Migrate to the Responses API

The Responses API is our new API primitive, an evolution of Chat Completions which brings added simplicity and powerful agentic primitives to your integrations.

While Chat Completions remains supported, Responses is recommended for all new projects.

About the Responses API

The Responses API is a unified interface for building powerful, agent-like applications. It contains:

Built-in tools like web search, file search, computer use, code interpreter, and remote MCPs.
Seamless multi-turn interactions that allow you to pass previous responses for higher accuracy reasoning results.
Native multimodal support for text and images.

Responses benefits

The Responses API contains several benefits over Chat Completions:

Better performance: Using reasoning models, like GPT-5, with Responses will result in better model intelligence when compared to Chat Completions. Our internal evals reveal a 3% improvement in SWE-bench with same prompt and setup.
Agentic by default: The Responses API is an agentic loop, allowing the model to call multiple tools, like web_search, image_generation, file_search, code_interpreter, remote MCP servers, as well as your own custom functions, within the span of one API request.
Lower costs: Results in lower costs due to improved cache utilization (40% to 80% improvement when compared to Chat Completions in internal tests).
Stateful context: Use store: true to maintain state from turn to turn, preserving reasoning and tool context from turn-to-turn.
Flexible inputs: Pass a string with input or a list of messages; use instructions for system-level guidance.
Encrypted reasoning: Opt-out of statefulness while still benefiting from advanced reasoning.
Future-proof: Future-proofed for upcoming models.

Capabilities	Chat Completions API	Responses API
Text generation
Audio		Coming soon
Vision
Structured Outputs
Function calling
Web search
File search
Computer use
Code interpreter
MCP
Image generation
Reasoning summaries

Examples

See how the Responses API compares to the Chat Completions API in specific scenarios.

Messages vs. Items

Both APIs make it easy to generate output from our models. The input to, and result of, a call to Chat completions is an array of Messages, while the Responses API uses Items. An Item is a union of many types, representing the range of possibilities of model actions. A message is a type of Item, as is a function_call or function_call_output. Unlike a Chat Completions Message, where many concerns are glued together into one object, Items are distinct from one another and better represent the basic unit of model context.

Additionally, Chat Completions can return multiple parallel generations as choices, using the n param. In Responses, we’ve removed this param, leaving only one generation.

Chat Completions API

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15from openai import OpenAI

client = OpenAI()

completion = client.chat.completions.create(
    model="gpt-5.6",
    messages=[
        {
            "role": "user",
            "content": "Write a one-sentence bedtime story about a unicorn.",
        }
    ],
)

print(completion.choices[0].message.content)

Responses API

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10from openai import OpenAI

client = OpenAI()

response = client.responses.create(
    model="gpt-5.6",
    input="Write a one-sentence bedtime story about a unicorn.",
)

print(response.output_text)

When you get a response back from the Responses API, the fields differ slightly. Instead of a message, you receive a typed response object with its own id. Responses are stored by default. Chat completions are stored by default for new accounts. To disable storage when using either API, set store: false.

The objects you receive back from these APIs will differ slightly. In Chat Completions, you receive an array of choices, each containing a message. In Responses, you receive an array of Items labeled output.

Chat Completions API

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19{
  "id": "chatcmpl-C9EDpkjH60VPPIB86j2zIhiR8kWiC",
  "object": "chat.completion",
  "created": 1756315657,
  "model": "gpt-5.5",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "Under a blanket of starlight, a sleepy unicorn tiptoed through moonlit meadows, gathering dreams like dew to tuck beneath its silver mane until morning.",
        "refusal": null,
        "annotations": []
      },
      "finish_reason": "stop"
    }
  ],
  ...
}

Responses API

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29{
  "id": "resp_68af4030592c81938ec0a5fbab4a3e9f05438e46b5f69a3b",
  "object": "response",
  "created_at": 1756315696,
  "model": "gpt-5.5",
  "output": [
    {
      "id": "rs_68af4030baa48193b0b43b4c2a176a1a05438e46b5f69a3b",
      "type": "reasoning",
      "content": [],
      "summary": []
    },
    {
      "id": "msg_68af40337e58819392e935fb404414d005438e46b5f69a3b",
      "type": "message",
      "status": "completed",
      "content": [
        {
          "type": "output_text",
          "annotations": [],
          "logprobs": [],
          "text": "Under a quilt of moonlight, a drowsy unicorn wandered through quiet meadows, brushing blossoms with her glowing horn so they sighed soft lullabies that carried every dreamer gently to sleep."
        }
      ],
      "role": "assistant"
    }
  ],
  ...
}

