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Magnus Hedemark
5428b18ddd
Greenfield SkillOpt: 3 epochs for Microsoft AutoGen skill. Conversational agent model, GroupChat patterns, code execution, nested chats, cancellation tokens, MCP integration. All API surfaces validated against microsoft.github.io/autogen docs.
1.5 KiB
1.5 KiB
AutoGen Conversation Patterns
Two-Agent Chat
from autogen_agentchat.agents import AssistantAgent, UserProxyAgent
assistant = AssistantAgent(name="assistant", model_client=model_client)
proxy = UserProxyAgent(name="proxy", human_input_mode="NEVER")
result = proxy.initiate_chat(assistant, message="What is AutoGen?", max_turns=2)
print(result.summary)
Termination Conditions
Prevent infinite loops:
proxy = UserProxyAgent(
name="proxy",
human_input_mode="NEVER",
is_termination_msg=lambda msg: "TERMINATE" in (msg.get("content", "") or ""),
max_consecutive_auto_reply=5,
)
# Or limit turns at chat level
result = proxy.initiate_chat(assistant, message="Hello", max_turns=10)
Cancellation Tokens
from autogen_core import CancellationToken
token = CancellationToken()
# Token can be used to cancel long-running operations
Nested Chats
Agent delegates work to a sub-conversation:
async def research_topic(query: str) -> str:
researcher = AssistantAgent(name="researcher", model_client=model_client)
fact_checker = AssistantAgent(name="fact_checker", model_client=model_client)
proxy = UserProxyAgent(name="proxy", human_input_mode="NEVER")
result = await proxy.initiate_chat(
researcher, message=f"Research: {query}", max_turns=5
)
return result.summary
# Register as a function the main agent can call
assistant.register_function(function_map={"research": research_topic})