mcp-run-python

from __future__ import annotations as _annotations from collections.abc import AsyncIterator, Iterator, Sequence from contextlib import AbstractAsyncContextManager, asynccontextmanager, contextmanager from typing import Any, overload from .. import ( _utils, messages as _messages, models, usage as _usage, ) from ..builtin_tools import AbstractBuiltinTool from ..output import OutputDataT, OutputSpec from ..run import AgentRun from ..settings import ModelSettings from ..tools import ( AgentDepsT, DeferredToolResults, Tool, ToolFuncEither, ) from ..toolsets import AbstractToolset from .abstract import AbstractAgent, EventStreamHandler, Instructions, RunOutputDataT class WrapperAgent(AbstractAgent[AgentDepsT, OutputDataT]): """Agent which wraps another agent. Does nothing on its own, used as a base class. """ def __init__(self, wrapped: AbstractAgent[AgentDepsT, OutputDataT]): self.wrapped = wrapped @property def model(self) -> models.Model | models.KnownModelName | str | None: return self.wrapped.model @property def name(self) -> str | None: return self.wrapped.name @name.setter def name(self, value: str | None) -> None: self.wrapped.name = value @property def deps_type(self) -> type: return self.wrapped.deps_type @property def output_type(self) -> OutputSpec[OutputDataT]: return self.wrapped.output_type @property def event_stream_handler(self) -> EventStreamHandler[AgentDepsT] | None: return self.wrapped.event_stream_handler @property def toolsets(self) -> Sequence[AbstractToolset[AgentDepsT]]: return self.wrapped.toolsets async def __aenter__(self) -> AbstractAgent[AgentDepsT, OutputDataT]: return await self.wrapped.__aenter__() async def __aexit__(self, *args: Any) -> bool | None: return await self.wrapped.__aexit__(*args) @overload def iter( self, user_prompt: str | Sequence[_messages.UserContent] | None = None, *, output_type: None = None, message_history: Sequence[_messages.ModelMessage] | None = None, deferred_tool_results: DeferredToolResults | None = None, model: models.Model | models.KnownModelName | str | None = None, deps: AgentDepsT = None, model_settings: ModelSettings | None = None, usage_limits: _usage.UsageLimits | None = None, usage: _usage.RunUsage | None = None, infer_name: bool = True, toolsets: Sequence[AbstractToolset[AgentDepsT]] | None = None, builtin_tools: Sequence[AbstractBuiltinTool] | None = None, ) -> AbstractAsyncContextManager[AgentRun[AgentDepsT, OutputDataT]]: ... @overload def iter( self, user_prompt: str | Sequence[_messages.UserContent] | None = None, *, output_type: OutputSpec[RunOutputDataT], message_history: Sequence[_messages.ModelMessage] | None = None, deferred_tool_results: DeferredToolResults | None = None, model: models.Model | models.KnownModelName | str | None = None, deps: AgentDepsT = None, model_settings: ModelSettings | None = None, usage_limits: _usage.UsageLimits | None = None, usage: _usage.RunUsage | None = None, infer_name: bool = True, toolsets: Sequence[AbstractToolset[AgentDepsT]] | None = None, builtin_tools: Sequence[AbstractBuiltinTool] | None = None, ) -> AbstractAsyncContextManager[AgentRun[AgentDepsT, RunOutputDataT]]: ... @asynccontextmanager async def iter( self, user_prompt: str | Sequence[_messages.UserContent] | None = None, *, output_type: OutputSpec[RunOutputDataT] | None = None, message_history: Sequence[_messages.ModelMessage] | None = None, deferred_tool_results: DeferredToolResults | None = None, model: models.Model | models.KnownModelName | str | None = None, deps: AgentDepsT = None, model_settings: ModelSettings | None = None, usage_limits: _usage.UsageLimits | None = None, usage: _usage.RunUsage | None = None, infer_name: bool = True, toolsets: Sequence[AbstractToolset[AgentDepsT]] | None = None, builtin_tools: Sequence[AbstractBuiltinTool] | None = None, ) -> AsyncIterator[AgentRun[AgentDepsT, Any]]: """A contextmanager which can be used to iterate over the agent graph's nodes as they are executed. This method builds an internal agent graph (using system prompts, tools and output schemas) and then returns an `AgentRun` object. The `AgentRun` can be used