Ability to generate handoff message from AssistantAgent (#3968)

* Ability to generate handoff message from AssistantAgent

* Fix mypy

* Validation

---------

Co-authored-by: Victor Dibia <victordibia@microsoft.com>

python/packages/autogen-agentchat/src/autogen_agentchat/agents/__init__.py

@@ -1,4 +1,4 @@
-from ._assistant_agent import AssistantAgent
+from ._assistant_agent import AssistantAgent, Handoff
 from ._base_chat_agent import BaseChatAgent
 from ._code_executor_agent import CodeExecutorAgent
 from ._coding_assistant_agent import CodingAssistantAgent
@@ -7,6 +7,7 @@ from ._tool_use_assistant_agent import ToolUseAssistantAgent
 __all__ = [
     "BaseChatAgent",
     "AssistantAgent",
+    "Handoff",
     "CodeExecutorAgent",
     "CodingAssistantAgent",
     "ToolUseAssistantAgent",

python/packages/autogen-agentchat/src/autogen_agentchat/agents/_assistant_agent.py

@@ -1,7 +1,7 @@
 import asyncio
 import json
 import logging
-from typing import Any, Awaitable, Callable, List, Sequence
+from typing import Any, Awaitable, Callable, Dict, List, Sequence
 
 from autogen_core.base import CancellationToken
 from autogen_core.components import FunctionCall
@@ -15,11 +15,12 @@ from autogen_core.components.models import (
     UserMessage,
 )
 from autogen_core.components.tools import FunctionTool, Tool
-from pydantic import BaseModel, ConfigDict
+from pydantic import BaseModel, ConfigDict, Field, model_validator
 
 from .. import EVENT_LOGGER_NAME
 from ..messages import (
     ChatMessage,
+    HandoffMessage,
     StopMessage,
     TextMessage,
 )
@@ -31,6 +32,9 @@ event_logger = logging.getLogger(EVENT_LOGGER_NAME)
 class ToolCallEvent(BaseModel):
     """A tool call event."""
 
+    source: str
+    """The source of the event."""
+
     tool_calls: List[FunctionCall]
     """The tool call message."""
 
@@ -40,12 +44,58 @@ class ToolCallEvent(BaseModel):
 class ToolCallResultEvent(BaseModel):
     """A tool call result event."""
 
+    source: str
+    """The source of the event."""
+
     tool_call_results: List[FunctionExecutionResult]
     """The tool call result message."""
 
     model_config = ConfigDict(arbitrary_types_allowed=True)
 
 
+class Handoff(BaseModel):
+    """Handoff configuration for :class:`AssistantAgent`."""
+
+    target: str
+    """The name of the target agent to handoff to."""
+
+    description: str = Field(default=None)
+    """The description of the handoff such as the condition under which it should happen and the target agent's ability.
+    If not provided, it is generated from the target agent's name."""
+
+    name: str = Field(default=None)
+    """The name of this handoff configuration. If not provided, it is generated from the target agent's name."""
+
+    message: str = Field(default=None)
+    """The message to the target agent.
+    If not provided, it is generated from the target agent's name."""
+
+    @model_validator(mode="before")
+    @classmethod
+    def set_defaults(cls, values: Dict[str, Any]) -> Dict[str, Any]:
+        if values.get("description") is None:
+            values["description"] = f"Handoff to {values['target']}."
+        if values.get("name") is None:
+            values["name"] = f"transfer_to_{values['target']}".lower()
+        else:
+            name = values["name"]
+            if not isinstance(name, str):
+                raise ValueError(f"Handoff name must be a string: {values['name']}")
+            # Check if name is a valid identifier.
+            if not name.isidentifier():
+                raise ValueError(f"Handoff name must be a valid identifier: {values['name']}")
+        if values.get("message") is None:
+            values["message"] = (
+                f"Transferred to {values['target']}, adopting the role of {values['target']} immediately."
+            )
+        return values
+
+    @property
+    def handoff_tool(self) -> Tool:
+        """Create a handoff tool from this handoff configuration."""
+        return FunctionTool(lambda: self.message, name=self.name, description=self.description)
+
+
 class AssistantAgent(BaseChatAgent):
     """An agent that provides assistance with tool use.
 
