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b31b4e508d
This PR adds callable as an option to specify conditional edges in
GraphFlow.
```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="Detect if the input is in Chinese. If it is, say 'yes', else say 'no', and nothing else.",
)
agent_b = AssistantAgent("B", model_client=model_client, system_message="Translate input to English.")
agent_c = AssistantAgent("C", model_client=model_client, system_message="Translate input to Chinese.")
# Create a directed graph with conditional branching flow A -> B ("yes"), A -> C (otherwise).
builder = DiGraphBuilder()
builder.add_node(agent_a).add_node(agent_b).add_node(agent_c)
# Create conditions as callables that check the message content.
builder.add_edge(agent_a, agent_b, condition=lambda msg: "yes" in msg.to_model_text())
builder.add_edge(agent_a, agent_c, condition=lambda msg: "yes" not in msg.to_model_text())
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="AutoGen is a framework for building AI agents."):
print(event)
asyncio.run(main())
```
---------
Co-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com>
Co-authored-by: ekzhu <320302+ekzhu@users.noreply.github.com>
AutoGen AgentChat
AgentChat is a high-level API for building multi-agent applications.
It is built on top of the autogen-core package.
For beginner users, AgentChat is the recommended starting point.
For advanced users, autogen-core's event-driven
programming model provides more flexibility and control over the underlying components.
AgentChat provides intuitive defaults, such as Agents with preset behaviors and Teams with predefined multi-agent design patterns.