diff --git a/python/packages/autogen-agentchat/src/autogen_agentchat/agents/_assistant_agent.py b/python/packages/autogen-agentchat/src/autogen_agentchat/agents/_assistant_agent.py
index e7ff6cc291a3..c46b994aa62d 100644
--- a/python/packages/autogen-agentchat/src/autogen_agentchat/agents/_assistant_agent.py
+++ b/python/packages/autogen-agentchat/src/autogen_agentchat/agents/_assistant_agent.py
@@ -31,7 +31,6 @@
LLMMessage,
ModelFamily,
SystemMessage,
- UserMessage,
)
from autogen_core.tools import BaseTool, FunctionTool
from pydantic import BaseModel
@@ -814,14 +813,13 @@ async def _add_messages_to_context(
messages: Sequence[ChatMessage],
) -> None:
"""
- Add incoming user (and possibly handoff) messages to the model context.
+ Add incoming messages to the model context.
"""
for msg in messages:
if isinstance(msg, HandoffMessage):
- # Add handoff context to the model context.
- for context_msg in msg.context:
- await model_context.add_message(context_msg)
- await model_context.add_message(UserMessage(content=msg.content, source=msg.source))
+ 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(
diff --git a/python/packages/autogen-agentchat/src/autogen_agentchat/agents/_base_chat_agent.py b/python/packages/autogen-agentchat/src/autogen_agentchat/agents/_base_chat_agent.py
index 94b89235df89..375e296c23bb 100644
--- a/python/packages/autogen-agentchat/src/autogen_agentchat/agents/_base_chat_agent.py
+++ b/python/packages/autogen-agentchat/src/autogen_agentchat/agents/_base_chat_agent.py
@@ -7,7 +7,6 @@
from ..base import ChatAgent, Response, TaskResult
from ..messages import (
AgentEvent,
- BaseChatMessage,
ChatMessage,
ModelClientStreamingChunkEvent,
TextMessage,
@@ -121,7 +120,7 @@ async def run(
text_msg = TextMessage(content=task, source="user")
input_messages.append(text_msg)
output_messages.append(text_msg)
- elif isinstance(task, BaseChatMessage):
+ elif isinstance(task, ChatMessage):
input_messages.append(task)
output_messages.append(task)
else:
@@ -129,7 +128,7 @@ async def run(
raise ValueError("Task list cannot be empty.")
# Task is a sequence of messages.
for msg in task:
- if isinstance(msg, BaseChatMessage):
+ if isinstance(msg, ChatMessage):
input_messages.append(msg)
output_messages.append(msg)
else:
@@ -159,7 +158,7 @@ async def run_stream(
input_messages.append(text_msg)
output_messages.append(text_msg)
yield text_msg
- elif isinstance(task, BaseChatMessage):
+ elif isinstance(task, ChatMessage):
input_messages.append(task)
output_messages.append(task)
yield task
@@ -167,7 +166,7 @@ async def run_stream(
if not task:
raise ValueError("Task list cannot be empty.")
for msg in task:
- if isinstance(msg, BaseChatMessage):
+ if isinstance(msg, ChatMessage):
input_messages.append(msg)
output_messages.append(msg)
yield msg
diff --git a/python/packages/autogen-agentchat/src/autogen_agentchat/agents/_code_executor_agent.py b/python/packages/autogen-agentchat/src/autogen_agentchat/agents/_code_executor_agent.py
index 089daa2a15f0..e94ce4550efb 100644
--- a/python/packages/autogen-agentchat/src/autogen_agentchat/agents/_code_executor_agent.py
+++ b/python/packages/autogen-agentchat/src/autogen_agentchat/agents/_code_executor_agent.py
@@ -21,7 +21,9 @@ class CodeExecutorAgentConfig(BaseModel):
class CodeExecutorAgent(BaseChatAgent, Component[CodeExecutorAgentConfig]):
- """An agent that extracts and executes code snippets found in received messages and returns the output.
+ """An agent that extracts and executes code snippets found in received
+ :class:`~autogen_agentchat.messages.TextMessage` messages and returns the output
+ of the code execution.
It is typically used within a team with another agent that generates code snippets to be executed.
diff --git a/python/packages/autogen-agentchat/src/autogen_agentchat/agents/_society_of_mind_agent.py b/python/packages/autogen-agentchat/src/autogen_agentchat/agents/_society_of_mind_agent.py
index 2eba918714b7..ac8f539653e3 100644
--- a/python/packages/autogen-agentchat/src/autogen_agentchat/agents/_society_of_mind_agent.py
+++ b/python/packages/autogen-agentchat/src/autogen_agentchat/agents/_society_of_mind_agent.py
@@ -1,7 +1,7 @@
from typing import Any, AsyncGenerator, List, Mapping, Sequence
from autogen_core import CancellationToken, Component, ComponentModel
-from autogen_core.models import ChatCompletionClient, LLMMessage, SystemMessage, UserMessage
+from autogen_core.models import ChatCompletionClient, LLMMessage, SystemMessage
from pydantic import BaseModel
from typing_extensions import Self
@@ -11,7 +11,6 @@
from ..base import TaskResult, Team
from ..messages import (
AgentEvent,
- BaseChatMessage,
ChatMessage,
ModelClientStreamingChunkEvent,
TextMessage,
@@ -167,13 +166,9 @@ async def on_messages_stream(
else:
# Generate a response using the model client.
llm_messages: List[LLMMessage] = [SystemMessage(content=self._instruction)]
- llm_messages.extend(
- [
- UserMessage(content=message.content, source=message.source)
- for message in inner_messages
- if isinstance(message, BaseChatMessage)
- ]
- )
+ for message in messages:
+ if isinstance(message, ChatMessage):
+ llm_messages.append(message.to_model_message())
llm_messages.append(SystemMessage(content=self._response_prompt))
completion = await self._model_client.create(messages=llm_messages, cancellation_token=cancellation_token)
assert isinstance(completion.content, str)
diff --git a/python/packages/autogen-agentchat/src/autogen_agentchat/agents/_user_proxy_agent.py b/python/packages/autogen-agentchat/src/autogen_agentchat/agents/_user_proxy_agent.py
index 3ca0ec890324..221832a8a70e 100644
--- a/python/packages/autogen-agentchat/src/autogen_agentchat/agents/_user_proxy_agent.py
+++ b/python/packages/autogen-agentchat/src/autogen_agentchat/agents/_user_proxy_agent.py
@@ -82,6 +82,7 @@ async def simple_user_agent():
cancellation_token=CancellationToken(),
)
)
+ assert isinstance(response.chat_message, TextMessage)
print(f"Your name is {response.chat_message.content}")
Example:
@@ -117,6 +118,7 @@ async def cancellable_user_agent():
)
)
response = await agent_task
+ assert isinstance(response.chat_message, TextMessage)
print(f"Your name is {response.chat_message.content}")
except Exception as e:
print(f"Exception: {e}")
diff --git a/python/packages/autogen-agentchat/src/autogen_agentchat/conditions/_terminations.py b/python/packages/autogen-agentchat/src/autogen_agentchat/conditions/_terminations.py
index 7ccddd1f6da4..c4ff24836abd 100644
--- a/python/packages/autogen-agentchat/src/autogen_agentchat/conditions/_terminations.py
+++ b/python/packages/autogen-agentchat/src/autogen_agentchat/conditions/_terminations.py
@@ -11,7 +11,6 @@
BaseChatMessage,
ChatMessage,
HandoffMessage,
- MultiModalMessage,
StopMessage,
TextMessage,
ToolCallExecutionEvent,
@@ -137,18 +136,12 @@ async def __call__(self, messages: Sequence[AgentEvent | ChatMessage]) -> StopMe
if self._sources is not None and message.source not in self._sources:
continue
- if isinstance(message.content, str) and self._termination_text in message.content:
+ content = message.to_text()
+ if self._termination_text in content:
self._terminated = True
return StopMessage(
content=f"Text '{self._termination_text}' mentioned", source="TextMentionTermination"
)
- elif isinstance(message, MultiModalMessage):
- for item in message.content:
- if isinstance(item, str) and self._termination_text in item:
- self._terminated = True
- return StopMessage(
- content=f"Text '{self._termination_text}' mentioned", source="TextMentionTermination"
- )
return None
async def reset(self) -> None:
diff --git a/python/packages/autogen-agentchat/src/autogen_agentchat/messages.py b/python/packages/autogen-agentchat/src/autogen_agentchat/messages.py
index 89500a50e344..f03ae61e239d 100644
--- a/python/packages/autogen-agentchat/src/autogen_agentchat/messages.py
+++ b/python/packages/autogen-agentchat/src/autogen_agentchat/messages.py
@@ -1,21 +1,69 @@
"""
This module defines various message types used for agent-to-agent communication.
-Each message type inherits either from the BaseChatMessage class or BaseAgentEvent
+Each message type inherits either from the ChatMessage class or BaseAgentEvent
class and includes specific fields relevant to the type of message being sent.
"""
-from abc import ABC
-from typing import Dict, List, Literal
+from abc import ABC, abstractmethod
+from typing import Any, Dict, Generic, List, Literal, Mapping, TypeVar
from autogen_core import FunctionCall, Image
from autogen_core.memory import MemoryContent
-from autogen_core.models import FunctionExecutionResult, LLMMessage, RequestUsage
-from pydantic import BaseModel, ConfigDict, Field
-from typing_extensions import Annotated
+from autogen_core.models import FunctionExecutionResult, LLMMessage, RequestUsage, UserMessage
+from pydantic import BaseModel, ConfigDict, computed_field
+from typing_extensions import Self
class BaseMessage(BaseModel, ABC):
- """Base class for all message types."""
+ """Base class for all message types in AgentChat. This is an abstract class
+ with default implementations for serialization and deserialization.
+
+ .. warning::
+
+ If you want to create a new message type, do not inherit from this class.
+ Instead, inherit from :class:`ChatMessage` or :class:`AgentEvent`
+ to clarify the purpose of the message type.
+
+ """
+
+ @computed_field
+ def type(self) -> str:
+ """The class name of this message."""
+ return self.__class__.__name__
+
+ def dump(self) -> Mapping[str, Any]:
+ """Convert the message to a JSON-serializable dictionary.
+
+ The default implementation uses the Pydantic model's `model_dump` method.
+
+ If you want to customize the serialization, override this method.
+ """
+ return self.model_dump()
+
+ @classmethod
+ def load(cls, data: Mapping[str, Any]) -> Self:
+ """Create a message from a dictionary of JSON-serializable data.
+
+ The default implementation uses the Pydantic model's `model_validate` method.
+ If you want to customize the deserialization, override this method.
+ """
+ return cls.model_validate(data)
+
+
+class ChatMessage(BaseMessage, ABC):
+ """Base class for chat messages.
+
+ .. note::
+
+ If you want to create a new message type that is used for agent-to-agent
+ communication, inherit from this class, or simply use
+ :class:`StructuredMessage` if your content type is a subclass of
+ Pydantic BaseModel.
+
+ This class is used for messages that are sent between agents in a chat
+ conversation. Agents are expected to process the content of the
+ message using models and return a response as another :class:`ChatMessage`.
+ """
source: str
"""The name of the agent that sent this message."""
@@ -28,89 +76,231 @@ class BaseMessage(BaseModel, ABC):
model_config = ConfigDict(arbitrary_types_allowed=True)
+ @abstractmethod
+ def to_text(self) -> str:
+ """Convert the content of the message to a string-only representation
+ that can be rendered in the console and inspected by the user or conditions.
+
+ This is not used for creating text-only content for models.
+ For :class:`ChatMessage` types, use :meth:`to_model_text` instead."""
+ ...
-class BaseChatMessage(BaseMessage, ABC):
- """Base class for chat messages."""
+ @abstractmethod
+ def to_model_text(self) -> str:
+ """Convert the content of the message to text-only representation.
+ This is used for creating text-only content for models.
- pass
+ This is not used for rendering the message in console. For that, use
+ :meth:`~BaseMessage.to_text`.
+ The difference between this and :meth:`to_model_message` is that this
+ is used to construct parts of the a message for the model client,
+ while :meth:`to_model_message` is used to create a complete message
+ for the model client.
+ """
+ ...
-class BaseAgentEvent(BaseMessage, ABC):
- """Base class for agent events."""
+ @abstractmethod
+ def to_model_message(self) -> UserMessage:
+ """Convert the message content to a :class:`~autogen_core.models.UserMessage`
+ for use with model client, e.g., :class:`~autogen_core.models.ChatCompletionClient`."""
+ ...
- pass
+class TextChatMessage(ChatMessage, ABC):
+ """Base class for all text-only :class:`ChatMessage` types.
+ It has implementations for :meth:`to_text`, :meth:`to_model_text`,
+ and :meth:`to_model_message` methods.
-class TextMessage(BaseChatMessage):
- """A text message."""
+ Inherit from this class if your message content type is a string.
+ """
content: str
"""The content of the message."""
- type: Literal["TextMessage"] = "TextMessage"
+ def to_text(self) -> str:
+ return self.content
+
+ def to_model_text(self) -> str:
+ return self.content
+
+ def to_model_message(self) -> UserMessage:
+ return UserMessage(content=self.content, source=self.source)
+
+
+class AgentEvent(BaseMessage, ABC):
+ """Base class for agent events.
+
+ .. note::
+
+ If you want to create a new message type for signaling observable events
+ to user and application, inherit from this class.
+
+ Agent events are used to signal actions and thoughts produced by agents
+ and teams to user and applications. They are not used for agent-to-agent
+ communication and are not expected to be processed by other agents.
+
+ You should override the :meth:`to_text` method if you want to provide
+ a custom rendering of the content.
+ """
+
+ source: str
+ """The name of the agent that sent this message."""
+
+ models_usage: RequestUsage | None = None
+ """The model client usage incurred when producing this message."""
+
+ metadata: Dict[str, str] = {}
+ """Additional metadata about the message."""
+
+ model_config = ConfigDict(arbitrary_types_allowed=True)
+
+ @abstractmethod
+ def to_text(self) -> str:
+ """Convert the content of the message to a string-only representation
+ that can be rendered in the console and inspected by the user.
+
+ This is not used for creating text-only content for models.
+ For :class:`ChatMessage` types, use :meth:`to_model_text` instead."""
+ ...
+
+
+StructuredContentType = TypeVar("StructuredContentType", bound=BaseModel, covariant=True)
+"""Type variable for structured content types."""
+
+
+class StructuredMessage(ChatMessage, Generic[StructuredContentType]):
+ """A :class:`ChatMessage` type with an unspecified content type.
+ To create a new structured message type, specify the content type
+ as a subclass of `Pydantic BaseModel <https://docs.pydantic.dev/latest/concepts/models/>`_.
-class MultiModalMessage(BaseChatMessage):
+ .. code-block:: python
+
+ from pydantic import BaseModel
+ from autogen_agentchat.messages import StructuredMessage
+
+
+ class MyMessageContent(BaseModel):
+ text: str
+ number: int
+
+
+ message = StructuredMessage[MyMessageContent](
+ content=MyMessageContent(text="Hello", number=42),
+ source="agent1",
+ )
+
+ print(message.to_text()) # {"text": "Hello", "number": 42}
+
+ """
+
+ content: StructuredContentType
+ """The content of the message. Must be a subclass of
+ `Pydantic BaseModel <https://docs.pydantic.dev/latest/concepts/models/>`_."""
+
+ def to_text(self) -> str:
+ return self.content.model_dump_json(indent=2)
+
+ def to_model_text(self) -> str:
+ return self.content.model_dump_json()
+
+ def to_model_message(self) -> UserMessage:
+ return UserMessage(
+ content=self.content.model_dump_json(),
+ source=self.source,
+ )
+
+
+class TextMessage(TextChatMessage):
+ """A text message with string-only content."""
+
+ ...
+
+
+class MultiModalMessage(ChatMessage):
"""A multimodal message."""
content: List[str | Image]
"""The content of the message."""
- type: Literal["MultiModalMessage"] = "MultiModalMessage"
-
-
-class StopMessage(BaseChatMessage):
+ def to_model_text(self, image_placeholder: str | None = "[image]") -> str:
+ """Convert the content of the message to a string-only representation.
+ If an image is present, it will be replaced with the image placeholder
+ by default, otherwise it will be a base64 string when set to None.
+ """
+ text = ""
+ for c in self.content:
+ if isinstance(c, str):
+ text += c
+ elif isinstance(c, Image):
+ if image_placeholder is not None:
+ text += f" {image_placeholder}"
+ else:
+ text += f" {c.to_base64()}"
+ return text
+
+ def to_text(self, iterm: bool = False) -> str:
+ result: List[str] = []
+ for c in self.content:
+ if isinstance(c, str):
+ result.append(c)
+ else:
+ if iterm:
+ # iTerm2 image rendering protocol: https://iterm2.com/documentation-images.html
+ image_data = c.to_base64()
+ result.append(f"\033]1337;File=inline=1:{image_data}\a\n")
+ else:
+ result.append("<image>")
+ return "\n".join(result)
+
+ def to_model_message(self) -> UserMessage:
+ return UserMessage(content=self.content, source=self.source)
+
+
+class StopMessage(TextChatMessage):
"""A message requesting stop of a conversation."""
- content: str
- """The content for the stop message."""
-
- type: Literal["StopMessage"] = "StopMessage"
+ ...
-class HandoffMessage(BaseChatMessage):
+class HandoffMessage(TextChatMessage):
"""A message requesting handoff of a conversation to another agent."""
target: str
"""The name of the target agent to handoff to."""
- content: str
- """The handoff message to the target agent."""
-
context: List[LLMMessage] = []
"""The model context to be passed to the target agent."""
- type: Literal["HandoffMessage"] = "HandoffMessage"
+
+class ToolCallSummaryMessage(TextChatMessage):
+ """A message signaling the summary of tool call results."""
+
+ ...
-class ToolCallRequestEvent(BaseAgentEvent):
+class ToolCallRequestEvent(AgentEvent):
"""An event signaling a request to use tools."""
content: List[FunctionCall]
"""The tool calls."""
- type: Literal["ToolCallRequestEvent"] = "ToolCallRequestEvent"
+ def to_text(self) -> str:
+ return str(self.content)
-class ToolCallExecutionEvent(BaseAgentEvent):
+class ToolCallExecutionEvent(AgentEvent):
"""An event signaling the execution of tool calls."""
content: List[FunctionExecutionResult]
"""The tool call results."""
- type: Literal["ToolCallExecutionEvent"] = "ToolCallExecutionEvent"
-
-
-class ToolCallSummaryMessage(BaseChatMessage):
- """A message signaling the summary of tool call results."""
-
- content: str
- """Summary of the the tool call results."""
-
- type: Literal["ToolCallSummaryMessage"] = "ToolCallSummaryMessage"
+ def to_text(self) -> str:
+ return str(self.content)
-class UserInputRequestedEvent(BaseAgentEvent):
+class UserInputRequestedEvent(AgentEvent):
"""An event signaling a that the user proxy has requested user input. Published prior to invoking the input callback."""
request_id: str
@@ -119,60 +309,117 @@ class UserInputRequestedEvent(BaseAgentEvent):
content: Literal[""] = ""
"""Empty content for compat with consumers expecting a content field."""
- type: Literal["UserInputRequestedEvent"] = "UserInputRequestedEvent"
+ def to_text(self) -> str:
+ return str(self.content)
-class MemoryQueryEvent(BaseAgentEvent):
+class MemoryQueryEvent(AgentEvent):
"""An event signaling the results of memory queries."""
content: List[MemoryContent]
"""The memory query results."""
- type: Literal["MemoryQueryEvent"] = "MemoryQueryEvent"
+ def to_text(self) -> str:
+ return str(self.content)
-class ModelClientStreamingChunkEvent(BaseAgentEvent):
+class ModelClientStreamingChunkEvent(AgentEvent):
"""An event signaling a text output chunk from a model client in streaming mode."""
content: str
- """The partial text chunk."""
+ """A string chunk from the model client."""
- type: Literal["ModelClientStreamingChunkEvent"] = "ModelClientStreamingChunkEvent"
+ def to_text(self) -> str:
+ return self.content
-class ThoughtEvent(BaseAgentEvent):
- """An event signaling the thought process of an agent.
+class ThoughtEvent(AgentEvent):
+ """An event signaling the thought process of a model.
It is used to communicate the reasoning tokens generated by a reasoning model,
or the extra text content generated by a function call."""
content: str
- """The thought process."""
-
- type: Literal["ThoughtEvent"] = "ThoughtEvent"
-
-
-ChatMessage = Annotated[
- TextMessage | MultiModalMessage | StopMessage | ToolCallSummaryMessage | HandoffMessage, Field(discriminator="type")
-]
-"""Messages for agent-to-agent communication only."""
-
-
-AgentEvent = Annotated[
- ToolCallRequestEvent
- | ToolCallExecutionEvent
- | MemoryQueryEvent
- | UserInputRequestedEvent
- | ModelClientStreamingChunkEvent
- | ThoughtEvent,
- Field(discriminator="type"),
-]
-"""Events emitted by agents and teams when they work, not used for agent-to-agent communication."""
+ """The thought process of the model."""
+
+ def to_text(self) -> str:
+ return self.content
+
+
+class MessageFactory:
+ """:meta private:
+
+ A factory for creating messages from JSON-serializable dictionaries.
+
+ This is useful for deserializing messages from JSON data.
+ """
+
+ def __init__(self) -> None:
+ self._message_types: Dict[str, type[AgentEvent | ChatMessage]] = {}
+ # Register all message types.
+ self._message_types[TextMessage.__name__] = TextMessage
+ self._message_types[MultiModalMessage.__name__] = MultiModalMessage
+ self._message_types[StopMessage.__name__] = StopMessage
+ self._message_types[ToolCallSummaryMessage.__name__] = ToolCallSummaryMessage
+ self._message_types[HandoffMessage.__name__] = HandoffMessage
+ self._message_types[ToolCallRequestEvent.__name__] = ToolCallRequestEvent
+ self._message_types[ToolCallExecutionEvent.__name__] = ToolCallExecutionEvent
+ self._message_types[MemoryQueryEvent.__name__] = MemoryQueryEvent
+ self._message_types[UserInputRequestedEvent.__name__] = UserInputRequestedEvent
+ self._message_types[ModelClientStreamingChunkEvent.__name__] = ModelClientStreamingChunkEvent
+ self._message_types[ThoughtEvent.__name__] = ThoughtEvent
+
+ def is_registered(self, message_type: type[AgentEvent | ChatMessage]) -> bool:
+ """Check if a message type is registered with the factory."""
+ # Get the class name of the message type.
+ class_name = message_type.__name__
+ # Check if the class name is already registered.
+ return class_name in self._message_types
+
+ def register(self, message_type: type[AgentEvent | ChatMessage]) -> None:
+ """Register a new message type with the factory."""
+ if self.is_registered(message_type):
+ raise ValueError(f"Message type {message_type} is already registered.")
+ if not issubclass(message_type, ChatMessage) and not issubclass(message_type, AgentEvent):
+ raise ValueError(f"Message type {message_type} must be a subclass of ChatMessage or AgentEvent.")
+ # Get the class name of the
+ class_name = message_type.__name__
+ # Check if the class name is already registered.
+ # Register the message type.
+ self._message_types[class_name] = message_type
+
+ def create(self, data: Mapping[str, Any]) -> AgentEvent | ChatMessage:
+ """Create a message from a dictionary of JSON-serializable data."""
+ # Get the type of the message from the dictionary.
+ message_type = data.get("type")
+ if message_type not in self._message_types:
+ raise ValueError(f"Unknown message type: {message_type}")
+ if not isinstance(message_type, str):
+ raise ValueError(f"Message type must be a string, got {type(message_type)}")
+
+ # Get the class for the message type.
+ message_class = self._message_types[message_type]
+
+ # Create an instance of the message class.
+ assert issubclass(message_class, ChatMessage) or issubclass(message_class, AgentEvent)
+ return message_class.load(data)
+
+
+# For backward compatibility
+BaseAgentEvent = AgentEvent
+BaseChatMessage = ChatMessage
__all__ = [
"AgentEvent",
"BaseMessage",
"ChatMessage",
+ "BaseChatMessage",
+ "BaseAgentEvent",
+ "AgentEvent",
+ "TextChatMessage",
+ "ChatMessage",
+ "StructuredContentType",
+ "StructuredMessage",
"HandoffMessage",
"MultiModalMessage",
"StopMessage",
@@ -184,4 +431,5 @@ class ThoughtEvent(BaseAgentEvent):
"UserInputRequestedEvent",
"ModelClientStreamingChunkEvent",
"ThoughtEvent",
+ "MessageFactory",
]
diff --git a/python/packages/autogen-agentchat/src/autogen_agentchat/state/_states.py b/python/packages/autogen-agentchat/src/autogen_agentchat/state/_states.py
index 16ddbc7472d6..ecc7b5f7cae7 100644
--- a/python/packages/autogen-agentchat/src/autogen_agentchat/state/_states.py
+++ b/python/packages/autogen-agentchat/src/autogen_agentchat/state/_states.py
@@ -1,15 +1,7 @@
-from typing import Annotated, Any, List, Mapping, Optional
+from typing import Any, List, Mapping, Optional
from pydantic import BaseModel, Field
-from ..messages import (
- AgentEvent,
- ChatMessage,
-)
-
-# Ensures pydantic can distinguish between types of events & messages.
-_AgentMessage = Annotated[AgentEvent | ChatMessage, Field(discriminator="type")]
-
class BaseState(BaseModel):
"""Base class for all saveable state"""
@@ -35,7 +27,7 @@ class TeamState(BaseState):
class BaseGroupChatManagerState(BaseState):
"""Base state for all group chat managers."""
- message_thread: List[_AgentMessage] = Field(default_factory=list)
+ message_thread: List[Mapping[str, Any]] = Field(default_factory=list)
current_turn: int = Field(default=0)
type: str = Field(default="BaseGroupChatManagerState")
@@ -44,7 +36,7 @@ class ChatAgentContainerState(BaseState):
"""State for a container of chat agents."""
agent_state: Mapping[str, Any] = Field(default_factory=dict)
- message_buffer: List[ChatMessage] = Field(default_factory=list)
+ message_buffer: List[Mapping[str, Any]] = Field(default_factory=list)
type: str = Field(default="ChatAgentContainerState")
diff --git a/python/packages/autogen-agentchat/src/autogen_agentchat/teams/_group_chat/_base_group_chat.py b/python/packages/autogen-agentchat/src/autogen_agentchat/teams/_group_chat/_base_group_chat.py
index ff6731b03f4a..9e4f77a3a135 100644
--- a/python/packages/autogen-agentchat/src/autogen_agentchat/teams/_group_chat/_base_group_chat.py
+++ b/python/packages/autogen-agentchat/src/autogen_agentchat/teams/_group_chat/_base_group_chat.py
@@ -19,8 +19,8 @@
from ...base import ChatAgent, TaskResult, Team, TerminationCondition
from ...messages import (
AgentEvent,
- BaseChatMessage,
ChatMessage,
+ MessageFactory,
ModelClientStreamingChunkEvent,
StopMessage,
TextMessage,
@@ -50,6 +50,7 @@ def __init__(
termination_condition: TerminationCondition | None = None,
max_turns: int | None = None,
runtime: AgentRuntime | None = None,
+ custom_message_types: List[type[AgentEvent | ChatMessage]] | None = None,
):
if len(participants) == 0:
raise ValueError("At least one participant is required.")
@@ -59,6 +60,10 @@ def __init__(
self._base_group_chat_manager_class = group_chat_manager_class
self._termination_condition = termination_condition
self._max_turns = max_turns
+ self._message_factory = MessageFactory()
+ if custom_message_types is not None:
+ for message_type in custom_message_types:
+ self._message_factory.register(message_type)
# The team ID is a UUID that is used to identify the team and its participants
# in the agent runtime. It is used to create unique topic types for each participant.
@@ -115,6 +120,7 @@ def _create_group_chat_manager_factory(
output_message_queue: asyncio.Queue[AgentEvent | ChatMessage | GroupChatTermination],
termination_condition: TerminationCondition | None,
max_turns: int | None,
+ message_factory: MessageFactory,
) -> Callable[[], SequentialRoutedAgent]: ...
def _create_participant_factory(
@@ -122,9 +128,10 @@ def _create_participant_factory(
parent_topic_type: str,
output_topic_type: str,
agent: ChatAgent,
+ message_factory: MessageFactory,
) -> Callable[[], ChatAgentContainer]:
def _factory() -> ChatAgentContainer:
- container = ChatAgentContainer(parent_topic_type, output_topic_type, agent)
+ container = ChatAgentContainer(parent_topic_type, output_topic_type, agent, message_factory)
return container
return _factory
@@ -140,7 +147,9 @@ async def _init(self, runtime: AgentRuntime) -> None:
await ChatAgentContainer.register(
runtime,
type=agent_type,
- factory=self._create_participant_factory(self._group_topic_type, self._output_topic_type, participant),
+ factory=self._create_participant_factory(
+ self._group_topic_type, self._output_topic_type, participant, self._message_factory
+ ),
)
# Add subscriptions for the participant.
# The participant should be able to receive messages from its own topic.
@@ -162,6 +171,7 @@ async def _init(self, runtime: AgentRuntime) -> None:
output_message_queue=self._output_message_queue,
termination_condition=self._termination_condition,
max_turns=self._max_turns,
+ message_factory=self._message_factory,
),
)
# Add subscriptions for the group chat manager.
@@ -393,16 +403,27 @@ async def main() -> None:
pass
elif isinstance(task, str):
messages = [TextMessage(content=task, source="user")]
- elif isinstance(task, BaseChatMessage):
+ elif isinstance(task, ChatMessage):
messages = [task]
- else:
+ elif isinstance(task, list):
if not task:
raise ValueError("Task list cannot be empty.")
messages = []
for msg in task:
- if not isinstance(msg, BaseChatMessage):
+ if not isinstance(msg, ChatMessage):
raise ValueError("All messages in task list must be valid ChatMessage types")
messages.append(msg)
+ else:
+ raise ValueError("Task must be a string, a ChatMessage, or a list of ChatMessage.")
+ # Check if the messages types are registered with the message factory.
+ if messages is not None:
+ for msg in messages:
+ if not self._message_factory.is_registered(msg.__class__):
+ raise ValueError(
+ f"Message type {msg.__class__} is not registered with the message factory. "
+ "Please register it with the message factory by adding it to the "
+ "custom_message_types list when creating the team."
+ )
if self._is_running:
raise ValueError("The team is already running, it cannot run again until it is stopped.")
diff --git a/python/packages/autogen-agentchat/src/autogen_agentchat/teams/_group_chat/_base_group_chat_manager.py b/python/packages/autogen-agentchat/src/autogen_agentchat/teams/_group_chat/_base_group_chat_manager.py
index 0cd45633728a..59653e5f31a5 100644
--- a/python/packages/autogen-agentchat/src/autogen_agentchat/teams/_group_chat/_base_group_chat_manager.py
+++ b/python/packages/autogen-agentchat/src/autogen_agentchat/teams/_group_chat/_base_group_chat_manager.py
@@ -5,7 +5,7 @@
from autogen_core import DefaultTopicId, MessageContext, event, rpc
from ...base import TerminationCondition
-from ...messages import AgentEvent, ChatMessage, StopMessage
+from ...messages import AgentEvent, ChatMessage, MessageFactory, StopMessage
from ._events import (
GroupChatAgentResponse,
GroupChatMessage,
@@ -40,8 +40,9 @@ def __init__(
participant_names: List[str],
participant_descriptions: List[str],
output_message_queue: asyncio.Queue[AgentEvent | ChatMessage | GroupChatTermination],
- termination_condition: TerminationCondition | None = None,
- max_turns: int | None = None,
+ termination_condition: TerminationCondition | None,
+ max_turns: int | None,
+ message_factory: MessageFactory,
):
super().__init__(
description="Group chat manager",
@@ -73,6 +74,7 @@ def __init__(
raise ValueError("The maximum number of turns must be greater than 0.")
self._max_turns = max_turns
self._current_turn = 0
+ self._message_factory = message_factory
@rpc
async def handle_start(self, message: GroupChatStart, ctx: MessageContext) -> None:
diff --git a/python/packages/autogen-agentchat/src/autogen_agentchat/teams/_group_chat/_chat_agent_container.py b/python/packages/autogen-agentchat/src/autogen_agentchat/teams/_group_chat/_chat_agent_container.py
index 7c86556e257a..d4a2adda8e87 100644
--- a/python/packages/autogen-agentchat/src/autogen_agentchat/teams/_group_chat/_chat_agent_container.py
+++ b/python/packages/autogen-agentchat/src/autogen_agentchat/teams/_group_chat/_chat_agent_container.py
@@ -2,8 +2,9 @@
from autogen_core import DefaultTopicId, MessageContext, event, rpc
+from autogen_agentchat.messages import AgentEvent, ChatMessage, MessageFactory
+
from ...base import ChatAgent, Response
-from ...messages import ChatMessage
from ...state import ChatAgentContainerState
from ._events import (
GroupChatAgentResponse,
@@ -26,9 +27,13 @@ class ChatAgentContainer(SequentialRoutedAgent):
parent_topic_type (str): The topic type of the parent orchestrator.
output_topic_type (str): The topic type for the output.
agent (ChatAgent): The agent to delegate message handling to.
+ message_factory (MessageFactory): The message factory to use for
+ creating messages from JSON data.
"""
- def __init__(self, parent_topic_type: str, output_topic_type: str, agent: ChatAgent) -> None:
+ def __init__(
+ self, parent_topic_type: str, output_topic_type: str, agent: ChatAgent, message_factory: MessageFactory
+ ) -> None:
super().__init__(
description=agent.description,
sequential_message_types=[
@@ -42,17 +47,19 @@ def __init__(self, parent_topic_type: str, output_topic_type: str, agent: ChatAg
self._output_topic_type = output_topic_type
self._agent = agent
self._message_buffer: List[ChatMessage] = []
+ self._message_factory = message_factory
@event
async def handle_start(self, message: GroupChatStart, ctx: MessageContext) -> None:
"""Handle a start event by appending the content to the buffer."""
if message.messages is not None:
- self._message_buffer.extend(message.messages)
+ for msg in message.messages:
+ self._buffer_message(msg)
@event
async def handle_agent_response(self, message: GroupChatAgentResponse, ctx: MessageContext) -> None:
"""Handle an agent response event by appending the content to the buffer."""
- self._message_buffer.append(message.agent_response.chat_message)
+ self._buffer_message(message.agent_response.chat_message)
@rpc
async def handle_reset(self, message: GroupChatReset, ctx: MessageContext) -> None:
@@ -68,17 +75,10 @@ async def handle_request(self, message: GroupChatRequestPublish, ctx: MessageCon
response: Response | None = None
async for msg in self._agent.on_messages_stream(self._message_buffer, ctx.cancellation_token):
if isinstance(msg, Response):
- # Log the response.
- await self.publish_message(
- GroupChatMessage(message=msg.chat_message),
- topic_id=DefaultTopicId(type=self._output_topic_type),
- )
+ await self._log_message(msg.chat_message)
response = msg
else:
- # Log the message.
- await self.publish_message(
- GroupChatMessage(message=msg), topic_id=DefaultTopicId(type=self._output_topic_type)
- )
+ await self._log_message(msg)
if response is None:
raise ValueError("The agent did not produce a final response. Check the agent's on_messages_stream method.")
@@ -90,6 +90,21 @@ async def handle_request(self, message: GroupChatRequestPublish, ctx: MessageCon
cancellation_token=ctx.cancellation_token,
)
+ def _buffer_message(self, message: ChatMessage) -> None:
+ if not self._message_factory.is_registered(message.__class__):
+ raise ValueError(f"Message type {message.__class__} is not registered.")
+ # Buffer the message.
+ self._message_buffer.append(message)
+
+ async def _log_message(self, message: AgentEvent | ChatMessage) -> None:
+ if not self._message_factory.is_registered(message.__class__):
+ raise ValueError(f"Message type {message.__class__} is not registered.")
+ # Log the message.
+ await self.publish_message(
+ GroupChatMessage(message=message),
+ topic_id=DefaultTopicId(type=self._output_topic_type),
+ )
+
@rpc
async def handle_pause(self, message: GroupChatPause, ctx: MessageContext) -> None:
"""Handle a pause event by pausing the agent."""
@@ -105,10 +120,18 @@ async def on_unhandled_message(self, message: Any, ctx: MessageContext) -> None:
async def save_state(self) -> Mapping[str, Any]:
agent_state = await self._agent.save_state()
- state = ChatAgentContainerState(agent_state=agent_state, message_buffer=list(self._message_buffer))
+ state = ChatAgentContainerState(
+ agent_state=agent_state, message_buffer=[message.dump() for message in self._message_buffer]
+ )
return state.model_dump()
async def load_state(self, state: Mapping[str, Any]) -> None:
container_state = ChatAgentContainerState.model_validate(state)
- self._message_buffer = list(container_state.message_buffer)
+ self._message_buffer = []
+ for message_data in container_state.message_buffer:
+ message = self._message_factory.create(message_data)
+ if isinstance(message, ChatMessage):
+ self._message_buffer.append(message)
+ else:
+ raise ValueError(f"Invalid message type in message buffer: {type(message)}")
await self._agent.load_state(container_state.agent_state)
diff --git a/python/packages/autogen-agentchat/src/autogen_agentchat/teams/_group_chat/_magentic_one/_magentic_one_group_chat.py b/python/packages/autogen-agentchat/src/autogen_agentchat/teams/_group_chat/_magentic_one/_magentic_one_group_chat.py
index d391f7b62ff8..66ff53fe18e9 100644
--- a/python/packages/autogen-agentchat/src/autogen_agentchat/teams/_group_chat/_magentic_one/_magentic_one_group_chat.py
+++ b/python/packages/autogen-agentchat/src/autogen_agentchat/teams/_group_chat/_magentic_one/_magentic_one_group_chat.py
@@ -9,7 +9,7 @@
from .... import EVENT_LOGGER_NAME, TRACE_LOGGER_NAME
from ....base import ChatAgent, TerminationCondition
-from ....messages import AgentEvent, ChatMessage
+from ....messages import AgentEvent, ChatMessage, MessageFactory
from .._base_group_chat import BaseGroupChat
from .._events import GroupChatTermination
from ._magentic_one_orchestrator import MagenticOneOrchestrator
@@ -131,6 +131,7 @@ def _create_group_chat_manager_factory(
output_message_queue: asyncio.Queue[AgentEvent | ChatMessage | GroupChatTermination],
termination_condition: TerminationCondition | None,
max_turns: int | None,
+ message_factory: MessageFactory,
) -> Callable[[], MagenticOneOrchestrator]:
return lambda: MagenticOneOrchestrator(
name,
@@ -140,6 +141,7 @@ def _create_group_chat_manager_factory(
participant_names,
participant_descriptions,
max_turns,
+ message_factory,
self._model_client,
self._max_stalls,
self._final_answer_prompt,
diff --git a/python/packages/autogen-agentchat/src/autogen_agentchat/teams/_group_chat/_magentic_one/_magentic_one_orchestrator.py b/python/packages/autogen-agentchat/src/autogen_agentchat/teams/_group_chat/_magentic_one/_magentic_one_orchestrator.py
index bfef2b4ed184..d442c8acb6bf 100644
--- a/python/packages/autogen-agentchat/src/autogen_agentchat/teams/_group_chat/_magentic_one/_magentic_one_orchestrator.py
+++ b/python/packages/autogen-agentchat/src/autogen_agentchat/teams/_group_chat/_magentic_one/_magentic_one_orchestrator.py
@@ -18,6 +18,7 @@
AgentEvent,
ChatMessage,
HandoffMessage,
+ MessageFactory,
MultiModalMessage,
StopMessage,
TextMessage,
@@ -26,7 +27,7 @@
ToolCallSummaryMessage,
)
from ....state import MagenticOneOrchestratorState
-from ....utils import content_to_str, remove_images
+from ....utils import remove_images
from .._base_group_chat_manager import BaseGroupChatManager
from .._events import (
GroupChatAgentResponse,
@@ -61,6 +62,7 @@ def __init__(
participant_names: List[str],
participant_descriptions: List[str],
max_turns: int | None,
+ message_factory: MessageFactory,
model_client: ChatCompletionClient,
max_stalls: int,
final_answer_prompt: str,
@@ -77,6 +79,7 @@ def __init__(
output_message_queue,
termination_condition,
max_turns,
+ message_factory,
)
self._model_client = model_client
self._max_stalls = max_stalls
@@ -147,7 +150,7 @@ async def handle_start(self, message: GroupChatStart, ctx: MessageContext) -> No
# Create the initial task ledger
#################################
# Combine all message contents for task
- self._task = " ".join([content_to_str(msg.content) for msg in message.messages])
+ self._task = " ".join([msg.to_model_text() for msg in message.messages])
planning_conversation: List[LLMMessage] = []
# 1. GATHER FACTS
@@ -203,7 +206,7 @@ async def validate_group_state(self, messages: List[ChatMessage] | None) -> None
async def save_state(self) -> Mapping[str, Any]:
state = MagenticOneOrchestratorState(
- message_thread=list(self._message_thread),
+ message_thread=[msg.dump() for msg in self._message_thread],
current_turn=self._current_turn,
task=self._task,
facts=self._facts,
@@ -215,7 +218,7 @@ async def save_state(self) -> Mapping[str, Any]:
async def load_state(self, state: Mapping[str, Any]) -> None:
orchestrator_state = MagenticOneOrchestratorState.model_validate(state)
- self._message_thread = orchestrator_state.message_thread
+ self._message_thread = [self._message_factory.create(message) for message in orchestrator_state.message_thread]
self._current_turn = orchestrator_state.current_turn
self._task = orchestrator_state.task
self._facts = orchestrator_state.facts
diff --git a/python/packages/autogen-agentchat/src/autogen_agentchat/teams/_group_chat/_round_robin_group_chat.py b/python/packages/autogen-agentchat/src/autogen_agentchat/teams/_group_chat/_round_robin_group_chat.py
index 0e630df2d7cb..0f06d9aeec9e 100644
--- a/python/packages/autogen-agentchat/src/autogen_agentchat/teams/_group_chat/_round_robin_group_chat.py
+++ b/python/packages/autogen-agentchat/src/autogen_agentchat/teams/_group_chat/_round_robin_group_chat.py
@@ -6,7 +6,7 @@
from typing_extensions import Self
from ...base import ChatAgent, TerminationCondition
-from ...messages import AgentEvent, ChatMessage
+from ...messages import AgentEvent, ChatMessage, MessageFactory
from ...state import RoundRobinManagerState
from ._base_group_chat import BaseGroupChat
from ._base_group_chat_manager import BaseGroupChatManager
@@ -26,7 +26,8 @@ def __init__(
participant_descriptions: List[str],
output_message_queue: asyncio.Queue[AgentEvent | ChatMessage | GroupChatTermination],
termination_condition: TerminationCondition | None,
- max_turns: int | None = None,
+ max_turns: int | None,
+ message_factory: MessageFactory,
) -> None:
super().__init__(
name,
@@ -38,6 +39,7 @@ def __init__(
output_message_queue,
termination_condition,
max_turns,
+ message_factory,
)
self._next_speaker_index = 0
@@ -53,7 +55,7 @@ async def reset(self) -> None:
async def save_state(self) -> Mapping[str, Any]:
state = RoundRobinManagerState(
- message_thread=list(self._message_thread),
+ message_thread=[message.dump() for message in self._message_thread],
current_turn=self._current_turn,
next_speaker_index=self._next_speaker_index,
)
@@ -61,7 +63,7 @@ async def save_state(self) -> Mapping[str, Any]:
async def load_state(self, state: Mapping[str, Any]) -> None:
round_robin_state = RoundRobinManagerState.model_validate(state)
- self._message_thread = list(round_robin_state.message_thread)
+ self._message_thread = [self._message_factory.create(message) for message in round_robin_state.message_thread]
self._current_turn = round_robin_state.current_turn
self._next_speaker_index = round_robin_state.next_speaker_index
@@ -164,6 +166,7 @@ def __init__(
termination_condition: TerminationCondition | None = None,
max_turns: int | None = None,
runtime: AgentRuntime | None = None,
+ custom_message_types: List[type[AgentEvent | ChatMessage]] | None = None,
) -> None:
super().__init__(
participants,
@@ -172,6 +175,7 @@ def __init__(
termination_condition=termination_condition,
max_turns=max_turns,
runtime=runtime,
+ custom_message_types=custom_message_types,
)
def _create_group_chat_manager_factory(
@@ -185,6 +189,7 @@ def _create_group_chat_manager_factory(
output_message_queue: asyncio.Queue[AgentEvent | ChatMessage | GroupChatTermination],
termination_condition: TerminationCondition | None,
max_turns: int | None,
+ message_factory: MessageFactory,
) -> Callable[[], RoundRobinGroupChatManager]:
def _factory() -> RoundRobinGroupChatManager:
return RoundRobinGroupChatManager(
@@ -197,6 +202,7 @@ def _factory() -> RoundRobinGroupChatManager:
output_message_queue,
termination_condition,
max_turns,
+ message_factory,
)
return _factory
diff --git a/python/packages/autogen-agentchat/src/autogen_agentchat/teams/_group_chat/_selector_group_chat.py b/python/packages/autogen-agentchat/src/autogen_agentchat/teams/_group_chat/_selector_group_chat.py
index 6a2231873a0c..7e1b68814ec3 100644
--- a/python/packages/autogen-agentchat/src/autogen_agentchat/teams/_group_chat/_selector_group_chat.py
+++ b/python/packages/autogen-agentchat/src/autogen_agentchat/teams/_group_chat/_selector_group_chat.py
@@ -14,9 +14,8 @@
from ...base import ChatAgent, TerminationCondition
from ...messages import (
AgentEvent,
- BaseAgentEvent,
ChatMessage,
- MultiModalMessage,
+ MessageFactory,
)
from ...state import SelectorManagerState
from ._base_group_chat import BaseGroupChat
@@ -49,6 +48,7 @@ def __init__(
output_message_queue: asyncio.Queue[AgentEvent | ChatMessage | GroupChatTermination],
termination_condition: TerminationCondition | None,
max_turns: int | None,
+ message_factory: MessageFactory,
model_client: ChatCompletionClient,
selector_prompt: str,
allow_repeated_speaker: bool,
@@ -66,6 +66,7 @@ def __init__(
output_message_queue,
termination_condition,
max_turns,
+ message_factory,
)
self._model_client = model_client
self._selector_prompt = selector_prompt
@@ -89,7 +90,7 @@ async def reset(self) -> None:
async def save_state(self) -> Mapping[str, Any]:
state = SelectorManagerState(
- message_thread=list(self._message_thread),
+ message_thread=[msg.dump() for msg in self._message_thread],
current_turn=self._current_turn,
previous_speaker=self._previous_speaker,
)
@@ -97,7 +98,7 @@ async def save_state(self) -> Mapping[str, Any]:
async def load_state(self, state: Mapping[str, Any]) -> None:
selector_state = SelectorManagerState.model_validate(state)
- self._message_thread = list(selector_state.message_thread)
+ self._message_thread = [self._message_factory.create(msg) for msg in selector_state.message_thread]
self._current_turn = selector_state.current_turn
self._previous_speaker = selector_state.previous_speaker
@@ -152,20 +153,10 @@ async def select_speaker(self, thread: List[AgentEvent | ChatMessage]) -> str:
# Construct the history of the conversation.
history_messages: List[str] = []
for msg in thread:
- if isinstance(msg, BaseAgentEvent):
- # Ignore agent events.
+ if not isinstance(msg, ChatMessage):
+ # Only process chat messages.
continue
- message = f"{msg.source}:"
- if isinstance(msg.content, str):
- message += f" {msg.content}"
- elif isinstance(msg, MultiModalMessage):
- for item in msg.content:
- if isinstance(item, str):
- message += f" {item}"
- else:
- message += " [Image]"
- else:
- raise ValueError(f"Unexpected message type in selector: {type(msg)}")
+ message = f"{msg.source}: {msg.to_model_text()}"
history_messages.append(
message.rstrip() + "\n\n"
) # Create some consistency for how messages are separated in the transcript
@@ -414,7 +405,7 @@ def check_calculation(x: int, y: int, answer: int) -> str:
)
def selector_func(messages: Sequence[AgentEvent | ChatMessage]) -> str | None:
- if len(messages) == 1 or messages[-1].content == "Incorrect!":
+ if len(messages) == 1 or messages[-1].to_text() == "Incorrect!":
return "Agent1"
if messages[-1].source == "Agent1":
return "Agent2"
@@ -457,6 +448,7 @@ def __init__(
max_selector_attempts: int = 3,
selector_func: Optional[SelectorFuncType] = None,
candidate_func: Optional[CandidateFuncType] = None,
+ custom_message_types: List[type[AgentEvent | ChatMessage]] | None = None,
):
super().__init__(
participants,
@@ -465,6 +457,7 @@ def __init__(
termination_condition=termination_condition,
max_turns=max_turns,
runtime=runtime,
+ custom_message_types=custom_message_types,
)
# Validate the participants.
if len(participants) < 2:
@@ -487,6 +480,7 @@ def _create_group_chat_manager_factory(
output_message_queue: asyncio.Queue[AgentEvent | ChatMessage | GroupChatTermination],
termination_condition: TerminationCondition | None,
max_turns: int | None,
+ message_factory: MessageFactory,
) -> Callable[[], BaseGroupChatManager]:
return lambda: SelectorGroupChatManager(
name,
@@ -498,6 +492,7 @@ def _create_group_chat_manager_factory(
output_message_queue,
termination_condition,
max_turns,
+ message_factory,
self._model_client,
self._selector_prompt,
self._allow_repeated_speaker,
diff --git a/python/packages/autogen-agentchat/src/autogen_agentchat/teams/_group_chat/_swarm_group_chat.py b/python/packages/autogen-agentchat/src/autogen_agentchat/teams/_group_chat/_swarm_group_chat.py
index f28965712b71..d76ba0fb1e44 100644
--- a/python/packages/autogen-agentchat/src/autogen_agentchat/teams/_group_chat/_swarm_group_chat.py
+++ b/python/packages/autogen-agentchat/src/autogen_agentchat/teams/_group_chat/_swarm_group_chat.py
@@ -5,7 +5,7 @@
from pydantic import BaseModel
from ...base import ChatAgent, TerminationCondition
-from ...messages import AgentEvent, ChatMessage, HandoffMessage
+from ...messages import AgentEvent, ChatMessage, HandoffMessage, MessageFactory
from ...state import SwarmManagerState
from ._base_group_chat import BaseGroupChat
from ._base_group_chat_manager import BaseGroupChatManager
@@ -26,6 +26,7 @@ def __init__(
output_message_queue: asyncio.Queue[AgentEvent | ChatMessage | GroupChatTermination],
termination_condition: TerminationCondition | None,
max_turns: int | None,
+ message_factory: MessageFactory,
) -> None:
super().__init__(
name,
@@ -37,6 +38,7 @@ def __init__(
output_message_queue,
termination_condition,
max_turns,
+ message_factory,
)
self._current_speaker = self._participant_names[0]
@@ -90,7 +92,7 @@ async def select_speaker(self, thread: List[AgentEvent | ChatMessage]) -> str:
async def save_state(self) -> Mapping[str, Any]:
state = SwarmManagerState(
- message_thread=list(self._message_thread),
+ message_thread=[msg.dump() for msg in self._message_thread],
current_turn=self._current_turn,
current_speaker=self._current_speaker,
)
@@ -98,7 +100,7 @@ async def save_state(self) -> Mapping[str, Any]:
async def load_state(self, state: Mapping[str, Any]) -> None:
swarm_state = SwarmManagerState.model_validate(state)
- self._message_thread = list(swarm_state.message_thread)
+ self._message_thread = [self._message_factory.create(message) for message in swarm_state.message_thread]
self._current_turn = swarm_state.current_turn
self._current_speaker = swarm_state.current_speaker
@@ -210,6 +212,7 @@ def __init__(
termination_condition: TerminationCondition | None = None,
max_turns: int | None = None,
runtime: AgentRuntime | None = None,
+ custom_message_types: List[type[AgentEvent | ChatMessage]] | None = None,
) -> None:
super().__init__(
participants,
@@ -218,6 +221,7 @@ def __init__(
termination_condition=termination_condition,
max_turns=max_turns,
runtime=runtime,
+ custom_message_types=custom_message_types,
)
# The first participant must be able to produce handoff messages.
first_participant = self._participants[0]
@@ -235,6 +239,7 @@ def _create_group_chat_manager_factory(
output_message_queue: asyncio.Queue[AgentEvent | ChatMessage | GroupChatTermination],
termination_condition: TerminationCondition | None,
max_turns: int | None,
+ message_factory: MessageFactory,
) -> Callable[[], SwarmGroupChatManager]:
def _factory() -> SwarmGroupChatManager:
return SwarmGroupChatManager(
@@ -247,6 +252,7 @@ def _factory() -> SwarmGroupChatManager:
output_message_queue,
termination_condition,
max_turns,
+ message_factory,
)
return _factory
diff --git a/python/packages/autogen-agentchat/src/autogen_agentchat/ui/_console.py b/python/packages/autogen-agentchat/src/autogen_agentchat/ui/_console.py
index 0a95c842ea08..524ee93a8315 100644
--- a/python/packages/autogen-agentchat/src/autogen_agentchat/ui/_console.py
+++ b/python/packages/autogen-agentchat/src/autogen_agentchat/ui/_console.py
@@ -5,7 +5,7 @@
from inspect import iscoroutinefunction
from typing import AsyncGenerator, Awaitable, Callable, Dict, List, Optional, TypeVar, Union, cast
-from autogen_core import CancellationToken, Image
+from autogen_core import CancellationToken
from autogen_core.models import RequestUsage
from autogen_agentchat.agents import UserProxyAgent
@@ -135,7 +135,11 @@ async def Console(
duration = time.time() - start_time
# Print final response.
- output = f"{'-' * 10} {message.chat_message.source} {'-' * 10}\n{_message_to_str(message.chat_message, render_image_iterm=render_image_iterm)}\n"
+ if isinstance(message.chat_message, MultiModalMessage):
+ final_content = message.chat_message.to_text(iterm=render_image_iterm)
+ else:
+ final_content = message.chat_message.to_text()
+ output = f"{'-' * 10} {message.chat_message.source} {'-' * 10}\n{final_content}\n"
if message.chat_message.models_usage:
if output_stats:
output += f"[Prompt tokens: {message.chat_message.models_usage.prompt_tokens}, Completion tokens: {message.chat_message.models_usage.completion_tokens}]\n"
@@ -171,16 +175,17 @@ async def Console(
# Print message sender.
await aprint(f"{'-' * 10} {message.source} {'-' * 10}", end="\n", flush=True)
if isinstance(message, ModelClientStreamingChunkEvent):
- await aprint(message.content, end="")
+ await aprint(message.to_text(), end="")
streaming_chunks.append(message.content)
else:
if streaming_chunks:
streaming_chunks.clear()
# Chunked messages are already printed, so we just print a newline.
await aprint("", end="\n", flush=True)
+ elif isinstance(message, MultiModalMessage):
+ await aprint(message.to_text(iterm=render_image_iterm), end="\n", flush=True)
else:
- # Print message content.
