pydantic_ai.agent

Agent dataclass

Bases: Generic[AgentDepsT, ResultDataT]

Class for defining "agents" - a way to have a specific type of "conversation" with an LLM.

Agents are generic in the dependency type they take AgentDepsT and the result data type they return, ResultDataT.

By default, if neither generic parameter is customised, agents have type Agent[None, str].

Minimal usage example:

from pydantic_ai import Agent

agent = Agent('openai:gpt-4o')
result = agent.run_sync('What is the capital of France?')
print(result.data)
#> Paris

Source code in pydantic_ai_slim/pydantic_ai/agent.py

@final
@dataclasses.dataclass(init=False)
class Agent(Generic[AgentDepsT, ResultDataT]):
    """Class for defining "agents" - a way to have a specific type of "conversation" with an LLM.

    Agents are generic in the dependency type they take [`AgentDepsT`][pydantic_ai.tools.AgentDepsT]
    and the result data type they return, [`ResultDataT`][pydantic_ai.result.ResultDataT].

    By default, if neither generic parameter is customised, agents have type `Agent[None, str]`.

    Minimal usage example:

    ```python
    from pydantic_ai import Agent

    agent = Agent('openai:gpt-4o')
    result = agent.run_sync('What is the capital of France?')
    print(result.data)
    #> Paris
    ```
    """

    # we use dataclass fields in order to conveniently know what attributes are available
    model: models.Model | models.KnownModelName | None
    """The default model configured for this agent."""

    name: str | None
    """The name of the agent, used for logging.

    If `None`, we try to infer the agent name from the call frame when the agent is first run.
    """
    end_strategy: EndStrategy
    """Strategy for handling tool calls when a final result is found."""

    model_settings: ModelSettings | None
    """Optional model request settings to use for this agents's runs, by default.

    Note, if `model_settings` is provided by `run`, `run_sync`, or `run_stream`, those settings will
    be merged with this value, with the runtime argument taking priority.
    """

    result_type: type[ResultDataT] = dataclasses.field(repr=False)
    """
    The type of the result data, used to validate the result data, defaults to `str`.
    """

    _deps_type: type[AgentDepsT] = dataclasses.field(repr=False)
    _result_tool_name: str = dataclasses.field(repr=False)
    _result_tool_description: str | None = dataclasses.field(repr=False)
    _result_schema: _result.ResultSchema[ResultDataT] | None = dataclasses.field(repr=False)
    _result_validators: list[_result.ResultValidator[AgentDepsT, ResultDataT]] = dataclasses.field(repr=False)
    _system_prompts: tuple[str, ...] = dataclasses.field(repr=False)
    _system_prompt_functions: list[_system_prompt.SystemPromptRunner[AgentDepsT]] = dataclasses.field(repr=False)
    _system_prompt_dynamic_functions: dict[str, _system_prompt.SystemPromptRunner[AgentDepsT]] = dataclasses.field(
        repr=False
    )
    _function_tools: dict[str, Tool[AgentDepsT]] = dataclasses.field(repr=False)
    _default_retries: int = dataclasses.field(repr=False)
    _max_result_retries: int = dataclasses.field(repr=False)
    _override_deps: _utils.Option[AgentDepsT] = dataclasses.field(default=None, repr=False)
    _override_model: _utils.Option[models.Model] = dataclasses.field(default=None, repr=False)

    def __init__(
        self,
        model: models.Model | models.KnownModelName | None = None,
        *,
        result_type: type[ResultDataT] = str,
        system_prompt: str | Sequence[str] = (),
        deps_type: type[AgentDepsT] = NoneType,
        name: str | None = None,
        model_settings: ModelSettings | None = None,
        retries: int = 1,
        result_tool_name: str = 'final_result',
        result_tool_description: str | None = None,
        result_retries: int | None = None,
        tools: Sequence[Tool[AgentDepsT] | ToolFuncEither[AgentDepsT, ...]] = (),
        defer_model_check: bool = False,
        end_strategy: EndStrategy = 'early',
    ):
        """Create an agent.

        Args:
            model: The default model to use for this agent, if not provide,
                you must provide the model when calling it.
            result_type: The type of the result data, used to validate the result data, defaults to `str`.
            system_prompt: Static system prompts to use for this agent, you can also register system
                prompts via a function with [`system_prompt`][pydantic_ai.Agent.system_prompt].
            deps_type: The type used for dependency injection, this parameter exists solely to allow you to fully
                parameterize the agent, and therefore get the best out of static type checking.
                If you're not using deps, but want type checking to pass, you can set `deps=None` to satisfy Pyright
                or add a type hint `: Agent[None, <return type>]`.
            name: The name of the agent, used for logging. If `None`, we try to infer the agent name from the call frame
                when the agent is first run.
            model_settings: Optional model request settings to use for this agent's runs, by default.
            retries: The default number of retries to allow before raising an error.
            result_tool_name: The name of the tool to use for the final result.
            result_tool_description: The description of the final result tool.
            result_retries: The maximum number of retries to allow for result validation, defaults to `retries`.
            tools: Tools to register with the agent, you can also register tools via the decorators
                [`@agent.tool`][pydantic_ai.Agent.tool] and [`@agent.tool_plain`][pydantic_ai.Agent.tool_plain].
            defer_model_check: by default, if you provide a [named][pydantic_ai.models.KnownModelName] model,
                it's evaluated to create a [`Model`][pydantic_ai.models.Model] instance immediately,
                which checks for the necessary environment variables. Set this to `false`
                to defer the evaluation until the first run. Useful if you want to
                [override the model][pydantic_ai.Agent.override] for testing.
            end_strategy: Strategy for handling tool calls that are requested alongside a final result.
                See [`EndStrategy`][pydantic_ai.agent.EndStrategy] for more information.
        """
        if model is None or defer_model_check:
            self.model = model
        else:
            self.model = models.infer_model(model)

        self.end_strategy = end_strategy
        self.name = name
        self.model_settings = model_settings
        self.result_type = result_type

        self._deps_type = deps_type

        self._result_tool_name = result_tool_name
        self._result_tool_description = result_tool_description
        self._result_schema: _result.ResultSchema[ResultDataT] | None = _result.ResultSchema[result_type].build(
            result_type, result_tool_name, result_tool_description
        )
        self._result_validators: list[_result.ResultValidator[AgentDepsT, ResultDataT]] = []

        self._system_prompts = (system_prompt,) if isinstance(system_prompt, str) else tuple(system_prompt)
        self._system_prompt_functions: list[_system_prompt.SystemPromptRunner[AgentDepsT]] = []
        self._system_prompt_dynamic_functions: dict[str, _system_prompt.SystemPromptRunner[AgentDepsT]] = {}

        self._function_tools: dict[str, Tool[AgentDepsT]] = {}

        self._default_retries = retries
        self._max_result_retries = result_retries if result_retries is not None else retries
        for tool in tools:
            if isinstance(tool, Tool):
                self._register_tool(tool)
            else:
                self._register_tool(Tool(tool))

    @overload
    async def run(
        self,
        user_prompt: str,
        *,
        result_type: None = None,
        message_history: list[_messages.ModelMessage] | None = None,
        model: models.Model | models.KnownModelName | None = None,
        deps: AgentDepsT = None,
        model_settings: ModelSettings | None = None,
        usage_limits: _usage.UsageLimits | None = None,
        usage: _usage.Usage | None = None,
        infer_name: bool = True,
    ) -> result.RunResult[ResultDataT]: ...

    @overload
    async def run(
        self,
        user_prompt: str,
        *,
        result_type: type[RunResultDataT],
        message_history: list[_messages.ModelMessage] | None = None,
        model: models.Model | models.KnownModelName | None = None,
        deps: AgentDepsT = None,
        model_settings: ModelSettings | None = None,
        usage_limits: _usage.UsageLimits | None = None,
        usage: _usage.Usage | None = None,
        infer_name: bool = True,
    ) -> result.RunResult[RunResultDataT]: ...

    async def run(
        self,
        user_prompt: str,
        *,
        message_history: list[_messages.ModelMessage] | None = None,
        model: models.Model | models.KnownModelName | None = None,
        deps: AgentDepsT = None,
        model_settings: ModelSettings | None = None,
        usage_limits: _usage.UsageLimits | None = None,
        usage: _usage.Usage | None = None,
        result_type: type[RunResultDataT] | None = None,
        infer_name: bool = True,
    ) -> result.RunResult[Any]:
        """Run the agent with a user prompt in async mode.

        Example:
        ```python
        from pydantic_ai import Agent

        agent = Agent('openai:gpt-4o')

        async def main():
            result = await agent.run('What is the capital of France?')
            print(result.data)
            #> Paris
        ```

        Args:
            result_type: Custom result type to use for this run, `result_type` may only be used if the agent has no
                result validators since result validators would expect an argument that matches the agent's result type.
            user_prompt: User input to start/continue the conversation.
            message_history: History of the conversation so far.
            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.
            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.