Additional differences

Responses are stored by default. Chat completions are stored by default for new accounts. To disable storage in either API, set store: false.
Reasoning models have a richer experience in the Responses API with improved tool usage. Starting with GPT-5.4, Chat Completions does not support tool calling with reasoning_effort values other than none.
Structured Outputs API shape is different. Instead of response_format, use text.format in Responses. Learn more in the Structured Outputs guide.
The function-calling API shape is different, both for the function config on the request, and function calls sent back in the response. See the full difference in the function calling guide.
The Responses SDK has an output_text helper, which the Chat Completions SDK does not have.
In Chat Completions, conversation state must be managed manually. The Responses API has compatibility with the Conversations API for persistent conversations, or the ability to pass a previous_response_id to easily chain Responses together.

Migrating from Chat Completions

Treat migration as three related changes: send requests to /v1/responses, read output from a typed output array, and choose how your application will carry state between turns.

1. Update generation endpoints

Start by updating your generation endpoints from post /v1/chat/completions to post /v1/responses.

If you are not using functions or multimodal inputs, simple message inputs are compatible from one API to the other:

Reuse simple message input

JavaScript

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15/** @type {OpenAI.ChatCompletionMessageParam[] & OpenAI.Responses.ResponseInput} */
const context = [
  { role: "system", content: "You are a helpful assistant." },
  { role: "user", content: "Hello!" },
];

const completion = await client.chat.completions.create({
  model: "gpt-5.6",
  messages: context,
});

const response = await client.responses.create({
  model: "gpt-5.6",
  input: context,
});

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8context = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "Hello!"},
]

completion = client.chat.completions.create(model="gpt-5.6", messages=context)

response = client.responses.create(model="gpt-5.6", input=context)

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37package main

import (
	"context"
	"fmt"

	"github.com/openai/openai-go/v3"
	"github.com/openai/openai-go/v3/responses"
)

func main() {
	client := openai.NewClient()

	completion, err := client.Chat.Completions.New(context.Background(), openai.ChatCompletionNewParams{
		Model: "gpt-5.6",
		Messages: []openai.ChatCompletionMessageParamUnion{
			openai.SystemMessage("You are a helpful assistant."),
			openai.UserMessage("Hello!"),
		},
	})
	if err != nil {
		panic(err)
	}
	fmt.Println(completion.Choices[0].Message.Content)

	response, err := client.Responses.New(context.Background(), responses.ResponseNewParams{
		Model: "gpt-5.6",
		Input: responses.ResponseNewParamsInputUnion{OfInputItemList: responses.ResponseInputParam{
			responses.ResponseInputItemParamOfMessage("You are a helpful assistant.", responses.EasyInputMessageRoleSystem),
			responses.ResponseInputItemParamOfMessage("Hello!", responses.EasyInputMessageRoleUser),
		}},
	})
	if err != nil {
		panic(err)
	}
	fmt.Println(response.OutputText())
}

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46import com.openai.client.OpenAIClient;
import com.openai.client.okhttp.OpenAIOkHttpClient;
import com.openai.models.chat.completions.ChatCompletionCreateParams;
import com.openai.models.responses.EasyInputMessage;
import com.openai.models.responses.ResponseCreateParams;
import com.openai.models.responses.ResponseInputItem;
import java.util.List;

var completion =
    client
        .chat()
        .completions()
        .create(
            ChatCompletionCreateParams.builder()
                .model("gpt-5.6")
                .addSystemMessage("You are a helpful assistant.")
                .addUserMessage("Hello!")
                .build());
completion.choices().stream()
    .flatMap(choice -> choice.message().content().stream())
    .forEach(System.out::println);

var response =
    client
        .responses()
        .create(
            ResponseCreateParams.builder()
                .model("gpt-5.6")
                .inputOfResponse(
                    List.of(
                        ResponseInputItem.ofEasyInputMessage(
                            EasyInputMessage.builder()
                                .role(EasyInputMessage.Role.SYSTEM)
                                .content("You are a helpful assistant.")
                                .build()),
                        ResponseInputItem.ofEasyInputMessage(
                            EasyInputMessage.builder()
                                .role(EasyInputMessage.Role.USER)
                                .content("Hello!")
                                .build())))
                .build());
response.output().stream()
    .flatMap(item -> item.message().stream())
    .flatMap(message -> message.content().stream())
    .flatMap(content -> content.outputText().stream())
    .forEach(text -> System.out.println(text.text()));