- await aprint(_message_to_str(message, render_image_iterm=render_image_iterm), end="\n", flush=True)
+ await aprint(message.to_text(), end="\n", flush=True)
if message.models_usage:
if output_stats:
await aprint(
@@ -195,25 +200,3 @@ async def Console(
raise ValueError("No TaskResult or Response was processed.")
return last_processed
-
-
-# iTerm2 image rendering protocol: https://iterm2.com/documentation-images.html
-def _image_to_iterm(image: Image) -> str:
- image_data = image.to_base64()
- return f"\033]1337;File=inline=1:{image_data}\a\n"
-
-
-def _message_to_str(message: AgentEvent | ChatMessage, *, render_image_iterm: bool = False) -> str:
- if isinstance(message, MultiModalMessage):
- result: List[str] = []
- for c in message.content:
- if isinstance(c, str):
- result.append(c)
- else:
- if render_image_iterm:
- result.append(_image_to_iterm(c))
- else:
- result.append("<image>")
- return "\n".join(result)
- else:
- return f"{message.content}"
diff --git a/python/packages/autogen-agentchat/src/autogen_agentchat/utils/_utils.py b/python/packages/autogen-agentchat/src/autogen_agentchat/utils/_utils.py
index 6de1178645fc..738b72e9b329 100644
--- a/python/packages/autogen-agentchat/src/autogen_agentchat/utils/_utils.py
+++ b/python/packages/autogen-agentchat/src/autogen_agentchat/utils/_utils.py
@@ -2,18 +2,24 @@
from autogen_core import FunctionCall, Image
from autogen_core.models import FunctionExecutionResult, LLMMessage, UserMessage
+from pydantic import BaseModel
# Type aliases for convenience
+_StructuredContent = BaseModel
_UserContent = Union[str, List[Union[str, Image]]]
_AssistantContent = Union[str, List[FunctionCall]]
_FunctionExecutionContent = List[FunctionExecutionResult]
_SystemContent = str
-def content_to_str(content: _UserContent | _AssistantContent | _FunctionExecutionContent | _SystemContent) -> str:
+def content_to_str(
+ content: _UserContent | _AssistantContent | _FunctionExecutionContent | _SystemContent | _StructuredContent,
+) -> str:
"""Convert the content of an LLMMessage to a string."""
if isinstance(content, str):
return content
+ elif isinstance(content, BaseModel):
+ return content.model_dump_json()
else:
result: List[str] = []
for c in content:
diff --git a/python/packages/autogen-agentchat/tests/test_assistant_agent.py b/python/packages/autogen-agentchat/tests/test_assistant_agent.py
index db4fe42b73c5..c061651b0ee1 100644
--- a/python/packages/autogen-agentchat/tests/test_assistant_agent.py
+++ b/python/packages/autogen-agentchat/tests/test_assistant_agent.py
@@ -12,6 +12,7 @@
MemoryQueryEvent,
ModelClientStreamingChunkEvent,
MultiModalMessage,
+ StructuredMessage,
TextMessage,
ThoughtEvent,
ToolCallExecutionEvent,
@@ -624,6 +625,23 @@ async def test_multi_modal_task(monkeypatch: pytest.MonkeyPatch) -> None:
assert len(result.messages) == 2
+@pytest.mark.asyncio
+async def test_run_with_structured_task() -> None:
+ class InputTask(BaseModel):
+ input: str
+ data: List[str]
+
+ model_client = ReplayChatCompletionClient(["Hello"])
+ agent = AssistantAgent(
+ name="assistant",
+ model_client=model_client,
+ )
+
+ task = StructuredMessage[InputTask](content=InputTask(input="Test", data=["Test1", "Test2"]), source="user")
+ result = await agent.run(task=task)
+ assert len(result.messages) == 2
+
+
@pytest.mark.asyncio
async def test_invalid_model_capabilities() -> None:
model = "random-model"
@@ -896,6 +914,7 @@ async def test_model_client_stream() -> None:
chunks: List[str] = []
async for message in agent.run_stream(task="task"):
if isinstance(message, TaskResult):
+ assert isinstance(message.messages[-1], TextMessage)
assert message.messages[-1].content == "Response to message 3"
elif isinstance(message, ModelClientStreamingChunkEvent):
chunks.append(message.content)
@@ -929,11 +948,14 @@ async def test_model_client_stream_with_tool_calls() -> None:
chunks: List[str] = []
async for message in agent.run_stream(task="task"):
if isinstance(message, TaskResult):
+ assert isinstance(message.messages[-1], TextMessage)
+ assert isinstance(message.messages[1], ToolCallRequestEvent)
assert message.messages[-1].content == "Example response 2 to task"
assert message.messages[1].content == [
FunctionCall(id="1", name="_pass_function", arguments=r'{"input": "task"}'),
FunctionCall(id="3", name="_echo_function", arguments=r'{"input": "task"}'),
]
+ assert isinstance(message.messages[2], ToolCallExecutionEvent)
assert message.messages[2].content == [
FunctionExecutionResult(call_id="1", content="pass", is_error=False, name="_pass_function"),
FunctionExecutionResult(call_id="3", content="task", is_error=False, name="_echo_function"),
diff --git a/python/packages/autogen-agentchat/tests/test_group_chat.py b/python/packages/autogen-agentchat/tests/test_group_chat.py
index 5ab1605a72b7..c0387ee84764 100644
--- a/python/packages/autogen-agentchat/tests/test_group_chat.py
+++ b/python/packages/autogen-agentchat/tests/test_group_chat.py
@@ -20,6 +20,7 @@
HandoffMessage,
MultiModalMessage,
StopMessage,
+ StructuredMessage,
TextMessage,
ToolCallExecutionEvent,
ToolCallRequestEvent,
@@ -44,6 +45,7 @@
from autogen_ext.code_executors.local import LocalCommandLineCodeExecutor
from autogen_ext.models.openai import OpenAIChatCompletionClient
from autogen_ext.models.replay import ReplayChatCompletionClient
+from pydantic import BaseModel
from utils import FileLogHandler
logger = logging.getLogger(EVENT_LOGGER_NAME)
@@ -101,6 +103,34 @@ async def on_reset(self, cancellation_token: CancellationToken) -> None:
self._last_message = None
+class _UnknownMessageType(ChatMessage):
+ content: str
+
+ def to_model_message(self) -> UserMessage:
+ raise NotImplementedError("This message type is not supported.")
+
+ def to_model_text(self) -> str:
+ raise NotImplementedError("This message type is not supported.")
+
+ def to_text(self) -> str:
+ raise NotImplementedError("This message type is not supported.")
+
+
+class _UnknownMessageTypeAgent(BaseChatAgent):
+ def __init__(self, name: str, description: str) -> None:
+ super().__init__(name, description)
+
+ @property
+ def produced_message_types(self) -> Sequence[type[ChatMessage]]:
+ return (_UnknownMessageType,)
+
+ async def on_messages(self, messages: Sequence[ChatMessage], cancellation_token: CancellationToken) -> Response:
+ return Response(chat_message=_UnknownMessageType(content="Unknown message type", source=self.name))
+
+ async def on_reset(self, cancellation_token: CancellationToken) -> None:
+ pass
+
+
class _StopAgent(_EchoAgent):
def __init__(self, name: str, description: str, *, stop_at: int = 1) -> None:
super().__init__(name, description)
@@ -122,6 +152,19 @@ def _pass_function(input: str) -> str:
return "pass"
+class _InputTask1(BaseModel):
+ task: str
+ data: List[str]
+
+
+class _InputTask2(BaseModel):
+ task: str
+ data: str
+
+
+TaskType = str | List[ChatMessage] | ChatMessage
+
+
@pytest_asyncio.fixture(params=["single_threaded", "embedded"]) # type: ignore
async def runtime(request: pytest.FixtureRequest) -> AsyncGenerator[AgentRuntime | None, None]:
if request.param == "single_threaded":
@@ -164,14 +207,11 @@ async def test_round_robin_group_chat(runtime: AgentRuntime | None) -> None:
"Hello, world!",
"TERMINATE",
]
- # Normalize the messages to remove \r\n and any leading/trailing whitespace.
- normalized_messages = [
- msg.content.replace("\r\n", "\n").rstrip("\n") if isinstance(msg.content, str) else msg.content
- for msg in result.messages
- ]
-
- # Assert that all expected messages are in the collected messages
- assert normalized_messages == expected_messages
+ for i in range(len(expected_messages)):
+ produced_message = result.messages[i]
+ assert isinstance(produced_message, TextMessage)
+ content = produced_message.content.replace("\r\n", "\n").rstrip("\n")
+ assert content == expected_messages[i]
assert result.stop_reason is not None and result.stop_reason == "Text 'TERMINATE' mentioned"
@@ -202,28 +242,89 @@ async def test_round_robin_group_chat(runtime: AgentRuntime | None) -> None:
model_client.reset()
index = 0
await team.reset()
- result_2 = await team.run(
- task=MultiModalMessage(content=["Write a program that prints 'Hello, world!'"], source="user")
- )
- assert result.messages[0].content == result_2.messages[0].content[0]
+ task = MultiModalMessage(content=["Write a program that prints 'Hello, world!'"], source="user")
+ result_2 = await team.run(task=task)
+ assert isinstance(result.messages[0], TextMessage)
+ assert isinstance(result_2.messages[0], MultiModalMessage)
+ assert result.messages[0].content == task.content[0]
assert result.messages[1:] == result_2.messages[1:]
@pytest.mark.asyncio
-async def test_round_robin_group_chat_state(runtime: AgentRuntime | None) -> None:
+async def test_round_robin_group_chat_unknown_task_message_type(runtime: AgentRuntime | None) -> None:
+ model_client = ReplayChatCompletionClient([])
+ agent1 = AssistantAgent("agent1", model_client=model_client)
+ agent2 = AssistantAgent("agent2", model_client=model_client)
+ termination = TextMentionTermination("TERMINATE")
+ team1 = RoundRobinGroupChat(
+ participants=[agent1, agent2],
+ termination_condition=termination,
+ runtime=runtime,
+ custom_message_types=[StructuredMessage[_InputTask2]],
+ )
+ with pytest.raises(ValueError, match=r"Message type .*StructuredMessage\[_InputTask1\].* is not registered"):
+ await team1.run(
+ task=StructuredMessage[_InputTask1](
+ content=_InputTask1(task="Write a program that prints 'Hello, world!'", data=["a", "b", "c"]),
+ source="user",
+ )
+ )
+
+
+@pytest.mark.asyncio
+async def test_round_robin_group_chat_unknown_agent_message_type() -> None:
+ model_client = ReplayChatCompletionClient(["Hello"])
+ agent1 = AssistantAgent("agent1", model_client=model_client)
+ agent2 = _UnknownMessageTypeAgent("agent2", "I am an unknown message type agent")
+ termination = TextMentionTermination("TERMINATE")
+ team1 = RoundRobinGroupChat(participants=[agent1, agent2], termination_condition=termination)
+ with pytest.raises(ValueError, match="Message type .*UnknownMessageType.* not registered"):
+ await team1.run(task=TextMessage(content="Write a program that prints 'Hello, world!'", source="user"))
+
+
+@pytest.mark.asyncio
+@pytest.mark.parametrize(
+ "task",
+ [
+ "Write a program that prints 'Hello, world!'",
+ [TextMessage(content="Write a program that prints 'Hello, world!'", source="user")],
+ [MultiModalMessage(content=["Write a program that prints 'Hello, world!'"], source="user")],
+ [
+ StructuredMessage[_InputTask1](
+ content=_InputTask1(task="Write a program that prints 'Hello, world!'", data=["a", "b", "c"]),
+ source="user",
+ ),
+ StructuredMessage[_InputTask2](
+ content=_InputTask2(task="Write a program that prints 'Hello, world!'", data="a"), source="user"
+ ),
+ ],
+ ],
+ ids=["text", "text_message", "multi_modal_message", "structured_message"],
+)
+async def test_round_robin_group_chat_state(task: TaskType, runtime: AgentRuntime | None) -> None:
model_client = ReplayChatCompletionClient(
["No facts", "No plan", "print('Hello, world!')", "TERMINATE"],
)
agent1 = AssistantAgent("agent1", model_client=model_client)
agent2 = AssistantAgent("agent2", model_client=model_client)
termination = TextMentionTermination("TERMINATE")
- team1 = RoundRobinGroupChat(participants=[agent1, agent2], termination_condition=termination, runtime=runtime)
- await team1.run(task="Write a program that prints 'Hello, world!'")
+ team1 = RoundRobinGroupChat(
+ participants=[agent1, agent2],
+ termination_condition=termination,
+ runtime=runtime,
+ custom_message_types=[StructuredMessage[_InputTask1], StructuredMessage[_InputTask2]],
+ )
+ await team1.run(task=task)
state = await team1.save_state()
agent3 = AssistantAgent("agent1", model_client=model_client)
agent4 = AssistantAgent("agent2", model_client=model_client)
- team2 = RoundRobinGroupChat(participants=[agent3, agent4], termination_condition=termination, runtime=runtime)
+ team2 = RoundRobinGroupChat(
+ participants=[agent3, agent4],
+ termination_condition=termination,
+ runtime=runtime,
+ custom_message_types=[StructuredMessage[_InputTask1], StructuredMessage[_InputTask2]],
+ )
await team2.load_state(state)
state2 = await team2.save_state()
assert state == state2
@@ -453,6 +554,7 @@ async def test_selector_group_chat(runtime: AgentRuntime | None) -> None:
task="Write a program that prints 'Hello, world!'",
)
assert len(result.messages) == 6
+ assert isinstance(result.messages[0], TextMessage)
assert result.messages[0].content == "Write a program that prints 'Hello, world!'"
assert result.messages[1].source == "agent3"
assert result.messages[2].source == "agent2"
@@ -485,7 +587,25 @@ async def test_selector_group_chat(runtime: AgentRuntime | None) -> None:
@pytest.mark.asyncio
-async def test_selector_group_chat_state(runtime: AgentRuntime | None) -> None:
+@pytest.mark.parametrize(
+ "task",
+ [
+ "Write a program that prints 'Hello, world!'",
+ [TextMessage(content="Write a program that prints 'Hello, world!'", source="user")],
+ [MultiModalMessage(content=["Write a program that prints 'Hello, world!'"], source="user")],
+ [
+ StructuredMessage[_InputTask1](
+ content=_InputTask1(task="Write a program that prints 'Hello, world!'", data=["a", "b", "c"]),
+ source="user",
+ ),
+ StructuredMessage[_InputTask2](
+ content=_InputTask2(task="Write a program that prints 'Hello, world!'", data="a"), source="user"
+ ),
+ ],
+ ],
+ ids=["text", "text_message", "multi_modal_message", "structured_message"],
+)
+async def test_selector_group_chat_state(task: TaskType, runtime: AgentRuntime | None) -> None:
model_client = ReplayChatCompletionClient(
["agent1", "No facts", "agent2", "No plan", "agent1", "print('Hello, world!')", "agent2", "TERMINATE"],
)
@@ -497,14 +617,18 @@ async def test_selector_group_chat_state(runtime: AgentRuntime | None) -> None:
termination_condition=termination,
model_client=model_client,
runtime=runtime,
+ custom_message_types=[StructuredMessage[_InputTask1], StructuredMessage[_InputTask2]],
)
- await team1.run(task="Write a program that prints 'Hello, world!'")
+ await team1.run(task=task)
state = await team1.save_state()
agent3 = AssistantAgent("agent1", model_client=model_client)
agent4 = AssistantAgent("agent2", model_client=model_client)
team2 = SelectorGroupChat(
- participants=[agent3, agent4], termination_condition=termination, model_client=model_client
+ participants=[agent3, agent4],
+ termination_condition=termination,
+ model_client=model_client,
+ custom_message_types=[StructuredMessage[_InputTask1], StructuredMessage[_InputTask2]],
)
await team2.load_state(state)
state2 = await team2.save_state()
@@ -545,6 +669,7 @@ async def test_selector_group_chat_two_speakers(runtime: AgentRuntime | None) ->
task="Write a program that prints 'Hello, world!'",
)
assert len(result.messages) == 5
+ assert isinstance(result.messages[0], TextMessage)
assert result.messages[0].content == "Write a program that prints 'Hello, world!'"
assert result.messages[1].source == "agent2"
assert result.messages[2].source == "agent1"
@@ -594,6 +719,7 @@ async def test_selector_group_chat_two_speakers_allow_repeated(runtime: AgentRun
)
result = await team.run(task="Write a program that prints 'Hello, world!'")
assert len(result.messages) == 4
+ assert isinstance(result.messages[0], TextMessage)
assert result.messages[0].content == "Write a program that prints 'Hello, world!'"
assert result.messages[1].source == "agent2"
assert result.messages[2].source == "agent2"
@@ -635,6 +761,7 @@ async def test_selector_group_chat_succcess_after_2_attempts(runtime: AgentRunti
)
result = await team.run(task="Write a program that prints 'Hello, world!'")
assert len(result.messages) == 2
+ assert isinstance(result.messages[0], TextMessage)
assert result.messages[0].content == "Write a program that prints 'Hello, world!'"
assert result.messages[1].source == "agent2"
@@ -659,6 +786,7 @@ async def test_selector_group_chat_fall_back_to_first_after_3_attempts(runtime:
)
result = await team.run(task="Write a program that prints 'Hello, world!'")
assert len(result.messages) == 2
+ assert isinstance(result.messages[0], TextMessage)
assert result.messages[0].content == "Write a program that prints 'Hello, world!'"
assert result.messages[1].source == "agent1"
@@ -679,6 +807,7 @@ async def test_selector_group_chat_fall_back_to_previous_after_3_attempts(runtim
)
result = await team.run(task="Write a program that prints 'Hello, world!'")
assert len(result.messages) == 3
+ assert isinstance(result.messages[0], TextMessage)
assert result.messages[0].content == "Write a program that prints 'Hello, world!'"
assert result.messages[1].source == "agent2"
assert result.messages[2].source == "agent2"
@@ -796,6 +925,12 @@ async def test_swarm_handoff(runtime: AgentRuntime | None) -> None:
team = Swarm([second_agent, first_agent, third_agent], termination_condition=termination, runtime=runtime)
result = await team.run(task="task")
assert len(result.messages) == 6
+ assert isinstance(result.messages[0], TextMessage)
+ assert isinstance(result.messages[1], HandoffMessage)
+ assert isinstance(result.messages[2], HandoffMessage)
+ assert isinstance(result.messages[3], HandoffMessage)
+ assert isinstance(result.messages[4], HandoffMessage)
+ assert isinstance(result.messages[5], HandoffMessage)
assert result.messages[0].content == "task"
assert result.messages[1].content == "Transferred to third_agent."
assert result.messages[2].content == "Transferred to first_agent."
@@ -839,6 +974,65 @@ async def test_swarm_handoff(runtime: AgentRuntime | None) -> None:
assert manager_1._current_speaker == manager_2._current_speaker # pyright: ignore
+@pytest.mark.asyncio
+@pytest.mark.parametrize(
+ "task",
+ [
+ "Write a program that prints 'Hello, world!'",
+ [TextMessage(content="Write a program that prints 'Hello, world!'", source="user")],
+ [MultiModalMessage(content=["Write a program that prints 'Hello, world!'"], source="user")],
+ [
+ StructuredMessage[_InputTask1](
+ content=_InputTask1(task="Write a program that prints 'Hello, world!'", data=["a", "b", "c"]),
+ source="user",
+ ),
+ StructuredMessage[_InputTask2](
+ content=_InputTask2(task="Write a program that prints 'Hello, world!'", data="a"), source="user"
+ ),
+ ],
+ ],
+ ids=["text", "text_message", "multi_modal_message", "structured_message"],
+)
+async def test_swarm_handoff_state(task: TaskType, runtime: AgentRuntime | None) -> None:
+ first_agent = _HandOffAgent("first_agent", description="first agent", next_agent="second_agent")
+ second_agent = _HandOffAgent("second_agent", description="second agent", next_agent="third_agent")
+ third_agent = _HandOffAgent("third_agent", description="third agent", next_agent="first_agent")
+
+ termination = MaxMessageTermination(6)
+ team1 = Swarm(
+ [second_agent, first_agent, third_agent],
+ termination_condition=termination,
+ runtime=runtime,
+ custom_message_types=[StructuredMessage[_InputTask1], StructuredMessage[_InputTask2]],
+ )
+ await team1.run(task=task)
+ state = await team1.save_state()
+
+ first_agent2 = _HandOffAgent("first_agent", description="first agent", next_agent="second_agent")
+ second_agent2 = _HandOffAgent("second_agent", description="second agent", next_agent="third_agent")
+ third_agent2 = _HandOffAgent("third_agent", description="third agent", next_agent="first_agent")
+ team2 = Swarm(
+ [second_agent2, first_agent2, third_agent2],
+ termination_condition=termination,
+ runtime=runtime,
+ custom_message_types=[StructuredMessage[_InputTask1], StructuredMessage[_InputTask2]],
+ )
+ await team2.load_state(state)
+ state2 = await team2.save_state()
+ assert state == state2
+
+ manager_1 = await team1._runtime.try_get_underlying_agent_instance( # pyright: ignore
+ AgentId(f"{team1._group_chat_manager_name}_{team1._team_id}", team1._team_id), # pyright: ignore
+ SwarmGroupChatManager, # pyright: ignore
+ )
+ manager_2 = await team2._runtime.try_get_underlying_agent_instance( # pyright: ignore
+ AgentId(f"{team2._group_chat_manager_name}_{team2._team_id}", team2._team_id), # pyright: ignore
+ SwarmGroupChatManager, # pyright: ignore
+ )
+ assert manager_1._message_thread == manager_2._message_thread # pyright: ignore
+ assert manager_1._current_speaker == manager_2._current_speaker # pyright: ignore
+
+
@pytest.mark.asyncio
async def test_swarm_handoff_using_tool_calls(runtime: AgentRuntime | None) -> None:
model_client = ReplayChatCompletionClient(
@@ -870,9 +1064,14 @@ async def test_swarm_handoff_using_tool_calls(runtime: AgentRuntime | None) -> N
team = Swarm([agent1, agent2], termination_condition=termination, runtime=runtime)
result = await team.run(task="task")
assert len(result.messages) == 7
+ assert isinstance(result.messages[0], TextMessage)
assert result.messages[0].content == "task"
assert isinstance(result.messages[1], ToolCallRequestEvent)
assert isinstance(result.messages[2], ToolCallExecutionEvent)
+ assert isinstance(result.messages[3], HandoffMessage)
+ assert isinstance(result.messages[4], HandoffMessage)
+ assert isinstance(result.messages[5], TextMessage)
+ assert isinstance(result.messages[6], TextMessage)
assert result.messages[3].content == "handoff to agent2"
assert result.messages[4].content == "Transferred to agent1."
assert result.messages[5].content == "Hello"
@@ -910,18 +1109,23 @@ async def test_swarm_pause_and_resume(runtime: AgentRuntime | None) -> None:
team = Swarm([second_agent, first_agent, third_agent], max_turns=1, runtime=runtime)
result = await team.run(task="task")
assert len(result.messages) == 2
+ assert isinstance(result.messages[0], TextMessage)
+ assert isinstance(result.messages[1], HandoffMessage)
assert result.messages[0].content == "task"
assert result.messages[1].content == "Transferred to third_agent."
# Resume with a new task.
result = await team.run(task="new task")
assert len(result.messages) == 2
+ assert isinstance(result.messages[0], TextMessage)
+ assert isinstance(result.messages[1], HandoffMessage)
assert result.messages[0].content == "new task"
assert result.messages[1].content == "Transferred to first_agent."
# Resume with the same task.
result = await team.run()
assert len(result.messages) == 1
+ assert isinstance(result.messages[0], HandoffMessage)
assert result.messages[0].content == "Transferred to second_agent."
@@ -996,8 +1200,10 @@ def tool2() -> str:
source="agent1",
context=expected_handoff_context,
)
+ assert isinstance(result.messages[4], TextMessage)
assert result.messages[4].content == "Hello"
assert result.messages[4].source == "agent2"
+ assert isinstance(result.messages[5], TextMessage)
assert result.messages[5].content == "TERMINATE"
assert result.messages[5].source == "agent2"
@@ -1020,17 +1226,26 @@ async def test_swarm_with_handoff_termination(runtime: AgentRuntime | None) -> N
# Start
result = await team.run(task="task")
assert len(result.messages) == 2
+ assert isinstance(result.messages[0], TextMessage)
+ assert isinstance(result.messages[1], HandoffMessage)
assert result.messages[0].content == "task"
assert result.messages[1].content == "Transferred to third_agent."
# Resume existing.
result = await team.run()
assert len(result.messages) == 3
+ assert isinstance(result.messages[0], HandoffMessage)
+ assert isinstance(result.messages[1], HandoffMessage)
+ assert isinstance(result.messages[2], HandoffMessage)
assert result.messages[0].content == "Transferred to first_agent."
assert result.messages[1].content == "Transferred to second_agent."
assert result.messages[2].content == "Transferred to third_agent."
# Resume new task.
result = await team.run(task="new task")
assert len(result.messages) == 4
+ assert isinstance(result.messages[0], TextMessage)
+ assert isinstance(result.messages[1], HandoffMessage)
+ assert isinstance(result.messages[2], HandoffMessage)
+ assert isinstance(result.messages[3], HandoffMessage)
assert result.messages[0].content == "new task"
assert result.messages[1].content == "Transferred to first_agent."
assert result.messages[2].content == "Transferred to second_agent."
@@ -1043,6 +1258,9 @@ async def test_swarm_with_handoff_termination(runtime: AgentRuntime | None) -> N
# Start
result = await team.run(task="task")
assert len(result.messages) == 3
+ assert isinstance(result.messages[0], TextMessage)
+ assert isinstance(result.messages[1], HandoffMessage)
+ assert isinstance(result.messages[2], HandoffMessage)
assert result.messages[0].content == "task"
assert result.messages[1].content == "Transferred to third_agent."
assert result.messages[2].content == "Transferred to non_existing_agent."
@@ -1055,6 +1273,10 @@ async def test_swarm_with_handoff_termination(runtime: AgentRuntime | None) -> N
# Resume with a HandoffMessage
result = await team.run(task=HandoffMessage(content="Handoff to first_agent.", target="first_agent", source="user"))
assert len(result.messages) == 4
+ assert isinstance(result.messages[0], HandoffMessage)
+ assert isinstance(result.messages[1], HandoffMessage)
+ assert isinstance(result.messages[2], HandoffMessage)
+ assert isinstance(result.messages[3], HandoffMessage)
assert result.messages[0].content == "Handoff to first_agent."
assert result.messages[1].content == "Transferred to second_agent."
assert result.messages[2].content == "Transferred to third_agent."
@@ -1081,6 +1303,10 @@ async def test_round_robin_group_chat_with_message_list(runtime: AgentRuntime |
# Verify the messages were processed in order
assert len(result.messages) == 4 # Initial messages + echo until termination
+ assert isinstance(result.messages[0], TextMessage)
+ assert isinstance(result.messages[1], TextMessage)
+ assert isinstance(result.messages[2], TextMessage)
+ assert isinstance(result.messages[3], TextMessage)
assert result.messages[0].content == "Message 1" # First message
assert result.messages[1].content == "Message 2" # Second message
assert result.messages[2].content == "Message 3" # Third message
diff --git a/python/packages/autogen-agentchat/tests/test_group_chat_endpoint.py b/python/packages/autogen-agentchat/tests/test_group_chat_endpoint.py
index 390a45e031f4..dd0c3ba71b7d 100644
--- a/python/packages/autogen-agentchat/tests/test_group_chat_endpoint.py
+++ b/python/packages/autogen-agentchat/tests/test_group_chat_endpoint.py
@@ -4,10 +4,7 @@
import pytest
from autogen_agentchat.agents import AssistantAgent
from autogen_agentchat.base import TaskResult
-from autogen_agentchat.messages import (
- AgentEvent,
- ChatMessage,
-)
+from autogen_agentchat.messages import AgentEvent, ChatMessage
from autogen_agentchat.teams import SelectorGroupChat
from autogen_agentchat.ui import Console
from autogen_core.models import ChatCompletionClient
diff --git a/python/packages/autogen-agentchat/tests/test_magentic_one_group_chat.py b/python/packages/autogen-agentchat/tests/test_magentic_one_group_chat.py
index 34228ba039f7..4213087d8ca6 100644
--- a/python/packages/autogen-agentchat/tests/test_magentic_one_group_chat.py
+++ b/python/packages/autogen-agentchat/tests/test_magentic_one_group_chat.py
@@ -134,8 +134,8 @@ async def test_magentic_one_group_chat_basic(runtime: AgentRuntime | None) -> No
)
result = await team.run(task="Write a program that prints 'Hello, world!'")
assert len(result.messages) == 5
- assert result.messages[2].content == "Continue task"
- assert result.messages[4].content == "print('Hello, world!')"
+ assert result.messages[2].to_text() == "Continue task"
+ assert result.messages[4].to_text() == "print('Hello, world!')"
assert result.stop_reason is not None and result.stop_reason == "Because"
# Test save and load.
@@ -214,8 +214,8 @@ async def test_magentic_one_group_chat_with_stalls(runtime: AgentRuntime | None)
)
result = await team.run(task="Write a program that prints 'Hello, world!'")
assert len(result.messages) == 6
- assert isinstance(result.messages[1].content, str)
+ assert isinstance(result.messages[1], TextMessage)
assert result.messages[1].content.startswith("\nWe are working to address the following user request:")
- assert isinstance(result.messages[4].content, str)
+ assert isinstance(result.messages[4], TextMessage)
assert result.messages[4].content.startswith("\nWe are working to address the following user request:")
assert result.stop_reason is not None and result.stop_reason == "test"
diff --git a/python/packages/autogen-agentchat/tests/test_messages.py b/python/packages/autogen-agentchat/tests/test_messages.py
new file mode 100644
index 000000000000..c3dd0acac836
--- /dev/null
+++ b/python/packages/autogen-agentchat/tests/test_messages.py
@@ -0,0 +1,93 @@
+import pytest
+from autogen_agentchat.messages import HandoffMessage, MessageFactory, StructuredMessage, TextMessage
+from pydantic import BaseModel
+
+
+class TestContent(BaseModel):
+ """Test content model."""
+
+ field1: str
+ field2: int
+
+
+def test_structured_message() -> None:
+ # Create a structured message with the test content
+ message = StructuredMessage[TestContent](
+ source="test_agent",
+ content=TestContent(field1="test", field2=42),
+ )
+
+ # Check that the message type is correct
+ assert message.type == "StructuredMessage[TestContent]" # type: ignore
+
+ # Check that the content is of the correct type
+ assert isinstance(message.content, TestContent)
+
+ # Check that the content fields are set correctly
+ assert message.content.field1 == "test"
+ assert message.content.field2 == 42
+
+ # Check that model_dump works correctly
+ dumped_message = message.model_dump()
+ assert dumped_message["source"] == "test_agent"
+ assert dumped_message["content"]["field1"] == "test"
+ assert dumped_message["content"]["field2"] == 42
+ assert dumped_message["type"] == "StructuredMessage[TestContent]"
+
+
+def test_message_factory() -> None:
+ factory = MessageFactory()
+
+ # Text message data
+ text_data = {
+ "type": "TextMessage",
+ "source": "test_agent",
+ "content": "Hello, world!",
+ }
+
+ # Create a TextMessage instance
+ text_message = factory.create(text_data)
+ assert isinstance(text_message, TextMessage)
+ assert text_message.source == "test_agent"
+ assert text_message.content == "Hello, world!"
+ assert text_message.type == "TextMessage" # type: ignore
+
+ # Handoff message data
+ handoff_data = {
+ "type": "HandoffMessage",
+ "source": "test_agent",
+ "content": "handoff to another agent",
+ "target": "target_agent",
+ }
+
+ # Create a HandoffMessage instance
+ handoff_message = factory.create(handoff_data)
+ assert isinstance(handoff_message, HandoffMessage)
+ assert handoff_message.source == "test_agent"
+ assert handoff_message.content == "handoff to another agent"
+ assert handoff_message.target == "target_agent"
+ assert handoff_message.type == "HandoffMessage" # type: ignore
+
+ # Structured message data
+ structured_data = {
+ "type": "StructuredMessage[TestContent]",
+ "source": "test_agent",
+ "content": {
+ "field1": "test",
+ "field2": 42,
+ },
+ }
+ # Create a StructuredMessage instance -- this will fail because the type
+ # is not registered in the factory.
+ with pytest.raises(ValueError):
+ structured_message = factory.create(structured_data)
+ # Register the StructuredMessage type in the factory
+ factory.register(StructuredMessage[TestContent])
+ # Create a StructuredMessage instance
+ structured_message = factory.create(structured_data)
+ assert isinstance(structured_message, StructuredMessage)
+ assert isinstance(structured_message.content, TestContent) # type: ignore
+ assert structured_message.source == "test_agent"
+ assert structured_message.content.field1 == "test"
+ assert structured_message.content.field2 == 42
+ assert structured_message.type == "StructuredMessage[TestContent]" # type: ignore
diff --git a/python/packages/autogen-core/docs/src/user-guide/agentchat-user-guide/custom-agents.ipynb b/python/packages/autogen-core/docs/src/user-guide/agentchat-user-guide/custom-agents.ipynb
index 1a526ee6106b..3f4a66896d41 100644
--- a/python/packages/autogen-core/docs/src/user-guide/agentchat-user-guide/custom-agents.ipynb
+++ b/python/packages/autogen-core/docs/src/user-guide/agentchat-user-guide/custom-agents.ipynb
@@ -1,739 +1,739 @@
{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Custom Agents\n",
- "\n",
- "You may have agents with behaviors that do not fall into a preset. \n",
- "In such cases, you can build custom agents.\n",
- "\n",
- "All agents in AgentChat inherit from {py:class}`~autogen_agentchat.agents.BaseChatAgent` \n",
- "class and implement the following abstract methods and attributes:\n",
- "\n",
- "- {py:meth}`~autogen_agentchat.agents.BaseChatAgent.on_messages`: The abstract method that defines the behavior of the agent in response to messages. This method is called when the agent is asked to provide a response in {py:meth}`~autogen_agentchat.agents.BaseChatAgent.run`. It returns a {py:class}`~autogen_agentchat.base.Response` object.\n",
- "- {py:meth}`~autogen_agentchat.agents.BaseChatAgent.on_reset`: The abstract method that resets the agent to its initial state. This method is called when the agent is asked to reset itself.\n",
- "- {py:attr}`~autogen_agentchat.agents.BaseChatAgent.produced_message_types`: The list of possible {py:class}`~autogen_agentchat.messages.ChatMessage` message types the agent can produce in its response.\n",
- "\n",
- "Optionally, you can implement the the {py:meth}`~autogen_agentchat.agents.BaseChatAgent.on_messages_stream` method to stream messages as they are generated by the agent. If this method is not implemented, the agent\n",
- "uses the default implementation of {py:meth}`~autogen_agentchat.agents.BaseChatAgent.on_messages_stream`\n",
- "that calls the {py:meth}`~autogen_agentchat.agents.BaseChatAgent.on_messages` method and\n",
- "yields all messages in the response."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## CountDownAgent\n",
- "\n",
- "In this example, we create a simple agent that counts down from a given number to zero,\n",
- "and produces a stream of messages with the current count."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "3...\n",
- "2...\n",
- "1...\n",
- "Done!\n"
- ]
- }
- ],
- "source": [
- "from typing import AsyncGenerator, List, Sequence\n",
- "\n",
- "from autogen_agentchat.agents import BaseChatAgent\n",
- "from autogen_agentchat.base import Response\n",
- "from autogen_agentchat.messages import AgentEvent, ChatMessage, TextMessage\n",
- "from autogen_core import CancellationToken\n",
- "\n",
- "\n",
- "class CountDownAgent(BaseChatAgent):\n",
- " def __init__(self, name: str, count: int = 3):\n",
- " super().__init__(name, \"A simple agent that counts down.\")\n",
- " self._count = count\n",
- "\n",
- " @property\n",
- " def produced_message_types(self) -> Sequence[type[ChatMessage]]:\n",
- " return (TextMessage,)\n",
- "\n",
- " async def on_messages(self, messages: Sequence[ChatMessage], cancellation_token: CancellationToken) -> Response:\n",
- " # Calls the on_messages_stream.\n",
- " response: Response | None = None\n",
- " async for message in self.on_messages_stream(messages, cancellation_token):\n",
- " if isinstance(message, Response):\n",
- " response = message\n",
- " assert response is not None\n",
- " return response\n",
- "\n",
- " async def on_messages_stream(\n",
- " self, messages: Sequence[ChatMessage], cancellation_token: CancellationToken\n",
- " ) -> AsyncGenerator[AgentEvent | ChatMessage | Response, None]:\n",
- " inner_messages: List[AgentEvent | ChatMessage] = []\n",
- " for i in range(self._count, 0, -1):\n",
- " msg = TextMessage(content=f\"{i}...\", source=self.name)\n",
- " inner_messages.append(msg)\n",
- " yield msg\n",
- " # The response is returned at the end of the stream.\n",
- " # It contains the final message and all the inner messages.\n",
- " yield Response(chat_message=TextMessage(content=\"Done!\", source=self.name), inner_messages=inner_messages)\n",
- "\n",
- " async def on_reset(self, cancellation_token: CancellationToken) -> None:\n",
- " pass\n",
- "\n",
- "\n",
- "async def run_countdown_agent() -> None:\n",
- " # Create a countdown agent.\n",
- " countdown_agent = CountDownAgent(\"countdown\")\n",
- "\n",
- " # Run the agent with a given task and stream the response.\n",
- " async for message in countdown_agent.on_messages_stream([], CancellationToken()):\n",
- " if isinstance(message, Response):\n",
- " print(message.chat_message.content)\n",
- " else:\n",
- " print(message.content)\n",
- "\n",
- "\n",
- "# Use asyncio.run(run_countdown_agent()) when running in a script.\n",
- "await run_countdown_agent()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## ArithmeticAgent\n",
- "\n",
- "In this example, we create an agent class that can perform simple arithmetic operations\n",
- "on a given integer. Then, we will use different instances of this agent class\n",
- "in a {py:class}`~autogen_agentchat.teams.SelectorGroupChat`\n",
- "to transform a given integer into another integer by applying a sequence of arithmetic operations.\n",
- "\n",
- "The `ArithmeticAgent` class takes an `operator_func` that takes an integer and returns an integer,\n",
- "after applying an arithmetic operation to the integer.\n",
- "In its `on_messages` method, it applies the `operator_func` to the integer in the input message,\n",
- "and returns a response with the result."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "from typing import Callable, Sequence\n",
- "\n",
- "from autogen_agentchat.agents import BaseChatAgent\n",
- "from autogen_agentchat.base import Response\n",
- "from autogen_agentchat.conditions import MaxMessageTermination\n",
- "from autogen_agentchat.messages import ChatMessage\n",
- "from autogen_agentchat.teams import SelectorGroupChat\n",
- "from autogen_agentchat.ui import Console\n",
- "from autogen_core import CancellationToken\n",
- "from autogen_ext.models.openai import OpenAIChatCompletionClient\n",
- "\n",
- "\n",
- "class ArithmeticAgent(BaseChatAgent):\n",
- " def __init__(self, name: str, description: str, operator_func: Callable[[int], int]) -> None:\n",
- " super().__init__(name, description=description)\n",
- " self._operator_func = operator_func\n",
- " self._message_history: List[ChatMessage] = []\n",
- "\n",
- " @property\n",
- " def produced_message_types(self) -> Sequence[type[ChatMessage]]:\n",
- " return (TextMessage,)\n",
- "\n",
- " async def on_messages(self, messages: Sequence[ChatMessage], cancellation_token: CancellationToken) -> Response:\n",
- " # Update the message history.\n",
- " # NOTE: it is possible the messages is an empty list, which means the agent was selected previously.\n",
- " self._message_history.extend(messages)\n",
- " # Parse the number in the last message.\n",
- " assert isinstance(self._message_history[-1], TextMessage)\n",
- " number = int(self._message_history[-1].content)\n",
- " # Apply the operator function to the number.\n",
- " result = self._operator_func(number)\n",
- " # Create a new message with the result.\n",
- " response_message = TextMessage(content=str(result), source=self.name)\n",
- " # Update the message history.\n",
- " self._message_history.append(response_message)\n",
- " # Return the response.\n",
- " return Response(chat_message=response_message)\n",
- "\n",
- " async def on_reset(self, cancellation_token: CancellationToken) -> None:\n",
- " pass"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "```{note}\n",
- "The `on_messages` method may be called with an empty list of messages, in which\n",
- "case it means the agent was called previously and is now being called again,\n",
- "without any new messages from the caller. So it is important to keep a history\n",
- "of the previous messages received by the agent, and use that history to generate\n",
- "the response.\n",
- "```"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Now we can create a {py:class}`~autogen_agentchat.teams.SelectorGroupChat` with 5 instances of `ArithmeticAgent`:\n",
- "\n",
- "- one that adds 1 to the input integer,\n",
- "- one that subtracts 1 from the input integer,\n",
- "- one that multiplies the input integer by 2,\n",
- "- one that divides the input integer by 2 and rounds down to the nearest integer, and\n",
- "- one that returns the input integer unchanged.\n",
- "\n",
- "We then create a {py:class}`~autogen_agentchat.teams.SelectorGroupChat` with these agents,\n",
- "and set the appropriate selector settings:\n",
- "\n",
- "- allow the same agent to be selected consecutively to allow for repeated operations, and\n",
- "- customize the selector prompt to tailor the model's response to the specific task."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "---------- user ----------\n",
- "Apply the operations to turn the given number into 25.\n",
- "---------- user ----------\n",
- "10\n",
- "---------- multiply_agent ----------\n",
- "20\n",
- "---------- add_agent ----------\n",
- "21\n",
- "---------- multiply_agent ----------\n",
- "42\n",
- "---------- divide_agent ----------\n",
- "21\n",
- "---------- add_agent ----------\n",
- "22\n",
- "---------- add_agent ----------\n",
- "23\n",
- "---------- add_agent ----------\n",
- "24\n",
- "---------- add_agent ----------\n",
- "25\n",
- "---------- Summary ----------\n",
- "Number of messages: 10\n",
- "Finish reason: Maximum number of messages 10 reached, current message count: 10\n",
- "Total prompt tokens: 0\n",
- "Total completion tokens: 0\n",
- "Duration: 2.40 seconds\n"
- ]
- }
- ],
- "source": [
- "async def run_number_agents() -> None:\n",
- " # Create agents for number operations.\n",
- " add_agent = ArithmeticAgent(\"add_agent\", \"Adds 1 to the number.\", lambda x: x + 1)\n",
- " multiply_agent = ArithmeticAgent(\"multiply_agent\", \"Multiplies the number by 2.\", lambda x: x * 2)\n",
- " subtract_agent = ArithmeticAgent(\"subtract_agent\", \"Subtracts 1 from the number.\", lambda x: x - 1)\n",
- " divide_agent = ArithmeticAgent(\"divide_agent\", \"Divides the number by 2 and rounds down.\", lambda x: x // 2)\n",
- " identity_agent = ArithmeticAgent(\"identity_agent\", \"Returns the number as is.\", lambda x: x)\n",
- "\n",
- " # The termination condition is to stop after 10 messages.\n",
- " termination_condition = MaxMessageTermination(10)\n",
- "\n",
- " # Create a selector group chat.\n",
- " selector_group_chat = SelectorGroupChat(\n",
- " [add_agent, multiply_agent, subtract_agent, divide_agent, identity_agent],\n",
- " model_client=OpenAIChatCompletionClient(model=\"gpt-4o\"),\n",
- " termination_condition=termination_condition,\n",
- " allow_repeated_speaker=True, # Allow the same agent to speak multiple times, necessary for this task.\n",
- " selector_prompt=(\n",
- " \"Available roles:\\n{roles}\\nTheir job descriptions:\\n{participants}\\n\"\n",
- " \"Current conversation history:\\n{history}\\n\"\n",
- " \"Please select the most appropriate role for the next message, and only return the role name.\"\n",
- " ),\n",
- " )\n",
- "\n",
- " # Run the selector group chat with a given task and stream the response.\n",
- " task: List[ChatMessage] = [\n",
- " TextMessage(content=\"Apply the operations to turn the given number into 25.\", source=\"user\"),\n",
- " TextMessage(content=\"10\", source=\"user\"),\n",
- " ]\n",
- " stream = selector_group_chat.run_stream(task=task)\n",
- " await Console(stream)\n",
- "\n",
- "\n",
- "# Use asyncio.run(run_number_agents()) when running in a script.\n",
- "await run_number_agents()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "From the output, we can see that the agents have successfully transformed the input integer\n",
- "from 10 to 25 by choosing appropriate agents that apply the arithmetic operations in sequence."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Using Custom Model Clients in Custom Agents\n",
- "\n",
- "One of the key features of the {py:class}`~autogen_agentchat.agents.AssistantAgent` preset in AgentChat is that it takes a `model_client` argument and can use it in responding to messages. However, in some cases, you may want your agent to use a custom model client that is not currently supported (see [supported model clients](https://microsoft.github.io/autogen/dev/user-guide/core-user-guide/components/model-clients.html)) or custom model behaviours. \n",
- "\n",
- "You can accomplish this with a custom agent that implements *your custom model client*.\n",
- "\n",
- "In the example below, we will walk through an example of a custom agent that uses the [Google Gemini SDK](https://github.com/googleapis/python-genai) directly to respond to messages.\n",
- "\n",
- "> **Note:** You will need to install the [Google Gemini SDK](https://github.com/googleapis/python-genai) to run this example. You can install it using the following command: \n",
- "\n",
- "```bash\n",
- "pip install google-genai\n",
- "``` "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# !pip install google-genai\n",
- "import os\n",
- "from typing import AsyncGenerator, Sequence\n",
- "\n",
- "from autogen_agentchat.agents import BaseChatAgent\n",
- "from autogen_agentchat.base import Response\n",
- "from autogen_agentchat.messages import AgentEvent, ChatMessage\n",
- "from autogen_core import CancellationToken\n",
- "from autogen_core.model_context import UnboundedChatCompletionContext\n",
- "from autogen_core.models import AssistantMessage, RequestUsage, UserMessage\n",
- "from google import genai\n",
- "from google.genai import types\n",
- "\n",
- "\n",
- "class GeminiAssistantAgent(BaseChatAgent):\n",
- " def __init__(\n",
- " self,\n",
- " name: str,\n",
- " description: str = \"An agent that provides assistance with ability to use tools.\",\n",
- " model: str = \"gemini-1.5-flash-002\",\n",
- " api_key: str = os.environ[\"GEMINI_API_KEY\"],\n",
- " system_message: str\n",
- " | None = \"You are a helpful assistant that can respond to messages. Reply with TERMINATE when the task has been completed.\",\n",
- " ):\n",
- " super().__init__(name=name, description=description)\n",
- " self._model_context = UnboundedChatCompletionContext()\n",
- " self._model_client = genai.Client(api_key=api_key)\n",
- " self._system_message = system_message\n",
- " self._model = model\n",
- "\n",
- " @property\n",
- " def produced_message_types(self) -> Sequence[type[ChatMessage]]:\n",
- " return (TextMessage,)\n",
- "\n",
- " async def on_messages(self, messages: Sequence[ChatMessage], cancellation_token: CancellationToken) -> Response:\n",
- " final_response = None\n",
- " async for message in self.on_messages_stream(messages, cancellation_token):\n",
- " if isinstance(message, Response):\n",
- " final_response = message\n",
- "\n",
- " if final_response is None:\n",
- " raise AssertionError(\"The stream should have returned the final result.\")\n",
- "\n",
- " return final_response\n",
- "\n",
- " async def on_messages_stream(\n",
- " self, messages: Sequence[ChatMessage], cancellation_token: CancellationToken\n",
- " ) -> AsyncGenerator[AgentEvent | ChatMessage | Response, None]:\n",
- " # Add messages to the model context\n",
- " for msg in messages:\n",
- " await self._model_context.add_message(UserMessage(content=msg.content, source=msg.source))\n",
- "\n",
- " # Get conversation history\n",
- " history = [\n",
- " (msg.source if hasattr(msg, \"source\") else \"system\")\n",
- " + \": \"\n",
- " + (msg.content if isinstance(msg.content, str) else \"\")\n",
- " + \"\\n\"\n",
- " for msg in await self._model_context.get_messages()\n",
- " ]\n",
- " # Generate response using Gemini\n",
- " response = self._model_client.models.generate_content(\n",
- " model=self._model,\n",
- " contents=f\"History: {history}\\nGiven the history, please provide a response\",\n",
- " config=types.GenerateContentConfig(\n",
- " system_instruction=self._system_message,\n",
- " temperature=0.3,\n",
- " ),\n",
- " )\n",
- "\n",
- " # Create usage metadata\n",
- " usage = RequestUsage(\n",
- " prompt_tokens=response.usage_metadata.prompt_token_count,\n",
- " completion_tokens=response.usage_metadata.candidates_token_count,\n",
- " )\n",
- "\n",
- " # Add response to model context\n",
- " await self._model_context.add_message(AssistantMessage(content=response.text, source=self.name))\n",
- "\n",
- " # Yield the final response\n",
- " yield Response(\n",
- " chat_message=TextMessage(content=response.text, source=self.name, models_usage=usage),\n",
- " inner_messages=[],\n",
- " )\n",
- "\n",
- " async def on_reset(self, cancellation_token: CancellationToken) -> None:\n",
- " \"\"\"Reset the assistant by clearing the model context.\"\"\"\n",
- " await self._model_context.clear()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 38,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "---------- user ----------\n",
- "What is the capital of New York?\n",
- "---------- gemini_assistant ----------\n",
- "Albany\n",
- "TERMINATE\n",
- "\n"
- ]
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Custom Agents\n",
+ "\n",
+ "You may have agents with behaviors that do not fall into a preset. \n",
+ "In such cases, you can build custom agents.\n",
+ "\n",
+ "All agents in AgentChat inherit from {py:class}`~autogen_agentchat.agents.BaseChatAgent` \n",
+ "class and implement the following abstract methods and attributes:\n",
+ "\n",
+ "- {py:meth}`~autogen_agentchat.agents.BaseChatAgent.on_messages`: The abstract method that defines the behavior of the agent in response to messages. This method is called when the agent is asked to provide a response in {py:meth}`~autogen_agentchat.agents.BaseChatAgent.run`. It returns a {py:class}`~autogen_agentchat.base.Response` object.\n",
+ "- {py:meth}`~autogen_agentchat.agents.BaseChatAgent.on_reset`: The abstract method that resets the agent to its initial state. This method is called when the agent is asked to reset itself.\n",
+ "- {py:attr}`~autogen_agentchat.agents.BaseChatAgent.produced_message_types`: The list of possible {py:class}`~autogen_agentchat.messages.ChatMessage` message types the agent can produce in its response.\n",
+ "\n",
+ "Optionally, you can implement the the {py:meth}`~autogen_agentchat.agents.BaseChatAgent.on_messages_stream` method to stream messages as they are generated by the agent. If this method is not implemented, the agent\n",
+ "uses the default implementation of {py:meth}`~autogen_agentchat.agents.BaseChatAgent.on_messages_stream`\n",
+ "that calls the {py:meth}`~autogen_agentchat.agents.BaseChatAgent.on_messages` method and\n",
+ "yields all messages in the response."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## CountDownAgent\n",
+ "\n",
+ "In this example, we create a simple agent that counts down from a given number to zero,\n",
+ "and produces a stream of messages with the current count."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "3...\n",
+ "2...\n",
+ "1...\n",
+ "Done!\n"
+ ]
+ }
+ ],
+ "source": [
+ "from typing import AsyncGenerator, List, Sequence\n",
+ "\n",
+ "from autogen_agentchat.agents import BaseChatAgent\n",
+ "from autogen_agentchat.base import Response\n",
+ "from autogen_agentchat.messages import AgentEvent, ChatMessage, TextMessage\n",
+ "from autogen_core import CancellationToken\n",
+ "\n",
+ "\n",
+ "class CountDownAgent(BaseChatAgent):\n",
+ " def __init__(self, name: str, count: int = 3):\n",
+ " super().__init__(name, \"A simple agent that counts down.\")\n",
+ " self._count = count\n",
+ "\n",
+ " @property\n",
+ " def produced_message_types(self) -> Sequence[type[ChatMessage]]:\n",
+ " return (TextMessage,)\n",
+ "\n",
+ " async def on_messages(self, messages: Sequence[ChatMessage], cancellation_token: CancellationToken) -> Response:\n",
+ " # Calls the on_messages_stream.\n",
+ " response: Response | None = None\n",
+ " async for message in self.on_messages_stream(messages, cancellation_token):\n",
+ " if isinstance(message, Response):\n",
+ " response = message\n",
+ " assert response is not None\n",
+ " return response\n",
+ "\n",
+ " async def on_messages_stream(\n",
+ " self, messages: Sequence[ChatMessage], cancellation_token: CancellationToken\n",
+ " ) -> AsyncGenerator[AgentEvent | ChatMessage | Response, None]:\n",
+ " inner_messages: List[AgentEvent | ChatMessage] = []\n",
+ " for i in range(self._count, 0, -1):\n",
+ " msg = TextMessage(content=f\"{i}...\", source=self.name)\n",
+ " inner_messages.append(msg)\n",
+ " yield msg\n",
+ " # The response is returned at the end of the stream.\n",
+ " # It contains the final message and all the inner messages.\n",
+ " yield Response(chat_message=TextMessage(content=\"Done!\", source=self.name), inner_messages=inner_messages)\n",
+ "\n",
+ " async def on_reset(self, cancellation_token: CancellationToken) -> None:\n",
+ " pass\n",
+ "\n",
+ "\n",
+ "async def run_countdown_agent() -> None:\n",
+ " # Create a countdown agent.\n",
+ " countdown_agent = CountDownAgent(\"countdown\")\n",
+ "\n",
+ " # Run the agent with a given task and stream the response.\n",
+ " async for message in countdown_agent.on_messages_stream([], CancellationToken()):\n",
+ " if isinstance(message, Response):\n",
+ " print(message.chat_message)\n",
+ " else:\n",
+ " print(message)\n",
+ "\n",
+ "\n",
+ "# Use asyncio.run(run_countdown_agent()) when running in a script.\n",
+ "await run_countdown_agent()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## ArithmeticAgent\n",
+ "\n",
+ "In this example, we create an agent class that can perform simple arithmetic operations\n",
+ "on a given integer. Then, we will use different instances of this agent class\n",
+ "in a {py:class}`~autogen_agentchat.teams.SelectorGroupChat`\n",
+ "to transform a given integer into another integer by applying a sequence of arithmetic operations.\n",
+ "\n",
+ "The `ArithmeticAgent` class takes an `operator_func` that takes an integer and returns an integer,\n",
+ "after applying an arithmetic operation to the integer.\n",
+ "In its `on_messages` method, it applies the `operator_func` to the integer in the input message,\n",
+ "and returns a response with the result."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from typing import Callable, Sequence\n",
+ "\n",
+ "from autogen_agentchat.agents import BaseChatAgent\n",
+ "from autogen_agentchat.base import Response\n",
+ "from autogen_agentchat.conditions import MaxMessageTermination\n",
+ "from autogen_agentchat.messages import ChatMessage\n",
+ "from autogen_agentchat.teams import SelectorGroupChat\n",
+ "from autogen_agentchat.ui import Console\n",
+ "from autogen_core import CancellationToken\n",
+ "from autogen_ext.models.openai import OpenAIChatCompletionClient\n",
+ "\n",
+ "\n",
+ "class ArithmeticAgent(BaseChatAgent):\n",
+ " def __init__(self, name: str, description: str, operator_func: Callable[[int], int]) -> None:\n",
+ " super().__init__(name, description=description)\n",
+ " self._operator_func = operator_func\n",
+ " self._message_history: List[ChatMessage] = []\n",
+ "\n",
+ " @property\n",
+ " def produced_message_types(self) -> Sequence[type[ChatMessage]]:\n",
+ " return (TextMessage,)\n",
+ "\n",
+ " async def on_messages(self, messages: Sequence[ChatMessage], cancellation_token: CancellationToken) -> Response:\n",
+ " # Update the message history.\n",
+ " # NOTE: it is possible the messages is an empty list, which means the agent was selected previously.\n",
+ " self._message_history.extend(messages)\n",
+ " # Parse the number in the last message.\n",
+ " assert isinstance(self._message_history[-1], TextMessage)\n",
+ " number = int(self._message_history[-1].content)\n",