        Returns:
            The result of the run.
        """
        if infer_name and self.name is None:
            self._infer_name(inspect.currentframe())
        model_used = await self._get_model(model)

        deps = self._get_deps(deps)
        new_message_index = len(message_history) if message_history else 0
        result_schema: _result.ResultSchema[RunResultDataT] | None = self._prepare_result_schema(result_type)

        # Build the graph
        graph = _agent_graph.build_agent_graph(self.name, self._deps_type, result_type or self.result_type)

        # Build the initial state
        state = _agent_graph.GraphAgentState(
            message_history=message_history[:] if message_history else [],
            usage=usage or _usage.Usage(),
            retries=0,
            run_step=0,
        )

        # We consider it a user error if a user tries to restrict the result type while having a result validator that
        # may change the result type from the restricted type to something else. Therefore, we consider the following
        # typecast reasonable, even though it is possible to violate it with otherwise-type-checked code.
        result_validators = cast(list[_result.ResultValidator[AgentDepsT, RunResultDataT]], self._result_validators)

        # TODO: Instead of this, copy the function tools to ensure they don't share current_retry state between agent
        #  runs. Requires some changes to `Tool` to make them copyable though.
        for v in self._function_tools.values():
            v.current_retry = 0

        model_settings = merge_model_settings(self.model_settings, model_settings)
        usage_limits = usage_limits or _usage.UsageLimits()

        with _logfire.span(
            '{agent_name} run {prompt=}',
            prompt=user_prompt,
            agent=self,
            model_name=model_used.name() if model_used else 'no-model',
            agent_name=self.name or 'agent',
        ) as run_span:
            # Build the deps object for the graph
            graph_deps = _agent_graph.GraphAgentDeps[AgentDepsT, RunResultDataT](
                user_deps=deps,
                prompt=user_prompt,
                new_message_index=new_message_index,
                model=model_used,
                model_settings=model_settings,
                usage_limits=usage_limits,
                max_result_retries=self._max_result_retries,
                end_strategy=self.end_strategy,
                result_schema=result_schema,
                result_tools=self._result_schema.tool_defs() if self._result_schema else [],
                result_validators=result_validators,
                function_tools=self._function_tools,
                run_span=run_span,
            )

            start_node = _agent_graph.UserPromptNode[AgentDepsT](
                user_prompt=user_prompt,
                system_prompts=self._system_prompts,
                system_prompt_functions=self._system_prompt_functions,
                system_prompt_dynamic_functions=self._system_prompt_dynamic_functions,
            )

            # Actually run
            end_result, _ = await graph.run(
                start_node,
                state=state,
                deps=graph_deps,
                infer_name=False,
            )

        # Build final run result
        # We don't do any advanced checking if the data is actually from a final result or not
        return result.RunResult(
            state.message_history,
            new_message_index,
            end_result.data,
            end_result.tool_name,
            state.usage,
        )

    @overload
    def run_sync(
        self,
        user_prompt: str,
        *,
        message_history: list[_messages.ModelMessage] | None = None,
        model: models.Model | models.KnownModelName | None = None,
        deps: AgentDepsT = None,
        model_settings: ModelSettings | None = None,
        usage_limits: _usage.UsageLimits | None = None,
        usage: _usage.Usage | None = None,
        infer_name: bool = True,
    ) -> result.RunResult[ResultDataT]: ...

    @overload
    def run_sync(
        self,
        user_prompt: str,
        *,
        result_type: type[RunResultDataT] | None,
        message_history: list[_messages.ModelMessage] | None = None,
        model: models.Model | models.KnownModelName | None = None,
        deps: AgentDepsT = None,
        model_settings: ModelSettings | None = None,
        usage_limits: _usage.UsageLimits | None = None,
        usage: _usage.Usage | None = None,
        infer_name: bool = True,
    ) -> result.RunResult[RunResultDataT]: ...

    def run_sync(
        self,
        user_prompt: str,
        *,
        result_type: type[RunResultDataT] | None = None,
        message_history: list[_messages.ModelMessage] | None = None,
        model: models.Model | models.KnownModelName | None = None,
        deps: AgentDepsT = None,
        model_settings: ModelSettings | None = None,
        usage_limits: _usage.UsageLimits | None = None,
        usage: _usage.Usage | None = None,
        infer_name: bool = True,
    ) -> result.RunResult[Any]:
        """Run the agent with a user prompt synchronously.

        This is a convenience method that wraps [`self.run`][pydantic_ai.Agent.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.data)
        #> Rome
        ```

        Args:
            result_type: Custom result type to use for this run, `result_type` may only be used if the agent has no
                result validators since result validators would expect an argument that matches the agent's result type.
            user_prompt: User input to start/continue the conversation.
            message_history: History of the conversation so far.
            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.
            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.

        Returns:
            The result of the run.
        """
        if infer_name and self.name is None:
            self._infer_name(inspect.currentframe())
        return asyncio.get_event_loop().run_until_complete(
            self.run(
                user_prompt,
                result_type=result_type,
                message_history=message_history,
                model=model,
                deps=deps,
                model_settings=model_settings,
                usage_limits=usage_limits,
                usage=usage,
                infer_name=False,
            )
        )

    @overload
    def run_stream(
        self,
        user_prompt: str,
        *,
        result_type: None = None,
        message_history: list[_messages.ModelMessage] | None = None,
        model: models.Model | models.KnownModelName | None = None,
        deps: AgentDepsT = None,
        model_settings: ModelSettings | None = None,
        usage_limits: _usage.UsageLimits | None = None,
        usage: _usage.Usage | None = None,
        infer_name: bool = True,
    ) -> AbstractAsyncContextManager[result.StreamedRunResult[AgentDepsT, ResultDataT]]: ...

    @overload
    def run_stream(
        self,
        user_prompt: str,
        *,
        result_type: type[RunResultDataT],
        message_history: list[_messages.ModelMessage] | None = None,
        model: models.Model | models.KnownModelName | None = None,
        deps: AgentDepsT = None,
        model_settings: ModelSettings | None = None,
        usage_limits: _usage.UsageLimits | None = None,
        usage: _usage.Usage | None = None,
        infer_name: bool = True,
    ) -> AbstractAsyncContextManager[result.StreamedRunResult[AgentDepsT, RunResultDataT]]: ...

    @asynccontextmanager
    async def run_stream(
        self,
        user_prompt: str,
        *,
        result_type: type[RunResultDataT] | None = None,
        message_history: list[_messages.ModelMessage] | None = None,
        model: models.Model | models.KnownModelName | None = None,
        deps: AgentDepsT = None,
        model_settings: ModelSettings | None = None,
        usage_limits: _usage.UsageLimits | None = None,
        usage: _usage.Usage | None = None,
        infer_name: bool = True,
    ) -> AsyncIterator[result.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_data())
                #> London
        ```

        Args:
            result_type: Custom result type to use for this run, `result_type` may only be used if the agent has no
                result validators since result validators would expect an argument that matches the agent's result type.
            user_prompt: User input to start/continue the conversation.
            message_history: History of the conversation so far.
            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.
            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.

        Returns:
            The result of the run.
        """
        if infer_name and self.name is None:
            # f_back because `asynccontextmanager` adds one frame
            if frame := inspect.currentframe():  # pragma: no branch
                self._infer_name(frame.f_back)
        model_used = await self._get_model(model)

        deps = self._get_deps(deps)
        new_message_index = len(message_history) if message_history else 0
        result_schema: _result.ResultSchema[RunResultDataT] | None = self._prepare_result_schema(result_type)

        # Build the graph
        graph = self._build_stream_graph(result_type)

        # Build the initial state
        graph_state = _agent_graph.GraphAgentState(
            message_history=message_history[:] if message_history else [],
            usage=usage or _usage.Usage(),
            retries=0,
            run_step=0,
        )

        # We consider it a user error if a user tries to restrict the result type while having a result validator that
        # may change the result type from the restricted type to something else. Therefore, we consider the following
        # typecast reasonable, even though it is possible to violate it with otherwise-type-checked code.
        result_validators = cast(list[_result.ResultValidator[AgentDepsT, RunResultDataT]], self._result_validators)

        # TODO: Instead of this, copy the function tools to ensure they don't share current_retry state between agent
        #  runs. Requires some changes to `Tool` to make them copyable though.
        for v in self._function_tools.values():
            v.current_retry = 0

        model_settings = merge_model_settings(self.model_settings, model_settings)
        usage_limits = usage_limits or _usage.UsageLimits()

        with _logfire.span(
            '{agent_name} run stream {prompt=}',
            prompt=user_prompt,
            agent=self,
            model_name=model_used.name(),
            agent_name=self.name or 'agent',
        ) as run_span:
            # Build the deps object for the graph
            graph_deps = _agent_graph.GraphAgentDeps[AgentDepsT, RunResultDataT](
                user_deps=deps,
                prompt=user_prompt,
                new_message_index=new_message_index,
                model=model_used,
                model_settings=model_settings,
                usage_limits=usage_limits,
                max_result_retries=self._max_result_retries,
                end_strategy=self.end_strategy,
                result_schema=result_schema,
                result_tools=self._result_schema.tool_defs() if self._result_schema else [],
                result_validators=result_validators,
                function_tools=self._function_tools,
                run_span=run_span,
            )

            start_node = _agent_graph.StreamUserPromptNode[AgentDepsT](
                user_prompt=user_prompt,
                system_prompts=self._system_prompts,
                system_prompt_functions=self._system_prompt_functions,
                system_prompt_dynamic_functions=self._system_prompt_dynamic_functions,
            )

            # Actually run
            node = start_node
            history: list[HistoryStep[_agent_graph.GraphAgentState, RunResultDataT]] = []
            while True:
                if isinstance(node, _agent_graph.StreamModelRequestNode):
                    node = cast(
                        _agent_graph.StreamModelRequestNode[
                            AgentDepsT, result.StreamedRunResult[AgentDepsT, RunResultDataT]
                        ],
                        node,
                    )
                    async with node.run_to_result(GraphRunContext(graph_state, graph_deps)) as r:
                        if isinstance(r, End):
                            yield r.data
                            break
                assert not isinstance(node, End)  # the previous line should be hit first
                node = await graph.next(
                    node,
                    history,
                    state=graph_state,
                    deps=graph_deps,
                    infer_name=False,
                )

    @contextmanager
    def override(
        self,
        *,
        deps: AgentDepsT | _utils.Unset = _utils.UNSET,
        model: models.Model | models.KnownModelName | _utils.Unset = _utils.UNSET,
    ) -> Iterator[None]:
        """Context manager to temporarily override agent dependencies and model.

