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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.
57 lines
1.5 KiB
Markdown
57 lines
1.5 KiB
Markdown
# AutoGen Conversation Patterns
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## Two-Agent Chat
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```python
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from autogen_agentchat.agents import AssistantAgent, UserProxyAgent
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assistant = AssistantAgent(name="assistant", model_client=model_client)
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proxy = UserProxyAgent(name="proxy", human_input_mode="NEVER")
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result = proxy.initiate_chat(assistant, message="What is AutoGen?", max_turns=2)
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print(result.summary)
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```
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## Termination Conditions
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Prevent infinite loops:
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```python
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proxy = UserProxyAgent(
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name="proxy",
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human_input_mode="NEVER",
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is_termination_msg=lambda msg: "TERMINATE" in (msg.get("content", "") or ""),
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max_consecutive_auto_reply=5,
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)
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# Or limit turns at chat level
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result = proxy.initiate_chat(assistant, message="Hello", max_turns=10)
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```
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## Cancellation Tokens
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```python
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from autogen_core import CancellationToken
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token = CancellationToken()
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# Token can be used to cancel long-running operations
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```
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## Nested Chats
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Agent delegates work to a sub-conversation:
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```python
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async def research_topic(query: str) -> str:
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researcher = AssistantAgent(name="researcher", model_client=model_client)
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fact_checker = AssistantAgent(name="fact_checker", model_client=model_client)
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proxy = UserProxyAgent(name="proxy", human_input_mode="NEVER")
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result = await proxy.initiate_chat(
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researcher, message=f"Research: {query}", max_turns=5
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)
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return result.summary
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# Register as a function the main agent can call
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assistant.register_function(function_map={"research": research_topic})
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```
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