Merge branch 'main' into list_messages_support

README.md

@@ -3,7 +3,8 @@
 <div align="center">
 <img src="https://microsoft.github.io/autogen/0.2/img/ag.svg" alt="AutoGen Logo" width="100">
 
-[![Twitter](https://img.shields.io/twitter/url/https/twitter.com/cloudposse.svg?style=social&label=Follow%20%40pyautogen)](https://twitter.com/pyautogen) [![GitHub Discussions](https://img.shields.io/badge/Discussions-Q%26A-green?logo=github)](https://github.com/microsoft/autogen/discussions) [![0.2 Docs](https://img.shields.io/badge/Docs-0.2-blue)](https://microsoft.github.io/autogen/0.2/) [![0.4 Docs](https://img.shields.io/badge/Docs-0.4-blue)](https://microsoft.github.io/autogen/dev/)
+[![Twitter](https://img.shields.io/twitter/url/https/twitter.com/cloudposse.svg?style=social&label=Follow%20%40pyautogen)](https://twitter.com/pyautogen) [![LinkedIn](https://img.shields.io/badge/LinkedIn-Company?style=flat&logo=linkedin&logoColor=white)](https://www.linkedin.com/company/105812540)
+[![GitHub Discussions](https://img.shields.io/badge/Discussions-Q%26A-green?logo=github)](https://github.com/microsoft/autogen/discussions) [![0.2 Docs](https://img.shields.io/badge/Docs-0.2-blue)](https://microsoft.github.io/autogen/0.2/) [![0.4 Docs](https://img.shields.io/badge/Docs-0.4-blue)](https://microsoft.github.io/autogen/dev/)
 [![PyPi autogen-core](https://img.shields.io/badge/PyPi-autogen--core-blue?logo=pypi)](https://pypi.org/project/autogen-core/0.4.0.dev8/) [![PyPi autogen-agentchat](https://img.shields.io/badge/PyPi-autogen--agentchat-blue?logo=pypi)](https://pypi.org/project/autogen-agentchat/0.4.0.dev8/) [![PyPi autogen-ext](https://img.shields.io/badge/PyPi-autogen--ext-blue?logo=pypi)](https://pypi.org/project/autogen-ext/0.4.0.dev8/)
 
 </div>
@@ -115,7 +116,8 @@ To use Azure OpenAI models, follow the instruction
 ```python
 import asyncio
 from autogen_agentchat.agents import AssistantAgent
-from autogen_agentchat.task import Console, TextMentionTermination
+from autogen_agentchat.ui import Console
+from autogen_agentchat.conditions import TextMentionTermination
 from autogen_agentchat.teams import RoundRobinGroupChat
 from autogen_ext.models import OpenAIChatCompletionClient
 

python/packages/agbench/benchmarks/AssistantBench/Templates/MagenticOne/scenario.py

@@ -8,15 +8,15 @@
 
 from typing import List
 
-from autogen_core.base import AgentId, AgentProxy, TopicId
+from autogen_core import AgentId, AgentProxy, TopicId
 from autogen_core.application import SingleThreadedAgentRuntime
 from autogen_core.application.logging import EVENT_LOGGER_NAME
 from autogen_core.components.models import (
     ChatCompletionClient,
     UserMessage,
     LLMMessage,
 )
-from autogen_core.components import DefaultSubscription, DefaultTopicId
+from autogen_core import DefaultSubscription, DefaultTopicId
 from autogen_core.components.code_executor import LocalCommandLineCodeExecutor
 from autogen_core.components.models import AssistantMessage
 

python/packages/agbench/benchmarks/GAIA/Templates/MagenticOne/scenario.py

@@ -8,7 +8,7 @@
 
 from typing import List
 
-from autogen_core.base import AgentId, AgentProxy, TopicId
+from autogen_core import AgentId, AgentProxy, TopicId
 from autogen_core.application import SingleThreadedAgentRuntime
 from autogen_core.application.logging import EVENT_LOGGER_NAME
 from autogen_core.components.models import (
@@ -17,7 +17,7 @@
     UserMessage,
     LLMMessage,
 )
-from autogen_core.components import DefaultSubscription, DefaultTopicId
+from autogen_core import DefaultSubscription, DefaultTopicId
 from autogen_core.components.code_executor import LocalCommandLineCodeExecutor
 from autogen_core.components.models import AssistantMessage
 

python/packages/agbench/benchmarks/HumanEval/Templates/MagenticOne/scenario.py

@@ -1,10 +1,10 @@
 import asyncio
 import logging
 
-from autogen_core.base import AgentId, AgentProxy, TopicId
+from autogen_core import AgentId, AgentProxy, TopicId
 from autogen_core.application import SingleThreadedAgentRuntime
 from autogen_core.application.logging import EVENT_LOGGER_NAME
-from autogen_core.components import DefaultSubscription, DefaultTopicId
+from autogen_core import DefaultSubscription, DefaultTopicId
 from autogen_core.components.code_executor import LocalCommandLineCodeExecutor
 from autogen_core.components.models import (
     UserMessage,
@@ -41,7 +41,7 @@ async def main() -> None:
     executor = AgentProxy(AgentId("Executor", "default"), runtime)
 
     await runtime.register(
-        "Orchestrator", 
+        "Orchestrator",
         lambda: RoundRobinOrchestrator([coder, executor]),
         subscriptions=lambda: [DefaultSubscription()],
     )

python/packages/agbench/benchmarks/WebArena/Templates/MagenticOne/scenario.py

@@ -7,10 +7,10 @@
 
 from typing import Any, Dict, List, Tuple, Union
 
-from autogen_core.base import AgentId, AgentProxy, TopicId
+from autogen_core import AgentId, AgentProxy, TopicId
 from autogen_core.application import SingleThreadedAgentRuntime
 from autogen_core.application.logging import EVENT_LOGGER_NAME
-from autogen_core.components import DefaultSubscription, DefaultTopicId
+from autogen_core import DefaultSubscription, DefaultTopicId
 from autogen_core.components.code_executor import LocalCommandLineCodeExecutor
 from autogen_core.components.models import (
     ChatCompletionClient,
@@ -105,7 +105,7 @@ async def main() -> None:
             task_prompt = task_prompt.replace(k, REPLACEMENTS[k])
         fh.write(task_prompt)
         TASK = json.loads(task_prompt)
-        if TASK["start_url"] == REDDIT: 
+        if TASK["start_url"] == REDDIT:
             TASK["start_url"] = TASK["start_url"] + "/forums/all"
 
     full_task = ""
@@ -150,7 +150,7 @@ async def main() -> None:
 
     # Round-robin orchestrator
     await runtime.register(
-        "round_robin_orc", 
+        "round_robin_orc",
         lambda: RoundRobinOrchestrator(agents=[web_surfer, login_assistant],),
         subscriptions=lambda: [DefaultSubscription()],
     )
@@ -163,7 +163,7 @@ async def main() -> None:
 
             runtime.start()
             await runtime.publish_message(
-                ResetMessage(), 
+                ResetMessage(),
                 topic_id=DefaultTopicId(),
             )
             await runtime.publish_message(
@@ -192,16 +192,16 @@ async def main() -> None:
         subscriptions=lambda: [DefaultSubscription()],
     )
     executor = AgentProxy(AgentId("ComputerTerminal", "default"), runtime)
-    
+
     await runtime.register(
         "FileSurfer",
         lambda: FileSurfer(model_client=client),
         subscriptions=lambda: [DefaultSubscription()],
     )
     file_surfer = AgentProxy(AgentId("FileSurfer", "default"), runtime)
-   
+
     await runtime.register(
-        "orchestrator", 
+        "orchestrator",
         lambda: LedgerOrchestrator(
             agents=[coder, executor, file_surfer, web_surfer],
             model_client=client,
@@ -251,7 +251,7 @@ async def main() -> None:
     page = actual_surfer._page
     cdp_session = await context.new_cdp_session(page)
     config_file = "full_task.json"
-    
+
     evaluator = evaluation_harness.evaluator_router(config_file)
     score = await evaluator(
         trajectory=evaluation_harness.make_answer_trajecotry(final_answer),
@@ -260,7 +260,7 @@ async def main() -> None:
         client=cdp_session,
     #    azure_config=llm_config,
     )
-    
+
     print("FINAL SCORE: " + str(score))
 
 

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

@@ -1,4 +1,4 @@
-from ._assistant_agent import AssistantAgent, Handoff
+from ._assistant_agent import AssistantAgent, Handoff  # type: ignore
 from ._base_chat_agent import BaseChatAgent
 from ._code_executor_agent import CodeExecutorAgent
 from ._coding_assistant_agent import CodingAssistantAgent

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

@@ -1,10 +1,10 @@
 import asyncio
 import json
 import logging
+import warnings
 from typing import Any, AsyncGenerator, Awaitable, Callable, Dict, List, Sequence
 
-from autogen_core.base import CancellationToken
-from autogen_core.components import FunctionCall
+from autogen_core import CancellationToken, FunctionCall
 from autogen_core.components.models import (
     AssistantMessage,
     ChatCompletionClient,
@@ -15,9 +15,10 @@
     UserMessage,
 )
 from autogen_core.components.tools import FunctionTool, Tool
-from pydantic import BaseModel, Field, model_validator
+from typing_extensions import deprecated
 
 from .. import EVENT_LOGGER_NAME
+from ..base import Handoff as HandoffBase
 from ..base import Response
 from ..messages import (
     AgentMessage,
@@ -33,51 +34,16 @@
 event_logger = logging.getLogger(EVENT_LOGGER_NAME)
 
 
-class Handoff(BaseModel):
-    """Handoff configuration for :class:`AssistantAgent`."""