@@ -55,8 +105,52 @@ class AssistantAgent(BaseChatAgent):
         name (str): The name of the agent.
         model_client (ChatCompletionClient): The model client to use for inference.
         tools (List[Tool | Callable[..., Any] | Callable[..., Awaitable[Any]]] | None, optional): The tools to register with the agent.
+        handoffs (List[Handoff | str] | None, optional): The handoff configurations for the agent, allowing it to transfer to other agents by responding with a HandoffMessage.
+            If a handoff is a string, it should represent the target agent's name.
         description (str, optional): The description of the agent.
         system_message (str, optional): The system message for the model.
+
+    Raises:
+        ValueError: If tool names are not unique.
+        ValueError: If handoff names are not unique.
+        ValueError: If handoff names are not unique from tool names.
+
+    Examples:
+
+        The following example demonstrates how to create an assistant agent with
+        a model client and generate a response to a simple task.
+
+        .. code-block:: python
+
+            from autogen_ext.models import OpenAIChatCompletionClient
+            from autogen_agentchat.agents import AssistantAgent
+            from autogen_agentchat.task import MaxMessageTermination
+
+            model_client = OpenAIChatCompletionClient(model="gpt-4o")
+            agent = AssistantAgent(name="assistant", model_client=model_client)
+
+            await agent.run("What is the capital of France?", termination_condition=MaxMessageTermination(2))
+
+
+        The following example demonstrates how to create an assistant agent with
+        a model client and a tool, and generate a response to a simple task using the tool.
+
+        .. code-block:: python
+
+            from autogen_ext.models import OpenAIChatCompletionClient
+            from autogen_agentchat.agents import AssistantAgent
+            from autogen_agentchat.task import MaxMessageTermination
+
+
+            async def get_current_time() -> str:
+                return "The current time is 12:00 PM."
+
+
+            model_client = OpenAIChatCompletionClient(model="gpt-4o")
+            agent = AssistantAgent(name="assistant", model_client=model_client, tools=[get_current_time])
+
+            await agent.run("What is the current time?", termination_condition=MaxMessageTermination(3))
+
     """
 
     def __init__(
@@ -65,6 +159,7 @@ class AssistantAgent(BaseChatAgent):
         model_client: ChatCompletionClient,
         *,
         tools: List[Tool | Callable[..., Any] | Callable[..., Awaitable[Any]]] | None = None,
+        handoffs: List[Handoff | str] | None = None,
         description: str = "An agent that provides assistance with ability to use tools.",
         system_message: str = "You are a helpful AI assistant. Solve tasks using your tools. Reply with 'TERMINATE' when the task has been completed.",
     ):
@@ -84,33 +179,71 @@ class AssistantAgent(BaseChatAgent):
                     self._tools.append(FunctionTool(tool, description=description))
                 else:
                     raise ValueError(f"Unsupported tool type: {type(tool)}")
+        # Check if tool names are unique.
+        tool_names = [tool.name for tool in self._tools]
+        if len(tool_names) != len(set(tool_names)):
+            raise ValueError(f"Tool names must be unique: {tool_names}")
+        # Handoff tools.
+        self._handoff_tools: List[Tool] = []
+        self._handoffs: Dict[str, Handoff] = {}
+        if handoffs is not None:
+            for handoff in handoffs:
+                if isinstance(handoff, str):
+                    handoff = Handoff(target=handoff)
+                if isinstance(handoff, Handoff):
+                    self._handoff_tools.append(handoff.handoff_tool)
+                    self._handoffs[handoff.name] = handoff
+                else:
+                    raise ValueError(f"Unsupported handoff type: {type(handoff)}")
+        # Check if handoff tool names are unique.
+        handoff_tool_names = [tool.name for tool in self._handoff_tools]
+        if len(handoff_tool_names) != len(set(handoff_tool_names)):
+            raise ValueError(f"Handoff names must be unique: {handoff_tool_names}")
+        # Check if handoff tool names not in tool names.
+        if any(name in tool_names for name in handoff_tool_names):
+            raise ValueError(
+                f"Handoff names must be unique from tool names. Handoff names: {handoff_tool_names}; tool names: {tool_names}"
+            )
         self._model_context: List[LLMMessage] = []
 
     async def on_messages(self, messages: Sequence[ChatMessage], cancellation_token: CancellationToken) -> ChatMessage:
         # Add messages to the model context.
         for msg in messages:
-            # TODO: add special handling for handoff messages
             self._model_context.append(UserMessage(content=msg.content, source=msg.source))
 
         # Generate an inference result based on the current model context.
         llm_messages = self._system_messages + self._model_context
-        result = await self._model_client.create(llm_messages, tools=self._tools, cancellation_token=cancellation_token)
+        result = await self._model_client.create(
+            llm_messages, tools=self._tools + self._handoff_tools, cancellation_token=cancellation_token
+        )
 
