from __future__ import annotations from collections.abc import AsyncIterable, AsyncIterator, Callable, Iterator, Sequence from contextlib import AbstractAsyncContextManager, asynccontextmanager, contextmanager from contextvars import ContextVar from typing import Any, overload from prefect import flow, task from prefect.context import FlowRunContext from prefect.utilities.asyncutils import run_coro_as_sync from typing_extensions import Never from pydantic_ai import ( AbstractToolset, AgentRunResultEvent, _utils, messages as _messages, models, usage as _usage, ) from pydantic_ai.agent import AbstractAgent, AgentRun, AgentRunResult, EventStreamHandler, WrapperAgent from pydantic_ai.agent.abstract import Instructions, RunOutputDataT from pydantic_ai.builtin_tools import AbstractBuiltinTool from pydantic_ai.exceptions import UserError from pydantic_ai.models import Model from pydantic_ai.output import OutputDataT, OutputSpec from pydantic_ai.result import StreamedRunResult from pydantic_ai.settings import ModelSettings from pydantic_ai.tools import ( AgentDepsT, DeferredToolResults, RunContext, Tool, ToolFuncEither, ) from ._model import PrefectModel from ._toolset import prefectify_toolset from ._types import TaskConfig, default_task_config class PrefectAgent(WrapperAgent[AgentDepsT, OutputDataT]): def __init__( self, wrapped: AbstractAgent[AgentDepsT, OutputDataT], *, name: str | None = None, event_stream_handler: EventStreamHandler[AgentDepsT] | None = None, mcp_task_config: TaskConfig | None = None, model_task_config: TaskConfig | None = None, tool_task_config: TaskConfig | None = None, tool_task_config_by_name: dict[str, TaskConfig | None] | None = None, event_stream_handler_task_config: TaskConfig | None = None, prefectify_toolset_func: Callable[ [AbstractToolset[AgentDepsT], TaskConfig, TaskConfig, dict[str, TaskConfig | None]], AbstractToolset[AgentDepsT], ] = prefectify_toolset, ): """Wrap an agent to enable it with Prefect durable flows, by automatically offloading model requests, tool calls, and MCP server communication to Prefect tasks. After wrapping, the original agent can still be used as normal outside of the Prefect flow. Args: wrapped: The agent to wrap. name: Optional unique agent name to use as the Prefect flow name prefix. If not provided, the agent's `name` will be used. event_stream_handler: Optional event stream handler to use instead of the one set on the wrapped agent. mcp_task_config: The base Prefect task config to use for MCP server tasks. If no config is provided, use the default settings of Prefect. model_task_config: The Prefect task config to use for model request tasks. If no config is provided, use the default settings of Prefect. tool_task_config: The default Prefect task config to use for tool calls. If no config is provided, use the default settings of Prefect. tool_task_config_by_name: Per-tool task configuration. Keys are tool names, values are TaskConfig or None (None disables task wrapping for that tool). event_stream_handler_task_config: The Prefect task config to use for the event stream handler task. If no config is provided, use the default settings of Prefect. prefectify_toolset_func: Optional function to use to prepare toolsets for Prefect by wrapping them in a `PrefectWrapperToolset` that moves methods that require IO to Prefect tasks. If not provided, only `FunctionToolset` and `MCPServer` will be prepared for Prefect. The function takes the toolset, the task config, the tool-specific task config, and the tool-specific task config by name. """ super().