to async-iterate over the nodes of the graph as they are executed. This is the API to use if you want to consume the outputs coming from each LLM model response, or the stream of events coming from the execution of tools. The `AgentRun` also provides methods to access the full message history, new messages, and usage statistics, and the final result of the run once it has completed. For more details, see the documentation of `AgentRun`. Example: ```python from pydantic_ai import Agent agent = Agent('openai:gpt-4o') async def main(): nodes = [] async with agent.iter('What is the capital of France?') as agent_run: async for node in agent_run: nodes.append(node) print(nodes) ''' [ UserPromptNode( user_prompt='What is the capital of France?', instructions_functions=[], system_prompts=(), system_prompt_functions=[], system_prompt_dynamic_functions={}, ), ModelRequestNode( request=ModelRequest( parts=[ UserPromptPart( content='What is the capital of France?', timestamp=datetime.datetime(...), ) ] ) ), CallToolsNode( model_response=ModelResponse( parts=[TextPart(content='The capital of France is Paris.')], usage=RequestUsage(input_tokens=56, output_tokens=7), model_name='gpt-4o', timestamp=datetime.datetime(...), ) ), End(data=FinalResult(output='The capital of France is Paris.')), ] ''' print(agent_run.result.output) #> The capital of France is Paris. ``` Args: user_prompt: User input to start/continue the conversation. output_type: Custom output type to use for this run, `output_type` may only be used if the agent has no output validators since output validators would expect an argument that matches the agent's output type. message_history: History of the conversation so far. deferred_tool_results: Optional results for deferred tool calls in the message history. model: Optional model to use for this run, required if `model` was not set when creating the agent. deps: Optional dependencies to use for this run. model_settings: Optional settings to use for this model's request. usage_limits: Optional limits on model request count or token usage. usage: Optional usage to start with, useful for resuming a conversation or agents used in tools. infer_name: Whether to try to infer the agent name from the call frame if it's not set. toolsets: Optional additional toolsets for this run. builtin_tools: Optional additional builtin tools for this run. Returns: The result of the run. """ async with self.wrapped.iter( user_prompt=user_prompt, output_type=output_type, message_history=message_history, deferred_tool_results=deferred_tool_results, model=model, deps=deps, model_settings=model_settings, usage_limits=usage_limits, usage=usage, infer_name=infer_name, toolsets=toolsets, builtin_tools=builtin_tools, ) as run: yield run @contextmanager def override( self, *, name: str | _utils.Unset = _utils.UNSET, deps: AgentDepsT | _utils.Unset = _utils.UNSET, model: models.Model | models.KnownModelName | str | _utils.Unset = _utils.UNSET, toolsets: Sequence[AbstractToolset[AgentDepsT]] | _utils.Unset = _utils.UNSET, tools: Sequence[Tool[AgentDepsT] | ToolFuncEither[AgentDepsT, ...]] | _utils.Unset = _utils.UNSET, instructions: Instructions[AgentDepsT] | _utils.Unset = _utils.UNSET, ) -> Iterator[None]: """Context manager to temporarily override agent name, dependencies, model, toolsets, tools, or instructions. This is particularly useful when testing. You can find an example of this [here](../testing.md#overriding-model-via-pytest-fixtures). Args: name: The name to use instead of the name passed to the agent constructor and agent run. deps: The dependencies to use instead of the dependencies passed to the agent run. model: The model to use instead of the model passed to the agent run. toolsets: The toolsets to use instead of the toolsets passed to the agent constructor and agent run. tools: The tools to use instead of the tools registered with the agent. instructions: The instructions to use instead of the instructions registered with the agent. """ with self.wrapped.override( name=name, deps=deps, model=model, toolsets=toolsets, tools=tools, instructions=instructions, ): yield