+ " # Apply the operator function to the number.\n",
+ " result = self._operator_func(number)\n",
+ " # Create a new message with the result.\n",
+ " response_message = TextMessage(content=str(result), source=self.name)\n",
+ " # Update the message history.\n",
+ " self._message_history.append(response_message)\n",
+ " # Return the response.\n",
+ " return Response(chat_message=response_message)\n",
+ "\n",
+ " async def on_reset(self, cancellation_token: CancellationToken) -> None:\n",
+ " pass"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "```{note}\n",
+ "The `on_messages` method may be called with an empty list of messages, in which\n",
+ "case it means the agent was called previously and is now being called again,\n",
+ "without any new messages from the caller. So it is important to keep a history\n",
+ "of the previous messages received by the agent, and use that history to generate\n",
+ "the response.\n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now we can create a {py:class}`~autogen_agentchat.teams.SelectorGroupChat` with 5 instances of `ArithmeticAgent`:\n",
+ "\n",
+ "- one that adds 1 to the input integer,\n",
+ "- one that subtracts 1 from the input integer,\n",
+ "- one that multiplies the input integer by 2,\n",
+ "- one that divides the input integer by 2 and rounds down to the nearest integer, and\n",
+ "- one that returns the input integer unchanged.\n",
+ "\n",
+ "We then create a {py:class}`~autogen_agentchat.teams.SelectorGroupChat` with these agents,\n",
+ "and set the appropriate selector settings:\n",
+ "\n",
+ "- allow the same agent to be selected consecutively to allow for repeated operations, and\n",
+ "- customize the selector prompt to tailor the model's response to the specific task."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "---------- user ----------\n",
+ "Apply the operations to turn the given number into 25.\n",
+ "---------- user ----------\n",
+ "10\n",
+ "---------- multiply_agent ----------\n",
+ "20\n",
+ "---------- add_agent ----------\n",
+ "21\n",
+ "---------- multiply_agent ----------\n",
+ "42\n",
+ "---------- divide_agent ----------\n",
+ "21\n",
+ "---------- add_agent ----------\n",
+ "22\n",
+ "---------- add_agent ----------\n",
+ "23\n",
+ "---------- add_agent ----------\n",
+ "24\n",
+ "---------- add_agent ----------\n",
+ "25\n",
+ "---------- Summary ----------\n",
+ "Number of messages: 10\n",
+ "Finish reason: Maximum number of messages 10 reached, current message count: 10\n",
+ "Total prompt tokens: 0\n",
+ "Total completion tokens: 0\n",
+ "Duration: 2.40 seconds\n"
+ ]
+ }
+ ],
+ "source": [
+ "async def run_number_agents() -> None:\n",
+ " # Create agents for number operations.\n",
+ " add_agent = ArithmeticAgent(\"add_agent\", \"Adds 1 to the number.\", lambda x: x + 1)\n",
+ " multiply_agent = ArithmeticAgent(\"multiply_agent\", \"Multiplies the number by 2.\", lambda x: x * 2)\n",
+ " subtract_agent = ArithmeticAgent(\"subtract_agent\", \"Subtracts 1 from the number.\", lambda x: x - 1)\n",
+ " divide_agent = ArithmeticAgent(\"divide_agent\", \"Divides the number by 2 and rounds down.\", lambda x: x // 2)\n",
+ " identity_agent = ArithmeticAgent(\"identity_agent\", \"Returns the number as is.\", lambda x: x)\n",
+ "\n",
+ " # The termination condition is to stop after 10 messages.\n",
+ " termination_condition = MaxMessageTermination(10)\n",
+ "\n",
+ " # Create a selector group chat.\n",
+ " selector_group_chat = SelectorGroupChat(\n",
+ " [add_agent, multiply_agent, subtract_agent, divide_agent, identity_agent],\n",
+ " model_client=OpenAIChatCompletionClient(model=\"gpt-4o\"),\n",
+ " termination_condition=termination_condition,\n",
+ " allow_repeated_speaker=True, # Allow the same agent to speak multiple times, necessary for this task.\n",
+ " selector_prompt=(\n",
+ " \"Available roles:\\n{roles}\\nTheir job descriptions:\\n{participants}\\n\"\n",
+ " \"Current conversation history:\\n{history}\\n\"\n",
+ " \"Please select the most appropriate role for the next message, and only return the role name.\"\n",
+ " ),\n",
+ " )\n",
+ "\n",
+ " # Run the selector group chat with a given task and stream the response.\n",
+ " task: List[ChatMessage] = [\n",
+ " TextMessage(content=\"Apply the operations to turn the given number into 25.\", source=\"user\"),\n",
+ " TextMessage(content=\"10\", source=\"user\"),\n",
+ " ]\n",
+ " stream = selector_group_chat.run_stream(task=task)\n",
+ " await Console(stream)\n",
+ "\n",
+ "\n",
+ "# Use asyncio.run(run_number_agents()) when running in a script.\n",
+ "await run_number_agents()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "From the output, we can see that the agents have successfully transformed the input integer\n",
+ "from 10 to 25 by choosing appropriate agents that apply the arithmetic operations in sequence."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Using Custom Model Clients in Custom Agents\n",
+ "\n",
+ "One of the key features of the {py:class}`~autogen_agentchat.agents.AssistantAgent` preset in AgentChat is that it takes a `model_client` argument and can use it in responding to messages. However, in some cases, you may want your agent to use a custom model client that is not currently supported (see [supported model clients](https://microsoft.github.io/autogen/dev/user-guide/core-user-guide/components/model-clients.html)) or custom model behaviours. \n",
+ "\n",
+ "You can accomplish this with a custom agent that implements *your custom model client*.\n",
+ "\n",
+ "In the example below, we will walk through an example of a custom agent that uses the [Google Gemini SDK](https://github.com/googleapis/python-genai) directly to respond to messages.\n",
+ "\n",
+ "> **Note:** You will need to install the [Google Gemini SDK](https://github.com/googleapis/python-genai) to run this example. You can install it using the following command: \n",
+ "\n",
+ "```bash\n",
+ "pip install google-genai\n",
+ "``` "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# !pip install google-genai\n",
+ "import os\n",
+ "from typing import AsyncGenerator, Sequence\n",
+ "\n",
+ "from autogen_agentchat.agents import BaseChatAgent\n",
+ "from autogen_agentchat.base import Response\n",
+ "from autogen_agentchat.messages import AgentEvent, ChatMessage\n",
+ "from autogen_core import CancellationToken\n",
+ "from autogen_core.model_context import UnboundedChatCompletionContext\n",
+ "from autogen_core.models import AssistantMessage, RequestUsage, UserMessage\n",
+ "from google import genai\n",
+ "from google.genai import types\n",
+ "\n",
+ "\n",
+ "class GeminiAssistantAgent(BaseChatAgent):\n",
+ " def __init__(\n",
+ " self,\n",
+ " name: str,\n",
+ " description: str = \"An agent that provides assistance with ability to use tools.\",\n",
+ " model: str = \"gemini-1.5-flash-002\",\n",
+ " api_key: str = os.environ[\"GEMINI_API_KEY\"],\n",
+ " system_message: str\n",
+ " | None = \"You are a helpful assistant that can respond to messages. Reply with TERMINATE when the task has been completed.\",\n",
+ " ):\n",
+ " super().__init__(name=name, description=description)\n",
+ " self._model_context = UnboundedChatCompletionContext()\n",
+ " self._model_client = genai.Client(api_key=api_key)\n",
+ " self._system_message = system_message\n",
+ " self._model = model\n",
+ "\n",
+ " @property\n",
+ " def produced_message_types(self) -> Sequence[type[ChatMessage]]:\n",
+ " return (TextMessage,)\n",
+ "\n",
+ " async def on_messages(self, messages: Sequence[ChatMessage], cancellation_token: CancellationToken) -> Response:\n",
+ " final_response = None\n",
+ " async for message in self.on_messages_stream(messages, cancellation_token):\n",
+ " if isinstance(message, Response):\n",
+ " final_response = message\n",
+ "\n",
+ " if final_response is None:\n",
+ " raise AssertionError(\"The stream should have returned the final result.\")\n",
+ "\n",
+ " return final_response\n",
+ "\n",
+ " async def on_messages_stream(\n",
+ " self, messages: Sequence[ChatMessage], cancellation_token: CancellationToken\n",
+ " ) -> AsyncGenerator[AgentEvent | ChatMessage | Response, None]:\n",
+ " # Add messages to the model context\n",
+ " for msg in messages:\n",
+ " await self._model_context.add_message(msg.to_model_message())\n",
+ "\n",
+ " # Get conversation history\n",
+ " history = [\n",
+ " (msg.source if hasattr(msg, \"source\") else \"system\")\n",
+ " + \": \"\n",
+ " + (msg.content if isinstance(msg.content, str) else \"\")\n",
+ " + \"\\n\"\n",
+ " for msg in await self._model_context.get_messages()\n",
+ " ]\n",
+ " # Generate response using Gemini\n",
+ " response = self._model_client.models.generate_content(\n",
+ " model=self._model,\n",
+ " contents=f\"History: {history}\\nGiven the history, please provide a response\",\n",
+ " config=types.GenerateContentConfig(\n",
+ " system_instruction=self._system_message,\n",
+ " temperature=0.3,\n",
+ " ),\n",
+ " )\n",
+ "\n",
+ " # Create usage metadata\n",
+ " usage = RequestUsage(\n",
+ " prompt_tokens=response.usage_metadata.prompt_token_count,\n",
+ " completion_tokens=response.usage_metadata.candidates_token_count,\n",
+ " )\n",
+ "\n",
+ " # Add response to model context\n",
+ " await self._model_context.add_message(AssistantMessage(content=response.text, source=self.name))\n",
+ "\n",
+ " # Yield the final response\n",
+ " yield Response(\n",
+ " chat_message=TextMessage(content=response.text, source=self.name, models_usage=usage),\n",
+ " inner_messages=[],\n",
+ " )\n",
+ "\n",
+ " async def on_reset(self, cancellation_token: CancellationToken) -> None:\n",
+ " \"\"\"Reset the assistant by clearing the model context.\"\"\"\n",
+ " await self._model_context.clear()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 38,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "---------- user ----------\n",
+ "What is the capital of New York?\n",
+ "---------- gemini_assistant ----------\n",
+ "Albany\n",
+ "TERMINATE\n",
+ "\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "TaskResult(messages=[TextMessage(source='user', models_usage=None, content='What is the capital of New York?', type='TextMessage'), TextMessage(source='gemini_assistant', models_usage=RequestUsage(prompt_tokens=46, completion_tokens=5), content='Albany\\nTERMINATE\\n', type='TextMessage')], stop_reason=None)"
+ ]
+ },
+ "execution_count": 38,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "gemini_assistant = GeminiAssistantAgent(\"gemini_assistant\")\n",
+ "await Console(gemini_assistant.run_stream(task=\"What is the capital of New York?\"))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In the example above, we have chosen to provide `model`, `api_key` and `system_message` as arguments - you can choose to provide any other arguments that are required by the model client you are using or fits with your application design. \n",
+ "\n",
+ "Now, let us explore how to use this custom agent as part of a team in AgentChat."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 39,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "---------- user ----------\n",
+ "Write a Haiku poem with 4 lines about the fall season.\n",
+ "---------- primary ----------\n",
+ "Crimson leaves cascade, \n",
+ "Whispering winds sing of change, \n",
+ "Chill wraps the fading, \n",
+ "Nature's quilt, rich and warm.\n",
+ "---------- gemini_critic ----------\n",
+ "The poem is good, but it has four lines instead of three. A haiku must have three lines with a 5-7-5 syllable structure. The content is evocative of autumn, but the form is incorrect. Please revise to adhere to the haiku's syllable structure.\n",
+ "\n",
+ "---------- primary ----------\n",
+ "Thank you for your feedback! Here’s a revised haiku that follows the 5-7-5 syllable structure:\n",
+ "\n",
+ "Crimson leaves drift down, \n",
+ "Chill winds whisper through the gold, \n",
+ "Autumn’s breath is near.\n",
+ "---------- gemini_critic ----------\n",
+ "The revised haiku is much improved. It correctly follows the 5-7-5 syllable structure and maintains the evocative imagery of autumn. APPROVE\n",
+ "\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "TaskResult(messages=[TextMessage(source='user', models_usage=None, content='Write a Haiku poem with 4 lines about the fall season.', type='TextMessage'), TextMessage(source='primary', models_usage=RequestUsage(prompt_tokens=33, completion_tokens=31), content=\"Crimson leaves cascade, \\nWhispering winds sing of change, \\nChill wraps the fading, \\nNature's quilt, rich and warm.\", type='TextMessage'), TextMessage(source='gemini_critic', models_usage=RequestUsage(prompt_tokens=86, completion_tokens=60), content=\"The poem is good, but it has four lines instead of three. A haiku must have three lines with a 5-7-5 syllable structure. The content is evocative of autumn, but the form is incorrect. Please revise to adhere to the haiku's syllable structure.\\n\", type='TextMessage'), TextMessage(source='primary', models_usage=RequestUsage(prompt_tokens=141, completion_tokens=49), content='Thank you for your feedback! Here’s a revised haiku that follows the 5-7-5 syllable structure:\\n\\nCrimson leaves drift down, \\nChill winds whisper through the gold, \\nAutumn’s breath is near.', type='TextMessage'), TextMessage(source='gemini_critic', models_usage=RequestUsage(prompt_tokens=211, completion_tokens=32), content='The revised haiku is much improved. It correctly follows the 5-7-5 syllable structure and maintains the evocative imagery of autumn. APPROVE\\n', type='TextMessage')], stop_reason=\"Text 'APPROVE' mentioned\")"
+ ]
+ },
+ "execution_count": 39,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from autogen_agentchat.agents import AssistantAgent\n",
+ "from autogen_agentchat.conditions import TextMentionTermination\n",
+ "from autogen_agentchat.teams import RoundRobinGroupChat\n",
+ "from autogen_agentchat.ui import Console\n",
+ "\n",
+ "model_client = OpenAIChatCompletionClient(model=\"gpt-4o-mini\")\n",
+ "\n",
+ "# Create the primary agent.\n",
+ "primary_agent = AssistantAgent(\n",
+ " \"primary\",\n",
+ " model_client=model_client,\n",
+ " system_message=\"You are a helpful AI assistant.\",\n",
+ ")\n",
+ "\n",
+ "# Create a critic agent based on our new GeminiAssistantAgent.\n",
+ "gemini_critic_agent = GeminiAssistantAgent(\n",
+ " \"gemini_critic\",\n",
+ " system_message=\"Provide constructive feedback. Respond with 'APPROVE' to when your feedbacks are addressed.\",\n",
+ ")\n",
+ "\n",
+ "\n",
+ "# Define a termination condition that stops the task if the critic approves or after 10 messages.\n",
+ "termination = TextMentionTermination(\"APPROVE\") | MaxMessageTermination(10)\n",
+ "\n",
+ "# Create a team with the primary and critic agents.\n",
+ "team = RoundRobinGroupChat([primary_agent, gemini_critic_agent], termination_condition=termination)\n",
+ "\n",
+ "await Console(team.run_stream(task=\"Write a Haiku poem with 4 lines about the fall season.\"))\n",
+ "await model_client.close()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In section above, we show several very important concepts:\n",
+ "- We have developed a custom agent that uses the Google Gemini SDK to respond to messages. \n",
+ "- We show that this custom agent can be used as part of the broader AgentChat ecosystem - in this case as a participant in a {py:class}`~autogen_agentchat.teams.RoundRobinGroupChat` as long as it inherits from {py:class}`~autogen_agentchat.agents.BaseChatAgent`.\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Making the Custom Agent Declarative \n",
+ "\n",
+ "Autogen provides a [Component](https://microsoft.github.io/autogen/dev/user-guide/core-user-guide/framework/component-config.html) interface for making the configuration of components serializable to a declarative format. This is useful for saving and loading configurations, and for sharing configurations with others. \n",
+ "\n",
+ "We accomplish this by inheriting from the `Component` class and implementing the `_from_config` and `_to_config` methods.\n",
+ "The declarative class can be serialized to a JSON format using the `dump_component` method, and deserialized from a JSON format using the `load_component` method."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "from typing import AsyncGenerator, Sequence\n",
+ "\n",
+ "from autogen_agentchat.agents import BaseChatAgent\n",
+ "from autogen_agentchat.base import Response\n",
+ "from autogen_agentchat.messages import AgentEvent, ChatMessage\n",
+ "from autogen_core import CancellationToken, Component\n",
+ "from pydantic import BaseModel\n",
+ "from typing_extensions import Self\n",
+ "\n",
+ "\n",
+ "class GeminiAssistantAgentConfig(BaseModel):\n",
+ " name: str\n",
+ " description: str = \"An agent that provides assistance with ability to use tools.\"\n",
+ " model: str = \"gemini-1.5-flash-002\"\n",
+ " system_message: str | None = None\n",
+ "\n",
+ "\n",
+ "class GeminiAssistantAgent(BaseChatAgent, Component[GeminiAssistantAgentConfig]): # type: ignore[no-redef]\n",
+ " component_config_schema = GeminiAssistantAgentConfig\n",
+ " # component_provider_override = \"mypackage.agents.GeminiAssistantAgent\"\n",
+ "\n",
+ " def __init__(\n",
+ " self,\n",
+ " name: str,\n",
+ " description: str = \"An agent that provides assistance with ability to use tools.\",\n",
+ " model: str = \"gemini-1.5-flash-002\",\n",
+ " api_key: str = os.environ[\"GEMINI_API_KEY\"],\n",
+ " system_message: str\n",
+ " | None = \"You are a helpful assistant that can respond to messages. Reply with TERMINATE when the task has been completed.\",\n",
+ " ):\n",
+ " super().__init__(name=name, description=description)\n",
+ " self._model_context = UnboundedChatCompletionContext()\n",
+ " self._model_client = genai.Client(api_key=api_key)\n",
+ " self._system_message = system_message\n",
+ " self._model = model\n",
+ "\n",
+ " @property\n",
+ " def produced_message_types(self) -> Sequence[type[ChatMessage]]:\n",
+ " return (TextMessage,)\n",
+ "\n",
+ " async def on_messages(self, messages: Sequence[ChatMessage], cancellation_token: CancellationToken) -> Response:\n",
+ " final_response = None\n",
+ " async for message in self.on_messages_stream(messages, cancellation_token):\n",
+ " if isinstance(message, Response):\n",
+ " final_response = message\n",
+ "\n",
+ " if final_response is None:\n",
+ " raise AssertionError(\"The stream should have returned the final result.\")\n",
+ "\n",
+ " return final_response\n",
+ "\n",
+ " async def on_messages_stream(\n",
+ " self, messages: Sequence[ChatMessage], cancellation_token: CancellationToken\n",
+ " ) -> AsyncGenerator[AgentEvent | ChatMessage | Response, None]:\n",
+ " # Add messages to the model context\n",
+ " for msg in messages:\n",
+ " await self._model_context.add_message(msg.to_model_message())\n",
+ "\n",
+ " # Get conversation history\n",
+ " history = [\n",
+ " (msg.source if hasattr(msg, \"source\") else \"system\")\n",
+ " + \": \"\n",
+ " + (msg.content if isinstance(msg.content, str) else \"\")\n",
+ " + \"\\n\"\n",
+ " for msg in await self._model_context.get_messages()\n",
+ " ]\n",
+ "\n",
+ " # Generate response using Gemini\n",
+ " response = self._model_client.models.generate_content(\n",
+ " model=self._model,\n",
+ " contents=f\"History: {history}\\nGiven the history, please provide a response\",\n",
+ " config=types.GenerateContentConfig(\n",
+ " system_instruction=self._system_message,\n",
+ " temperature=0.3,\n",
+ " ),\n",
+ " )\n",
+ "\n",
+ " # Create usage metadata\n",
+ " usage = RequestUsage(\n",
+ " prompt_tokens=response.usage_metadata.prompt_token_count,\n",
+ " completion_tokens=response.usage_metadata.candidates_token_count,\n",
+ " )\n",
+ "\n",
+ " # Add response to model context\n",
+ " await self._model_context.add_message(AssistantMessage(content=response.text, source=self.name))\n",
+ "\n",
+ " # Yield the final response\n",
+ " yield Response(\n",
+ " chat_message=TextMessage(content=response.text, source=self.name, models_usage=usage),\n",
+ " inner_messages=[],\n",
+ " )\n",
+ "\n",
+ " async def on_reset(self, cancellation_token: CancellationToken) -> None:\n",
+ " \"\"\"Reset the assistant by clearing the model context.\"\"\"\n",
+ " await self._model_context.clear()\n",
+ "\n",
+ " @classmethod\n",
+ " def _from_config(cls, config: GeminiAssistantAgentConfig) -> Self:\n",
+ " return cls(\n",
+ " name=config.name, description=config.description, model=config.model, system_message=config.system_message\n",
+ " )\n",
+ "\n",
+ " def _to_config(self) -> GeminiAssistantAgentConfig:\n",
+ " return GeminiAssistantAgentConfig(\n",
+ " name=self.name,\n",
+ " description=self.description,\n",
+ " model=self._model,\n",
+ " system_message=self._system_message,\n",
+ " )"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now that we have the required methods implemented, we can now load and dump the custom agent to and from a JSON format, and then load the agent from the JSON format.\n",
+ " \n",
+ " > Note: You should set the `component_provider_override` class variable to the full path of the module containing the custom agent class e.g., (`mypackage.agents.GeminiAssistantAgent`). This is used by `load_component` method to determine how to instantiate the class. \n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 41,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "{\n",
+ " \"provider\": \"__main__.GeminiAssistantAgent\",\n",
+ " \"component_type\": \"agent\",\n",
+ " \"version\": 1,\n",
+ " \"component_version\": 1,\n",
+ " \"description\": null,\n",
+ " \"label\": \"GeminiAssistantAgent\",\n",
+ " \"config\": {\n",
+ " \"name\": \"gemini_assistant\",\n",
+ " \"description\": \"An agent that provides assistance with ability to use tools.\",\n",
+ " \"model\": \"gemini-1.5-flash-002\",\n",
+ " \"system_message\": \"You are a helpful assistant that can respond to messages. Reply with TERMINATE when the task has been completed.\"\n",
+ " }\n",
+ "}\n",
+ "<__main__.GeminiAssistantAgent object at 0x11a5c5a90>\n"
+ ]
+ }
+ ],
+ "source": [
+ "gemini_assistant = GeminiAssistantAgent(\"gemini_assistant\")\n",
+ "config = gemini_assistant.dump_component()\n",
+ "print(config.model_dump_json(indent=2))\n",
+ "loaded_agent = GeminiAssistantAgent.load_component(config)\n",
+ "print(loaded_agent)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Next Steps \n",
+ "\n",
+ "So far, we have seen how to create custom agents, add custom model clients to agents, and make custom agents declarative. There are a few ways in which this basic sample can be extended:\n",
+ "\n",
+ "- Extend the Gemini model client to handle function calling similar to the {py:class}`~autogen_agentchat.agents.AssistantAgent` class. https://ai.google.dev/gemini-api/docs/function-calling \n",
+ "- Implement a package with a custom agent and experiment with using its declarative format in a tool like [AutoGen Studio](https://microsoft.github.io/autogen/stable/user-guide/autogenstudio-user-guide/index.html)."
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": ".venv",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.12.3"
+ }
},
- {
- "data": {
- "text/plain": [
- "TaskResult(messages=[TextMessage(source='user', models_usage=None, content='What is the capital of New York?', type='TextMessage'), TextMessage(source='gemini_assistant', models_usage=RequestUsage(prompt_tokens=46, completion_tokens=5), content='Albany\\nTERMINATE\\n', type='TextMessage')], stop_reason=None)"
- ]
- },
- "execution_count": 38,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "gemini_assistant = GeminiAssistantAgent(\"gemini_assistant\")\n",
- "await Console(gemini_assistant.run_stream(task=\"What is the capital of New York?\"))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "In the example above, we have chosen to provide `model`, `api_key` and `system_message` as arguments - you can choose to provide any other arguments that are required by the model client you are using or fits with your application design. \n",
- "\n",
- "Now, let us explore how to use this custom agent as part of a team in AgentChat."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 39,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "---------- user ----------\n",
- "Write a Haiku poem with 4 lines about the fall season.\n",
- "---------- primary ----------\n",
- "Crimson leaves cascade, \n",
- "Whispering winds sing of change, \n",
- "Chill wraps the fading, \n",
- "Nature's quilt, rich and warm.\n",
- "---------- gemini_critic ----------\n",
- "The poem is good, but it has four lines instead of three. A haiku must have three lines with a 5-7-5 syllable structure. The content is evocative of autumn, but the form is incorrect. Please revise to adhere to the haiku's syllable structure.\n",
- "\n",
- "---------- primary ----------\n",
- "Thank you for your feedback! Here’s a revised haiku that follows the 5-7-5 syllable structure:\n",
- "\n",
- "Crimson leaves drift down, \n",
- "Chill winds whisper through the gold, \n",
- "Autumn’s breath is near.\n",
- "---------- gemini_critic ----------\n",
- "The revised haiku is much improved. It correctly follows the 5-7-5 syllable structure and maintains the evocative imagery of autumn. APPROVE\n",
- "\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "TaskResult(messages=[TextMessage(source='user', models_usage=None, content='Write a Haiku poem with 4 lines about the fall season.', type='TextMessage'), TextMessage(source='primary', models_usage=RequestUsage(prompt_tokens=33, completion_tokens=31), content=\"Crimson leaves cascade, \\nWhispering winds sing of change, \\nChill wraps the fading, \\nNature's quilt, rich and warm.\", type='TextMessage'), TextMessage(source='gemini_critic', models_usage=RequestUsage(prompt_tokens=86, completion_tokens=60), content=\"The poem is good, but it has four lines instead of three. A haiku must have three lines with a 5-7-5 syllable structure. The content is evocative of autumn, but the form is incorrect. Please revise to adhere to the haiku's syllable structure.\\n\", type='TextMessage'), TextMessage(source='primary', models_usage=RequestUsage(prompt_tokens=141, completion_tokens=49), content='Thank you for your feedback! Here’s a revised haiku that follows the 5-7-5 syllable structure:\\n\\nCrimson leaves drift down, \\nChill winds whisper through the gold, \\nAutumn’s breath is near.', type='TextMessage'), TextMessage(source='gemini_critic', models_usage=RequestUsage(prompt_tokens=211, completion_tokens=32), content='The revised haiku is much improved. It correctly follows the 5-7-5 syllable structure and maintains the evocative imagery of autumn. APPROVE\\n', type='TextMessage')], stop_reason=\"Text 'APPROVE' mentioned\")"
- ]
- },
- "execution_count": 39,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "from autogen_agentchat.agents import AssistantAgent\n",
- "from autogen_agentchat.conditions import TextMentionTermination\n",
- "from autogen_agentchat.teams import RoundRobinGroupChat\n",
- "from autogen_agentchat.ui import Console\n",
- "\n",
- "model_client = OpenAIChatCompletionClient(model=\"gpt-4o-mini\")\n",
- "\n",
- "# Create the primary agent.\n",
- "primary_agent = AssistantAgent(\n",
- " \"primary\",\n",
- " model_client=model_client,\n",
- " system_message=\"You are a helpful AI assistant.\",\n",
- ")\n",
- "\n",
- "# Create a critic agent based on our new GeminiAssistantAgent.\n",
- "gemini_critic_agent = GeminiAssistantAgent(\n",
- " \"gemini_critic\",\n",
- " system_message=\"Provide constructive feedback. Respond with 'APPROVE' to when your feedbacks are addressed.\",\n",
- ")\n",
- "\n",
- "\n",
- "# Define a termination condition that stops the task if the critic approves or after 10 messages.\n",
- "termination = TextMentionTermination(\"APPROVE\") | MaxMessageTermination(10)\n",
- "\n",
- "# Create a team with the primary and critic agents.\n",
- "team = RoundRobinGroupChat([primary_agent, gemini_critic_agent], termination_condition=termination)\n",
- "\n",
- "await Console(team.run_stream(task=\"Write a Haiku poem with 4 lines about the fall season.\"))\n",
- "await model_client.close()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "In section above, we show several very important concepts:\n",
- "- We have developed a custom agent that uses the Google Gemini SDK to respond to messages. \n",
- "- We show that this custom agent can be used as part of the broader AgentChat ecosystem - in this case as a participant in a {py:class}`~autogen_agentchat.teams.RoundRobinGroupChat` as long as it inherits from {py:class}`~autogen_agentchat.agents.BaseChatAgent`.\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Making the Custom Agent Declarative \n",
- "\n",
- "Autogen provides a [Component](https://microsoft.github.io/autogen/dev/user-guide/core-user-guide/framework/component-config.html) interface for making the configuration of components serializable to a declarative format. This is useful for saving and loading configurations, and for sharing configurations with others. \n",
- "\n",
- "We accomplish this by inheriting from the `Component` class and implementing the `_from_config` and `_to_config` methods.\n",
- "The declarative class can be serialized to a JSON format using the `dump_component` method, and deserialized from a JSON format using the `load_component` method."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 40,
- "metadata": {},
- "outputs": [],
- "source": [
- "import os\n",
- "from typing import AsyncGenerator, Sequence\n",
- "\n",
- "from autogen_agentchat.agents import BaseChatAgent\n",
- "from autogen_agentchat.base import Response\n",
- "from autogen_agentchat.messages import AgentEvent, ChatMessage\n",
- "from autogen_core import CancellationToken, Component\n",
- "from pydantic import BaseModel\n",
- "from typing_extensions import Self\n",
- "\n",
- "\n",
- "class GeminiAssistantAgentConfig(BaseModel):\n",
- " name: str\n",
- " description: str = \"An agent that provides assistance with ability to use tools.\"\n",
- " model: str = \"gemini-1.5-flash-002\"\n",
- " system_message: str | None = None\n",
- "\n",
- "\n",
- "class GeminiAssistantAgent(BaseChatAgent, Component[GeminiAssistantAgentConfig]): # type: ignore[no-redef]\n",
- " component_config_schema = GeminiAssistantAgentConfig\n",
- " # component_provider_override = \"mypackage.agents.GeminiAssistantAgent\"\n",
- "\n",
- " def __init__(\n",
- " self,\n",
- " name: str,\n",
- " description: str = \"An agent that provides assistance with ability to use tools.\",\n",
- " model: str = \"gemini-1.5-flash-002\",\n",
- " api_key: str = os.environ[\"GEMINI_API_KEY\"],\n",
- " system_message: str\n",
- " | None = \"You are a helpful assistant that can respond to messages. Reply with TERMINATE when the task has been completed.\",\n",
- " ):\n",
- " super().__init__(name=name, description=description)\n",
- " self._model_context = UnboundedChatCompletionContext()\n",
- " self._model_client = genai.Client(api_key=api_key)\n",
- " self._system_message = system_message\n",
- " self._model = model\n",
- "\n",
- " @property\n",
- " def produced_message_types(self) -> Sequence[type[ChatMessage]]:\n",
- " return (TextMessage,)\n",
- "\n",
- " async def on_messages(self, messages: Sequence[ChatMessage], cancellation_token: CancellationToken) -> Response:\n",
- " final_response = None\n",
- " async for message in self.on_messages_stream(messages, cancellation_token):\n",
- " if isinstance(message, Response):\n",
- " final_response = message\n",
- "\n",
- " if final_response is None:\n",
- " raise AssertionError(\"The stream should have returned the final result.\")\n",
- "\n",
- " return final_response\n",
- "\n",
- " async def on_messages_stream(\n",
- " self, messages: Sequence[ChatMessage], cancellation_token: CancellationToken\n",
- " ) -> AsyncGenerator[AgentEvent | ChatMessage | Response, None]:\n",
- " # Add messages to the model context\n",
- " for msg in messages:\n",
- " await self._model_context.add_message(UserMessage(content=msg.content, source=msg.source))\n",
- "\n",
- " # Get conversation history\n",
- " history = [\n",
- " (msg.source if hasattr(msg, \"source\") else \"system\")\n",
- " + \": \"\n",
- " + (msg.content if isinstance(msg.content, str) else \"\")\n",
- " + \"\\n\"\n",
- " for msg in await self._model_context.get_messages()\n",
- " ]\n",
- "\n",
- " # Generate response using Gemini\n",
- " response = self._model_client.models.generate_content(\n",
- " model=self._model,\n",
- " contents=f\"History: {history}\\nGiven the history, please provide a response\",\n",
- " config=types.GenerateContentConfig(\n",
- " system_instruction=self._system_message,\n",
- " temperature=0.3,\n",
- " ),\n",
- " )\n",
- "\n",
- " # Create usage metadata\n",
- " usage = RequestUsage(\n",
- " prompt_tokens=response.usage_metadata.prompt_token_count,\n",
- " completion_tokens=response.usage_metadata.candidates_token_count,\n",
- " )\n",
- "\n",
- " # Add response to model context\n",
- " await self._model_context.add_message(AssistantMessage(content=response.text, source=self.name))\n",
- "\n",
- " # Yield the final response\n",
- " yield Response(\n",
- " chat_message=TextMessage(content=response.text, source=self.name, models_usage=usage),\n",
- " inner_messages=[],\n",
- " )\n",
- "\n",
- " async def on_reset(self, cancellation_token: CancellationToken) -> None:\n",
- " \"\"\"Reset the assistant by clearing the model context.\"\"\"\n",
- " await self._model_context.clear()\n",
- "\n",
- " @classmethod\n",
- " def _from_config(cls, config: GeminiAssistantAgentConfig) -> Self:\n",
- " return cls(\n",
- " name=config.name, description=config.description, model=config.model, system_message=config.system_message\n",
- " )\n",
- "\n",
- " def _to_config(self) -> GeminiAssistantAgentConfig:\n",
- " return GeminiAssistantAgentConfig(\n",
- " name=self.name,\n",
- " description=self.description,\n",
- " model=self._model,\n",
- " system_message=self._system_message,\n",
- " )"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Now that we have the required methods implemented, we can now load and dump the custom agent to and from a JSON format, and then load the agent from the JSON format.\n",
- " \n",
- " > Note: You should set the `component_provider_override` class variable to the full path of the module containing the custom agent class e.g., (`mypackage.agents.GeminiAssistantAgent`). This is used by `load_component` method to determine how to instantiate the class. \n",
- " "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 41,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "{\n",
- " \"provider\": \"__main__.GeminiAssistantAgent\",\n",
- " \"component_type\": \"agent\",\n",
- " \"version\": 1,\n",
- " \"component_version\": 1,\n",
- " \"description\": null,\n",
- " \"label\": \"GeminiAssistantAgent\",\n",
- " \"config\": {\n",
- " \"name\": \"gemini_assistant\",\n",
- " \"description\": \"An agent that provides assistance with ability to use tools.\",\n",
- " \"model\": \"gemini-1.5-flash-002\",\n",
- " \"system_message\": \"You are a helpful assistant that can respond to messages. Reply with TERMINATE when the task has been completed.\"\n",
- " }\n",
- "}\n",
- "<__main__.GeminiAssistantAgent object at 0x11a5c5a90>\n"
- ]
- }
- ],
- "source": [
- "gemini_assistant = GeminiAssistantAgent(\"gemini_assistant\")\n",
- "config = gemini_assistant.dump_component()\n",
- "print(config.model_dump_json(indent=2))\n",
- "loaded_agent = GeminiAssistantAgent.load_component(config)\n",
- "print(loaded_agent)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Next Steps \n",
- "\n",
- "So far, we have seen how to create custom agents, add custom model clients to agents, and make custom agents declarative. There are a few ways in which this basic sample can be extended:\n",
- "\n",
- "- Extend the Gemini model client to handle function calling similar to the {py:class}`~autogen_agentchat.agents.AssistantAgent` class. https://ai.google.dev/gemini-api/docs/function-calling \n",
- "- Implement a package with a custom agent and experiment with using its declarative format in a tool like [AutoGen Studio](https://microsoft.github.io/autogen/stable/user-guide/autogenstudio-user-guide/index.html)."
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": ".venv",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.11.9"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat": 4,
+ "nbformat_minor": 2
}
diff --git a/python/packages/autogen-core/docs/src/user-guide/agentchat-user-guide/migration-guide.md b/python/packages/autogen-core/docs/src/user-guide/agentchat-user-guide/migration-guide.md
index d0533435fe42..9d505d7718a9 100644
--- a/python/packages/autogen-core/docs/src/user-guide/agentchat-user-guide/migration-guide.md
+++ b/python/packages/autogen-core/docs/src/user-guide/agentchat-user-guide/migration-guide.md
@@ -691,7 +691,7 @@ async def main() -> None:
if user_input == "exit":
break
response = await assistant.on_messages([TextMessage(content=user_input, source="user")], CancellationToken())
- print("Assistant:", response.chat_message.content)
+ print("Assistant:", response.chat_message.to_text())
await model_client.close()
asyncio.run(main())
@@ -1331,7 +1331,7 @@ async def main() -> None:
if user_input == "exit":
break
response = await assistant.on_messages([TextMessage(content=user_input, source="user")], CancellationToken())
- print("Assistant:", response.chat_message.content)
+ print("Assistant:", response.chat_message.to_text())
await model_client.close()
diff --git a/python/packages/autogen-core/docs/src/user-guide/agentchat-user-guide/selector-group-chat.ipynb b/python/packages/autogen-core/docs/src/user-guide/agentchat-user-guide/selector-group-chat.ipynb
index 522c26b2098c..fdc2b9a9d51f 100644
--- a/python/packages/autogen-core/docs/src/user-guide/agentchat-user-guide/selector-group-chat.ipynb
+++ b/python/packages/autogen-core/docs/src/user-guide/agentchat-user-guide/selector-group-chat.ipynb
@@ -1,1026 +1,1026 @@
{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Selector Group Chat"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "{py:class}`~autogen_agentchat.teams.SelectorGroupChat` implements a team where participants take turns broadcasting messages to all other members. A generative model (e.g., an LLM) selects the next speaker based on the shared context, enabling dynamic, context-aware collaboration.\n",
- "\n",
- "Key features include:\n",
- "\n",
- "- Model-based speaker selection\n",
- "- Configurable participant roles and descriptions\n",
- "- Prevention of consecutive turns by the same speaker (optional)\n",
- "- Customizable selection prompting\n",
- "- Customizable selection function to override the default model-based selection\n",
- "- Customizable candidate function to narrow-down the set of agents for selection using model\n",
- "\n",
- "```{note}\n",
- "{py:class}`~autogen_agentchat.teams.SelectorGroupChat` is a high-level API. For more control and customization, refer to the [Group Chat Pattern](../core-user-guide/design-patterns/group-chat.ipynb) in the Core API documentation to implement your own group chat logic.\n",
- "```\n",
- "\n",
- "## How Does it Work?\n",
- "\n",
- "{py:class}`~autogen_agentchat.teams.SelectorGroupChat` is a group chat similar to {py:class}`~autogen_agentchat.teams.RoundRobinGroupChat`,\n",
- "but with a model-based next speaker selection mechanism.\n",
- "When the team receives a task through {py:meth}`~autogen_agentchat.teams.BaseGroupChat.run` or {py:meth}`~autogen_agentchat.teams.BaseGroupChat.run_stream`,\n",
- "the following steps are executed:\n",
- "\n",
- "1. The team analyzes the current conversation context, including the conversation history and participants' {py:attr}`~autogen_agentchat.base.ChatAgent.name` and {py:attr}`~autogen_agentchat.base.ChatAgent.description` attributes, to determine the next speaker using a model. By default, the team will not select the same speak consecutively unless it is the only agent available. This can be changed by setting `allow_repeated_speaker=True`. You can also override the model by providing a custom selection function.\n",
- "2. The team prompts the selected speaker agent to provide a response, which is then **broadcasted** to all other participants.\n",
- "3. The termination condition is checked to determine if the conversation should end, if not, the process repeats from step 1.\n",
- "4. When the conversation ends, the team returns the {py:class}`~autogen_agentchat.base.TaskResult` containing the conversation history from this task.\n",
- "\n",
- "Once the team finishes the task, the conversation context is kept within the team and all participants, so the next task can continue from the previous conversation context.\n",
- "You can reset the conversation context by calling {py:meth}`~autogen_agentchat.teams.BaseGroupChat.reset`.\n",
- "\n",
- "In this section, we will demonstrate how to use {py:class}`~autogen_agentchat.teams.SelectorGroupChat` with a simple example for a web search and data analysis task."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Example: Web Search/Analysis"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [],
- "source": [
- "from typing import List, Sequence\n",
- "\n",
- "from autogen_agentchat.agents import AssistantAgent, UserProxyAgent\n",
- "from autogen_agentchat.conditions import MaxMessageTermination, TextMentionTermination\n",
- "from autogen_agentchat.messages import AgentEvent, ChatMessage\n",
- "from autogen_agentchat.teams import SelectorGroupChat\n",
- "from autogen_agentchat.ui import Console\n",
- "from autogen_ext.models.openai import OpenAIChatCompletionClient"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Agents\n",
- "\n",
- "\n",
- "\n",
- "This system uses three specialized agents:\n",
- "\n",
- "- **Planning Agent**: The strategic coordinator that breaks down complex tasks into manageable subtasks. \n",
- "- **Web Search Agent**: An information retrieval specialist that interfaces with the `search_web_tool`.\n",
- "- **Data Analyst Agent**: An agent specialist in performing calculations equipped with `percentage_change_tool`. "
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "The tools `search_web_tool` and `percentage_change_tool` are external tools that the agents can use to perform their tasks."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Note: This example uses mock tools instead of real APIs for demonstration purposes\n",
- "def search_web_tool(query: str) -> str:\n",
- " if \"2006-2007\" in query:\n",
- " return \"\"\"Here are the total points scored by Miami Heat players in the 2006-2007 season:\n",
- " Udonis Haslem: 844 points\n",
- " Dwayne Wade: 1397 points\n",
- " James Posey: 550 points\n",
- " ...\n",
- " \"\"\"\n",
- " elif \"2007-2008\" in query:\n",
- " return \"The number of total rebounds for Dwayne Wade in the Miami Heat season 2007-2008 is 214.\"\n",
- " elif \"2008-2009\" in query:\n",
- " return \"The number of total rebounds for Dwayne Wade in the Miami Heat season 2008-2009 is 398.\"\n",
- " return \"No data found.\"\n",
- "\n",
- "\n",
- "def percentage_change_tool(start: float, end: float) -> float:\n",
- " return ((end - start) / start) * 100"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Let's create the specialized agents using the {py:class}`~autogen_agentchat.agents.AssistantAgent` class.\n",
- "It is important to note that the agents' {py:attr}`~autogen_agentchat.base.ChatAgent.name` and {py:attr}`~autogen_agentchat.base.ChatAgent.description` attributes are used by the model to determine the next speaker,\n",
- "so it is recommended to provide meaningful names and descriptions."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [],
- "source": [
- "model_client = OpenAIChatCompletionClient(model=\"gpt-4o\")\n",
- "\n",
- "planning_agent = AssistantAgent(\n",
- " \"PlanningAgent\",\n",
- " description=\"An agent for planning tasks, this agent should be the first to engage when given a new task.\",\n",
- " model_client=model_client,\n",
- " system_message=\"\"\"\n",
- " You are a planning agent.\n",
- " Your job is to break down complex tasks into smaller, manageable subtasks.\n",
- " Your team members are:\n",
- " WebSearchAgent: Searches for information\n",
- " DataAnalystAgent: Performs calculations\n",
- "\n",
- " You only plan and delegate tasks - you do not execute them yourself.\n",
- "\n",
- " When assigning tasks, use this format:\n",
- " 1. <agent> : <task>\n",
- "\n",
- " After all tasks are complete, summarize the findings and end with \"TERMINATE\".\n",
- " \"\"\",\n",
- ")\n",
- "\n",
- "web_search_agent = AssistantAgent(\n",
- " \"WebSearchAgent\",\n",
- " description=\"An agent for searching information on the web.\",\n",
- " tools=[search_web_tool],\n",
- " model_client=model_client,\n",
- " system_message=\"\"\"\n",
- " You are a web search agent.\n",
- " Your only tool is search_tool - use it to find information.\n",
- " You make only one search call at a time.\n",
- " Once you have the results, you never do calculations based on them.\n",
- " \"\"\",\n",
- ")\n",
- "\n",
- "data_analyst_agent = AssistantAgent(\n",
- " \"DataAnalystAgent\",\n",
- " description=\"An agent for performing calculations.\",\n",
- " model_client=model_client,\n",
- " tools=[percentage_change_tool],\n",
- " system_message=\"\"\"\n",
- " You are a data analyst.\n",
- " Given the tasks you have been assigned, you should analyze the data and provide results using the tools provided.\n",
- " If you have not seen the data, ask for it.\n",
- " \"\"\",\n",
- ")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "```{note}\n",
- "By default, {py:class}`~autogen_agentchat.agents.AssistantAgent` returns the\n",
- "tool output as the response. If your tool does not return a well-formed\n",
- "string in natural language format, you may want to add a reflection step\n",
- "within the agent by setting `reflect_on_tool_use=True` when creating the agent.\n",
- "This will allow the agent to reflect on the tool output and provide a natural\n",
- "language response.\n",
- "```"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Workflow\n",
- "\n",
- "1. The task is received by the {py:class}`~autogen_agentchat.teams.SelectorGroupChat` which, based on agent descriptions, selects the most appropriate agent to handle the initial task (typically the Planning Agent).\n",
- "\n",
- "2. The **Planning Agent** analyzes the task and breaks it down into subtasks, assigning each to the most appropriate agent using the format:\n",
- " `<agent> : <task>`\n",
- "\n",
- "3. Based on the conversation context and agent descriptions, the {py:class}`~autogen_agent.teams.SelectorGroupChat` manager dynamically selects the next agent to handle their assigned subtask.\n",
- "\n",
- "4. The **Web Search Agent** performs searches one at a time, storing results in the shared conversation history.\n",
- "\n",
- "5. The **Data Analyst** processes the gathered information using available calculation tools when selected.\n",
- "\n",
- "6. The workflow continues with agents being dynamically selected until either:\n",
- " - The Planning Agent determines all subtasks are complete and sends \"TERMINATE\"\n",
- " - An alternative termination condition is met (e.g., a maximum number of messages)\n",
- "\n",
- "When defining your agents, make sure to include a helpful {py:attr}`~autogen_agentchat.base.ChatAgent.description` since this is used to decide which agent to select next."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Termination Conditions\n",
- "\n",
- "Let's use two termination conditions:\n",
- "{py:class}`~autogen_agentchat.conditions.TextMentionTermination` to end the conversation when the Planning Agent sends \"TERMINATE\",\n",
- "and {py:class}`~autogen_agentchat.conditions.MaxMessageTermination` to limit the conversation to 25 messages to avoid infinite loop."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {},
- "outputs": [],
- "source": [
- "text_mention_termination = TextMentionTermination(\"TERMINATE\")\n",
- "max_messages_termination = MaxMessageTermination(max_messages=25)\n",
- "termination = text_mention_termination | max_messages_termination"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Selector Prompt\n",
- "\n",
- "{py:class}`~autogen_agentchat.teams.SelectorGroupChat` uses a model to select\n",
- "the next speaker based on the conversation context.\n",
- "We will use a custom selector prompt to properly align with the workflow."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {},
- "outputs": [],
- "source": [
- "selector_prompt = \"\"\"Select an agent to perform task.\n",
- "\n",
- "{roles}\n",
- "\n",
- "Current conversation context:\n",
- "{history}\n",
- "\n",
- "Read the above conversation, then select an agent from {participants} to perform the next task.\n",
- "Make sure the planner agent has assigned tasks before other agents start working.\n",
- "Only select one agent.\n",
- "\"\"\""
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "```{tip}\n",
- "Try not to overload the model with too much instruction in the selector prompt.\n",
- "\n",
- "What is too much? It depends on the capabilities of the model you are using.\n",
- "For GPT-4o and equivalents, you can use a selector prompt with a condition for when each speaker should be selected.\n",
- "For smaller models such as Phi-4, you should keep the selector prompt as simple as possible\n",
- "such as the one used in this example.\n",
- "\n",
- "Generally, if you find yourself writing multiple conditions for each agent,\n",
- "it is a sign that you should consider using a custom selection function,\n",
- "or breaking down the task into smaller, sequential tasks to be handled by\n",
- "separate agents or teams.\n",
- "```"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Running the Team\n",
- "\n",
- "Let's create the team with the agents, termination conditions, and custom selector prompt."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {},
- "outputs": [],
- "source": [
- "team = SelectorGroupChat(\n",
- " [planning_agent, web_search_agent, data_analyst_agent],\n",
- " model_client=model_client,\n",
- " termination_condition=termination,\n",
- " selector_prompt=selector_prompt,\n",
- " allow_repeated_speaker=True, # Allow an agent to speak multiple turns in a row.\n",
- ")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Now we run the team with a task to find information about an NBA player."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {},
- "outputs": [],
- "source": [
- "task = \"Who was the Miami Heat player with the highest points in the 2006-2007 season, and what was the percentage change in his total rebounds between the 2007-2008 and 2008-2009 seasons?\""
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "---------- user ----------\n",
- "Who was the Miami Heat player with the highest points in the 2006-2007 season, and what was the percentage change in his total rebounds between the 2007-2008 and 2008-2009 seasons?\n",
- "---------- PlanningAgent ----------\n",
- "To complete this task, we need to perform the following subtasks:\n",
- "\n",
- "1. Find out which Miami Heat player had the highest points in the 2006-2007 season.\n",
- "2. Gather data on this player's total rebounds for the 2007-2008 season.\n",
- "3. Gather data on this player's total rebounds for the 2008-2009 season.\n",
- "4. Calculate the percentage change in the player's total rebounds between the 2007-2008 and 2008-2009 seasons.\n",
- "\n",
- "I'll assign these tasks accordingly:\n",
- "\n",
- "1. WebSearchAgent: Search for the Miami Heat player with the highest points in the 2006-2007 NBA season.\n",
- "2. WebSearchAgent: Find the total rebounds for this player in the 2007-2008 NBA season.\n",
- "3. WebSearchAgent: Find the total rebounds for this player in the 2008-2009 NBA season.\n",
- "4. DataAnalystAgent: Calculate the percentage change in total rebounds from the 2007-2008 season to the 2008-2009 season for this player.\n",
- "---------- WebSearchAgent ----------\n",
- "[FunctionCall(id='call_89tUNHaAM0kKQYPJLleGUKK7', arguments='{\"query\":\"Miami Heat player highest points 2006-2007 season\"}', name='search_web_tool')]\n",
- "---------- WebSearchAgent ----------\n",
- "[FunctionExecutionResult(content='Here are the total points scored by Miami Heat players in the 2006-2007 season:\\n Udonis Haslem: 844 points\\n Dwayne Wade: 1397 points\\n James Posey: 550 points\\n ...\\n ', name='search_web_tool', call_id='call_89tUNHaAM0kKQYPJLleGUKK7', is_error=False)]\n",
- "---------- WebSearchAgent ----------\n",
- "Here are the total points scored by Miami Heat players in the 2006-2007 season:\n",
- " Udonis Haslem: 844 points\n",
- " Dwayne Wade: 1397 points\n",
- " James Posey: 550 points\n",
- " ...\n",
- " \n",
- "---------- WebSearchAgent ----------\n",
- "The Miami Heat player with the highest points in the 2006-2007 season was Dwyane Wade, with 1,397 points.\n",
- "\n",
- "Next, I will search for Dwyane Wade's total rebounds for the 2007-2008 season.\n",
- "---------- WebSearchAgent ----------\n",
- "[FunctionCall(id='call_RC55TkSjG3JXRuVOTPrcE1RL', arguments='{\"query\":\"Dwyane Wade total rebounds 2007-2008 season\"}', name='search_web_tool')]\n",
- "---------- WebSearchAgent ----------\n",
- "[FunctionExecutionResult(content='The number of total rebounds for Dwayne Wade in the Miami Heat season 2007-2008 is 214.', name='search_web_tool', call_id='call_RC55TkSjG3JXRuVOTPrcE1RL', is_error=False)]\n",
- "---------- WebSearchAgent ----------\n",
- "The number of total rebounds for Dwayne Wade in the Miami Heat season 2007-2008 is 214.\n",
- "---------- WebSearchAgent ----------\n",
- "[FunctionCall(id='call_pBXoABrErDow0rZjw3tjOZol', arguments='{\"query\":\"Dwyane Wade total rebounds 2008-2009 season\"}', name='search_web_tool')]\n",
- "---------- WebSearchAgent ----------\n",
- "[FunctionExecutionResult(content='The number of total rebounds for Dwayne Wade in the Miami Heat season 2008-2009 is 398.', name='search_web_tool', call_id='call_pBXoABrErDow0rZjw3tjOZol', is_error=False)]\n",
- "---------- WebSearchAgent ----------\n",
- "The number of total rebounds for Dwayne Wade in the Miami Heat season 2008-2009 is 398.\n",
- "---------- DataAnalystAgent ----------\n",
- "[FunctionCall(id='call_qMxxXtcJsiK8KFSSCx3zm0is', arguments='{\"start\":214,\"end\":398}', name='percentage_change_tool')]\n",
- "---------- DataAnalystAgent ----------\n",
- "[FunctionExecutionResult(content='85.98130841121495', name='percentage_change_tool', call_id='call_qMxxXtcJsiK8KFSSCx3zm0is', is_error=False)]\n",
- "---------- DataAnalystAgent ----------\n",
- "85.98130841121495\n",
- "---------- PlanningAgent ----------\n",
- "The player with the highest points for the Miami Heat in the 2006-2007 NBA season was Dwyane Wade, who scored 1,397 points. The percentage change in Dwyane Wade's total rebounds from 214 in the 2007-2008 season to 398 in the 2008-2009 season is approximately 85.98%.\n",
- "\n",
- "TERMINATE\n"
- ]
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Selector Group Chat"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "{py:class}`~autogen_agentchat.teams.SelectorGroupChat` implements a team where participants take turns broadcasting messages to all other members. A generative model (e.g., an LLM) selects the next speaker based on the shared context, enabling dynamic, context-aware collaboration.\n",