        This is particularly useful when testing.
        You can find an example of this [here](../testing-evals.md#overriding-model-via-pytest-fixtures).

        Args:
            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.
        """
        if _utils.is_set(deps):
            override_deps_before = self._override_deps
            self._override_deps = _utils.Some(deps)
        else:
            override_deps_before = _utils.UNSET

        # noinspection PyTypeChecker
        if _utils.is_set(model):
            override_model_before = self._override_model
            # noinspection PyTypeChecker
            self._override_model = _utils.Some(models.infer_model(model))  # pyright: ignore[reportArgumentType]
        else:
            override_model_before = _utils.UNSET

        try:
            yield
        finally:
            if _utils.is_set(override_deps_before):
                self._override_deps = override_deps_before
            if _utils.is_set(override_model_before):
                self._override_model = override_model_before

    @overload
    def system_prompt(
        self, func: Callable[[RunContext[AgentDepsT]], str], /
    ) -> Callable[[RunContext[AgentDepsT]], str]: ...

    @overload
    def system_prompt(
        self, func: Callable[[RunContext[AgentDepsT]], Awaitable[str]], /
    ) -> Callable[[RunContext[AgentDepsT]], Awaitable[str]]: ...

    @overload
    def system_prompt(self, func: Callable[[], str], /) -> Callable[[], str]: ...

    @overload
    def system_prompt(self, func: Callable[[], Awaitable[str]], /) -> Callable[[], Awaitable[str]]: ...

    @overload
    def system_prompt(
        self, /, *, dynamic: bool = False
    ) -> Callable[[_system_prompt.SystemPromptFunc[AgentDepsT]], _system_prompt.SystemPromptFunc[AgentDepsT]]: ...

    def system_prompt(
        self,
        func: _system_prompt.SystemPromptFunc[AgentDepsT] | None = None,
        /,
        *,
        dynamic: bool = False,
    ) -> (
        Callable[[_system_prompt.SystemPromptFunc[AgentDepsT]], _system_prompt.SystemPromptFunc[AgentDepsT]]
        | _system_prompt.SystemPromptFunc[AgentDepsT]
    ):
        """Decorator to register a system prompt function.

        Optionally takes [`RunContext`][pydantic_ai.tools.RunContext] as its only argument.
        Can decorate a sync or async functions.

        The decorator can be used either bare (`agent.system_prompt`) or as a function call
        (`agent.system_prompt(...)`), see the examples below.

        Overloads for every possible signature of `system_prompt` are included so the decorator doesn't obscure
        the type of the function, see `tests/typed_agent.py` for tests.

        Args:
            func: The function to decorate
            dynamic: If True, the system prompt will be reevaluated even when `messages_history` is provided,
                see [`SystemPromptPart.dynamic_ref`][pydantic_ai.messages.SystemPromptPart.dynamic_ref]

        Example:
        ```python
        from pydantic_ai import Agent, RunContext

        agent = Agent('test', deps_type=str)

        @agent.system_prompt
        def simple_system_prompt() -> str:
            return 'foobar'

        @agent.system_prompt(dynamic=True)
        async def async_system_prompt(ctx: RunContext[str]) -> str:
            return f'{ctx.deps} is the best'
        ```
        """
        if func is None:

            def decorator(
                func_: _system_prompt.SystemPromptFunc[AgentDepsT],
            ) -> _system_prompt.SystemPromptFunc[AgentDepsT]:
                runner = _system_prompt.SystemPromptRunner[AgentDepsT](func_, dynamic=dynamic)
                self._system_prompt_functions.append(runner)
                if dynamic:
                    self._system_prompt_dynamic_functions[func_.__qualname__] = runner
                return func_

            return decorator
        else:
            assert not dynamic, "dynamic can't be True in this case"
            self._system_prompt_functions.append(_system_prompt.SystemPromptRunner[AgentDepsT](func, dynamic=dynamic))
            return func

    @overload
    def result_validator(
        self, func: Callable[[RunContext[AgentDepsT], ResultDataT], ResultDataT], /
    ) -> Callable[[RunContext[AgentDepsT], ResultDataT], ResultDataT]: ...

    @overload
    def result_validator(
        self, func: Callable[[RunContext[AgentDepsT], ResultDataT], Awaitable[ResultDataT]], /
    ) -> Callable[[RunContext[AgentDepsT], ResultDataT], Awaitable[ResultDataT]]: ...

    @overload
    def result_validator(
        self, func: Callable[[ResultDataT], ResultDataT], /
    ) -> Callable[[ResultDataT], ResultDataT]: ...

    @overload
    def result_validator(
        self, func: Callable[[ResultDataT], Awaitable[ResultDataT]], /
    ) -> Callable[[ResultDataT], Awaitable[ResultDataT]]: ...

    def result_validator(
        self, func: _result.ResultValidatorFunc[AgentDepsT, ResultDataT], /
    ) -> _result.ResultValidatorFunc[AgentDepsT, ResultDataT]:
        """Decorator to register a result validator function.

        Optionally takes [`RunContext`][pydantic_ai.tools.RunContext] as its first argument.
        Can decorate a sync or async functions.

        Overloads for every possible signature of `result_validator` are included so the decorator doesn't obscure
        the type of the function, see `tests/typed_agent.py` for tests.

        Example:
        ```python
        from pydantic_ai import Agent, ModelRetry, RunContext

        agent = Agent('test', deps_type=str)

        @agent.result_validator
        def result_validator_simple(data: str) -> str:
            if 'wrong' in data:
                raise ModelRetry('wrong response')
            return data

        @agent.result_validator
        async def result_validator_deps(ctx: RunContext[str], data: str) -> str:
            if ctx.deps in data:
                raise ModelRetry('wrong response')
            return data

        result = agent.run_sync('foobar', deps='spam')
        print(result.data)
        #> success (no tool calls)
        ```
        """
        self._result_validators.append(_result.ResultValidator[AgentDepsT, Any](func))
        return func

    @overload
    def tool(self, func: ToolFuncContext[AgentDepsT, ToolParams], /) -> ToolFuncContext[AgentDepsT, ToolParams]: ...

    @overload
    def tool(
        self,
        /,
        *,
        retries: int | None = None,
        prepare: ToolPrepareFunc[AgentDepsT] | None = None,
        docstring_format: DocstringFormat = 'auto',
        require_parameter_descriptions: bool = False,
    ) -> Callable[[ToolFuncContext[AgentDepsT, ToolParams]], ToolFuncContext[AgentDepsT, ToolParams]]: ...

    def tool(
        self,
        func: ToolFuncContext[AgentDepsT, ToolParams] | None = None,
        /,
        *,
        retries: int | None = None,
        prepare: ToolPrepareFunc[AgentDepsT] | None = None,
        docstring_format: DocstringFormat = 'auto',
        require_parameter_descriptions: bool = False,
    ) -> Any:
        """Decorator to register a tool function which takes [`RunContext`][pydantic_ai.tools.RunContext] as its first argument.

        Can decorate a sync or async functions.

        The docstring is inspected to extract both the tool description and description of each parameter,
        [learn more](../tools.md#function-tools-and-schema).

        We can't add overloads for every possible signature of tool, since the return type is a recursive union
        so the signature of functions decorated with `@agent.tool` is obscured.

        Example:
        ```python
        from pydantic_ai import Agent, RunContext

        agent = Agent('test', deps_type=int)

        @agent.tool
        def foobar(ctx: RunContext[int], x: int) -> int:
            return ctx.deps + x

        @agent.tool(retries=2)
        async def spam(ctx: RunContext[str], y: float) -> float:
            return ctx.deps + y

        result = agent.run_sync('foobar', deps=1)
        print(result.data)
        #> {"foobar":1,"spam":1.0}
        ```

        Args:
            func: The tool function to register.
            retries: The number of retries to allow for this tool, defaults to the agent's default retries,
                which defaults to 1.
            prepare: custom method to prepare the tool definition for each step, return `None` to omit this
                tool from a given step. This is useful if you want to customise a tool at call time,
                or omit it completely from a step. See [`ToolPrepareFunc`][pydantic_ai.tools.ToolPrepareFunc].
            docstring_format: The format of the docstring, see [`DocstringFormat`][pydantic_ai.tools.DocstringFormat].
                Defaults to `'auto'`, such that the format is inferred from the structure of the docstring.
            require_parameter_descriptions: If True, raise an error if a parameter description is missing. Defaults to False.
        """
        if func is None:

            def tool_decorator(
                func_: ToolFuncContext[AgentDepsT, ToolParams],
            ) -> ToolFuncContext[AgentDepsT, ToolParams]:
                # noinspection PyTypeChecker
                self._register_function(func_, True, retries, prepare, docstring_format, require_parameter_descriptions)
                return func_

            return tool_decorator
        else:
            # noinspection PyTypeChecker
            self._register_function(func, True, retries, prepare, docstring_format, require_parameter_descriptions)
            return func

    @overload
    def tool_plain(self, func: ToolFuncPlain[ToolParams], /) -> ToolFuncPlain[ToolParams]: ...

    @overload
    def tool_plain(
        self,
        /,
        *,
        retries: int | None = None,
        prepare: ToolPrepareFunc[AgentDepsT] | None = None,
        docstring_format: DocstringFormat = 'auto',
        require_parameter_descriptions: bool = False,
    ) -> Callable[[ToolFuncPlain[ToolParams]], ToolFuncPlain[ToolParams]]: ...

    def tool_plain(
        self,
        func: ToolFuncPlain[ToolParams] | None = None,
        /,
        *,
        retries: int | None = None,
        prepare: ToolPrepareFunc[AgentDepsT] | None = None,
        docstring_format: DocstringFormat = 'auto',
        require_parameter_descriptions: bool = False,
    ) -> Any:
        """Decorator to register a tool function which DOES NOT take `RunContext` as an argument.