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19require "openai"

client = OpenAI::Client.new
messages = [
  {role: :system, content: "You are a helpful assistant."},
  {role: :user, content: "Hello!"}
]

completion = client.chat.completions.create(
  model: "gpt-5.6",
  messages: messages
)
puts(completion.choices.fetch(0).message.content)

response = client.responses.create(
  model: "gpt-5.6",
  input: messages
)
puts(response.output_text)

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20INPUT='[
  { "role": "system", "content": "You are a helpful assistant." },
  { "role": "user", "content": "Hello!" }
]'

curl -s https://api.openai.com/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $OPENAI_API_KEY" \
  -d "{
    \"model\": \"gpt-5.6\",
    \"messages\": $INPUT
  }"

curl -s https://api.openai.com/v1/responses \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $OPENAI_API_KEY" \
  -d "{
    \"model\": \"gpt-5.6\",
    \"input\": $INPUT
  }"

With Chat Completions, you create a messages array and read the model text from completion.choices[0].message.content.

Generate text from a model

JavaScript

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11import OpenAI from "openai";
const client = new OpenAI({ apiKey: process.env.OPENAI_API_KEY });

const completion = await client.chat.completions.create({
  model: "gpt-5.6",
  messages: [
    { role: "system", content: "You are a helpful assistant." },
    { role: "user", content: "Hello!" },
  ],
});
console.log(completion.choices[0].message.content);

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12from openai import OpenAI

client = OpenAI()

completion = client.chat.completions.create(
    model="gpt-5.6",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Hello!"},
    ],
)
print(completion.choices[0].message.content)

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24package main

import (
	"context"
	"fmt"

	"github.com/openai/openai-go/v3"
)

func main() {
	client := openai.NewClient()

	completion, err := client.Chat.Completions.New(context.Background(), openai.ChatCompletionNewParams{
		Model: "gpt-5.6",
		Messages: []openai.ChatCompletionMessageParamUnion{
			openai.SystemMessage("You are a helpful assistant."),
			openai.UserMessage("Hello!"),
		},
	})
	if err != nil {
		panic(err)
	}
	fmt.Println(completion.Choices[0].Message.Content)
}

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14import com.openai.client.OpenAIClient;
import com.openai.client.okhttp.OpenAIOkHttpClient;
import com.openai.models.chat.completions.ChatCompletionCreateParams;

ChatCompletionCreateParams params =
    ChatCompletionCreateParams.builder()
        .model("gpt-5.6")
        .addSystemMessage("You are a helpful assistant.")
        .addUserMessage("Hello!")
        .build();

client.chat().completions().create(params).choices().stream()
    .flatMap(choice -> choice.message().content().stream())
    .forEach(System.out::println);

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13require "openai"

client = OpenAI::Client.new

completion = client.chat.completions.create(
  model: "gpt-5.6",
  messages: [
    {role: :system, content: "You are a helpful assistant."},
    {role: :user, content: "Hello!"}
  ]
)

puts(completion.choices.fetch(0).message.content)

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10curl https://api.openai.com/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $OPENAI_API_KEY" \
  -d '{
      "model": "gpt-5.6",
      "messages": [
          {"role": "system", "content": "You are a helpful assistant."},
          {"role": "user", "content": "Hello!"}
      ]
  }'

With Responses, you can separate instructions and input at the top level and read generated text from response.output_text.

Generate text from a model

JavaScript

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10import OpenAI from "openai";
const client = new OpenAI({ apiKey: process.env.OPENAI_API_KEY });

const response = await client.responses.create({
  model: "gpt-5.6",
  instructions: "You are a helpful assistant.",
  input: "Hello!",
});

console.log(response.output_text);

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8from openai import OpenAI

client = OpenAI()

response = client.responses.create(
    model="gpt-5.6", instructions="You are a helpful assistant.", input="Hello!"
)
print(response.output_text)

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23package main

import (
	"context"
	"fmt"