+ "\n",
+ "Key features include:\n",
+ "\n",
+ "- Model-based speaker selection\n",
+ "- Configurable participant roles and descriptions\n",
+ "- Prevention of consecutive turns by the same speaker (optional)\n",
+ "- Customizable selection prompting\n",
+ "- Customizable selection function to override the default model-based selection\n",
+ "- Customizable candidate function to narrow-down the set of agents for selection using model\n",
+ "\n",
+ "```{note}\n",
+ "{py:class}`~autogen_agentchat.teams.SelectorGroupChat` is a high-level API. For more control and customization, refer to the [Group Chat Pattern](../core-user-guide/design-patterns/group-chat.ipynb) in the Core API documentation to implement your own group chat logic.\n",
+ "```\n",
+ "\n",
+ "## How Does it Work?\n",
+ "\n",
+ "{py:class}`~autogen_agentchat.teams.SelectorGroupChat` is a group chat similar to {py:class}`~autogen_agentchat.teams.RoundRobinGroupChat`,\n",
+ "but with a model-based next speaker selection mechanism.\n",
+ "When the team receives a task through {py:meth}`~autogen_agentchat.teams.BaseGroupChat.run` or {py:meth}`~autogen_agentchat.teams.BaseGroupChat.run_stream`,\n",
+ "the following steps are executed:\n",
+ "\n",
+ "1. The team analyzes the current conversation context, including the conversation history and participants' {py:attr}`~autogen_agentchat.base.ChatAgent.name` and {py:attr}`~autogen_agentchat.base.ChatAgent.description` attributes, to determine the next speaker using a model. By default, the team will not select the same speak consecutively unless it is the only agent available. This can be changed by setting `allow_repeated_speaker=True`. You can also override the model by providing a custom selection function.\n",
+ "2. The team prompts the selected speaker agent to provide a response, which is then **broadcasted** to all other participants.\n",
+ "3. The termination condition is checked to determine if the conversation should end, if not, the process repeats from step 1.\n",
+ "4. When the conversation ends, the team returns the {py:class}`~autogen_agentchat.base.TaskResult` containing the conversation history from this task.\n",
+ "\n",
+ "Once the team finishes the task, the conversation context is kept within the team and all participants, so the next task can continue from the previous conversation context.\n",
+ "You can reset the conversation context by calling {py:meth}`~autogen_agentchat.teams.BaseGroupChat.reset`.\n",
+ "\n",
+ "In this section, we will demonstrate how to use {py:class}`~autogen_agentchat.teams.SelectorGroupChat` with a simple example for a web search and data analysis task."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Example: Web Search/Analysis"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from typing import List, Sequence\n",
+ "\n",
+ "from autogen_agentchat.agents import AssistantAgent, UserProxyAgent\n",
+ "from autogen_agentchat.conditions import MaxMessageTermination, TextMentionTermination\n",
+ "from autogen_agentchat.messages import AgentEvent, ChatMessage\n",
+ "from autogen_agentchat.teams import SelectorGroupChat\n",
+ "from autogen_agentchat.ui import Console\n",
+ "from autogen_ext.models.openai import OpenAIChatCompletionClient"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Agents\n",
+ "\n",
+ "\n",
+ "\n",
+ "This system uses three specialized agents:\n",
+ "\n",
+ "- **Planning Agent**: The strategic coordinator that breaks down complex tasks into manageable subtasks. \n",
+ "- **Web Search Agent**: An information retrieval specialist that interfaces with the `search_web_tool`.\n",
+ "- **Data Analyst Agent**: An agent specialist in performing calculations equipped with `percentage_change_tool`. "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The tools `search_web_tool` and `percentage_change_tool` are external tools that the agents can use to perform their tasks."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Note: This example uses mock tools instead of real APIs for demonstration purposes\n",
+ "def search_web_tool(query: str) -> str:\n",
+ " if \"2006-2007\" in query:\n",
+ " return \"\"\"Here are the total points scored by Miami Heat players in the 2006-2007 season:\n",
+ " Udonis Haslem: 844 points\n",
+ " Dwayne Wade: 1397 points\n",
+ " James Posey: 550 points\n",
+ " ...\n",
+ " \"\"\"\n",
+ " elif \"2007-2008\" in query:\n",
+ " return \"The number of total rebounds for Dwayne Wade in the Miami Heat season 2007-2008 is 214.\"\n",
+ " elif \"2008-2009\" in query:\n",
+ " return \"The number of total rebounds for Dwayne Wade in the Miami Heat season 2008-2009 is 398.\"\n",
+ " return \"No data found.\"\n",
+ "\n",
+ "\n",
+ "def percentage_change_tool(start: float, end: float) -> float:\n",
+ " return ((end - start) / start) * 100"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let's create the specialized agents using the {py:class}`~autogen_agentchat.agents.AssistantAgent` class.\n",
+ "It is important to note that the agents' {py:attr}`~autogen_agentchat.base.ChatAgent.name` and {py:attr}`~autogen_agentchat.base.ChatAgent.description` attributes are used by the model to determine the next speaker,\n",
+ "so it is recommended to provide meaningful names and descriptions."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "model_client = OpenAIChatCompletionClient(model=\"gpt-4o\")\n",
+ "\n",
+ "planning_agent = AssistantAgent(\n",
+ " \"PlanningAgent\",\n",
+ " description=\"An agent for planning tasks, this agent should be the first to engage when given a new task.\",\n",
+ " model_client=model_client,\n",
+ " system_message=\"\"\"\n",
+ " You are a planning agent.\n",
+ " Your job is to break down complex tasks into smaller, manageable subtasks.\n",
+ " Your team members are:\n",
+ " WebSearchAgent: Searches for information\n",
+ " DataAnalystAgent: Performs calculations\n",
+ "\n",
+ " You only plan and delegate tasks - you do not execute them yourself.\n",
+ "\n",
+ " When assigning tasks, use this format:\n",
+ " 1. <agent> : <task>\n",
+ "\n",
+ " After all tasks are complete, summarize the findings and end with \"TERMINATE\".\n",
+ " \"\"\",\n",
+ ")\n",
+ "\n",
+ "web_search_agent = AssistantAgent(\n",
+ " \"WebSearchAgent\",\n",
+ " description=\"An agent for searching information on the web.\",\n",
+ " tools=[search_web_tool],\n",
+ " model_client=model_client,\n",
+ " system_message=\"\"\"\n",
+ " You are a web search agent.\n",
+ " Your only tool is search_tool - use it to find information.\n",
+ " You make only one search call at a time.\n",
+ " Once you have the results, you never do calculations based on them.\n",
+ " \"\"\",\n",
+ ")\n",
+ "\n",
+ "data_analyst_agent = AssistantAgent(\n",
+ " \"DataAnalystAgent\",\n",
+ " description=\"An agent for performing calculations.\",\n",
+ " model_client=model_client,\n",
+ " tools=[percentage_change_tool],\n",
+ " system_message=\"\"\"\n",
+ " You are a data analyst.\n",
+ " Given the tasks you have been assigned, you should analyze the data and provide results using the tools provided.\n",
+ " If you have not seen the data, ask for it.\n",
+ " \"\"\",\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "```{note}\n",
+ "By default, {py:class}`~autogen_agentchat.agents.AssistantAgent` returns the\n",
+ "tool output as the response. If your tool does not return a well-formed\n",
+ "string in natural language format, you may want to add a reflection step\n",
+ "within the agent by setting `reflect_on_tool_use=True` when creating the agent.\n",
+ "This will allow the agent to reflect on the tool output and provide a natural\n",
+ "language response.\n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Workflow\n",
+ "\n",
+ "1. The task is received by the {py:class}`~autogen_agentchat.teams.SelectorGroupChat` which, based on agent descriptions, selects the most appropriate agent to handle the initial task (typically the Planning Agent).\n",
+ "\n",
+ "2. The **Planning Agent** analyzes the task and breaks it down into subtasks, assigning each to the most appropriate agent using the format:\n",
+ " `<agent> : <task>`\n",
+ "\n",
+ "3. Based on the conversation context and agent descriptions, the {py:class}`~autogen_agent.teams.SelectorGroupChat` manager dynamically selects the next agent to handle their assigned subtask.\n",
+ "\n",
+ "4. The **Web Search Agent** performs searches one at a time, storing results in the shared conversation history.\n",
+ "\n",
+ "5. The **Data Analyst** processes the gathered information using available calculation tools when selected.\n",
+ "\n",
+ "6. The workflow continues with agents being dynamically selected until either:\n",
+ " - The Planning Agent determines all subtasks are complete and sends \"TERMINATE\"\n",
+ " - An alternative termination condition is met (e.g., a maximum number of messages)\n",
+ "\n",
+ "When defining your agents, make sure to include a helpful {py:attr}`~autogen_agentchat.base.ChatAgent.description` since this is used to decide which agent to select next."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Termination Conditions\n",
+ "\n",
+ "Let's use two termination conditions:\n",
+ "{py:class}`~autogen_agentchat.conditions.TextMentionTermination` to end the conversation when the Planning Agent sends \"TERMINATE\",\n",
+ "and {py:class}`~autogen_agentchat.conditions.MaxMessageTermination` to limit the conversation to 25 messages to avoid infinite loop."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "text_mention_termination = TextMentionTermination(\"TERMINATE\")\n",
+ "max_messages_termination = MaxMessageTermination(max_messages=25)\n",
+ "termination = text_mention_termination | max_messages_termination"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Selector Prompt\n",
+ "\n",
+ "{py:class}`~autogen_agentchat.teams.SelectorGroupChat` uses a model to select\n",
+ "the next speaker based on the conversation context.\n",
+ "We will use a custom selector prompt to properly align with the workflow."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "selector_prompt = \"\"\"Select an agent to perform task.\n",
+ "\n",
+ "{roles}\n",
+ "\n",
+ "Current conversation context:\n",
+ "{history}\n",
+ "\n",
+ "Read the above conversation, then select an agent from {participants} to perform the next task.\n",
+ "Make sure the planner agent has assigned tasks before other agents start working.\n",
+ "Only select one agent.\n",
+ "\"\"\""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "```{tip}\n",
+ "Try not to overload the model with too much instruction in the selector prompt.\n",
+ "\n",
+ "What is too much? It depends on the capabilities of the model you are using.\n",
+ "For GPT-4o and equivalents, you can use a selector prompt with a condition for when each speaker should be selected.\n",
+ "For smaller models such as Phi-4, you should keep the selector prompt as simple as possible\n",
+ "such as the one used in this example.\n",
+ "\n",
+ "Generally, if you find yourself writing multiple conditions for each agent,\n",
+ "it is a sign that you should consider using a custom selection function,\n",
+ "or breaking down the task into smaller, sequential tasks to be handled by\n",
+ "separate agents or teams.\n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Running the Team\n",
+ "\n",
+ "Let's create the team with the agents, termination conditions, and custom selector prompt."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "team = SelectorGroupChat(\n",
+ " [planning_agent, web_search_agent, data_analyst_agent],\n",
+ " model_client=model_client,\n",
+ " termination_condition=termination,\n",
+ " selector_prompt=selector_prompt,\n",
+ " allow_repeated_speaker=True, # Allow an agent to speak multiple turns in a row.\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now we run the team with a task to find information about an NBA player."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "task = \"Who was the Miami Heat player with the highest points in the 2006-2007 season, and what was the percentage change in his total rebounds between the 2007-2008 and 2008-2009 seasons?\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "---------- user ----------\n",
+ "Who was the Miami Heat player with the highest points in the 2006-2007 season, and what was the percentage change in his total rebounds between the 2007-2008 and 2008-2009 seasons?\n",
+ "---------- PlanningAgent ----------\n",
+ "To complete this task, we need to perform the following subtasks:\n",
+ "\n",
+ "1. Find out which Miami Heat player had the highest points in the 2006-2007 season.\n",
+ "2. Gather data on this player's total rebounds for the 2007-2008 season.\n",
+ "3. Gather data on this player's total rebounds for the 2008-2009 season.\n",
+ "4. Calculate the percentage change in the player's total rebounds between the 2007-2008 and 2008-2009 seasons.\n",
+ "\n",
+ "I'll assign these tasks accordingly:\n",
+ "\n",
+ "1. WebSearchAgent: Search for the Miami Heat player with the highest points in the 2006-2007 NBA season.\n",
+ "2. WebSearchAgent: Find the total rebounds for this player in the 2007-2008 NBA season.\n",
+ "3. WebSearchAgent: Find the total rebounds for this player in the 2008-2009 NBA season.\n",
+ "4. DataAnalystAgent: Calculate the percentage change in total rebounds from the 2007-2008 season to the 2008-2009 season for this player.\n",
+ "---------- WebSearchAgent ----------\n",
+ "[FunctionCall(id='call_89tUNHaAM0kKQYPJLleGUKK7', arguments='{\"query\":\"Miami Heat player highest points 2006-2007 season\"}', name='search_web_tool')]\n",
+ "---------- WebSearchAgent ----------\n",
+ "[FunctionExecutionResult(content='Here are the total points scored by Miami Heat players in the 2006-2007 season:\\n Udonis Haslem: 844 points\\n Dwayne Wade: 1397 points\\n James Posey: 550 points\\n ...\\n ', name='search_web_tool', call_id='call_89tUNHaAM0kKQYPJLleGUKK7', is_error=False)]\n",
+ "---------- WebSearchAgent ----------\n",
+ "Here are the total points scored by Miami Heat players in the 2006-2007 season:\n",
+ " Udonis Haslem: 844 points\n",
+ " Dwayne Wade: 1397 points\n",
+ " James Posey: 550 points\n",
+ " ...\n",
+ " \n",
+ "---------- WebSearchAgent ----------\n",
+ "The Miami Heat player with the highest points in the 2006-2007 season was Dwyane Wade, with 1,397 points.\n",
+ "\n",
+ "Next, I will search for Dwyane Wade's total rebounds for the 2007-2008 season.\n",
+ "---------- WebSearchAgent ----------\n",
+ "[FunctionCall(id='call_RC55TkSjG3JXRuVOTPrcE1RL', arguments='{\"query\":\"Dwyane Wade total rebounds 2007-2008 season\"}', name='search_web_tool')]\n",
+ "---------- WebSearchAgent ----------\n",
+ "[FunctionExecutionResult(content='The number of total rebounds for Dwayne Wade in the Miami Heat season 2007-2008 is 214.', name='search_web_tool', call_id='call_RC55TkSjG3JXRuVOTPrcE1RL', is_error=False)]\n",
+ "---------- WebSearchAgent ----------\n",
+ "The number of total rebounds for Dwayne Wade in the Miami Heat season 2007-2008 is 214.\n",
+ "---------- WebSearchAgent ----------\n",
+ "[FunctionCall(id='call_pBXoABrErDow0rZjw3tjOZol', arguments='{\"query\":\"Dwyane Wade total rebounds 2008-2009 season\"}', name='search_web_tool')]\n",
+ "---------- WebSearchAgent ----------\n",
+ "[FunctionExecutionResult(content='The number of total rebounds for Dwayne Wade in the Miami Heat season 2008-2009 is 398.', name='search_web_tool', call_id='call_pBXoABrErDow0rZjw3tjOZol', is_error=False)]\n",
+ "---------- WebSearchAgent ----------\n",
+ "The number of total rebounds for Dwayne Wade in the Miami Heat season 2008-2009 is 398.\n",
+ "---------- DataAnalystAgent ----------\n",
+ "[FunctionCall(id='call_qMxxXtcJsiK8KFSSCx3zm0is', arguments='{\"start\":214,\"end\":398}', name='percentage_change_tool')]\n",
+ "---------- DataAnalystAgent ----------\n",
+ "[FunctionExecutionResult(content='85.98130841121495', name='percentage_change_tool', call_id='call_qMxxXtcJsiK8KFSSCx3zm0is', is_error=False)]\n",
+ "---------- DataAnalystAgent ----------\n",
+ "85.98130841121495\n",
+ "---------- PlanningAgent ----------\n",
+ "The player with the highest points for the Miami Heat in the 2006-2007 NBA season was Dwyane Wade, who scored 1,397 points. The percentage change in Dwyane Wade's total rebounds from 214 in the 2007-2008 season to 398 in the 2008-2009 season is approximately 85.98%.\n",
+ "\n",
+ "TERMINATE\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "TaskResult(messages=[TextMessage(source='user', models_usage=None, metadata={}, content='Who was the Miami Heat player with the highest points in the 2006-2007 season, and what was the percentage change in his total rebounds between the 2007-2008 and 2008-2009 seasons?', type='TextMessage'), TextMessage(source='PlanningAgent', models_usage=RequestUsage(prompt_tokens=161, completion_tokens=220), metadata={}, content=\"To complete this task, we need to perform the following subtasks:\\n\\n1. Find out which Miami Heat player had the highest points in the 2006-2007 season.\\n2. Gather data on this player's total rebounds for the 2007-2008 season.\\n3. Gather data on this player's total rebounds for the 2008-2009 season.\\n4. Calculate the percentage change in the player's total rebounds between the 2007-2008 and 2008-2009 seasons.\\n\\nI'll assign these tasks accordingly:\\n\\n1. WebSearchAgent: Search for the Miami Heat player with the highest points in the 2006-2007 NBA season.\\n2. WebSearchAgent: Find the total rebounds for this player in the 2007-2008 NBA season.\\n3. WebSearchAgent: Find the total rebounds for this player in the 2008-2009 NBA season.\\n4. DataAnalystAgent: Calculate the percentage change in total rebounds from the 2007-2008 season to the 2008-2009 season for this player.\", type='TextMessage'), ToolCallRequestEvent(source='WebSearchAgent', models_usage=RequestUsage(prompt_tokens=368, completion_tokens=27), metadata={}, content=[FunctionCall(id='call_89tUNHaAM0kKQYPJLleGUKK7', arguments='{\"query\":\"Miami Heat player highest points 2006-2007 season\"}', name='search_web_tool')], type='ToolCallRequestEvent'), ToolCallExecutionEvent(source='WebSearchAgent', models_usage=None, metadata={}, content=[FunctionExecutionResult(content='Here are the total points scored by Miami Heat players in the 2006-2007 season:\\n Udonis Haslem: 844 points\\n Dwayne Wade: 1397 points\\n James Posey: 550 points\\n ...\\n ', name='search_web_tool', call_id='call_89tUNHaAM0kKQYPJLleGUKK7', is_error=False)], type='ToolCallExecutionEvent'), ToolCallSummaryMessage(source='WebSearchAgent', models_usage=None, metadata={}, content='Here are the total points scored by Miami Heat players in the 2006-2007 season:\\n Udonis Haslem: 844 points\\n Dwayne Wade: 1397 points\\n James Posey: 550 points\\n ...\\n ', type='ToolCallSummaryMessage'), ThoughtEvent(source='WebSearchAgent', models_usage=None, metadata={}, content=\"The Miami Heat player with the highest points in the 2006-2007 season was Dwyane Wade, with 1,397 points.\\n\\nNext, I will search for Dwyane Wade's total rebounds for the 2007-2008 season.\", type='ThoughtEvent'), ToolCallRequestEvent(source='WebSearchAgent', models_usage=RequestUsage(prompt_tokens=460, completion_tokens=83), metadata={}, content=[FunctionCall(id='call_RC55TkSjG3JXRuVOTPrcE1RL', arguments='{\"query\":\"Dwyane Wade total rebounds 2007-2008 season\"}', name='search_web_tool')], type='ToolCallRequestEvent'), ToolCallExecutionEvent(source='WebSearchAgent', models_usage=None, metadata={}, content=[FunctionExecutionResult(content='The number of total rebounds for Dwayne Wade in the Miami Heat season 2007-2008 is 214.', name='search_web_tool', call_id='call_RC55TkSjG3JXRuVOTPrcE1RL', is_error=False)], type='ToolCallExecutionEvent'), ToolCallSummaryMessage(source='WebSearchAgent', models_usage=None, metadata={}, content='The number of total rebounds for Dwayne Wade in the Miami Heat season 2007-2008 is 214.', type='ToolCallSummaryMessage'), ToolCallRequestEvent(source='WebSearchAgent', models_usage=RequestUsage(prompt_tokens=585, completion_tokens=28), metadata={}, content=[FunctionCall(id='call_pBXoABrErDow0rZjw3tjOZol', arguments='{\"query\":\"Dwyane Wade total rebounds 2008-2009 season\"}', name='search_web_tool')], type='ToolCallRequestEvent'), ToolCallExecutionEvent(source='WebSearchAgent', models_usage=None, metadata={}, content=[FunctionExecutionResult(content='The number of total rebounds for Dwayne Wade in the Miami Heat season 2008-2009 is 398.', name='search_web_tool', call_id='call_pBXoABrErDow0rZjw3tjOZol', is_error=False)], type='ToolCallExecutionEvent'), ToolCallSummaryMessage(source='WebSearchAgent', models_usage=None, metadata={}, content='The number of total rebounds for Dwayne Wade in the Miami Heat season 2008-2009 is 398.', type='ToolCallSummaryMessage'), ToolCallRequestEvent(source='DataAnalystAgent', models_usage=RequestUsage(prompt_tokens=496, completion_tokens=21), metadata={}, content=[FunctionCall(id='call_qMxxXtcJsiK8KFSSCx3zm0is', arguments='{\"start\":214,\"end\":398}', name='percentage_change_tool')], type='ToolCallRequestEvent'), ToolCallExecutionEvent(source='DataAnalystAgent', models_usage=None, metadata={}, content=[FunctionExecutionResult(content='85.98130841121495', name='percentage_change_tool', call_id='call_qMxxXtcJsiK8KFSSCx3zm0is', is_error=False)], type='ToolCallExecutionEvent'), ToolCallSummaryMessage(source='DataAnalystAgent', models_usage=None, metadata={}, content='85.98130841121495', type='ToolCallSummaryMessage'), TextMessage(source='PlanningAgent', models_usage=RequestUsage(prompt_tokens=528, completion_tokens=80), metadata={}, content=\"The player with the highest points for the Miami Heat in the 2006-2007 NBA season was Dwyane Wade, who scored 1,397 points. The percentage change in Dwyane Wade's total rebounds from 214 in the 2007-2008 season to 398 in the 2008-2009 season is approximately 85.98%.\\n\\nTERMINATE\", type='TextMessage')], stop_reason=\"Text 'TERMINATE' mentioned\")"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Use asyncio.run(...) if you are running this in a script.\n",
+ "await Console(team.run_stream(task=task))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "As we can see, after the Web Search Agent conducts the necessary searches and the Data Analyst Agent completes the necessary calculations, we find that Dwayne Wade was the Miami Heat player with the highest points in the 2006-2007 season, and the percentage change in his total rebounds between the 2007-2008 and 2008-2009 seasons is 85.98%!"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Custom Selector Function"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Often times we want better control over the selection process.\n",
+ "To this end, we can set the `selector_func` argument with a custom selector function to override the default model-based selection.\n",
+ "This allows us to implement more complex selection logic and state-based transitions.\n",
+ "\n",
+ "For instance, we want the Planning Agent to speak immediately after any specialized agent to check the progress.\n",
+ "\n",
+ "```{note}\n",
+ "Returning `None` from the custom selector function will use the default model-based selection.\n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "---------- user ----------\n",
+ "Who was the Miami Heat player with the highest points in the 2006-2007 season, and what was the percentage change in his total rebounds between the 2007-2008 and 2008-2009 seasons?\n",
+ "---------- PlanningAgent ----------\n",
+ "To answer this question, we need to follow these steps: \n",
+ "\n",
+ "1. Identify the Miami Heat player with the highest points in the 2006-2007 season.\n",
+ "2. Retrieve the total rebounds of that player for the 2007-2008 and 2008-2009 seasons.\n",
+ "3. Calculate the percentage change in his total rebounds between the two seasons.\n",
+ "\n",
+ "Let's delegate these tasks:\n",
+ "\n",
+ "1. WebSearchAgent: Find the Miami Heat player with the highest points in the 2006-2007 NBA season.\n",
+ "2. WebSearchAgent: Retrieve the total rebounds for the identified player during the 2007-2008 NBA season.\n",
+ "3. WebSearchAgent: Retrieve the total rebounds for the identified player during the 2008-2009 NBA season.\n",
+ "4. DataAnalystAgent: Calculate the percentage change in total rebounds between the 2007-2008 and 2008-2009 seasons for the player found.\n",
+ "---------- WebSearchAgent ----------\n",
+ "[FunctionCall(id='call_Pz82ndNLSV4cH0Sg6g7ArP4L', arguments='{\"query\":\"Miami Heat player highest points 2006-2007 season\"}', name='search_web_tool')]\n",
+ "---------- WebSearchAgent ----------\n",
+ "[FunctionExecutionResult(content='Here are the total points scored by Miami Heat players in the 2006-2007 season:\\n Udonis Haslem: 844 points\\n Dwayne Wade: 1397 points\\n James Posey: 550 points\\n ...\\n ', call_id='call_Pz82ndNLSV4cH0Sg6g7ArP4L')]\n",
+ "---------- WebSearchAgent ----------\n",
+ "Here are the total points scored by Miami Heat players in the 2006-2007 season:\n",
+ " Udonis Haslem: 844 points\n",
+ " Dwayne Wade: 1397 points\n",
+ " James Posey: 550 points\n",
+ " ...\n",
+ " \n",
+ "---------- PlanningAgent ----------\n",
+ "Great! Dwyane Wade was the Miami Heat player with the highest points in the 2006-2007 season. Now, let's continue with the next tasks:\n",
+ "\n",
+ "2. WebSearchAgent: Retrieve the total rebounds for Dwyane Wade during the 2007-2008 NBA season.\n",
+ "3. WebSearchAgent: Retrieve the total rebounds for Dwyane Wade during the 2008-2009 NBA season.\n",
+ "---------- WebSearchAgent ----------\n",
+ "[FunctionCall(id='call_3qv9so2DXFZIHtzqDIfXoFID', arguments='{\"query\": \"Dwyane Wade total rebounds 2007-2008 season\"}', name='search_web_tool'), FunctionCall(id='call_Vh7zzzWUeiUAvaYjP0If0k1k', arguments='{\"query\": \"Dwyane Wade total rebounds 2008-2009 season\"}', name='search_web_tool')]\n",
+ "---------- WebSearchAgent ----------\n",
+ "[FunctionExecutionResult(content='The number of total rebounds for Dwayne Wade in the Miami Heat season 2007-2008 is 214.', call_id='call_3qv9so2DXFZIHtzqDIfXoFID'), FunctionExecutionResult(content='The number of total rebounds for Dwayne Wade in the Miami Heat season 2008-2009 is 398.', call_id='call_Vh7zzzWUeiUAvaYjP0If0k1k')]\n",
+ "---------- WebSearchAgent ----------\n",
+ "The number of total rebounds for Dwayne Wade in the Miami Heat season 2007-2008 is 214.\n",
+ "The number of total rebounds for Dwayne Wade in the Miami Heat season 2008-2009 is 398.\n",
+ "---------- PlanningAgent ----------\n",
+ "Now let's calculate the percentage change in total rebounds between the 2007-2008 and 2008-2009 seasons for Dwyane Wade.\n",
+ "\n",
+ "4. DataAnalystAgent: Calculate the percentage change in total rebounds for Dwyane Wade between the 2007-2008 and 2008-2009 seasons.\n",
+ "---------- DataAnalystAgent ----------\n",
+ "[FunctionCall(id='call_FXnPSr6JVGfAWs3StIizbt2V', arguments='{\"start\":214,\"end\":398}', name='percentage_change_tool')]\n",
+ "---------- DataAnalystAgent ----------\n",
+ "[FunctionExecutionResult(content='85.98130841121495', call_id='call_FXnPSr6JVGfAWs3StIizbt2V')]\n",
+ "---------- DataAnalystAgent ----------\n",
+ "85.98130841121495\n",
+ "---------- PlanningAgent ----------\n",
+ "Dwyane Wade was the Miami Heat player with the highest points in the 2006-2007 season, scoring a total of 1397 points. The percentage change in his total rebounds from the 2007-2008 season (214 rebounds) to the 2008-2009 season (398 rebounds) is approximately 86.0%.\n",
+ "\n",
+ "TERMINATE\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "TaskResult(messages=[TextMessage(source='user', models_usage=None, content='Who was the Miami Heat player with the highest points in the 2006-2007 season, and what was the percentage change in his total rebounds between the 2007-2008 and 2008-2009 seasons?', type='TextMessage'), TextMessage(source='PlanningAgent', models_usage=RequestUsage(prompt_tokens=161, completion_tokens=192), content=\"To answer this question, we need to follow these steps: \\n\\n1. Identify the Miami Heat player with the highest points in the 2006-2007 season.\\n2. Retrieve the total rebounds of that player for the 2007-2008 and 2008-2009 seasons.\\n3. Calculate the percentage change in his total rebounds between the two seasons.\\n\\nLet's delegate these tasks:\\n\\n1. WebSearchAgent: Find the Miami Heat player with the highest points in the 2006-2007 NBA season.\\n2. WebSearchAgent: Retrieve the total rebounds for the identified player during the 2007-2008 NBA season.\\n3. WebSearchAgent: Retrieve the total rebounds for the identified player during the 2008-2009 NBA season.\\n4. DataAnalystAgent: Calculate the percentage change in total rebounds between the 2007-2008 and 2008-2009 seasons for the player found.\", type='TextMessage'), ToolCallRequestEvent(source='WebSearchAgent', models_usage=RequestUsage(prompt_tokens=340, completion_tokens=27), content=[FunctionCall(id='call_Pz82ndNLSV4cH0Sg6g7ArP4L', arguments='{\"query\":\"Miami Heat player highest points 2006-2007 season\"}', name='search_web_tool')], type='ToolCallRequestEvent'), ToolCallExecutionEvent(source='WebSearchAgent', models_usage=None, content=[FunctionExecutionResult(content='Here are the total points scored by Miami Heat players in the 2006-2007 season:\\n Udonis Haslem: 844 points\\n Dwayne Wade: 1397 points\\n James Posey: 550 points\\n ...\\n ', call_id='call_Pz82ndNLSV4cH0Sg6g7ArP4L')], type='ToolCallExecutionEvent'), ToolCallSummaryMessage(source='WebSearchAgent', models_usage=None, content='Here are the total points scored by Miami Heat players in the 2006-2007 season:\\n Udonis Haslem: 844 points\\n Dwayne Wade: 1397 points\\n James Posey: 550 points\\n ...\\n ', type='ToolCallSummaryMessage'), TextMessage(source='PlanningAgent', models_usage=RequestUsage(prompt_tokens=420, completion_tokens=87), content=\"Great! Dwyane Wade was the Miami Heat player with the highest points in the 2006-2007 season. Now, let's continue with the next tasks:\\n\\n2. WebSearchAgent: Retrieve the total rebounds for Dwyane Wade during the 2007-2008 NBA season.\\n3. WebSearchAgent: Retrieve the total rebounds for Dwyane Wade during the 2008-2009 NBA season.\", type='TextMessage'), ToolCallRequestEvent(source='WebSearchAgent', models_usage=RequestUsage(prompt_tokens=525, completion_tokens=71), content=[FunctionCall(id='call_3qv9so2DXFZIHtzqDIfXoFID', arguments='{\"query\": \"Dwyane Wade total rebounds 2007-2008 season\"}', name='search_web_tool'), FunctionCall(id='call_Vh7zzzWUeiUAvaYjP0If0k1k', arguments='{\"query\": \"Dwyane Wade total rebounds 2008-2009 season\"}', name='search_web_tool')], type='ToolCallRequestEvent'), ToolCallExecutionEvent(source='WebSearchAgent', models_usage=None, content=[FunctionExecutionResult(content='The number of total rebounds for Dwayne Wade in the Miami Heat season 2007-2008 is 214.', call_id='call_3qv9so2DXFZIHtzqDIfXoFID'), FunctionExecutionResult(content='The number of total rebounds for Dwayne Wade in the Miami Heat season 2008-2009 is 398.', call_id='call_Vh7zzzWUeiUAvaYjP0If0k1k')], type='ToolCallExecutionEvent'), ToolCallSummaryMessage(source='WebSearchAgent', models_usage=None, content='The number of total rebounds for Dwayne Wade in the Miami Heat season 2007-2008 is 214.\\nThe number of total rebounds for Dwayne Wade in the Miami Heat season 2008-2009 is 398.', type='ToolCallSummaryMessage'), TextMessage(source='PlanningAgent', models_usage=RequestUsage(prompt_tokens=569, completion_tokens=68), content=\"Now let's calculate the percentage change in total rebounds between the 2007-2008 and 2008-2009 seasons for Dwyane Wade.\\n\\n4. DataAnalystAgent: Calculate the percentage change in total rebounds for Dwyane Wade between the 2007-2008 and 2008-2009 seasons.\", type='TextMessage'), ToolCallRequestEvent(source='DataAnalystAgent', models_usage=RequestUsage(prompt_tokens=627, completion_tokens=21), content=[FunctionCall(id='call_FXnPSr6JVGfAWs3StIizbt2V', arguments='{\"start\":214,\"end\":398}', name='percentage_change_tool')], type='ToolCallRequestEvent'), ToolCallExecutionEvent(source='DataAnalystAgent', models_usage=None, content=[FunctionExecutionResult(content='85.98130841121495', call_id='call_FXnPSr6JVGfAWs3StIizbt2V')], type='ToolCallExecutionEvent'), ToolCallSummaryMessage(source='DataAnalystAgent', models_usage=None, content='85.98130841121495', type='ToolCallSummaryMessage'), TextMessage(source='PlanningAgent', models_usage=RequestUsage(prompt_tokens=659, completion_tokens=76), content='Dwyane Wade was the Miami Heat player with the highest points in the 2006-2007 season, scoring a total of 1397 points. The percentage change in his total rebounds from the 2007-2008 season (214 rebounds) to the 2008-2009 season (398 rebounds) is approximately 86.0%.\\n\\nTERMINATE', type='TextMessage')], stop_reason=\"Text 'TERMINATE' mentioned\")"
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "def selector_func(messages: Sequence[AgentEvent | ChatMessage]) -> str | None:\n",
+ " if messages[-1].source != planning_agent.name:\n",
+ " return planning_agent.name\n",
+ " return None\n",
+ "\n",
+ "\n",
+ "# Reset the previous team and run the chat again with the selector function.\n",
+ "await team.reset()\n",
+ "team = SelectorGroupChat(\n",
+ " [planning_agent, web_search_agent, data_analyst_agent],\n",
+ " model_client=model_client,\n",
+ " termination_condition=termination,\n",
+ " selector_prompt=selector_prompt,\n",
+ " allow_repeated_speaker=True,\n",
+ " selector_func=selector_func,\n",
+ ")\n",
+ "\n",
+ "await Console(team.run_stream(task=task))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "You can see from the conversation log that the Planning Agent always speaks immediately after the specialized agents.\n",
+ "\n",
+ "```{tip}\n",
+ "Each participant agent only makes one step (executing tools, generating a response, etc.)\n",
+ "on each turn. \n",
+ "If you want an {py:class}`~autogen_agentchat.agents.AssistantAgent` to repeat\n",
+ "until it stop returning a {py:class}`~autogen_agentchat.messages.ToolCallSummaryMessage`\n",
+ "when it has finished running all the tools it needs to run, you can do so by\n",
+ "checking the last message and returning the agent if it is a\n",
+ "{py:class}`~autogen_agentchat.messages.ToolCallSummaryMessage`.\n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Custom Candidate Function"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "One more possible requirement might be to automatically select the next speaker from a filtered list of agents.\n",
+ "For this, we can set `candidate_func` parameter with a custom candidate function to filter down the list of potential agents for speaker selection for each turn of groupchat.\n",
+ "\n",
+ "This allow us to restrict speaker selection to a specific set of agents after a given agent.\n",
+ "\n",
+ "\n",
+ "```{note}\n",
+ "The `candidate_func` is only valid if `selector_func` is not set.\n",
+ "Returning `None` or an empty list `[]` from the custom candidate function will raise a `ValueError`.\n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "---------- user ----------\n",
+ "Who was the Miami Heat player with the highest points in the 2006-2007 season, and what was the percentage change in his total rebounds between the 2007-2008 and 2008-2009 seasons?\n",
+ "---------- PlanningAgent ----------\n",
+ "To answer this question, we'll break it down into two main subtasks:\n",
+ "\n",
+ "1. Identify the Miami Heat player with the highest points in the 2006-2007 season.\n",
+ "2. Calculate the percentage change in that player's total rebounds between the 2007-2008 and 2008-2009 seasons.\n",
+ "\n",
+ "Let's assign these tasks:\n",
+ "\n",
+ "1. WebSearchAgent: Search for the Miami Heat player with the highest points in the 2006-2007 NBA season.\n",
+ "2. WebSearchAgent: Find the total rebound statistics for that identified player for both the 2007-2008 and 2008-2009 NBA seasons.\n",
+ "3. DataAnalystAgent: Calculate the percentage change in the player's total rebounds between the 2007-2008 and 2008-2009 seasons once the data is retrieved.\n",
+ "---------- WebSearchAgent ----------\n",
+ "[FunctionCall(id='call_WtR5KTfEIxs3jIO25gjAw7dF', arguments='{\"query\":\"Miami Heat highest points scorer 2006-2007 NBA season\"}', name='search_web_tool')]\n",
+ "---------- WebSearchAgent ----------\n",
+ "[FunctionExecutionResult(content='Here are the total points scored by Miami Heat players in the 2006-2007 season:\\n Udonis Haslem: 844 points\\n Dwayne Wade: 1397 points\\n James Posey: 550 points\\n ...\\n ', name='search_web_tool', call_id='call_WtR5KTfEIxs3jIO25gjAw7dF', is_error=False)]\n",
+ "---------- WebSearchAgent ----------\n",
+ "Here are the total points scored by Miami Heat players in the 2006-2007 season:\n",
+ " Udonis Haslem: 844 points\n",
+ " Dwayne Wade: 1397 points\n",
+ " James Posey: 550 points\n",
+ " ...\n",
+ " \n",
+ "---------- DataAnalystAgent ----------\n",
+ "[FunctionCall(id='call_9HA3DEacUl4WuG2G2PtRkXAO', arguments='{\"start\": 432, \"end\": 527}', name='percentage_change_tool')]\n",
+ "---------- DataAnalystAgent ----------\n",
+ "[FunctionExecutionResult(content='21.99074074074074', name='percentage_change_tool', call_id='call_9HA3DEacUl4WuG2G2PtRkXAO', is_error=False)]\n",
+ "---------- DataAnalystAgent ----------\n",
+ "21.99074074074074\n",
+ "---------- PlanningAgent ----------\n",
+ "It seems we've missed some context there, so let's assign the subtasks again for clarity:\n",
+ "\n",
+ "Based on the search results, Dwyane Wade had the highest points for the Miami Heat in the 2006-2007 season with 1397 points.\n",
+ "\n",
+ "Now, let's find the necessary rebound statistics:\n",
+ "\n",
+ "2. WebSearchAgent: Find Dwyane Wade's total rebound statistics for both the 2007-2008 and 2008-2009 NBA seasons.\n",
+ "3. DataAnalystAgent: Once the data is retrieved, calculate the percentage change in Dwyane Wade's total rebounds between the 2007-2008 and 2008-2009 seasons.\n",
+ "---------- WebSearchAgent ----------\n",
+ "[FunctionCall(id='call_3i1wTDSjkGg6Ev8YKYWkZK55', arguments='{\"query\": \"Dwyane Wade total rebounds 2007-2008 NBA season\"}', name='search_web_tool'), FunctionCall(id='call_NRAs6jHxXRi8zsvpW5WlHAaU', arguments='{\"query\": \"Dwyane Wade total rebounds 2008-2009 NBA season\"}', name='search_web_tool')]\n",
+ "---------- WebSearchAgent ----------\n",
+ "[FunctionExecutionResult(content='The number of total rebounds for Dwayne Wade in the Miami Heat season 2007-2008 is 214.', name='search_web_tool', call_id='call_3i1wTDSjkGg6Ev8YKYWkZK55', is_error=False), FunctionExecutionResult(content='The number of total rebounds for Dwayne Wade in the Miami Heat season 2008-2009 is 398.', name='search_web_tool', call_id='call_NRAs6jHxXRi8zsvpW5WlHAaU', is_error=False)]\n",
+ "---------- WebSearchAgent ----------\n",
+ "The number of total rebounds for Dwayne Wade in the Miami Heat season 2007-2008 is 214.\n",
+ "The number of total rebounds for Dwayne Wade in the Miami Heat season 2008-2009 is 398.\n",
+ "---------- PlanningAgent ----------\n",
+ "The total rebounds for Dwyane Wade in the 2007-2008 season were 214, and in the 2008-2009 season, they were 398.\n",
+ "\n",
+ "Now, let's calculate the percentage change.\n",
+ "\n",
+ "3. DataAnalystAgent: Calculate the percentage change in Dwyane Wade's total rebounds from the 2007-2008 season to the 2008-2009 season.\n",
+ "---------- DataAnalystAgent ----------\n",
+ "[FunctionCall(id='call_XECA7ezz7VIKbf8IbZYSCSpI', arguments='{\"start\":214,\"end\":398}', name='percentage_change_tool')]\n",
+ "---------- DataAnalystAgent ----------\n",
+ "[FunctionExecutionResult(content='85.98130841121495', name='percentage_change_tool', call_id='call_XECA7ezz7VIKbf8IbZYSCSpI', is_error=False)]\n",
+ "---------- DataAnalystAgent ----------\n",
+ "85.98130841121495\n",
+ "---------- PlanningAgent ----------\n",
+ "The Miami Heat player with the highest points in the 2006-2007 season was Dwyane Wade, with 1397 points. The percentage change in his total rebounds between the 2007-2008 and 2008-2009 seasons was approximately 85.98%.\n",
+ "\n",
+ "TERMINATE\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "TaskResult(messages=[TextMessage(source='user', models_usage=None, metadata={}, content='Who was the Miami Heat player with the highest points in the 2006-2007 season, and what was the percentage change in his total rebounds between the 2007-2008 and 2008-2009 seasons?', type='TextMessage'), TextMessage(source='PlanningAgent', models_usage=RequestUsage(prompt_tokens=161, completion_tokens=169), metadata={}, content=\"To answer this question, we'll break it down into two main subtasks:\\n\\n1. Identify the Miami Heat player with the highest points in the 2006-2007 season.\\n2. Calculate the percentage change in that player's total rebounds between the 2007-2008 and 2008-2009 seasons.\\n\\nLet's assign these tasks:\\n\\n1. WebSearchAgent: Search for the Miami Heat player with the highest points in the 2006-2007 NBA season.\\n2. WebSearchAgent: Find the total rebound statistics for that identified player for both the 2007-2008 and 2008-2009 NBA seasons.\\n3. DataAnalystAgent: Calculate the percentage change in the player's total rebounds between the 2007-2008 and 2008-2009 seasons once the data is retrieved.\", type='TextMessage'), ToolCallRequestEvent(source='WebSearchAgent', models_usage=RequestUsage(prompt_tokens=324, completion_tokens=28), metadata={}, content=[FunctionCall(id='call_WtR5KTfEIxs3jIO25gjAw7dF', arguments='{\"query\":\"Miami Heat highest points scorer 2006-2007 NBA season\"}', name='search_web_tool')], type='ToolCallRequestEvent'), ToolCallExecutionEvent(source='WebSearchAgent', models_usage=None, metadata={}, content=[FunctionExecutionResult(content='Here are the total points scored by Miami Heat players in the 2006-2007 season:\\n Udonis Haslem: 844 points\\n Dwayne Wade: 1397 points\\n James Posey: 550 points\\n ...\\n ', name='search_web_tool', call_id='call_WtR5KTfEIxs3jIO25gjAw7dF', is_error=False)], type='ToolCallExecutionEvent'), ToolCallSummaryMessage(source='WebSearchAgent', models_usage=None, metadata={}, content='Here are the total points scored by Miami Heat players in the 2006-2007 season:\\n Udonis Haslem: 844 points\\n Dwayne Wade: 1397 points\\n James Posey: 550 points\\n ...\\n ', type='ToolCallSummaryMessage'), ToolCallRequestEvent(source='DataAnalystAgent', models_usage=RequestUsage(prompt_tokens=390, completion_tokens=37), metadata={}, content=[FunctionCall(id='call_9HA3DEacUl4WuG2G2PtRkXAO', arguments='{\"start\": 432, \"end\": 527}', name='percentage_change_tool')], type='ToolCallRequestEvent'), ToolCallExecutionEvent(source='DataAnalystAgent', models_usage=None, metadata={}, content=[FunctionExecutionResult(content='21.99074074074074', name='percentage_change_tool', call_id='call_9HA3DEacUl4WuG2G2PtRkXAO', is_error=False)], type='ToolCallExecutionEvent'), ToolCallSummaryMessage(source='DataAnalystAgent', models_usage=None, metadata={}, content='21.99074074074074', type='ToolCallSummaryMessage'), TextMessage(source='PlanningAgent', models_usage=RequestUsage(prompt_tokens=413, completion_tokens=137), metadata={}, content=\"It seems we've missed some context there, so let's assign the subtasks again for clarity:\\n\\nBased on the search results, Dwyane Wade had the highest points for the Miami Heat in the 2006-2007 season with 1397 points.\\n\\nNow, let's find the necessary rebound statistics:\\n\\n2. WebSearchAgent: Find Dwyane Wade's total rebound statistics for both the 2007-2008 and 2008-2009 NBA seasons.\\n3. DataAnalystAgent: Once the data is retrieved, calculate the percentage change in Dwyane Wade's total rebounds between the 2007-2008 and 2008-2009 seasons.\", type='TextMessage'), ToolCallRequestEvent(source='WebSearchAgent', models_usage=RequestUsage(prompt_tokens=576, completion_tokens=73), metadata={}, content=[FunctionCall(id='call_3i1wTDSjkGg6Ev8YKYWkZK55', arguments='{\"query\": \"Dwyane Wade total rebounds 2007-2008 NBA season\"}', name='search_web_tool'), FunctionCall(id='call_NRAs6jHxXRi8zsvpW5WlHAaU', arguments='{\"query\": \"Dwyane Wade total rebounds 2008-2009 NBA season\"}', name='search_web_tool')], type='ToolCallRequestEvent'), ToolCallExecutionEvent(source='WebSearchAgent', models_usage=None, metadata={}, content=[FunctionExecutionResult(content='The number of total rebounds for Dwayne Wade in the Miami Heat season 2007-2008 is 214.', name='search_web_tool', call_id='call_3i1wTDSjkGg6Ev8YKYWkZK55', is_error=False), FunctionExecutionResult(content='The number of total rebounds for Dwayne Wade in the Miami Heat season 2008-2009 is 398.', name='search_web_tool', call_id='call_NRAs6jHxXRi8zsvpW5WlHAaU', is_error=False)], type='ToolCallExecutionEvent'), ToolCallSummaryMessage(source='WebSearchAgent', models_usage=None, metadata={}, content='The number of total rebounds for Dwayne Wade in the Miami Heat season 2007-2008 is 214.\\nThe number of total rebounds for Dwayne Wade in the Miami Heat season 2008-2009 is 398.', type='ToolCallSummaryMessage'), TextMessage(source='PlanningAgent', models_usage=RequestUsage(prompt_tokens=612, completion_tokens=84), metadata={}, content=\"The total rebounds for Dwyane Wade in the 2007-2008 season were 214, and in the 2008-2009 season, they were 398.\\n\\nNow, let's calculate the percentage change.\\n\\n3. DataAnalystAgent: Calculate the percentage change in Dwyane Wade's total rebounds from the 2007-2008 season to the 2008-2009 season.\", type='TextMessage'), ToolCallRequestEvent(source='DataAnalystAgent', models_usage=RequestUsage(prompt_tokens=720, completion_tokens=21), metadata={}, content=[FunctionCall(id='call_XECA7ezz7VIKbf8IbZYSCSpI', arguments='{\"start\":214,\"end\":398}', name='percentage_change_tool')], type='ToolCallRequestEvent'), ToolCallExecutionEvent(source='DataAnalystAgent', models_usage=None, metadata={}, content=[FunctionExecutionResult(content='85.98130841121495', name='percentage_change_tool', call_id='call_XECA7ezz7VIKbf8IbZYSCSpI', is_error=False)], type='ToolCallExecutionEvent'), ToolCallSummaryMessage(source='DataAnalystAgent', models_usage=None, metadata={}, content='85.98130841121495', type='ToolCallSummaryMessage'), TextMessage(source='PlanningAgent', models_usage=RequestUsage(prompt_tokens=718, completion_tokens=63), metadata={}, content='The Miami Heat player with the highest points in the 2006-2007 season was Dwyane Wade, with 1397 points. The percentage change in his total rebounds between the 2007-2008 and 2008-2009 seasons was approximately 85.98%.\\n\\nTERMINATE', type='TextMessage')], stop_reason=\"Text 'TERMINATE' mentioned\")"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "def candidate_func(messages: Sequence[AgentEvent | ChatMessage]) -> List[str]:\n",
+ " # keep planning_agent first one to plan out the tasks\n",
+ " if messages[-1].source == \"user\":\n",
+ " return [planning_agent.name]\n",
+ "\n",
+ " # if previous agent is planning_agent and if it explicitely asks for web_search_agent\n",
+ " # or data_analyst_agent or both (in-case of re-planning or re-assignment of tasks)\n",
+ " # then return those specific agents\n",
+ " last_message = messages[-1]\n",
+ " if last_message.source == planning_agent.name:\n",
+ " participants = []\n",
+ " if web_search_agent.name in last_message.to_text():\n",
+ " participants.append(web_search_agent.name)\n",
+ " if data_analyst_agent.name in last_message.to_text():\n",
+ " participants.append(data_analyst_agent.name)\n",
+ " if participants:\n",
+ " return participants # SelectorGroupChat will select from the remaining two agents.\n",
+ "\n",
+ " # we can assume that the task is finished once the web_search_agent\n",
+ " # and data_analyst_agent have took their turns, thus we send\n",
+ " # in planning_agent to terminate the chat\n",
+ " previous_set_of_agents = set(message.source for message in messages)\n",
+ " if web_search_agent.name in previous_set_of_agents and data_analyst_agent.name in previous_set_of_agents:\n",
+ " return [planning_agent.name]\n",
+ "\n",
+ " # if no-conditions are met then return all the agents\n",
+ " return [planning_agent.name, web_search_agent.name, data_analyst_agent.name]\n",
+ "\n",
+ "\n",
+ "# Reset the previous team and run the chat again with the selector function.\n",
+ "await team.reset()\n",
+ "team = SelectorGroupChat(\n",
+ " [planning_agent, web_search_agent, data_analyst_agent],\n",
+ " model_client=model_client,\n",
+ " termination_condition=termination,\n",
+ " candidate_func=candidate_func,\n",
+ ")\n",
+ "\n",
+ "await Console(team.run_stream(task=task))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "You can see from the conversation log that the Planning Agent returns to conversation once the Web Search Agent and Data Analyst Agent took their turns and it finds that the task was not finished as expected so it called the WebSearchAgent again to get rebound values and then called DataAnalysetAgent to get the percentage change."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## User Feedback\n",
+ "\n",
+ "We can add {py:class}`~autogen_agentchat.agents.UserProxyAgent` to the team to\n",
+ "provide user feedback during a run.\n",
+ "See [Human-in-the-Loop](./tutorial/human-in-the-loop.ipynb) for more details\n",
+ "about {py:class}`~autogen_agentchat.agents.UserProxyAgent`.\n",
+ "\n",
+ "To use the {py:class}`~autogen_agentchat.agents.UserProxyAgent` in the \n",
+ "web search example, we simply add it to the team and update the selector function\n",
+ "to always check for user feedback after the planning agent speaks.\n",
+ "If the user responds with `\"APPROVE\"`, the conversation continues, otherwise,\n",
+ "the planning agent tries again, until the user approves."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "---------- user ----------\n",
+ "Who was the Miami Heat player with the highest points in the 2006-2007 season, and what was the percentage change in his total rebounds between the 2007-2008 and 2008-2009 seasons?\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "---------- PlanningAgent ----------\n",
+ "To address the user's query, we will need to perform the following tasks:\n",
+ "\n",
+ "1. Identify the Miami Heat player with the highest points in the 2006-2007 season.\n",
+ "2. Find the total rebounds for that player in the 2007-2008 season.\n",
+ "3. Find the total rebounds for that player in the 2008-2009 season.\n",
+ "4. Calculate the percentage change in the total rebounds between the 2007-2008 and 2008-2009 seasons.\n",
+ "\n",
+ "Let's assign these tasks:\n",
+ "\n",
+ "1. **WebSearchAgent**: Identify the Miami Heat player with the highest points in the 2006-2007 season.\n",
+ " \n",
+ "(Task 2 and 3 depend on the result of Task 1. We'll proceed with Tasks 2 and 3 once Task 1 is complete.)\n",
+ "---------- UserProxyAgent ----------\n",
+ "approve\n",
+ "---------- WebSearchAgent ----------\n",
+ "[FunctionCall(id='call_0prr3fUnG5CtisUG7QeygW0w', arguments='{\"query\":\"Miami Heat highest points scorer 2006-2007 NBA season\"}', name='search_web_tool')]\n",
+ "---------- WebSearchAgent ----------\n",
+ "[FunctionExecutionResult(content='Here are the total points scored by Miami Heat players in the 2006-2007 season:\\n Udonis Haslem: 844 points\\n Dwayne Wade: 1397 points\\n James Posey: 550 points\\n ...\\n ', call_id='call_0prr3fUnG5CtisUG7QeygW0w')]\n",
+ "---------- WebSearchAgent ----------\n",
+ "Here are the total points scored by Miami Heat players in the 2006-2007 season:\n",
+ " Udonis Haslem: 844 points\n",
+ " Dwayne Wade: 1397 points\n",
+ " James Posey: 550 points\n",
+ " ...\n",
+ " \n",
+ "---------- PlanningAgent ----------\n",
+ "Dwyane Wade was the Miami Heat player with the highest points in the 2006-2007 season, scoring 1397 points.\n",
+ "\n",
+ "Next, we need to find Dwyane Wade's total rebounds for the 2007-2008 and 2008-2009 seasons:\n",
+ "\n",
+ "2. **WebSearchAgent**: Find Dwyane Wade's total rebounds for the 2007-2008 season.\n",
+ "3. **WebSearchAgent**: Find Dwyane Wade's total rebounds for the 2008-2009 season.\n",
+ "---------- UserProxyAgent ----------\n",
+ "approve\n",
+ "---------- WebSearchAgent ----------\n",
+ "[FunctionCall(id='call_fBZe80NaBfruOVGwRWbhXyRm', arguments='{\"query\": \"Dwyane Wade total rebounds 2007-2008 NBA season\"}', name='search_web_tool'), FunctionCall(id='call_cURYibna4fGxySiL7IYt0c3s', arguments='{\"query\": \"Dwyane Wade total rebounds 2008-2009 NBA season\"}', name='search_web_tool')]\n",
+ "---------- WebSearchAgent ----------\n",
+ "[FunctionExecutionResult(content='The number of total rebounds for Dwayne Wade in the Miami Heat season 2007-2008 is 214.', call_id='call_fBZe80NaBfruOVGwRWbhXyRm'), FunctionExecutionResult(content='The number of total rebounds for Dwayne Wade in the Miami Heat season 2008-2009 is 398.', call_id='call_cURYibna4fGxySiL7IYt0c3s')]\n",
+ "---------- WebSearchAgent ----------\n",
+ "The number of total rebounds for Dwayne Wade in the Miami Heat season 2007-2008 is 214.\n",
+ "The number of total rebounds for Dwayne Wade in the Miami Heat season 2008-2009 is 398.\n",
+ "---------- PlanningAgent ----------\n",
+ "Now that we have Dwyane Wade's total rebounds for both seasons, we can calculate the percentage change:\n",
+ "\n",
+ "4. **DataAnalystAgent**: Calculate the percentage change in Dwyane Wade's total rebounds from the 2007-2008 season (214 rebounds) to the 2008-2009 season (398 rebounds).\n",
+ "---------- UserProxyAgent ----------\n",
+ "approve\n",
+ "---------- DataAnalystAgent ----------\n",
+ "[FunctionCall(id='call_z3uog7t2x0z1Suzl5hACF9hY', arguments='{\"start\":214,\"end\":398}', name='percentage_change_tool')]\n",
+ "---------- DataAnalystAgent ----------\n",
+ "[FunctionExecutionResult(content='85.98130841121495', call_id='call_z3uog7t2x0z1Suzl5hACF9hY')]\n",
+ "---------- DataAnalystAgent ----------\n",
+ "85.98130841121495\n",
+ "---------- PlanningAgent ----------\n",
+ "Dwyane Wade was the Miami Heat player with the highest points in the 2006-2007 season, scoring 1397 points. His total rebounds increased from 214 in the 2007-2008 season to 398 in the 2008-2009 season, which is a percentage change of approximately 85.98%.\n",
+ "\n",