        Can decorate a sync or async functions.

        The docstring is inspected to extract both the tool description and description of each parameter,
        [learn more](../tools.md#function-tools-and-schema).

        We can't add overloads for every possible signature of tool, since the return type is a recursive union
        so the signature of functions decorated with `@agent.tool` is obscured.

        Example:
        ```python
        from pydantic_ai import Agent, RunContext

        agent = Agent('test')

        @agent.tool
        def foobar(ctx: RunContext[int]) -> int:
            return 123

        @agent.tool(retries=2)
        async def spam(ctx: RunContext[str]) -> float:
            return 3.14

        result = agent.run_sync('foobar', deps=1)
        print(result.data)
        #> {"foobar":123,"spam":3.14}
        ```

        Args:
            func: The tool function to register.
            retries: The number of retries to allow for this tool, defaults to the agent's default retries,
                which defaults to 1.
            prepare: custom method to prepare the tool definition for each step, return `None` to omit this
                tool from a given step. This is useful if you want to customise a tool at call time,
                or omit it completely from a step. See [`ToolPrepareFunc`][pydantic_ai.tools.ToolPrepareFunc].
            docstring_format: The format of the docstring, see [`DocstringFormat`][pydantic_ai.tools.DocstringFormat].
                Defaults to `'auto'`, such that the format is inferred from the structure of the docstring.
            require_parameter_descriptions: If True, raise an error if a parameter description is missing. Defaults to False.
        """
        if func is None:

            def tool_decorator(func_: ToolFuncPlain[ToolParams]) -> ToolFuncPlain[ToolParams]:
                # noinspection PyTypeChecker
                self._register_function(
                    func_, False, retries, prepare, docstring_format, require_parameter_descriptions
                )
                return func_

            return tool_decorator
        else:
            self._register_function(func, False, retries, prepare, docstring_format, require_parameter_descriptions)
            return func

    def _register_function(
        self,
        func: ToolFuncEither[AgentDepsT, ToolParams],
        takes_ctx: bool,
        retries: int | None,
        prepare: ToolPrepareFunc[AgentDepsT] | None,
        docstring_format: DocstringFormat,
        require_parameter_descriptions: bool,
    ) -> None:
        """Private utility to register a function as a tool."""
        retries_ = retries if retries is not None else self._default_retries
        tool = Tool[AgentDepsT](
            func,
            takes_ctx=takes_ctx,
            max_retries=retries_,
            prepare=prepare,
            docstring_format=docstring_format,
            require_parameter_descriptions=require_parameter_descriptions,
        )
        self._register_tool(tool)

    def _register_tool(self, tool: Tool[AgentDepsT]) -> None:
        """Private utility to register a tool instance."""
        if tool.max_retries is None:
            # noinspection PyTypeChecker
            tool = dataclasses.replace(tool, max_retries=self._default_retries)

        if tool.name in self._function_tools:
            raise exceptions.UserError(f'Tool name conflicts with existing tool: {tool.name!r}')

        if self._result_schema and tool.name in self._result_schema.tools:
            raise exceptions.UserError(f'Tool name conflicts with result schema name: {tool.name!r}')

        self._function_tools[tool.name] = tool

    async def _get_model(self, model: models.Model | models.KnownModelName | None) -> models.Model:
        """Create a model configured for this agent.

        Args:
            model: model to use for this run, required if `model` was not set when creating the agent.

        Returns:
            The model used
        """
        model_: models.Model
        if some_model := self._override_model:
            # we don't want `override()` to cover up errors from the model not being defined, hence this check
            if model is None and self.model is None:
                raise exceptions.UserError(
                    '`model` must be set either when creating the agent or when calling it. '
                    '(Even when `override(model=...)` is customizing the model that will actually be called)'
                )
            model_ = some_model.value
        elif model is not None:
            model_ = models.infer_model(model)
        elif self.model is not None:
            # noinspection PyTypeChecker
            model_ = self.model = models.infer_model(self.model)
        else:
            raise exceptions.UserError('`model` must be set either when creating the agent or when calling it.')

        return model_

    def _get_deps(self: Agent[T, ResultDataT], deps: T) -> T:
        """Get deps for a run.

        If we've overridden deps via `_override_deps`, use that, otherwise use the deps passed to the call.

        We could do runtime type checking of deps against `self._deps_type`, but that's a slippery slope.
        """
        if some_deps := self._override_deps:
            return some_deps.value
        else:
            return deps

    def _infer_name(self, function_frame: FrameType | None) -> None:
        """Infer the agent name from the call frame.

        Usage should be `self._infer_name(inspect.currentframe())`.
        """
        assert self.name is None, 'Name already set'
        if function_frame is not None:  # pragma: no branch
            if parent_frame := function_frame.f_back:  # pragma: no branch
                for name, item in parent_frame.f_locals.items():
                    if item is self:
                        self.name = name
                        return
                if parent_frame.f_locals != parent_frame.f_globals:
                    # if we couldn't find the agent in locals and globals are a different dict, try globals
                    for name, item in parent_frame.f_globals.items():
                        if item is self:
                            self.name = name
                            return

    @property
    @deprecated(
        'The `last_run_messages` attribute has been removed, use `capture_run_messages` instead.', category=None
    )
    def last_run_messages(self) -> list[_messages.ModelMessage]:
        raise AttributeError('The `last_run_messages` attribute has been removed, use `capture_run_messages` instead.')

    def _build_graph(
        self, result_type: type[RunResultDataT] | None
    ) -> Graph[_agent_graph.GraphAgentState, _agent_graph.GraphAgentDeps[AgentDepsT, Any], Any]:
        return _agent_graph.build_agent_graph(self.name, self._deps_type, result_type or self.result_type)

    def _build_stream_graph(
        self, result_type: type[RunResultDataT] | None
    ) -> Graph[_agent_graph.GraphAgentState, _agent_graph.GraphAgentDeps[AgentDepsT, Any], Any]:
        return _agent_graph.build_agent_stream_graph(self.name, self._deps_type, result_type or self.result_type)

    def _prepare_result_schema(
        self, result_type: type[RunResultDataT] | None
    ) -> _result.ResultSchema[RunResultDataT] | None:
        if result_type is not None:
            if self._result_validators:
                raise exceptions.UserError('Cannot set a custom run `result_type` when the agent has result validators')
            return _result.ResultSchema[result_type].build(
                result_type, self._result_tool_name, self._result_tool_description
            )
        else:
            return self._result_schema  # pyright: ignore[reportReturnType]

model instance-attribute

model: Model | KnownModelName | None

The default model configured for this agent.

__init__

__init__(
    model: Model | KnownModelName | None = None,
    *,
    result_type: type[ResultDataT] = str,
    system_prompt: str | Sequence[str] = (),
    deps_type: type[AgentDepsT] = NoneType,
    name: str | None = None,
    model_settings: ModelSettings | None = None,
    retries: int = 1,
    result_tool_name: str = "final_result",
    result_tool_description: str | None = None,
    result_retries: int | None = None,
    tools: Sequence[
        Tool[AgentDepsT] | ToolFuncEither[AgentDepsT, ...]
    ] = (),
    defer_model_check: bool = False,
    end_strategy: EndStrategy = "early"
)

Create an agent.

Parameters:

Name	Type	Description	Default
model	Model | KnownModelName | None	The default model to use for this agent, if not provide, you must provide the model when calling it.	None
result_type	type[ResultDataT]	The type of the result data, used to validate the result data, defaults to str.	str
system_prompt	str | Sequence[str]	Static system prompts to use for this agent, you can also register system prompts via a function with system_prompt.	()
deps_type	type[AgentDepsT]	The type used for dependency injection, this parameter exists solely to allow you to fully parameterize the agent, and therefore get the best out of static type checking. If you're not using deps, but want type checking to pass, you can set deps=None to satisfy Pyright or add a type hint : Agent[None, <return type>].	NoneType
name	str | None	The name of the agent, used for logging. If None, we try to infer the agent name from the call frame when the agent is first run.	None
model_settings	ModelSettings | None	Optional model request settings to use for this agent's runs, by default.	None
retries	int	The default number of retries to allow before raising an error.	1
result_tool_name	str	The name of the tool to use for the final result.	'final_result'
result_tool_description	str | None	The description of the final result tool.	None
result_retries	int | None	The maximum number of retries to allow for result validation, defaults to retries.	None
tools	Sequence[Tool[AgentDepsT] | ToolFuncEither[AgentDepsT, ...]]	Tools to register with the agent, you can also register tools via the decorators @agent.tool and @agent.tool_plain.	()
defer_model_check	bool	by default, if you provide a named model, it's evaluated to create a Model instance immediately, which checks for the necessary environment variables. Set this to false to defer the evaluation until the first run. Useful if you want to override the model for testing.	False
end_strategy	EndStrategy	Strategy for handling tool calls that are requested alongside a final result. See EndStrategy for more information.	'early'

Source code in pydantic_ai_slim/pydantic_ai/agent.py

def __init__(
    self,
    model: models.Model | models.KnownModelName | None = None,
    *,
    result_type: type[ResultDataT] = str,
    system_prompt: str | Sequence[str] = (),
    deps_type: type[AgentDepsT] = NoneType,
    name: str | None = None,
    model_settings: ModelSettings | None = None,
    retries: int = 1,
    result_tool_name: str = 'final_result',
    result_tool_description: str | None = None,
    result_retries: int | None = None,
    tools: Sequence[Tool[AgentDepsT] | ToolFuncEither[AgentDepsT, ...]] = (),
    defer_model_check: bool = False,
    end_strategy: EndStrategy = 'early',
):
    """Create an agent.

