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Producing Structured Output with Agents

This tutorial step shows you how to produce structured output with an agent, where the agent is built on the Azure OpenAI Chat Completion service.

Important

Not all agent types support structured output. This step uses a ChatClientAgent, which does support structured output.

Prerequisites

For prerequisites and installing NuGet packages, see the Create and run a simple agent step in this tutorial.

Create the agent with structured output

The ChatClientAgent is built on top of any IChatClient implementation. The ChatClientAgent uses the support for structured output that's provided by the underlying chat client.

When creating the agent, you have the option to provide the default ChatOptions instance to use for the underlying chat client. This ChatOptions instance allows you to pick a preferred ChatResponseFormat.

Various options for ResponseFormat are available:

This example creates an agent that produces structured output in the form of a JSON object that conforms to a specific schema.

The easiest way to produce the schema is to define a type that represents the structure of the output you want from the agent, and then use the AIJsonUtilities.CreateJsonSchema method to create a schema from the type.

using System.Text.Json;
using System.Text.Json.Serialization;
using Microsoft.Extensions.AI;

public class PersonInfo
{
    public string? Name { get; set; }
    public int? Age { get; set; }
    public string? Occupation { get; set; }
}

JsonElement schema = AIJsonUtilities.CreateJsonSchema(typeof(PersonInfo));

You can then create a ChatOptions instance that uses this schema for the response format.

using Microsoft.Extensions.AI;

ChatOptions chatOptions = new()
{
    ResponseFormat = ChatResponseFormat.ForJsonSchema(
        schema: schema,
        schemaName: "PersonInfo",
        schemaDescription: "Information about a person including their name, age, and occupation")
};

This ChatOptions instance can be used when creating the agent.

using System;
using Azure.AI.OpenAI;
using Azure.Identity;
using Microsoft.Agents.AI;
using OpenAI;

AIAgent agent = new AzureOpenAIClient(
    new Uri("https://<myresource>.openai.azure.com"),
    new AzureCliCredential())
        .GetChatClient("gpt-4o-mini")
        .CreateAIAgent(new ChatClientAgentOptions()
        {
            Name = "HelpfulAssistant",
            Instructions = "You are a helpful assistant.",
            ChatOptions = chatOptions
        });

Now you can just run the agent with some textual information that the agent can use to fill in the structured output.

var response = await agent.RunAsync("Please provide information about John Smith, who is a 35-year-old software engineer.");

The agent response can then be deserialized into the PersonInfo class using the Deserialize<T> method on the response object.

var personInfo = response.Deserialize<PersonInfo>(JsonSerializerOptions.Web);
Console.WriteLine($"Name: {personInfo.Name}, Age: {personInfo.Age}, Occupation: {personInfo.Occupation}");

When streaming, the agent response is streamed as a series of updates, and you can only deserialize the response once all the updates have been received. You must assemble all the updates into a single response before deserializing it.

var updates = agent.RunStreamingAsync("Please provide information about John Smith, who is a 35-year-old software engineer.");
personInfo = (await updates.ToAgentRunResponseAsync()).Deserialize<PersonInfo>(JsonSerializerOptions.Web);

This tutorial step shows you how to produce structured output with an agent, where the agent is built on the Azure OpenAI Chat Completion service.

Important

Not all agent types support structured output. The ChatAgent supports structured output when used with compatible chat clients.

Prerequisites

For prerequisites and installing packages, see the Create and run a simple agent step in this tutorial.

Create the agent with structured output

The ChatAgent is built on top of any chat client implementation that supports structured output. The ChatAgent uses the response_format parameter to specify the desired output schema.

When creating or running the agent, you can provide a Pydantic model that defines the structure of the expected output.

Various response formats are supported based on the underlying chat client capabilities.

This example creates an agent that produces structured output in the form of a JSON object that conforms to a Pydantic model schema.

First, define a Pydantic model that represents the structure of the output you want from the agent:

from pydantic import BaseModel

class PersonInfo(BaseModel):
    """Information about a person."""
    name: str | None = None
    age: int | None = None
    occupation: str | None = None

Now you can create an agent using the Azure OpenAI Chat Client:

from agent_framework.azure import AzureOpenAIChatClient
from azure.identity import AzureCliCredential

# Create the agent using Azure OpenAI Chat Client
agent = AzureOpenAIChatClient(credential=AzureCliCredential()).create_agent(
    name="HelpfulAssistant",
    instructions="You are a helpful assistant that extracts person information from text."
)

Now you can run the agent with some textual information and specify the structured output format using the response_format parameter:

response = await agent.run(
    "Please provide information about John Smith, who is a 35-year-old software engineer.",
    response_format=PersonInfo
)

The agent response will contain the structured output in the value property, which can be accessed directly as a Pydantic model instance:

if response.value:
    person_info = response.value
    print(f"Name: {person_info.name}, Age: {person_info.age}, Occupation: {person_info.occupation}")
else:
    print("No structured data found in response")

When streaming, the agent response is streamed as a series of updates. To get the structured output, you must collect all the updates and then access the final response value:

from agent_framework import AgentRunResponse

# Get structured response from streaming agent using AgentRunResponse.from_agent_response_generator
# This method collects all streaming updates and combines them into a single AgentRunResponse
final_response = await AgentRunResponse.from_agent_response_generator(
    agent.run_stream(query, response_format=PersonInfo),
    output_format_type=PersonInfo,
)

if final_response.value:
    person_info = final_response.value
    print(f"Name: {person_info.name}, Age: {person_info.age}, Occupation: {person_info.occupation}")

Next steps

Using an agent as a function tool

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