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agent_dhal/agentdhal_agentchat/tools/_agent.py

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+from agentdhal_core import Component, ComponentModel
+from pydantic import BaseModel
+from typing_extensions import Self
+
+from agentdhal_agentchat.agents import BaseChatAgent
+
+from ._task_runner_tool import TaskRunnerTool
+
+
+class AgentToolConfig(BaseModel):
+    """Configuration for the AgentTool."""
+
+    agent: ComponentModel
+    """The agent to be used for running the task."""
+
+    return_value_as_last_message: bool = False
+    """Whether to return the value as the last message of the task result."""
+
+
+class AgentTool(TaskRunnerTool, Component[AgentToolConfig]):
+    """Tool that can be used to run a task using an agent.
+
+    The tool returns the result of the task execution as a :class:`~agentdhal_agentchat.base.TaskResult` object.
+
+    .. important::
+        When using AgentTool, you **must** disable parallel tool calls in the model client configuration
+        to avoid concurrency issues. Agents cannot run concurrently as they maintain internal state
+        that would conflict with parallel execution. For example, set ``parallel_tool_calls=False``
+        for :class:`~agentdhal_extensions.models.openai.OpenAIChatCompletionClient` and
+        :class:`~agentdhal_extensions.models.openai.AzureOpenAIChatCompletionClient`.
+
+    Args:
+        agent (BaseChatAgent): The agent to be used for running the task.
+        return_value_as_last_message (bool): Whether to use the last message content of the task result
+            as the return value of the tool in :meth:`~agentdhal_agentchat.tools.TaskRunnerTool.return_value_as_string`.
+            If set to True, the last message content will be returned as a string.
+            If set to False, the tool will return all messages in the task result as a string concatenated together,
+            with each message prefixed by its source (e.g., "writer: ...", "assistant: ...").
+
+    Example:
+
+        .. code-block:: python
+
+            import asyncio
+
+            from agentdhal_agentchat.agents import AssistantAgent
+            from agentdhal_agentchat.tools import AgentTool
+            from agentdhal_agentchat.ui import Console
+            from agentdhal_extensions.models.openai import OpenAIChatCompletionClient
+
+
+            async def main() -> None:
+                model_client = OpenAIChatCompletionClient(model="gpt-4.1")
+                writer = AssistantAgent(
+                    name="writer",
+                    description="A writer agent for generating text.",
+                    model_client=model_client,
+                    system_message="Write well.",
+                )
+                writer_tool = AgentTool(agent=writer)
+
+                # Create model client with parallel tool calls disabled for the main agent
+                main_model_client = OpenAIChatCompletionClient(model="gpt-4.1", parallel_tool_calls=False)
+                assistant = AssistantAgent(
+                    name="assistant",
+                    model_client=main_model_client,
+                    tools=[writer_tool],
+                    system_message="You are a helpful assistant.",
+                )
+                await Console(assistant.run_stream(task="Write a poem about the sea."))
+
+
+            asyncio.run(main())
+    """
+
+    component_config_schema = AgentToolConfig
+    component_provider_override = "agentdhal_agentchat.tools.AgentTool"
+
+    def __init__(self, agent: BaseChatAgent, return_value_as_last_message: bool = False) -> None:
+        self._agent = agent
+        super().__init__(
+            agent, agent.name, agent.description, return_value_as_last_message=return_value_as_last_message
+        )
+
+    def _to_config(self) -> AgentToolConfig:
+        return AgentToolConfig(
+            agent=self._agent.dump_component(),
+            return_value_as_last_message=self._return_value_as_last_message,
+        )
+
+    @classmethod
+    def _from_config(cls, config: AgentToolConfig) -> Self:
+        return cls(BaseChatAgent.load_component(config.agent), config.return_value_as_last_message)