         # Add the response to the model context.
         self._model_context.append(AssistantMessage(content=result.content, source=self.name))
 
         # Run tool calls until the model produces a string response.
         while isinstance(result.content, list) and all(isinstance(item, FunctionCall) for item in result.content):
-            event_logger.debug(ToolCallEvent(tool_calls=result.content))
+            event_logger.debug(ToolCallEvent(tool_calls=result.content, source=self.name))
             # Execute the tool calls.
             results = await asyncio.gather(
                 *[self._execute_tool_call(call, cancellation_token) for call in result.content]
             )
-            event_logger.debug(ToolCallResultEvent(tool_call_results=results))
+            event_logger.debug(ToolCallResultEvent(tool_call_results=results, source=self.name))
             self._model_context.append(FunctionExecutionResultMessage(content=results))
+
+            # Detect handoff requests.
+            handoffs: List[Handoff] = []
+            for call in result.content:
+                if call.name in self._handoffs:
+                    handoffs.append(self._handoffs[call.name])
+            if len(handoffs) > 0:
+                if len(handoffs) > 1:
+                    raise ValueError(f"Multiple handoffs detected: {[handoff.name for handoff in handoffs]}")
+                # Respond with a handoff message.
+                return HandoffMessage(content=handoffs[0].message, target=handoffs[0].target, source=self.name)
+
             # Generate an inference result based on the current model context.
             result = await self._model_client.create(
-                self._model_context, tools=self._tools, cancellation_token=cancellation_token
+                self._model_context, tools=self._tools + self._handoff_tools, cancellation_token=cancellation_token
             )
             self._model_context.append(AssistantMessage(content=result.content, source=self.name))
 
@@ -127,9 +260,9 @@ class AssistantAgent(BaseChatAgent):
     ) -> FunctionExecutionResult:
         """Execute a tool call and return the result."""
         try:
-            if not self._tools:
+            if not self._tools + self._handoff_tools:
                 raise ValueError("No tools are available.")
-            tool = next((t for t in self._tools if t.name == tool_call.name), None)
+            tool = next((t for t in self._tools + self._handoff_tools if t.name == tool_call.name), None)
             if tool is None:
                 raise ValueError(f"The tool '{tool_call.name}' is not available.")
             arguments = json.loads(tool_call.arguments)

python/packages/autogen-agentchat/src/autogen_agentchat/messages.py

@@ -35,8 +35,11 @@ class StopMessage(BaseMessage):
 class HandoffMessage(BaseMessage):
     """A message requesting handoff of a conversation to another agent."""
 
+    target: str
+    """The name of the target agent to handoff to."""
+
     content: str
-    """The agent name to handoff the conversation to."""
+    """The handoff message to the target agent."""
 
 
 ChatMessage = TextMessage | MultiModalMessage | StopMessage | HandoffMessage

python/packages/autogen-agentchat/src/autogen_agentchat/teams/_group_chat/_swarm_group_chat.py

@@ -37,7 +37,7 @@ class SwarmGroupChatManager(BaseGroupChatManager):
     async def select_speaker(self, thread: List[GroupChatPublishEvent]) -> str:
         """Select a speaker from the participants based on handoff message."""
         if len(thread) > 0 and isinstance(thread[-1].agent_message, HandoffMessage):
-            self._current_speaker = thread[-1].agent_message.content
+            self._current_speaker = thread[-1].agent_message.target
             if self._current_speaker not in self._participant_topic_types:
                 raise ValueError("The selected speaker in the handoff message is not a participant.")
             event_logger.debug(GroupChatSelectSpeakerEvent(selected_speaker=self._current_speaker, source=self.id))
@@ -47,7 +47,40 @@ class SwarmGroupChatManager(BaseGroupChatManager):
 
 
 class Swarm(BaseGroupChat):
-    """(Experimental) A group chat that selects the next speaker based on handoff message only."""
+    """A group chat team that selects the next speaker based on handoff message only.
+
+    The first participant in the list of participants is the initial speaker.
+    The next speaker is selected based on the :class:`~autogen_agentchat.messages.HandoffMessage` message
+    sent by the current speaker. If no handoff message is sent, the current speaker
+    continues to be the speaker.
+
+    Args:
+        participants (List[ChatAgent]): The agents participating in the group chat. The first agent in the list is the initial speaker.
+
+    Examples:
+
+        .. code-block:: python
+
+            from autogen_ext.models import OpenAIChatCompletionClient
+            from autogen_agentchat.agents import AssistantAgent
+            from autogen_agentchat.teams import Swarm
+            from autogen_agentchat.task import MaxMessageTermination
+
+            model_client = OpenAIChatCompletionClient(model="gpt-4o")
+
+            agent1 = AssistantAgent(
+                "Alice",
+                model_client=model_client,
+                handoffs=["Bob"],
+                system_message="You are Alice and you only answer questions about yourself.",
+            )
+            agent2 = AssistantAgent(
+                "Bob", model_client=model_client, system_message="You are Bob and your birthday is on 1st January."
+            )
+
+            team = Swarm([agent1, agent2])
+            await team.run("What is bob's birthday?", termination_condition=MaxMessageTermination(3))
+    """
 
