModelMessage] = field(default_factory=list) def add(self, part: ModelRequestPart | ModelResponsePart) -> None:  """Add a new part, creating a new request or response message if necessary.""" last_message = self.messages[-1] if self.messages else None if isinstance(part, get_union_args(ModelRequestPart)): part = cast(ModelRequestPart, part) if isinstance(last_message, ModelRequest): last_message.parts = [*last_message.parts, part] else: self.messages.append(ModelRequest(parts=[part])) else: part = cast(ModelResponsePart, part) if isinstance(last_message, ModelResponse): last_message.parts = [*last_message.parts, part] else: self.messages.append(ModelResponse(parts=[part])) 

add

add(part: ModelRequestPart | ModelResponsePart) -> None

Add a new part, creating a new request or response message if necessary.

Source code in pydantic_ai_slim/pydantic_ai/ui/_messages_builder.py

def add(self, part: ModelRequestPart | ModelResponsePart) -> None:  """Add a new part, creating a new request or response message if necessary.""" last_message = self.messages[-1] if self.messages else None if isinstance(part, get_union_args(ModelRequestPart)): part = cast(ModelRequestPart, part) if isinstance(last_message, ModelRequest): last_message.parts = [*last_message.parts, part] else: self.messages.append(ModelRequest(parts=[part])) else: part = cast(ModelResponsePart, part) if isinstance(last_message, ModelResponse): last_message.parts = [*last_message.parts, part] else: self.messages.append(ModelResponse(parts=[part])) 

pydantic_ai.ui

StateDeps dataclass

StateHandler

state property writable

UIAdapter dataclass

agent instance-attribute

run_input instance-attribute

accept class-attribute instance-attribute

from_request async classmethod

build_run_input abstractmethod classmethod

load_messages abstractmethod classmethod

dump_messages classmethod

build_event_stream abstractmethod

messages abstractmethod cached property

toolset cached property

state cached property

transform_stream

encode_stream

streaming_response

run_stream_native

run_stream

dispatch_request async classmethod

SSE_CONTENT_TYPE module-attribute

NativeEvent module-attribute

OnCompleteFunc module-attribute

UIEventStream dataclass

accept class-attribute instance-attribute

message_id class-attribute instance-attribute

new_message_id

response_headers property

content_type property

encode_event abstractmethod

encode_stream async

streaming_response

transform_stream async

handle_event async

handle_part_start async

handle_part_delta async

handle_part_end async

before_stream async

after_stream async

on_error async

before_request async

after_request async

before_response async

after_response async

handle_text_start async

handle_text_delta async

handle_text_end async

handle_thinking_start async

handle_thinking_delta async

handle_thinking_end async

handle_tool_call_start async

handle_tool_call_delta async

handle_tool_call_end async

handle_builtin_tool_call_start async

handle_builtin_tool_call_end async

handle_builtin_tool_return async

handle_file async

handle_final_result async

handle_function_tool_call async

handle_function_tool_result async

handle_run_result async

MessagesBuilder dataclass

add

`pydantic_ai.ui`

StateDeps `dataclass`

state `property` `writable`

UIAdapter `dataclass`

agent `instance-attribute`

run_input `instance-attribute`

accept `class-attribute` `instance-attribute`

from_request `async` `classmethod`

build_run_input `abstractmethod` `classmethod`

load_messages `abstractmethod` `classmethod`

dump_messages `classmethod`

build_event_stream `abstractmethod`

messages `abstractmethod` `cached` `property`

toolset `cached` `property`

state `cached` `property`

dispatch_request `async` `classmethod`

SSE_CONTENT_TYPE `module-attribute`

NativeEvent `module-attribute`

OnCompleteFunc `module-attribute`

UIEventStream `dataclass`

accept `class-attribute` `instance-attribute`

message_id `class-attribute` `instance-attribute`

response_headers `property`

content_type `property`

encode_event `abstractmethod`

encode_stream `async`

transform_stream `async`

handle_event `async`

handle_part_start `async`

handle_part_delta `async`

handle_part_end `async`

before_stream `async`

after_stream `async`

on_error `async`

before_request `async`

after_request `async`

before_response `async`

after_response `async`

handle_text_start `async`

handle_text_delta `async`

handle_text_end `async`

handle_thinking_start `async`

handle_thinking_delta `async`

handle_thinking_end `async`

handle_tool_call_start `async`

handle_tool_call_delta `async`

handle_tool_call_end `async`

handle_builtin_tool_call_start `async`

handle_builtin_tool_call_end `async`

handle_builtin_tool_return `async`

handle_file `async`

handle_final_result `async`

handle_function_tool_call `async`

handle_function_tool_result `async`

handle_run_result `async`

MessagesBuilder `dataclass`