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@ekzhu ekzhu released this 05 Jun 00:37
· 15 commits to main since this release
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.
 

# 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:

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 team and print the events.
    async for event in team.run_stream(task="Write a short story about a cat."):
        print(event)


asyncio.run(main())

Agent B and C will run concurrently in separate coroutines.

  • Enable concurrent execution of agents in GraphFlow by @ekzhu in #6545

Callable conditions for GraphFlow edges

Now you can use lambda functions or other callables to specify edge conditions in GraphFlow. This addresses the issue of the keyword substring-based conditions cannot cover all possibilities and leading to "cannot find next agent" bug.

NOTE: callable conditions are currently experimental, and it cannot be serialized with the graph.

  • Add callable condition for GraphFlow edges by @ekzhu in #6623

New Agent: OpenAIAgent

  • Feature: Add OpenAIAgent backed by OpenAI Response API by @jay-thakur in #6418

MCP Improvement

AssistantAgent Improvement

Code Executors Improvement

  • Add option to auto-delete temporary files in LocalCommandLineCodeExecutor by @holtvogt in #6556
  • Include all output to error output in docker jupyter code executor by @ekzhu in #6572

OpenAIChatCompletionClient Improvement

OllamaChatCompletionClient Improvement

AnthropicBedrockChatCompletionClient Improvement

MagenticOneGroupChat Improvement

  • Use structured output for m1 orchestrator by @ekzhu in #6540

Other Changes

New Contributors

Full Changelog: python-v0.5.7...python-v0.6.0

Contributors

ekzhu, victordibia, and 19 other contributors
Assets 2
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