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dark_hal/agent_dhal/agentdhal_agentchat/agents/_assistant_agent.py

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2026-03-13 12:56:43 -07:00
from __future__ import annotations
import asyncio
import json
import logging
import uuid
import warnings
from typing import (
Any,
AsyncGenerator,
Awaitable,
Callable,
Dict,
List,
Mapping,
Optional,
Sequence,
Tuple,
TypeVar,
Union,
)
from agentdhal_core import CancellationToken, Component, ComponentModel, FunctionCall
from agentdhal_core.memory import Memory
from agentdhal_core.model_context import (
ChatCompletionContext,
UnboundedChatCompletionContext,
)
from agentdhal_core.models import (
AssistantMessage,
ChatCompletionClient,
CreateResult,
FunctionExecutionResult,
FunctionExecutionResultMessage,
LLMMessage,
SystemMessage,
)
from agentdhal_core.tools import BaseTool, FunctionTool, StaticStreamWorkbench, ToolResult, Workbench
from pydantic import BaseModel, Field
from typing_extensions import Self
from .. import EVENT_LOGGER_NAME
from ..base import Handoff as HandoffBase
from ..base import Response
from ..messages import (
BaseAgentEvent,
BaseChatMessage,
HandoffMessage,
MemoryQueryEvent,
ModelClientStreamingChunkEvent,
StructuredMessage,
StructuredMessageFactory,
TextMessage,
ThoughtEvent,
ToolCallExecutionEvent,
ToolCallRequestEvent,
ToolCallSummaryMessage,
)
from ..state import AssistantAgentState
from ..utils import remove_images
from ._base_chat_agent import BaseChatAgent
event_logger = logging.getLogger(EVENT_LOGGER_NAME)
# Add type variables for more specific typing
T = TypeVar("T", bound=BaseModel)
R = TypeVar("R", bound=BaseModel)
class HalConfig(BaseModel):
"""The declarative configuration for the assistant agent."""
name: str
model_client: ComponentModel
tools: List[ComponentModel] | None = None
workbench: List[ComponentModel] | None = None
handoffs: List[HandoffBase | str] | None = None
model_context: ComponentModel | None = None
memory: List[ComponentModel] | None = None
description: str
system_message: str | None = None
model_client_stream: bool = False
reflect_on_tool_use: bool
tool_call_summary_format: str
max_tool_iterations: int = Field(default=1, ge=1)
metadata: Dict[str, str] | None = None
structured_message_factory: ComponentModel | None = None
class Hal(BaseChatAgent, Component[AssistantAgentConfig]):
"""An agent that provides assistance with tool use.
The :meth:`on_messages` returns a :class:`~agentdhal_agentchat.base.Response`
in which :attr:`~agentdhal_agentchat.base.Response.chat_message` is the final
response message.
The :meth:`on_messages_stream` creates an async generator that produces
the inner messages as they are created, and the :class:`~agentdhal_agentchat.base.Response`
object as the last item before closing the generator.
The :meth:`BaseChatAgent.run` method returns a :class:`~agentdhal_agentchat.base.TaskResult`
containing the messages produced by the agent. In the list of messages,
:attr:`~agentdhal_agentchat.base.TaskResult.messages`,
the last message is the final response message.
The :meth:`BaseChatAgent.run_stream` method creates an async generator that produces
the inner messages as they are created, and the :class:`~agentdhal_agentchat.base.TaskResult`
object as the last item before closing the generator.
.. attention::
The caller must only pass the new messages to the agent on each call
to the :meth:`on_messages`, :meth:`on_messages_stream`, :meth:`BaseChatAgent.run`,
or :meth:`BaseChatAgent.run_stream` methods.
The agent maintains its state between calls to these methods.
Do not pass the entire conversation history to the agent on each call.
.. warning::
The assistant agent is not thread-safe or coroutine-safe.
It should not be shared between multiple tasks or coroutines, and it should
not call its methods concurrently.
The following diagram shows how the assistant agent works:
.. image:: ../../images/assistant-agent.svg
**Structured output:**
If the `output_content_type` is set, the agent will respond with a :class:`~agentdhal_agentchat.messages.StructuredMessage`
instead of a :class:`~agentdhal_agentchat.messages.TextMessage` in the final response by default.
.. note::
Currently, setting `output_content_type` prevents the agent from being
able to call `load_component` and `dum_component` methods for serializable
configuration. This will be fixed soon in the future.
**Tool call behavior:**
* If the model returns no tool call, then the response is immediately returned as a :class:`~agentdhal_agentchat.messages.TextMessage` or a :class:`~agentdhal_agentchat.messages.StructuredMessage` (when using structured output) in :attr:`~agentdhal_agentchat.base.Response.chat_message`. This ends the tool call iteration loop regardless of the `max_tool_iterations` setting.
* When the model returns tool calls, they will be executed right away:
- When `reflect_on_tool_use` is False, the tool call results are returned as a :class:`~agentdhal_agentchat.messages.ToolCallSummaryMessage` in :attr:`~agentdhal_agentchat.base.Response.chat_message`. You can customise the summary with either a static format string (`tool_call_summary_format`) **or** a callable (`tool_call_summary_formatter`); the callable is evaluated once per tool call.
- When `reflect_on_tool_use` is True, the another model inference is made using the tool calls and results, and final response is returned as a :class:`~agentdhal_agentchat.messages.TextMessage` or a :class:`~agentdhal_agentchat.messages.StructuredMessage` (when using structured output) in :attr:`~agentdhal_agentchat.base.Response.chat_message`.
- `reflect_on_tool_use` is set to `True` by default when `output_content_type` is set.
- `reflect_on_tool_use` is set to `False` by default when `output_content_type` is not set.
* If the model returns multiple tool calls, they will be executed concurrently. To disable parallel tool calls you need to configure the model client. For example, set `parallel_tool_calls=False` for :class:`~agentdhal_extensions.models.openai.OpenAIChatCompletionClient` and :class:`~agentdhal_extensions.models.openai.AzureOpenAIChatCompletionClient`.
* The `max_tool_iterations` parameter controls how many sequential tool call iterations the agent can perform in a single run. When set to 1 (default), the agent executes tool calls once and returns the result. When set higher, the agent can make additional model calls to execute more tool calls if the model continues to request them, enabling multi-step tool-based workflows. The agent stops when either the model returns a text response (instead of tool calls) or the maximum number of iterations is reached.
.. tip::
By default, the tool call results are returned as the response when tool
calls are made, so pay close attention to how the tools' return values
are formatted—especially if another agent expects a specific schema.
* Use **`tool_call_summary_format`** for a simple static template.
* Use **`tool_call_summary_formatter`** for full programmatic control
(e.g., "hide large success payloads, show full details on error").
*Note*: `tool_call_summary_formatter` is **not serializable** and will
be ignored when an agent is loaded from, or exported to, YAML/JSON
configuration files.
**Hand off behavior:**
* If a handoff is triggered, a :class:`~agentdhal_agentchat.messages.HandoffMessage` will be returned in :attr:`~agentdhal_agentchat.base.Response.chat_message`.
* If there are tool calls, they will also be executed right away before returning the handoff.
* The tool calls and results are passed to the target agent through :attr:`~agentdhal_agentchat.messages.HandoffMessage.context`.
.. note::
If multiple handoffs are detected, only the first handoff is executed.
To avoid this, disable parallel tool calls in the model client configuration.