+ "TERMINATE\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "TaskResult(messages=[TextMessage(source='user', models_usage=None, content='Who was the Miami Heat player with the highest points in the 2006-2007 season, and what was the percentage change in his total rebounds between the 2007-2008 and 2008-2009 seasons?', type='TextMessage'), TextMessage(source='PlanningAgent', models_usage=RequestUsage(prompt_tokens=161, completion_tokens=166), content=\"To address the user's query, we will need to perform the following tasks:\\n\\n1. Identify the Miami Heat player with the highest points in the 2006-2007 season.\\n2. Find the total rebounds for that player in the 2007-2008 season.\\n3. Find the total rebounds for that player in the 2008-2009 season.\\n4. Calculate the percentage change in the total rebounds between the 2007-2008 and 2008-2009 seasons.\\n\\nLet's assign these tasks:\\n\\n1. **WebSearchAgent**: Identify the Miami Heat player with the highest points in the 2006-2007 season.\\n \\n(Task 2 and 3 depend on the result of Task 1. We'll proceed with Tasks 2 and 3 once Task 1 is complete.)\", type='TextMessage'), UserInputRequestedEvent(source='UserProxyAgent', models_usage=None, request_id='2a433f88-f886-4b39-a078-ea1acdcb2f9d', content='', type='UserInputRequestedEvent'), TextMessage(source='UserProxyAgent', models_usage=None, content='approve', type='TextMessage'), ToolCallRequestEvent(source='WebSearchAgent', models_usage=RequestUsage(prompt_tokens=323, completion_tokens=28), content=[FunctionCall(id='call_0prr3fUnG5CtisUG7QeygW0w', arguments='{\"query\":\"Miami Heat highest points scorer 2006-2007 NBA season\"}', name='search_web_tool')], type='ToolCallRequestEvent'), ToolCallExecutionEvent(source='WebSearchAgent', models_usage=None, content=[FunctionExecutionResult(content='Here are the total points scored by Miami Heat players in the 2006-2007 season:\\n Udonis Haslem: 844 points\\n Dwayne Wade: 1397 points\\n James Posey: 550 points\\n ...\\n ', call_id='call_0prr3fUnG5CtisUG7QeygW0w')], type='ToolCallExecutionEvent'), ToolCallSummaryMessage(source='WebSearchAgent', models_usage=None, content='Here are the total points scored by Miami Heat players in the 2006-2007 season:\\n Udonis Haslem: 844 points\\n Dwayne Wade: 1397 points\\n James Posey: 550 points\\n ...\\n ', type='ToolCallSummaryMessage'), TextMessage(source='PlanningAgent', models_usage=RequestUsage(prompt_tokens=403, completion_tokens=112), content=\"Dwyane Wade was the Miami Heat player with the highest points in the 2006-2007 season, scoring 1397 points.\\n\\nNext, we need to find Dwyane Wade's total rebounds for the 2007-2008 and 2008-2009 seasons:\\n\\n2. **WebSearchAgent**: Find Dwyane Wade's total rebounds for the 2007-2008 season.\\n3. **WebSearchAgent**: Find Dwyane Wade's total rebounds for the 2008-2009 season.\", type='TextMessage'), UserInputRequestedEvent(source='UserProxyAgent', models_usage=None, request_id='23dd4570-2391-41e9-aeea-86598499792c', content='', type='UserInputRequestedEvent'), TextMessage(source='UserProxyAgent', models_usage=None, content='approve', type='TextMessage'), ToolCallRequestEvent(source='WebSearchAgent', models_usage=RequestUsage(prompt_tokens=543, completion_tokens=73), content=[FunctionCall(id='call_fBZe80NaBfruOVGwRWbhXyRm', arguments='{\"query\": \"Dwyane Wade total rebounds 2007-2008 NBA season\"}', name='search_web_tool'), FunctionCall(id='call_cURYibna4fGxySiL7IYt0c3s', arguments='{\"query\": \"Dwyane Wade total rebounds 2008-2009 NBA season\"}', name='search_web_tool')], type='ToolCallRequestEvent'), ToolCallExecutionEvent(source='WebSearchAgent', models_usage=None, content=[FunctionExecutionResult(content='The number of total rebounds for Dwayne Wade in the Miami Heat season 2007-2008 is 214.', call_id='call_fBZe80NaBfruOVGwRWbhXyRm'), FunctionExecutionResult(content='The number of total rebounds for Dwayne Wade in the Miami Heat season 2008-2009 is 398.', call_id='call_cURYibna4fGxySiL7IYt0c3s')], type='ToolCallExecutionEvent'), ToolCallSummaryMessage(source='WebSearchAgent', models_usage=None, content='The number of total rebounds for Dwayne Wade in the Miami Heat season 2007-2008 is 214.\\nThe number of total rebounds for Dwayne Wade in the Miami Heat season 2008-2009 is 398.', type='ToolCallSummaryMessage'), TextMessage(source='PlanningAgent', models_usage=RequestUsage(prompt_tokens=586, completion_tokens=70), content=\"Now that we have Dwyane Wade's total rebounds for both seasons, we can calculate the percentage change:\\n\\n4. **DataAnalystAgent**: Calculate the percentage change in Dwyane Wade's total rebounds from the 2007-2008 season (214 rebounds) to the 2008-2009 season (398 rebounds).\", type='TextMessage'), UserInputRequestedEvent(source='UserProxyAgent', models_usage=None, request_id='e849d193-4ab3-4558-8560-7dbc062a0aee', content='', type='UserInputRequestedEvent'), TextMessage(source='UserProxyAgent', models_usage=None, content='approve', type='TextMessage'), ToolCallRequestEvent(source='DataAnalystAgent', models_usage=RequestUsage(prompt_tokens=655, completion_tokens=21), content=[FunctionCall(id='call_z3uog7t2x0z1Suzl5hACF9hY', arguments='{\"start\":214,\"end\":398}', name='percentage_change_tool')], type='ToolCallRequestEvent'), ToolCallExecutionEvent(source='DataAnalystAgent', models_usage=None, content=[FunctionExecutionResult(content='85.98130841121495', call_id='call_z3uog7t2x0z1Suzl5hACF9hY')], type='ToolCallExecutionEvent'), ToolCallSummaryMessage(source='DataAnalystAgent', models_usage=None, content='85.98130841121495', type='ToolCallSummaryMessage'), TextMessage(source='PlanningAgent', models_usage=RequestUsage(prompt_tokens=687, completion_tokens=74), content='Dwyane Wade was the Miami Heat player with the highest points in the 2006-2007 season, scoring 1397 points. His total rebounds increased from 214 in the 2007-2008 season to 398 in the 2008-2009 season, which is a percentage change of approximately 85.98%.\\n\\nTERMINATE', type='TextMessage')], stop_reason=\"Text 'TERMINATE' mentioned\")"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "user_proxy_agent = UserProxyAgent(\"UserProxyAgent\", description=\"A proxy for the user to approve or disapprove tasks.\")\n",
+ "\n",
+ "\n",
+ "def selector_func_with_user_proxy(messages: Sequence[AgentEvent | ChatMessage]) -> str | None:\n",
+ " if messages[-1].source != planning_agent.name and messages[-1].source != user_proxy_agent.name:\n",
+ " # Planning agent should be the first to engage when given a new task, or check progress.\n",
+ " return planning_agent.name\n",
+ " if messages[-1].source == planning_agent.name:\n",
+ " if messages[-2].source == user_proxy_agent.name and \"APPROVE\" in messages[-1].content.upper(): # type: ignore\n",
+ " # User has approved the plan, proceed to the next agent.\n",
+ " return None\n",
+ " # Use the user proxy agent to get the user's approval to proceed.\n",
+ " return user_proxy_agent.name\n",
+ " if messages[-1].source == user_proxy_agent.name:\n",
+ " # If the user does not approve, return to the planning agent.\n",
+ " if \"APPROVE\" not in messages[-1].content.upper(): # type: ignore\n",
+ " return planning_agent.name\n",
+ " return None\n",
+ "\n",
+ "\n",
+ "# Reset the previous agents and run the chat again with the user proxy agent and selector function.\n",
+ "await team.reset()\n",
+ "team = SelectorGroupChat(\n",
+ " [planning_agent, web_search_agent, data_analyst_agent, user_proxy_agent],\n",
+ " model_client=model_client,\n",
+ " termination_condition=termination,\n",
+ " selector_prompt=selector_prompt,\n",
+ " selector_func=selector_func_with_user_proxy,\n",
+ " allow_repeated_speaker=True,\n",
+ ")\n",
+ "\n",
+ "await Console(team.run_stream(task=task))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now, the user's feedback is incorporated into the conversation flow,\n",
+ "and the user can approve or reject the planning agent's decisions."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Using Reasoning Models\n",
+ "\n",
+ "So far in the examples, we have used a `gpt-4o` model. Models like `gpt-4o`\n",
+ "and `gemini-1.5-flash` are great at following instructions, so you can\n",
+ "have relatively detailed instructions in the selector prompt for the team and the \n",
+ "system messages for each agent to guide their behavior.\n",
+ "\n",
+ "However, if you are using a reasoning model like `o3-mini`, you will need to\n",
+ "keep the selector prompt and system messages as simple and to the point as possible.\n",
+ "This is because the reasoning models are already good at coming up with their own \n",
+ "instructions given the context provided to them.\n",
+ "\n",
+ "This also means that we don't need a planning agent to break down the task\n",
+ "anymore, since the {py:class}`~autogen_agentchat.teams.SelectorGroupChat` that\n",
+ "uses a reasoning model can do that on its own.\n",
+ "\n",
+ "In the following example, we will use `o3-mini` as the model for the\n",
+ "agents and the team, and we will not use a planning agent.\n",
+ "Also, we are keeping the selector prompt and system messages as simple as possible."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "model_client = OpenAIChatCompletionClient(model=\"o3-mini\")\n",
+ "\n",
+ "web_search_agent = AssistantAgent(\n",
+ " \"WebSearchAgent\",\n",
+ " description=\"An agent for searching information on the web.\",\n",
+ " tools=[search_web_tool],\n",
+ " model_client=model_client,\n",
+ " system_message=\"\"\"Use web search tool to find information.\"\"\",\n",
+ ")\n",
+ "\n",
+ "data_analyst_agent = AssistantAgent(\n",
+ " \"DataAnalystAgent\",\n",
+ " description=\"An agent for performing calculations.\",\n",
+ " model_client=model_client,\n",
+ " tools=[percentage_change_tool],\n",
+ " system_message=\"\"\"Use tool to perform calculation. If you have not seen the data, ask for it.\"\"\",\n",
+ ")\n",
+ "\n",
+ "user_proxy_agent = UserProxyAgent(\n",
+ " \"UserProxyAgent\",\n",
+ " description=\"A user to approve or disapprove tasks.\",\n",
+ ")\n",
+ "\n",
+ "selector_prompt = \"\"\"Select an agent to perform task.\n",
+ "\n",
+ "{roles}\n",
+ "\n",
+ "Current conversation context:\n",
+ "{history}\n",
+ "\n",
+ "Read the above conversation, then select an agent from {participants} to perform the next task.\n",
+ "When the task is complete, let the user approve or disapprove the task.\n",
+ "\"\"\"\n",
+ "\n",
+ "team = SelectorGroupChat(\n",
+ " [web_search_agent, data_analyst_agent, user_proxy_agent],\n",
+ " model_client=model_client,\n",
+ " termination_condition=termination, # Use the same termination condition as before.\n",
+ " selector_prompt=selector_prompt,\n",
+ " allow_repeated_speaker=True,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "---------- user ----------\n",
+ "Who was the Miami Heat player with the highest points in the 2006-2007 season, and what was the percentage change in his total rebounds between the 2007-2008 and 2008-2009 seasons?\n",
+ "---------- WebSearchAgent ----------\n",
+ "[FunctionCall(id='call_hl7EP6Lp5jj5wEdxeNHTwUVG', arguments='{\"query\": \"Who was the Miami Heat player with the highest points in the 2006-2007 season Miami Heat statistics Dwyane Wade rebounds percentage change 2007-2008 2008-2009 seasons\"}', name='search_web_tool')]\n",
+ "---------- WebSearchAgent ----------\n",
+ "[FunctionExecutionResult(content='Here are the total points scored by Miami Heat players in the 2006-2007 season:\\n Udonis Haslem: 844 points\\n Dwayne Wade: 1397 points\\n James Posey: 550 points\\n ...\\n ', call_id='call_hl7EP6Lp5jj5wEdxeNHTwUVG', is_error=False)]\n",
+ "---------- WebSearchAgent ----------\n",
+ "Here are the total points scored by Miami Heat players in the 2006-2007 season:\n",
+ " Udonis Haslem: 844 points\n",
+ " Dwayne Wade: 1397 points\n",
+ " James Posey: 550 points\n",
+ " ...\n",
+ " \n",
+ "---------- DataAnalystAgent ----------\n",
+ "I found that in the 2006–2007 season the player with the highest points was Dwyane Wade (with 1,397 points). Could you please provide Dwyane Wade’s total rebounds for the 2007–2008 and the 2008–2009 seasons so I can calculate the percentage change?\n",
+ "---------- WebSearchAgent ----------\n",
+ "[FunctionCall(id='call_lppGTILXDvO9waPwKO66ehK6', arguments='{\"query\": \"Dwyane Wade total rebounds 2007-2008 and 2008-2009 seasons for Miami Heat\"}', name='search_web_tool')]\n",
+ "---------- WebSearchAgent ----------\n",
+ "[FunctionExecutionResult(content='The number of total rebounds for Dwayne Wade in the Miami Heat season 2007-2008 is 214.', call_id='call_lppGTILXDvO9waPwKO66ehK6', is_error=False)]\n",
+ "---------- WebSearchAgent ----------\n",
+ "The number of total rebounds for Dwayne Wade in the Miami Heat season 2007-2008 is 214.\n",
+ "---------- DataAnalystAgent ----------\n",
+ "Could you please provide Dwyane Wade’s total rebounds in the 2008-2009 season?\n",
+ "---------- WebSearchAgent ----------\n",
+ "[FunctionCall(id='call_r8DBcbJtQfdtugLtyTrqOvoK', arguments='{\"query\": \"Dwyane Wade total rebounds 2008-2009 season Miami Heat\"}', name='search_web_tool')]\n",
+ "---------- WebSearchAgent ----------\n",
+ "[FunctionExecutionResult(content='The number of total rebounds for Dwayne Wade in the Miami Heat season 2008-2009 is 398.', call_id='call_r8DBcbJtQfdtugLtyTrqOvoK', is_error=False)]\n",
+ "---------- WebSearchAgent ----------\n",
+ "The number of total rebounds for Dwayne Wade in the Miami Heat season 2008-2009 is 398.\n",
+ "---------- DataAnalystAgent ----------\n",
+ "[FunctionCall(id='call_4jejv1wM7V1osbBCxJze8aQM', arguments='{\"start\": 214, \"end\": 398}', name='percentage_change_tool')]\n",
+ "---------- DataAnalystAgent ----------\n",
+ "[FunctionExecutionResult(content='85.98130841121495', call_id='call_4jejv1wM7V1osbBCxJze8aQM', is_error=False)]\n",
+ "---------- DataAnalystAgent ----------\n",
+ "85.98130841121495\n",
+ "---------- DataAnalystAgent ----------\n",
+ "Dwyane Wade was the Miami Heat player with the highest total points (1,397) during the 2006-2007 season. His total rebounds increased by approximately 86% from 214 in the 2007-2008 season to 398 in the 2008-2009 season.\n",
+ "---------- UserProxyAgent ----------\n",
+ "Approve. TERMINATE\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "TaskResult(messages=[TextMessage(source='user', models_usage=None, content='Who was the Miami Heat player with the highest points in the 2006-2007 season, and what was the percentage change in his total rebounds between the 2007-2008 and 2008-2009 seasons?', type='TextMessage'), ToolCallRequestEvent(source='WebSearchAgent', models_usage=RequestUsage(prompt_tokens=103, completion_tokens=384), content=[FunctionCall(id='call_hl7EP6Lp5jj5wEdxeNHTwUVG', arguments='{\"query\": \"Who was the Miami Heat player with the highest points in the 2006-2007 season Miami Heat statistics Dwyane Wade rebounds percentage change 2007-2008 2008-2009 seasons\"}', name='search_web_tool')], type='ToolCallRequestEvent'), ToolCallExecutionEvent(source='WebSearchAgent', models_usage=None, content=[FunctionExecutionResult(content='Here are the total points scored by Miami Heat players in the 2006-2007 season:\\n Udonis Haslem: 844 points\\n Dwayne Wade: 1397 points\\n James Posey: 550 points\\n ...\\n ', call_id='call_hl7EP6Lp5jj5wEdxeNHTwUVG', is_error=False)], type='ToolCallExecutionEvent'), ToolCallSummaryMessage(source='WebSearchAgent', models_usage=None, content='Here are the total points scored by Miami Heat players in the 2006-2007 season:\\n Udonis Haslem: 844 points\\n Dwayne Wade: 1397 points\\n James Posey: 550 points\\n ...\\n ', type='ToolCallSummaryMessage'), TextMessage(source='DataAnalystAgent', models_usage=RequestUsage(prompt_tokens=183, completion_tokens=1038), content='I found that in the 2006–2007 season the player with the highest points was Dwyane Wade (with 1,397 points). Could you please provide Dwyane Wade’s total rebounds for the 2007–2008 and the 2008–2009 seasons so I can calculate the percentage change?', type='TextMessage'), ToolCallRequestEvent(source='WebSearchAgent', models_usage=RequestUsage(prompt_tokens=299, completion_tokens=109), content=[FunctionCall(id='call_lppGTILXDvO9waPwKO66ehK6', arguments='{\"query\": \"Dwyane Wade total rebounds 2007-2008 and 2008-2009 seasons for Miami Heat\"}', name='search_web_tool')], type='ToolCallRequestEvent'), ToolCallExecutionEvent(source='WebSearchAgent', models_usage=None, content=[FunctionExecutionResult(content='The number of total rebounds for Dwayne Wade in the Miami Heat season 2007-2008 is 214.', call_id='call_lppGTILXDvO9waPwKO66ehK6', is_error=False)], type='ToolCallExecutionEvent'), ToolCallSummaryMessage(source='WebSearchAgent', models_usage=None, content='The number of total rebounds for Dwayne Wade in the Miami Heat season 2007-2008 is 214.', type='ToolCallSummaryMessage'), TextMessage(source='DataAnalystAgent', models_usage=RequestUsage(prompt_tokens=291, completion_tokens=224), content='Could you please provide Dwyane Wade’s total rebounds in the 2008-2009 season?', type='TextMessage'), ToolCallRequestEvent(source='WebSearchAgent', models_usage=RequestUsage(prompt_tokens=401, completion_tokens=37), content=[FunctionCall(id='call_r8DBcbJtQfdtugLtyTrqOvoK', arguments='{\"query\": \"Dwyane Wade total rebounds 2008-2009 season Miami Heat\"}', name='search_web_tool')], type='ToolCallRequestEvent'), ToolCallExecutionEvent(source='WebSearchAgent', models_usage=None, content=[FunctionExecutionResult(content='The number of total rebounds for Dwayne Wade in the Miami Heat season 2008-2009 is 398.', call_id='call_r8DBcbJtQfdtugLtyTrqOvoK', is_error=False)], type='ToolCallExecutionEvent'), ToolCallSummaryMessage(source='WebSearchAgent', models_usage=None, content='The number of total rebounds for Dwayne Wade in the Miami Heat season 2008-2009 is 398.', type='ToolCallSummaryMessage'), ToolCallRequestEvent(source='DataAnalystAgent', models_usage=RequestUsage(prompt_tokens=353, completion_tokens=158), content=[FunctionCall(id='call_4jejv1wM7V1osbBCxJze8aQM', arguments='{\"start\": 214, \"end\": 398}', name='percentage_change_tool')], type='ToolCallRequestEvent'), ToolCallExecutionEvent(source='DataAnalystAgent', models_usage=None, content=[FunctionExecutionResult(content='85.98130841121495', call_id='call_4jejv1wM7V1osbBCxJze8aQM', is_error=False)], type='ToolCallExecutionEvent'), ToolCallSummaryMessage(source='DataAnalystAgent', models_usage=None, content='85.98130841121495', type='ToolCallSummaryMessage'), TextMessage(source='DataAnalystAgent', models_usage=RequestUsage(prompt_tokens=394, completion_tokens=138), content='Dwyane Wade was the Miami Heat player with the highest total points (1,397) during the 2006-2007 season. His total rebounds increased by approximately 86% from 214 in the 2007-2008 season to 398 in the 2008-2009 season.', type='TextMessage'), UserInputRequestedEvent(source='UserProxyAgent', models_usage=None, request_id='b3b05408-73fc-47d4-b832-16c9f447cd6e', content='', type='UserInputRequestedEvent'), TextMessage(source='UserProxyAgent', models_usage=None, content='Approve. TERMINATE', type='TextMessage')], stop_reason=\"Text 'TERMINATE' mentioned\")"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "await Console(team.run_stream(task=task))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "```{tip}\n",
+ "For more guidance on how to prompt reasoning models, see the\n",
+ "Azure AI Services Blog on [Prompt Engineering for OpenAI's O1 and O3-mini Reasoning Models](https://techcommunity.microsoft.com/blog/azure-ai-services-blog/prompt-engineering-for-openai%E2%80%99s-o1-and-o3-mini-reasoning-models/4374010)\n",
+ "```"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": ".venv",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.12.3"
+ }
},
- {
- "data": {
- "text/plain": [
- "TaskResult(messages=[TextMessage(source='user', models_usage=None, metadata={}, content='Who was the Miami Heat player with the highest points in the 2006-2007 season, and what was the percentage change in his total rebounds between the 2007-2008 and 2008-2009 seasons?', type='TextMessage'), TextMessage(source='PlanningAgent', models_usage=RequestUsage(prompt_tokens=161, completion_tokens=220), metadata={}, content=\"To complete this task, we need to perform the following subtasks:\\n\\n1. Find out which Miami Heat player had the highest points in the 2006-2007 season.\\n2. Gather data on this player's total rebounds for the 2007-2008 season.\\n3. Gather data on this player's total rebounds for the 2008-2009 season.\\n4. Calculate the percentage change in the player's total rebounds between the 2007-2008 and 2008-2009 seasons.\\n\\nI'll assign these tasks accordingly:\\n\\n1. WebSearchAgent: Search for the Miami Heat player with the highest points in the 2006-2007 NBA season.\\n2. WebSearchAgent: Find the total rebounds for this player in the 2007-2008 NBA season.\\n3. WebSearchAgent: Find the total rebounds for this player in the 2008-2009 NBA season.\\n4. DataAnalystAgent: Calculate the percentage change in total rebounds from the 2007-2008 season to the 2008-2009 season for this player.\", type='TextMessage'), ToolCallRequestEvent(source='WebSearchAgent', models_usage=RequestUsage(prompt_tokens=368, completion_tokens=27), metadata={}, content=[FunctionCall(id='call_89tUNHaAM0kKQYPJLleGUKK7', arguments='{\"query\":\"Miami Heat player highest points 2006-2007 season\"}', name='search_web_tool')], type='ToolCallRequestEvent'), ToolCallExecutionEvent(source='WebSearchAgent', models_usage=None, metadata={}, content=[FunctionExecutionResult(content='Here are the total points scored by Miami Heat players in the 2006-2007 season:\\n Udonis Haslem: 844 points\\n Dwayne Wade: 1397 points\\n James Posey: 550 points\\n ...\\n ', name='search_web_tool', call_id='call_89tUNHaAM0kKQYPJLleGUKK7', is_error=False)], type='ToolCallExecutionEvent'), ToolCallSummaryMessage(source='WebSearchAgent', models_usage=None, metadata={}, content='Here are the total points scored by Miami Heat players in the 2006-2007 season:\\n Udonis Haslem: 844 points\\n Dwayne Wade: 1397 points\\n James Posey: 550 points\\n ...\\n ', type='ToolCallSummaryMessage'), ThoughtEvent(source='WebSearchAgent', models_usage=None, metadata={}, content=\"The Miami Heat player with the highest points in the 2006-2007 season was Dwyane Wade, with 1,397 points.\\n\\nNext, I will search for Dwyane Wade's total rebounds for the 2007-2008 season.\", type='ThoughtEvent'), ToolCallRequestEvent(source='WebSearchAgent', models_usage=RequestUsage(prompt_tokens=460, completion_tokens=83), metadata={}, content=[FunctionCall(id='call_RC55TkSjG3JXRuVOTPrcE1RL', arguments='{\"query\":\"Dwyane Wade total rebounds 2007-2008 season\"}', name='search_web_tool')], type='ToolCallRequestEvent'), ToolCallExecutionEvent(source='WebSearchAgent', models_usage=None, metadata={}, content=[FunctionExecutionResult(content='The number of total rebounds for Dwayne Wade in the Miami Heat season 2007-2008 is 214.', name='search_web_tool', call_id='call_RC55TkSjG3JXRuVOTPrcE1RL', is_error=False)], type='ToolCallExecutionEvent'), ToolCallSummaryMessage(source='WebSearchAgent', models_usage=None, metadata={}, content='The number of total rebounds for Dwayne Wade in the Miami Heat season 2007-2008 is 214.', type='ToolCallSummaryMessage'), ToolCallRequestEvent(source='WebSearchAgent', models_usage=RequestUsage(prompt_tokens=585, completion_tokens=28), metadata={}, content=[FunctionCall(id='call_pBXoABrErDow0rZjw3tjOZol', arguments='{\"query\":\"Dwyane Wade total rebounds 2008-2009 season\"}', name='search_web_tool')], type='ToolCallRequestEvent'), ToolCallExecutionEvent(source='WebSearchAgent', models_usage=None, metadata={}, content=[FunctionExecutionResult(content='The number of total rebounds for Dwayne Wade in the Miami Heat season 2008-2009 is 398.', name='search_web_tool', call_id='call_pBXoABrErDow0rZjw3tjOZol', is_error=False)], type='ToolCallExecutionEvent'), ToolCallSummaryMessage(source='WebSearchAgent', models_usage=None, metadata={}, content='The number of total rebounds for Dwayne Wade in the Miami Heat season 2008-2009 is 398.', type='ToolCallSummaryMessage'), ToolCallRequestEvent(source='DataAnalystAgent', models_usage=RequestUsage(prompt_tokens=496, completion_tokens=21), metadata={}, content=[FunctionCall(id='call_qMxxXtcJsiK8KFSSCx3zm0is', arguments='{\"start\":214,\"end\":398}', name='percentage_change_tool')], type='ToolCallRequestEvent'), ToolCallExecutionEvent(source='DataAnalystAgent', models_usage=None, metadata={}, content=[FunctionExecutionResult(content='85.98130841121495', name='percentage_change_tool', call_id='call_qMxxXtcJsiK8KFSSCx3zm0is', is_error=False)], type='ToolCallExecutionEvent'), ToolCallSummaryMessage(source='DataAnalystAgent', models_usage=None, metadata={}, content='85.98130841121495', type='ToolCallSummaryMessage'), TextMessage(source='PlanningAgent', models_usage=RequestUsage(prompt_tokens=528, completion_tokens=80), metadata={}, content=\"The player with the highest points for the Miami Heat in the 2006-2007 NBA season was Dwyane Wade, who scored 1,397 points. The percentage change in Dwyane Wade's total rebounds from 214 in the 2007-2008 season to 398 in the 2008-2009 season is approximately 85.98%.\\n\\nTERMINATE\", type='TextMessage')], stop_reason=\"Text 'TERMINATE' mentioned\")"
- ]
- },
- "execution_count": 8,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# Use asyncio.run(...) if you are running this in a script.\n",
- "await Console(team.run_stream(task=task))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "As we can see, after the Web Search Agent conducts the necessary searches and the Data Analyst Agent completes the necessary calculations, we find that Dwayne Wade was the Miami Heat player with the highest points in the 2006-2007 season, and the percentage change in his total rebounds between the 2007-2008 and 2008-2009 seasons is 85.98%!"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Custom Selector Function"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Often times we want better control over the selection process.\n",
- "To this end, we can set the `selector_func` argument with a custom selector function to override the default model-based selection.\n",
- "This allows us to implement more complex selection logic and state-based transitions.\n",
- "\n",
- "For instance, we want the Planning Agent to speak immediately after any specialized agent to check the progress.\n",
- "\n",
- "```{note}\n",
- "Returning `None` from the custom selector function will use the default model-based selection.\n",
- "```"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "---------- user ----------\n",
- "Who was the Miami Heat player with the highest points in the 2006-2007 season, and what was the percentage change in his total rebounds between the 2007-2008 and 2008-2009 seasons?\n",
- "---------- PlanningAgent ----------\n",
- "To answer this question, we need to follow these steps: \n",
- "\n",
- "1. Identify the Miami Heat player with the highest points in the 2006-2007 season.\n",
- "2. Retrieve the total rebounds of that player for the 2007-2008 and 2008-2009 seasons.\n",
- "3. Calculate the percentage change in his total rebounds between the two seasons.\n",
- "\n",
- "Let's delegate these tasks:\n",
- "\n",
- "1. WebSearchAgent: Find the Miami Heat player with the highest points in the 2006-2007 NBA season.\n",
- "2. WebSearchAgent: Retrieve the total rebounds for the identified player during the 2007-2008 NBA season.\n",
- "3. WebSearchAgent: Retrieve the total rebounds for the identified player during the 2008-2009 NBA season.\n",
- "4. DataAnalystAgent: Calculate the percentage change in total rebounds between the 2007-2008 and 2008-2009 seasons for the player found.\n",
- "---------- WebSearchAgent ----------\n",
- "[FunctionCall(id='call_Pz82ndNLSV4cH0Sg6g7ArP4L', arguments='{\"query\":\"Miami Heat player highest points 2006-2007 season\"}', name='search_web_tool')]\n",
- "---------- WebSearchAgent ----------\n",
- "[FunctionExecutionResult(content='Here are the total points scored by Miami Heat players in the 2006-2007 season:\\n Udonis Haslem: 844 points\\n Dwayne Wade: 1397 points\\n James Posey: 550 points\\n ...\\n ', call_id='call_Pz82ndNLSV4cH0Sg6g7ArP4L')]\n",
- "---------- WebSearchAgent ----------\n",
- "Here are the total points scored by Miami Heat players in the 2006-2007 season:\n",
- " Udonis Haslem: 844 points\n",
- " Dwayne Wade: 1397 points\n",
- " James Posey: 550 points\n",
- " ...\n",
- " \n",
- "---------- PlanningAgent ----------\n",
- "Great! Dwyane Wade was the Miami Heat player with the highest points in the 2006-2007 season. Now, let's continue with the next tasks:\n",
- "\n",
- "2. WebSearchAgent: Retrieve the total rebounds for Dwyane Wade during the 2007-2008 NBA season.\n",
- "3. WebSearchAgent: Retrieve the total rebounds for Dwyane Wade during the 2008-2009 NBA season.\n",
- "---------- WebSearchAgent ----------\n",
- "[FunctionCall(id='call_3qv9so2DXFZIHtzqDIfXoFID', arguments='{\"query\": \"Dwyane Wade total rebounds 2007-2008 season\"}', name='search_web_tool'), FunctionCall(id='call_Vh7zzzWUeiUAvaYjP0If0k1k', arguments='{\"query\": \"Dwyane Wade total rebounds 2008-2009 season\"}', name='search_web_tool')]\n",
- "---------- WebSearchAgent ----------\n",
- "[FunctionExecutionResult(content='The number of total rebounds for Dwayne Wade in the Miami Heat season 2007-2008 is 214.', call_id='call_3qv9so2DXFZIHtzqDIfXoFID'), FunctionExecutionResult(content='The number of total rebounds for Dwayne Wade in the Miami Heat season 2008-2009 is 398.', call_id='call_Vh7zzzWUeiUAvaYjP0If0k1k')]\n",
- "---------- WebSearchAgent ----------\n",
- "The number of total rebounds for Dwayne Wade in the Miami Heat season 2007-2008 is 214.\n",
- "The number of total rebounds for Dwayne Wade in the Miami Heat season 2008-2009 is 398.\n",
- "---------- PlanningAgent ----------\n",
- "Now let's calculate the percentage change in total rebounds between the 2007-2008 and 2008-2009 seasons for Dwyane Wade.\n",
- "\n",
- "4. DataAnalystAgent: Calculate the percentage change in total rebounds for Dwyane Wade between the 2007-2008 and 2008-2009 seasons.\n",
- "---------- DataAnalystAgent ----------\n",
- "[FunctionCall(id='call_FXnPSr6JVGfAWs3StIizbt2V', arguments='{\"start\":214,\"end\":398}', name='percentage_change_tool')]\n",
- "---------- DataAnalystAgent ----------\n",
- "[FunctionExecutionResult(content='85.98130841121495', call_id='call_FXnPSr6JVGfAWs3StIizbt2V')]\n",
- "---------- DataAnalystAgent ----------\n",
- "85.98130841121495\n",
- "---------- PlanningAgent ----------\n",
- "Dwyane Wade was the Miami Heat player with the highest points in the 2006-2007 season, scoring a total of 1397 points. The percentage change in his total rebounds from the 2007-2008 season (214 rebounds) to the 2008-2009 season (398 rebounds) is approximately 86.0%.\n",
- "\n",
- "TERMINATE\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "TaskResult(messages=[TextMessage(source='user', models_usage=None, content='Who was the Miami Heat player with the highest points in the 2006-2007 season, and what was the percentage change in his total rebounds between the 2007-2008 and 2008-2009 seasons?', type='TextMessage'), TextMessage(source='PlanningAgent', models_usage=RequestUsage(prompt_tokens=161, completion_tokens=192), content=\"To answer this question, we need to follow these steps: \\n\\n1. Identify the Miami Heat player with the highest points in the 2006-2007 season.\\n2. Retrieve the total rebounds of that player for the 2007-2008 and 2008-2009 seasons.\\n3. Calculate the percentage change in his total rebounds between the two seasons.\\n\\nLet's delegate these tasks:\\n\\n1. WebSearchAgent: Find the Miami Heat player with the highest points in the 2006-2007 NBA season.\\n2. WebSearchAgent: Retrieve the total rebounds for the identified player during the 2007-2008 NBA season.\\n3. WebSearchAgent: Retrieve the total rebounds for the identified player during the 2008-2009 NBA season.\\n4. DataAnalystAgent: Calculate the percentage change in total rebounds between the 2007-2008 and 2008-2009 seasons for the player found.\", type='TextMessage'), ToolCallRequestEvent(source='WebSearchAgent', models_usage=RequestUsage(prompt_tokens=340, completion_tokens=27), content=[FunctionCall(id='call_Pz82ndNLSV4cH0Sg6g7ArP4L', arguments='{\"query\":\"Miami Heat player highest points 2006-2007 season\"}', name='search_web_tool')], type='ToolCallRequestEvent'), ToolCallExecutionEvent(source='WebSearchAgent', models_usage=None, content=[FunctionExecutionResult(content='Here are the total points scored by Miami Heat players in the 2006-2007 season:\\n Udonis Haslem: 844 points\\n Dwayne Wade: 1397 points\\n James Posey: 550 points\\n ...\\n ', call_id='call_Pz82ndNLSV4cH0Sg6g7ArP4L')], type='ToolCallExecutionEvent'), ToolCallSummaryMessage(source='WebSearchAgent', models_usage=None, content='Here are the total points scored by Miami Heat players in the 2006-2007 season:\\n Udonis Haslem: 844 points\\n Dwayne Wade: 1397 points\\n James Posey: 550 points\\n ...\\n ', type='ToolCallSummaryMessage'), TextMessage(source='PlanningAgent', models_usage=RequestUsage(prompt_tokens=420, completion_tokens=87), content=\"Great! Dwyane Wade was the Miami Heat player with the highest points in the 2006-2007 season. Now, let's continue with the next tasks:\\n\\n2. WebSearchAgent: Retrieve the total rebounds for Dwyane Wade during the 2007-2008 NBA season.\\n3. WebSearchAgent: Retrieve the total rebounds for Dwyane Wade during the 2008-2009 NBA season.\", type='TextMessage'), ToolCallRequestEvent(source='WebSearchAgent', models_usage=RequestUsage(prompt_tokens=525, completion_tokens=71), content=[FunctionCall(id='call_3qv9so2DXFZIHtzqDIfXoFID', arguments='{\"query\": \"Dwyane Wade total rebounds 2007-2008 season\"}', name='search_web_tool'), FunctionCall(id='call_Vh7zzzWUeiUAvaYjP0If0k1k', arguments='{\"query\": \"Dwyane Wade total rebounds 2008-2009 season\"}', name='search_web_tool')], type='ToolCallRequestEvent'), ToolCallExecutionEvent(source='WebSearchAgent', models_usage=None, content=[FunctionExecutionResult(content='The number of total rebounds for Dwayne Wade in the Miami Heat season 2007-2008 is 214.', call_id='call_3qv9so2DXFZIHtzqDIfXoFID'), FunctionExecutionResult(content='The number of total rebounds for Dwayne Wade in the Miami Heat season 2008-2009 is 398.', call_id='call_Vh7zzzWUeiUAvaYjP0If0k1k')], type='ToolCallExecutionEvent'), ToolCallSummaryMessage(source='WebSearchAgent', models_usage=None, content='The number of total rebounds for Dwayne Wade in the Miami Heat season 2007-2008 is 214.\\nThe number of total rebounds for Dwayne Wade in the Miami Heat season 2008-2009 is 398.', type='ToolCallSummaryMessage'), TextMessage(source='PlanningAgent', models_usage=RequestUsage(prompt_tokens=569, completion_tokens=68), content=\"Now let's calculate the percentage change in total rebounds between the 2007-2008 and 2008-2009 seasons for Dwyane Wade.\\n\\n4. DataAnalystAgent: Calculate the percentage change in total rebounds for Dwyane Wade between the 2007-2008 and 2008-2009 seasons.\", type='TextMessage'), ToolCallRequestEvent(source='DataAnalystAgent', models_usage=RequestUsage(prompt_tokens=627, completion_tokens=21), content=[FunctionCall(id='call_FXnPSr6JVGfAWs3StIizbt2V', arguments='{\"start\":214,\"end\":398}', name='percentage_change_tool')], type='ToolCallRequestEvent'), ToolCallExecutionEvent(source='DataAnalystAgent', models_usage=None, content=[FunctionExecutionResult(content='85.98130841121495', call_id='call_FXnPSr6JVGfAWs3StIizbt2V')], type='ToolCallExecutionEvent'), ToolCallSummaryMessage(source='DataAnalystAgent', models_usage=None, content='85.98130841121495', type='ToolCallSummaryMessage'), TextMessage(source='PlanningAgent', models_usage=RequestUsage(prompt_tokens=659, completion_tokens=76), content='Dwyane Wade was the Miami Heat player with the highest points in the 2006-2007 season, scoring a total of 1397 points. The percentage change in his total rebounds from the 2007-2008 season (214 rebounds) to the 2008-2009 season (398 rebounds) is approximately 86.0%.\\n\\nTERMINATE', type='TextMessage')], stop_reason=\"Text 'TERMINATE' mentioned\")"
- ]
- },
- "execution_count": 10,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "def selector_func(messages: Sequence[AgentEvent | ChatMessage]) -> str | None:\n",
- " if messages[-1].source != planning_agent.name:\n",
- " return planning_agent.name\n",
- " return None\n",
- "\n",
- "\n",
- "# Reset the previous team and run the chat again with the selector function.\n",
- "await team.reset()\n",
- "team = SelectorGroupChat(\n",
- " [planning_agent, web_search_agent, data_analyst_agent],\n",
- " model_client=model_client,\n",
- " termination_condition=termination,\n",
- " selector_prompt=selector_prompt,\n",
- " allow_repeated_speaker=True,\n",
- " selector_func=selector_func,\n",
- ")\n",
- "\n",
- "await Console(team.run_stream(task=task))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "You can see from the conversation log that the Planning Agent always speaks immediately after the specialized agents.\n",
- "\n",
- "```{tip}\n",
- "Each participant agent only makes one step (executing tools, generating a response, etc.)\n",
- "on each turn. \n",
- "If you want an {py:class}`~autogen_agentchat.agents.AssistantAgent` to repeat\n",
- "until it stop returning a {py:class}`~autogen_agentchat.messages.ToolCallSummaryMessage`\n",
- "when it has finished running all the tools it needs to run, you can do so by\n",
- "checking the last message and returning the agent if it is a\n",
- "{py:class}`~autogen_agentchat.messages.ToolCallSummaryMessage`.\n",
- "```"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Custom Candidate Function"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "One more possible requirement might be to automatically select the next speaker from a filtered list of agents.\n",
- "For this, we can set `candidate_func` parameter with a custom candidate function to filter down the list of potential agents for speaker selection for each turn of groupchat.\n",
- "\n",
- "This allow us to restrict speaker selection to a specific set of agents after a given agent.\n",
- "\n",
- "\n",
- "```{note}\n",
- "The `candidate_func` is only valid if `selector_func` is not set.\n",
- "Returning `None` or an empty list `[]` from the custom candidate function will raise a `ValueError`.\n",
- "```"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "---------- user ----------\n",
- "Who was the Miami Heat player with the highest points in the 2006-2007 season, and what was the percentage change in his total rebounds between the 2007-2008 and 2008-2009 seasons?\n",
- "---------- PlanningAgent ----------\n",
- "To answer this question, we'll break it down into two main subtasks:\n",
- "\n",
- "1. Identify the Miami Heat player with the highest points in the 2006-2007 season.\n",
- "2. Calculate the percentage change in that player's total rebounds between the 2007-2008 and 2008-2009 seasons.\n",
- "\n",
- "Let's assign these tasks:\n",
- "\n",
- "1. WebSearchAgent: Search for the Miami Heat player with the highest points in the 2006-2007 NBA season.\n",
- "2. WebSearchAgent: Find the total rebound statistics for that identified player for both the 2007-2008 and 2008-2009 NBA seasons.\n",
- "3. DataAnalystAgent: Calculate the percentage change in the player's total rebounds between the 2007-2008 and 2008-2009 seasons once the data is retrieved.\n",
- "---------- WebSearchAgent ----------\n",
- "[FunctionCall(id='call_WtR5KTfEIxs3jIO25gjAw7dF', arguments='{\"query\":\"Miami Heat highest points scorer 2006-2007 NBA season\"}', name='search_web_tool')]\n",
- "---------- WebSearchAgent ----------\n",
- "[FunctionExecutionResult(content='Here are the total points scored by Miami Heat players in the 2006-2007 season:\\n Udonis Haslem: 844 points\\n Dwayne Wade: 1397 points\\n James Posey: 550 points\\n ...\\n ', name='search_web_tool', call_id='call_WtR5KTfEIxs3jIO25gjAw7dF', is_error=False)]\n",
- "---------- WebSearchAgent ----------\n",
- "Here are the total points scored by Miami Heat players in the 2006-2007 season:\n",
- " Udonis Haslem: 844 points\n",
- " Dwayne Wade: 1397 points\n",
- " James Posey: 550 points\n",
- " ...\n",
- " \n",
- "---------- DataAnalystAgent ----------\n",
- "[FunctionCall(id='call_9HA3DEacUl4WuG2G2PtRkXAO', arguments='{\"start\": 432, \"end\": 527}', name='percentage_change_tool')]\n",
- "---------- DataAnalystAgent ----------\n",
- "[FunctionExecutionResult(content='21.99074074074074', name='percentage_change_tool', call_id='call_9HA3DEacUl4WuG2G2PtRkXAO', is_error=False)]\n",
- "---------- DataAnalystAgent ----------\n",
- "21.99074074074074\n",
- "---------- PlanningAgent ----------\n",
- "It seems we've missed some context there, so let's assign the subtasks again for clarity:\n",
- "\n",
- "Based on the search results, Dwyane Wade had the highest points for the Miami Heat in the 2006-2007 season with 1397 points.\n",
- "\n",
- "Now, let's find the necessary rebound statistics:\n",
- "\n",
- "2. WebSearchAgent: Find Dwyane Wade's total rebound statistics for both the 2007-2008 and 2008-2009 NBA seasons.\n",
- "3. DataAnalystAgent: Once the data is retrieved, calculate the percentage change in Dwyane Wade's total rebounds between the 2007-2008 and 2008-2009 seasons.\n",
- "---------- WebSearchAgent ----------\n",
- "[FunctionCall(id='call_3i1wTDSjkGg6Ev8YKYWkZK55', arguments='{\"query\": \"Dwyane Wade total rebounds 2007-2008 NBA season\"}', name='search_web_tool'), FunctionCall(id='call_NRAs6jHxXRi8zsvpW5WlHAaU', arguments='{\"query\": \"Dwyane Wade total rebounds 2008-2009 NBA season\"}', name='search_web_tool')]\n",
- "---------- WebSearchAgent ----------\n",
- "[FunctionExecutionResult(content='The number of total rebounds for Dwayne Wade in the Miami Heat season 2007-2008 is 214.', name='search_web_tool', call_id='call_3i1wTDSjkGg6Ev8YKYWkZK55', is_error=False), FunctionExecutionResult(content='The number of total rebounds for Dwayne Wade in the Miami Heat season 2008-2009 is 398.', name='search_web_tool', call_id='call_NRAs6jHxXRi8zsvpW5WlHAaU', is_error=False)]\n",
- "---------- WebSearchAgent ----------\n",
- "The number of total rebounds for Dwayne Wade in the Miami Heat season 2007-2008 is 214.\n",
- "The number of total rebounds for Dwayne Wade in the Miami Heat season 2008-2009 is 398.\n",
- "---------- PlanningAgent ----------\n",
- "The total rebounds for Dwyane Wade in the 2007-2008 season were 214, and in the 2008-2009 season, they were 398.\n",
- "\n",
- "Now, let's calculate the percentage change.\n",
- "\n",
- "3. DataAnalystAgent: Calculate the percentage change in Dwyane Wade's total rebounds from the 2007-2008 season to the 2008-2009 season.\n",
- "---------- DataAnalystAgent ----------\n",
- "[FunctionCall(id='call_XECA7ezz7VIKbf8IbZYSCSpI', arguments='{\"start\":214,\"end\":398}', name='percentage_change_tool')]\n",
- "---------- DataAnalystAgent ----------\n",
- "[FunctionExecutionResult(content='85.98130841121495', name='percentage_change_tool', call_id='call_XECA7ezz7VIKbf8IbZYSCSpI', is_error=False)]\n",
- "---------- DataAnalystAgent ----------\n",
- "85.98130841121495\n",
- "---------- PlanningAgent ----------\n",
- "The Miami Heat player with the highest points in the 2006-2007 season was Dwyane Wade, with 1397 points. The percentage change in his total rebounds between the 2007-2008 and 2008-2009 seasons was approximately 85.98%.\n",
- "\n",
- "TERMINATE\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "TaskResult(messages=[TextMessage(source='user', models_usage=None, metadata={}, content='Who was the Miami Heat player with the highest points in the 2006-2007 season, and what was the percentage change in his total rebounds between the 2007-2008 and 2008-2009 seasons?', type='TextMessage'), TextMessage(source='PlanningAgent', models_usage=RequestUsage(prompt_tokens=161, completion_tokens=169), metadata={}, content=\"To answer this question, we'll break it down into two main subtasks:\\n\\n1. Identify the Miami Heat player with the highest points in the 2006-2007 season.\\n2. Calculate the percentage change in that player's total rebounds between the 2007-2008 and 2008-2009 seasons.\\n\\nLet's assign these tasks:\\n\\n1. WebSearchAgent: Search for the Miami Heat player with the highest points in the 2006-2007 NBA season.\\n2. WebSearchAgent: Find the total rebound statistics for that identified player for both the 2007-2008 and 2008-2009 NBA seasons.\\n3. DataAnalystAgent: Calculate the percentage change in the player's total rebounds between the 2007-2008 and 2008-2009 seasons once the data is retrieved.\", type='TextMessage'), ToolCallRequestEvent(source='WebSearchAgent', models_usage=RequestUsage(prompt_tokens=324, completion_tokens=28), metadata={}, content=[FunctionCall(id='call_WtR5KTfEIxs3jIO25gjAw7dF', arguments='{\"query\":\"Miami Heat highest points scorer 2006-2007 NBA season\"}', name='search_web_tool')], type='ToolCallRequestEvent'), ToolCallExecutionEvent(source='WebSearchAgent', models_usage=None, metadata={}, content=[FunctionExecutionResult(content='Here are the total points scored by Miami Heat players in the 2006-2007 season:\\n Udonis Haslem: 844 points\\n Dwayne Wade: 1397 points\\n James Posey: 550 points\\n ...\\n ', name='search_web_tool', call_id='call_WtR5KTfEIxs3jIO25gjAw7dF', is_error=False)], type='ToolCallExecutionEvent'), ToolCallSummaryMessage(source='WebSearchAgent', models_usage=None, metadata={}, content='Here are the total points scored by Miami Heat players in the 2006-2007 season:\\n Udonis Haslem: 844 points\\n Dwayne Wade: 1397 points\\n James Posey: 550 points\\n ...\\n ', type='ToolCallSummaryMessage'), ToolCallRequestEvent(source='DataAnalystAgent', models_usage=RequestUsage(prompt_tokens=390, completion_tokens=37), metadata={}, content=[FunctionCall(id='call_9HA3DEacUl4WuG2G2PtRkXAO', arguments='{\"start\": 432, \"end\": 527}', name='percentage_change_tool')], type='ToolCallRequestEvent'), ToolCallExecutionEvent(source='DataAnalystAgent', models_usage=None, metadata={}, content=[FunctionExecutionResult(content='21.99074074074074', name='percentage_change_tool', call_id='call_9HA3DEacUl4WuG2G2PtRkXAO', is_error=False)], type='ToolCallExecutionEvent'), ToolCallSummaryMessage(source='DataAnalystAgent', models_usage=None, metadata={}, content='21.99074074074074', type='ToolCallSummaryMessage'), TextMessage(source='PlanningAgent', models_usage=RequestUsage(prompt_tokens=413, completion_tokens=137), metadata={}, content=\"It seems we've missed some context there, so let's assign the subtasks again for clarity:\\n\\nBased on the search results, Dwyane Wade had the highest points for the Miami Heat in the 2006-2007 season with 1397 points.\\n\\nNow, let's find the necessary rebound statistics:\\n\\n2. WebSearchAgent: Find Dwyane Wade's total rebound statistics for both the 2007-2008 and 2008-2009 NBA seasons.\\n3. DataAnalystAgent: Once the data is retrieved, calculate the percentage change in Dwyane Wade's total rebounds between the 2007-2008 and 2008-2009 seasons.\", type='TextMessage'), ToolCallRequestEvent(source='WebSearchAgent', models_usage=RequestUsage(prompt_tokens=576, completion_tokens=73), metadata={}, content=[FunctionCall(id='call_3i1wTDSjkGg6Ev8YKYWkZK55', arguments='{\"query\": \"Dwyane Wade total rebounds 2007-2008 NBA season\"}', name='search_web_tool'), FunctionCall(id='call_NRAs6jHxXRi8zsvpW5WlHAaU', arguments='{\"query\": \"Dwyane Wade total rebounds 2008-2009 NBA season\"}', name='search_web_tool')], type='ToolCallRequestEvent'), ToolCallExecutionEvent(source='WebSearchAgent', models_usage=None, metadata={}, content=[FunctionExecutionResult(content='The number of total rebounds for Dwayne Wade in the Miami Heat season 2007-2008 is 214.', name='search_web_tool', call_id='call_3i1wTDSjkGg6Ev8YKYWkZK55', is_error=False), FunctionExecutionResult(content='The number of total rebounds for Dwayne Wade in the Miami Heat season 2008-2009 is 398.', name='search_web_tool', call_id='call_NRAs6jHxXRi8zsvpW5WlHAaU', is_error=False)], type='ToolCallExecutionEvent'), ToolCallSummaryMessage(source='WebSearchAgent', models_usage=None, metadata={}, content='The number of total rebounds for Dwayne Wade in the Miami Heat season 2007-2008 is 214.\\nThe number of total rebounds for Dwayne Wade in the Miami Heat season 2008-2009 is 398.', type='ToolCallSummaryMessage'), TextMessage(source='PlanningAgent', models_usage=RequestUsage(prompt_tokens=612, completion_tokens=84), metadata={}, content=\"The total rebounds for Dwyane Wade in the 2007-2008 season were 214, and in the 2008-2009 season, they were 398.\\n\\nNow, let's calculate the percentage change.\\n\\n3. DataAnalystAgent: Calculate the percentage change in Dwyane Wade's total rebounds from the 2007-2008 season to the 2008-2009 season.\", type='TextMessage'), ToolCallRequestEvent(source='DataAnalystAgent', models_usage=RequestUsage(prompt_tokens=720, completion_tokens=21), metadata={}, content=[FunctionCall(id='call_XECA7ezz7VIKbf8IbZYSCSpI', arguments='{\"start\":214,\"end\":398}', name='percentage_change_tool')], type='ToolCallRequestEvent'), ToolCallExecutionEvent(source='DataAnalystAgent', models_usage=None, metadata={}, content=[FunctionExecutionResult(content='85.98130841121495', name='percentage_change_tool', call_id='call_XECA7ezz7VIKbf8IbZYSCSpI', is_error=False)], type='ToolCallExecutionEvent'), ToolCallSummaryMessage(source='DataAnalystAgent', models_usage=None, metadata={}, content='85.98130841121495', type='ToolCallSummaryMessage'), TextMessage(source='PlanningAgent', models_usage=RequestUsage(prompt_tokens=718, completion_tokens=63), metadata={}, content='The Miami Heat player with the highest points in the 2006-2007 season was Dwyane Wade, with 1397 points. The percentage change in his total rebounds between the 2007-2008 and 2008-2009 seasons was approximately 85.98%.\\n\\nTERMINATE', type='TextMessage')], stop_reason=\"Text 'TERMINATE' mentioned\")"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "def candidate_func(messages: Sequence[AgentEvent | ChatMessage]) -> List[str]:\n",
- " # keep planning_agent first one to plan out the tasks\n",
- " if messages[-1].source == \"user\":\n",
- " return [planning_agent.name]\n",
- "\n",
- " # if previous agent is planning_agent and if it explicitely asks for web_search_agent\n",
- " # or data_analyst_agent or both (in-case of re-planning or re-assignment of tasks)\n",
- " # then return those specific agents\n",
- " last_message = messages[-1]\n",
- " if last_message.source == planning_agent.name:\n",
- " participants = []\n",
- " if web_search_agent.name in last_message.content:\n",
- " participants.append(web_search_agent.name)\n",
- " if data_analyst_agent.name in last_message.content:\n",
- " participants.append(data_analyst_agent.name)\n",
- " if participants:\n",
- " return participants # SelectorGroupChat will select from the remaining two agents.\n",
- "\n",
- " # we can assume that the task is finished once the web_search_agent\n",
- " # and data_analyst_agent have took their turns, thus we send\n",
- " # in planning_agent to terminate the chat\n",
- " previous_set_of_agents = set(message.source for message in messages)\n",
- " if web_search_agent.name in previous_set_of_agents and data_analyst_agent.name in previous_set_of_agents:\n",
- " return [planning_agent.name]\n",
- "\n",
- " # if no-conditions are met then return all the agents\n",
- " return [planning_agent.name, web_search_agent.name, data_analyst_agent.name]\n",
- "\n",
- "\n",
- "# Reset the previous team and run the chat again with the selector function.\n",
- "await team.reset()\n",
- "team = SelectorGroupChat(\n",
- " [planning_agent, web_search_agent, data_analyst_agent],\n",
- " model_client=model_client,\n",
- " termination_condition=termination,\n",
- " candidate_func=candidate_func,\n",
- ")\n",
- "\n",
- "await Console(team.run_stream(task=task))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "You can see from the conversation log that the Planning Agent returns to conversation once the Web Search Agent and Data Analyst Agent took their turns and it finds that the task was not finished as expected so it called the WebSearchAgent again to get rebound values and then called DataAnalysetAgent to get the percentage change."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## User Feedback\n",
- "\n",
- "We can add {py:class}`~autogen_agentchat.agents.UserProxyAgent` to the team to\n",
- "provide user feedback during a run.\n",
- "See [Human-in-the-Loop](./tutorial/human-in-the-loop.ipynb) for more details\n",
- "about {py:class}`~autogen_agentchat.agents.UserProxyAgent`.\n",
- "\n",
- "To use the {py:class}`~autogen_agentchat.agents.UserProxyAgent` in the \n",
- "web search example, we simply add it to the team and update the selector function\n",
- "to always check for user feedback after the planning agent speaks.\n",
- "If the user responds with `\"APPROVE\"`, the conversation continues, otherwise,\n",
- "the planning agent tries again, until the user approves."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "---------- user ----------\n",
- "Who was the Miami Heat player with the highest points in the 2006-2007 season, and what was the percentage change in his total rebounds between the 2007-2008 and 2008-2009 seasons?\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "---------- PlanningAgent ----------\n",
- "To address the user's query, we will need to perform the following tasks:\n",
- "\n",
- "1. Identify the Miami Heat player with the highest points in the 2006-2007 season.\n",
- "2. Find the total rebounds for that player in the 2007-2008 season.\n",
- "3. Find the total rebounds for that player in the 2008-2009 season.\n",
- "4. Calculate the percentage change in the total rebounds between the 2007-2008 and 2008-2009 seasons.\n",
- "\n",
- "Let's assign these tasks:\n",
- "\n",
- "1. **WebSearchAgent**: Identify the Miami Heat player with the highest points in the 2006-2007 season.\n",
- " \n",
- "(Task 2 and 3 depend on the result of Task 1. We'll proceed with Tasks 2 and 3 once Task 1 is complete.)\n",
- "---------- UserProxyAgent ----------\n",
- "approve\n",
- "---------- WebSearchAgent ----------\n",
- "[FunctionCall(id='call_0prr3fUnG5CtisUG7QeygW0w', arguments='{\"query\":\"Miami Heat highest points scorer 2006-2007 NBA season\"}', name='search_web_tool')]\n",
- "---------- WebSearchAgent ----------\n",
- "[FunctionExecutionResult(content='Here are the total points scored by Miami Heat players in the 2006-2007 season:\\n Udonis Haslem: 844 points\\n Dwayne Wade: 1397 points\\n James Posey: 550 points\\n ...\\n ', call_id='call_0prr3fUnG5CtisUG7QeygW0w')]\n",
- "---------- WebSearchAgent ----------\n",
- "Here are the total points scored by Miami Heat players in the 2006-2007 season:\n",
- " Udonis Haslem: 844 points\n",
- " Dwayne Wade: 1397 points\n",
- " James Posey: 550 points\n",
- " ...\n",
- " \n",
- "---------- PlanningAgent ----------\n",
- "Dwyane Wade was the Miami Heat player with the highest points in the 2006-2007 season, scoring 1397 points.\n",
- "\n",
- "Next, we need to find Dwyane Wade's total rebounds for the 2007-2008 and 2008-2009 seasons:\n",
- "\n",
- "2. **WebSearchAgent**: Find Dwyane Wade's total rebounds for the 2007-2008 season.\n",
- "3. **WebSearchAgent**: Find Dwyane Wade's total rebounds for the 2008-2009 season.\n",
- "---------- UserProxyAgent ----------\n",
- "approve\n",
- "---------- WebSearchAgent ----------\n",
- "[FunctionCall(id='call_fBZe80NaBfruOVGwRWbhXyRm', arguments='{\"query\": \"Dwyane Wade total rebounds 2007-2008 NBA season\"}', name='search_web_tool'), FunctionCall(id='call_cURYibna4fGxySiL7IYt0c3s', arguments='{\"query\": \"Dwyane Wade total rebounds 2008-2009 NBA season\"}', name='search_web_tool')]\n",
- "---------- WebSearchAgent ----------\n",
