Class AgentTool

src/google/adk/tools/agent_tool.py:107–326

A tool that wraps an agent. This tool allows an agent to be called as a tool within a larger application. The agent's input schema is used to define the tool's input parameters, and the agent's output is returned as the tool's result. Attributes: agent: The agent to wrap. skip_summ

class AgentTool(BaseTool):
  """A tool that wraps an agent.

  This tool allows an agent to be called as a tool within a larger application.
  The agent's input schema is used to define the tool's input parameters, and
  the agent's output is returned as the tool's result.

  Attributes:
    agent: The agent to wrap.
    skip_summarization: Whether to skip summarization of the agent output.
    include_plugins: Whether to propagate plugins from the parent runner context
      to the agent's runner. When True (default), the agent will inherit all
      plugins from its parent. Set to False to run the agent with an isolated
      plugin environment.
  """

  def __init__(
      self,
      agent: BaseAgent,
      skip_summarization: bool = False,
      *,
      include_plugins: bool = True,
      propagate_grounding_metadata: bool = False,
  ):
    self.agent = agent
    self.skip_summarization: bool = skip_summarization
    self.include_plugins = include_plugins
    self.propagate_grounding_metadata = propagate_grounding_metadata

    super().__init__(name=agent.name, description=agent.description)

  @model_validator(mode='before')
  @classmethod
  def populate_name(cls, data: Any) -> Any:
    data['name'] = data['agent'].name
    return data

  @override
  def _get_declaration(self) -> types.FunctionDeclaration:
    from ..utils.variant_utils import GoogleLLMVariant

    input_schema = _get_input_schema(self.agent)
    output_schema = _get_output_schema(self.agent)

    if input_schema:
      result = _automatic_function_calling_util.build_function_declaration(
          func=input_schema, variant=self._api_variant
      )
      # Override the description with the agent's description
      result.description = self.agent.description
    else:
      if is_feature_enabled(FeatureName.JSON_SCHEMA_FOR_FUNC_DECL):
        result = types.FunctionDeclaration(
            name=self.name,
            description=self.agent.description,
            parameters_json_schema={
                'type': 'object',
                'properties': {