+@deprecated("Moved to autogen_agentchat.base.Handoff. Will remove in 0.4.0.", stacklevel=2)
+class Handoff(HandoffBase):
+    """[DEPRECATED] Handoff configuration. Moved to :class:`autogen_agentchat.base.Handoff`. Will remove in 0.4.0."""
 
-    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."""
-
-        def _handoff_tool() -> str:
-            return self.message
-
-        return FunctionTool(_handoff_tool, name=self.name, description=self.description)
+    def model_post_init(self, __context: Any) -> None:
+        warnings.warn(
+            "Handoff was moved to autogen_agentchat.base.Handoff. Importing from this will be removed in 0.4.0.",
+            DeprecationWarning,
+            stacklevel=2,
+        )
 
 
 class AssistantAgent(BaseChatAgent):
@@ -87,7 +53,7 @@ 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,
+        handoffs (List[HandoffBase | str] | None, optional): The handoff configurations for the agent,
             allowing it to transfer to other agents by responding with a :class:`HandoffMessage`.
             The transfer is only executed when the team is in :class:`~autogen_agentchat.teams.Swarm`.
             If a handoff is a string, it should represent the target agent's name.
@@ -107,7 +73,7 @@ class AssistantAgent(BaseChatAgent):
         .. code-block:: python
 
             import asyncio
-            from autogen_core.base import CancellationToken
+            from autogen_core import CancellationToken
             from autogen_ext.models import OpenAIChatCompletionClient
             from autogen_agentchat.agents import AssistantAgent
             from autogen_agentchat.messages import TextMessage
@@ -139,8 +105,8 @@ async def main() -> None:
             from autogen_ext.models import OpenAIChatCompletionClient
             from autogen_agentchat.agents import AssistantAgent
             from autogen_agentchat.messages import TextMessage
-            from autogen_agentchat.task import Console
-            from autogen_core.base import CancellationToken
+            from autogen_agentchat.ui import Console
+            from autogen_core import CancellationToken
 
 
             async def get_current_time() -> str:
@@ -169,7 +135,7 @@ async def main() -> None:
         .. code-block:: python
 
             import asyncio
-            from autogen_core.base import CancellationToken
+            from autogen_core import CancellationToken
             from autogen_ext.models import OpenAIChatCompletionClient
             from autogen_agentchat.agents import AssistantAgent
             from autogen_agentchat.messages import TextMessage
@@ -204,7 +170,7 @@ def __init__(
         model_client: ChatCompletionClient,
         *,
         tools: List[Tool | Callable[..., Any] | Callable[..., Awaitable[Any]]] | None = None,
-        handoffs: List[Handoff | str] | None = None,
+        handoffs: List[HandoffBase | str] | None = None,
         description: str = "An agent that provides assistance with ability to use tools.",
         system_message: str
         | None = "You are a helpful AI assistant. Solve tasks using your tools. Reply with TERMINATE when the task has been completed.",
@@ -236,14 +202,14 @@ def __init__(
             raise ValueError(f"Tool names must be unique: {tool_names}")
         # Handoff tools.
         self._handoff_tools: List[Tool] = []
-        self._handoffs: Dict[str, Handoff] = {}
+        self._handoffs: Dict[str, HandoffBase] = {}
         if handoffs is not None:
             if model_client.capabilities["function_calling"] is False:
                 raise ValueError("The model does not support function calling, which is needed for handoffs.")