- "[FunctionExecutionResult(content='The number of total rebounds for Dwayne Wade in the Miami Heat season 2007-2008 is 214.', call_id='call_fBZe80NaBfruOVGwRWbhXyRm'), FunctionExecutionResult(content='The number of total rebounds for Dwayne Wade in the Miami Heat season 2008-2009 is 398.', call_id='call_cURYibna4fGxySiL7IYt0c3s')]\n",
- "---------- WebSearchAgent ----------\n",
- "The number of total rebounds for Dwayne Wade in the Miami Heat season 2007-2008 is 214.\n",
- "The number of total rebounds for Dwayne Wade in the Miami Heat season 2008-2009 is 398.\n",
- "---------- PlanningAgent ----------\n",
- "Now that we have Dwyane Wade's total rebounds for both seasons, we can calculate the percentage change:\n",
- "\n",
- "4. **DataAnalystAgent**: Calculate the percentage change in Dwyane Wade's total rebounds from the 2007-2008 season (214 rebounds) to the 2008-2009 season (398 rebounds).\n",
- "---------- UserProxyAgent ----------\n",
- "approve\n",
- "---------- DataAnalystAgent ----------\n",
- "[FunctionCall(id='call_z3uog7t2x0z1Suzl5hACF9hY', arguments='{\"start\":214,\"end\":398}', name='percentage_change_tool')]\n",
- "---------- DataAnalystAgent ----------\n",
- "[FunctionExecutionResult(content='85.98130841121495', call_id='call_z3uog7t2x0z1Suzl5hACF9hY')]\n",
- "---------- DataAnalystAgent ----------\n",
- "85.98130841121495\n",
- "---------- PlanningAgent ----------\n",
- "Dwyane Wade was the Miami Heat player with the highest points in the 2006-2007 season, scoring 1397 points. His total rebounds increased from 214 in the 2007-2008 season to 398 in the 2008-2009 season, which is a percentage change of approximately 85.98%.\n",
- "\n",
- "TERMINATE\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "TaskResult(messages=[TextMessage(source='user', models_usage=None, content='Who was the Miami Heat player with the highest points in the 2006-2007 season, and what was the percentage change in his total rebounds between the 2007-2008 and 2008-2009 seasons?', type='TextMessage'), TextMessage(source='PlanningAgent', models_usage=RequestUsage(prompt_tokens=161, completion_tokens=166), content=\"To address the user's query, we will need to perform the following tasks:\\n\\n1. Identify the Miami Heat player with the highest points in the 2006-2007 season.\\n2. Find the total rebounds for that player in the 2007-2008 season.\\n3. Find the total rebounds for that player in the 2008-2009 season.\\n4. Calculate the percentage change in the total rebounds between the 2007-2008 and 2008-2009 seasons.\\n\\nLet's assign these tasks:\\n\\n1. **WebSearchAgent**: Identify the Miami Heat player with the highest points in the 2006-2007 season.\\n \\n(Task 2 and 3 depend on the result of Task 1. We'll proceed with Tasks 2 and 3 once Task 1 is complete.)\", type='TextMessage'), UserInputRequestedEvent(source='UserProxyAgent', models_usage=None, request_id='2a433f88-f886-4b39-a078-ea1acdcb2f9d', content='', type='UserInputRequestedEvent'), TextMessage(source='UserProxyAgent', models_usage=None, content='approve', type='TextMessage'), ToolCallRequestEvent(source='WebSearchAgent', models_usage=RequestUsage(prompt_tokens=323, completion_tokens=28), content=[FunctionCall(id='call_0prr3fUnG5CtisUG7QeygW0w', arguments='{\"query\":\"Miami Heat highest points scorer 2006-2007 NBA season\"}', name='search_web_tool')], type='ToolCallRequestEvent'), ToolCallExecutionEvent(source='WebSearchAgent', models_usage=None, content=[FunctionExecutionResult(content='Here are the total points scored by Miami Heat players in the 2006-2007 season:\\n Udonis Haslem: 844 points\\n Dwayne Wade: 1397 points\\n James Posey: 550 points\\n ...\\n ', call_id='call_0prr3fUnG5CtisUG7QeygW0w')], type='ToolCallExecutionEvent'), ToolCallSummaryMessage(source='WebSearchAgent', models_usage=None, content='Here are the total points scored by Miami Heat players in the 2006-2007 season:\\n Udonis Haslem: 844 points\\n Dwayne Wade: 1397 points\\n James Posey: 550 points\\n ...\\n ', type='ToolCallSummaryMessage'), TextMessage(source='PlanningAgent', models_usage=RequestUsage(prompt_tokens=403, completion_tokens=112), content=\"Dwyane Wade was the Miami Heat player with the highest points in the 2006-2007 season, scoring 1397 points.\\n\\nNext, we need to find Dwyane Wade's total rebounds for the 2007-2008 and 2008-2009 seasons:\\n\\n2. **WebSearchAgent**: Find Dwyane Wade's total rebounds for the 2007-2008 season.\\n3. **WebSearchAgent**: Find Dwyane Wade's total rebounds for the 2008-2009 season.\", type='TextMessage'), UserInputRequestedEvent(source='UserProxyAgent', models_usage=None, request_id='23dd4570-2391-41e9-aeea-86598499792c', content='', type='UserInputRequestedEvent'), TextMessage(source='UserProxyAgent', models_usage=None, content='approve', type='TextMessage'), ToolCallRequestEvent(source='WebSearchAgent', models_usage=RequestUsage(prompt_tokens=543, completion_tokens=73), content=[FunctionCall(id='call_fBZe80NaBfruOVGwRWbhXyRm', arguments='{\"query\": \"Dwyane Wade total rebounds 2007-2008 NBA season\"}', name='search_web_tool'), FunctionCall(id='call_cURYibna4fGxySiL7IYt0c3s', arguments='{\"query\": \"Dwyane Wade total rebounds 2008-2009 NBA season\"}', name='search_web_tool')], type='ToolCallRequestEvent'), ToolCallExecutionEvent(source='WebSearchAgent', models_usage=None, content=[FunctionExecutionResult(content='The number of total rebounds for Dwayne Wade in the Miami Heat season 2007-2008 is 214.', call_id='call_fBZe80NaBfruOVGwRWbhXyRm'), FunctionExecutionResult(content='The number of total rebounds for Dwayne Wade in the Miami Heat season 2008-2009 is 398.', call_id='call_cURYibna4fGxySiL7IYt0c3s')], type='ToolCallExecutionEvent'), ToolCallSummaryMessage(source='WebSearchAgent', models_usage=None, content='The number of total rebounds for Dwayne Wade in the Miami Heat season 2007-2008 is 214.\\nThe number of total rebounds for Dwayne Wade in the Miami Heat season 2008-2009 is 398.', type='ToolCallSummaryMessage'), TextMessage(source='PlanningAgent', models_usage=RequestUsage(prompt_tokens=586, completion_tokens=70), content=\"Now that we have Dwyane Wade's total rebounds for both seasons, we can calculate the percentage change:\\n\\n4. **DataAnalystAgent**: Calculate the percentage change in Dwyane Wade's total rebounds from the 2007-2008 season (214 rebounds) to the 2008-2009 season (398 rebounds).\", type='TextMessage'), UserInputRequestedEvent(source='UserProxyAgent', models_usage=None, request_id='e849d193-4ab3-4558-8560-7dbc062a0aee', content='', type='UserInputRequestedEvent'), TextMessage(source='UserProxyAgent', models_usage=None, content='approve', type='TextMessage'), ToolCallRequestEvent(source='DataAnalystAgent', models_usage=RequestUsage(prompt_tokens=655, completion_tokens=21), content=[FunctionCall(id='call_z3uog7t2x0z1Suzl5hACF9hY', arguments='{\"start\":214,\"end\":398}', name='percentage_change_tool')], type='ToolCallRequestEvent'), ToolCallExecutionEvent(source='DataAnalystAgent', models_usage=None, content=[FunctionExecutionResult(content='85.98130841121495', call_id='call_z3uog7t2x0z1Suzl5hACF9hY')], type='ToolCallExecutionEvent'), ToolCallSummaryMessage(source='DataAnalystAgent', models_usage=None, content='85.98130841121495', type='ToolCallSummaryMessage'), TextMessage(source='PlanningAgent', models_usage=RequestUsage(prompt_tokens=687, completion_tokens=74), content='Dwyane Wade was the Miami Heat player with the highest points in the 2006-2007 season, scoring 1397 points. His total rebounds increased from 214 in the 2007-2008 season to 398 in the 2008-2009 season, which is a percentage change of approximately 85.98%.\\n\\nTERMINATE', type='TextMessage')], stop_reason=\"Text 'TERMINATE' mentioned\")"
- ]
- },
- "execution_count": 9,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "user_proxy_agent = UserProxyAgent(\"UserProxyAgent\", description=\"A proxy for the user to approve or disapprove tasks.\")\n",
- "\n",
- "\n",
- "def selector_func_with_user_proxy(messages: Sequence[AgentEvent | ChatMessage]) -> str | None:\n",
- " if messages[-1].source != planning_agent.name and messages[-1].source != user_proxy_agent.name:\n",
- " # Planning agent should be the first to engage when given a new task, or check progress.\n",
- " return planning_agent.name\n",
- " if messages[-1].source == planning_agent.name:\n",
- " if messages[-2].source == user_proxy_agent.name and \"APPROVE\" in messages[-1].content.upper(): # type: ignore\n",
- " # User has approved the plan, proceed to the next agent.\n",
- " return None\n",
- " # Use the user proxy agent to get the user's approval to proceed.\n",
- " return user_proxy_agent.name\n",
- " if messages[-1].source == user_proxy_agent.name:\n",
- " # If the user does not approve, return to the planning agent.\n",
- " if \"APPROVE\" not in messages[-1].content.upper(): # type: ignore\n",
- " return planning_agent.name\n",
- " return None\n",
- "\n",
- "\n",
- "# Reset the previous agents and run the chat again with the user proxy agent and selector function.\n",
- "await team.reset()\n",
- "team = SelectorGroupChat(\n",
- " [planning_agent, web_search_agent, data_analyst_agent, user_proxy_agent],\n",
- " model_client=model_client,\n",
- " termination_condition=termination,\n",
- " selector_prompt=selector_prompt,\n",
- " selector_func=selector_func_with_user_proxy,\n",
- " allow_repeated_speaker=True,\n",
- ")\n",
- "\n",
- "await Console(team.run_stream(task=task))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Now, the user's feedback is incorporated into the conversation flow,\n",
- "and the user can approve or reject the planning agent's decisions."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Using Reasoning Models\n",
- "\n",
- "So far in the examples, we have used a `gpt-4o` model. Models like `gpt-4o`\n",
- "and `gemini-1.5-flash` are great at following instructions, so you can\n",
- "have relatively detailed instructions in the selector prompt for the team and the \n",
- "system messages for each agent to guide their behavior.\n",
- "\n",
- "However, if you are using a reasoning model like `o3-mini`, you will need to\n",
- "keep the selector prompt and system messages as simple and to the point as possible.\n",
- "This is because the reasoning models are already good at coming up with their own \n",
- "instructions given the context provided to them.\n",
- "\n",
- "This also means that we don't need a planning agent to break down the task\n",
- "anymore, since the {py:class}`~autogen_agentchat.teams.SelectorGroupChat` that\n",
- "uses a reasoning model can do that on its own.\n",
- "\n",
- "In the following example, we will use `o3-mini` as the model for the\n",
- "agents and the team, and we will not use a planning agent.\n",
- "Also, we are keeping the selector prompt and system messages as simple as possible."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {},
- "outputs": [],
- "source": [
- "model_client = OpenAIChatCompletionClient(model=\"o3-mini\")\n",
- "\n",
- "web_search_agent = AssistantAgent(\n",
- " \"WebSearchAgent\",\n",
- " description=\"An agent for searching information on the web.\",\n",
- " tools=[search_web_tool],\n",
- " model_client=model_client,\n",
- " system_message=\"\"\"Use web search tool to find information.\"\"\",\n",
- ")\n",
- "\n",
- "data_analyst_agent = AssistantAgent(\n",
- " \"DataAnalystAgent\",\n",
- " description=\"An agent for performing calculations.\",\n",
- " model_client=model_client,\n",
- " tools=[percentage_change_tool],\n",
- " system_message=\"\"\"Use tool to perform calculation. If you have not seen the data, ask for it.\"\"\",\n",
- ")\n",
- "\n",
- "user_proxy_agent = UserProxyAgent(\n",
- " \"UserProxyAgent\",\n",
- " description=\"A user to approve or disapprove tasks.\",\n",
- ")\n",
- "\n",
- "selector_prompt = \"\"\"Select an agent to perform task.\n",
- "\n",
- "{roles}\n",
- "\n",
- "Current conversation context:\n",
- "{history}\n",
- "\n",
- "Read the above conversation, then select an agent from {participants} to perform the next task.\n",
- "When the task is complete, let the user approve or disapprove the task.\n",
- "\"\"\"\n",
- "\n",
- "team = SelectorGroupChat(\n",
- " [web_search_agent, data_analyst_agent, user_proxy_agent],\n",
- " model_client=model_client,\n",
- " termination_condition=termination, # Use the same termination condition as before.\n",
- " selector_prompt=selector_prompt,\n",
- " allow_repeated_speaker=True,\n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "---------- user ----------\n",
- "Who was the Miami Heat player with the highest points in the 2006-2007 season, and what was the percentage change in his total rebounds between the 2007-2008 and 2008-2009 seasons?\n",
- "---------- WebSearchAgent ----------\n",
- "[FunctionCall(id='call_hl7EP6Lp5jj5wEdxeNHTwUVG', arguments='{\"query\": \"Who was the Miami Heat player with the highest points in the 2006-2007 season Miami Heat statistics Dwyane Wade rebounds percentage change 2007-2008 2008-2009 seasons\"}', name='search_web_tool')]\n",
- "---------- WebSearchAgent ----------\n",
- "[FunctionExecutionResult(content='Here are the total points scored by Miami Heat players in the 2006-2007 season:\\n Udonis Haslem: 844 points\\n Dwayne Wade: 1397 points\\n James Posey: 550 points\\n ...\\n ', call_id='call_hl7EP6Lp5jj5wEdxeNHTwUVG', is_error=False)]\n",
- "---------- WebSearchAgent ----------\n",
- "Here are the total points scored by Miami Heat players in the 2006-2007 season:\n",
- " Udonis Haslem: 844 points\n",
- " Dwayne Wade: 1397 points\n",
- " James Posey: 550 points\n",
- " ...\n",
- " \n",
- "---------- DataAnalystAgent ----------\n",
- "I found that in the 2006–2007 season the player with the highest points was Dwyane Wade (with 1,397 points). Could you please provide Dwyane Wade’s total rebounds for the 2007–2008 and the 2008–2009 seasons so I can calculate the percentage change?\n",
- "---------- WebSearchAgent ----------\n",
- "[FunctionCall(id='call_lppGTILXDvO9waPwKO66ehK6', arguments='{\"query\": \"Dwyane Wade total rebounds 2007-2008 and 2008-2009 seasons for Miami Heat\"}', name='search_web_tool')]\n",
- "---------- WebSearchAgent ----------\n",
- "[FunctionExecutionResult(content='The number of total rebounds for Dwayne Wade in the Miami Heat season 2007-2008 is 214.', call_id='call_lppGTILXDvO9waPwKO66ehK6', is_error=False)]\n",
- "---------- WebSearchAgent ----------\n",
- "The number of total rebounds for Dwayne Wade in the Miami Heat season 2007-2008 is 214.\n",
- "---------- DataAnalystAgent ----------\n",
- "Could you please provide Dwyane Wade’s total rebounds in the 2008-2009 season?\n",
- "---------- WebSearchAgent ----------\n",
- "[FunctionCall(id='call_r8DBcbJtQfdtugLtyTrqOvoK', arguments='{\"query\": \"Dwyane Wade total rebounds 2008-2009 season Miami Heat\"}', name='search_web_tool')]\n",
- "---------- WebSearchAgent ----------\n",
- "[FunctionExecutionResult(content='The number of total rebounds for Dwayne Wade in the Miami Heat season 2008-2009 is 398.', call_id='call_r8DBcbJtQfdtugLtyTrqOvoK', is_error=False)]\n",
- "---------- WebSearchAgent ----------\n",
- "The number of total rebounds for Dwayne Wade in the Miami Heat season 2008-2009 is 398.\n",
- "---------- DataAnalystAgent ----------\n",
- "[FunctionCall(id='call_4jejv1wM7V1osbBCxJze8aQM', arguments='{\"start\": 214, \"end\": 398}', name='percentage_change_tool')]\n",
- "---------- DataAnalystAgent ----------\n",
- "[FunctionExecutionResult(content='85.98130841121495', call_id='call_4jejv1wM7V1osbBCxJze8aQM', is_error=False)]\n",
- "---------- DataAnalystAgent ----------\n",
- "85.98130841121495\n",
- "---------- DataAnalystAgent ----------\n",
- "Dwyane Wade was the Miami Heat player with the highest total points (1,397) during the 2006-2007 season. His total rebounds increased by approximately 86% from 214 in the 2007-2008 season to 398 in the 2008-2009 season.\n",
- "---------- UserProxyAgent ----------\n",
- "Approve. TERMINATE\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "TaskResult(messages=[TextMessage(source='user', models_usage=None, content='Who was the Miami Heat player with the highest points in the 2006-2007 season, and what was the percentage change in his total rebounds between the 2007-2008 and 2008-2009 seasons?', type='TextMessage'), ToolCallRequestEvent(source='WebSearchAgent', models_usage=RequestUsage(prompt_tokens=103, completion_tokens=384), content=[FunctionCall(id='call_hl7EP6Lp5jj5wEdxeNHTwUVG', arguments='{\"query\": \"Who was the Miami Heat player with the highest points in the 2006-2007 season Miami Heat statistics Dwyane Wade rebounds percentage change 2007-2008 2008-2009 seasons\"}', name='search_web_tool')], type='ToolCallRequestEvent'), ToolCallExecutionEvent(source='WebSearchAgent', models_usage=None, content=[FunctionExecutionResult(content='Here are the total points scored by Miami Heat players in the 2006-2007 season:\\n Udonis Haslem: 844 points\\n Dwayne Wade: 1397 points\\n James Posey: 550 points\\n ...\\n ', call_id='call_hl7EP6Lp5jj5wEdxeNHTwUVG', is_error=False)], type='ToolCallExecutionEvent'), ToolCallSummaryMessage(source='WebSearchAgent', models_usage=None, content='Here are the total points scored by Miami Heat players in the 2006-2007 season:\\n Udonis Haslem: 844 points\\n Dwayne Wade: 1397 points\\n James Posey: 550 points\\n ...\\n ', type='ToolCallSummaryMessage'), TextMessage(source='DataAnalystAgent', models_usage=RequestUsage(prompt_tokens=183, completion_tokens=1038), content='I found that in the 2006–2007 season the player with the highest points was Dwyane Wade (with 1,397 points). Could you please provide Dwyane Wade’s total rebounds for the 2007–2008 and the 2008–2009 seasons so I can calculate the percentage change?', type='TextMessage'), ToolCallRequestEvent(source='WebSearchAgent', models_usage=RequestUsage(prompt_tokens=299, completion_tokens=109), content=[FunctionCall(id='call_lppGTILXDvO9waPwKO66ehK6', arguments='{\"query\": \"Dwyane Wade total rebounds 2007-2008 and 2008-2009 seasons for Miami Heat\"}', name='search_web_tool')], type='ToolCallRequestEvent'), ToolCallExecutionEvent(source='WebSearchAgent', models_usage=None, content=[FunctionExecutionResult(content='The number of total rebounds for Dwayne Wade in the Miami Heat season 2007-2008 is 214.', call_id='call_lppGTILXDvO9waPwKO66ehK6', is_error=False)], type='ToolCallExecutionEvent'), ToolCallSummaryMessage(source='WebSearchAgent', models_usage=None, content='The number of total rebounds for Dwayne Wade in the Miami Heat season 2007-2008 is 214.', type='ToolCallSummaryMessage'), TextMessage(source='DataAnalystAgent', models_usage=RequestUsage(prompt_tokens=291, completion_tokens=224), content='Could you please provide Dwyane Wade’s total rebounds in the 2008-2009 season?', type='TextMessage'), ToolCallRequestEvent(source='WebSearchAgent', models_usage=RequestUsage(prompt_tokens=401, completion_tokens=37), content=[FunctionCall(id='call_r8DBcbJtQfdtugLtyTrqOvoK', arguments='{\"query\": \"Dwyane Wade total rebounds 2008-2009 season Miami Heat\"}', name='search_web_tool')], type='ToolCallRequestEvent'), ToolCallExecutionEvent(source='WebSearchAgent', models_usage=None, content=[FunctionExecutionResult(content='The number of total rebounds for Dwayne Wade in the Miami Heat season 2008-2009 is 398.', call_id='call_r8DBcbJtQfdtugLtyTrqOvoK', is_error=False)], type='ToolCallExecutionEvent'), ToolCallSummaryMessage(source='WebSearchAgent', models_usage=None, content='The number of total rebounds for Dwayne Wade in the Miami Heat season 2008-2009 is 398.', type='ToolCallSummaryMessage'), ToolCallRequestEvent(source='DataAnalystAgent', models_usage=RequestUsage(prompt_tokens=353, completion_tokens=158), content=[FunctionCall(id='call_4jejv1wM7V1osbBCxJze8aQM', arguments='{\"start\": 214, \"end\": 398}', name='percentage_change_tool')], type='ToolCallRequestEvent'), ToolCallExecutionEvent(source='DataAnalystAgent', models_usage=None, content=[FunctionExecutionResult(content='85.98130841121495', call_id='call_4jejv1wM7V1osbBCxJze8aQM', is_error=False)], type='ToolCallExecutionEvent'), ToolCallSummaryMessage(source='DataAnalystAgent', models_usage=None, content='85.98130841121495', type='ToolCallSummaryMessage'), TextMessage(source='DataAnalystAgent', models_usage=RequestUsage(prompt_tokens=394, completion_tokens=138), content='Dwyane Wade was the Miami Heat player with the highest total points (1,397) during the 2006-2007 season. His total rebounds increased by approximately 86% from 214 in the 2007-2008 season to 398 in the 2008-2009 season.', type='TextMessage'), UserInputRequestedEvent(source='UserProxyAgent', models_usage=None, request_id='b3b05408-73fc-47d4-b832-16c9f447cd6e', content='', type='UserInputRequestedEvent'), TextMessage(source='UserProxyAgent', models_usage=None, content='Approve. TERMINATE', type='TextMessage')], stop_reason=\"Text 'TERMINATE' mentioned\")"
- ]
- },
- "execution_count": 9,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "await Console(team.run_stream(task=task))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "```{tip}\n",
- "For more guidance on how to prompt reasoning models, see the\n",
- "Azure AI Services Blog on [Prompt Engineering for OpenAI's O1 and O3-mini Reasoning Models](https://techcommunity.microsoft.com/blog/azure-ai-services-blog/prompt-engineering-for-openai%E2%80%99s-o1-and-o3-mini-reasoning-models/4374010)\n",
- "```"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": ".venv",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.12.3"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat": 4,
+ "nbformat_minor": 2
}
diff --git a/python/packages/autogen-core/docs/src/user-guide/agentchat-user-guide/tracing.ipynb b/python/packages/autogen-core/docs/src/user-guide/agentchat-user-guide/tracing.ipynb
index 8e6f07b90482..9c3014e59cc5 100644
--- a/python/packages/autogen-core/docs/src/user-guide/agentchat-user-guide/tracing.ipynb
+++ b/python/packages/autogen-core/docs/src/user-guide/agentchat-user-guide/tracing.ipynb
@@ -1,403 +1,402 @@
{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Tracing and Observability\n",
- "\n",
- "AutoGen has [built-in support for tracing](https://microsoft.github.io/autogen/dev/user-guide/core-user-guide/framework/telemetry.html) and observability for collecting comprehensive records on the execution of your application. This feature is useful for debugging, performance analysis, and understanding the flow of your application.\n",
- "\n",
- "This capability is powered by the [OpenTelemetry](https://opentelemetry.io/) library, which means you can use any OpenTelemetry-compatible backend to collect and analyze traces.\n",
- "\n",
- "## Setup\n",
- "\n",
- "To begin, you need to install the OpenTelemetry Python package. You can do this using pip:\n",
- "\n",
- "```bash\n",
- "pip install opentelemetry-sdk\n",
- "```\n",
- "\n",
- "Once you have the SDK installed, the simplest way to set up tracing in AutoGen is to:\n",
- "\n",
- "1. Configure an OpenTelemetry tracer provider\n",
- "2. Set up an exporter to send traces to your backend\n",
- "3. Connect the tracer provider to the AutoGen runtime\n",
- "\n",
- "## Telemetry Backend\n",
- "\n",
- "To collect and view traces, you need to set up a telemetry backend. Several open-source options are available, including Jaeger, Zipkin. For this example, we will use Jaeger as our telemetry backend.\n",
- "\n",
- "For a quick start, you can run Jaeger locally using Docker:\n",
- "\n",
- "```bash\n",
- "docker run -d --name jaeger \\\n",
- " -e COLLECTOR_OTLP_ENABLED=true \\\n",
- " -p 16686:16686 \\\n",
- " -p 4317:4317 \\\n",
- " -p 4318:4318 \\\n",
- " jaegertracing/all-in-one:latest\n",
- "```\n",
- "\n",
- "This command starts a Jaeger instance that listens on port 16686 for the Jaeger UI and port 4317 for the OpenTelemetry collector. You can access the Jaeger UI at `http://localhost:16686`.\n",
- "\n",
- "## Instrumenting an AgentChat Team\n",
- "\n",
- "In the following section, we will review how to enable tracing with an AutoGen GroupChat team. The AutoGen runtime already supports open telemetry (automatically logging message metadata). To begin, we will create a tracing service that will be used to instrument the AutoGen runtime. "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "from opentelemetry import trace\n",
- "from opentelemetry.exporter.otlp.proto.grpc.trace_exporter import OTLPSpanExporter\n",
- "from opentelemetry.sdk.resources import Resource\n",
- "from opentelemetry.sdk.trace import TracerProvider\n",
- "from opentelemetry.sdk.trace.export import BatchSpanProcessor\n",
- "\n",
- "otel_exporter = OTLPSpanExporter(endpoint=\"http://localhost:4317\", insecure=True)\n",
- "tracer_provider = TracerProvider(resource=Resource({\"service.name\": \"autogen-test-agentchat\"}))\n",
- "span_processor = BatchSpanProcessor(otel_exporter)\n",
- "tracer_provider.add_span_processor(span_processor)\n",
- "trace.set_tracer_provider(tracer_provider)\n",
- "\n",
- "# we will get reference this tracer later using its service name\n",
- "# tracer = trace.get_tracer(\"autogen-test-agentchat\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "\n",
- "All of the code to create a [team](./tutorial/teams.ipynb) should already be familiar to you. An important note here is that all AgentChat agents and teams are run using the AutoGen core API runtime. In turn, the runtime is already instrumented to log [runtime messaging events (metadata)] (https://github.com/microsoft/autogen/blob/main/python/packages/autogen-core/src/autogen_core/_telemetry/_tracing_config.py) including:\n",
- "\n",
- "- **create**: When a message is created\n",
- "- **send**: When a message is sent\n",
- "- **publish**: When a message is published\n",
- "- **receive**: When a message is received\n",
- "- **intercept**: When a message is intercepted\n",
- "- **process**: When a message is processed\n",
- "- **ack**: When a message is acknowledged \n",
- " "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [],
- "source": [
- "from autogen_agentchat.agents import AssistantAgent\n",
- "from autogen_agentchat.conditions import MaxMessageTermination, TextMentionTermination\n",
- "from autogen_agentchat.teams import SelectorGroupChat\n",
- "from autogen_agentchat.ui import Console\n",
- "from autogen_core import SingleThreadedAgentRuntime\n",
- "from autogen_ext.models.openai import OpenAIChatCompletionClient\n",
- "\n",
- "\n",
- "def search_web_tool(query: str) -> str:\n",
- " if \"2006-2007\" in query:\n",
- " return \"\"\"Here are the total points scored by Miami Heat players in the 2006-2007 season:\n",
- " Udonis Haslem: 844 points\n",
- " Dwayne Wade: 1397 points\n",
- " James Posey: 550 points\n",
- " ...\n",
- " \"\"\"\n",
- " elif \"2007-2008\" in query:\n",
- " return \"The number of total rebounds for Dwayne Wade in the Miami Heat season 2007-2008 is 214.\"\n",
- " elif \"2008-2009\" in query:\n",
- " return \"The number of total rebounds for Dwayne Wade in the Miami Heat season 2008-2009 is 398.\"\n",
- " return \"No data found.\"\n",
- "\n",
- "\n",
- "def percentage_change_tool(start: float, end: float) -> float:\n",
- " return ((end - start) / start) * 100\n",
- "\n",
- "\n",
- "async def main() -> None:\n",
- " model_client = OpenAIChatCompletionClient(model=\"gpt-4o\")\n",
- "\n",
- " planning_agent = AssistantAgent(\n",
- " \"PlanningAgent\",\n",
- " description=\"An agent for planning tasks, this agent should be the first to engage when given a new task.\",\n",
- " model_client=model_client,\n",
- " system_message=\"\"\"\n",
- " You are a planning agent.\n",
- " Your job is to break down complex tasks into smaller, manageable subtasks.\n",
- " Your team members are:\n",
- " WebSearchAgent: Searches for information\n",
- " DataAnalystAgent: Performs calculations\n",
- "\n",
- " You only plan and delegate tasks - you do not execute them yourself.\n",
- "\n",
- " When assigning tasks, use this format:\n",
- " 1. <agent> : <task>\n",
- "\n",
- " After all tasks are complete, summarize the findings and end with \"TERMINATE\".\n",
- " \"\"\",\n",
- " )\n",
- "\n",
- " web_search_agent = AssistantAgent(\n",
- " \"WebSearchAgent\",\n",
- " description=\"An agent for searching information on the web.\",\n",
- " tools=[search_web_tool],\n",
- " model_client=model_client,\n",
- " system_message=\"\"\"\n",
- " You are a web search agent.\n",
- " Your only tool is search_tool - use it to find information.\n",
- " You make only one search call at a time.\n",
- " Once you have the results, you never do calculations based on them.\n",
- " \"\"\",\n",
- " )\n",
- "\n",
- " data_analyst_agent = AssistantAgent(\n",
- " \"DataAnalystAgent\",\n",
- " description=\"An agent for performing calculations.\",\n",
- " model_client=model_client,\n",
- " tools=[percentage_change_tool],\n",
- " system_message=\"\"\"\n",
- " You are a data analyst.\n",
- " Given the tasks you have been assigned, you should analyze the data and provide results using the tools provided.\n",
- " If you have not seen the data, ask for it.\n",
- " \"\"\",\n",
- " )\n",
- "\n",
- " text_mention_termination = TextMentionTermination(\"TERMINATE\")\n",
- " max_messages_termination = MaxMessageTermination(max_messages=25)\n",
- " termination = text_mention_termination | max_messages_termination\n",
- "\n",
- " selector_prompt = \"\"\"Select an agent to perform task.\n",
- "\n",
- " {roles}\n",
- "\n",
- " Current conversation context:\n",
- " {history}\n",
- "\n",
- " Read the above conversation, then select an agent from {participants} to perform the next task.\n",
- " Make sure the planner agent has assigned tasks before other agents start working.\n",
- " Only select one agent.\n",
- " \"\"\"\n",
- "\n",
- " task = \"Who was the Miami Heat player with the highest points in the 2006-2007 season, and what was the percentage change in his total rebounds between the 2007-2008 and 2008-2009 seasons?\"\n",
- "\n",
- " tracer = trace.get_tracer(\"autogen-test-agentchat\")\n",
- " with tracer.start_as_current_span(\"runtime\"):\n",
- " team = SelectorGroupChat(\n",
- " [planning_agent, web_search_agent, data_analyst_agent],\n",
- " model_client=model_client,\n",
- " termination_condition=termination,\n",
- " selector_prompt=selector_prompt,\n",
- " allow_repeated_speaker=True,\n",
- " )\n",
- " await Console(team.run_stream(task=task))\n",
- "\n",
- " await model_client.close()\n",
- "\n",
- "\n",
- "# asyncio.run(main())"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "---------- user ----------\n",
- "Who was the Miami Heat player with the highest points in the 2006-2007 season, and what was the percentage change in his total rebounds between the 2007-2008 and 2008-2009 seasons?\n",
- "---------- PlanningAgent ----------\n",
- "To accomplish this, we can break down the tasks as follows:\n",
- "\n",
- "1. WebSearchAgent: Search for the Miami Heat player with the highest points during the 2006-2007 NBA season.\n",
- "2. WebSearchAgent: Find the total rebounds for the identified player in both the 2007-2008 and 2008-2009 NBA seasons.\n",
- "3. DataAnalystAgent: Calculate the percentage change in total rebounds for the player between the 2007-2008 and 2008-2009 seasons.\n",
- "\n",
- "Once these tasks are complete, I will summarize the findings.\n",
- "---------- WebSearchAgent ----------\n",
- "[FunctionCall(id='call_PUhxZyR0CTlWCY4uwd5Zh3WO', arguments='{\"query\":\"Miami Heat highest points scorer 2006-2007 season\"}', name='search_web_tool')]\n",
- "---------- WebSearchAgent ----------\n",
- "[FunctionExecutionResult(content='Here are the total points scored by Miami Heat players in the 2006-2007 season:\\n Udonis Haslem: 844 points\\n Dwayne Wade: 1397 points\\n James Posey: 550 points\\n ...\\n ', name='search_web_tool', call_id='call_PUhxZyR0CTlWCY4uwd5Zh3WO', is_error=False)]\n",
- "---------- WebSearchAgent ----------\n",
- "Here are the total points scored by Miami Heat players in the 2006-2007 season:\n",
- " Udonis Haslem: 844 points\n",
- " Dwayne Wade: 1397 points\n",
- " James Posey: 550 points\n",
- " ...\n",
- " \n",
- "---------- WebSearchAgent ----------\n",
- "Dwyane Wade was the Miami Heat player with the highest points in the 2006-2007 season, scoring 1,397 points. Now, let's find his total rebounds for the 2007-2008 and 2008-2009 NBA seasons.\n",
- "---------- WebSearchAgent ----------\n",
- "[FunctionCall(id='call_GL7KkWKj9ejIM8FfpgXe2dPk', arguments='{\"query\": \"Dwyane Wade total rebounds 2007-2008 season\"}', name='search_web_tool'), FunctionCall(id='call_X81huZoiA30zIjSAIDgb8ebe', arguments='{\"query\": \"Dwyane Wade total rebounds 2008-2009 season\"}', name='search_web_tool')]\n",
- "---------- WebSearchAgent ----------\n",
- "[FunctionExecutionResult(content='The number of total rebounds for Dwayne Wade in the Miami Heat season 2007-2008 is 214.', name='search_web_tool', call_id='call_GL7KkWKj9ejIM8FfpgXe2dPk', is_error=False), FunctionExecutionResult(content='The number of total rebounds for Dwayne Wade in the Miami Heat season 2008-2009 is 398.', name='search_web_tool', call_id='call_X81huZoiA30zIjSAIDgb8ebe', is_error=False)]\n",
- "---------- WebSearchAgent ----------\n",
- "The number of total rebounds for Dwayne Wade in the Miami Heat season 2007-2008 is 214.\n",
- "The number of total rebounds for Dwayne Wade in the Miami Heat season 2008-2009 is 398.\n",
- "---------- DataAnalystAgent ----------\n",
- "[FunctionCall(id='call_kB50RkFVqHptA7FOf0lL2RS8', arguments='{\"start\":214,\"end\":398}', name='percentage_change_tool')]\n",
- "---------- DataAnalystAgent ----------\n",
- "[FunctionExecutionResult(content='85.98130841121495', name='percentage_change_tool', call_id='call_kB50RkFVqHptA7FOf0lL2RS8', is_error=False)]\n",
- "---------- DataAnalystAgent ----------\n",
- "85.98130841121495\n",
- "---------- PlanningAgent ----------\n",
- "The Miami Heat player with the highest points during the 2006-2007 NBA season was Dwayne Wade, who scored 1,397 points. The percentage increase in his total rebounds from the 2007-2008 season (214 rebounds) to the 2008-2009 season (398 rebounds) was approximately 86%.\n",
- "\n",
- "TERMINATE\n"
- ]
- }
- ],
- "source": [
- "await main()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "You can then use the Jaeger UI to view the traces collected from the application run above. \n",
- "\n",
- ""
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Custom Traces \n",
- "\n",
- "So far, we are logging only the default events that are generated by the AutoGen runtime (message created, publish etc). However, you can also create custom spans to log specific events in your application. \n",
- "\n",
- "In the example below, we will show how to log messages from the `RoundRobinGroupChat` team as they are generated by adding custom spans around the team to log runtime events and spans to log messages generated by the team.\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\n",
- "-- primary_agent -- : Leaves cascade like gold, \n",
- "Whispering winds cool the earth.\n",
- "primary_agent: Leaves cascade like gold, \n",
- "Whispering winds cool the earth.\n",
- "\n",
- "-- critic_agent -- : Your haiku beautifully captures the essence of the fall season with vivid imagery. However, it appears to have six syllables in the second line, which should traditionally be five. Here's a revised version keeping the 5-7-5 syllable structure:\n",
- "\n",
- "Leaves cascade like gold, \n",
- "Whispering winds cool the air. \n",
- "\n",
- "Please adjust the second line to reflect a five-syllable count. Thank you!\n",
- "critic_agent: Your haiku beautifully captures the essence of the fall season with vivid imagery. However, it appears to have six syllables in the second line, which should traditionally be five. Here's a revised version keeping the 5-7-5 syllable structure:\n",
- "\n",
- "Leaves cascade like gold, \n",
- "Whispering winds cool the air. \n",
- "\n",
- "Please adjust the second line to reflect a five-syllable count. Thank you!\n",
- "\n",
- "-- primary_agent -- : Leaves cascade like gold, \n",
- "Whispering winds cool the air.\n",
- "primary_agent: Leaves cascade like gold, \n",
- "Whispering winds cool the air.\n",
- "\n",
- "-- critic_agent -- : APPROVE\n",
- "critic_agent: APPROVE\n"
- ]
- }
- ],
- "source": [
- "from autogen_agentchat.base import TaskResult\n",
- "from autogen_agentchat.conditions import ExternalTermination\n",
- "from autogen_agentchat.teams import RoundRobinGroupChat\n",
- "from autogen_core import CancellationToken\n",
- "\n",
- "\n",
- "async def run_agents() -> None:\n",
- " # Create an OpenAI model client.\n",
- " model_client = OpenAIChatCompletionClient(model=\"gpt-4o-2024-08-06\")\n",
- "\n",
- " # Create the primary agent.\n",
- " primary_agent = AssistantAgent(\n",
- " \"primary_agent\",\n",
- " model_client=model_client,\n",
- " system_message=\"You are a helpful AI assistant.\",\n",
- " )\n",
- "\n",
- " # Create the critic agent.\n",
- " critic_agent = AssistantAgent(\n",
- " \"critic_agent\",\n",
- " model_client=model_client,\n",
- " system_message=\"Provide constructive feedback. Respond with 'APPROVE' to when your feedbacks are addressed.\",\n",
- " )\n",
- "\n",
- " # Define a termination condition that stops the task if the critic approves.\n",
- " text_termination = TextMentionTermination(\"APPROVE\")\n",
- "\n",
- " tracer = trace.get_tracer(\"autogen-test-agentchat\")\n",
- " with tracer.start_as_current_span(\"runtime_round_robin_events\"):\n",
- " team = RoundRobinGroupChat([primary_agent, critic_agent], termination_condition=text_termination)\n",
- "\n",
- " response_stream = team.run_stream(task=\"Write a 2 line haiku about the fall season\")\n",
- " async for response in response_stream:\n",
- " async for response in response_stream:\n",
- " if not isinstance(response, TaskResult):\n",
- " print(f\"\\n-- {response.source} -- : {response.content}\")\n",
- " with tracer.start_as_current_span(f\"agent_message.{response.source}\") as message_span:\n",
- " content = response.content if isinstance(response.content, str) else str(response.content)\n",
- " message_span.set_attribute(\"agent.name\", response.source)\n",
- " message_span.set_attribute(\"message.content\", content)\n",
- " print(f\"{response.source}: {response.content}\")\n",
- "\n",
- " await model_client.close()\n",
- "\n",
- "\n",
- "await run_agents()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "In the code above, we create a new span for each message sent by the agent. We set attributes on the span to include the agent's name and the message content. This allows us to trace the flow of messages through our application and understand how they are processed."
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": ".venv",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.11.9"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 2
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Tracing and Observability\n",
+ "\n",
+ "AutoGen has [built-in support for tracing](https://microsoft.github.io/autogen/dev/user-guide/core-user-guide/framework/telemetry.html) and observability for collecting comprehensive records on the execution of your application. This feature is useful for debugging, performance analysis, and understanding the flow of your application.\n",
+ "\n",
+ "This capability is powered by the [OpenTelemetry](https://opentelemetry.io/) library, which means you can use any OpenTelemetry-compatible backend to collect and analyze traces.\n",
+ "\n",
+ "## Setup\n",
+ "\n",
+ "To begin, you need to install the OpenTelemetry Python package. You can do this using pip:\n",
+ "\n",
+ "```bash\n",
+ "pip install opentelemetry-sdk\n",
+ "```\n",
+ "\n",
+ "Once you have the SDK installed, the simplest way to set up tracing in AutoGen is to:\n",
+ "\n",
+ "1. Configure an OpenTelemetry tracer provider\n",
+ "2. Set up an exporter to send traces to your backend\n",
+ "3. Connect the tracer provider to the AutoGen runtime\n",
+ "\n",
+ "## Telemetry Backend\n",
+ "\n",
+ "To collect and view traces, you need to set up a telemetry backend. Several open-source options are available, including Jaeger, Zipkin. For this example, we will use Jaeger as our telemetry backend.\n",
+ "\n",
+ "For a quick start, you can run Jaeger locally using Docker:\n",
+ "\n",
+ "```bash\n",
+ "docker run -d --name jaeger \\\n",
+ " -e COLLECTOR_OTLP_ENABLED=true \\\n",
+ " -p 16686:16686 \\\n",
+ " -p 4317:4317 \\\n",
+ " -p 4318:4318 \\\n",
+ " jaegertracing/all-in-one:latest\n",
+ "```\n",
+ "\n",
+ "This command starts a Jaeger instance that listens on port 16686 for the Jaeger UI and port 4317 for the OpenTelemetry collector. You can access the Jaeger UI at `http://localhost:16686`.\n",
+ "\n",
+ "## Instrumenting an AgentChat Team\n",
+ "\n",
+ "In the following section, we will review how to enable tracing with an AutoGen GroupChat team. The AutoGen runtime already supports open telemetry (automatically logging message metadata). To begin, we will create a tracing service that will be used to instrument the AutoGen runtime. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from opentelemetry import trace\n",
+ "from opentelemetry.exporter.otlp.proto.grpc.trace_exporter import OTLPSpanExporter\n",
+ "from opentelemetry.sdk.resources import Resource\n",
+ "from opentelemetry.sdk.trace import TracerProvider\n",
+ "from opentelemetry.sdk.trace.export import BatchSpanProcessor\n",
+ "\n",
+ "otel_exporter = OTLPSpanExporter(endpoint=\"http://localhost:4317\", insecure=True)\n",
+ "tracer_provider = TracerProvider(resource=Resource({\"service.name\": \"autogen-test-agentchat\"}))\n",
+ "span_processor = BatchSpanProcessor(otel_exporter)\n",
+ "tracer_provider.add_span_processor(span_processor)\n",
+ "trace.set_tracer_provider(tracer_provider)\n",
+ "\n",
+ "# we will get reference this tracer later using its service name\n",
+ "# tracer = trace.get_tracer(\"autogen-test-agentchat\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n",
+ "\n",
+ "All of the code to create a [team](./tutorial/teams.ipynb) should already be familiar to you. An important note here is that all AgentChat agents and teams are run using the AutoGen core API runtime. In turn, the runtime is already instrumented to log [runtime messaging events (metadata)] (https://github.com/microsoft/autogen/blob/main/python/packages/autogen-core/src/autogen_core/_telemetry/_tracing_config.py) including:\n",
+ "\n",
+ "- **create**: When a message is created\n",
+ "- **send**: When a message is sent\n",
+ "- **publish**: When a message is published\n",
+ "- **receive**: When a message is received\n",
+ "- **intercept**: When a message is intercepted\n",
+ "- **process**: When a message is processed\n",
+ "- **ack**: When a message is acknowledged \n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from autogen_agentchat.agents import AssistantAgent\n",
+ "from autogen_agentchat.conditions import MaxMessageTermination, TextMentionTermination\n",
+ "from autogen_agentchat.teams import SelectorGroupChat\n",
+ "from autogen_agentchat.ui import Console\n",
+ "from autogen_core import SingleThreadedAgentRuntime\n",
+ "from autogen_ext.models.openai import OpenAIChatCompletionClient\n",
+ "\n",
+ "\n",
+ "def search_web_tool(query: str) -> str:\n",
+ " if \"2006-2007\" in query:\n",
+ " return \"\"\"Here are the total points scored by Miami Heat players in the 2006-2007 season:\n",
+ " Udonis Haslem: 844 points\n",
+ " Dwayne Wade: 1397 points\n",
+ " James Posey: 550 points\n",
+ " ...\n",
+ " \"\"\"\n",
+ " elif \"2007-2008\" in query:\n",
+ " return \"The number of total rebounds for Dwayne Wade in the Miami Heat season 2007-2008 is 214.\"\n",
+ " elif \"2008-2009\" in query:\n",
+ " return \"The number of total rebounds for Dwayne Wade in the Miami Heat season 2008-2009 is 398.\"\n",
+ " return \"No data found.\"\n",
+ "\n",
+ "\n",
+ "def percentage_change_tool(start: float, end: float) -> float:\n",
+ " return ((end - start) / start) * 100\n",
+ "\n",
+ "\n",
+ "async def main() -> None:\n",
+ " model_client = OpenAIChatCompletionClient(model=\"gpt-4o\")\n",
+ "\n",
+ " planning_agent = AssistantAgent(\n",
+ " \"PlanningAgent\",\n",
+ " description=\"An agent for planning tasks, this agent should be the first to engage when given a new task.\",\n",
+ " model_client=model_client,\n",
+ " system_message=\"\"\"\n",
+ " You are a planning agent.\n",
+ " Your job is to break down complex tasks into smaller, manageable subtasks.\n",
+ " Your team members are:\n",
+ " WebSearchAgent: Searches for information\n",
+ " DataAnalystAgent: Performs calculations\n",
+ "\n",
+ " You only plan and delegate tasks - you do not execute them yourself.\n",
+ "\n",
+ " When assigning tasks, use this format:\n",
+ " 1. <agent> : <task>\n",
+ "\n",
+ " After all tasks are complete, summarize the findings and end with \"TERMINATE\".\n",
+ " \"\"\",\n",
+ " )\n",
+ "\n",
+ " web_search_agent = AssistantAgent(\n",
+ " \"WebSearchAgent\",\n",
+ " description=\"An agent for searching information on the web.\",\n",
+ " tools=[search_web_tool],\n",
+ " model_client=model_client,\n",
+ " system_message=\"\"\"\n",
+ " You are a web search agent.\n",
+ " Your only tool is search_tool - use it to find information.\n",
+ " You make only one search call at a time.\n",
+ " Once you have the results, you never do calculations based on them.\n",
+ " \"\"\",\n",
+ " )\n",
+ "\n",
+ " data_analyst_agent = AssistantAgent(\n",
+ " \"DataAnalystAgent\",\n",
+ " description=\"An agent for performing calculations.\",\n",
+ " model_client=model_client,\n",
+ " tools=[percentage_change_tool],\n",
+ " system_message=\"\"\"\n",
+ " You are a data analyst.\n",
+ " Given the tasks you have been assigned, you should analyze the data and provide results using the tools provided.\n",
+ " If you have not seen the data, ask for it.\n",
+ " \"\"\",\n",
+ " )\n",
+ "\n",
+ " text_mention_termination = TextMentionTermination(\"TERMINATE\")\n",
+ " max_messages_termination = MaxMessageTermination(max_messages=25)\n",
+ " termination = text_mention_termination | max_messages_termination\n",
+ "\n",
+ " selector_prompt = \"\"\"Select an agent to perform task.\n",
+ "\n",
+ " {roles}\n",
+ "\n",
+ " Current conversation context:\n",
+ " {history}\n",
+ "\n",
+ " Read the above conversation, then select an agent from {participants} to perform the next task.\n",
+ " Make sure the planner agent has assigned tasks before other agents start working.\n",
+ " Only select one agent.\n",
+ " \"\"\"\n",
+ "\n",
+ " task = \"Who was the Miami Heat player with the highest points in the 2006-2007 season, and what was the percentage change in his total rebounds between the 2007-2008 and 2008-2009 seasons?\"\n",
+ "\n",
+ " tracer = trace.get_tracer(\"autogen-test-agentchat\")\n",
+ " with tracer.start_as_current_span(\"runtime\"):\n",
+ " team = SelectorGroupChat(\n",
+ " [planning_agent, web_search_agent, data_analyst_agent],\n",
+ " model_client=model_client,\n",
+ " termination_condition=termination,\n",
+ " selector_prompt=selector_prompt,\n",
+ " allow_repeated_speaker=True,\n",
+ " )\n",
+ " await Console(team.run_stream(task=task))\n",
+ "\n",
+ " await model_client.close()\n",
+ "\n",
+ "\n",
+ "# asyncio.run(main())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "---------- user ----------\n",
+ "Who was the Miami Heat player with the highest points in the 2006-2007 season, and what was the percentage change in his total rebounds between the 2007-2008 and 2008-2009 seasons?\n",
+ "---------- PlanningAgent ----------\n",
+ "To accomplish this, we can break down the tasks as follows:\n",
+ "\n",
+ "1. WebSearchAgent: Search for the Miami Heat player with the highest points during the 2006-2007 NBA season.\n",
+ "2. WebSearchAgent: Find the total rebounds for the identified player in both the 2007-2008 and 2008-2009 NBA seasons.\n",
+ "3. DataAnalystAgent: Calculate the percentage change in total rebounds for the player between the 2007-2008 and 2008-2009 seasons.\n",
+ "\n",
+ "Once these tasks are complete, I will summarize the findings.\n",
+ "---------- WebSearchAgent ----------\n",
+ "[FunctionCall(id='call_PUhxZyR0CTlWCY4uwd5Zh3WO', arguments='{\"query\":\"Miami Heat highest points scorer 2006-2007 season\"}', name='search_web_tool')]\n",
+ "---------- WebSearchAgent ----------\n",
+ "[FunctionExecutionResult(content='Here are the total points scored by Miami Heat players in the 2006-2007 season:\\n Udonis Haslem: 844 points\\n Dwayne Wade: 1397 points\\n James Posey: 550 points\\n ...\\n ', name='search_web_tool', call_id='call_PUhxZyR0CTlWCY4uwd5Zh3WO', is_error=False)]\n",
+ "---------- WebSearchAgent ----------\n",
+ "Here are the total points scored by Miami Heat players in the 2006-2007 season:\n",
+ " Udonis Haslem: 844 points\n",
+ " Dwayne Wade: 1397 points\n",
+ " James Posey: 550 points\n",
+ " ...\n",
+ " \n",
+ "---------- WebSearchAgent ----------\n",
+ "Dwyane Wade was the Miami Heat player with the highest points in the 2006-2007 season, scoring 1,397 points. Now, let's find his total rebounds for the 2007-2008 and 2008-2009 NBA seasons.\n",
+ "---------- WebSearchAgent ----------\n",
+ "[FunctionCall(id='call_GL7KkWKj9ejIM8FfpgXe2dPk', arguments='{\"query\": \"Dwyane Wade total rebounds 2007-2008 season\"}', name='search_web_tool'), FunctionCall(id='call_X81huZoiA30zIjSAIDgb8ebe', arguments='{\"query\": \"Dwyane Wade total rebounds 2008-2009 season\"}', name='search_web_tool')]\n",
+ "---------- WebSearchAgent ----------\n",
+ "[FunctionExecutionResult(content='The number of total rebounds for Dwayne Wade in the Miami Heat season 2007-2008 is 214.', name='search_web_tool', call_id='call_GL7KkWKj9ejIM8FfpgXe2dPk', is_error=False), FunctionExecutionResult(content='The number of total rebounds for Dwayne Wade in the Miami Heat season 2008-2009 is 398.', name='search_web_tool', call_id='call_X81huZoiA30zIjSAIDgb8ebe', is_error=False)]\n",
+ "---------- WebSearchAgent ----------\n",
+ "The number of total rebounds for Dwayne Wade in the Miami Heat season 2007-2008 is 214.\n",
+ "The number of total rebounds for Dwayne Wade in the Miami Heat season 2008-2009 is 398.\n",
+ "---------- DataAnalystAgent ----------\n",
+ "[FunctionCall(id='call_kB50RkFVqHptA7FOf0lL2RS8', arguments='{\"start\":214,\"end\":398}', name='percentage_change_tool')]\n",
+ "---------- DataAnalystAgent ----------\n",
+ "[FunctionExecutionResult(content='85.98130841121495', name='percentage_change_tool', call_id='call_kB50RkFVqHptA7FOf0lL2RS8', is_error=False)]\n",
+ "---------- DataAnalystAgent ----------\n",
+ "85.98130841121495\n",
+ "---------- PlanningAgent ----------\n",
+ "The Miami Heat player with the highest points during the 2006-2007 NBA season was Dwayne Wade, who scored 1,397 points. The percentage increase in his total rebounds from the 2007-2008 season (214 rebounds) to the 2008-2009 season (398 rebounds) was approximately 86%.\n",
+ "\n",
+ "TERMINATE\n"
+ ]
+ }
+ ],
+ "source": [
+ "await main()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "You can then use the Jaeger UI to view the traces collected from the application run above. \n",
+ "\n",
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Custom Traces \n",
+ "\n",
+ "So far, we are logging only the default events that are generated by the AutoGen runtime (message created, publish etc). However, you can also create custom spans to log specific events in your application. \n",
+ "\n",
+ "In the example below, we will show how to log messages from the `RoundRobinGroupChat` team as they are generated by adding custom spans around the team to log runtime events and spans to log messages generated by the team.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "-- primary_agent -- : Leaves cascade like gold, \n",
+ "Whispering winds cool the earth.\n",
+ "primary_agent: Leaves cascade like gold, \n",
+ "Whispering winds cool the earth.\n",
+ "\n",
+ "-- critic_agent -- : Your haiku beautifully captures the essence of the fall season with vivid imagery. However, it appears to have six syllables in the second line, which should traditionally be five. Here's a revised version keeping the 5-7-5 syllable structure:\n",
+ "\n",
+ "Leaves cascade like gold, \n",
+ "Whispering winds cool the air. \n",
+ "\n",
+ "Please adjust the second line to reflect a five-syllable count. Thank you!\n",
+ "critic_agent: Your haiku beautifully captures the essence of the fall season with vivid imagery. However, it appears to have six syllables in the second line, which should traditionally be five. Here's a revised version keeping the 5-7-5 syllable structure:\n",
+ "\n",
+ "Leaves cascade like gold, \n",
+ "Whispering winds cool the air. \n",
+ "\n",
+ "Please adjust the second line to reflect a five-syllable count. Thank you!\n",
+ "\n",
+ "-- primary_agent -- : Leaves cascade like gold, \n",
+ "Whispering winds cool the air.\n",
+ "primary_agent: Leaves cascade like gold, \n",
+ "Whispering winds cool the air.\n",
+ "\n",
+ "-- critic_agent -- : APPROVE\n",
+ "critic_agent: APPROVE\n"
+ ]
+ }
+ ],
+ "source": [
+ "from autogen_agentchat.base import TaskResult\n",
+ "from autogen_agentchat.conditions import ExternalTermination\n",
+ "from autogen_agentchat.teams import RoundRobinGroupChat\n",
+ "from autogen_core import CancellationToken\n",
+ "\n",
+ "\n",
+ "async def run_agents() -> None:\n",
+ " # Create an OpenAI model client.\n",
+ " model_client = OpenAIChatCompletionClient(model=\"gpt-4o-2024-08-06\")\n",
+ "\n",
+ " # Create the primary agent.\n",
+ " primary_agent = AssistantAgent(\n",
+ " \"primary_agent\",\n",
+ " model_client=model_client,\n",
+ " system_message=\"You are a helpful AI assistant.\",\n",
+ " )\n",
+ "\n",
+ " # Create the critic agent.\n",
+ " critic_agent = AssistantAgent(\n",
+ " \"critic_agent\",\n",
+ " model_client=model_client,\n",
+ " system_message=\"Provide constructive feedback. Respond with 'APPROVE' to when your feedbacks are addressed.\",\n",
+ " )\n",
+ "\n",
+ " # Define a termination condition that stops the task if the critic approves.\n",
+ " text_termination = TextMentionTermination(\"APPROVE\")\n",
+ "\n",
+ " tracer = trace.get_tracer(\"autogen-test-agentchat\")\n",
+ " with tracer.start_as_current_span(\"runtime_round_robin_events\"):\n",
+ " team = RoundRobinGroupChat([primary_agent, critic_agent], termination_condition=text_termination)\n",
+ "\n",
+ " response_stream = team.run_stream(task=\"Write a 2 line haiku about the fall season\")\n",
+ " async for response in response_stream:\n",
+ " async for response in response_stream:\n",
+ " if not isinstance(response, TaskResult):\n",
+ " print(f\"\\n-- {response.source} -- : {response.to_text()}\")\n",
+ " with tracer.start_as_current_span(f\"agent_message.{response.source}\") as message_span:\n",
+ " message_span.set_attribute(\"agent.name\", response.source)\n",
+ " message_span.set_attribute(\"message.content\", response.to_text())\n",
+ " print(f\"{response.source}: {response.to_text()}\")\n",
+ "\n",
+ " await model_client.close()\n",
+ "\n",
+ "\n",
+ "await run_agents()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n",
+ "In the code above, we create a new span for each message sent by the agent. We set attributes on the span to include the agent's name and the message content. This allows us to trace the flow of messages through our application and understand how they are processed."