**Limit context size sent to the model:**
You can limit the number of messages sent to the model by setting
the `model_context` parameter to a :class:`~agentdhal_core.model_context.BufferedChatCompletionContext`.
This will limit the number of recent messages sent to the model and can be useful
when the model has a limit on the number of tokens it can process.
Another option is to use a :class:`~agentdhal_core.model_context.TokenLimitedChatCompletionContext`
which will limit the number of tokens sent to the model.
You can also create your own model context by subclassing
:class:`~agentdhal_core.model_context.ChatCompletionContext`.
**Streaming mode:**
The assistant agent can be used in streaming mode by setting `model_client_stream=True`.
In this mode, the :meth:`on_messages_stream` and :meth:`BaseChatAgent.run_stream` methods will also yield
:class:`~agentdhal_agentchat.messages.ModelClientStreamingChunkEvent`
messages as the model client produces chunks of response.
The chunk messages will not be included in the final response's inner messages.
Args:
name (str): The name of the agent.
model_client (ChatCompletionClient): The model client to use for inference.
tools (List[BaseTool[Any, Any] | Callable[..., Any] | Callable[..., Awaitable[Any]]] | None, optional): The tools to register with the agent.
workbench (Workbench | Sequence[Workbench] | None, optional): The workbench or list of workbenches to use for the agent.
Tools cannot be used when workbench is set and vice versa.
handoffs (List[HandoffBase | str] | None, optional): The handoff configurations for the agent,
allowing it to transfer to other agents by responding with a :class:`HandoffMessage`.
The transfer is only executed when the team is in :class:`~agentdhal_agentchat.teams.Swarm`.
If a handoff is a string, it should represent the target agent's name.
model_context (ChatCompletionContext | None, optional): The model context for storing and retrieving :class:`~agentdhal_core.models.LLMMessage`. It can be preloaded with initial messages. The initial messages will be cleared when the agent is reset.
description (str, optional): The description of the agent.
system_message (str, optional): The system message for the model. If provided, it will be prepended to the messages in the model context when making an inference. Set to `None` to disable.
model_client_stream (bool, optional): If `True`, the model client will be used in streaming mode.
:meth:`on_messages_stream` and :meth:`BaseChatAgent.run_stream` methods will also yield :class:`~agentdhal_agentchat.messages.ModelClientStreamingChunkEvent`
messages as the model client produces chunks of response. Defaults to `False`.
reflect_on_tool_use (bool, optional): If `True`, the agent will make another model inference using the tool call and result
to generate a response. If `False`, the tool call result will be returned as the response. By default, if `output_content_type` is set, this will be `True`;
if `output_content_type` is not set, this will be `False`.
output_content_type (type[BaseModel] | None, optional): The output content type for :class:`~agentdhal_agentchat.messages.StructuredMessage` response as a Pydantic model.
This will be used with the model client to generate structured output.
If this is set, the agent will respond with a :class:`~agentdhal_agentchat.messages.StructuredMessage` instead of a :class:`~agentdhal_agentchat.messages.TextMessage`
in the final response, unless `reflect_on_tool_use` is `False` and a tool call is made.
output_content_type_format (str | None, optional): (Experimental) The format string used for the content of a :class:`~agentdhal_agentchat.messages.StructuredMessage` response.
max_tool_iterations (int, optional): The maximum number of tool iterations to perform until the model stops making tool calls. Defaults to `1`, which means the agent will
only execute the tool calls made by the model once, and return the result as a :class:`~agentdhal_agentchat.messages.ToolCallSummaryMessage`,
or a :class:`~agentdhal_agentchat.messages.TextMessage` or a :class:`~agentdhal_agentchat.messages.StructuredMessage` (when using structured output)
in :attr:`~agentdhal_agentchat.base.Response.chat_message` as the final response.
As soon as the model stops making tool calls, the agent will stop executing tool calls and return the result as the final response.
The value must be greater than or equal to 1.
tool_call_summary_format (str, optional): Static format string applied to each tool call result when composing the :class:`~agentdhal_agentchat.messages.ToolCallSummaryMessage`.
Defaults to ``"{result}"``. Ignored if `tool_call_summary_formatter` is provided. When `reflect_on_tool_use` is ``False``, the summaries for all tool
calls are concatenated with a newline ('\\n') and returned as the response. Placeholders available in the template:
`{tool_name}`, `{arguments}`, `{result}`, `{is_error}`.
tool_call_summary_formatter (Callable[[FunctionCall, FunctionExecutionResult], str] | None, optional):
Callable that receives the ``FunctionCall`` and its ``FunctionExecutionResult`` and returns the summary string.
Overrides `tool_call_summary_format` when supplied and allows conditional logic — for example, emitting static string like
``"Tool FooBar executed successfully."`` on success and a full payload (including all passed arguments etc.) only on failure.
**Limitation**: The callable is *not serializable*; values provided via YAML/JSON configs are ignored.
.. note::
`tool_call_summary_formatter` is intended for in-code use only. It cannot currently be saved or restored via
configuration files.
memory (Sequence[Memory] | None, optional): The memory store to use for the agent. Defaults to `None`.
metadata (Dict[str, str] | None, optional): Optional metadata for tracking.
Raises:
ValueError: If tool names are not unique.
ValueError: If handoff names are not unique.
ValueError: If handoff names are not unique from tool names.
ValueError: If maximum number of tool iterations is less than 1.
Examples:
**Example 1: basic agent**
The following example demonstrates how to create an assistant agent with
a model client and generate a response to a simple task.
.. code-block:: python
import asyncio
from agentdhal_extensions.models.openai import OpenAIChatCompletionClient
from agentdhal_agentchat.agents import AssistantAgent
async def main() -> None:
model_client = OpenAIChatCompletionClient(
model="gpt-4o",
# api_key = "your_openai_api_key"
)
agent = AssistantAgent(name="assistant", model_client=model_client)
result = await agent.run(task="Name two cities in North America.")
print(result)
asyncio.run(main())
**Example 2: model client token streaming**
This example demonstrates how to create an assistant agent with
a model client and generate a token stream by setting `model_client_stream=True`.
.. code-block:: python
import asyncio
from agentdhal_extensions.models.openai import OpenAIChatCompletionClient
from agentdhal_agentchat.agents import AssistantAgent
async def main() -> None:
model_client = OpenAIChatCompletionClient(
model="gpt-4o",
# api_key = "your_openai_api_key"
)
agent = AssistantAgent(
name="assistant",
model_client=model_client,
model_client_stream=True,
)
stream = agent.run_stream(task="Name two cities in North America.")