     def __init__(self, participants: List[ChatAgent]):
         super().__init__(participants, group_chat_manager_class=SwarmGroupChatManager)

python/packages/autogen-agentchat/tests/test_assistant_agent.py

@@ -1,10 +1,14 @@
 import asyncio
 import json
+import logging
 from typing import Any, AsyncGenerator, List
 
 import pytest
-from autogen_agentchat.agents import AssistantAgent
+from autogen_agentchat import EVENT_LOGGER_NAME
-from autogen_agentchat.messages import StopMessage, TextMessage
+from autogen_agentchat.agents import AssistantAgent, Handoff
+from autogen_agentchat.logging import FileLogHandler
+from autogen_agentchat.messages import HandoffMessage, StopMessage, TextMessage
+from autogen_core.base import CancellationToken
 from autogen_core.components.tools import FunctionTool
 from autogen_ext.models import OpenAIChatCompletionClient
 from openai.resources.chat.completions import AsyncCompletions
@@ -14,6 +18,10 @@ from openai.types.chat.chat_completion_message import ChatCompletionMessage
 from openai.types.chat.chat_completion_message_tool_call import ChatCompletionMessageToolCall, Function
 from openai.types.completion_usage import CompletionUsage
 
+logger = logging.getLogger(EVENT_LOGGER_NAME)
+logger.setLevel(logging.DEBUG)
+logger.addHandler(FileLogHandler("test_assistant_agent.log"))
+
 
 class _MockChatCompletion:
     def __init__(self, chat_completions: List[ChatCompletion]) -> None:
@@ -107,3 +115,51 @@ async def test_run_with_tools(monkeypatch: pytest.MonkeyPatch) -> None:
     assert isinstance(result.messages[0], TextMessage)
     assert isinstance(result.messages[1], TextMessage)
     assert isinstance(result.messages[2], StopMessage)
+
+
+@pytest.mark.asyncio
+async def test_handoffs(monkeypatch: pytest.MonkeyPatch) -> None:
+    handoff = Handoff(target="agent2")
+    model = "gpt-4o-2024-05-13"
+    chat_completions = [
+        ChatCompletion(
+            id="id1",
+            choices=[
+                Choice(
+                    finish_reason="tool_calls",
+                    index=0,
+                    message=ChatCompletionMessage(
+                        content=None,
+                        tool_calls=[
+                            ChatCompletionMessageToolCall(
+                                id="1",
+                                type="function",
+                                function=Function(
+                                    name=handoff.name,
+                                    arguments=json.dumps({}),
+                                ),
+                            )
+                        ],
+                        role="assistant",
+                    ),
+                )
+            ],
+            created=0,
+            model=model,
+            object="chat.completion",
+            usage=CompletionUsage(prompt_tokens=0, completion_tokens=0, total_tokens=0),
+        ),
+    ]
+    mock = _MockChatCompletion(chat_completions)
+    monkeypatch.setattr(AsyncCompletions, "create", mock.mock_create)
+    tool_use_agent = AssistantAgent(
+        "tool_use_agent",
+        model_client=OpenAIChatCompletionClient(model=model, api_key=""),
+        tools=[_pass_function, _fail_function, FunctionTool(_echo_function, description="Echo")],
+        handoffs=[handoff],
+    )
+    response = await tool_use_agent.on_messages(
+        [TextMessage(content="task", source="user")], cancellation_token=CancellationToken()
+    )
+    assert isinstance(response, HandoffMessage)
+    assert response.target == "agent2"

python/packages/autogen-agentchat/tests/test_group_chat.py

@@ -10,6 +10,7 @@ from autogen_agentchat.agents import (
     AssistantAgent,
     BaseChatAgent,
     CodeExecutorAgent,
+    Handoff,
 )
 from autogen_agentchat.logging import FileLogHandler
 from autogen_agentchat.messages import (
@@ -415,11 +416,11 @@ class _HandOffAgent(BaseChatAgent):
         self._next_agent = next_agent
 
     async def on_messages(self, messages: Sequence[ChatMessage], cancellation_token: CancellationToken) -> ChatMessage:
-        return HandoffMessage(content=self._next_agent, source=self.name)
+        return HandoffMessage(content=f"Transferred to {self._next_agent}.", target=self._next_agent, source=self.name)
 