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": ".venv",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.12.3"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
}
diff --git a/python/packages/autogen-core/docs/src/user-guide/agentchat-user-guide/tutorial/agents.ipynb b/python/packages/autogen-core/docs/src/user-guide/agentchat-user-guide/tutorial/agents.ipynb
index 35c9052dee75..c927e775ad46 100644
--- a/python/packages/autogen-core/docs/src/user-guide/agentchat-user-guide/tutorial/agents.ipynb
+++ b/python/packages/autogen-core/docs/src/user-guide/agentchat-user-guide/tutorial/agents.ipynb
@@ -1,847 +1,848 @@
{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Agents\n",
- "\n",
- "AutoGen AgentChat provides a set of preset Agents, each with variations in how an agent might respond to messages.\n",
- "All agents share the following attributes and methods:\n",
- "\n",
- "- {py:attr}`~autogen_agentchat.agents.BaseChatAgent.name`: The unique name of the agent.\n",
- "- {py:attr}`~autogen_agentchat.agents.BaseChatAgent.description`: The description of the agent in text.\n",
- "- {py:meth}`~autogen_agentchat.agents.BaseChatAgent.on_messages`: Send the agent a sequence of {py:class}`~autogen_agentchat.messages.ChatMessage` get a {py:class}`~autogen_agentchat.base.Response`. **It is important to note that agents are expected to be stateful and this method is expected to be called with new messages, not the complete history**.\n",
- "- {py:meth}`~autogen_agentchat.agents.BaseChatAgent.on_messages_stream`: Same as {py:meth}`~autogen_agentchat.agents.BaseChatAgent.on_messages` but returns an iterator of {py:class}`~autogen_agentchat.messages.AgentEvent` or {py:class}`~autogen_agentchat.messages.ChatMessage` followed by a {py:class}`~autogen_agentchat.base.Response` as the last item.\n",
- "- {py:meth}`~autogen_agentchat.agents.BaseChatAgent.on_reset`: Reset the agent to its initial state.\n",
- "- {py:meth}`~autogen_agentchat.agents.BaseChatAgent.run` and {py:meth}`~autogen_agentchat.agents.BaseChatAgent.run_stream`: convenience methods that call {py:meth}`~autogen_agentchat.agents.BaseChatAgent.on_messages` and {py:meth}`~autogen_agentchat.agents.BaseChatAgent.on_messages_stream` respectively but offer the same interface as [Teams](./teams.ipynb).\n",
- "\n",
- "See {py:mod}`autogen_agentchat.messages` for more information on AgentChat message types.\n",
- "\n",
- "\n",
- "## Assistant Agent\n",
- "\n",
- "{py:class}`~autogen_agentchat.agents.AssistantAgent` is a built-in agent that\n",
- "uses a language model and has the ability to use tools."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [],
- "source": [
- "from autogen_agentchat.agents import AssistantAgent\n",
- "from autogen_agentchat.messages import TextMessage\n",
- "from autogen_agentchat.ui import Console\n",
- "from autogen_core import CancellationToken\n",
- "from autogen_ext.models.openai import OpenAIChatCompletionClient"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Define a tool that searches the web for information.\n",
- "async def web_search(query: str) -> str:\n",
- " \"\"\"Find information on the web\"\"\"\n",
- " return \"AutoGen is a programming framework for building multi-agent applications.\"\n",
- "\n",
- "\n",
- "# Create an agent that uses the OpenAI GPT-4o model.\n",
- "model_client = OpenAIChatCompletionClient(\n",
- " model=\"gpt-4o\",\n",
- " # api_key=\"YOUR_API_KEY\",\n",
- ")\n",
- "agent = AssistantAgent(\n",
- " name=\"assistant\",\n",
- " model_client=model_client,\n",
- " tools=[web_search],\n",
- " system_message=\"Use tools to solve tasks.\",\n",
- ")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "## Getting Responses\n",
- "\n",
- "We can use the {py:meth}`~autogen_agentchat.agents.AssistantAgent.on_messages` method to get the agent response to a given message.\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[ToolCallRequestEvent(source='assistant', models_usage=RequestUsage(prompt_tokens=598, completion_tokens=16), content=[FunctionCall(id='call_9UWYM1CgE3ZbnJcSJavNDB79', arguments='{\"query\":\"AutoGen\"}', name='web_search')], type='ToolCallRequestEvent'), ToolCallExecutionEvent(source='assistant', models_usage=None, content=[FunctionExecutionResult(content='AutoGen is a programming framework for building multi-agent applications.', call_id='call_9UWYM1CgE3ZbnJcSJavNDB79', is_error=False)], type='ToolCallExecutionEvent')]\n",
- "source='assistant' models_usage=None content='AutoGen is a programming framework for building multi-agent applications.' type='ToolCallSummaryMessage'\n"
- ]
- }
- ],
- "source": [
- "async def assistant_run() -> None:\n",
- " response = await agent.on_messages(\n",
- " [TextMessage(content=\"Find information on AutoGen\", source=\"user\")],\n",
- " cancellation_token=CancellationToken(),\n",
- " )\n",
- " print(response.inner_messages)\n",
- " print(response.chat_message)\n",
- "\n",
- "\n",
- "# Use asyncio.run(assistant_run()) when running in a script.\n",
- "await assistant_run()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "The call to the {py:meth}`~autogen_agentchat.agents.AssistantAgent.on_messages` method\n",
- "returns a {py:class}`~autogen_agentchat.base.Response`\n",
- "that contains the agent's final response in the {py:attr}`~autogen_agentchat.base.Response.chat_message` attribute,\n",
- "as well as a list of inner messages in the {py:attr}`~autogen_agentchat.base.Response.inner_messages` attribute,\n",
- "which stores the agent's \"thought process\" that led to the final response.\n",
- "\n",
- "```{note}\n",
- "It is important to note that {py:meth}`~autogen_agentchat.agents.AssistantAgent.on_messages`\n",
- "will update the internal state of the agent -- it will add the messages to the agent's\n",
- "history. So you should call this method with new messages.\n",
- "**You should not repeatedly call this method with the same messages or the complete history.**\n",
- "```\n",
- "\n",
- "```{note}\n",
- "Unlike in v0.2 AgentChat, the tools are executed by the same agent directly within\n",
- "the same call to {py:meth}`~autogen_agentchat.agents.AssistantAgent.on_messages`.\n",
- "By default, the agent will return the result of the tool call as the final response.\n",
- "```\n",
- "\n",
- "You can also call the {py:meth}`~autogen_agentchat.agents.BaseChatAgent.run` method, which is a convenience method that calls {py:meth}`~autogen_agentchat.agents.BaseChatAgent.on_messages`. \n",
- "It follows the same interface as [Teams](./teams.ipynb) and returns a {py:class}`~autogen_agentchat.base.TaskResult` object."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Multi-Modal Input\n",
- "\n",
- "The {py:class}`~autogen_agentchat.agents.AssistantAgent` can handle multi-modal input\n",
- "by providing the input as a {py:class}`~autogen_agentchat.messages.MultiModalMessage`."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [
- {
- "data": {
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\"/>"
- ],
- "text/plain": [
- "<autogen_core._image.Image at 0x12a29f9b0>"
- ]
- },
- "execution_count": 9,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "from io import BytesIO\n",
- "\n",
- "import PIL\n",
- "import requests\n",
- "from autogen_agentchat.messages import MultiModalMessage\n",
- "from autogen_core import Image\n",
- "\n",
- "# Create a multi-modal message with random image and text.\n",
- "pil_image = PIL.Image.open(BytesIO(requests.get(\"https://picsum.photos/300/200\").content))\n",
- "img = Image(pil_image)\n",
- "multi_modal_message = MultiModalMessage(content=[\"Can you describe the content of this image?\", img], source=\"user\")\n",
- "img"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "The image depicts a vintage car, likely from the 1930s or 1940s, with a sleek, classic design. The car seems to be customized or well-maintained, as indicated by its shiny exterior and lowered stance. It has a prominent grille and round headlights. There's a license plate on the front with the text \"FARMER BOY.\" The setting appears to be a street with old-style buildings in the background, suggesting a historical or retro theme.\n"
- ]
- }
- ],
- "source": [
- "# Use asyncio.run(...) when running in a script.\n",
- "response = await agent.on_messages([multi_modal_message], CancellationToken())\n",
- "print(response.chat_message.content)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "You can also use {py:class}`~autogen_agentchat.messages.MultiModalMessage` as a `task`\n",
- "input to the {py:meth}`~autogen_agentchat.agents.BaseChatAgent.run` method."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Streaming Messages\n",
- "\n",
- "We can also stream each message as it is generated by the agent by using the\n",
- "{py:meth}`~autogen_agentchat.agents.AssistantAgent.on_messages_stream` method,\n",
- "and use {py:class}`~autogen_agentchat.ui.Console` to print the messages\n",
- "as they appear to the console."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "---------- assistant ----------\n",
- "[FunctionCall(id='call_fSp5iTGVm2FKw5NIvfECSqNd', arguments='{\"query\":\"AutoGen information\"}', name='web_search')]\n",
- "[Prompt tokens: 61, Completion tokens: 16]\n",
- "---------- assistant ----------\n",
- "[FunctionExecutionResult(content='AutoGen is a programming framework for building multi-agent applications.', call_id='call_fSp5iTGVm2FKw5NIvfECSqNd')]\n",
- "---------- assistant ----------\n",
- "AutoGen is a programming framework designed for building multi-agent applications. If you need more detailed information or specific aspects about AutoGen, feel free to ask!\n",
- "[Prompt tokens: 93, Completion tokens: 32]\n",
- "---------- Summary ----------\n",
- "Number of inner messages: 2\n",
- "Total prompt tokens: 154\n",
- "Total completion tokens: 48\n",
- "Duration: 4.30 seconds\n"
- ]
- }
- ],
- "source": [
- "async def assistant_run_stream() -> None:\n",
- " # Option 1: read each message from the stream (as shown in the previous example).\n",
- " # async for message in agent.on_messages_stream(\n",
- " # [TextMessage(content=\"Find information on AutoGen\", source=\"user\")],\n",
- " # cancellation_token=CancellationToken(),\n",
- " # ):\n",
- " # print(message)\n",
- "\n",
- " # Option 2: use Console to print all messages as they appear.\n",
- " await Console(\n",
- " agent.on_messages_stream(\n",
- " [TextMessage(content=\"Find information on AutoGen\", source=\"user\")],\n",
- " cancellation_token=CancellationToken(),\n",
- " ),\n",
- " output_stats=True, # Enable stats printing.\n",
- " )\n",
- "\n",
- "\n",
- "# Use asyncio.run(assistant_run_stream()) when running in a script.\n",
- "await assistant_run_stream()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "The {py:meth}`~autogen_agentchat.agents.AssistantAgent.on_messages_stream` method\n",
- "returns an asynchronous generator that yields each inner message generated by the agent,\n",
- "with the final item being the response message in the {py:attr}`~autogen_agentchat.base.Response.chat_message` attribute.\n",
- "\n",
- "From the messages, you can observe that the assistant agent utilized the `web_search` tool to\n",
- "gather information and responded based on the search results.\n",
- "\n",
- "You can also use {py:meth}`~autogen_agentchat.agents.BaseChatAgent.run_stream` to get the same streaming behavior as {py:meth}`~autogen_agentchat.agents.BaseChatAgent.on_messages_stream`. It follows the same interface as [Teams](./teams.ipynb)."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Using Tools\n",
- "\n",
- "Large Language Models (LLMs) are typically limited to generating text or code responses. \n",
- "However, many complex tasks benefit from the ability to use external tools that perform specific actions,\n",
- "such as fetching data from APIs or databases.\n",
- "\n",
- "To address this limitation, modern LLMs can now accept a list of available tool schemas \n",
- "(descriptions of tools and their arguments) and generate a tool call message. \n",
- "This capability is known as **Tool Calling** or **Function Calling** and \n",
- "is becoming a popular pattern in building intelligent agent-based applications.\n",
- "Refer to the documentation from [OpenAI](https://platform.openai.com/docs/guides/function-calling) \n",
- "and [Anthropic](https://docs.anthropic.com/en/docs/build-with-claude/tool-use) for more information about tool calling in LLMs.\n",
- "\n",
- "In AgentChat, the {py:class}`~autogen_agentchat.agents.AssistantAgent` can use tools to perform specific actions.\n",
- "The `web_search` tool is one such tool that allows the assistant agent to search the web for information.\n",
- "A custom tool can be a Python function or a subclass of the {py:class}`~autogen_core.tools.BaseTool`.\n",
- "\n",
- "```{note}\n",
- "For how to use model clients directly with tools, refer to the [Tools](../../core-user-guide/components/tools.ipynb) section\n",
- "in the Core User Guide.\n",
- "```\n",
- "\n",
- "By default, when {py:class}`~autogen_agentchat.agents.AssistantAgent` executes a tool,\n",
- "it will return the tool's output as a string in {py:class}`~autogen_agentchat.messages.ToolCallSummaryMessage` in its response.\n",
- "If your tool does not return a well-formed string in natural language, you\n",
- "can add a reflection step to have the model summarize the tool's output,\n",
- "by setting the `reflect_on_tool_use=True` parameter in the {py:class}`~autogen_agentchat.agents.AssistantAgent` constructor.\n",
- "\n",
- "### Built-in Tools\n",
- "\n",
- "AutoGen Extension provides a set of built-in tools that can be used with the Assistant Agent.\n",
- "Head over to the [API documentation](../../../reference/index.md) for all the available tools\n",
- "under the `autogen_ext.tools` namespace. For example, you can find the following tools:\n",
- "\n",
- "- {py:mod}`~autogen_ext.tools.graphrag`: Tools for using GraphRAG index.\n",
- "- {py:mod}`~autogen_ext.tools.http`: Tools for making HTTP requests.\n",
- "- {py:mod}`~autogen_ext.tools.langchain`: Adaptor for using LangChain tools.\n",
- "- {py:mod}`~autogen_ext.tools.mcp`: Tools for using Model Chat Protocol (MCP) servers."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Function Tool\n",
- "\n",
- "The {py:class}`~autogen_agentchat.agents.AssistantAgent` automatically\n",
- "converts a Python function into a {py:class}`~autogen_core.tools.FunctionTool`\n",
- "which can be used as a tool by the agent and automatically generates the tool schema\n",
- "from the function signature and docstring.\n",
- "\n",
- "The `web_search_func` tool is an example of a function tool.\n",
- "The schema is automatically generated."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "{'name': 'web_search_func',\n",
- " 'description': 'Find information on the web',\n",
- " 'parameters': {'type': 'object',\n",
- " 'properties': {'query': {'description': 'query',\n",
- " 'title': 'Query',\n",
- " 'type': 'string'}},\n",
- " 'required': ['query'],\n",
- " 'additionalProperties': False},\n",
- " 'strict': False}"
- ]
- },
- "execution_count": 5,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "from autogen_core.tools import FunctionTool\n",
- "\n",
- "\n",
- "# Define a tool using a Python function.\n",
- "async def web_search_func(query: str) -> str:\n",
- " \"\"\"Find information on the web\"\"\"\n",
- " return \"AutoGen is a programming framework for building multi-agent applications.\"\n",
- "\n",
- "\n",
- "# This step is automatically performed inside the AssistantAgent if the tool is a Python function.\n",
- "web_search_function_tool = FunctionTool(web_search_func, description=\"Find information on the web\")\n",
- "# The schema is provided to the model during AssistantAgent's on_messages call.\n",
- "web_search_function_tool.schema"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Model Context Protocol Tools\n",
- "\n",
- "The {py:class}`~autogen_agentchat.agents.AssistantAgent` can also use tools that are\n",
- "served from a Model Context Protocol (MCP) server\n",
- "using {py:func}`~autogen_ext.tools.mcp.mcp_server_tools`."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Seattle, located in Washington state, is the most populous city in the state and a major city in the Pacific Northwest region of the United States. It's known for its vibrant cultural scene, significant economic presence, and rich history. Here are some key points about Seattle from the Wikipedia page:\n",
- "\n",
- "1. **History and Geography**: Seattle is situated between Puget Sound and Lake Washington, with the Cascade Range to the east and the Olympic Mountains to the west. Its history is deeply rooted in Native American heritage and its development was accelerated with the arrival of settlers in the 19th century. The city was officially incorporated in 1869.\n",
- "\n",
- "2. **Economy**: Seattle is a major economic hub with a diverse economy anchored by sectors like aerospace, technology, and retail. It's home to influential companies such as Amazon and Starbucks, and has a significant impact on the tech industry due to companies like Microsoft and other technology enterprises in the surrounding area.\n",
- "\n",
- "3. **Cultural Significance**: Known for its music scene, Seattle was the birthplace of grunge music in the early 1990s. It also boasts significant attractions like the Space Needle, Pike Place Market, and the Seattle Art Museum. \n",
- "\n",
- "4. **Education and Innovation**: The city hosts important educational institutions, with the University of Washington being a leading research university. Seattle is recognized for fostering innovation and is a leader in environmental sustainability efforts.\n",
- "\n",
- "5. **Demographics and Diversity**: Seattle is noted for its diverse population, reflected in its rich cultural tapestry. It has seen a significant increase in population, leading to urban development and changes in its social landscape.\n",
- "\n",
- "These points highlight Seattle as a dynamic city with a significant cultural, economic, and educational influence within the United States and beyond.\n"
- ]
- }
- ],
- "source": [
- "from autogen_agentchat.agents import AssistantAgent\n",
- "from autogen_ext.models.openai import OpenAIChatCompletionClient\n",
- "from autogen_ext.tools.mcp import StdioServerParams, mcp_server_tools\n",
- "\n",
- "# Get the fetch tool from mcp-server-fetch.\n",
- "fetch_mcp_server = StdioServerParams(command=\"uvx\", args=[\"mcp-server-fetch\"])\n",
- "tools = await mcp_server_tools(fetch_mcp_server)\n",
- "\n",
- "# Create an agent that can use the fetch tool.\n",
- "model_client = OpenAIChatCompletionClient(model=\"gpt-4o\")\n",
- "agent = AssistantAgent(name=\"fetcher\", model_client=model_client, tools=tools, reflect_on_tool_use=True) # type: ignore\n",
- "\n",
- "# Let the agent fetch the content of a URL and summarize it.\n",
- "result = await agent.run(task=\"Summarize the content of https://en.wikipedia.org/wiki/Seattle\")\n",
- "print(result.messages[-1].content)\n",
- "\n",
- "# Close the connection to the model client.\n",
- "await model_client.close()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Langchain Tools\n",
- "\n",
- "You can also use tools from the Langchain library\n",
- "by wrapping them in {py:class}`~autogen_ext.tools.langchain.LangChainToolAdapter`."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "---------- assistant ----------\n",
- "[FunctionCall(id='call_BEYRkf53nBS1G2uG60wHP0zf', arguments='{\"query\":\"df[\\'Age\\'].mean()\"}', name='python_repl_ast')]\n",
- "[Prompt tokens: 111, Completion tokens: 22]\n",
- "---------- assistant ----------\n",
- "[FunctionExecutionResult(content='29.69911764705882', call_id='call_BEYRkf53nBS1G2uG60wHP0zf')]\n",
- "---------- assistant ----------\n",
- "29.69911764705882\n",
- "---------- Summary ----------\n",
- "Number of inner messages: 2\n",
- "Total prompt tokens: 111\n",
- "Total completion tokens: 22\n",
- "Duration: 0.62 seconds\n"
- ]
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Agents\n",
+ "\n",
+ "AutoGen AgentChat provides a set of preset Agents, each with variations in how an agent might respond to messages.\n",
+ "All agents share the following attributes and methods:\n",
+ "\n",
+ "- {py:attr}`~autogen_agentchat.agents.BaseChatAgent.name`: The unique name of the agent.\n",
+ "- {py:attr}`~autogen_agentchat.agents.BaseChatAgent.description`: The description of the agent in text.\n",
+ "- {py:meth}`~autogen_agentchat.agents.BaseChatAgent.on_messages`: Send the agent a sequence of {py:class}`~autogen_agentchat.messages.ChatMessage` and get a {py:class}`~autogen_agentchat.base.Response`. **It is important to note that agents are expected to be stateful and this method is expected to be called with new messages, not the complete history**.\n",
+ "- {py:meth}`~autogen_agentchat.agents.BaseChatAgent.on_messages_stream`: Same as {py:meth}`~autogen_agentchat.agents.BaseChatAgent.on_messages` but returns an iterator of {py:class}`~autogen_agentchat.messages.AgentEvent` or {py:class}`~autogen_agentchat.messages.ChatMessage` followed by a {py:class}`~autogen_agentchat.base.Response` as the last item.\n",
+ "- {py:meth}`~autogen_agentchat.agents.BaseChatAgent.on_reset`: Reset the agent to its initial state.\n",
+ "- {py:meth}`~autogen_agentchat.agents.BaseChatAgent.run` and {py:meth}`~autogen_agentchat.agents.BaseChatAgent.run_stream`: convenience methods that call {py:meth}`~autogen_agentchat.agents.BaseChatAgent.on_messages` and {py:meth}`~autogen_agentchat.agents.BaseChatAgent.on_messages_stream` respectively but offer the same interface as [Teams](./teams.ipynb).\n",
+ "\n",
+ "See {py:mod}`autogen_agentchat.messages` for more information on AgentChat message types.\n",
+ "\n",
+ "\n",
+ "## Assistant Agent\n",
+ "\n",
+ "{py:class}`~autogen_agentchat.agents.AssistantAgent` is a built-in agent that\n",
+ "uses a language model and has the ability to use tools."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from autogen_agentchat.agents import AssistantAgent\n",
+ "from autogen_agentchat.messages import TextMessage\n",
+ "from autogen_agentchat.ui import Console\n",
+ "from autogen_core import CancellationToken\n",
+ "from autogen_ext.models.openai import OpenAIChatCompletionClient"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Define a tool that searches the web for information.\n",
+ "async def web_search(query: str) -> str:\n",
+ " \"\"\"Find information on the web\"\"\"\n",
+ " return \"AutoGen is a programming framework for building multi-agent applications.\"\n",
+ "\n",
+ "\n",
+ "# Create an agent that uses the OpenAI GPT-4o model.\n",
+ "model_client = OpenAIChatCompletionClient(\n",
+ " model=\"gpt-4o\",\n",
+ " # api_key=\"YOUR_API_KEY\",\n",
+ ")\n",
+ "agent = AssistantAgent(\n",
+ " name=\"assistant\",\n",
+ " model_client=model_client,\n",
+ " tools=[web_search],\n",
+ " system_message=\"Use tools to solve tasks.\",\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n",
+ "## Getting Responses\n",
+ "\n",
+ "We can use the {py:meth}`~autogen_agentchat.agents.AssistantAgent.on_messages` method to get the agent response to a given message.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[ToolCallRequestEvent(source='assistant', models_usage=RequestUsage(prompt_tokens=598, completion_tokens=16), content=[FunctionCall(id='call_9UWYM1CgE3ZbnJcSJavNDB79', arguments='{\"query\":\"AutoGen\"}', name='web_search')], type='ToolCallRequestEvent'), ToolCallExecutionEvent(source='assistant', models_usage=None, content=[FunctionExecutionResult(content='AutoGen is a programming framework for building multi-agent applications.', call_id='call_9UWYM1CgE3ZbnJcSJavNDB79', is_error=False)], type='ToolCallExecutionEvent')]\n",
+ "source='assistant' models_usage=None content='AutoGen is a programming framework for building multi-agent applications.' type='ToolCallSummaryMessage'\n"
+ ]
+ }
+ ],
+ "source": [
+ "async def assistant_run() -> None:\n",
+ " response = await agent.on_messages(\n",
+ " [TextMessage(content=\"Find information on AutoGen\", source=\"user\")],\n",
+ " cancellation_token=CancellationToken(),\n",
+ " )\n",
+ " print(response.inner_messages)\n",
+ " print(response.chat_message)\n",
+ "\n",
+ "\n",
+ "# Use asyncio.run(assistant_run()) when running in a script.\n",
+ "await assistant_run()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The call to the {py:meth}`~autogen_agentchat.agents.AssistantAgent.on_messages` method\n",
+ "returns a {py:class}`~autogen_agentchat.base.Response`\n",
+ "that contains the agent's final response in the {py:attr}`~autogen_agentchat.base.Response.chat_message` attribute,\n",
+ "as well as a list of inner messages in the {py:attr}`~autogen_agentchat.base.Response.inner_messages` attribute,\n",
+ "which stores the agent's \"thought process\" that led to the final response.\n",
+ "\n",
+ "```{note}\n",
+ "It is important to note that {py:meth}`~autogen_agentchat.agents.AssistantAgent.on_messages`\n",
+ "will update the internal state of the agent -- it will add the messages to the agent's\n",
+ "history. So you should call this method with new messages.\n",
+ "**You should not repeatedly call this method with the same messages or the complete history.**\n",
+ "```\n",
+ "\n",
+ "```{note}\n",
+ "Unlike in v0.2 AgentChat, the tools are executed by the same agent directly within\n",
+ "the same call to {py:meth}`~autogen_agentchat.agents.AssistantAgent.on_messages`.\n",
+ "By default, the agent will return the result of the tool call as the final response.\n",
+ "```\n",
+ "\n",
+ "You can also call the {py:meth}`~autogen_agentchat.agents.BaseChatAgent.run` method, which is a convenience method that calls {py:meth}`~autogen_agentchat.agents.BaseChatAgent.on_messages`. \n",
+ "It follows the same interface as [Teams](./teams.ipynb) and returns a {py:class}`~autogen_agentchat.base.TaskResult` object."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Multi-Modal Input\n",
+ "\n",
+ "The {py:class}`~autogen_agentchat.agents.AssistantAgent` can handle multi-modal input\n",
+ "by providing the input as a {py:class}`~autogen_agentchat.messages.MultiModalMessage`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
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\"/>"
+ ],
+ "text/plain": [
+ "<autogen_core._image.Image at 0x12a29f9b0>"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from io import BytesIO\n",
+ "\n",
+ "import PIL\n",
+ "import requests\n",
+ "from autogen_agentchat.messages import MultiModalMessage\n",
+ "from autogen_core import Image\n",
+ "\n",
+ "# Create a multi-modal message with random image and text.\n",
+ "pil_image = PIL.Image.open(BytesIO(requests.get(\"https://picsum.photos/300/200\").content))\n",
+ "img = Image(pil_image)\n",
+ "multi_modal_message = MultiModalMessage(content=[\"Can you describe the content of this image?\", img], source=\"user\")\n",
+ "img"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "The image depicts a vintage car, likely from the 1930s or 1940s, with a sleek, classic design. The car seems to be customized or well-maintained, as indicated by its shiny exterior and lowered stance. It has a prominent grille and round headlights. There's a license plate on the front with the text \"FARMER BOY.\" The setting appears to be a street with old-style buildings in the background, suggesting a historical or retro theme.\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Use asyncio.run(...) when running in a script.\n",
+ "response = await agent.on_messages([multi_modal_message], CancellationToken())\n",
+ "print(response.chat_message)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "You can also use {py:class}`~autogen_agentchat.messages.MultiModalMessage` as a `task`\n",
+ "input to the {py:meth}`~autogen_agentchat.agents.BaseChatAgent.run` method."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Streaming Messages\n",
+ "\n",
+ "We can also stream each message as it is generated by the agent by using the\n",
+ "{py:meth}`~autogen_agentchat.agents.AssistantAgent.on_messages_stream` method,\n",
+ "and use {py:class}`~autogen_agentchat.ui.Console` to print the messages\n",
+ "as they appear to the console."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "---------- assistant ----------\n",
+ "[FunctionCall(id='call_fSp5iTGVm2FKw5NIvfECSqNd', arguments='{\"query\":\"AutoGen information\"}', name='web_search')]\n",
+ "[Prompt tokens: 61, Completion tokens: 16]\n",
+ "---------- assistant ----------\n",
+ "[FunctionExecutionResult(content='AutoGen is a programming framework for building multi-agent applications.', call_id='call_fSp5iTGVm2FKw5NIvfECSqNd')]\n",
+ "---------- assistant ----------\n",
+ "AutoGen is a programming framework designed for building multi-agent applications. If you need more detailed information or specific aspects about AutoGen, feel free to ask!\n",
+ "[Prompt tokens: 93, Completion tokens: 32]\n",
+ "---------- Summary ----------\n",
+ "Number of inner messages: 2\n",
+ "Total prompt tokens: 154\n",
+ "Total completion tokens: 48\n",
+ "Duration: 4.30 seconds\n"
+ ]
+ }
+ ],
+ "source": [
+ "async def assistant_run_stream() -> None:\n",
+ " # Option 1: read each message from the stream (as shown in the previous example).\n",
+ " # async for message in agent.on_messages_stream(\n",
+ " # [TextMessage(content=\"Find information on AutoGen\", source=\"user\")],\n",
+ " # cancellation_token=CancellationToken(),\n",
+ " # ):\n",
+ " # print(message)\n",
+ "\n",
+ " # Option 2: use Console to print all messages as they appear.\n",
+ " await Console(\n",
+ " agent.on_messages_stream(\n",
+ " [TextMessage(content=\"Find information on AutoGen\", source=\"user\")],\n",
+ " cancellation_token=CancellationToken(),\n",
+ " ),\n",
+ " output_stats=True, # Enable stats printing.\n",
+ " )\n",
+ "\n",
+ "\n",
+ "# Use asyncio.run(assistant_run_stream()) when running in a script.\n",
+ "await assistant_run_stream()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The {py:meth}`~autogen_agentchat.agents.AssistantAgent.on_messages_stream` method\n",
+ "returns an asynchronous generator that yields each inner message generated by the agent,\n",
+ "with the final item being the response message in the {py:attr}`~autogen_agentchat.base.Response.chat_message` attribute.\n",
+ "\n",
+ "From the messages, you can observe that the assistant agent utilized the `web_search` tool to\n",
+ "gather information and responded based on the search results.\n",
+ "\n",
+ "You can also use {py:meth}`~autogen_agentchat.agents.BaseChatAgent.run_stream` to get the same streaming behavior as {py:meth}`~autogen_agentchat.agents.BaseChatAgent.on_messages_stream`. It follows the same interface as [Teams](./teams.ipynb)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Using Tools\n",
+ "\n",
+ "Large Language Models (LLMs) are typically limited to generating text or code responses. \n",
+ "However, many complex tasks benefit from the ability to use external tools that perform specific actions,\n",
+ "such as fetching data from APIs or databases.\n",
+ "\n",
+ "To address this limitation, modern LLMs can now accept a list of available tool schemas \n",
+ "(descriptions of tools and their arguments) and generate a tool call message. \n",
+ "This capability is known as **Tool Calling** or **Function Calling** and \n",
+ "is becoming a popular pattern in building intelligent agent-based applications.\n",
+ "Refer to the documentation from [OpenAI](https://platform.openai.com/docs/guides/function-calling) \n",
+ "and [Anthropic](https://docs.anthropic.com/en/docs/build-with-claude/tool-use) for more information about tool calling in LLMs.\n",
+ "\n",
+ "In AgentChat, the {py:class}`~autogen_agentchat.agents.AssistantAgent` can use tools to perform specific actions.\n",
+ "The `web_search` tool is one such tool that allows the assistant agent to search the web for information.\n",
+ "A custom tool can be a Python function or a subclass of the {py:class}`~autogen_core.tools.BaseTool`.\n",
+ "\n",
+ "```{note}\n",
+ "For how to use model clients directly with tools, refer to the [Tools](../../core-user-guide/components/tools.ipynb) section\n",
+ "in the Core User Guide.\n",
+ "```\n",
+ "\n",
+ "By default, when {py:class}`~autogen_agentchat.agents.AssistantAgent` executes a tool,\n",
+ "it will return the tool's output as a string in {py:class}`~autogen_agentchat.messages.ToolCallSummaryMessage` in its response.\n",
+ "If your tool does not return a well-formed string in natural language, you\n",
+ "can add a reflection step to have the model summarize the tool's output,\n",
+ "by setting the `reflect_on_tool_use=True` parameter in the {py:class}`~autogen_agentchat.agents.AssistantAgent` constructor.\n",
+ "\n",
+ "### Built-in Tools\n",
+ "\n",
+ "AutoGen Extension provides a set of built-in tools that can be used with the Assistant Agent.\n",
+ "Head over to the [API documentation](../../../reference/index.md) for all the available tools\n",
+ "under the `autogen_ext.tools` namespace. For example, you can find the following tools:\n",
+ "\n",
+ "- {py:mod}`~autogen_ext.tools.graphrag`: Tools for using GraphRAG index.\n",
+ "- {py:mod}`~autogen_ext.tools.http`: Tools for making HTTP requests.\n",
+ "- {py:mod}`~autogen_ext.tools.langchain`: Adaptor for using LangChain tools.\n",
+ "- {py:mod}`~autogen_ext.tools.mcp`: Tools for using Model Chat Protocol (MCP) servers."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Function Tool\n",
+ "\n",
+ "The {py:class}`~autogen_agentchat.agents.AssistantAgent` automatically\n",
+ "converts a Python function into a {py:class}`~autogen_core.tools.FunctionTool`\n",
+ "which can be used as a tool by the agent and automatically generates the tool schema\n",
+ "from the function signature and docstring.\n",
+ "\n",
+ "The `web_search_func` tool is an example of a function tool.\n",
+ "The schema is automatically generated."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'name': 'web_search_func',\n",
+ " 'description': 'Find information on the web',\n",
+ " 'parameters': {'type': 'object',\n",
+ " 'properties': {'query': {'description': 'query',\n",
+ " 'title': 'Query',\n",
+ " 'type': 'string'}},\n",
+ " 'required': ['query'],\n",
+ " 'additionalProperties': False},\n",
+ " 'strict': False}"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from autogen_core.tools import FunctionTool\n",
+ "\n",
+ "\n",
+ "# Define a tool using a Python function.\n",
+ "async def web_search_func(query: str) -> str:\n",
+ " \"\"\"Find information on the web\"\"\"\n",
+ " return \"AutoGen is a programming framework for building multi-agent applications.\"\n",
+ "\n",
+ "\n",
+ "# This step is automatically performed inside the AssistantAgent if the tool is a Python function.\n",
+ "web_search_function_tool = FunctionTool(web_search_func, description=\"Find information on the web\")\n",
+ "# The schema is provided to the model during AssistantAgent's on_messages call.\n",
+ "web_search_function_tool.schema"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Model Context Protocol Tools\n",
+ "\n",
+ "The {py:class}`~autogen_agentchat.agents.AssistantAgent` can also use tools that are\n",
+ "served from a Model Context Protocol (MCP) server\n",
+ "using {py:func}`~autogen_ext.tools.mcp.mcp_server_tools`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Seattle, located in Washington state, is the most populous city in the state and a major city in the Pacific Northwest region of the United States. It's known for its vibrant cultural scene, significant economic presence, and rich history. Here are some key points about Seattle from the Wikipedia page:\n",
+ "\n",
+ "1. **History and Geography**: Seattle is situated between Puget Sound and Lake Washington, with the Cascade Range to the east and the Olympic Mountains to the west. Its history is deeply rooted in Native American heritage and its development was accelerated with the arrival of settlers in the 19th century. The city was officially incorporated in 1869.\n",
+ "\n",
+ "2. **Economy**: Seattle is a major economic hub with a diverse economy anchored by sectors like aerospace, technology, and retail. It's home to influential companies such as Amazon and Starbucks, and has a significant impact on the tech industry due to companies like Microsoft and other technology enterprises in the surrounding area.\n",
+ "\n",
+ "3. **Cultural Significance**: Known for its music scene, Seattle was the birthplace of grunge music in the early 1990s. It also boasts significant attractions like the Space Needle, Pike Place Market, and the Seattle Art Museum. \n",
+ "\n",
+ "4. **Education and Innovation**: The city hosts important educational institutions, with the University of Washington being a leading research university. Seattle is recognized for fostering innovation and is a leader in environmental sustainability efforts.\n",
+ "\n",
+ "5. **Demographics and Diversity**: Seattle is noted for its diverse population, reflected in its rich cultural tapestry. It has seen a significant increase in population, leading to urban development and changes in its social landscape.\n",
+ "\n",
+ "These points highlight Seattle as a dynamic city with a significant cultural, economic, and educational influence within the United States and beyond.\n"
+ ]
+ }
+ ],
+ "source": [
+ "from autogen_agentchat.agents import AssistantAgent\n",
+ "from autogen_ext.models.openai import OpenAIChatCompletionClient\n",
+ "from autogen_ext.tools.mcp import StdioServerParams, mcp_server_tools\n",
+ "\n",
+ "# Get the fetch tool from mcp-server-fetch.\n",
+ "fetch_mcp_server = StdioServerParams(command=\"uvx\", args=[\"mcp-server-fetch\"])\n",
+ "tools = await mcp_server_tools(fetch_mcp_server)\n",
+ "\n",
+ "# Create an agent that can use the fetch tool.\n",
+ "model_client = OpenAIChatCompletionClient(model=\"gpt-4o\")\n",
+ "agent = AssistantAgent(name=\"fetcher\", model_client=model_client, tools=tools, reflect_on_tool_use=True) # type: ignore\n",
+ "\n",
+ "# Let the agent fetch the content of a URL and summarize it.\n",
+ "result = await agent.run(task=\"Summarize the content of https://en.wikipedia.org/wiki/Seattle\")\n",
+ "assert isinstance(result.messages[-1], TextMessage)\n",
+ "print(result.messages[-1].content)\n",
+ "\n",
+ "# Close the connection to the model client.\n",
+ "await model_client.close()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Langchain Tools\n",
+ "\n",
+ "You can also use tools from the Langchain library\n",
+ "by wrapping them in {py:class}`~autogen_ext.tools.langchain.LangChainToolAdapter`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "---------- assistant ----------\n",
+ "[FunctionCall(id='call_BEYRkf53nBS1G2uG60wHP0zf', arguments='{\"query\":\"df[\\'Age\\'].mean()\"}', name='python_repl_ast')]\n",
+ "[Prompt tokens: 111, Completion tokens: 22]\n",
+ "---------- assistant ----------\n",
+ "[FunctionExecutionResult(content='29.69911764705882', call_id='call_BEYRkf53nBS1G2uG60wHP0zf')]\n",
+ "---------- assistant ----------\n",
+ "29.69911764705882\n",
+ "---------- Summary ----------\n",
+ "Number of inner messages: 2\n",
+ "Total prompt tokens: 111\n",
+ "Total completion tokens: 22\n",
+ "Duration: 0.62 seconds\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "Response(chat_message=ToolCallSummaryMessage(source='assistant', models_usage=None, content='29.69911764705882', type='ToolCallSummaryMessage'), inner_messages=[ToolCallRequestEvent(source='assistant', models_usage=RequestUsage(prompt_tokens=111, completion_tokens=22), content=[FunctionCall(id='call_BEYRkf53nBS1G2uG60wHP0zf', arguments='{\"query\":\"df[\\'Age\\'].mean()\"}', name='python_repl_ast')], type='ToolCallRequestEvent'), ToolCallExecutionEvent(source='assistant', models_usage=None, content=[FunctionExecutionResult(content='29.69911764705882', call_id='call_BEYRkf53nBS1G2uG60wHP0zf')], type='ToolCallExecutionEvent')])"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "import pandas as pd\n",
+ "from autogen_ext.tools.langchain import LangChainToolAdapter\n",
+ "from langchain_experimental.tools.python.tool import PythonAstREPLTool\n",
+ "\n",
+ "df = pd.read_csv(\"https://raw.githubusercontent.com/pandas-dev/pandas/main/doc/data/titanic.csv\")\n",
+ "tool = LangChainToolAdapter(PythonAstREPLTool(locals={\"df\": df}))\n",
+ "model_client = OpenAIChatCompletionClient(model=\"gpt-4o\")\n",
+ "agent = AssistantAgent(\n",
+ " \"assistant\", tools=[tool], model_client=model_client, system_message=\"Use the `df` variable to access the dataset.\"\n",
+ ")\n",
+ "await Console(\n",
+ " agent.on_messages_stream(\n",
+ " [TextMessage(content=\"What's the average age of the passengers?\", source=\"user\")], CancellationToken()\n",
+ " ),\n",
+ " output_stats=True,\n",
+ ")\n",
+ "\n",
+ "await model_client.close()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Parallel Tool Calls\n",
+ "\n",
+ "Some models support parallel tool calls, which can be useful for tasks that require multiple tools to be called simultaneously.\n",
+ "By default, if the model client produces multiple tool calls, {py:class}`~autogen_agentchat.agents.AssistantAgent`\n",
+ "will call the tools in parallel.\n",
+ "\n",
+ "You may want to disable parallel tool calls when the tools have side effects that may interfere with each other, or,\n",
+ "when agent behavior needs to be consistent across different models.\n",
+ "This should be done at the model client level.\n",
+ "\n",
+ "For {py:class}`~autogen_ext.models.openai.OpenAIChatCompletionClient` and {py:class}`~autogen_ext.models.openai.AzureOpenAIChatCompletionClient`,\n",
+ "set `parallel_tool_calls=False` to disable parallel tool calls."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "model_client_no_parallel_tool_call = OpenAIChatCompletionClient(\n",
+ " model=\"gpt-4o\",\n",
+ " parallel_tool_calls=False, # type: ignore\n",
+ ")\n",
+ "agent_no_parallel_tool_call = AssistantAgent(\n",
+ " name=\"assistant\",\n",
+ " model_client=model_client_no_parallel_tool_call,\n",
+ " tools=[web_search],\n",
+ " system_message=\"Use tools to solve tasks.\",\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Running an Agent in a Loop\n",
+ "\n",
+ "The {py:class}`~autogen_agentchat.agents.AssistantAgent` executes one\n",
+ "step at a time: one model call, followed by one tool call (or parallel tool calls), and then\n",
+ "an optional reflection.\n",
+ "\n",
+ "To run it in a loop, for example, running it until it stops producing\n",
+ "tool calls, please refer to [Single-Agent Team](./teams.ipynb#single-agent-team)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Structured Output\n",
+ "\n",
+ "Structured output allows models to return structured JSON text with pre-defined schema\n",
+ "provided by the application. Different from JSON-mode, the schema can be provided\n",
+ "as a [Pydantic BaseModel](https://docs.pydantic.dev/latest/concepts/models/)\n",
+ "class, which can also be used to validate the output. \n",
+ "\n",
+ "```{note}\n",
+ "Structured output is only available for models that support it. It also\n",
+ "requires the model client to support structured output as well.\n",
+ "Currently, the {py:class}`~autogen_ext.models.openai.OpenAIChatCompletionClient`\n",
+ "and {py:class}`~autogen_ext.models.openai.AzureOpenAIChatCompletionClient`\n",
+ "support structured output.\n",
+ "```\n",
+ "\n",
+ "Structured output is also useful for incorporating Chain-of-Thought\n",
+ "reasoning in the agent's responses.\n",
+ "See the example below for how to use structured output with the assistant agent."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "---------- user ----------\n",
+ "I am happy.\n",
+ "---------- assistant ----------\n",
+ "{\"thoughts\":\"The user explicitly states that they are happy.\",\"response\":\"happy\"}\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "TaskResult(messages=[TextMessage(source='user', models_usage=None, content='I am happy.', type='TextMessage'), TextMessage(source='assistant', models_usage=RequestUsage(prompt_tokens=89, completion_tokens=18), content='{\"thoughts\":\"The user explicitly states that they are happy.\",\"response\":\"happy\"}', type='TextMessage')], stop_reason=None)"
+ ]
+ },
+ "execution_count": 2,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from typing import Literal\n",
+ "\n",
+ "from pydantic import BaseModel\n",
+ "\n",
+ "\n",
+ "# The response format for the agent as a Pydantic base model.\n",
+ "class AgentResponse(BaseModel):\n",
+ " thoughts: str\n",
+ " response: Literal[\"happy\", \"sad\", \"neutral\"]\n",
+ "\n",
+ "\n",
+ "# Create an agent that uses the OpenAI GPT-4o model with the custom response format.\n",
+ "model_client = OpenAIChatCompletionClient(\n",
+ " model=\"gpt-4o\",\n",
+ " response_format=AgentResponse, # type: ignore\n",
+ ")\n",
+ "agent = AssistantAgent(\n",
+ " \"assistant\",\n",
+ " model_client=model_client,\n",
+ " system_message=\"Categorize the input as happy, sad, or neutral following the JSON format.\",\n",
+ ")\n",
+ "\n",
+ "await Console(agent.run_stream(task=\"I am happy.\"))\n",
+ "await model_client.close()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Streaming Tokens\n",
+ "\n",
+ "You can stream the tokens generated by the model client by setting `model_client_stream=True`.\n",
+ "This will cause the agent to yield {py:class}`~autogen_agentchat.messages.ModelClientStreamingChunkEvent` messages\n",
+ "in {py:meth}`~autogen_agentchat.agents.BaseChatAgent.on_messages_stream` and {py:meth}`~autogen_agentchat.agents.BaseChatAgent.run_stream`.\n",
+ "\n",
+ "The underlying model API must support streaming tokens for this to work.\n",
+ "Please check with your model provider to see if this is supported."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "source='assistant' models_usage=None content='Two' type='ModelClientStreamingChunkEvent'\n",
+ "source='assistant' models_usage=None content=' cities' type='ModelClientStreamingChunkEvent'\n",
+ "source='assistant' models_usage=None content=' in' type='ModelClientStreamingChunkEvent'\n",
+ "source='assistant' models_usage=None content=' South' type='ModelClientStreamingChunkEvent'\n",
+ "source='assistant' models_usage=None content=' America' type='ModelClientStreamingChunkEvent'\n",
+ "source='assistant' models_usage=None content=' are' type='ModelClientStreamingChunkEvent'\n",
+ "source='assistant' models_usage=None content=' Buenos' type='ModelClientStreamingChunkEvent'\n",
+ "source='assistant' models_usage=None content=' Aires' type='ModelClientStreamingChunkEvent'\n",
+ "source='assistant' models_usage=None content=' in' type='ModelClientStreamingChunkEvent'\n",
+ "source='assistant' models_usage=None content=' Argentina' type='ModelClientStreamingChunkEvent'\n",
+ "source='assistant' models_usage=None content=' and' type='ModelClientStreamingChunkEvent'\n",
+ "source='assistant' models_usage=None content=' São' type='ModelClientStreamingChunkEvent'\n",
+ "source='assistant' models_usage=None content=' Paulo' type='ModelClientStreamingChunkEvent'\n",
+ "source='assistant' models_usage=None content=' in' type='ModelClientStreamingChunkEvent'\n",
+ "source='assistant' models_usage=None content=' Brazil' type='ModelClientStreamingChunkEvent'\n",
+ "source='assistant' models_usage=None content='.' type='ModelClientStreamingChunkEvent'\n",
+ "Response(chat_message=TextMessage(source='assistant', models_usage=RequestUsage(prompt_tokens=0, completion_tokens=0), content='Two cities in South America are Buenos Aires in Argentina and São Paulo in Brazil.', type='TextMessage'), inner_messages=[])\n"
+ ]
+ }
+ ],
+ "source": [
+ "model_client = OpenAIChatCompletionClient(model=\"gpt-4o\")\n",
+ "\n",
+ "streaming_assistant = AssistantAgent(\n",
+ " name=\"assistant\",\n",
+ " model_client=model_client,\n",
+ " system_message=\"You are a helpful assistant.\",\n",
+ " model_client_stream=True, # Enable streaming tokens.\n",
+ ")\n",
+ "\n",
+ "# Use an async function and asyncio.run() in a script.\n",
+ "async for message in streaming_assistant.on_messages_stream( # type: ignore\n",
+ " [TextMessage(content=\"Name two cities in South America\", source=\"user\")],\n",
+ " cancellation_token=CancellationToken(),\n",
+ "):\n",
+ " print(message)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "You can see the streaming chunks in the output above.\n",
+ "The chunks are generated by the model client and are yielded by the agent as they are received.\n",
+ "The final response, the concatenation of all the chunks, is yielded right after the last chunk.\n",
+ "\n",
+ "Similarly, {py:meth}`~autogen_agentchat.agents.BaseChatAgent.run_stream` will also yield the same streaming chunks,\n",
+ "followed by a full text message right after the last chunk."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "source='user' models_usage=None content='Name two cities in North America.' type='TextMessage'\n",
+ "source='assistant' models_usage=None content='Two' type='ModelClientStreamingChunkEvent'\n",
+ "source='assistant' models_usage=None content=' cities' type='ModelClientStreamingChunkEvent'\n",
+ "source='assistant' models_usage=None content=' in' type='ModelClientStreamingChunkEvent'\n",
+ "source='assistant' models_usage=None content=' North' type='ModelClientStreamingChunkEvent'\n",
+ "source='assistant' models_usage=None content=' America' type='ModelClientStreamingChunkEvent'\n",
+ "source='assistant' models_usage=None content=' are' type='ModelClientStreamingChunkEvent'\n",
+ "source='assistant' models_usage=None content=' New' type='ModelClientStreamingChunkEvent'\n",
+ "source='assistant' models_usage=None content=' York' type='ModelClientStreamingChunkEvent'\n",
+ "source='assistant' models_usage=None content=' City' type='ModelClientStreamingChunkEvent'\n",
+ "source='assistant' models_usage=None content=' in' type='ModelClientStreamingChunkEvent'\n",
+ "source='assistant' models_usage=None content=' the' type='ModelClientStreamingChunkEvent'\n",
+ "source='assistant' models_usage=None content=' United' type='ModelClientStreamingChunkEvent'\n",
+ "source='assistant' models_usage=None content=' States' type='ModelClientStreamingChunkEvent'\n",
+ "source='assistant' models_usage=None content=' and' type='ModelClientStreamingChunkEvent'\n",
+ "source='assistant' models_usage=None content=' Toronto' type='ModelClientStreamingChunkEvent'\n",
+ "source='assistant' models_usage=None content=' in' type='ModelClientStreamingChunkEvent'\n",
+ "source='assistant' models_usage=None content=' Canada' type='ModelClientStreamingChunkEvent'\n",
+ "source='assistant' models_usage=None content='.' type='ModelClientStreamingChunkEvent'\n",
+ "source='assistant' models_usage=RequestUsage(prompt_tokens=0, completion_tokens=0) content='Two cities in North America are New York City in the United States and Toronto in Canada.' type='TextMessage'\n",
+ "TaskResult(messages=[TextMessage(source='user', models_usage=None, content='Name two cities in North America.', type='TextMessage'), TextMessage(source='assistant', models_usage=RequestUsage(prompt_tokens=0, completion_tokens=0), content='Two cities in North America are New York City in the United States and Toronto in Canada.', type='TextMessage')], stop_reason=None)\n"
+ ]
+ }
+ ],
+ "source": [
+ "async for message in streaming_assistant.run_stream(task=\"Name two cities in North America.\"): # type: ignore\n",
+ " print(message)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Using Model Context\n",
+ "\n",
+ "{py:class}`~autogen_agentchat.agents.AssistantAgent` has a `model_context`\n",
+ "parameter that can be used to pass in a {py:class}`~autogen_core.model_context.ChatCompletionContext`\n",
+ "object. This allows the agent to use different model contexts, such as\n",
+ "{py:class}`~autogen_core.model_context.BufferedChatCompletionContext` to\n",
+ "limit the context sent to the model.\n",
+ "\n",
+ "By default, {py:class}`~autogen_agentchat.agents.AssistantAgent` uses\n",
+ "the {py:class}`~autogen_core.model_context.UnboundedChatCompletionContext`\n",
+ "which sends the full conversation history to the model. To limit the context\n",
+ "to the last `n` messages, you can use the {py:class}`~autogen_core.model_context.BufferedChatCompletionContext`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from autogen_core.model_context import BufferedChatCompletionContext\n",
+ "\n",
+ "# Create an agent that uses only the last 5 messages in the context to generate responses.\n",
+ "agent = AssistantAgent(\n",
+ " name=\"assistant\",\n",
+ " model_client=model_client,\n",
+ " tools=[web_search],\n",
+ " system_message=\"Use tools to solve tasks.\",\n",
+ " model_context=BufferedChatCompletionContext(buffer_size=5), # Only use the last 5 messages in the context.\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Other Preset Agents\n",
+ "\n",
+ "The following preset agents are available:\n",
+ "\n",
+ "- {py:class}`~autogen_agentchat.agents.UserProxyAgent`: An agent that takes user input returns it as responses.\n",
+ "- {py:class}`~autogen_agentchat.agents.CodeExecutorAgent`: An agent that can execute code.\n",
+ "- {py:class}`~autogen_ext.agents.openai.OpenAIAssistantAgent`: An agent that is backed by an OpenAI Assistant, with ability to use custom tools.\n",
+ "- {py:class}`~autogen_ext.agents.web_surfer.MultimodalWebSurfer`: A multi-modal agent that can search the web and visit web pages for information.\n",
+ "- {py:class}`~autogen_ext.agents.file_surfer.FileSurfer`: An agent that can search and browse local files for information.\n",
+ "- {py:class}`~autogen_ext.agents.video_surfer.VideoSurfer`: An agent that can watch videos for information."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Next Step\n",
+ "\n",
+ "Having explored the usage of the {py:class}`~autogen_agentchat.agents.AssistantAgent`, we can now proceed to the next section to learn about the teams feature in AgentChat.\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<!-- ## CodingAssistantAgent\n",
+ "\n",
+ "Generates responses (text and code) using an LLM upon receipt of a message. It takes a `system_message` argument that defines or sets the tone for how the agent's LLM should respond. \n",
+ "\n",
+ "```python\n",
+ "\n",
+ "writing_assistant_agent = CodingAssistantAgent(\n",
+ " name=\"writing_assistant_agent\",\n",
+ " system_message=\"You are a helpful assistant that solve tasks by generating text responses and code.\",\n",
+ " model_client=model_client,\n",
+ ")\n",
+ "`\n",
+ "\n",
+ "We can explore or test the behavior of the agent by sending a message to it using the {py:meth}`~autogen_agentchat.agents.BaseChatAgent.on_messages` method. \n",
+ "\n",
+ "```python\n",
+ "result = await writing_assistant_agent.on_messages(\n",
+ " messages=[\n",
+ " TextMessage(content=\"What is the weather right now in France?\", source=\"user\"),\n",
+ " ],\n",
+ " cancellation_token=CancellationToken(),\n",
+ ")\n",
+ "print(result) -->"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": ".venv",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.12.3"
+ }
},
- {
- "data": {
- "text/plain": [
- "Response(chat_message=ToolCallSummaryMessage(source='assistant', models_usage=None, content='29.69911764705882', type='ToolCallSummaryMessage'), inner_messages=[ToolCallRequestEvent(source='assistant', models_usage=RequestUsage(prompt_tokens=111, completion_tokens=22), content=[FunctionCall(id='call_BEYRkf53nBS1G2uG60wHP0zf', arguments='{\"query\":\"df[\\'Age\\'].mean()\"}', name='python_repl_ast')], type='ToolCallRequestEvent'), ToolCallExecutionEvent(source='assistant', models_usage=None, content=[FunctionExecutionResult(content='29.69911764705882', call_id='call_BEYRkf53nBS1G2uG60wHP0zf')], type='ToolCallExecutionEvent')])"
- ]
- },
- "execution_count": 5,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "import pandas as pd\n",
- "from autogen_ext.tools.langchain import LangChainToolAdapter\n",
- "from langchain_experimental.tools.python.tool import PythonAstREPLTool\n",
- "\n",
- "df = pd.read_csv(\"https://raw.githubusercontent.com/pandas-dev/pandas/main/doc/data/titanic.csv\")\n",
- "tool = LangChainToolAdapter(PythonAstREPLTool(locals={\"df\": df}))\n",
- "model_client = OpenAIChatCompletionClient(model=\"gpt-4o\")\n",
- "agent = AssistantAgent(\n",
- " \"assistant\", tools=[tool], model_client=model_client, system_message=\"Use the `df` variable to access the dataset.\"\n",
- ")\n",
- "await Console(\n",
- " agent.on_messages_stream(\n",
- " [TextMessage(content=\"What's the average age of the passengers?\", source=\"user\")], CancellationToken()\n",
- " ),\n",
- " output_stats=True,\n",
- ")\n",