	"github.com/openai/openai-go/v3"
	"github.com/openai/openai-go/v3/responses"
)

func main() {
	client := openai.NewClient()

	response, err := client.Responses.New(context.Background(), responses.ResponseNewParams{
		Model:        "gpt-5.6",
		Instructions: openai.String("You are a helpful assistant."),
		Input:        responses.ResponseNewParamsInputUnion{OfString: openai.String("Hello!")},
	})
	if err != nil {
		panic(err)
	}
	fmt.Println(response.OutputText())
}

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16import com.openai.client.OpenAIClient;
import com.openai.client.okhttp.OpenAIOkHttpClient;
import com.openai.models.responses.ResponseCreateParams;

ResponseCreateParams params =
    ResponseCreateParams.builder()
        .model("gpt-5.6")
        .input("Hello!")
        .instructions("You are a helpful assistant.")
        .build();

client.responses().create(params).output().stream()
    .flatMap(item -> item.message().stream())
    .flatMap(message -> message.content().stream())
    .flatMap(content -> content.outputText().stream())
    .forEach(text -> System.out.println(text.text()));

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11require "openai"

client = OpenAI::Client.new

response = client.responses.create(
  model: "gpt-5.6",
  instructions: "You are a helpful assistant.",
  input: "Hello!"
)

puts(response.output_text)

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8curl https://api.openai.com/v1/responses \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $OPENAI_API_KEY" \
  -d '{
      "model": "gpt-5.6",
      "instructions": "You are a helpful assistant.",
      "input": "Hello!"
  }'

2. Map Messages to Items

Chat Completions uses messages as both input and output. Responses uses input and output arrays of typed Items. A message is one Item type, alongside Items such as reasoning, function_call, and function_call_output.

Chat Completions concept	Responses mapping
`messages[]`	`input`, as a string or an array of input Items
System or developer guidance	Top-level `instructions`, or compatible message Items when you need to preserve an existing transcript
User message	An input message Item with `role: "user"`
Assistant message	An output message Item in `response.output`; pass it back in `input` if you manually manage state
Tool or function call	A `function_call` output Item
Tool or function result	A `function_call_output` input Item linked to the call with `call_id`
Multiple generations with `n`	Not available in Responses; make separate requests if you need multiple candidate outputs

When you only need the final text, use the SDK output_text helper. When your flow uses reasoning, tools, or multimodal output, iterate over response.output and handle each Item by its type.

3. Update multi-turn conversations

If you have multi-turn conversations in your application, update your context logic. Responses gives you three common state-management options:

Use previous_response_id when you want OpenAI to manage prior response context. Resend stable instructions on each request, because previous_response_id does not carry over the previous response’s top-level instructions.
Pass prior output Items back into the next request when you need to manage or trim context yourself.
Use the Conversations API when you need a persistent conversation object.

In Chat Completions, you store the transcript and send the accumulated messages array on each request.

Multi-turn conversation

JavaScript

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17/** @type {OpenAI.ChatCompletionMessageParam[]} */
let messages = [
  { role: "system", content: "You are a helpful assistant." },
  { role: "user", content: "What is the capital of France?" },
];
const res1 = await client.chat.completions.create({
  model: "gpt-5.6",
  messages,
});

messages = messages.concat([res1.choices[0].message]);
messages.push({ role: "user", content: "And its population?" });

const res2 = await client.chat.completions.create({
  model: "gpt-5.6",
  messages,
});

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10messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "What is the capital of France?"},
]
res1 = client.chat.completions.create(model="gpt-5.6", messages=messages)

messages += [res1.choices[0].message]
messages += [{"role": "user", "content": "And its population?"}]

res2 = client.chat.completions.create(model="gpt-5.6", messages=messages)

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28package main

import (
	"context"
	"fmt"

	"github.com/openai/openai-go/v3"
)

func main() {
	client := openai.NewClient()
	messages := []openai.ChatCompletionMessageParamUnion{
		openai.SystemMessage("You are a helpful assistant."),
		openai.UserMessage("What is the capital of France?"),
	}

	first, err := client.Chat.Completions.New(context.Background(), openai.ChatCompletionNewParams{Model: "gpt-5.6", Messages: messages})
	if err != nil {
		panic(err)
	}
	messages = append(messages, openai.AssistantMessage(first.Choices[0].Message.Content), openai.UserMessage("And its population?"))

	second, err := client.Chat.Completions.New(context.Background(), openai.ChatCompletionNewParams{Model: "gpt-5.6", Messages: messages})
	if err != nil {
		panic(err)
	}
	fmt.Println(second.Choices[0].Message.Content)
}

