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DigiJ
4
agent_dhal/agentdhal_agentchat/tools/__init__.py
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4
agent_dhal/agentdhal_agentchat/tools/__init__.py
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from ._agent import AgentTool
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from ._team import TeamTool
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__all__ = ["AgentTool", "TeamTool"]
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93
agent_dhal/agentdhal_agentchat/tools/_agent.py
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93
agent_dhal/agentdhal_agentchat/tools/_agent.py
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from agentdhal_core import Component, ComponentModel
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from pydantic import BaseModel
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from typing_extensions import Self
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from agentdhal_agentchat.agents import BaseChatAgent
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from ._task_runner_tool import TaskRunnerTool
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class AgentToolConfig(BaseModel):
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"""Configuration for the AgentTool."""
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agent: ComponentModel
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"""The agent to be used for running the task."""
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return_value_as_last_message: bool = False
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"""Whether to return the value as the last message of the task result."""
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class AgentTool(TaskRunnerTool, Component[AgentToolConfig]):
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"""Tool that can be used to run a task using an agent.
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The tool returns the result of the task execution as a :class:`~agentdhal_agentchat.base.TaskResult` object.
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.. important::
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When using AgentTool, you **must** disable parallel tool calls in the model client configuration
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to avoid concurrency issues. Agents cannot run concurrently as they maintain internal state
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that would conflict with parallel execution. For example, set ``parallel_tool_calls=False``
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for :class:`~agentdhal_extensions.models.openai.OpenAIChatCompletionClient` and
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:class:`~agentdhal_extensions.models.openai.AzureOpenAIChatCompletionClient`.
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Args:
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agent (BaseChatAgent): The agent to be used for running the task.
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return_value_as_last_message (bool): Whether to use the last message content of the task result
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as the return value of the tool in :meth:`~agentdhal_agentchat.tools.TaskRunnerTool.return_value_as_string`.
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If set to True, the last message content will be returned as a string.
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If set to False, the tool will return all messages in the task result as a string concatenated together,
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with each message prefixed by its source (e.g., "writer: ...", "assistant: ...").
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Example:
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.. code-block:: python
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import asyncio
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from agentdhal_agentchat.agents import AssistantAgent
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from agentdhal_agentchat.tools import AgentTool
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from agentdhal_agentchat.ui import Console
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from agentdhal_extensions.models.openai import OpenAIChatCompletionClient
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async def main() -> None:
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model_client = OpenAIChatCompletionClient(model="gpt-4.1")
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writer = AssistantAgent(
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name="writer",
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description="A writer agent for generating text.",
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model_client=model_client,
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system_message="Write well.",
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)
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writer_tool = AgentTool(agent=writer)
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# Create model client with parallel tool calls disabled for the main agent
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main_model_client = OpenAIChatCompletionClient(model="gpt-4.1", parallel_tool_calls=False)
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assistant = AssistantAgent(
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name="assistant",
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model_client=main_model_client,
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tools=[writer_tool],
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system_message="You are a helpful assistant.",
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)
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await Console(assistant.run_stream(task="Write a poem about the sea."))
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asyncio.run(main())
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"""
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component_config_schema = AgentToolConfig
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component_provider_override = "agentdhal_agentchat.tools.AgentTool"
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def __init__(self, agent: BaseChatAgent, return_value_as_last_message: bool = False) -> None:
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self._agent = agent
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super().__init__(
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agent, agent.name, agent.description, return_value_as_last_message=return_value_as_last_message
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)
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def _to_config(self) -> AgentToolConfig:
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return AgentToolConfig(
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agent=self._agent.dump_component(),
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return_value_as_last_message=self._return_value_as_last_message,
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)
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@classmethod
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def _from_config(cls, config: AgentToolConfig) -> Self:
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return cls(BaseChatAgent.load_component(config.agent), config.return_value_as_last_message)
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72
agent_dhal/agentdhal_agentchat/tools/_task_runner_tool.py
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72
agent_dhal/agentdhal_agentchat/tools/_task_runner_tool.py
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from abc import ABC
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from typing import Annotated, Any, AsyncGenerator, List, Mapping
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from agentdhal_core import CancellationToken
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from agentdhal_core.tools import BaseStreamTool
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from pydantic import BaseModel
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from ..agents import BaseChatAgent
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from ..base import TaskResult
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from ..messages import BaseAgentEvent, BaseChatMessage
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from ..teams import BaseGroupChat
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class TaskRunnerToolArgs(BaseModel):
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"""Input for the TaskRunnerTool."""
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task: Annotated[str, "The task to be executed."]
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class TaskRunnerTool(BaseStreamTool[TaskRunnerToolArgs, BaseAgentEvent | BaseChatMessage, TaskResult], ABC):
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"""An base class for tool that can be used to run a task using a team or an agent."""
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component_type = "tool"
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def __init__(
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self,
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task_runner: BaseGroupChat | BaseChatAgent,
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name: str,
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description: str,
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return_value_as_last_message: bool,
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) -> None:
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self._task_runner = task_runner
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self._return_value_as_last_message = return_value_as_last_message
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super().__init__(
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args_type=TaskRunnerToolArgs,
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return_type=TaskResult,
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name=name,
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description=description,
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strict=True,
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)
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async def run(self, args: TaskRunnerToolArgs, cancellation_token: CancellationToken) -> TaskResult:
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"""Run the task and return the result."""
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return await self._task_runner.run(task=args.task, cancellation_token=cancellation_token)
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async def run_stream(
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self, args: TaskRunnerToolArgs, cancellation_token: CancellationToken
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) -> AsyncGenerator[BaseAgentEvent | BaseChatMessage | TaskResult, None]:
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"""Run the task and yield events or messages as they are produced, the final :class:`TaskResult`
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will be yielded at the end."""