    Args:
        model: The default model to use for this agent, if not provide,
            you must provide the model when calling it.
        result_type: The type of the result data, used to validate the result data, defaults to `str`.
        system_prompt: Static system prompts to use for this agent, you can also register system
            prompts via a function with [`system_prompt`][pydantic_ai.Agent.system_prompt].
        deps_type: The type used for dependency injection, this parameter exists solely to allow you to fully
            parameterize the agent, and therefore get the best out of static type checking.
            If you're not using deps, but want type checking to pass, you can set `deps=None` to satisfy Pyright
            or add a type hint `: Agent[None, <return type>]`.
        name: The name of the agent, used for logging. If `None`, we try to infer the agent name from the call frame
            when the agent is first run.
        model_settings: Optional model request settings to use for this agent's runs, by default.
        retries: The default number of retries to allow before raising an error.
        result_tool_name: The name of the tool to use for the final result.
        result_tool_description: The description of the final result tool.
        result_retries: The maximum number of retries to allow for result validation, defaults to `retries`.
        tools: Tools to register with the agent, you can also register tools via the decorators
            [`@agent.tool`][pydantic_ai.Agent.tool] and [`@agent.tool_plain`][pydantic_ai.Agent.tool_plain].
        defer_model_check: by default, if you provide a [named][pydantic_ai.models.KnownModelName] model,
            it's evaluated to create a [`Model`][pydantic_ai.models.Model] instance immediately,
            which checks for the necessary environment variables. Set this to `false`
            to defer the evaluation until the first run. Useful if you want to
            [override the model][pydantic_ai.Agent.override] for testing.
        end_strategy: Strategy for handling tool calls that are requested alongside a final result.
            See [`EndStrategy`][pydantic_ai.agent.EndStrategy] for more information.
    """
    if model is None or defer_model_check:
        self.model = model
    else:
        self.model = models.infer_model(model)

    self.end_strategy = end_strategy
    self.name = name
    self.model_settings = model_settings
    self.result_type = result_type

    self._deps_type = deps_type

    self._result_tool_name = result_tool_name
    self._result_tool_description = result_tool_description
    self._result_schema: _result.ResultSchema[ResultDataT] | None = _result.ResultSchema[result_type].build(
        result_type, result_tool_name, result_tool_description
    )
    self._result_validators: list[_result.ResultValidator[AgentDepsT, ResultDataT]] = []

    self._system_prompts = (system_prompt,) if isinstance(system_prompt, str) else tuple(system_prompt)
    self._system_prompt_functions: list[_system_prompt.SystemPromptRunner[AgentDepsT]] = []
    self._system_prompt_dynamic_functions: dict[str, _system_prompt.SystemPromptRunner[AgentDepsT]] = {}

    self._function_tools: dict[str, Tool[AgentDepsT]] = {}

    self._default_retries = retries
    self._max_result_retries = result_retries if result_retries is not None else retries
    for tool in tools:
        if isinstance(tool, Tool):
            self._register_tool(tool)
        else:
            self._register_tool(Tool(tool))

end_strategy instance-attribute

end_strategy: EndStrategy = end_strategy

Strategy for handling tool calls when a final result is found.

name instance-attribute

name: str | None = name

The name of the agent, used for logging.

If None, we try to infer the agent name from the call frame when the agent is first run.

model_settings instance-attribute

model_settings: ModelSettings | None = model_settings

Optional model request settings to use for this agents's runs, by default.

Note, if model_settings is provided by run, run_sync, or run_stream, those settings will be merged with this value, with the runtime argument taking priority.

result_type class-attribute instance-attribute

result_type: type[ResultDataT] = result_type

The type of the result data, used to validate the result data, defaults to str.

run async

run(
    user_prompt: str,
    *,
    result_type: None = None,
    message_history: list[ModelMessage] | None = None,
    model: Model | KnownModelName | None = None,
    deps: AgentDepsT = None,
    model_settings: ModelSettings | None = None,
    usage_limits: UsageLimits | None = None,
    usage: Usage | None = None,
    infer_name: bool = True
) -> RunResult[ResultDataT]

run(
    user_prompt: str,
    *,
    result_type: type[RunResultDataT],
    message_history: list[ModelMessage] | None = None,
    model: Model | KnownModelName | None = None,
    deps: AgentDepsT = None,
    model_settings: ModelSettings | None = None,
    usage_limits: UsageLimits | None = None,
    usage: Usage | None = None,
    infer_name: bool = True
) -> RunResult[RunResultDataT]

run(
    user_prompt: str,
    *,
    message_history: list[ModelMessage] | None = None,
    model: Model | KnownModelName | None = None,
    deps: AgentDepsT = None,
    model_settings: ModelSettings | None = None,
    usage_limits: UsageLimits | None = None,
    usage: Usage | None = None,
    result_type: type[RunResultDataT] | None = None,
    infer_name: bool = True
) -> RunResult[Any]

Run the agent with a user prompt in async mode.

Example:

from pydantic_ai import Agent

agent = Agent('openai:gpt-4o')

async def main():
    result = await agent.run('What is the capital of France?')
    print(result.data)
    #> Paris

Parameters:

Name	Type	Description	Default
result_type	type[RunResultDataT] | None	Custom result type to use for this run, result_type may only be used if the agent has no result validators since result validators would expect an argument that matches the agent's result type.	None
user_prompt	str	User input to start/continue the conversation.	required
message_history	list[ModelMessage] | None	History of the conversation so far.	None
model	Model | KnownModelName | None	Optional model to use for this run, required if model was not set when creating the agent.	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	Usage | 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

Returns:

Type	Description
RunResult[Any]	The result of the run.

Source code in pydantic_ai_slim/pydantic_ai/agent.py

async def run(
    self,
    user_prompt: str,
    *,
    message_history: list[_messages.ModelMessage] | None = None,
    model: models.Model | models.KnownModelName | None = None,
    deps: AgentDepsT = None,
    model_settings: ModelSettings | None = None,
    usage_limits: _usage.UsageLimits | None = None,
    usage: _usage.Usage | None = None,
    result_type: type[RunResultDataT] | None = None,
    infer_name: bool = True,
) -> result.RunResult[Any]:
    """Run the agent with a user prompt in async mode.

    Example:
    ```python
    from pydantic_ai import Agent

    agent = Agent('openai:gpt-4o')

    async def main():
        result = await agent.run('What is the capital of France?')
        print(result.data)
        #> Paris
    ```

    Args:
        result_type: Custom result type to use for this run, `result_type` may only be used if the agent has no
            result validators since result validators would expect an argument that matches the agent's result type.
        user_prompt: User input to start/continue the conversation.
        message_history: History of the conversation so far.
        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.
        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.

    Returns:
        The result of the run.
    """
    if infer_name and self.name is None:
        self._infer_name(inspect.currentframe())
    model_used = await self._get_model(model)

    deps = self._get_deps(deps)
    new_message_index = len(message_history) if message_history else 0
    result_schema: _result.ResultSchema[RunResultDataT] | None = self._prepare_result_schema(result_type)

    # Build the graph
    graph = _agent_graph.build_agent_graph(self.name, self._deps_type, result_type or self.result_type)

    # Build the initial state
    state = _agent_graph.GraphAgentState(
        message_history=message_history[:] if message_history else [],
        usage=usage or _usage.Usage(),
        retries=0,
        run_step=0,
    )

    # We consider it a user error if a user tries to restrict the result type while having a result validator that
    # may change the result type from the restricted type to something else. Therefore, we consider the following
    # typecast reasonable, even though it is possible to violate it with otherwise-type-checked code.
    result_validators = cast(list[_result.ResultValidator[AgentDepsT, RunResultDataT]], self._result_validators)

    # TODO: Instead of this, copy the function tools to ensure they don't share current_retry state between agent
    #  runs. Requires some changes to `Tool` to make them copyable though.
    for v in self._function_tools.values():
        v.current_retry = 0

    model_settings = merge_model_settings(self.model_settings, model_settings)
    usage_limits = usage_limits or _usage.UsageLimits()

    with _logfire.span(
        '{agent_name} run {prompt=}',
        prompt=user_prompt,
        agent=self,
        model_name=model_used.name() if model_used else 'no-model',
        agent_name=self.name or 'agent',
    ) as run_span:
        # Build the deps object for the graph
        graph_deps = _agent_graph.GraphAgentDeps[AgentDepsT, RunResultDataT](
            user_deps=deps,
            prompt=user_prompt,
            new_message_index=new_message_index,
            model=model_used,
            model_settings=model_settings,
            usage_limits=usage_limits,
            max_result_retries=self._max_result_retries,
            end_strategy=self.end_strategy,
            result_schema=result_schema,
            result_tools=self._result_schema.tool_defs() if self._result_schema else [],
            result_validators=result_validators,
            function_tools=self._function_tools,
            run_span=run_span,
        )

        start_node = _agent_graph.UserPromptNode[AgentDepsT](
            user_prompt=user_prompt,
            system_prompts=self._system_prompts,
            system_prompt_functions=self._system_prompt_functions,
            system_prompt_dynamic_functions=self._system_prompt_dynamic_functions,
        )

        # Actually run
        end_result, _ = await graph.run(
            start_node,
            state=state,
            deps=graph_deps,
            infer_name=False,
        )

    # Build final run result
    # We don't do any advanced checking if the data is actually from a final result or not
    return result.RunResult(
        state.message_history,
        new_message_index,
        end_result.data,
        end_result.tool_name,
        state.usage,
    )

run_sync

run_sync(
    user_prompt: str,
    *,
    message_history: list[ModelMessage] | None = None,
    model: Model | KnownModelName | None = None,
    deps: AgentDepsT = None,
    model_settings: ModelSettings | None = None,
    usage_limits: UsageLimits | None = None,
    usage: Usage | None = None,
    infer_name: bool = True
) -> RunResult[ResultDataT]

run_sync(
    user_prompt: str,
    *,
    result_type: type[RunResultDataT] | None,
    message_history: list[ModelMessage] | None = None,
    model: Model | KnownModelName | None = None,
    deps: AgentDepsT = None,
    model_settings: ModelSettings | None = None,
    usage_limits: UsageLimits | None = None,
    usage: Usage | None = None,
    infer_name: bool = True
) -> RunResult[RunResultDataT]

run_sync(
    user_prompt: str,
    *,
    result_type: type[RunResultDataT] | None = None,
    message_history: list[ModelMessage] | None = None,
    model: Model | KnownModelName | None = None,
    deps: AgentDepsT = None,
    model_settings: ModelSettings | None = None,
    usage_limits: UsageLimits | None = None,
    usage: Usage | None = None,
    infer_name: bool = True
) -> RunResult[Any]

Run the agent with a user prompt synchronously.