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24import com.openai.client.OpenAIClient;
import com.openai.client.okhttp.OpenAIOkHttpClient;
import com.openai.models.chat.completions.ChatCompletionCreateParams;

var params =
    ChatCompletionCreateParams.builder()
        .model("gpt-5.6")
        .addSystemMessage("You are a helpful assistant.")
        .addUserMessage("What is the capital of France?")
        .build();
var first = client.chat().completions().create(params);

var second =
    client
        .chat()
        .completions()
        .create(
            params.toBuilder()
                .addAssistantMessage(first.choices().get(0).message().content().orElseThrow())
                .addUserMessage("And its population?")
                .build());
second.choices().stream()
    .flatMap(choice -> choice.message().content().stream())
    .forEach(System.out::println);

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21require "openai"

client = OpenAI::Client.new
messages = [
  {role: :system, content: "You are a helpful assistant."},
  {role: :user, content: "What is the capital of France?"}
]

first = client.chat.completions.create(
  model: "gpt-5.6",
  messages: messages
)
messages << {role: :assistant, content: first.choices.fetch(0).message.content}
messages << {role: :user, content: "And its population?"}

second = client.chat.completions.create(
  model: "gpt-5.6",
  messages: messages
)

puts(second.choices.fetch(0).message.content)

With Responses, you can manually pass outputs from one response into the input of another.

Multi-turn conversation

JavaScript

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18/** @type {OpenAI.Responses.ResponseInput} */
let context = [{ role: "user", content: "What is the capital of France?" }];

const res1 = await client.responses.create({
  model: "gpt-5.6",
  input: context,
});

// Append the first response’s output to context
context = context.concat(res1.output);

// Add the next user message
context.push({ role: "user", content: "And its population?" });

const res2 = await client.responses.create({
  model: "gpt-5.6",
  input: context,
});

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16context = [{"role": "user", "content": "What is the capital of France?"}]
res1 = client.responses.create(
    model="gpt-5.6",
    input=context,
)

# Append the first response's output to context
context += res1.output

# Add the next user message
context += [{"role": "user", "content": "And its population?"}]

res2 = client.responses.create(
    model="gpt-5.6",
    input=context,
)

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46package main

import (
	"context"
	"encoding/json"
	"fmt"

	"github.com/openai/openai-go/v3"
	"github.com/openai/openai-go/v3/responses"
)

func main() {
	client := openai.NewClient()
	contextItems := responses.ResponseInputParam{
		responses.ResponseInputItemParamOfMessage("What is the capital of France?", responses.EasyInputMessageRoleUser),
	}
	first, err := client.Responses.New(context.Background(), responses.ResponseNewParams{
		Model: "gpt-5.6",
		Input: responses.ResponseNewParamsInputUnion{OfInputItemList: contextItems},
	})
	if err != nil {
		panic(err)
	}
	contextItems = append(contextItems, outputAsInput(first.Output)...)
	contextItems = append(contextItems, responses.ResponseInputItemParamOfMessage("And its population?", responses.EasyInputMessageRoleUser))
	second, err := client.Responses.New(context.Background(), responses.ResponseNewParams{
		Model: "gpt-5.6",
		Input: responses.ResponseNewParamsInputUnion{OfInputItemList: contextItems},
	})
	if err != nil {
		panic(err)
	}
	fmt.Println(second.OutputText())
}

func outputAsInput(output []responses.ResponseOutputItemUnion) []responses.ResponseInputItemUnionParam {
	input := make([]responses.ResponseInputItemUnionParam, 0, len(output))
	for _, item := range output {
		var converted responses.ResponseInputItemUnion
		if err := json.Unmarshal([]byte(item.RawJSON()), &converted); err != nil {
			panic(err)
		}
		input = append(input, converted.ToParam())
	}
	return input
}