async for message in stream:
print(message)
asyncio.run(main())
.. code-block:: text
source='user' models_usage=None metadata={} content='Name two cities in North America.' type='TextMessage'
source='assistant' models_usage=None metadata={} content='Two' type='ModelClientStreamingChunkEvent'
source='assistant' models_usage=None metadata={} content=' cities' type='ModelClientStreamingChunkEvent'
source='assistant' models_usage=None metadata={} content=' in' type='ModelClientStreamingChunkEvent'
source='assistant' models_usage=None metadata={} content=' North' type='ModelClientStreamingChunkEvent'
source='assistant' models_usage=None metadata={} content=' America' type='ModelClientStreamingChunkEvent'
source='assistant' models_usage=None metadata={} content=' are' type='ModelClientStreamingChunkEvent'
source='assistant' models_usage=None metadata={} content=' New' type='ModelClientStreamingChunkEvent'
source='assistant' models_usage=None metadata={} content=' York' type='ModelClientStreamingChunkEvent'
source='assistant' models_usage=None metadata={} content=' City' type='ModelClientStreamingChunkEvent'
source='assistant' models_usage=None metadata={} content=' and' type='ModelClientStreamingChunkEvent'
source='assistant' models_usage=None metadata={} content=' Toronto' type='ModelClientStreamingChunkEvent'
source='assistant' models_usage=None metadata={} content='.' type='ModelClientStreamingChunkEvent'
source='assistant' models_usage=None metadata={} content=' TERMIN' type='ModelClientStreamingChunkEvent'
source='assistant' models_usage=None metadata={} content='ATE' type='ModelClientStreamingChunkEvent'
source='assistant' models_usage=RequestUsage(prompt_tokens=0, completion_tokens=0) metadata={} content='Two cities in North America are New York City and Toronto. TERMINATE' type='TextMessage'
messages=[TextMessage(source='user', models_usage=None, metadata={}, content='Name two cities in North America.', type='TextMessage'), TextMessage(source='assistant', models_usage=RequestUsage(prompt_tokens=0, completion_tokens=0), metadata={}, content='Two cities in North America are New York City and Toronto. TERMINATE', type='TextMessage')] stop_reason=None
**Example 3: agent with tools**
The following example demonstrates how to create an assistant agent with
a model client and a tool, generate a stream of messages for a task, and
print the messages to the console using :class:`~agentdhal_agentchat.ui.Console`.
The tool is a simple function that returns the current time.
Under the hood, the function is wrapped in a :class:`~agentdhal_core.tools.FunctionTool`
and used with the agent's model client. The doc string of the function
is used as the tool description, the function name is used as the tool name,
and the function signature including the type hints is used as the tool arguments.
.. code-block:: python
import asyncio
from agentdhal_extensions.models.openai import OpenAIChatCompletionClient
from agentdhal_agentchat.agents import AssistantAgent
from agentdhal_agentchat.ui import Console
async def get_current_time() -> str:
return "The current time is 12:00 PM."
async def main() -> None:
model_client = OpenAIChatCompletionClient(
model="gpt-4o",
# api_key = "your_openai_api_key"
)
agent = AssistantAgent(name="assistant", model_client=model_client, tools=[get_current_time])
await Console(agent.run_stream(task="What is the current time?"))
asyncio.run(main())
**Example 4: agent with max_tool_iterations**
The following example demonstrates how to use the `max_tool_iterations` parameter
to control how many times the agent can execute tool calls in a single run.
This is useful when you want the agent to perform multiple sequential tool
operations to reach a goal.
.. code-block:: python
import asyncio
from agentdhal_extensions.models.openai import OpenAIChatCompletionClient
from agentdhal_agentchat.agents import AssistantAgent
from agentdhal_agentchat.ui import Console
# Global counter state
counter = 0
def increment_counter() -> str:
\"\"\"Increment the counter by 1 and return the current value.\"\"\"
global counter
counter += 1
return f"Counter incremented to: {counter}"
def get_counter() -> str:
\"\"\"Get the current counter value.\"\"\"
global counter
return f"Current counter value: {counter}"
async def main() -> None:
model_client = OpenAIChatCompletionClient(
model="gpt-4o",
# api_key = "your_openai_api_key"
)
# Create agent with max_tool_iterations=5 to allow multiple tool calls
agent = AssistantAgent(
name="assistant",
model_client=model_client,
tools=[increment_counter, get_counter],
max_tool_iterations=5, # Allow up to 5 tool call iterations
reflect_on_tool_use=True, # Get a final summary after tool calls
)
await Console(agent.run_stream(task="Increment the counter 3 times and then tell me the final value."))
asyncio.run(main())
**Example 5: agent with Model-Context Protocol (MCP) workbench**
The following example demonstrates how to create an assistant agent with
a model client and an :class:`~agentdhal_extensions.tools.mcp.McpWorkbench` for
interacting with a Model-Context Protocol (MCP) server.
.. code-block:: python
import asyncio
from agentdhal_agentchat.agents import AssistantAgent
from agentdhal_agentchat.ui import Console
from agentdhal_extensions.models.openai import OpenAIChatCompletionClient
from agentdhal_extensions.tools.mcp import StdioServerParams, McpWorkbench
async def main() -> None:
params = StdioServerParams(
command="uvx",
args=["mcp-server-fetch"],
read_timeout_seconds=60,
)
# You can also use `start()` and `stop()` to manage the session.
async with McpWorkbench(server_params=params) as workbench:
model_client = OpenAIChatCompletionClient(model="gpt-4.1-nano")
assistant = AssistantAgent(
name="Assistant",
model_client=model_client,
workbench=workbench,
reflect_on_tool_use=True,
)
await Console(
assistant.run_stream(task="Go to https://github.com/microsoft/autogen and tell me what you see.")
)
asyncio.run(main())
**Example 6: agent with structured output and tool**
The following example demonstrates how to create an assistant agent with
a model client configured to use structured output and a tool.
Note that you need to use :class:`~agentdhal_core.tools.FunctionTool` to create the tool
and the `strict=True` is required for structured output mode.
Because the model is configured to use structured output, the output
reflection response will be a JSON formatted string.
.. code-block:: python
import asyncio
from typing import Literal
from agentdhal_agentchat.agents import AssistantAgent
from agentdhal_agentchat.ui import Console
from agentdhal_core.tools import FunctionTool
from agentdhal_extensions.models.openai import OpenAIChatCompletionClient
from pydantic import BaseModel
# Define the structured output format.
class AgentResponse(BaseModel):
thoughts: str
response: Literal["happy", "sad", "neutral"]
# Define the function to be called as a tool.
def sentiment_analysis(text: str) -> str:
\"\"\"Given a text, return the sentiment.\"\"\"
return "happy" if "happy" in text else "sad" if "sad" in text else "neutral"
# Create a FunctionTool instance with `strict=True`,
# which is required for structured output mode.
tool = FunctionTool(sentiment_analysis, description="Sentiment Analysis", strict=True)
# Create an OpenAIChatCompletionClient instance that supports structured output.
model_client = OpenAIChatCompletionClient(
model="gpt-4o-mini",
)
# Create an AssistantAgent instance that uses the tool and model client.
agent = AssistantAgent(
name="assistant",
model_client=model_client,
tools=[tool],
system_message="Use the tool to analyze sentiment.",
output_content_type=AgentResponse,
)
async def main() -> None:
stream = agent.run_stream(task="I am happy today!")
await Console(stream)
asyncio.run(main())
.. code-block:: text
---------- assistant ----------
[FunctionCall(id='call_tIZjAVyKEDuijbBwLY6RHV2p', arguments='{"text":"I am happy today!"}', name='sentiment_analysis')]
---------- assistant ----------
[FunctionExecutionResult(content='happy', call_id='call_tIZjAVyKEDuijbBwLY6RHV2p', is_error=False)]
---------- assistant ----------
{"thoughts":"The user expresses a clear positive emotion by stating they are happy today, suggesting an upbeat mood.","response":"happy"}
**Example 7: agent with bounded model context**
The following example shows how to use a
:class:`~agentdhal_core.model_context.BufferedChatCompletionContext`
that only keeps the last 2 messages (1 user + 1 assistant).
Bounded model context is useful when the model has a limit on the
number of tokens it can process.