This is a convenience method that wraps self.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:

from pydantic_ai import Agent

agent = Agent('openai:gpt-4o')

result_sync = agent.run_sync('What is the capital of Italy?')
print(result_sync.data)
#> Rome

Parameters:

Name	Type	Description	Default
result_type	type[RunResultDataT] | None	Custom result type to use for this run, result_type may only be used if the agent has no result validators since result validators would expect an argument that matches the agent's result type.	None
user_prompt	str	User input to start/continue the conversation.	required
message_history	list[ModelMessage] | None	History of the conversation so far.	None
model	Model | KnownModelName | None	Optional model to use for this run, required if model was not set when creating the agent.	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	Usage | 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

Returns:

Type	Description
RunResult[Any]	The result of the run.

Source code in pydantic_ai_slim/pydantic_ai/agent.py

def run_sync(
    self,
    user_prompt: str,
    *,
    result_type: type[RunResultDataT] | None = None,
    message_history: list[_messages.ModelMessage] | None = None,
    model: models.Model | models.KnownModelName | None = None,
    deps: AgentDepsT = None,
    model_settings: ModelSettings | None = None,
    usage_limits: _usage.UsageLimits | None = None,
    usage: _usage.Usage | None = None,
    infer_name: bool = True,
) -> result.RunResult[Any]:
    """Run the agent with a user prompt synchronously.

    This is a convenience method that wraps [`self.run`][pydantic_ai.Agent.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.data)
    #> Rome
    ```

    Args:
        result_type: Custom result type to use for this run, `result_type` may only be used if the agent has no
            result validators since result validators would expect an argument that matches the agent's result type.
        user_prompt: User input to start/continue the conversation.
        message_history: History of the conversation so far.
        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.
        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.

    Returns:
        The result of the run.
    """
    if infer_name and self.name is None:
        self._infer_name(inspect.currentframe())
    return asyncio.get_event_loop().run_until_complete(
        self.run(
            user_prompt,
            result_type=result_type,
            message_history=message_history,
            model=model,
            deps=deps,
            model_settings=model_settings,
            usage_limits=usage_limits,
            usage=usage,
            infer_name=False,
        )
    )

run_stream async

run_stream(
    user_prompt: str,
    *,
    result_type: None = None,
    message_history: list[ModelMessage] | None = None,
    model: Model | KnownModelName | None = None,
    deps: AgentDepsT = None,
    model_settings: ModelSettings | None = None,
    usage_limits: UsageLimits | None = None,
    usage: Usage | None = None,
    infer_name: bool = True
) -> AbstractAsyncContextManager[
    StreamedRunResult[AgentDepsT, ResultDataT]
]

run_stream(
    user_prompt: str,
    *,
    result_type: type[RunResultDataT],
    message_history: list[ModelMessage] | None = None,
    model: Model | KnownModelName | None = None,
    deps: AgentDepsT = None,
    model_settings: ModelSettings | None = None,
    usage_limits: UsageLimits | None = None,
    usage: Usage | None = None,
    infer_name: bool = True
) -> AbstractAsyncContextManager[
    StreamedRunResult[AgentDepsT, RunResultDataT]
]

run_stream(
    user_prompt: str,
    *,
    result_type: type[RunResultDataT] | None = None,
    message_history: list[ModelMessage] | None = None,
    model: Model | KnownModelName | None = None,
    deps: AgentDepsT = None,
    model_settings: ModelSettings | None = None,
    usage_limits: UsageLimits | None = None,
    usage: Usage | None = None,
    infer_name: bool = True
) -> AsyncIterator[StreamedRunResult[AgentDepsT, Any]]

Run the agent with a user prompt in async mode, returning a streamed response.

Example:

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_data())
        #> London

Parameters:

Name	Type	Description	Default
result_type	type[RunResultDataT] | None	Custom result type to use for this run, result_type may only be used if the agent has no result validators since result validators would expect an argument that matches the agent's result type.	None
user_prompt	str	User input to start/continue the conversation.	required
message_history	list[ModelMessage] | None	History of the conversation so far.	None
model	Model | KnownModelName | None	Optional model to use for this run, required if model was not set when creating the agent.	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	Usage | 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

Returns:

Type	Description
AsyncIterator[StreamedRunResult[AgentDepsT, Any]]	The result of the run.

Source code in pydantic_ai_slim/pydantic_ai/agent.py

@asynccontextmanager
async def run_stream(
    self,
    user_prompt: str,
    *,
    result_type: type[RunResultDataT] | None = None,
    message_history: list[_messages.ModelMessage] | None = None,
    model: models.Model | models.KnownModelName | None = None,
    deps: AgentDepsT = None,
    model_settings: ModelSettings | None = None,
    usage_limits: _usage.UsageLimits | None = None,
    usage: _usage.Usage | None = None,
    infer_name: bool = True,
) -> AsyncIterator[result.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_data())
            #> London
    ```

    Args:
        result_type: Custom result type to use for this run, `result_type` may only be used if the agent has no
            result validators since result validators would expect an argument that matches the agent's result type.
        user_prompt: User input to start/continue the conversation.
        message_history: History of the conversation so far.
        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.
        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.

    Returns:
        The result of the run.
    """
    if infer_name and self.name is None:
        # f_back because `asynccontextmanager` adds one frame
        if frame := inspect.currentframe():  # pragma: no branch
            self._infer_name(frame.f_back)
    model_used = await self._get_model(model)

    deps = self._get_deps(deps)
    new_message_index = len(message_history) if message_history else 0
    result_schema: _result.ResultSchema[RunResultDataT] | None = self._prepare_result_schema(result_type)

    # Build the graph
    graph = self._build_stream_graph(result_type)

    # Build the initial state
    graph_state = _agent_graph.GraphAgentState(
        message_history=message_history[:] if message_history else [],
        usage=usage or _usage.Usage(),
        retries=0,
        run_step=0,
    )

    # We consider it a user error if a user tries to restrict the result type while having a result validator that
    # may change the result type from the restricted type to something else. Therefore, we consider the following
    # typecast reasonable, even though it is possible to violate it with otherwise-type-checked code.
    result_validators = cast(list[_result.ResultValidator[AgentDepsT, RunResultDataT]], self._result_validators)

    # TODO: Instead of this, copy the function tools to ensure they don't share current_retry state between agent
    #  runs. Requires some changes to `Tool` to make them copyable though.
    for v in self._function_tools.values():
        v.current_retry = 0

    model_settings = merge_model_settings(self.model_settings, model_settings)
    usage_limits = usage_limits or _usage.UsageLimits()

    with _logfire.span(
        '{agent_name} run stream {prompt=}',
        prompt=user_prompt,
        agent=self,
        model_name=model_used.name(),
        agent_name=self.name or 'agent',
    ) as run_span:
        # Build the deps object for the graph
        graph_deps = _agent_graph.GraphAgentDeps[AgentDepsT, RunResultDataT](
            user_deps=deps,
            prompt=user_prompt,
            new_message_index=new_message_index,
            model=model_used,
            model_settings=model_settings,
            usage_limits=usage_limits,
            max_result_retries=self._max_result_retries,
            end_strategy=self.end_strategy,
            result_schema=result_schema,
            result_tools=self._result_schema.tool_defs() if self._result_schema else [],
            result_validators=result_validators,
            function_tools=self._function_tools,
            run_span=run_span,
        )

        start_node = _agent_graph.StreamUserPromptNode[AgentDepsT](
            user_prompt=user_prompt,
            system_prompts=self._system_prompts,
            system_prompt_functions=self._system_prompt_functions,
            system_prompt_dynamic_functions=self._system_prompt_dynamic_functions,
        )

        # Actually run
        node = start_node
        history: list[HistoryStep[_agent_graph.GraphAgentState, RunResultDataT]] = []
        while True:
            if isinstance(node, _agent_graph.StreamModelRequestNode):
                node = cast(
                    _agent_graph.StreamModelRequestNode[
                        AgentDepsT, result.StreamedRunResult[AgentDepsT, RunResultDataT]
                    ],
                    node,
                )
                async with node.run_to_result(GraphRunContext(graph_state, graph_deps)) as r:
                    if isinstance(r, End):
                        yield r.data
                        break
            assert not isinstance(node, End)  # the previous line should be hit first
            node = await graph.next(
                node,
                history,
                state=graph_state,
                deps=graph_deps,
                infer_name=False,
            )

override

override(
    *,
    deps: AgentDepsT | Unset = UNSET,
    model: Model | KnownModelName | Unset = UNSET
) -> Iterator[None]

Context manager to temporarily override agent dependencies and model.

This is particularly useful when testing. You can find an example of this here.