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async for event in self._task_runner.run_stream(task=args.task, cancellation_token=cancellation_token):
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yield event
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def return_value_as_string(self, value: TaskResult) -> str:
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"""Convert the task result to a string."""
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if self._return_value_as_last_message:
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if value.messages and isinstance(value.messages[-1], BaseChatMessage):
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return value.messages[-1].to_model_text()
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raise ValueError("The last message is not a BaseChatMessage.")
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parts: List[str] = []
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for message in value.messages:
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if isinstance(message, BaseChatMessage):
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if message.source == "user":
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continue
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parts.append(f"{message.source}: {message.to_model_text()}")
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return "\n\n".join(parts)
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async def save_state_json(self) -> Mapping[str, Any]:
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return await self._task_runner.save_state()
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async def load_state_json(self, state: Mapping[str, Any]) -> None:
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await self._task_runner.load_state(state)
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133
agent_dhal/agentdhal_agentchat/tools/_team.py
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133
agent_dhal/agentdhal_agentchat/tools/_team.py
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@@ -0,0 +1,133 @@
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from agentdhal_core import Component, ComponentModel
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from pydantic import BaseModel
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from typing_extensions import Self
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from agentdhal_agentchat.teams import BaseGroupChat
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from ._task_runner_tool import TaskRunnerTool
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class TeamToolConfig(BaseModel):
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"""Configuration for the TeamTool."""
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name: str
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"""The name of the tool."""
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description: str
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"""The name and description of the tool."""
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team: ComponentModel
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"""The team to be used for running the task."""
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return_value_as_last_message: bool = False
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"""Whether to return the value as the last message of the task result."""
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class TeamTool(TaskRunnerTool, Component[TeamToolConfig]):
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"""Tool that can be used to run a task.
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The tool returns the result of the task execution as a :class:`~agentdhal_agentchat.base.TaskResult` object.
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.. important::
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When using TeamTool, you **must** disable parallel tool calls in the model client configuration
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to avoid concurrency issues. Teams cannot run concurrently as they maintain internal state
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that would conflict with parallel execution. For example, set ``parallel_tool_calls=False``
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for :class:`~agentdhal_extensions.models.openai.OpenAIChatCompletionClient` and
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:class:`~agentdhal_extensions.models.openai.AzureOpenAIChatCompletionClient`.
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Args:
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team (BaseGroupChat): The team to be used for running the task.
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name (str): The name of the tool.
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description (str): The description of the tool.
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return_value_as_last_message (bool): Whether to use the last message content of the task result
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as the return value of the tool in :meth:`~agentdhal_agentchat.tools.TaskRunnerTool.return_value_as_string`.
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If set to True, the last message content will be returned as a string.
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If set to False, the tool will return all messages in the task result as a string concatenated together,
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with each message prefixed by its source (e.g., "writer: ...", "assistant: ...").
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Example:
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.. code-block:: python
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from agentdhal_agentchat.agents import AssistantAgent
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from agentdhal_agentchat.conditions import SourceMatchTermination
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from agentdhal_agentchat.teams import RoundRobinGroupChat
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from agentdhal_agentchat.tools import TeamTool
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from agentdhal_agentchat.ui import Console
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from agentdhal_extensions.models.openai import OpenAIChatCompletionClient
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async def main() -> None:
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# Disable parallel tool calls when using TeamTool
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model_client = OpenAIChatCompletionClient(model="gpt-4.1")
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writer = AssistantAgent(name="writer", model_client=model_client, system_message="You are a helpful assistant.")
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reviewer = AssistantAgent(
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name="reviewer", model_client=model_client, system_message="You are a critical reviewer."
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)
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summarizer = AssistantAgent(
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name="summarizer",
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model_client=model_client,
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system_message="You combine the review and produce a revised response.",
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)
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team = RoundRobinGroupChat(
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[writer, reviewer, summarizer], termination_condition=SourceMatchTermination(sources=["summarizer"])
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)
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# Create a TeamTool that uses the team to run tasks, returning the last message as the result.
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tool = TeamTool(
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team=team,
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name="writing_team",
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description="A tool for writing tasks.",
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return_value_as_last_message=True,
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)
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# Create model client with parallel tool calls disabled for the main agent
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main_model_client = OpenAIChatCompletionClient(model="gpt-4.1", parallel_tool_calls=False)
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main_agent = AssistantAgent(
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name="main_agent",
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model_client=main_model_client,
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system_message="You are a helpful assistant that can use the writing tool.",
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tools=[tool],
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)
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# For handling each events manually.
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# async for message in main_agent.run_stream(
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# task="Write a short story about a robot learning to love.",
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# ):
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# print(message)
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# Use Console to display the messages in a more readable format.
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await Console(
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main_agent.run_stream(
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task="Write a short story about a robot learning to love.",
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)
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)
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if __name__ == "__main__":
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import asyncio
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asyncio.run(main())
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"""
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component_config_schema = TeamToolConfig
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component_provider_override = "agentdhal_agentchat.tools.TeamTool"
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def __init__(
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self, team: BaseGroupChat, name: str, description: str, return_value_as_last_message: bool = False
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) -> None:
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self._team = team
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super().__init__(team, name, description, return_value_as_last_message=return_value_as_last_message)
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def _to_config(self) -> TeamToolConfig:
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return TeamToolConfig(
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name=self._name,
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description=self._description,
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team=self._team.dump_component(),
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return_value_as_last_message=self._return_value_as_last_message,
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)
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@classmethod
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def _from_config(cls, config: TeamToolConfig) -> Self:
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return cls(
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BaseGroupChat.load_component(config.team),
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config.name,
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config.description,
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config.return_value_as_last_message,
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)
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