.. code-block:: python
import asyncio
from agentdhal_agentchat.agents import AssistantAgent
from agentdhal_core.model_context import BufferedChatCompletionContext
from agentdhal_extensions.models.openai import OpenAIChatCompletionClient
async def main() -> None:
# Create a model client.
model_client = OpenAIChatCompletionClient(
model="gpt-4o-mini",
# api_key = "your_openai_api_key"
)
# Create a model context that only keeps the last 2 messages (1 user + 1 assistant).
model_context = BufferedChatCompletionContext(buffer_size=2)
# Create an AssistantAgent instance with the model client and context.
agent = AssistantAgent(
name="assistant",
model_client=model_client,
model_context=model_context,
system_message="You are a helpful assistant.",
)
result = await agent.run(task="Name two cities in North America.")
print(result.messages[-1].content) # type: ignore
result = await agent.run(task="My favorite color is blue.")
print(result.messages[-1].content) # type: ignore
result = await agent.run(task="Did I ask you any question?")
print(result.messages[-1].content) # type: ignore
asyncio.run(main())
.. code-block:: text
Two cities in North America are New York City and Toronto.
That's great! Blue is often associated with calmness and serenity. Do you have a specific shade of blue that you like, or any particular reason why it's your favorite?
No, you didn't ask a question. I apologize for any misunderstanding. If you have something specific you'd like to discuss or ask, feel free to let me know!
**Example 8: agent with memory**
The following example shows how to use a list-based memory with the assistant agent.
The memory is preloaded with some initial content.
Under the hood, the memory is used to update the model context
before making an inference, using the :meth:`~agentdhal_core.memory.Memory.update_context` method.
.. code-block:: python
import asyncio
from agentdhal_agentchat.agents import AssistantAgent
from agentdhal_core.memory import ListMemory, MemoryContent
from agentdhal_extensions.models.openai import OpenAIChatCompletionClient
async def main() -> None:
# Create a model client.
model_client = OpenAIChatCompletionClient(
model="gpt-4o-mini",
# api_key = "your_openai_api_key"
)
# Create a list-based memory with some initial content.
memory = ListMemory()
await memory.add(MemoryContent(content="User likes pizza.", mime_type="text/plain"))
await memory.add(MemoryContent(content="User dislikes cheese.", mime_type="text/plain"))
# Create an AssistantAgent instance with the model client and memory.
agent = AssistantAgent(
name="assistant",
model_client=model_client,
memory=[memory],
system_message="You are a helpful assistant.",
)
result = await agent.run(task="What is a good dinner idea?")
print(result.messages[-1].content) # type: ignore
asyncio.run(main())
.. code-block:: text
How about making a delicious pizza without cheese? You can create a flavorful veggie pizza with a variety of toppings. Here's a quick idea:
**Veggie Tomato Sauce Pizza**
- Start with a pizza crust (store-bought or homemade).
- Spread a layer of marinara or tomato sauce evenly over the crust.
- Top with your favorite vegetables like bell peppers, mushrooms, onions, olives, and spinach.
- Add some protein if you'd like, such as grilled chicken or pepperoni (ensure it's cheese-free).
- Sprinkle with herbs like oregano and basil, and maybe a drizzle of olive oil.
- Bake according to the crust instructions until the edges are golden and the veggies are cooked.
Serve it with a side salad or some garlic bread to complete the meal! Enjoy your dinner!
**Example 9: agent with `o1-mini`**
The following example shows how to use `o1-mini` model with the assistant agent.
.. code-block:: python
import asyncio
from agentdhal_extensions.models.openai import OpenAIChatCompletionClient
from agentdhal_agentchat.agents import AssistantAgent
async def main() -> None:
model_client = OpenAIChatCompletionClient(
model="o1-mini",
# api_key = "your_openai_api_key"
)
# The system message is not supported by the o1 series model.
agent = AssistantAgent(name="assistant", model_client=model_client, system_message=None)
result = await agent.run(task="What is the capital of France?")
print(result.messages[-1].content) # type: ignore
asyncio.run(main())
.. note::
The `o1-preview` and `o1-mini` models do not support system message and function calling.
So the `system_message` should be set to `None` and the `tools` and `handoffs` should not be set.
See `o1 beta limitations <https://platform.openai.com/docs/guides/reasoning#beta-limitations>`_ for more details.
**Example 10: agent using reasoning model with custom model context.**
The following example shows how to use a reasoning model (DeepSeek R1) with the assistant agent.
The model context is used to filter out the thought field from the assistant message.
.. code-block:: python
import asyncio
from typing import List
from agentdhal_agentchat.agents import AssistantAgent
from agentdhal_core.model_context import UnboundedChatCompletionContext
from agentdhal_core.models import AssistantMessage, LLMMessage, ModelFamily
from agentdhal_extensions.models.ollama import OllamaChatCompletionClient
class ReasoningModelContext(UnboundedChatCompletionContext):
\"\"\"A model context for reasoning models.\"\"\"
async def get_messages(self) -> List[LLMMessage]:
messages = await super().get_messages()
# Filter out thought field from AssistantMessage.
messages_out: List[LLMMessage] = []
for message in messages:
if isinstance(message, AssistantMessage):
message.thought = None
messages_out.append(message)
return messages_out
# Create an instance of the model client for DeepSeek R1 hosted locally on Ollama.
model_client = OllamaChatCompletionClient(
model="deepseek-r1:8b",
model_info={
"vision": False,
"function_calling": False,
"json_output": False,
"family": ModelFamily.R1,
"structured_output": True,
},
)
agent = AssistantAgent(
"reasoning_agent",
model_client=model_client,
model_context=ReasoningModelContext(), # Use the custom model context.
)
async def run_reasoning_agent() -> None:
result = await agent.run(task="What is the capital of France?")
print(result)
asyncio.run(run_reasoning_agent())
For detailed examples and usage, see the Examples section below.
"""
component_version = 2
component_config_schema = AssistantAgentConfig
component_provider_override = "agentdhal_agentchat.agents.AssistantAgent"
def __init__(
self,
name: str,
model_client: ChatCompletionClient,
*,
tools: List[BaseTool[Any, Any] | Callable[..., Any] | Callable[..., Awaitable[Any]]] | None = None,
workbench: Workbench | Sequence[Workbench] | None = None,
handoffs: List[HandoffBase | str] | None = None,
model_context: ChatCompletionContext | None = None,
description: str = "An agent that provides assistance with ability to use tools.",
system_message: (
str | None
) = "You are a helpful AI assistant. Solve tasks using your tools. Reply with TERMINATE when the task has been completed.",
model_client_stream: bool = False,
reflect_on_tool_use: bool | None = None,
max_tool_iterations: int = 1,
tool_call_summary_format: str = "{result}",
tool_call_summary_formatter: Callable[[FunctionCall, FunctionExecutionResult], str] | None = None,
output_content_type: type[BaseModel] | None = None,
output_content_type_format: str | None = None,
memory: Sequence[Memory] | None = None,
metadata: Dict[str, str] | None = None,
):
super().__init__(name=name, description=description)
self._metadata = metadata or {}
self._model_client = model_client
self._model_client_stream = model_client_stream
self._output_content_type: type[BaseModel] | None = output_content_type
self._output_content_type_format = output_content_type_format
self._structured_message_factory: StructuredMessageFactory | None = None
if output_content_type is not None:
self._structured_message_factory = StructuredMessageFactory(
input_model=output_content_type, format_string=output_content_type_format
)
self._memory = None
if memory is not None:
if isinstance(memory, list):
self._memory = memory
else:
raise TypeError(f"Expected Memory, List[Memory], or None, got {type(memory)}")
self._system_messages: List[SystemMessage] = []
if system_message is None:
self._system_messages = []
else:
self._system_messages = [SystemMessage(content=system_message)]
self._tools: List[BaseTool[Any, Any]] = []
if tools is not None:
if model_client.model_info["function_calling"] is False:
raise ValueError("The model does not support function calling.")