Parameters:

Name	Type	Description	Default
deps	AgentDepsT | Unset	The dependencies to use instead of the dependencies passed to the agent run.	UNSET
model	Model | KnownModelName | Unset	The model to use instead of the model passed to the agent run.	UNSET

Source code in pydantic_ai_slim/pydantic_ai/agent.py

@contextmanager
def override(
    self,
    *,
    deps: AgentDepsT | _utils.Unset = _utils.UNSET,
    model: models.Model | models.KnownModelName | _utils.Unset = _utils.UNSET,
) -> Iterator[None]:
    """Context manager to temporarily override agent dependencies and model.

    This is particularly useful when testing.
    You can find an example of this [here](../testing-evals.md#overriding-model-via-pytest-fixtures).

    Args:
        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.
    """
    if _utils.is_set(deps):
        override_deps_before = self._override_deps
        self._override_deps = _utils.Some(deps)
    else:
        override_deps_before = _utils.UNSET

    # noinspection PyTypeChecker
    if _utils.is_set(model):
        override_model_before = self._override_model
        # noinspection PyTypeChecker
        self._override_model = _utils.Some(models.infer_model(model))  # pyright: ignore[reportArgumentType]
    else:
        override_model_before = _utils.UNSET

    try:
        yield
    finally:
        if _utils.is_set(override_deps_before):
            self._override_deps = override_deps_before
        if _utils.is_set(override_model_before):
            self._override_model = override_model_before

system_prompt

system_prompt(
    func: Callable[[RunContext[AgentDepsT]], str]
) -> Callable[[RunContext[AgentDepsT]], str]

system_prompt(
    func: Callable[[RunContext[AgentDepsT]], Awaitable[str]]
) -> Callable[[RunContext[AgentDepsT]], Awaitable[str]]

system_prompt(func: Callable[[], str]) -> Callable[[], str]

system_prompt(
    func: Callable[[], Awaitable[str]]
) -> Callable[[], Awaitable[str]]

system_prompt(*, dynamic: bool = False) -> Callable[
    [SystemPromptFunc[AgentDepsT]],
    SystemPromptFunc[AgentDepsT],
]

system_prompt(
    func: SystemPromptFunc[AgentDepsT] | None = None,
    /,
    *,
    dynamic: bool = False,
) -> (
    Callable[
        [SystemPromptFunc[AgentDepsT]],
        SystemPromptFunc[AgentDepsT],
    ]
    | SystemPromptFunc[AgentDepsT]
)

Decorator to register a system prompt function.

Optionally takes RunContext as its only argument. Can decorate a sync or async functions.

The decorator can be used either bare (agent.system_prompt) or as a function call (agent.system_prompt(...)), see the examples below.

Overloads for every possible signature of system_prompt are included so the decorator doesn't obscure the type of the function, see tests/typed_agent.py for tests.

Parameters:

Name	Type	Description	Default
func	SystemPromptFunc[AgentDepsT] | None	The function to decorate	None
dynamic	bool	If True, the system prompt will be reevaluated even when messages_history is provided, see SystemPromptPart.dynamic_ref	False

Example:

from pydantic_ai import Agent, RunContext

agent = Agent('test', deps_type=str)

@agent.system_prompt
def simple_system_prompt() -> str:
    return 'foobar'

@agent.system_prompt(dynamic=True)
async def async_system_prompt(ctx: RunContext[str]) -> str:
    return f'{ctx.deps} is the best'

Source code in pydantic_ai_slim/pydantic_ai/agent.py

def system_prompt(
    self,
    func: _system_prompt.SystemPromptFunc[AgentDepsT] | None = None,
    /,
    *,
    dynamic: bool = False,
) -> (
    Callable[[_system_prompt.SystemPromptFunc[AgentDepsT]], _system_prompt.SystemPromptFunc[AgentDepsT]]
    | _system_prompt.SystemPromptFunc[AgentDepsT]
):
    """Decorator to register a system prompt function.

    Optionally takes [`RunContext`][pydantic_ai.tools.RunContext] as its only argument.
    Can decorate a sync or async functions.

    The decorator can be used either bare (`agent.system_prompt`) or as a function call
    (`agent.system_prompt(...)`), see the examples below.

    Overloads for every possible signature of `system_prompt` are included so the decorator doesn't obscure
    the type of the function, see `tests/typed_agent.py` for tests.

    Args:
        func: The function to decorate
        dynamic: If True, the system prompt will be reevaluated even when `messages_history` is provided,
            see [`SystemPromptPart.dynamic_ref`][pydantic_ai.messages.SystemPromptPart.dynamic_ref]

    Example:
    ```python
    from pydantic_ai import Agent, RunContext

    agent = Agent('test', deps_type=str)

    @agent.system_prompt
    def simple_system_prompt() -> str:
        return 'foobar'

    @agent.system_prompt(dynamic=True)
    async def async_system_prompt(ctx: RunContext[str]) -> str:
        return f'{ctx.deps} is the best'
    ```
    """
    if func is None:

        def decorator(
            func_: _system_prompt.SystemPromptFunc[AgentDepsT],
        ) -> _system_prompt.SystemPromptFunc[AgentDepsT]:
            runner = _system_prompt.SystemPromptRunner[AgentDepsT](func_, dynamic=dynamic)
            self._system_prompt_functions.append(runner)
            if dynamic:
                self._system_prompt_dynamic_functions[func_.__qualname__] = runner
            return func_

        return decorator
    else:
        assert not dynamic, "dynamic can't be True in this case"
        self._system_prompt_functions.append(_system_prompt.SystemPromptRunner[AgentDepsT](func, dynamic=dynamic))
        return func

result_validator

result_validator(
    func: Callable[
        [RunContext[AgentDepsT], ResultDataT], ResultDataT
    ]
) -> Callable[
    [RunContext[AgentDepsT], ResultDataT], ResultDataT
]

result_validator(
    func: Callable[
        [RunContext[AgentDepsT], ResultDataT],
        Awaitable[ResultDataT],
    ]
) -> Callable[
    [RunContext[AgentDepsT], ResultDataT],
    Awaitable[ResultDataT],
]

result_validator(
    func: Callable[[ResultDataT], ResultDataT]
) -> Callable[[ResultDataT], ResultDataT]

result_validator(
    func: Callable[[ResultDataT], Awaitable[ResultDataT]]
) -> Callable[[ResultDataT], Awaitable[ResultDataT]]

result_validator(
    func: ResultValidatorFunc[AgentDepsT, ResultDataT]
) -> ResultValidatorFunc[AgentDepsT, ResultDataT]

Decorator to register a result validator function.

Optionally takes RunContext as its first argument. Can decorate a sync or async functions.

Overloads for every possible signature of result_validator are included so the decorator doesn't obscure the type of the function, see tests/typed_agent.py for tests.

Example:

from pydantic_ai import Agent, ModelRetry, RunContext

agent = Agent('test', deps_type=str)

@agent.result_validator
def result_validator_simple(data: str) -> str:
    if 'wrong' in data:
        raise ModelRetry('wrong response')
    return data

@agent.result_validator
async def result_validator_deps(ctx: RunContext[str], data: str) -> str:
    if ctx.deps in data:
        raise ModelRetry('wrong response')
    return data

result = agent.run_sync('foobar', deps='spam')
print(result.data)
#> success (no tool calls)

Source code in pydantic_ai_slim/pydantic_ai/agent.py

def result_validator(
    self, func: _result.ResultValidatorFunc[AgentDepsT, ResultDataT], /
) -> _result.ResultValidatorFunc[AgentDepsT, ResultDataT]:
    """Decorator to register a result validator function.

    Optionally takes [`RunContext`][pydantic_ai.tools.RunContext] as its first argument.
    Can decorate a sync or async functions.

    Overloads for every possible signature of `result_validator` are included so the decorator doesn't obscure
    the type of the function, see `tests/typed_agent.py` for tests.

    Example:
    ```python
    from pydantic_ai import Agent, ModelRetry, RunContext

    agent = Agent('test', deps_type=str)

    @agent.result_validator
    def result_validator_simple(data: str) -> str:
        if 'wrong' in data:
            raise ModelRetry('wrong response')
        return data

    @agent.result_validator
    async def result_validator_deps(ctx: RunContext[str], data: str) -> str:
        if ctx.deps in data:
            raise ModelRetry('wrong response')
        return data

    result = agent.run_sync('foobar', deps='spam')
    print(result.data)
    #> success (no tool calls)
    ```
    """
    self._result_validators.append(_result.ResultValidator[AgentDepsT, Any](func))
    return func

tool

tool(
    func: ToolFuncContext[AgentDepsT, ToolParams]
) -> ToolFuncContext[AgentDepsT, ToolParams]

tool(
    *,
    retries: int | None = None,
    prepare: ToolPrepareFunc[AgentDepsT] | None = None,
    docstring_format: DocstringFormat = "auto",
    require_parameter_descriptions: bool = False
) -> Callable[
    [ToolFuncContext[AgentDepsT, ToolParams]],
    ToolFuncContext[AgentDepsT, ToolParams],
]

tool(
    func: (
        ToolFuncContext[AgentDepsT, ToolParams] | None
    ) = None,
    /,
    *,
    retries: int | None = None,
    prepare: ToolPrepareFunc[AgentDepsT] | None = None,
    docstring_format: DocstringFormat = "auto",
    require_parameter_descriptions: bool = False,
) -> Any

Decorator to register a tool function which takes RunContext as its first argument.

Can decorate a sync or async functions.

The docstring is inspected to extract both the tool description and description of each parameter, learn more.

We can't add overloads for every possible signature of tool, since the return type is a recursive union so the signature of functions decorated with @agent.tool is obscured.

