Microsoft AutoGen 发布: python-v0.6.0

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## What's New ### Change to `BaseGroupChatManager.select_speaker` and support for concurrent agents in `GraphFlow` We made a type hint change to the `select_speaker` method of `BaseGroupChatManager` to allow for a list of agent names as a return value. This makes it possible to support concurrent agents in `GraphFlow`, such as in a fan-out-fan-in pattern. ```python # Original signature: async def select_speaker(self, thread: Sequence[BaseAgentEvent | BaseChatMessage]) -> str: ... # New signature: async def select_speaker(self, thread: Sequence[BaseAgentEvent | BaseChatMessage]) -> List[str] | str: ... ``` Now you can run `GraphFlow` with concurrent agents as follows: ```python import asyncio from autogen_agentchat.agents import AssistantAgent from autogen_agentchat.conditions import MaxMessageTermination from autogen_agentchat.teams import DiGraphBuilder, GraphFlow from autogen_ext.models.openai import OpenAIChatCompletionClient async def main(): # Initialize agents with OpenAI model clients. model_client = OpenAIChatCompletionClient(model="gpt-4.1-nano") agent_a = AssistantAgent("A", model_client=model_client, system_message="You are a helpful assistant.") agent_b = AssistantAgent("B", model_client=model_client, system_message="Translate input to Chinese.") agent_c = AssistantAgent("C", model_client=model_client, system_message="Translate input to Japanese.") # Create a directed graph with fan-out flow A -> (B, C). builder = DiGraphBuilder() builder.add_node(agent_a).add_node(agent_b).add_node(agent_c) builder.add_edge(agent_a, agent_b).add_edge(agent_a, agent_c) graph = builder.build() # Create a GraphFlow team with the directed graph. team = GraphFlow( participants=[agent_a, agent_b, agent_c], graph=graph, termination_condition=MaxMessageTermination(5), ) # Run the t