for tool in tools:
if isinstance(tool, BaseTool):
self._tools.append(tool)
elif callable(tool):
if hasattr(tool, "__doc__") and tool.__doc__ is not None:
description = tool.__doc__
else:
description = ""
self._tools.append(FunctionTool(tool, description=description))
else:
raise ValueError(f"Unsupported tool type: {type(tool)}")
# Check if tool names are unique.
tool_names = [tool.name for tool in self._tools]
if len(tool_names) != len(set(tool_names)):
raise ValueError(f"Tool names must be unique: {tool_names}")
# Handoff tools.
self._handoff_tools: List[BaseTool[Any, Any]] = []
self._handoffs: Dict[str, HandoffBase] = {}
if handoffs is not None:
if model_client.model_info["function_calling"] is False:
raise ValueError("The model does not support function calling, which is needed for handoffs.")
for handoff in handoffs:
if isinstance(handoff, str):
handoff = HandoffBase(target=handoff)
if isinstance(handoff, HandoffBase):
self._handoff_tools.append(handoff.handoff_tool)
self._handoffs[handoff.name] = handoff
else:
raise ValueError(f"Unsupported handoff type: {type(handoff)}")
# Check if handoff tool names are unique.
handoff_tool_names = [tool.name for tool in self._handoff_tools]
if len(handoff_tool_names) != len(set(handoff_tool_names)):
raise ValueError(f"Handoff names must be unique: {handoff_tool_names}")
# Create sets for faster lookup
tool_names_set = set(tool_names)
handoff_tool_names_set = set(handoff_tool_names)
# Check if there's any overlap between handoff tool names and tool names
overlap = tool_names_set.intersection(handoff_tool_names_set)
# Also check if any handoff target name matches a tool name
# This handles the case where a handoff is specified directly with a string that matches a tool name
for handoff in handoffs or []:
if isinstance(handoff, str) and handoff in tool_names_set:
raise ValueError("Handoff names must be unique from tool names")
elif isinstance(handoff, HandoffBase) and handoff.target in tool_names_set:
raise ValueError("Handoff names must be unique from tool names")
if overlap:
raise ValueError("Handoff names must be unique from tool names")
if workbench is not None:
if self._tools:
raise ValueError("Tools cannot be used with a workbench.")
if isinstance(workbench, Sequence):
self._workbench = workbench
else:
self._workbench = [workbench]
else:
self._workbench = [StaticStreamWorkbench(self._tools)]
if model_context is not None:
self._model_context = model_context
else:
self._model_context = UnboundedChatCompletionContext()
if self._output_content_type is not None and reflect_on_tool_use is None:
# If output_content_type is set, we need to reflect on tool use by default.
self._reflect_on_tool_use = True
elif reflect_on_tool_use is None:
self._reflect_on_tool_use = False
else:
self._reflect_on_tool_use = reflect_on_tool_use
# Tool call loop
self._max_tool_iterations = max_tool_iterations
if self._max_tool_iterations < 1:
raise ValueError(
f"Maximum number of tool iterations must be greater than or equal to 1, got {max_tool_iterations}"
)
self._tool_call_summary_format = tool_call_summary_format
self._tool_call_summary_formatter = tool_call_summary_formatter
self._is_running = False
@property
def produced_message_types(self) -> Sequence[type[BaseChatMessage]]:
"""Get the types of messages this agent can produce.
Returns:
Sequence of message types this agent can generate
"""
types: List[type[BaseChatMessage]] = [TextMessage, ToolCallSummaryMessage, HandoffMessage]
if self._structured_message_factory is not None:
types.append(StructuredMessage)
return types
@property
def model_context(self) -> ChatCompletionContext:
"""Get the model context used by this agent.
Returns:
The chat completion context for this agent
"""
return self._model_context
async def on_messages(
self,
messages: Sequence[BaseChatMessage],
cancellation_token: CancellationToken,
) -> Response:
"""Process incoming messages and generate a response.
Args:
messages: Sequence of messages to process
cancellation_token: Token for cancelling operation
Returns:
Response containing the agent's reply
"""
async for message in self.on_messages_stream(messages, cancellation_token):
if isinstance(message, Response):
return message
raise AssertionError("The stream should have returned the final result.")
async def on_messages_stream(
self,
messages: Sequence[BaseChatMessage],
cancellation_token: CancellationToken,
) -> AsyncGenerator[Union[BaseAgentEvent, BaseChatMessage, Response], None]:
"""Process messages and stream the response.
Args:
messages: Sequence of messages to process
cancellation_token: Token for cancelling operation
Yields:
Events, messages and final response during processing
"""
# Gather all relevant state here
agent_name = self.name
model_context = self._model_context
memory = self._memory
system_messages = self._system_messages
workbench = self._workbench
handoff_tools = self._handoff_tools
handoffs = self._handoffs
model_client = self._model_client
model_client_stream = self._model_client_stream
reflect_on_tool_use = self._reflect_on_tool_use
max_tool_iterations = self._max_tool_iterations
tool_call_summary_format = self._tool_call_summary_format
tool_call_summary_formatter = self._tool_call_summary_formatter
output_content_type = self._output_content_type
# STEP 1: Add new user/handoff messages to the model context
await self._add_messages_to_context(
model_context=model_context,
messages=messages,
)
# STEP 2: Update model context with any relevant memory
inner_messages: List[BaseAgentEvent | BaseChatMessage] = []
for event_msg in await self._update_model_context_with_memory(
memory=memory,
model_context=model_context,
agent_name=agent_name,
):
inner_messages.append(event_msg)
yield event_msg
# STEP 3: Generate a message ID for correlation between streaming chunks and final message
message_id = str(uuid.uuid4())
# STEP 4: Run the first inference
model_result = None
async for inference_output in self._call_llm(
model_client=model_client,
model_client_stream=model_client_stream,
system_messages=system_messages,
model_context=model_context,
workbench=workbench,
handoff_tools=handoff_tools,
agent_name=agent_name,
cancellation_token=cancellation_token,
output_content_type=output_content_type,
message_id=message_id,
):
if isinstance(inference_output, CreateResult):
model_result = inference_output
else:
# Streaming chunk event
yield inference_output
assert model_result is not None, "No model result was produced."