             for handoff in handoffs:
                 if isinstance(handoff, str):
-                    handoff = Handoff(target=handoff)
-                if isinstance(handoff, Handoff):
+                    handoff = HandoffBase(target=handoff)
+                if isinstance(handoff, HandoffBase):
                     self._handoff_tools.append(handoff.handoff_tool)
                     self._handoffs[handoff.name] = handoff
                 else:
@@ -312,7 +278,7 @@ async def on_messages_stream(
             yield tool_call_result_msg
 
             # Detect handoff requests.
-            handoffs: List[Handoff] = []
+            handoffs: List[HandoffBase] = []
             for call in result.content:
                 if call.name in self._handoffs:
                     handoffs.append(self._handoffs[call.name])

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

@@ -1,7 +1,7 @@
 from abc import ABC, abstractmethod
 from typing import AsyncGenerator, List, Sequence
 
-from autogen_core.base import CancellationToken
+from autogen_core import CancellationToken
 
 from ..base import ChatAgent, Response, TaskResult
 from ..messages import AgentMessage, ChatMessage, HandoffMessage, MultiModalMessage, StopMessage, TextMessage

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

@@ -1,6 +1,6 @@
 from typing import List, Sequence
 
-from autogen_core.base import CancellationToken
+from autogen_core import CancellationToken
 from autogen_core.components.code_executor import CodeBlock, CodeExecutor, extract_markdown_code_blocks
 
 from ..base import Response
@@ -28,7 +28,7 @@ class CodeExecutorAgent(BaseChatAgent):
             from autogen_agentchat.agents import CodeExecutorAgent
             from autogen_agentchat.messages import TextMessage
             from autogen_ext.code_executors import DockerCommandLineCodeExecutor
-            from autogen_core.base import CancellationToken
+            from autogen_core import CancellationToken
 
 
             async def run_code_executor_agent() -> None:

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

@@ -1,7 +1,6 @@
 from typing import AsyncGenerator, List, Sequence
 
-from autogen_core.base import CancellationToken
-from autogen_core.components import Image
+from autogen_core import CancellationToken, Image
 from autogen_core.components.models import ChatCompletionClient
 from autogen_core.components.models._types import SystemMessage
 
@@ -43,7 +42,7 @@ class SocietyOfMindAgent(BaseChatAgent):
         from autogen_agentchat.agents import AssistantAgent, SocietyOfMindAgent
         from autogen_ext.models import OpenAIChatCompletionClient
         from autogen_agentchat.teams import RoundRobinGroupChat
-        from autogen_agentchat.task import MaxMessageTermination
+        from autogen_agentchat.conditions import MaxMessageTermination
 
 
         async def main() -> None:

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

@@ -2,7 +2,7 @@
 from inspect import iscoroutinefunction
 from typing import Awaitable, Callable, List, Optional, Sequence, Union, cast
 
-from autogen_core.base import CancellationToken
+from autogen_core import CancellationToken
 
 from ..base import Response
 from ..messages import ChatMessage, HandoffMessage, TextMessage
@@ -33,7 +33,7 @@ class UserProxyAgent(BaseChatAgent):
 
         For typical use cases that involve
         slow human responses, it is recommended to use termination conditions
-        such as :class:`~autogen_agentchat.task.HandoffTermination` or :class:`~autogen_agentchat.task.SourceMatchTermination`
+        such as :class:`~autogen_agentchat.conditions.HandoffTermination` or :class:`~autogen_agentchat.conditions.SourceMatchTermination`
         to stop the running team and return the control to the application.
         You can run the team again with the user input. This way, the state of the team
         can be saved and restored when the user responds.

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

@@ -1,4 +1,5 @@
 from ._chat_agent import ChatAgent, Response
+from ._handoff import Handoff
 from ._task import TaskResult, TaskRunner
 from ._team import Team
 from ._termination import TerminatedException, TerminationCondition
@@ -11,4 +12,5 @@
     "TerminationCondition",
     "TaskResult",
     "TaskRunner",
+    "Handoff",
 ]