Producing Structured Outputs with Agents

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

Prerequisites

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

Define a type for structured outputs

First, define a type that represents the structure of the output you want from the agent.

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

Create the agent

Create a ChatClientAgent using the Azure AI Projects Client.

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

AIAgent agent = new AIProjectClient(
    new Uri("<your-foundry-project-endpoint>"),
    new DefaultAzureCredential())
        .AsAIAgent(
            model: "gpt-4o-mini",
            name: "HelpfulAssistant",
            instructions: "You are a helpful assistant.");

Structured outputs with RunAsync<T>

The RunAsync<T> method is available on the AIAgent base class. It accepts a generic type parameter that specifies the structured outputs type. This approach is applicable when the structured outputs type is known at compile time and a typed result instance is needed. It supports primitives, arrays, and complex types.

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

Console.WriteLine($"Name: {response.Result.Name}, Age: {response.Result.Age}, Occupation: {response.Result.Occupation}");

Structured outputs with ResponseFormat

Structured outputs can be configured by setting the ResponseFormat property on AgentRunOptions at invocation time, or at agent initialization time for agents that support it, such as ChatClientAgent and Foundry Agent.

This approach is applicable when:

• The structured outputs type is not known at compile time.
• The schema is represented as raw JSON.
• Structured outputs can only be configured at agent creation time.
• Only the raw JSON text is needed without deserialization.
• Inter-agent collaboration is used.

Various options for ResponseFormat are available:

• A built-in ChatResponseFormat.Text property: The response will be plain text.
• A built-in ChatResponseFormat.Json property: The response will be a JSON object without any particular schema.
• A custom ChatResponseFormatJson instance: The response will be a JSON object that conforms to a specific schema.

// Instead of using List<string> directly, create a wrapper type:
public class MovieListWrapper
{
    public List<string> Movies { get; set; }
}

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

AgentRunOptions runOptions = new()
{
    ResponseFormat = ChatResponseFormat.ForJsonSchema<PersonInfo>()
};

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

PersonInfo personInfo = JsonSerializer.Deserialize<PersonInfo>(response.Text, JsonSerializerOptions.Web)!;

Console.WriteLine($"Name: {personInfo.Name}, Age: {personInfo.Age}, Occupation: {personInfo.Occupation}");

The ResponseFormat can also be specified using a raw JSON schema string, which is useful when there is no corresponding .NET type available, such as for declarative agents or schemas loaded from external configuration:

string jsonSchema = """
{
    "type": "object",
    "properties": {
        "name": { "type": "string" },
        "age": { "type": "integer" },
        "occupation": { "type": "string" }
    },
    "required": ["name", "age", "occupation"]
}
""";

AgentRunOptions runOptions = new()
{
    ResponseFormat = ChatResponseFormat.ForJsonSchema(JsonElement.Parse(jsonSchema), "PersonInfo", "Information about a person")
};

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

JsonElement result = JsonSerializer.Deserialize<JsonElement>(response.Text);

Console.WriteLine($"Name: {result.GetProperty("name").GetString()}, Age: {result.GetProperty("age").GetInt32()}, Occupation: {result.GetProperty("occupation").GetString()}");

Structured outputs with streaming

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.

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

AIAgent agent = new AIProjectClient(
    new Uri("<your-foundry-project-endpoint>"),
    new DefaultAzureCredential())
        .AsAIAgent(new ChatClientAgentOptions()
        {
            Name = "HelpfulAssistant",
            ChatOptions = new()
            {
                ModelId = "gpt-4o-mini",
                Instructions = "You are a helpful assistant.",
                ResponseFormat = ChatResponseFormat.ForJsonSchema<PersonInfo>()
            }
        });

> [!WARNING]
> `DefaultAzureCredential` is convenient for development but requires careful consideration in production. In production, consider using a specific credential (e.g., `ManagedIdentityCredential`) to avoid latency issues, unintended credential probing, and potential security risks from fallback mechanisms.

IAsyncEnumerable<AgentResponseUpdate> updates = agent.RunStreamingAsync("Please provide information about John Smith, who is a 35-year-old software engineer.");

AgentResponse response = await updates.ToAgentResponseAsync();

PersonInfo personInfo = JsonSerializer.Deserialize<PersonInfo>(response.Text)!;

Console.WriteLine($"Name: {personInfo.Name}, Age: {personInfo.Age}, Occupation: {personInfo.Occupation}");

Structured outputs with agents with no structured outputs capabilities

Some agents don't natively support structured outputs, either because it's not part of the protocol or because the agents use language models without structured outputs capabilities. One possible approach is to create a custom decorator agent that wraps any AIAgent and uses an additional LLM call via a chat client to convert the agent's text response into structured JSON.