# --- NEW: If the model produced a hidden "thought," yield it as an event ---
if model_result.thought:
thought_event = ThoughtEvent(content=model_result.thought, source=agent_name)
yield thought_event
inner_messages.append(thought_event)
# Add the assistant message to the model context (including thought if present)
await model_context.add_message(
AssistantMessage(
content=model_result.content,
source=agent_name,
thought=getattr(model_result, "thought", None),
)
)
# STEP 5: Process the model output
async for output_event in self._process_model_result(
model_result=model_result,
inner_messages=inner_messages,
cancellation_token=cancellation_token,
agent_name=agent_name,
system_messages=system_messages,
model_context=model_context,
workbench=workbench,
handoff_tools=handoff_tools,
handoffs=handoffs,
model_client=model_client,
model_client_stream=model_client_stream,
reflect_on_tool_use=reflect_on_tool_use,
max_tool_iterations=max_tool_iterations,
tool_call_summary_format=tool_call_summary_format,
tool_call_summary_formatter=tool_call_summary_formatter,
output_content_type=output_content_type,
message_id=message_id,
format_string=self._output_content_type_format,
):
yield output_event
@staticmethod
async def _add_messages_to_context(
model_context: ChatCompletionContext,
messages: Sequence[BaseChatMessage],
) -> None:
"""
Add incoming messages to the model context.
"""
for msg in messages:
if isinstance(msg, HandoffMessage):
for llm_msg in msg.context:
await model_context.add_message(llm_msg)
await model_context.add_message(msg.to_model_message())
@staticmethod
async def _update_model_context_with_memory(
memory: Optional[Sequence[Memory]],
model_context: ChatCompletionContext,
agent_name: str,
) -> List[MemoryQueryEvent]:
"""Update model context with memory content.
Args:
memory: Optional sequence of memory stores to query
model_context: Context to update with memory content
agent_name: Name of the agent for event tracking
Returns:
List of memory query events generated during update
"""
events: List[MemoryQueryEvent] = []
if memory:
for mem in memory:
update_context_result = await mem.update_context(model_context)
if update_context_result and len(update_context_result.memories.results) > 0:
memory_query_event_msg = MemoryQueryEvent(
content=update_context_result.memories.results,
source=agent_name,
)
events.append(memory_query_event_msg)
return events
@classmethod
async def _call_llm(
cls,
model_client: ChatCompletionClient,
model_client_stream: bool,
system_messages: List[SystemMessage],
model_context: ChatCompletionContext,
workbench: Sequence[Workbench],
handoff_tools: List[BaseTool[Any, Any]],
agent_name: str,
cancellation_token: CancellationToken,
output_content_type: type[BaseModel] | None,
message_id: str,
) -> AsyncGenerator[Union[CreateResult, ModelClientStreamingChunkEvent], None]:
"""Call the language model with given context and configuration.
Args:
model_client: Client for model inference
model_client_stream: Whether to stream responses
system_messages: System messages to include
model_context: Context containing message history
workbench: Available workbenches
handoff_tools: Tools for handling handoffs
agent_name: Name of the agent
cancellation_token: Token for cancelling operation
output_content_type: Optional type for structured output
Returns:
Generator yielding model results or streaming chunks
"""
all_messages = await model_context.get_messages()
llm_messages = cls._get_compatible_context(model_client=model_client, messages=system_messages + all_messages)
tools = [tool for wb in workbench for tool in await wb.list_tools()] + handoff_tools
if model_client_stream:
model_result: Optional[CreateResult] = None
async for chunk in model_client.create_stream(
llm_messages,
tools=tools,
json_output=output_content_type,
cancellation_token=cancellation_token,
):
if isinstance(chunk, CreateResult):
model_result = chunk
elif isinstance(chunk, str):
yield ModelClientStreamingChunkEvent(content=chunk, source=agent_name, full_message_id=message_id)
else:
raise RuntimeError(f"Invalid chunk type: {type(chunk)}")
if model_result is None:
raise RuntimeError("No final model result in streaming mode.")
yield model_result
else:
model_result = await model_client.create(
llm_messages,
tools=tools,
cancellation_token=cancellation_token,
json_output=output_content_type,
)
yield model_result
@classmethod
async def _process_model_result(
cls,
model_result: CreateResult,
inner_messages: List[BaseAgentEvent | BaseChatMessage],
cancellation_token: CancellationToken,
agent_name: str,
system_messages: List[SystemMessage],
model_context: ChatCompletionContext,
workbench: Sequence[Workbench],
handoff_tools: List[BaseTool[Any, Any]],
handoffs: Dict[str, HandoffBase],
model_client: ChatCompletionClient,
model_client_stream: bool,
reflect_on_tool_use: bool,
tool_call_summary_format: str,
tool_call_summary_formatter: Callable[[FunctionCall, FunctionExecutionResult], str] | None,
max_tool_iterations: int,
output_content_type: type[BaseModel] | None,
message_id: str,
format_string: str | None = None,
) -> AsyncGenerator[BaseAgentEvent | BaseChatMessage | Response, None]:
"""
Handle final or partial responses from model_result, including tool calls, handoffs,
and reflection if needed. Supports tool call loops when enabled.
"""
# Tool call loop implementation with streaming support
current_model_result = model_result
# This variable is needed for the final summary/reflection step
executed_calls_and_results: List[Tuple[FunctionCall, FunctionExecutionResult]] = []
for loop_iteration in range(max_tool_iterations):
# If direct text response (string), we're done
if isinstance(current_model_result.content, str):
# Use the passed message ID for the final message
if output_content_type:
content = output_content_type.model_validate_json(current_model_result.content)
yield Response(
chat_message=StructuredMessage[output_content_type]( # type: ignore[valid-type]
content=content,
source=agent_name,
models_usage=current_model_result.usage,
format_string=format_string,
id=message_id,
),
inner_messages=inner_messages,
)
else:
yield Response(
chat_message=TextMessage(
content=current_model_result.content,
source=agent_name,
models_usage=current_model_result.usage,
id=message_id,
),
inner_messages=inner_messages,
)
return
# Otherwise, we have function calls
assert isinstance(current_model_result.content, list) and all(
isinstance(item, FunctionCall) for item in current_model_result.content
)
# STEP 4A: Yield ToolCallRequestEvent
tool_call_msg = ToolCallRequestEvent(
content=current_model_result.content,
source=agent_name,
models_usage=current_model_result.usage,
)
event_logger.debug(tool_call_msg)
inner_messages.append(tool_call_msg)
yield tool_call_msg
# STEP 4B: Execute tool calls with streaming support
# Use a queue to handle streaming results from tool calls.
stream = asyncio.Queue[BaseAgentEvent | BaseChatMessage | None]()
async def _execute_tool_calls(
function_calls: List[FunctionCall],
stream_queue: asyncio.Queue[BaseAgentEvent | BaseChatMessage | None],
) -> List[Tuple[FunctionCall, FunctionExecutionResult]]:
results = await asyncio.gather(
*[
cls._execute_tool_call(
tool_call=call,
workbench=workbench,
handoff_tools=handoff_tools,
agent_name=agent_name,
cancellation_token=cancellation_token,
stream=stream_queue,
)
for call in function_calls
]
)
# Signal the end of streaming by putting None in the queue.
stream_queue.put_nowait(None)
return results
task = asyncio.create_task(_execute_tool_calls(current_model_result.content, stream))
while True:
event = await stream.get()
if event is None:
# End of streaming, break the loop.
break
if isinstance(event, BaseAgentEvent) or isinstance(event, BaseChatMessage):
yield event
inner_messages.append(event)
else:
raise RuntimeError(f"Unexpected event type: {type(event)}")
# Wait for all tool calls to complete.
executed_calls_and_results = await task
exec_results = [result for _, result in executed_calls_and_results]
# Yield ToolCallExecutionEvent
tool_call_result_msg = ToolCallExecutionEvent(
content=exec_results,
source=agent_name,
)
event_logger.debug(tool_call_result_msg)
await model_context.add_message(FunctionExecutionResultMessage(content=exec_results))
inner_messages.append(tool_call_result_msg)
yield tool_call_result_msg
# STEP 4C: Check for handoff
handoff_output = cls._check_and_handle_handoff(
model_result=current_model_result,
executed_calls_and_results=executed_calls_and_results,
inner_messages=inner_messages,
handoffs=handoffs,
agent_name=agent_name,
)
if handoff_output:
yield handoff_output
return
# STEP 4D: Check if we should continue the loop.
# If we are on the last iteration, break to the summary/reflection step.
if loop_iteration == max_tool_iterations - 1:
break
# Continue the loop: make another model call using _call_llm
next_model_result: Optional[CreateResult] = None
async for llm_output in cls._call_llm(
model_client=model_client,
model_client_stream=model_client_stream,
system_messages=system_messages,
model_context=model_context,
workbench=workbench,
handoff_tools=handoff_tools,
agent_name=agent_name,
cancellation_token=cancellation_token,
output_content_type=output_content_type,
message_id=message_id, # Use same message ID for consistency
):
if isinstance(llm_output, CreateResult):
next_model_result = llm_output
else:
# Streaming chunk event
yield llm_output
assert next_model_result is not None, "No model result was produced in tool call loop."
current_model_result = next_model_result
# Yield thought event if present
if current_model_result.thought:
thought_event = ThoughtEvent(content=current_model_result.thought, source=agent_name)
yield thought_event
inner_messages.append(thought_event)
# Add the assistant message to the model context (including thought if present)
await model_context.add_message(
AssistantMessage(
content=current_model_result.content,
source=agent_name,
thought=getattr(current_model_result, "thought", None),
)
)
# After the loop, reflect or summarize tool results
if reflect_on_tool_use:
async for reflection_response in cls._reflect_on_tool_use_flow(
system_messages=system_messages,
model_client=model_client,
model_client_stream=model_client_stream,
model_context=model_context,
workbench=workbench,
handoff_tools=handoff_tools,
agent_name=agent_name,
inner_messages=inner_messages,
output_content_type=output_content_type,
cancellation_token=cancellation_token,
):
yield reflection_response
else:
yield cls._summarize_tool_use(
executed_calls_and_results=executed_calls_and_results,
inner_messages=inner_messages,
handoffs=handoffs,
tool_call_summary_format=tool_call_summary_format,
tool_call_summary_formatter=tool_call_summary_formatter,
agent_name=agent_name,
)
return
@staticmethod
def _check_and_handle_handoff(
model_result: CreateResult,
executed_calls_and_results: List[Tuple[FunctionCall, FunctionExecutionResult]],
inner_messages: List[BaseAgentEvent | BaseChatMessage],
handoffs: Dict[str, HandoffBase],
agent_name: str,
) -> Optional[Response]:
"""Check for and handle any handoff requests in the model result.
Args:
model_result: Result from model inference
executed_calls_and_results: List of executed tool calls and their results
inner_messages: List of messages generated during processing
handoffs: Dictionary of available handoff configurations
agent_name: Name of the agent
Returns:
Optional response containing handoff message if handoff detected
"""
handoff_reqs = [
call for call in model_result.content if isinstance(call, FunctionCall) and call.name in handoffs
]
if len(handoff_reqs) > 0:
# We have at least one handoff function call
selected_handoff = handoffs[handoff_reqs[0].name]
if len(handoff_reqs) > 1:
warnings.warn(
(
f"Multiple handoffs detected. Only the first is executed: "
f"{[handoffs[c.name].name for c in handoff_reqs]}. "
"Disable parallel tool calls in the model client to avoid this warning."
),
stacklevel=2,
)
# Collect normal tool calls (not handoff) into the handoff context
tool_calls: List[FunctionCall] = []
tool_call_results: List[FunctionExecutionResult] = []
# Collect the results returned by handoff_tool. By default, the message attribute will returned.
selected_handoff_message = selected_handoff.message
for exec_call, exec_result in executed_calls_and_results:
if exec_call.name not in handoffs:
tool_calls.append(exec_call)
tool_call_results.append(exec_result)
elif exec_call.name == selected_handoff.name:
selected_handoff_message = exec_result.content
handoff_context: List[LLMMessage] = []
if len(tool_calls) > 0:
# Include the thought in the AssistantMessage if model_result has it
handoff_context.append(
AssistantMessage(
content=tool_calls,
source=agent_name,
thought=getattr(model_result, "thought", None),
)
)
handoff_context.append(FunctionExecutionResultMessage(content=tool_call_results))
elif model_result.thought:
# If no tool calls, but a thought exists, include it in the context
handoff_context.append(
AssistantMessage(
content=model_result.thought,
source=agent_name,
)
)
# Return response for the first handoff
return Response(
chat_message=HandoffMessage(
content=selected_handoff_message,
target=selected_handoff.target,
source=agent_name,
context=handoff_context,
),
inner_messages=inner_messages,
)
return None
@classmethod
async def _reflect_on_tool_use_flow(
cls,
system_messages: List[SystemMessage],
model_client: ChatCompletionClient,
model_client_stream: bool,
model_context: ChatCompletionContext,
workbench: Sequence[Workbench],
handoff_tools: List[BaseTool[Any, Any]],
agent_name: str,
inner_messages: List[BaseAgentEvent | BaseChatMessage],
output_content_type: type[BaseModel] | None,
cancellation_token: CancellationToken,
) -> AsyncGenerator[Response | ModelClientStreamingChunkEvent | ThoughtEvent, None]:
"""
If reflect_on_tool_use=True, we do another inference based on tool results
and yield the final text response (or streaming chunks).
"""
all_messages = system_messages + await model_context.get_messages()
llm_messages = cls._get_compatible_context(model_client=model_client, messages=all_messages)
reflection_result: Optional[CreateResult] = None
# Generate a message ID for correlation between chunks and final message in reflection flow
reflection_message_id = str(uuid.uuid4())
if model_client_stream:
async for chunk in model_client.create_stream(
llm_messages,
json_output=output_content_type,
cancellation_token=cancellation_token,
tool_choice="none", # Do not use tools in reflection flow.
):
if isinstance(chunk, CreateResult):
reflection_result = chunk
elif isinstance(chunk, str):
yield ModelClientStreamingChunkEvent(
content=chunk, source=agent_name, full_message_id=reflection_message_id
)
else:
raise RuntimeError(f"Invalid chunk type: {type(chunk)}")
else:
reflection_result = await model_client.create(
llm_messages,
json_output=output_content_type,
cancellation_token=cancellation_token,
tool_choice="none", # Do not use tools in reflection flow.
)
if not reflection_result or not isinstance(reflection_result.content, str):
raise RuntimeError("Reflect on tool use produced no valid text response.")