Example:

from pydantic_ai import Agent, RunContext

agent = Agent('test', deps_type=int)

@agent.tool
def foobar(ctx: RunContext[int], x: int) -> int:
    return ctx.deps + x

@agent.tool(retries=2)
async def spam(ctx: RunContext[str], y: float) -> float:
    return ctx.deps + y

result = agent.run_sync('foobar', deps=1)
print(result.data)
#> {"foobar":1,"spam":1.0}

Parameters:

Name	Type	Description	Default
func	ToolFuncContext[AgentDepsT, ToolParams] | None	The tool function to register.	None
retries	int | None	The number of retries to allow for this tool, defaults to the agent's default retries, which defaults to 1.	None
prepare	ToolPrepareFunc[AgentDepsT] | None	custom method to prepare the tool definition for each step, return None to omit this tool from a given step. This is useful if you want to customise a tool at call time, or omit it completely from a step. See ToolPrepareFunc.	None
docstring_format	DocstringFormat	The format of the docstring, see DocstringFormat. Defaults to 'auto', such that the format is inferred from the structure of the docstring.	'auto'
require_parameter_descriptions	bool	If True, raise an error if a parameter description is missing. Defaults to False.	False

Source code in pydantic_ai_slim/pydantic_ai/agent.py

def tool(
    self,
    func: ToolFuncContext[AgentDepsT, ToolParams] | None = None,
    /,
    *,
    retries: int | None = None,
    prepare: ToolPrepareFunc[AgentDepsT] | None = None,
    docstring_format: DocstringFormat = 'auto',
    require_parameter_descriptions: bool = False,
) -> Any:
    """Decorator to register a tool function which takes [`RunContext`][pydantic_ai.tools.RunContext] as its first argument.

    Can decorate a sync or async functions.

    The docstring is inspected to extract both the tool description and description of each parameter,
    [learn more](../tools.md#function-tools-and-schema).

    We can't add overloads for every possible signature of tool, since the return type is a recursive union
    so the signature of functions decorated with `@agent.tool` is obscured.

    Example:
    ```python
    from pydantic_ai import Agent, RunContext

    agent = Agent('test', deps_type=int)

    @agent.tool
    def foobar(ctx: RunContext[int], x: int) -> int:
        return ctx.deps + x

    @agent.tool(retries=2)
    async def spam(ctx: RunContext[str], y: float) -> float:
        return ctx.deps + y

    result = agent.run_sync('foobar', deps=1)
    print(result.data)
    #> {"foobar":1,"spam":1.0}
    ```

    Args:
        func: The tool function to register.
        retries: The number of retries to allow for this tool, defaults to the agent's default retries,
            which defaults to 1.
        prepare: custom method to prepare the tool definition for each step, return `None` to omit this
            tool from a given step. This is useful if you want to customise a tool at call time,
            or omit it completely from a step. See [`ToolPrepareFunc`][pydantic_ai.tools.ToolPrepareFunc].
        docstring_format: The format of the docstring, see [`DocstringFormat`][pydantic_ai.tools.DocstringFormat].
            Defaults to `'auto'`, such that the format is inferred from the structure of the docstring.
        require_parameter_descriptions: If True, raise an error if a parameter description is missing. Defaults to False.
    """
    if func is None:

        def tool_decorator(
            func_: ToolFuncContext[AgentDepsT, ToolParams],
        ) -> ToolFuncContext[AgentDepsT, ToolParams]:
            # noinspection PyTypeChecker
            self._register_function(func_, True, retries, prepare, docstring_format, require_parameter_descriptions)
            return func_

        return tool_decorator
    else:
        # noinspection PyTypeChecker
        self._register_function(func, True, retries, prepare, docstring_format, require_parameter_descriptions)
        return func

tool_plain

tool_plain(
    func: ToolFuncPlain[ToolParams],
) -> ToolFuncPlain[ToolParams]

tool_plain(
    *,
    retries: int | None = None,
    prepare: ToolPrepareFunc[AgentDepsT] | None = None,
    docstring_format: DocstringFormat = "auto",
    require_parameter_descriptions: bool = False
) -> Callable[
    [ToolFuncPlain[ToolParams]], ToolFuncPlain[ToolParams]
]

tool_plain(
    func: ToolFuncPlain[ToolParams] | None = None,
    /,
    *,
    retries: int | None = None,
    prepare: ToolPrepareFunc[AgentDepsT] | None = None,
    docstring_format: DocstringFormat = "auto",
    require_parameter_descriptions: bool = False,
) -> Any

Decorator to register a tool function which DOES NOT take RunContext as an argument.

Can decorate a sync or async functions.

The docstring is inspected to extract both the tool description and description of each parameter, learn more.

We can't add overloads for every possible signature of tool, since the return type is a recursive union so the signature of functions decorated with @agent.tool is obscured.

Example:

from pydantic_ai import Agent, RunContext

agent = Agent('test')

@agent.tool
def foobar(ctx: RunContext[int]) -> int:
    return 123

@agent.tool(retries=2)
async def spam(ctx: RunContext[str]) -> float:
    return 3.14

result = agent.run_sync('foobar', deps=1)
print(result.data)
#> {"foobar":123,"spam":3.14}

Parameters:

Name	Type	Description	Default
func	ToolFuncPlain[ToolParams] | None	The tool function to register.	None
retries	int | None	The number of retries to allow for this tool, defaults to the agent's default retries, which defaults to 1.	None
prepare	ToolPrepareFunc[AgentDepsT] | None	custom method to prepare the tool definition for each step, return None to omit this tool from a given step. This is useful if you want to customise a tool at call time, or omit it completely from a step. See ToolPrepareFunc.	None
docstring_format	DocstringFormat	The format of the docstring, see DocstringFormat. Defaults to 'auto', such that the format is inferred from the structure of the docstring.	'auto'
require_parameter_descriptions	bool	If True, raise an error if a parameter description is missing. Defaults to False.	False

Source code in pydantic_ai_slim/pydantic_ai/agent.py

def tool_plain(
    self,
    func: ToolFuncPlain[ToolParams] | None = None,
    /,
    *,
    retries: int | None = None,
    prepare: ToolPrepareFunc[AgentDepsT] | None = None,
    docstring_format: DocstringFormat = 'auto',
    require_parameter_descriptions: bool = False,
) -> Any:
    """Decorator to register a tool function which DOES NOT take `RunContext` as an argument.

    Can decorate a sync or async functions.

    The docstring is inspected to extract both the tool description and description of each parameter,
    [learn more](../tools.md#function-tools-and-schema).

    We can't add overloads for every possible signature of tool, since the return type is a recursive union
    so the signature of functions decorated with `@agent.tool` is obscured.

    Example:
    ```python
    from pydantic_ai import Agent, RunContext

    agent = Agent('test')

    @agent.tool
    def foobar(ctx: RunContext[int]) -> int:
        return 123

    @agent.tool(retries=2)
    async def spam(ctx: RunContext[str]) -> float:
        return 3.14

    result = agent.run_sync('foobar', deps=1)
    print(result.data)
    #> {"foobar":123,"spam":3.14}
    ```

    Args:
        func: The tool function to register.
        retries: The number of retries to allow for this tool, defaults to the agent's default retries,
            which defaults to 1.
        prepare: custom method to prepare the tool definition for each step, return `None` to omit this
            tool from a given step. This is useful if you want to customise a tool at call time,
            or omit it completely from a step. See [`ToolPrepareFunc`][pydantic_ai.tools.ToolPrepareFunc].
        docstring_format: The format of the docstring, see [`DocstringFormat`][pydantic_ai.tools.DocstringFormat].
            Defaults to `'auto'`, such that the format is inferred from the structure of the docstring.
        require_parameter_descriptions: If True, raise an error if a parameter description is missing. Defaults to False.
    """
    if func is None:

        def tool_decorator(func_: ToolFuncPlain[ToolParams]) -> ToolFuncPlain[ToolParams]:
            # noinspection PyTypeChecker
            self._register_function(
                func_, False, retries, prepare, docstring_format, require_parameter_descriptions
            )
            return func_

        return tool_decorator
    else:
        self._register_function(func, False, retries, prepare, docstring_format, require_parameter_descriptions)
        return func

EndStrategy module-attribute

EndStrategy = Literal['early', 'exhaustive']

The strategy for handling multiple tool calls when a final result is found.

• 'early': Stop processing other tool calls once a final result is found
• 'exhaustive': Process all tool calls even after finding a final result

capture_run_messages

capture_run_messages() -> Iterator[list[ModelMessage]]

Context manager to access the messages used in a run, run_sync, or run_stream call.

Useful when a run may raise an exception, see model errors for more information.

Examples:

from pydantic_ai import Agent, capture_run_messages

agent = Agent('test')

with capture_run_messages() as messages:
    try:
        result = agent.run_sync('foobar')
    except Exception:
        print(messages)
        raise

Note

If you call run, run_sync, or run_stream more than once within a single capture_run_messages context, messages will represent the messages exchanged during the first call only.

Source code in pydantic_ai_slim/pydantic_ai/_agent_graph.py

@contextmanager
def capture_run_messages() -> Iterator[list[_messages.ModelMessage]]:
    """Context manager to access the messages used in a [`run`][pydantic_ai.Agent.run], [`run_sync`][pydantic_ai.Agent.run_sync], or [`run_stream`][pydantic_ai.Agent.run_stream] call.

    Useful when a run may raise an exception, see [model errors](../agents.md#model-errors) for more information.

    Examples:
    ```python
    from pydantic_ai import Agent, capture_run_messages

    agent = Agent('test')

    with capture_run_messages() as messages:
        try:
            result = agent.run_sync('foobar')
        except Exception:
            print(messages)
            raise
    ```

    !!! note
        If you call `run`, `run_sync`, or `run_stream` more than once within a single `capture_run_messages` context,
        `messages` will represent the messages exchanged during the first call only.
    """
    try:
        yield _messages_ctx_var.get().messages
    except LookupError:
        messages: list[_messages.ModelMessage] = []
        token = _messages_ctx_var.set(_RunMessages(messages))
        try:
            yield messages
        finally:
            _messages_ctx_var.reset(token)