- "\n",
- "await model_client.close()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Parallel Tool Calls\n",
- "\n",
- "Some models support parallel tool calls, which can be useful for tasks that require multiple tools to be called simultaneously.\n",
- "By default, if the model client produces multiple tool calls, {py:class}`~autogen_agentchat.agents.AssistantAgent`\n",
- "will call the tools in parallel.\n",
- "\n",
- "You may want to disable parallel tool calls when the tools have side effects that may interfere with each other, or,\n",
- "when agent behavior needs to be consistent across different models.\n",
- "This should be done at the model client level.\n",
- "\n",
- "For {py:class}`~autogen_ext.models.openai.OpenAIChatCompletionClient` and {py:class}`~autogen_ext.models.openai.AzureOpenAIChatCompletionClient`,\n",
- "set `parallel_tool_calls=False` to disable parallel tool calls."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "model_client_no_parallel_tool_call = OpenAIChatCompletionClient(\n",
- " model=\"gpt-4o\",\n",
- " parallel_tool_calls=False, # type: ignore\n",
- ")\n",
- "agent_no_parallel_tool_call = AssistantAgent(\n",
- " name=\"assistant\",\n",
- " model_client=model_client_no_parallel_tool_call,\n",
- " tools=[web_search],\n",
- " system_message=\"Use tools to solve tasks.\",\n",
- ")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Running an Agent in a Loop\n",
- "\n",
- "The {py:class}`~autogen_agentchat.agents.AssistantAgent` executes one\n",
- "step at a time: one model call, followed by one tool call (or parallel tool calls), and then\n",
- "an optional reflection.\n",
- "\n",
- "To run it in a loop, for example, running it until it stops producing\n",
- "tool calls, please refer to [Single-Agent Team](./teams.ipynb#single-agent-team)."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Structured Output\n",
- "\n",
- "Structured output allows models to return structured JSON text with pre-defined schema\n",
- "provided by the application. Different from JSON-mode, the schema can be provided\n",
- "as a [Pydantic BaseModel](https://docs.pydantic.dev/latest/concepts/models/)\n",
- "class, which can also be used to validate the output. \n",
- "\n",
- "```{note}\n",
- "Structured output is only available for models that support it. It also\n",
- "requires the model client to support structured output as well.\n",
- "Currently, the {py:class}`~autogen_ext.models.openai.OpenAIChatCompletionClient`\n",
- "and {py:class}`~autogen_ext.models.openai.AzureOpenAIChatCompletionClient`\n",
- "support structured output.\n",
- "```\n",
- "\n",
- "Structured output is also useful for incorporating Chain-of-Thought\n",
- "reasoning in the agent's responses.\n",
- "See the example below for how to use structured output with the assistant agent."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "---------- user ----------\n",
- "I am happy.\n",
- "---------- assistant ----------\n",
- "{\"thoughts\":\"The user explicitly states that they are happy.\",\"response\":\"happy\"}\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "TaskResult(messages=[TextMessage(source='user', models_usage=None, content='I am happy.', type='TextMessage'), TextMessage(source='assistant', models_usage=RequestUsage(prompt_tokens=89, completion_tokens=18), content='{\"thoughts\":\"The user explicitly states that they are happy.\",\"response\":\"happy\"}', type='TextMessage')], stop_reason=None)"
- ]
- },
- "execution_count": 2,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "from typing import Literal\n",
- "\n",
- "from pydantic import BaseModel\n",
- "\n",
- "\n",
- "# The response format for the agent as a Pydantic base model.\n",
- "class AgentResponse(BaseModel):\n",
- " thoughts: str\n",
- " response: Literal[\"happy\", \"sad\", \"neutral\"]\n",
- "\n",
- "\n",
- "# Create an agent that uses the OpenAI GPT-4o model with the custom response format.\n",
- "model_client = OpenAIChatCompletionClient(\n",
- " model=\"gpt-4o\",\n",
- " response_format=AgentResponse, # type: ignore\n",
- ")\n",
- "agent = AssistantAgent(\n",
- " \"assistant\",\n",
- " model_client=model_client,\n",
- " system_message=\"Categorize the input as happy, sad, or neutral following the JSON format.\",\n",
- ")\n",
- "\n",
- "await Console(agent.run_stream(task=\"I am happy.\"))\n",
- "await model_client.close()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Streaming Tokens\n",
- "\n",
- "You can stream the tokens generated by the model client by setting `model_client_stream=True`.\n",
- "This will cause the agent to yield {py:class}`~autogen_agentchat.messages.ModelClientStreamingChunkEvent` messages\n",
- "in {py:meth}`~autogen_agentchat.agents.BaseChatAgent.on_messages_stream` and {py:meth}`~autogen_agentchat.agents.BaseChatAgent.run_stream`.\n",
- "\n",
- "The underlying model API must support streaming tokens for this to work.\n",
- "Please check with your model provider to see if this is supported."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "source='assistant' models_usage=None content='Two' type='ModelClientStreamingChunkEvent'\n",
- "source='assistant' models_usage=None content=' cities' type='ModelClientStreamingChunkEvent'\n",
- "source='assistant' models_usage=None content=' in' type='ModelClientStreamingChunkEvent'\n",
- "source='assistant' models_usage=None content=' South' type='ModelClientStreamingChunkEvent'\n",
- "source='assistant' models_usage=None content=' America' type='ModelClientStreamingChunkEvent'\n",
- "source='assistant' models_usage=None content=' are' type='ModelClientStreamingChunkEvent'\n",
- "source='assistant' models_usage=None content=' Buenos' type='ModelClientStreamingChunkEvent'\n",
- "source='assistant' models_usage=None content=' Aires' type='ModelClientStreamingChunkEvent'\n",
- "source='assistant' models_usage=None content=' in' type='ModelClientStreamingChunkEvent'\n",
- "source='assistant' models_usage=None content=' Argentina' type='ModelClientStreamingChunkEvent'\n",
- "source='assistant' models_usage=None content=' and' type='ModelClientStreamingChunkEvent'\n",
- "source='assistant' models_usage=None content=' São' type='ModelClientStreamingChunkEvent'\n",
- "source='assistant' models_usage=None content=' Paulo' type='ModelClientStreamingChunkEvent'\n",
- "source='assistant' models_usage=None content=' in' type='ModelClientStreamingChunkEvent'\n",
- "source='assistant' models_usage=None content=' Brazil' type='ModelClientStreamingChunkEvent'\n",
- "source='assistant' models_usage=None content='.' type='ModelClientStreamingChunkEvent'\n",
- "Response(chat_message=TextMessage(source='assistant', models_usage=RequestUsage(prompt_tokens=0, completion_tokens=0), content='Two cities in South America are Buenos Aires in Argentina and São Paulo in Brazil.', type='TextMessage'), inner_messages=[])\n"
- ]
- }
- ],
- "source": [
- "model_client = OpenAIChatCompletionClient(model=\"gpt-4o\")\n",
- "\n",
- "streaming_assistant = AssistantAgent(\n",
- " name=\"assistant\",\n",
- " model_client=model_client,\n",
- " system_message=\"You are a helpful assistant.\",\n",
- " model_client_stream=True, # Enable streaming tokens.\n",
- ")\n",
- "\n",
- "# Use an async function and asyncio.run() in a script.\n",
- "async for message in streaming_assistant.on_messages_stream( # type: ignore\n",
- " [TextMessage(content=\"Name two cities in South America\", source=\"user\")],\n",
- " cancellation_token=CancellationToken(),\n",
- "):\n",
- " print(message)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "You can see the streaming chunks in the output above.\n",
- "The chunks are generated by the model client and are yielded by the agent as they are received.\n",
- "The final response, the concatenation of all the chunks, is yielded right after the last chunk.\n",
- "\n",
- "Similarly, {py:meth}`~autogen_agentchat.agents.BaseChatAgent.run_stream` will also yield the same streaming chunks,\n",
- "followed by a full text message right after the last chunk."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "source='user' models_usage=None content='Name two cities in North America.' type='TextMessage'\n",
- "source='assistant' models_usage=None content='Two' type='ModelClientStreamingChunkEvent'\n",
- "source='assistant' models_usage=None content=' cities' type='ModelClientStreamingChunkEvent'\n",
- "source='assistant' models_usage=None content=' in' type='ModelClientStreamingChunkEvent'\n",
- "source='assistant' models_usage=None content=' North' type='ModelClientStreamingChunkEvent'\n",
- "source='assistant' models_usage=None content=' America' type='ModelClientStreamingChunkEvent'\n",
- "source='assistant' models_usage=None content=' are' type='ModelClientStreamingChunkEvent'\n",
- "source='assistant' models_usage=None content=' New' type='ModelClientStreamingChunkEvent'\n",
- "source='assistant' models_usage=None content=' York' type='ModelClientStreamingChunkEvent'\n",
- "source='assistant' models_usage=None content=' City' type='ModelClientStreamingChunkEvent'\n",
- "source='assistant' models_usage=None content=' in' type='ModelClientStreamingChunkEvent'\n",
- "source='assistant' models_usage=None content=' the' type='ModelClientStreamingChunkEvent'\n",
- "source='assistant' models_usage=None content=' United' type='ModelClientStreamingChunkEvent'\n",
- "source='assistant' models_usage=None content=' States' type='ModelClientStreamingChunkEvent'\n",
- "source='assistant' models_usage=None content=' and' type='ModelClientStreamingChunkEvent'\n",
- "source='assistant' models_usage=None content=' Toronto' type='ModelClientStreamingChunkEvent'\n",
- "source='assistant' models_usage=None content=' in' type='ModelClientStreamingChunkEvent'\n",
- "source='assistant' models_usage=None content=' Canada' type='ModelClientStreamingChunkEvent'\n",
- "source='assistant' models_usage=None content='.' type='ModelClientStreamingChunkEvent'\n",
- "source='assistant' models_usage=RequestUsage(prompt_tokens=0, completion_tokens=0) content='Two cities in North America are New York City in the United States and Toronto in Canada.' type='TextMessage'\n",
- "TaskResult(messages=[TextMessage(source='user', models_usage=None, content='Name two cities in North America.', type='TextMessage'), TextMessage(source='assistant', models_usage=RequestUsage(prompt_tokens=0, completion_tokens=0), content='Two cities in North America are New York City in the United States and Toronto in Canada.', type='TextMessage')], stop_reason=None)\n"
- ]
- }
- ],
- "source": [
- "async for message in streaming_assistant.run_stream(task=\"Name two cities in North America.\"): # type: ignore\n",
- " print(message)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Using Model Context\n",
- "\n",
- "{py:class}`~autogen_agentchat.agents.AssistantAgent` has a `model_context`\n",
- "parameter that can be used to pass in a {py:class}`~autogen_core.model_context.ChatCompletionContext`\n",
- "object. This allows the agent to use different model contexts, such as\n",
- "{py:class}`~autogen_core.model_context.BufferedChatCompletionContext` to\n",
- "limit the context sent to the model.\n",
- "\n",
- "By default, {py:class}`~autogen_agentchat.agents.AssistantAgent` uses\n",
- "the {py:class}`~autogen_core.model_context.UnboundedChatCompletionContext`\n",
- "which sends the full conversation history to the model. To limit the context\n",
- "to the last `n` messages, you can use the {py:class}`~autogen_core.model_context.BufferedChatCompletionContext`."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "from autogen_core.model_context import BufferedChatCompletionContext\n",
- "\n",
- "# Create an agent that uses only the last 5 messages in the context to generate responses.\n",
- "agent = AssistantAgent(\n",
- " name=\"assistant\",\n",
- " model_client=model_client,\n",
- " tools=[web_search],\n",
- " system_message=\"Use tools to solve tasks.\",\n",
- " model_context=BufferedChatCompletionContext(buffer_size=5), # Only use the last 5 messages in the context.\n",
- ")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Other Preset Agents\n",
- "\n",
- "The following preset agents are available:\n",
- "\n",
- "- {py:class}`~autogen_agentchat.agents.UserProxyAgent`: An agent that takes user input returns it as responses.\n",
- "- {py:class}`~autogen_agentchat.agents.CodeExecutorAgent`: An agent that can execute code.\n",
- "- {py:class}`~autogen_ext.agents.openai.OpenAIAssistantAgent`: An agent that is backed by an OpenAI Assistant, with ability to use custom tools.\n",
- "- {py:class}`~autogen_ext.agents.web_surfer.MultimodalWebSurfer`: A multi-modal agent that can search the web and visit web pages for information.\n",
- "- {py:class}`~autogen_ext.agents.file_surfer.FileSurfer`: An agent that can search and browse local files for information.\n",
- "- {py:class}`~autogen_ext.agents.video_surfer.VideoSurfer`: An agent that can watch videos for information."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Next Step\n",
- "\n",
- "Having explored the usage of the {py:class}`~autogen_agentchat.agents.AssistantAgent`, we can now proceed to the next section to learn about the teams feature in AgentChat.\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<!-- ## CodingAssistantAgent\n",
- "\n",
- "Generates responses (text and code) using an LLM upon receipt of a message. It takes a `system_message` argument that defines or sets the tone for how the agent's LLM should respond. \n",
- "\n",
- "```python\n",
- "\n",
- "writing_assistant_agent = CodingAssistantAgent(\n",
- " name=\"writing_assistant_agent\",\n",
- " system_message=\"You are a helpful assistant that solve tasks by generating text responses and code.\",\n",
- " model_client=model_client,\n",
- ")\n",
- "`\n",
- "\n",
- "We can explore or test the behavior of the agent by sending a message to it using the {py:meth}`~autogen_agentchat.agents.BaseChatAgent.on_messages` method. \n",
- "\n",
- "```python\n",
- "result = await writing_assistant_agent.on_messages(\n",
- " messages=[\n",
- " TextMessage(content=\"What is the weather right now in France?\", source=\"user\"),\n",
- " ],\n",
- " cancellation_token=CancellationToken(),\n",
- ")\n",
- "print(result) -->"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": ".venv",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.12.3"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat": 4,
+ "nbformat_minor": 2
}
diff --git a/python/packages/autogen-core/docs/src/user-guide/agentchat-user-guide/tutorial/state.ipynb b/python/packages/autogen-core/docs/src/user-guide/agentchat-user-guide/tutorial/state.ipynb
index 5fd628ac1dd2..05e799eb1821 100644
--- a/python/packages/autogen-core/docs/src/user-guide/agentchat-user-guide/tutorial/state.ipynb
+++ b/python/packages/autogen-core/docs/src/user-guide/agentchat-user-guide/tutorial/state.ipynb
@@ -1,359 +1,359 @@
{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Managing State \n",
- "\n",
- "So far, we have discussed how to build components in a multi-agent application - agents, teams, termination conditions. In many cases, it is useful to save the state of these components to disk and load them back later. This is particularly useful in a web application where stateless endpoints respond to requests and need to load the state of the application from persistent storage.\n",
- "\n",
- "In this notebook, we will discuss how to save and load the state of agents, teams, and termination conditions. \n",
- " \n",
- "\n",
- "## Saving and Loading Agents\n",
- "\n",
- "We can get the state of an agent by calling {py:meth}`~autogen_agentchat.agents.AssistantAgent.save_state` method on \n",
- "an {py:class}`~autogen_agentchat.agents.AssistantAgent`. "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "In Tanganyika's embrace so wide and deep, \n",
- "Ancient waters cradle secrets they keep, \n",
- "Echoes of time where horizons sleep. \n"
- ]
- }
- ],
- "source": [
- "from autogen_agentchat.agents import AssistantAgent\n",
- "from autogen_agentchat.conditions import MaxMessageTermination\n",
- "from autogen_agentchat.messages import TextMessage\n",
- "from autogen_agentchat.teams import RoundRobinGroupChat\n",
- "from autogen_agentchat.ui import Console\n",
- "from autogen_core import CancellationToken\n",
- "from autogen_ext.models.openai import OpenAIChatCompletionClient\n",
- "\n",
- "model_client = OpenAIChatCompletionClient(model=\"gpt-4o-2024-08-06\")\n",
- "\n",
- "assistant_agent = AssistantAgent(\n",
- " name=\"assistant_agent\",\n",
- " system_message=\"You are a helpful assistant\",\n",
- " model_client=model_client,\n",
- ")\n",
- "\n",
- "# Use asyncio.run(...) when running in a script.\n",
- "response = await assistant_agent.on_messages(\n",
- " [TextMessage(content=\"Write a 3 line poem on lake tangayika\", source=\"user\")], CancellationToken()\n",
- ")\n",
- "print(response.chat_message.content)\n",
- "await model_client.close()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "{'type': 'AssistantAgentState', 'version': '1.0.0', 'llm_messages': [{'content': 'Write a 3 line poem on lake tangayika', 'source': 'user', 'type': 'UserMessage'}, {'content': \"In Tanganyika's embrace so wide and deep, \\nAncient waters cradle secrets they keep, \\nEchoes of time where horizons sleep. \", 'source': 'assistant_agent', 'type': 'AssistantMessage'}]}\n"
- ]
- }
- ],
- "source": [
- "agent_state = await assistant_agent.save_state()\n",
- "print(agent_state)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "The last line of the poem was: \"Echoes of time where horizons sleep.\"\n"
- ]
- }
- ],
- "source": [
- "model_client = OpenAIChatCompletionClient(model=\"gpt-4o-2024-08-06\")\n",
- "\n",
- "new_assistant_agent = AssistantAgent(\n",
- " name=\"assistant_agent\",\n",
- " system_message=\"You are a helpful assistant\",\n",
- " model_client=model_client,\n",
- ")\n",
- "await new_assistant_agent.load_state(agent_state)\n",
- "\n",
- "# Use asyncio.run(...) when running in a script.\n",
- "response = await new_assistant_agent.on_messages(\n",
- " [TextMessage(content=\"What was the last line of the previous poem you wrote\", source=\"user\")], CancellationToken()\n",
- ")\n",
- "print(response.chat_message.content)\n",
- "await model_client.close()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "```{note}\n",
- "For {py:class}`~autogen_agentchat.agents.AssistantAgent`, its state consists of the model_context.\n",
- "If your write your own custom agent, consider overriding the {py:meth}`~autogen_agentchat.agents.BaseChatAgent.save_state` and {py:meth}`~autogen_agentchat.agents.BaseChatAgent.load_state` methods to customize the behavior. The default implementations save and load an empty state.\n",
- "```"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Saving and Loading Teams \n",
- "\n",
- "We can get the state of a team by calling `save_state` method on the team and load it back by calling `load_state` method on the team. \n",
- "\n",
- "When we call `save_state` on a team, it saves the state of all the agents in the team.\n",
- "\n",
- "We will begin by creating a simple {py:class}`~autogen_agentchat.teams.RoundRobinGroupChat` team with a single agent and ask it to write a poem. "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "---------- user ----------\n",
- "Write a beautiful poem 3-line about lake tangayika\n",
- "---------- assistant_agent ----------\n",
- "In Tanganyika's gleam, beneath the azure skies, \n",
- "Whispers of ancient waters, in tranquil guise, \n",
- "Nature's mirror, where dreams and serenity lie.\n",
- "[Prompt tokens: 29, Completion tokens: 34]\n",
- "---------- Summary ----------\n",
- "Number of messages: 2\n",
- "Finish reason: Maximum number of messages 2 reached, current message count: 2\n",
- "Total prompt tokens: 29\n",
- "Total completion tokens: 34\n",
- "Duration: 0.71 seconds\n"
- ]
- }
- ],
- "source": [
- "model_client = OpenAIChatCompletionClient(model=\"gpt-4o-2024-08-06\")\n",
- "\n",
- "# Define a team.\n",
- "assistant_agent = AssistantAgent(\n",
- " name=\"assistant_agent\",\n",
- " system_message=\"You are a helpful assistant\",\n",
- " model_client=model_client,\n",
- ")\n",
- "agent_team = RoundRobinGroupChat([assistant_agent], termination_condition=MaxMessageTermination(max_messages=2))\n",
- "\n",
- "# Run the team and stream messages to the console.\n",
- "stream = agent_team.run_stream(task=\"Write a beautiful poem 3-line about lake tangayika\")\n",
- "\n",
- "# Use asyncio.run(...) when running in a script.\n",
- "await Console(stream)\n",
- "\n",
- "# Save the state of the agent team.\n",
- "team_state = await agent_team.save_state()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "If we reset the team (simulating instantiation of the team), and ask the question `What was the last line of the poem you wrote?`, we see that the team is unable to accomplish this as there is no reference to the previous run."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "---------- user ----------\n",
- "What was the last line of the poem you wrote?\n",
- "---------- assistant_agent ----------\n",
- "I'm sorry, but I am unable to recall or access previous interactions, including any specific poem I may have composed in our past conversations. If you like, I can write a new poem for you.\n",
- "[Prompt tokens: 28, Completion tokens: 40]\n",
- "---------- Summary ----------\n",
- "Number of messages: 2\n",
- "Finish reason: Maximum number of messages 2 reached, current message count: 2\n",
- "Total prompt tokens: 28\n",
- "Total completion tokens: 40\n",
- "Duration: 0.70 seconds\n"
- ]
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Managing State \n",
+ "\n",
+ "So far, we have discussed how to build components in a multi-agent application - agents, teams, termination conditions. In many cases, it is useful to save the state of these components to disk and load them back later. This is particularly useful in a web application where stateless endpoints respond to requests and need to load the state of the application from persistent storage.\n",
+ "\n",
+ "In this notebook, we will discuss how to save and load the state of agents, teams, and termination conditions. \n",
+ " \n",
+ "\n",
+ "## Saving and Loading Agents\n",
+ "\n",
+ "We can get the state of an agent by calling {py:meth}`~autogen_agentchat.agents.AssistantAgent.save_state` method on \n",
+ "an {py:class}`~autogen_agentchat.agents.AssistantAgent`. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "In Tanganyika's embrace so wide and deep, \n",
+ "Ancient waters cradle secrets they keep, \n",
+ "Echoes of time where horizons sleep. \n"
+ ]
+ }
+ ],
+ "source": [
+ "from autogen_agentchat.agents import AssistantAgent\n",
+ "from autogen_agentchat.conditions import MaxMessageTermination\n",
+ "from autogen_agentchat.messages import TextMessage\n",
+ "from autogen_agentchat.teams import RoundRobinGroupChat\n",
+ "from autogen_agentchat.ui import Console\n",
+ "from autogen_core import CancellationToken\n",
+ "from autogen_ext.models.openai import OpenAIChatCompletionClient\n",
+ "\n",
+ "model_client = OpenAIChatCompletionClient(model=\"gpt-4o-2024-08-06\")\n",
+ "\n",
+ "assistant_agent = AssistantAgent(\n",
+ " name=\"assistant_agent\",\n",
+ " system_message=\"You are a helpful assistant\",\n",
+ " model_client=model_client,\n",
+ ")\n",
+ "\n",
+ "# Use asyncio.run(...) when running in a script.\n",
+ "response = await assistant_agent.on_messages(\n",
+ " [TextMessage(content=\"Write a 3 line poem on lake tangayika\", source=\"user\")], CancellationToken()\n",
+ ")\n",
+ "print(response.chat_message)\n",
+ "await model_client.close()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "{'type': 'AssistantAgentState', 'version': '1.0.0', 'llm_messages': [{'content': 'Write a 3 line poem on lake tangayika', 'source': 'user', 'type': 'UserMessage'}, {'content': \"In Tanganyika's embrace so wide and deep, \\nAncient waters cradle secrets they keep, \\nEchoes of time where horizons sleep. \", 'source': 'assistant_agent', 'type': 'AssistantMessage'}]}\n"
+ ]
+ }
+ ],
+ "source": [
+ "agent_state = await assistant_agent.save_state()\n",
+ "print(agent_state)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "The last line of the poem was: \"Echoes of time where horizons sleep.\"\n"
+ ]
+ }
+ ],
+ "source": [
+ "model_client = OpenAIChatCompletionClient(model=\"gpt-4o-2024-08-06\")\n",
+ "\n",
+ "new_assistant_agent = AssistantAgent(\n",
+ " name=\"assistant_agent\",\n",
+ " system_message=\"You are a helpful assistant\",\n",
+ " model_client=model_client,\n",
+ ")\n",
+ "await new_assistant_agent.load_state(agent_state)\n",
+ "\n",
+ "# Use asyncio.run(...) when running in a script.\n",
+ "response = await new_assistant_agent.on_messages(\n",
+ " [TextMessage(content=\"What was the last line of the previous poem you wrote\", source=\"user\")], CancellationToken()\n",
+ ")\n",
+ "print(response.chat_message)\n",
+ "await model_client.close()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "```{note}\n",
+ "For {py:class}`~autogen_agentchat.agents.AssistantAgent`, its state consists of the model_context.\n",
+ "If your write your own custom agent, consider overriding the {py:meth}`~autogen_agentchat.agents.BaseChatAgent.save_state` and {py:meth}`~autogen_agentchat.agents.BaseChatAgent.load_state` methods to customize the behavior. The default implementations save and load an empty state.\n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Saving and Loading Teams \n",
+ "\n",
+ "We can get the state of a team by calling `save_state` method on the team and load it back by calling `load_state` method on the team. \n",
+ "\n",
+ "When we call `save_state` on a team, it saves the state of all the agents in the team.\n",
+ "\n",
+ "We will begin by creating a simple {py:class}`~autogen_agentchat.teams.RoundRobinGroupChat` team with a single agent and ask it to write a poem. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "---------- user ----------\n",
+ "Write a beautiful poem 3-line about lake tangayika\n",
+ "---------- assistant_agent ----------\n",
+ "In Tanganyika's gleam, beneath the azure skies, \n",
+ "Whispers of ancient waters, in tranquil guise, \n",
+ "Nature's mirror, where dreams and serenity lie.\n",
+ "[Prompt tokens: 29, Completion tokens: 34]\n",
+ "---------- Summary ----------\n",
+ "Number of messages: 2\n",
+ "Finish reason: Maximum number of messages 2 reached, current message count: 2\n",
+ "Total prompt tokens: 29\n",
+ "Total completion tokens: 34\n",
+ "Duration: 0.71 seconds\n"
+ ]
+ }
+ ],
+ "source": [
+ "model_client = OpenAIChatCompletionClient(model=\"gpt-4o-2024-08-06\")\n",
+ "\n",
+ "# Define a team.\n",
+ "assistant_agent = AssistantAgent(\n",
+ " name=\"assistant_agent\",\n",
+ " system_message=\"You are a helpful assistant\",\n",
+ " model_client=model_client,\n",
+ ")\n",
+ "agent_team = RoundRobinGroupChat([assistant_agent], termination_condition=MaxMessageTermination(max_messages=2))\n",
+ "\n",
+ "# Run the team and stream messages to the console.\n",
+ "stream = agent_team.run_stream(task=\"Write a beautiful poem 3-line about lake tangayika\")\n",
+ "\n",
+ "# Use asyncio.run(...) when running in a script.\n",
+ "await Console(stream)\n",
+ "\n",
+ "# Save the state of the agent team.\n",
+ "team_state = await agent_team.save_state()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "If we reset the team (simulating instantiation of the team), and ask the question `What was the last line of the poem you wrote?`, we see that the team is unable to accomplish this as there is no reference to the previous run."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "---------- user ----------\n",
+ "What was the last line of the poem you wrote?\n",
+ "---------- assistant_agent ----------\n",
+ "I'm sorry, but I am unable to recall or access previous interactions, including any specific poem I may have composed in our past conversations. If you like, I can write a new poem for you.\n",
+ "[Prompt tokens: 28, Completion tokens: 40]\n",
+ "---------- Summary ----------\n",
+ "Number of messages: 2\n",
+ "Finish reason: Maximum number of messages 2 reached, current message count: 2\n",
+ "Total prompt tokens: 28\n",
+ "Total completion tokens: 40\n",
+ "Duration: 0.70 seconds\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "TaskResult(messages=[TextMessage(source='user', models_usage=None, content='What was the last line of the poem you wrote?', type='TextMessage'), TextMessage(source='assistant_agent', models_usage=RequestUsage(prompt_tokens=28, completion_tokens=40), content=\"I'm sorry, but I am unable to recall or access previous interactions, including any specific poem I may have composed in our past conversations. If you like, I can write a new poem for you.\", type='TextMessage')], stop_reason='Maximum number of messages 2 reached, current message count: 2')"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "await agent_team.reset()\n",
+ "stream = agent_team.run_stream(task=\"What was the last line of the poem you wrote?\")\n",
+ "await Console(stream)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Next, we load the state of the team and ask the same question. We see that the team is able to accurately return the last line of the poem it wrote."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "{'type': 'TeamState', 'version': '1.0.0', 'agent_states': {'group_chat_manager/a55364ad-86fd-46ab-9449-dcb5260b1e06': {'type': 'RoundRobinManagerState', 'version': '1.0.0', 'message_thread': [{'source': 'user', 'models_usage': None, 'content': 'Write a beautiful poem 3-line about lake tangayika', 'type': 'TextMessage'}, {'source': 'assistant_agent', 'models_usage': {'prompt_tokens': 29, 'completion_tokens': 34}, 'content': \"In Tanganyika's gleam, beneath the azure skies, \\nWhispers of ancient waters, in tranquil guise, \\nNature's mirror, where dreams and serenity lie.\", 'type': 'TextMessage'}], 'current_turn': 0, 'next_speaker_index': 0}, 'collect_output_messages/a55364ad-86fd-46ab-9449-dcb5260b1e06': {}, 'assistant_agent/a55364ad-86fd-46ab-9449-dcb5260b1e06': {'type': 'ChatAgentContainerState', 'version': '1.0.0', 'agent_state': {'type': 'AssistantAgentState', 'version': '1.0.0', 'llm_messages': [{'content': 'Write a beautiful poem 3-line about lake tangayika', 'source': 'user', 'type': 'UserMessage'}, {'content': \"In Tanganyika's gleam, beneath the azure skies, \\nWhispers of ancient waters, in tranquil guise, \\nNature's mirror, where dreams and serenity lie.\", 'source': 'assistant_agent', 'type': 'AssistantMessage'}]}, 'message_buffer': []}}, 'team_id': 'a55364ad-86fd-46ab-9449-dcb5260b1e06'}\n",
+ "---------- user ----------\n",
+ "What was the last line of the poem you wrote?\n",
+ "---------- assistant_agent ----------\n",
+ "The last line of the poem I wrote is: \n",
+ "\"Nature's mirror, where dreams and serenity lie.\"\n",
+ "[Prompt tokens: 86, Completion tokens: 22]\n",
+ "---------- Summary ----------\n",
+ "Number of messages: 2\n",
+ "Finish reason: Maximum number of messages 2 reached, current message count: 2\n",
+ "Total prompt tokens: 86\n",
+ "Total completion tokens: 22\n",
+ "Duration: 0.96 seconds\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "TaskResult(messages=[TextMessage(source='user', models_usage=None, content='What was the last line of the poem you wrote?', type='TextMessage'), TextMessage(source='assistant_agent', models_usage=RequestUsage(prompt_tokens=86, completion_tokens=22), content='The last line of the poem I wrote is: \\n\"Nature\\'s mirror, where dreams and serenity lie.\"', type='TextMessage')], stop_reason='Maximum number of messages 2 reached, current message count: 2')"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "print(team_state)\n",
+ "\n",
+ "# Load team state.\n",
+ "await agent_team.load_state(team_state)\n",
+ "stream = agent_team.run_stream(task=\"What was the last line of the poem you wrote?\")\n",
+ "await Console(stream)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Persisting State (File or Database)\n",
+ "\n",
+ "In many cases, we may want to persist the state of the team to disk (or a database) and load it back later. State is a dictionary that can be serialized to a file or written to a database."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "---------- user ----------\n",
+ "What was the last line of the poem you wrote?\n",
+ "---------- assistant_agent ----------\n",
+ "The last line of the poem I wrote is: \n",
+ "\"Nature's mirror, where dreams and serenity lie.\"\n",
+ "[Prompt tokens: 86, Completion tokens: 22]\n",
+ "---------- Summary ----------\n",
+ "Number of messages: 2\n",
+ "Finish reason: Maximum number of messages 2 reached, current message count: 2\n",
+ "Total prompt tokens: 86\n",
+ "Total completion tokens: 22\n",
+ "Duration: 0.72 seconds\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "TaskResult(messages=[TextMessage(source='user', models_usage=None, content='What was the last line of the poem you wrote?', type='TextMessage'), TextMessage(source='assistant_agent', models_usage=RequestUsage(prompt_tokens=86, completion_tokens=22), content='The last line of the poem I wrote is: \\n\"Nature\\'s mirror, where dreams and serenity lie.\"', type='TextMessage')], stop_reason='Maximum number of messages 2 reached, current message count: 2')"
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "import json\n",
+ "\n",
+ "## save state to disk\n",
+ "\n",
+ "with open(\"coding/team_state.json\", \"w\") as f:\n",
+ " json.dump(team_state, f)\n",
+ "\n",
+ "## load state from disk\n",
+ "with open(\"coding/team_state.json\", \"r\") as f:\n",
+ " team_state = json.load(f)\n",
+ "\n",
+ "new_agent_team = RoundRobinGroupChat([assistant_agent], termination_condition=MaxMessageTermination(max_messages=2))\n",
+ "await new_agent_team.load_state(team_state)\n",
+ "stream = new_agent_team.run_stream(task=\"What was the last line of the poem you wrote?\")\n",
+ "await Console(stream)\n",
+ "await model_client.close()"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "agnext",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.9"
+ }
},
- {
- "data": {
- "text/plain": [
- "TaskResult(messages=[TextMessage(source='user', models_usage=None, content='What was the last line of the poem you wrote?', type='TextMessage'), TextMessage(source='assistant_agent', models_usage=RequestUsage(prompt_tokens=28, completion_tokens=40), content=\"I'm sorry, but I am unable to recall or access previous interactions, including any specific poem I may have composed in our past conversations. If you like, I can write a new poem for you.\", type='TextMessage')], stop_reason='Maximum number of messages 2 reached, current message count: 2')"
- ]
- },
- "execution_count": 6,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "await agent_team.reset()\n",
- "stream = agent_team.run_stream(task=\"What was the last line of the poem you wrote?\")\n",
- "await Console(stream)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Next, we load the state of the team and ask the same question. We see that the team is able to accurately return the last line of the poem it wrote."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "{'type': 'TeamState', 'version': '1.0.0', 'agent_states': {'group_chat_manager/a55364ad-86fd-46ab-9449-dcb5260b1e06': {'type': 'RoundRobinManagerState', 'version': '1.0.0', 'message_thread': [{'source': 'user', 'models_usage': None, 'content': 'Write a beautiful poem 3-line about lake tangayika', 'type': 'TextMessage'}, {'source': 'assistant_agent', 'models_usage': {'prompt_tokens': 29, 'completion_tokens': 34}, 'content': \"In Tanganyika's gleam, beneath the azure skies, \\nWhispers of ancient waters, in tranquil guise, \\nNature's mirror, where dreams and serenity lie.\", 'type': 'TextMessage'}], 'current_turn': 0, 'next_speaker_index': 0}, 'collect_output_messages/a55364ad-86fd-46ab-9449-dcb5260b1e06': {}, 'assistant_agent/a55364ad-86fd-46ab-9449-dcb5260b1e06': {'type': 'ChatAgentContainerState', 'version': '1.0.0', 'agent_state': {'type': 'AssistantAgentState', 'version': '1.0.0', 'llm_messages': [{'content': 'Write a beautiful poem 3-line about lake tangayika', 'source': 'user', 'type': 'UserMessage'}, {'content': \"In Tanganyika's gleam, beneath the azure skies, \\nWhispers of ancient waters, in tranquil guise, \\nNature's mirror, where dreams and serenity lie.\", 'source': 'assistant_agent', 'type': 'AssistantMessage'}]}, 'message_buffer': []}}, 'team_id': 'a55364ad-86fd-46ab-9449-dcb5260b1e06'}\n",
- "---------- user ----------\n",
- "What was the last line of the poem you wrote?\n",
- "---------- assistant_agent ----------\n",
- "The last line of the poem I wrote is: \n",
- "\"Nature's mirror, where dreams and serenity lie.\"\n",
- "[Prompt tokens: 86, Completion tokens: 22]\n",
- "---------- Summary ----------\n",
- "Number of messages: 2\n",
- "Finish reason: Maximum number of messages 2 reached, current message count: 2\n",
- "Total prompt tokens: 86\n",
- "Total completion tokens: 22\n",
- "Duration: 0.96 seconds\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "TaskResult(messages=[TextMessage(source='user', models_usage=None, content='What was the last line of the poem you wrote?', type='TextMessage'), TextMessage(source='assistant_agent', models_usage=RequestUsage(prompt_tokens=86, completion_tokens=22), content='The last line of the poem I wrote is: \\n\"Nature\\'s mirror, where dreams and serenity lie.\"', type='TextMessage')], stop_reason='Maximum number of messages 2 reached, current message count: 2')"
- ]
- },
- "execution_count": 7,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "print(team_state)\n",
- "\n",
- "# Load team state.\n",
- "await agent_team.load_state(team_state)\n",
- "stream = agent_team.run_stream(task=\"What was the last line of the poem you wrote?\")\n",
- "await Console(stream)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Persisting State (File or Database)\n",
- "\n",
- "In many cases, we may want to persist the state of the team to disk (or a database) and load it back later. State is a dictionary that can be serialized to a file or written to a database."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "---------- user ----------\n",
- "What was the last line of the poem you wrote?\n",
- "---------- assistant_agent ----------\n",
- "The last line of the poem I wrote is: \n",
- "\"Nature's mirror, where dreams and serenity lie.\"\n",
- "[Prompt tokens: 86, Completion tokens: 22]\n",
- "---------- Summary ----------\n",
- "Number of messages: 2\n",
- "Finish reason: Maximum number of messages 2 reached, current message count: 2\n",
- "Total prompt tokens: 86\n",
- "Total completion tokens: 22\n",
- "Duration: 0.72 seconds\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "TaskResult(messages=[TextMessage(source='user', models_usage=None, content='What was the last line of the poem you wrote?', type='TextMessage'), TextMessage(source='assistant_agent', models_usage=RequestUsage(prompt_tokens=86, completion_tokens=22), content='The last line of the poem I wrote is: \\n\"Nature\\'s mirror, where dreams and serenity lie.\"', type='TextMessage')], stop_reason='Maximum number of messages 2 reached, current message count: 2')"
- ]
- },
- "execution_count": 13,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "import json\n",
- "\n",
- "## save state to disk\n",
- "\n",
- "with open(\"coding/team_state.json\", \"w\") as f:\n",
- " json.dump(team_state, f)\n",
- "\n",
- "## load state from disk\n",
- "with open(\"coding/team_state.json\", \"r\") as f:\n",
- " team_state = json.load(f)\n",
- "\n",
- "new_agent_team = RoundRobinGroupChat([assistant_agent], termination_condition=MaxMessageTermination(max_messages=2))\n",
- "await new_agent_team.load_state(team_state)\n",
- "stream = new_agent_team.run_stream(task=\"What was the last line of the poem you wrote?\")\n",
- "await Console(stream)\n",
- "await model_client.close()"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "agnext",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.11.9"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat": 4,
+ "nbformat_minor": 2
}
diff --git a/python/packages/autogen-core/docs/src/user-guide/core-user-guide/framework/agent-and-agent-runtime.ipynb b/python/packages/autogen-core/docs/src/user-guide/core-user-guide/framework/agent-and-agent-runtime.ipynb
index e8e34a73b420..7f18ac2bce3e 100644
--- a/python/packages/autogen-core/docs/src/user-guide/core-user-guide/framework/agent-and-agent-runtime.ipynb
+++ b/python/packages/autogen-core/docs/src/user-guide/core-user-guide/framework/agent-and-agent-runtime.ipynb
@@ -133,7 +133,7 @@
" response = await self._delegate.on_messages(\n",
" [TextMessage(content=message.content, source=\"user\")], ctx.cancellation_token\n",
" )\n",
- " print(f\"{self.id.type} responded: {response.chat_message.content}\")"
+ " print(f\"{self.id.type} responded: {response.chat_message}\")"
]
},
{
diff --git a/python/packages/autogen-ext/src/autogen_ext/agents/file_surfer/_file_surfer.py b/python/packages/autogen-ext/src/autogen_ext/agents/file_surfer/_file_surfer.py
index aec34cc6364b..f569b8d967d0 100644
--- a/python/packages/autogen-ext/src/autogen_ext/agents/file_surfer/_file_surfer.py
+++ b/python/packages/autogen-ext/src/autogen_ext/agents/file_surfer/_file_surfer.py
@@ -7,7 +7,6 @@
from autogen_agentchat.base import Response
from autogen_agentchat.messages import (
ChatMessage,
- MultiModalMessage,
TextMessage,
)
from autogen_agentchat.utils import remove_images
@@ -90,11 +89,7 @@ def produced_message_types(self) -> Sequence[type[ChatMessage]]:
async def on_messages(self, messages: Sequence[ChatMessage], cancellation_token: CancellationToken) -> Response:
for chat_message in messages:
- if isinstance(chat_message, TextMessage | MultiModalMessage):
- self._chat_history.append(UserMessage(content=chat_message.content, source=chat_message.source))
- else:
- raise ValueError(f"Unexpected message in FileSurfer: {chat_message}")
-
+ self._chat_history.append(chat_message.to_model_message())
try:
_, content = await self._generate_reply(cancellation_token=cancellation_token)
self._chat_history.append(AssistantMessage(content=content, source=self.name))
diff --git a/python/packages/autogen-ext/src/autogen_ext/agents/openai/_openai_assistant_agent.py b/python/packages/autogen-ext/src/autogen_ext/agents/openai/_openai_assistant_agent.py
index 81b881704f77..419c517b3e6d 100644
--- a/python/packages/autogen-ext/src/autogen_ext/agents/openai/_openai_assistant_agent.py
+++ b/python/packages/autogen-ext/src/autogen_ext/agents/openai/_openai_assistant_agent.py
@@ -26,16 +26,12 @@
from autogen_agentchat.messages import (
AgentEvent,
ChatMessage,
- HandoffMessage,
- MultiModalMessage,
- StopMessage,
TextMessage,
ToolCallExecutionEvent,
ToolCallRequestEvent,
)
-from autogen_core import CancellationToken, FunctionCall
-from autogen_core.models._model_client import ChatCompletionClient
-from autogen_core.models._types import FunctionExecutionResult
+from autogen_core import CancellationToken, FunctionCall, Image
+from autogen_core.models import ChatCompletionClient, FunctionExecutionResult
from autogen_core.tools import FunctionTool, Tool
from pydantic import BaseModel, Field
@@ -52,6 +48,12 @@
from openai.types.beta.function_tool_param import FunctionToolParam
from openai.types.beta.thread import Thread, ToolResources, ToolResourcesCodeInterpreter
from openai.types.beta.threads import Message, MessageDeleted, Run
+from openai.types.beta.threads.image_url_content_block_param import ImageURLContentBlockParam
+from openai.types.beta.threads.image_url_param import ImageURLParam
+from openai.types.beta.threads.message_content_part_param import (
+ MessageContentPartParam,
+)
+from openai.types.beta.threads.text_content_block_param import TextContentBlockParam
from openai.types.shared_params.function_definition import FunctionDefinition
from openai.types.vector_store import VectorStore
@@ -406,10 +408,7 @@ async def on_messages_stream(
# Process all messages in sequence
for message in messages:
- if isinstance(message, (TextMessage, MultiModalMessage)):
- await self.handle_text_message(str(message.content), cancellation_token)
- elif isinstance(message, (StopMessage, HandoffMessage)):
- await self.handle_text_message(message.content, cancellation_token)
+ await self.handle_incoming_message(message, cancellation_token)
# Inner messages for tool calls
inner_messages: List[AgentEvent | ChatMessage] = []
@@ -519,8 +518,21 @@ async def on_messages_stream(
chat_message = TextMessage(source=self.name, content=text_content[0].text.value)
yield Response(chat_message=chat_message, inner_messages=inner_messages)
- async def handle_text_message(self, content: str, cancellation_token: CancellationToken) -> None:
+ async def handle_incoming_message(self, message: ChatMessage, cancellation_token: CancellationToken) -> None:
"""Handle regular text messages by adding them to the thread."""
+ content: str | List[MessageContentPartParam] | None = None
+ llm_message = message.to_model_message()
+ if isinstance(llm_message.content, str):
+ content = llm_message.content
+ else:
+ content = []
+ for c in llm_message.content:
+ if isinstance(c, str):
+ content.append(TextContentBlockParam(text=c, type="text"))
+ elif isinstance(c, Image):
+ content.append(ImageURLContentBlockParam(image_url=ImageURLParam(url=c.data_uri), type="image_url"))
+ else:
+ raise ValueError(f"Unsupported content type: {type(c)} in {message}")
await cancellation_token.link_future(
asyncio.ensure_future(
self._client.beta.threads.messages.create(
diff --git a/python/packages/autogen-ext/src/autogen_ext/agents/web_surfer/_multimodal_web_surfer.py b/python/packages/autogen-ext/src/autogen_ext/agents/web_surfer/_multimodal_web_surfer.py
index f4fb3abd10ea..8e48f187dea0 100644
--- a/python/packages/autogen-ext/src/autogen_ext/agents/web_surfer/_multimodal_web_surfer.py
+++ b/python/packages/autogen-ext/src/autogen_ext/agents/web_surfer/_multimodal_web_surfer.py
@@ -432,10 +432,8 @@ async def on_messages_stream(
self, messages: Sequence[ChatMessage], cancellation_token: CancellationToken
) -> AsyncGenerator[AgentEvent | ChatMessage | Response, None]:
for chat_message in messages:
- if isinstance(chat_message, TextMessage | MultiModalMessage):
- self._chat_history.append(UserMessage(content=chat_message.content, source=chat_message.source))
- else:
- raise ValueError(f"Unexpected message in MultiModalWebSurfer: {chat_message}")
+ self._chat_history.append(chat_message.to_model_message())
+
self.inner_messages: List[AgentEvent | ChatMessage] = []
self.model_usage: List[RequestUsage] = []
try:
diff --git a/python/packages/autogen-ext/src/autogen_ext/experimental/task_centric_memory/utils/apprentice.py b/python/packages/autogen-ext/src/autogen_ext/experimental/task_centric_memory/utils/apprentice.py
index a8104c0ebc44..8619d7ae789a 100644
--- a/python/packages/autogen-ext/src/autogen_ext/experimental/task_centric_memory/utils/apprentice.py
+++ b/python/packages/autogen-ext/src/autogen_ext/experimental/task_centric_memory/utils/apprentice.py
@@ -192,7 +192,7 @@ async def _assign_task_to_assistant_agent(self, task: str) -> Tuple[Any, Any]:
task_result: TaskResult = await assistant_agent.run(task=TextMessage(content=task, source="User"))
messages: Sequence[AgentEvent | ChatMessage] = task_result.messages
message: AgentEvent | ChatMessage = messages[-1]
- response_str = message.content
+ response_str = message.to_text()
# Log the model call
self.logger.log_model_task(
@@ -245,12 +245,7 @@ async def _assign_task_to_magentic_one(self, task: str) -> Tuple[str, str]:
response_str_list: List[str] = []
for message in messages:
- content = message.content
- if isinstance(content, str):
- content_str = content
- else:
- content_str = "Not a string."
- response_str_list.append(content_str)
+ response_str_list.append(message.to_text())
response_str = "\n".join(response_str_list)
self.logger.info("\n----- RESPONSE -----\n\n{}\n".format(response_str))
diff --git a/python/packages/autogen-ext/src/autogen_ext/experimental/task_centric_memory/utils/page_logger.py b/python/packages/autogen-ext/src/autogen_ext/experimental/task_centric_memory/utils/page_logger.py
index 92964dfbec12..806524ad8570 100644
--- a/python/packages/autogen-ext/src/autogen_ext/experimental/task_centric_memory/utils/page_logger.py
+++ b/python/packages/autogen-ext/src/autogen_ext/experimental/task_centric_memory/utils/page_logger.py
@@ -345,7 +345,7 @@ def log_model_task(
messages: Sequence[AgentEvent | ChatMessage] = task_result.messages
message = messages[-1]
- response_str = message.content
+ response_str = message.to_text()
if not isinstance(response_str, str):
response_str = "??"
diff --git a/python/packages/autogen-ext/src/autogen_ext/tools/http/_http_tool.py b/python/packages/autogen-ext/src/autogen_ext/tools/http/_http_tool.py
index 451d5826bad7..9cbd600abc7c 100644
--- a/python/packages/autogen-ext/src/autogen_ext/tools/http/_http_tool.py
+++ b/python/packages/autogen-ext/src/autogen_ext/tools/http/_http_tool.py
@@ -126,7 +126,7 @@ async def main():
[TextMessage(content="Can you base64 decode the value 'YWJjZGU=', please?", source="user")],
CancellationToken(),
)
- print(response.chat_message.content)
+ print(response.chat_message)
asyncio.run(main())
diff --git a/python/packages/autogen-ext/src/autogen_ext/tools/mcp/_factory.py b/python/packages/autogen-ext/src/autogen_ext/tools/mcp/_factory.py
index 3eb8634b3698..3b8c2356b79f 100644
--- a/python/packages/autogen-ext/src/autogen_ext/tools/mcp/_factory.py
+++ b/python/packages/autogen-ext/src/autogen_ext/tools/mcp/_factory.py
@@ -105,7 +105,7 @@ async def main() -> None:
# Let the agent fetch the content of a URL and summarize it.
result = await agent.run(task="Summarize the content of https://en.wikipedia.org/wiki/Seattle")
- print(result.messages[-1].content)
+ print(result.messages[-1])
asyncio.run(main())
diff --git a/python/packages/autogen-ext/src/autogen_ext/ui/_rich_console.py b/python/packages/autogen-ext/src/autogen_ext/ui/_rich_console.py
index 1951205e8ed5..3614a25c76ef 100644
--- a/python/packages/autogen-ext/src/autogen_ext/ui/_rich_console.py
+++ b/python/packages/autogen-ext/src/autogen_ext/ui/_rich_console.py
@@ -61,7 +61,7 @@ def _extract_message_content(message: AgentEvent | ChatMessage) -> Tuple[List[st
text_parts = [item for item in message.content if isinstance(item, str)]
image_parts = [item for item in message.content if isinstance(item, Image)]
else:
- text_parts = [str(message.content)]
+ text_parts = [message.to_text()]
image_parts = []
return text_parts, image_parts
diff --git a/python/packages/autogen-ext/tests/test_filesurfer_agent.py b/python/packages/autogen-ext/tests/test_filesurfer_agent.py
index 470bb270a9ef..04fc46365475 100644
--- a/python/packages/autogen-ext/tests/test_filesurfer_agent.py
+++ b/python/packages/autogen-ext/tests/test_filesurfer_agent.py
@@ -8,6 +8,7 @@
import aiofiles
import pytest
from autogen_agentchat import EVENT_LOGGER_NAME
+from autogen_agentchat.messages import TextMessage
from autogen_ext.agents.file_surfer import FileSurfer
from autogen_ext.models.openai import OpenAIChatCompletionClient
from openai.resources.chat.completions import AsyncCompletions
@@ -140,9 +141,11 @@ async def test_run_filesurfer(monkeypatch: pytest.MonkeyPatch) -> None:
# Get the FileSurfer to read the file, and the directory
assert agent._name == "FileSurfer" # pyright: ignore[reportPrivateUsage]
result = await agent.run(task="Please read the test file")
+ assert isinstance(result.messages[1], TextMessage)
assert "# FileSurfer test H1" in result.messages[1].content
result = await agent.run(task="Please read the test directory")
+ assert isinstance(result.messages[1], TextMessage)
assert "# Index of " in result.messages[1].content
assert "test_filesurfer_agent.html" in result.messages[1].content
diff --git a/python/packages/autogen-ext/tests/test_openai_assistant_agent.py b/python/packages/autogen-ext/tests/test_openai_assistant_agent.py
index 43bd3447737a..2213d6f6a486 100644
--- a/python/packages/autogen-ext/tests/test_openai_assistant_agent.py
+++ b/python/packages/autogen-ext/tests/test_openai_assistant_agent.py
@@ -8,7 +8,7 @@
import aiofiles
import pytest
-from autogen_agentchat.messages import ChatMessage, TextMessage
+from autogen_agentchat.messages import ChatMessage, TextMessage, ToolCallRequestEvent
from autogen_core import CancellationToken
from autogen_core.tools._base import BaseTool, Tool
from autogen_ext.agents.openai import OpenAIAssistantAgent
@@ -250,8 +250,7 @@ async def fake_async_aiofiles_open(*args: Any, **kwargs: Dict[str, Any]) -> Asyn
message = TextMessage(source="user", content="What is the first sentence of the jungle scout book?")
response = await agent.on_messages([message], cancellation_token)
- assert response.chat_message.content is not None
- assert isinstance(response.chat_message.content, str)
+ assert isinstance(response.chat_message, TextMessage)
assert len(response.chat_message.content) > 0
await agent.delete_uploaded_files(cancellation_token)
@@ -271,8 +270,7 @@ async def test_code_interpreter(
message = TextMessage(source="user", content="I need to solve the equation `3x + 11 = 14`. Can you help me?")
response = await agent.on_messages([message], cancellation_token)
- assert response.chat_message.content is not None
- assert isinstance(response.chat_message.content, str)
+ assert isinstance(response.chat_message, TextMessage)
assert len(response.chat_message.content) > 0
assert "x = 1" in response.chat_message.content.lower()
@@ -326,12 +324,11 @@ async def test_quiz_creation(
response = await agent.on_messages([message], cancellation_token)
# Check that the final response has non-empty inner messages (i.e. tool call events).
- assert response.chat_message.content is not None
- assert isinstance(response.chat_message.content, str)
+ assert isinstance(response.chat_message, TextMessage)
assert len(response.chat_message.content) > 0
assert isinstance(response.inner_messages, list)
# Ensure that at least one inner message has non-empty content.
- assert any(hasattr(tool_msg, "content") and tool_msg.content for tool_msg in response.inner_messages)
+ assert any(isinstance(msg, ToolCallRequestEvent) for msg in response.inner_messages)
await agent.delete_assistant(cancellation_token)
@@ -357,14 +354,14 @@ async def test_on_reset_behavior(client: AsyncOpenAI, cancellation_token: Cancel
message1 = TextMessage(source="user", content="What is my name?")
response1 = await agent.on_messages([message1], cancellation_token)
- assert isinstance(response1.chat_message.content, str)
+ assert isinstance(response1.chat_message, TextMessage)
assert "john" in response1.chat_message.content.lower()
await agent.on_reset(cancellation_token)
message2 = TextMessage(source="user", content="What is my name?")
response2 = await agent.on_messages([message2], cancellation_token)
- assert isinstance(response2.chat_message.content, str)
+ assert isinstance(response2.chat_message, TextMessage)
assert "john" in response2.chat_message.content.lower()
await agent.delete_assistant(cancellation_token)
diff --git a/python/samples/agentchat_chess_game/main.py b/python/samples/agentchat_chess_game/main.py
index e12db1d94778..914659cf5899 100644
--- a/python/samples/agentchat_chess_game/main.py
+++ b/python/samples/agentchat_chess_game/main.py
@@ -1,5 +1,6 @@
import argparse
import asyncio
+from autogen_agentchat.messages import TextMessage
import yaml
import random
@@ -78,11 +79,10 @@ async def get_ai_move(board: chess.Board, player: AssistantAgent, max_tries: int
while count < max_tries:
result = await Console(player.run_stream(task=task))
count += 1
- response = result.messages[-1].content
- assert isinstance(response, str)
+ assert isinstance(result.messages[-1], TextMessage)
# Check if the response is a valid UC move.
try:
- move = chess.Move.from_uci(extract_move(response))
+ move = chess.Move.from_uci(extract_move(result.messages[-1].content))
except (ValueError, IndexError):
task = "Invalid format. Please read instruction.\n" + get_ai_prompt(board)
continue
diff --git a/python/samples/agentchat_streamlit/agent.py b/python/samples/agentchat_streamlit/agent.py
index cbe588828838..acf2f9ed52f4 100644
--- a/python/samples/agentchat_streamlit/agent.py
+++ b/python/samples/agentchat_streamlit/agent.py
@@ -22,5 +22,5 @@ async def chat(self, prompt: str) -> str:
[TextMessage(content=prompt, source="user")],
CancellationToken(),
)
- assert isinstance(response.chat_message.content, str)
+ assert isinstance(response.chat_message, TextMessage)
return response.chat_message.content