# --- NEW: If the reflection produced a thought, yield it ---
if reflection_result.thought:
thought_event = ThoughtEvent(content=reflection_result.thought, source=agent_name)
yield thought_event
inner_messages.append(thought_event)
# Add to context (including thought if present)
await model_context.add_message(
AssistantMessage(
content=reflection_result.content,
source=agent_name,
thought=getattr(reflection_result, "thought", None),
)
)
if output_content_type:
content = output_content_type.model_validate_json(reflection_result.content)
yield Response(
chat_message=StructuredMessage[output_content_type]( # type: ignore[valid-type]
content=content,
source=agent_name,
models_usage=reflection_result.usage,
id=reflection_message_id,
),
inner_messages=inner_messages,
)
else:
yield Response(
chat_message=TextMessage(
content=reflection_result.content,
source=agent_name,
models_usage=reflection_result.usage,
id=reflection_message_id,
),
inner_messages=inner_messages,
)
@staticmethod
def _summarize_tool_use(
executed_calls_and_results: List[Tuple[FunctionCall, FunctionExecutionResult]],
inner_messages: List[BaseAgentEvent | BaseChatMessage],
handoffs: Dict[str, HandoffBase],
tool_call_summary_format: str,
tool_call_summary_formatter: Callable[[FunctionCall, FunctionExecutionResult], str] | None,
agent_name: str,
) -> Response:
"""
If reflect_on_tool_use=False, create a summary message of all tool calls.
"""
# Filter out calls which were actually handoffs
normal_tool_calls = [(call, result) for call, result in executed_calls_and_results if call.name not in handoffs]
def default_tool_call_summary_formatter(call: FunctionCall, result: FunctionExecutionResult) -> str:
return tool_call_summary_format.format(
tool_name=call.name,
arguments=call.arguments,
result=result.content,
is_error=result.is_error,
)
summary_formatter = tool_call_summary_formatter or default_tool_call_summary_formatter
tool_call_summaries = [summary_formatter(call, result) for call, result in normal_tool_calls]
tool_call_summary = "\n".join(tool_call_summaries)
return Response(
chat_message=ToolCallSummaryMessage(
content=tool_call_summary,
source=agent_name,
tool_calls=[call for call, _ in normal_tool_calls],
results=[result for _, result in normal_tool_calls],
),
inner_messages=inner_messages,
)
@staticmethod
async def _execute_tool_call(
tool_call: FunctionCall,
workbench: Sequence[Workbench],
handoff_tools: List[BaseTool[Any, Any]],
agent_name: str,
cancellation_token: CancellationToken,
stream: asyncio.Queue[BaseAgentEvent | BaseChatMessage | None],
) -> Tuple[FunctionCall, FunctionExecutionResult]:
"""Execute a single tool call and return the result."""
# Load the arguments from the tool call.
try:
arguments = json.loads(tool_call.arguments)
except json.JSONDecodeError as e:
return (
tool_call,
FunctionExecutionResult(
content=f"Error: {e}",
call_id=tool_call.id,
is_error=True,
name=tool_call.name,
),
)
# Check if the tool call is a handoff.
# TODO: consider creating a combined workbench to handle both handoff and normal tools.
for handoff_tool in handoff_tools:
if tool_call.name == handoff_tool.name:
# Run handoff tool call.
result = await handoff_tool.run_json(arguments, cancellation_token, call_id=tool_call.id)
result_as_str = handoff_tool.return_value_as_string(result)
return (
tool_call,
FunctionExecutionResult(
content=result_as_str,
call_id=tool_call.id,
is_error=False,
name=tool_call.name,
),
)
# Handle normal tool call using workbench.
for wb in workbench:
tools = await wb.list_tools()
if any(t["name"] == tool_call.name for t in tools):
if isinstance(wb, StaticStreamWorkbench):
tool_result: ToolResult | None = None
async for event in wb.call_tool_stream(
name=tool_call.name,
arguments=arguments,
cancellation_token=cancellation_token,
call_id=tool_call.id,
):
if isinstance(event, ToolResult):
tool_result = event
elif isinstance(event, BaseAgentEvent) or isinstance(event, BaseChatMessage):
await stream.put(event)
else:
warnings.warn(
f"Unexpected event type: {type(event)} in tool call streaming.",
UserWarning,
stacklevel=2,
)
assert isinstance(tool_result, ToolResult), "Tool result should not be None in streaming mode."
else:
tool_result = await wb.call_tool(
name=tool_call.name,
arguments=arguments,
cancellation_token=cancellation_token,
call_id=tool_call.id,
)
return (
tool_call,
FunctionExecutionResult(
content=tool_result.to_text(),
call_id=tool_call.id,
is_error=tool_result.is_error,
name=tool_call.name,
),
)
return (
tool_call,
FunctionExecutionResult(
content=f"Error: tool '{tool_call.name}' not found in any workbench",
call_id=tool_call.id,
is_error=True,
name=tool_call.name,
),
)
async def on_reset(self, cancellation_token: CancellationToken) -> None:
"""Reset the assistant agent to its initialization state."""
await self._model_context.clear()
async def save_state(self) -> Mapping[str, Any]:
"""Save the current state of the assistant agent."""
model_context_state = await self._model_context.save_state()
return AssistantAgentState(llm_context=model_context_state).model_dump()
async def load_state(self, state: Mapping[str, Any]) -> None:
"""Load the state of the assistant agent"""
assistant_agent_state = AssistantAgentState.model_validate(state)
# Load the model context state.
await self._model_context.load_state(assistant_agent_state.llm_context)
@staticmethod
def _get_compatible_context(model_client: ChatCompletionClient, messages: List[LLMMessage]) -> Sequence[LLMMessage]:
"""Ensure that the messages are compatible with the underlying client, by removing images if needed."""
if model_client.model_info["vision"]:
return messages
else:
return remove_images(messages)
def _to_config(self) -> AssistantAgentConfig:
"""Convert the assistant agent to a declarative config."""
return AssistantAgentConfig(
name=self.name,
model_client=self._model_client.dump_component(),
tools=None, # versionchanged:: v0.5.5 Now tools are not serialized, Cause they are part of the workbench.
workbench=[wb.dump_component() for wb in self._workbench] if self._workbench else None,
handoffs=list(self._handoffs.values()) if self._handoffs else None,
model_context=self._model_context.dump_component(),
memory=[memory.dump_component() for memory in self._memory] if self._memory else None,
description=self.description,
system_message=self._system_messages[0].content
if self._system_messages and isinstance(self._system_messages[0].content, str)
else None,
model_client_stream=self._model_client_stream,
reflect_on_tool_use=self._reflect_on_tool_use,
max_tool_iterations=self._max_tool_iterations,
tool_call_summary_format=self._tool_call_summary_format,
structured_message_factory=self._structured_message_factory.dump_component()
if self._structured_message_factory
else None,
metadata=self._metadata,
)
@classmethod
def _from_config(cls, config: AssistantAgentConfig) -> Self:
"""Create an assistant agent from a declarative config."""
if config.structured_message_factory:
structured_message_factory = StructuredMessageFactory.load_component(config.structured_message_factory)
format_string = structured_message_factory.format_string
output_content_type = structured_message_factory.ContentModel
else:
format_string = None
output_content_type = None
return cls(
name=config.name,
model_client=ChatCompletionClient.load_component(config.model_client),
workbench=[Workbench.load_component(wb) for wb in config.workbench] if config.workbench else None,
handoffs=config.handoffs,
model_context=ChatCompletionContext.load_component(config.model_context) if config.model_context else None,
tools=[BaseTool.load_component(tool) for tool in config.tools] if config.tools else None,
memory=[Memory.load_component(memory) for memory in config.memory] if config.memory else None,
description=config.description,
system_message=config.system_message,
model_client_stream=config.model_client_stream,
reflect_on_tool_use=config.reflect_on_tool_use,
max_tool_iterations=config.max_tool_iterations,
tool_call_summary_format=config.tool_call_summary_format,
output_content_type=output_content_type,
output_content_type_format=format_string,
metadata=config.metadata,
)
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