 
 @pytest.mark.asyncio
-async def test_swarm() -> None:
+async def test_swarm_handoff() -> 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")
@@ -428,8 +429,81 @@ async def test_swarm() -> None:
     result = await team.run("task", termination_condition=MaxMessageTermination(6))
     assert len(result.messages) == 6
     assert result.messages[0].content == "task"
-    assert result.messages[1].content == "third_agent"
+    assert result.messages[1].content == "Transferred to third_agent."
-    assert result.messages[2].content == "first_agent"
+    assert result.messages[2].content == "Transferred to first_agent."
-    assert result.messages[3].content == "second_agent"
+    assert result.messages[3].content == "Transferred to second_agent."
-    assert result.messages[4].content == "third_agent"
+    assert result.messages[4].content == "Transferred to third_agent."
-    assert result.messages[5].content == "first_agent"
+    assert result.messages[5].content == "Transferred to first_agent."
+
+
+@pytest.mark.asyncio
+async def test_swarm_handoff_using_tool_calls(monkeypatch: pytest.MonkeyPatch) -> None:
+    model = "gpt-4o-2024-05-13"
+    chat_completions = [
+        ChatCompletion(
+            id="id1",
+            choices=[
+                Choice(
+                    finish_reason="tool_calls",
+                    index=0,
+                    message=ChatCompletionMessage(
+                        content=None,
+                        tool_calls=[
+                            ChatCompletionMessageToolCall(
+                                id="1",
+                                type="function",
+                                function=Function(
+                                    name="handoff_to_agent2",
+                                    arguments=json.dumps({}),
+                                ),
+                            )
+                        ],
+                        role="assistant",
+                    ),
+                )
+            ],
+            created=0,
+            model=model,
+            object="chat.completion",
+            usage=CompletionUsage(prompt_tokens=0, completion_tokens=0, total_tokens=0),
+        ),
+        ChatCompletion(
+            id="id2",
+            choices=[
+                Choice(finish_reason="stop", index=0, message=ChatCompletionMessage(content="Hello", role="assistant"))
+            ],
+            created=0,
+            model=model,
+            object="chat.completion",
+            usage=CompletionUsage(prompt_tokens=0, completion_tokens=0, total_tokens=0),
+        ),
+        ChatCompletion(
+            id="id2",
+            choices=[
+                Choice(
+                    finish_reason="stop", index=0, message=ChatCompletionMessage(content="TERMINATE", role="assistant")
+                )
+            ],
+            created=0,
+            model=model,
+            object="chat.completion",
+            usage=CompletionUsage(prompt_tokens=0, completion_tokens=0, total_tokens=0),
+        ),
+    ]
+    mock = _MockChatCompletion(chat_completions)
+    monkeypatch.setattr(AsyncCompletions, "create", mock.mock_create)
+
+    agnet1 = AssistantAgent(
+        "agent1",
+        model_client=OpenAIChatCompletionClient(model=model, api_key=""),
+        handoffs=[Handoff(target="agent2", name="handoff_to_agent2", message="handoff to agent2")],
+    )
+    agent2 = _HandOffAgent("agent2", description="agent 2", next_agent="agent1")
+    team = Swarm([agnet1, agent2])
+    result = await team.run("task", termination_condition=StopMessageTermination())
+    assert len(result.messages) == 5
+    assert result.messages[0].content == "task"
+    assert result.messages[1].content == "handoff to agent2"
+    assert result.messages[2].content == "Transferred to agent1."
+    assert result.messages[3].content == "Hello"
+    assert result.messages[4].content == "TERMINATE"

python/packages/autogen-core/docs/src/user-guide/core-user-guide/framework/model-clients.ipynb

@@ -32,7 +32,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from autogen_ext.models import OpenAIChatCompletionClient, UserMessage\n",
+    "from autogen_core.components.models import UserMessage\n",
+    "from autogen_ext.models import OpenAIChatCompletionClient\n",
     "\n",
     "# Create an OpenAI model client.\n",
     "model_client = OpenAIChatCompletionClient(\n",
@@ -500,7 +501,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "autogen_core",
+   "display_name": ".venv",
    "language": "python",
    "name": "python3"
   },
@@ -514,7 +515,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.9"
+   "version": "3.11.5"
   }
  },
  "nbformat": 4,