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Resolves #5934 This PR adds ability for `AssistantAgent` to generate a `StructuredMessage[T]` where `T` is the content type in base model. How to use? ```python from typing import Literal from pydantic import BaseModel from autogen_agentchat.agents import AssistantAgent from autogen_ext.models.openai import OpenAIChatCompletionClient from autogen_agentchat.ui import Console # The response format for the agent as a Pydantic base model. class AgentResponse(BaseModel): thoughts: str response: Literal["happy", "sad", "neutral"] # Create an agent that uses the OpenAI GPT-4o model which supports structured output. model_client = OpenAIChatCompletionClient(model="gpt-4o") agent = AssistantAgent( "assistant", model_client=model_client, system_message="Categorize the input as happy, sad, or neutral following the JSON format.", # Setting the output format to AgentResponse to force the agent to produce a JSON string as response. output_content_type=AgentResponse, ) result = await Console(agent.run_stream(task="I am happy.")) # Check the last message in the result, validate its type, and print the thoughts and response. assert isinstance(result.messages[-1], StructuredMessage) assert isinstance(result.messages[-1].content, AgentResponse) print("Thought: ", result.messages[-1].content.thoughts) print("Response: ", result.messages[-1].content.response) await model_client.close() ``` ``` ---------- user ---------- I am happy. ---------- assistant ---------- { "thoughts": "The user explicitly states they are happy.", "response": "happy" } Thought: The user explicitly states they are happy. Response: happy ``` --------- Co-authored-by: Victor Dibia <victordibia@microsoft.com>
AutoGen Core
AutoGen core offers an easy way to quickly build event-driven, distributed, scalable, resilient AI agent systems. Agents are developed by using the Actor model. You can build and run your agent system locally and easily move to a distributed system in the cloud when you are ready.