For a reference implementation of this pattern that you can adapt to your own requirements, see the StructuredOutputAgent sample.

Streaming example

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

Prerequisites

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

Create the agent with structured outputs

The Agent is built on top of any chat client implementation that supports structured outputs. The Agent uses the response_format key in the options dict to specify the desired output schema.

When running the agent, you can provide either:

• A Pydantic model that defines the structure of the expected output.
• A JSON schema mapping (dict) when you want parsed JSON without defining a model class.

You can pass the options dict at runtime via agent.run(..., options={"response_format": ...}), or set it at agent creation time via the default_options dict.

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

The first example creates an agent that produces structured outputs 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:

import os
from agent_framework.openai import OpenAIChatCompletionClient
from azure.identity import AzureCliCredential

# Create the agent using Azure OpenAI Chat Client
agent = OpenAIChatCompletionClient(
    model=os.environ["AZURE_OPENAI_CHAT_COMPLETION_MODEL"],
    azure_endpoint=os.environ["AZURE_OPENAI_ENDPOINT"],
    api_version=os.getenv("AZURE_OPENAI_API_VERSION"),
    credential=AzureCliCredential(),
).as_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 outputs format using the response_format key in the options dict:

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

For a Pydantic model response format, the agent response contains the structured outputs in the value property as a 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")

Use a JSON schema mapping

If you already have a JSON schema as a Python mapping, pass that schema directly as the response_format value in the options dict. In this mode, response.value contains the parsed JSON value (typically a dict or list) instead of a Pydantic model instance.

person_info_schema = {
    "type": "object",
    "properties": {
        "name": {"type": "string"},
        "age": {"type": "integer"},
        "occupation": {"type": "string"},
    },
    "required": ["name", "age", "occupation"],
}

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

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

When streaming, agent.run(..., stream=True) returns a ResponseStream. The stream's built-in finalizer automatically handles structured outputs parsing, so you can iterate for real-time updates and then call get_final_response() to get the parsed result:

# Stream updates in real time, then get the structured result
stream = agent.run(query, stream=True, options={"response_format": PersonInfo})
async for update in stream:
    print(update.text, end="", flush=True)

# get_final_response() returns the AgentResponse with the parsed value
final_response = await stream.get_final_response()

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

The same rule applies when response_format is a JSON schema mapping: final_response.value contains parsed JSON instead of a Pydantic model instance.

If you don't need to process individual streaming updates, you can skip iteration entirely — get_final_response() will automatically consume the stream:

stream = agent.run(query, stream=True, options={"response_format": PersonInfo})
final_response = await stream.get_final_response()

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

Complete example

# Copyright (c) Microsoft. All rights reserved.

import asyncio

from agent_framework.openai import OpenAIChatClient
from pydantic import BaseModel

"""
OpenAI Responses Client with Structured Outputs Example

This sample demonstrates using structured outputs capabilities with OpenAI Responses Client,
showing Pydantic model integration for type-safe response parsing and data extraction.
"""


class OutputStruct(BaseModel):
    """A structured outputs model for testing purposes."""

    city: str
    description: str


async def non_streaming_example() -> None:
    print("=== Non-streaming example ===")

    agent = OpenAIChatClient().as_agent(
        name="CityAgent",
        instructions="You are a helpful agent that describes cities in a structured format.",
    )

    query = "Tell me about Paris, France"
    print(f"User: {query}")

    result = await agent.run(query, options={"response_format": OutputStruct})

    if structured_data := result.value:
        print("Structured Outputs Agent:")
        print(f"City: {structured_data.city}")
        print(f"Description: {structured_data.description}")
    else:
        print(f"Failed to parse response: {result.text}")


async def streaming_example() -> None:
    print("=== Streaming example ===")

    agent = OpenAIChatClient().as_agent(
        name="CityAgent",
        instructions="You are a helpful agent that describes cities in a structured format.",
    )

    query = "Tell me about Tokyo, Japan"
    print(f"User: {query}")

    # Stream updates in real time using ResponseStream
    stream = agent.run(query, stream=True, options={"response_format": OutputStruct})
    async for update in stream:
        if update.text:
            print(update.text, end="", flush=True)
    print()

    # get_final_response() returns the AgentResponse with structured outputs parsed
    result = await stream.get_final_response()

    if structured_data := result.value:
        print("Structured Outputs (from streaming with ResponseStream):")
        print(f"City: {structured_data.city}")
        print(f"Description: {structured_data.description}")
    else:
        print(f"Failed to parse response: {result.text}")


async def main() -> None:
    print("=== OpenAI Responses Agent with Structured Outputs ===")

    await non_streaming_example()
    await streaming_example()


if __name__ == "__main__":
    asyncio.run(main())