__init__(wrapped) self._name = name or wrapped.name self._event_stream_handler = event_stream_handler if self._name is None: raise UserError( "An agent needs to have a unique `name` in order to be used with Prefect. The name will be used to identify the agent's flows and tasks." ) # Merge the config with the default Prefect config self._mcp_task_config = default_task_config | (mcp_task_config or {}) self._model_task_config = default_task_config | (model_task_config or {}) self._tool_task_config = default_task_config | (tool_task_config or {}) self._tool_task_config_by_name = tool_task_config_by_name or {} self._event_stream_handler_task_config = default_task_config | (event_stream_handler_task_config or {}) if not isinstance(wrapped.model, Model): raise UserError( 'An agent needs to have a `model` in order to be used with Prefect, it cannot be set at agent run time.' ) prefect_model = PrefectModel( wrapped.model, task_config=self._model_task_config, event_stream_handler=self.event_stream_handler, ) self._model = prefect_model def _prefectify_toolset(toolset: AbstractToolset[AgentDepsT]) -> AbstractToolset[AgentDepsT]: """Convert a toolset to its Prefect equivalent.""" return prefectify_toolset_func( toolset, self._mcp_task_config, self._tool_task_config, self._tool_task_config_by_name, ) prefect_toolsets = [toolset.visit_and_replace(_prefectify_toolset) for toolset in wrapped.toolsets] self._toolsets = prefect_toolsets # Context variable to track when we're inside this agent's Prefect flow self._in_prefect_agent_flow: ContextVar[bool] = ContextVar( f'_in_prefect_agent_flow_{self._name}', default=False ) @property def name(self) -> str | None: return self._name @name.setter def name(self, value: str | None) -> None: # pragma: no cover raise UserError( 'The agent name cannot be changed after creation. If you need to change the name, create a new agent.' ) @property def model(self) -> Model: return self._model @property def event_stream_handler(self) -> EventStreamHandler[AgentDepsT] | None: handler = self._event_stream_handler or super().event_stream_handler if handler is None: return None elif FlowRunContext.get() is not None: # Special case if it's in a Prefect flow, we need to iterate through all events and call the handler. return self._call_event_stream_handler_in_flow else: return handler async def _call_event_stream_handler_in_flow( self, ctx: RunContext[AgentDepsT], stream: AsyncIterable[_messages.AgentStreamEvent] ) -> None: handler = self._event_stream_handler or super().event_stream_handler assert handler is not None # Create a task to handle each event @task(name='Handle Stream Event', **self._event_stream_handler_task_config) async def event_stream_handler_task(event: _messages.AgentStreamEvent) -> None: async def streamed_response(): yield event await handler(ctx, streamed_response()) async for event in stream: await event_stream_handler_task(event) @property def toolsets(self) -> Sequence[AbstractToolset[AgentDepsT]]: with self._prefect_overrides(): return super().toolsets @contextmanager def _prefect_overrides(self) -> Iterator[None]: # Override with PrefectModel and PrefectMCPServer in the toolsets. with super().override(model=self._model, toolsets=self._toolsets, tools=[]): yield @overload async def run( self, user_prompt: str | Sequence[_messages.UserContent] | None = None, *, output_type: None = None, message_history: Sequence[_messages.ModelMessage] | None = None, deferred_tool_results: DeferredToolResults | None = None, model: models.Model | models.KnownModelName | str | None = None, instructions: Instructions[AgentDepsT] = None, deps: AgentDepsT = None, model_settings: ModelSettings | None = None, usage_limits: _usage.UsageLimits | None = None, usage: _usage.RunUsage | None = None, infer_name: bool = True, toolsets: Sequence[AbstractToolset[AgentDepsT]] | None = None, builtin_tools: Sequence[AbstractBuiltinTool] | None = None, event_stream_handler: EventStreamHandler[AgentDepsT] | None = None, ) -> AgentRunResult[OutputDataT]: ... @overload async def run( self, user_prompt: str | Sequence[_messages.UserContent] | None = None, *, output_type: OutputSpec[RunOutputDataT], message_history: Sequence[_messages.ModelMessage] | None = None, deferred_tool_results: DeferredToolResults | None = None, model: models.Model | models.KnownModelName | str | None = None, instructions: Instructions[AgentDepsT] = None, deps: AgentDepsT = None, model_settings: ModelSettings | None = None, usage_limits: _usage.UsageLimits | None = None, usage: _usage.RunUsage | None = None, infer_name: bool = True, toolsets: Sequence[AbstractToolset[AgentDepsT]] | None = None, builtin_tools: Sequence[AbstractBuiltinTool] | None = None, event_stream_handler: EventStreamHandler[AgentDepsT] | None = None, ) -> AgentRunResult[RunOutputDataT]: ... async def run( self, user_prompt: str | Sequence[_messages.UserContent] | None = None, *, output_type: OutputSpec[RunOutputDataT] | None = None, message_history: Sequence[_messages.ModelMessage] | None = None, deferred_tool_results: DeferredToolResults | None = None, model: models.Model | models.KnownModelName | str | None = None, instructions: Instructions[AgentDepsT] = None, deps: AgentDepsT = None, model_settings: ModelSettings | None = None, usage_limits: _usage.UsageLimits | None = None, usage: _usage.RunUsage | None = None, infer_name: bool = True, toolsets: Sequence[AbstractToolset[AgentDepsT]] | None = None, builtin_tools: Sequence[AbstractBuiltinTool] | None = None, event_stream_handler: EventStreamHandler[AgentDepsT] | None = None, **_deprecated_kwargs: Never, ) -> AgentRunResult[Any]: """Run the agent with a user prompt in async mode. This method builds an internal agent graph (using system prompts, tools and result schemas) and then runs the graph to completion. The result of the run is returned. Example: ```python from pydantic_ai import Agent agent = Agent('openai:gpt-4o') async def main(): agent_run = await agent.run('What is the capital of France?') print(agent_run.output) #> The capital of France is Paris. ``` Args: user_prompt: User input to start/continue the conversation. 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. event_stream_handler: Optional event stream handler to use for this run. builtin_tools: Optional additional builtin tools for this run. Returns: The result of the run. """ @flow(name=f'{self._name} Run') async def wrapped_run_flow() -> AgentRunResult[Any]: # Mark that we're inside a PrefectAgent flow token = self._in_prefect_agent_flow.set(True) try: with self._prefect_overrides(): result = await super(WrapperAgent, self).run( user_prompt, 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, event_stream_handler=event_stream_handler, ) return result finally: self._in_prefect_agent_flow.reset(token) return await wrapped_run_flow() @overload def run_sync( self, user_prompt: str | Sequence[_messages.UserContent] | None = None, *, output_type: None = None, message_history: Sequence[_messages.ModelMessage] | None = None, deferred_tool_results: DeferredToolResults | None = None, model: models.Model | models.KnownModelName | str | None = None, instructions: Instructions[AgentDepsT] = None, deps: AgentDepsT = None, model_settings: ModelSettings | None = None, usage_limits: _usage.UsageLimits | None = None, usage: _usage.RunUsage | None = None, infer_name: bool = True, toolsets: Sequence[AbstractToolset[AgentDepsT]] | None = None, builtin_tools: Sequence[AbstractBuiltinTool] | None = None, event_stream_handler: EventStreamHandler[AgentDepsT] | None = None, ) -> AgentRunResult[OutputDataT]: ... @overload def run_sync( self, user_prompt: str | Sequence[_messages.UserContent] | None = None, *, output_type: OutputSpec[RunOutputDataT], message_history: Sequence[_messages.ModelMessage] | None = None, deferred_tool_results: DeferredToolResults | None = None, model: models.Model | models.KnownModelName | str | None = None, instructions: Instructions[AgentDepsT] = None, deps: AgentDepsT = None, model_settings: ModelSettings | None = None, usage_limits: _usage.UsageLimits | None = None, usage: _usage.RunUsage | None = None, infer_name: bool = True, toolsets: Sequence[AbstractToolset[AgentDepsT]] | None = None, builtin_tools: Sequence[AbstractBuiltinTool] | None = None, event_stream_handler: EventStreamHandler[AgentDepsT] | None = None, ) -> AgentRunResult[RunOutputDataT]: ... def run_sync( self, user_prompt: str | Sequence[_messages.UserContent] | None = None, *, output_type: OutputSpec[RunOutputDataT] | None = None, message_history: Sequence[_messages.ModelMessage] | None = None, deferred_tool_results: DeferredToolResults | None = None, model: models.Model | models.KnownModelName | str | None = None, instructions: Instructions[AgentDepsT] = None, deps: AgentDepsT = None, model_settings: ModelSettings | None = None, usage_limits: _usage.UsageLimits | None = None, usage: _usage.RunUsage | None = None, infer_name: bool = True, toolsets: Sequence[AbstractToolset[AgentDepsT]] | None = None, builtin_tools: Sequence[AbstractBuiltinTool] | None = None, event_stream_handler: EventStreamHandler[AgentDepsT] | None = None, **_deprecated_kwargs: Never, ) -> AgentRunResult[Any]: """Synchronously run the agent with a user prompt. This is a convenience method that wraps [`self.run`][pydantic_ai.agent.AbstractAgent.run] with `loop.run_until_complete(...)`. You therefore can't use this method inside async code or if there's an active event loop. Example: ```python from pydantic_ai import Agent agent = Agent('openai:gpt-4o') result_sync = agent.run_sync('What is the capital of Italy?') print(result_sync.output) #> The capital of Italy is Rome. ``` Args: user_prompt: User input to start/continue the conversation. 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. event_stream_handler: Optional event stream handler to use for this run. builtin_tools: Optional additional builtin tools for this run. Returns: The result of the run. """ @flow(name=f'{self._name} Sync Run') def wrapped_run_sync_flow() -> AgentRunResult[Any]: # Mark that we're inside a PrefectAgent flow token = self._in_prefect_agent_flow.set(True) try: with self._prefect_overrides(): # Using `run_coro_as_sync` from Prefect with async `run` to avoid event loop conflicts. result = run_coro_as_sync( super(PrefectAgent, self).run( user_prompt, 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, event_stream_handler=event_stream_handler, ) ) return result finally: self._in_prefect_agent_flow.reset(token) return wrapped_run_sync_flow() @overload def run_stream( self, user_prompt: str | Sequence[_messages.UserContent] | None = None, *, output_type: None = None, message_history: Sequence[_messages.ModelMessage] | None = None, deferred_tool_results: DeferredToolResults | None = None, model: models.Model | models.KnownModelName | str | None = None, instructions: Instructions[AgentDepsT] = None, deps: AgentDepsT = None, model_settings: ModelSettings | None = None, usage_limits: _usage.UsageLimits | None = None, usage: _usage.RunUsage | None = None, infer_name: bool = True, toolsets: Sequence[AbstractToolset[AgentDepsT]] | None = None, builtin_tools: Sequence[AbstractBuiltinTool] | None = None, event_stream_handler: EventStreamHandler[AgentDepsT] | None = None, ) -> AbstractAsyncContextManager[StreamedRunResult[AgentDepsT, OutputDataT]]: ... @overload def run_stream( self, user_prompt: str | Sequence[_messages.UserContent] | None = None, *, output_type: OutputSpec[RunOutputDataT], message_history: Sequence[_messages.ModelMessage] | None = None, deferred_tool_results: DeferredToolResults | None = None, model: models.Model | models.KnownModelName | str | None = None, instructions: Instructions[AgentDepsT] = None, deps: AgentDepsT = None, model_settings: ModelSettings | None = None, usage_limits: _usage.UsageLimits | None = None, usage: _usage.RunUsage | None = None, infer_name: bool = True, toolsets: Sequence[AbstractToolset[AgentDepsT]] | None = None, builtin_tools: Sequence[AbstractBuiltinTool] | None = None, event_stream_handler: EventStreamHandler[AgentDepsT] | None = None, ) -> AbstractAsyncContextManager[StreamedRunResult[AgentDepsT, RunOutputDataT]]: ... @asynccontextmanager async def run_stream( self, user_prompt: str | Sequence[_messages.UserContent] | None = None, *, output_type: OutputSpec[RunOutputDataT] | None = None, message_history: Sequence[_messages.ModelMessage] | None = None, deferred_tool_results: DeferredToolResults | None = None, model: models.Model | models.KnownModelName | str | None = None, instructions: Instructions[AgentDepsT] = None, deps: AgentDepsT = None, model_settings: ModelSettings | None = None, usage_limits: _usage.UsageLimits | None = None, usage: _usage.RunUsage | None = None, infer_name: bool = True, toolsets: Sequence[AbstractToolset[AgentDepsT]] | None = None, builtin_tools: Sequence[AbstractBuiltinTool] | None = None, event_stream_handler: EventStreamHandler[AgentDepsT] | None = None, **_deprecated_kwargs: Never, ) -> AsyncIterator[StreamedRunResult[AgentDepsT, Any]]: """Run the agent with a user prompt in async mode, returning a streamed response. Example: ```python from pydantic_ai import Agent agent = Agent('openai:gpt-4o') async def main(): async with agent.run_stream('What is the capital of the UK?') as response: print(await response.get_output()) #> The capital of the UK is London. ``` Args: user_prompt: User input to start/continue the conversation. 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 for this run. event_stream_handler: Optional event stream handler to use for this run. It will receive all the events up until the final result is found, which you can then read or stream from inside the context manager. Returns: The result of the run. """ if FlowRunContext.get() is not None: raise UserError( '`agent.run_stream()` cannot be used inside a Prefect flow. ' 'Set an `event_stream_handler` on the agent and use `agent.run()` instead.' ) async with super().run_stream( user_prompt, 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, event_stream_handler=event_stream_handler, builtin_tools=builtin_tools, **_deprecated_kwargs, ) as result: yield result @overload def run_stream_events( self, user_prompt: str | Sequence[_messages.UserContent] | None = None, *, output_type: None = None, message_history: Sequence[_messages.ModelMessage] | None = None, deferred_tool_results: DeferredToolResults | None = None, model: models.Model | models.KnownModelName | str | None = None, instructions: Instructions[AgentDepsT] = None, deps: AgentDepsT = None, model_settings: ModelSettings | None = None, usage_limits: _usage.UsageLimits | None = None, usage: _usage.RunUsage | None = None, infer_name: bool = True, toolsets: Sequence[AbstractToolset[AgentDepsT]] | None = None, builtin_tools: Sequence[AbstractBuiltinTool] | None = None, ) -> AsyncIterator[_messages.AgentStreamEvent | AgentRunResultEvent[OutputDataT]]: ... @overload def run_stream_events( self, user_prompt: str | Sequence[_messages.UserContent] | None = None, *, output_type: OutputSpec[RunOutputDataT], message_history: Sequence[_messages.ModelMessage] | None = None, deferred_tool_results: DeferredToolResults | None = None, model: models.Model | models.KnownModelName | str | None = None, instructions: Instructions[AgentDepsT] = None, deps: AgentDepsT = None, model_settings: ModelSettings | None = None, usage_limits: _usage.UsageLimits | None = None, usage: _usage.RunUsage | None = None, infer_name: bool = True, toolsets: Sequence[AbstractToolset[AgentDepsT]] | None = None, builtin_tools: Sequence[AbstractBuiltinTool] | None = None, ) -> AsyncIterator[_messages.AgentStreamEvent | AgentRunResultEvent[RunOutputDataT]]: ... def run_stream_events( self, user_prompt: str | Sequence[_messages.UserContent] | None = None, *, output_type: OutputSpec[RunOutputDataT] | None = None, message_history: Sequence[_messages.ModelMessage] | None = None, deferred_tool_results: DeferredToolResults | None = None, model: models.Model | models.KnownModelName | str | None = None, instructions: Instructions[AgentDepsT] = None, deps: AgentDepsT = None, model_settings: ModelSettings | None = None, usage_limits: _usage.UsageLimits | None = None, usage: _usage.RunUsage | None = None, infer_name: bool = True, toolsets: Sequence[AbstractToolset[AgentDepsT]] | None = None, builtin_tools: Sequence[AbstractBuiltinTool] | None = None, ) -> AsyncIterator[_messages.AgentStreamEvent | AgentRunResultEvent[Any]]: """Run the agent with a user prompt in async mode and stream events from the run. This is a convenience method that wraps [`self.run`][pydantic_ai.agent.AbstractAgent.run] and uses the `event_stream_handler` kwarg to get a stream of events from the run. Example: ```python from pydantic_ai import Agent, AgentRunResultEvent, AgentStreamEvent agent = Agent('openai:gpt-4o') async def main(): events: list[AgentStreamEvent | AgentRunResultEvent] = [] async for event in agent.run_stream_events('What is the capital of France?'): events.append(event) print(events) ''' [ PartStartEvent(index=0, part=TextPart(content='The capital of ')), FinalResultEvent(tool_name=None, tool_call_id=None), PartDeltaEvent(index=0, delta=TextPartDelta(content_delta='France is Paris. ')), PartEndEvent( index=0, part=TextPart(content='The capital of France is Paris. ') ), AgentRunResultEvent( result=AgentRunResult(output='The capital of France is Paris. ') ), ] ''' ``` Arguments are the same as for [`self.run`][pydantic_ai.agent.AbstractAgent.run], except that `event_stream_handler` is now allowed. Args: user_prompt: User input to start/continue the conversation. 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 for this run. Returns: An async iterable of stream events `AgentStreamEvent` and finally a `AgentRunResultEvent` with the final run result. """ if FlowRunContext.get() is not None: raise UserError( '`agent.run_stream_events()` cannot be used inside a Prefect flow. ' 'Set an `event_stream_handler` on the agent and use `agent.run()` instead.' ) return super().run_stream_events( user_prompt, 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, ) @overload def iter( self, user_prompt: str | Sequence[_messages.UserContent] | None = None, *, output_type: None = None, message_history: Sequence[_messages.ModelMessage] | None = None, deferred_tool_results: DeferredToolResults | None = None, model: models.Model | models.KnownModelName | str | None = None, instructions: Instructions[AgentDepsT] = None, deps: AgentDepsT = None, model_settings: ModelSettings | None = None, usage_limits: _usage.UsageLimits | None = None, usage: _usage.RunUsage | None = None, infer_name: bool = True, toolsets: Sequence[AbstractToolset[AgentDepsT]] | None = None, builtin_tools: Sequence[AbstractBuiltinTool] | None = None, ) -> AbstractAsyncContextManager[AgentRun[AgentDepsT, OutputDataT]]: ... @overload def iter( self, user_prompt: str | Sequence[_messages.UserContent] | None = None, *, output_type: OutputSpec[RunOutputDataT], message_history: Sequence[_messages.ModelMessage] | None = None, deferred_tool_results: DeferredToolResults | None = None, model: models.Model | models.KnownModelName | str | None = None, instructions: Instructions[AgentDepsT] = None, deps: AgentDepsT = None, model_settings: ModelSettings | None = None, usage_limits: _usage.UsageLimits | None = None, usage: _usage.RunUsage | None = None, infer_name: bool = True, toolsets: Sequence[AbstractToolset[AgentDepsT]] | None = None, builtin_tools: Sequence[AbstractBuiltinTool] | None = None, ) -> AbstractAsyncContextManager[AgentRun[AgentDepsT, RunOutputDataT]]: ... @asynccontextmanager async def iter( self, user_prompt: str | Sequence[_messages.UserContent] | None = None, *, output_type: OutputSpec[RunOutputDataT] | None = None, message_history: Sequence[_messages.ModelMessage] | None = None, deferred_tool_results: DeferredToolResults | None = None, model: models.Model | models.KnownModelName | str | None = None, instructions: Instructions[AgentDepsT] = None, deps: AgentDepsT = None, model_settings: ModelSettings | None = None, usage_limits: _usage.UsageLimits | None = None, usage: _usage.RunUsage | None = None, infer_name: bool = True, toolsets: Sequence[AbstractToolset[AgentDepsT]] | None = None, builtin_tools: Sequence[AbstractBuiltinTool] | None = None, ) -> AsyncIterator[AgentRun[AgentDepsT, Any]]: """A contextmanager which can be used to iterate over the agent graph's nodes as they are executed. This method builds an internal agent graph (using system prompts, tools and output schemas) and then returns an `AgentRun` object. The `AgentRun` can be used to async-iterate over the nodes of the graph as they are executed. This is the API to use if you want to consume the outputs coming from each LLM model response, or the stream of events coming from the execution of tools. The `AgentRun` also provides methods to access the full message history, new messages, and usage statistics, and the final result of the run once it has completed. For more details, see the documentation of `AgentRun`. Example: ```python from pydantic_ai import Agent agent = Agent('openai:gpt-4o') async def main(): nodes = [] async with agent.iter('What is the capital of France?') as agent_run: async for node in agent_run: nodes.append(node) print(nodes) ''' [ UserPromptNode( user_prompt='What is the capital of France?', instructions_functions=[], system_prompts=(), system_prompt_functions=[], system_prompt_dynamic_functions={}, ), ModelRequestNode( request=ModelRequest( parts=[ UserPromptPart( content='What is the capital of France?', timestamp=datetime.datetime(...), ) ], run_id='...', ) ), CallToolsNode( model_response=ModelResponse( parts=[TextPart(content='The capital of France is Paris.')], usage=RequestUsage(input_tokens=56, output_tokens=7), model_name='gpt-4o', timestamp=datetime.datetime(...), run_id='...', ) ), End(data=FinalResult(output='The capital of France is Paris.')), ] ''' print(agent_run.result.output) #> The capital of France is Paris. ``` Args: user_prompt: User input to start/continue the conversation. 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. deps: Optional dependencies to use for this run. instructions: Optional additional instructions 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 for this run. Returns: The result of the run. """ if model is not None and not isinstance(model, PrefectModel): raise UserError( 'Non-Prefect model cannot be set at agent run time inside a Prefect flow, it must be set at agent creation time.' ) with self._prefect_overrides(): async with super().iter( user_prompt=user_prompt, 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, ) as run: yield run @contextmanager def override( self, *, name: str | _utils.Unset = _utils.UNSET, deps: AgentDepsT | _utils.Unset = _utils.UNSET, model: models.Model | models.KnownModelName | str | _utils.Unset = _utils.UNSET, toolsets: Sequence[AbstractToolset[AgentDepsT]] | _utils.Unset = _utils.UNSET, tools: Sequence[Tool[AgentDepsT] | ToolFuncEither[AgentDepsT, ...]] | _utils.Unset = _utils.UNSET, instructions: Instructions[AgentDepsT] | _utils.Unset = _utils.UNSET, ) -> Iterator[None]: """Context manager to temporarily override agent dependencies, model, toolsets, tools, or instructions. This is particularly useful when testing. You can find an example of this [here](../testing.md#overriding-model-via-pytest-fixtures). Args: name: The name to use instead of the name passed to the agent constructor and agent run. deps: The dependencies to use instead of the dependencies passed to the agent run. model: The model to use instead of the model passed to the agent run. toolsets: The toolsets to use instead of the toolsets passed to the agent constructor and agent run. tools: The tools to use instead of the tools registered with the agent. instructions: The instructions to use instead of the instructions registered with the agent. """ if _utils.is_set(model) and not isinstance(model, PrefectModel): raise UserError( 'Non-Prefect model cannot be contextually overridden inside a Prefect flow, it must be set at agent creation time.' ) with super().override( name=name, deps=deps, model=model, toolsets=toolsets, tools=tools, instructions=instructions ): yield