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40import com.openai.client.OpenAIClient;
import com.openai.client.okhttp.OpenAIOkHttpClient;
import com.openai.core.JsonValue;
import com.openai.models.responses.EasyInputMessage;
import com.openai.models.responses.ResponseCreateParams;
import com.openai.models.responses.ResponseInputItem;
import java.util.ArrayList;

var history = new ArrayList<ResponseInputItem>();
history.add(
    ResponseInputItem.ofEasyInputMessage(
        EasyInputMessage.builder()
            .role(EasyInputMessage.Role.USER)
            .content("What is the capital of France?")
            .build()));

var first =
    client
        .responses()
        .create(
            ResponseCreateParams.builder().model("gpt-5.6").inputOfResponse(history).build());
first.output().stream()
    .map(item -> JsonValue.from(item).convert(ResponseInputItem.class))
    .forEach(history::add);
history.add(
    ResponseInputItem.ofEasyInputMessage(
        EasyInputMessage.builder()
            .role(EasyInputMessage.Role.USER)
            .content("And its population?")
            .build()));

client
    .responses()
    .create(ResponseCreateParams.builder().model("gpt-5.6").inputOfResponse(history).build())
    .output()
    .stream()
    .flatMap(item -> item.message().stream())
    .flatMap(message -> message.content().stream())
    .flatMap(content -> content.outputText().stream())
    .forEach(text -> System.out.println(text.text()));

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18require "openai"

client = OpenAI::Client.new
context = [{role: :user, content: "What is the capital of France?"}]

first = client.responses.create(
  model: "gpt-5.6",
  input: context
)
context.concat(first.output.map(&:to_h))
context << {role: :user, content: "And its population?"}

second = client.responses.create(
  model: "gpt-5.6",
  input: context
)

puts(second.output_text)

You can also use previous_response_id to reference the previous response and create response chains or forks.

Multi-turn conversation

JavaScript

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12const res1 = await client.responses.create({
  model: "gpt-5.6",
  input: "What is the capital of France?",
  store: true,
});

const res2 = await client.responses.create({
  model: "gpt-5.6",
  input: "And its population?",
  previous_response_id: res1.id,
  store: true,
});

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10res1 = client.responses.create(
    model="gpt-5.6", input="What is the capital of France?", store=True
)

res2 = client.responses.create(
    model="gpt-5.6",
    input="And its population?",
    previous_response_id=res1.id,
    store=True,
)

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33package main

import (
	"context"
	"fmt"

	"github.com/openai/openai-go/v3"
	"github.com/openai/openai-go/v3/responses"
)

func main() {
	client := openai.NewClient()

	first, err := client.Responses.New(context.Background(), responses.ResponseNewParams{
		Model: "gpt-5.6",
		Store: openai.Bool(true),
		Input: responses.ResponseNewParamsInputUnion{OfString: openai.String("What is the capital of France?")},
	})
	if err != nil {
		panic(err)
	}

	second, err := client.Responses.New(context.Background(), responses.ResponseNewParams{
		Model:              "gpt-5.6",
		Store:              openai.Bool(true),
		PreviousResponseID: openai.String(first.ID),
		Input:              responses.ResponseNewParamsInputUnion{OfString: openai.String("And its population?")},
	})
	if err != nil {
		panic(err)
	}
	fmt.Println(second.OutputText())
}

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29import com.openai.client.OpenAIClient;
import com.openai.client.okhttp.OpenAIOkHttpClient;
import com.openai.models.responses.ResponseCreateParams;

var first =
    client
        .responses()
        .create(
            ResponseCreateParams.builder()
                .model("gpt-5.6")
                .input("What is the capital of France?")
                .store(true)
                .build());

var second =
    client
        .responses()
        .create(
            ResponseCreateParams.builder()
                .model("gpt-5.6")
                .input("And its population?")
                .previousResponseId(first.id())
                .store(true)
                .build());
second.output().stream()
    .flatMap(item -> item.message().stream())
    .flatMap(message -> message.content().stream())
    .flatMap(content -> content.outputText().stream())
    .forEach(text -> System.out.println(text.text()));

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18require "openai"

client = OpenAI::Client.new

first = client.responses.create(
  model: "gpt-5.6",
  input: "What is the capital of France?",
  store: true
)

second = client.responses.create(
  model: "gpt-5.6",
  previous_response_id: first.id,
  input: "And its population?",
  store: true
)

puts(second.output_text)

Even when using previous_response_id, all previous input tokens for responses in the chain are billed as input tokens in the API.

4. Decide when to use statefulness

Responses are stored by default. Chat Completions are stored by default for new accounts. To disable storage in either API, set store: false.

Some organizations, such as those with Zero Data Retention (ZDR) requirements, cannot use the Responses API in a stateful way due to compliance or data retention policies. To support these cases, OpenAI offers encrypted reasoning items, allowing you to keep your workflow stateless while still benefiting from reasoning items.

To disable statefulness but still take advantage of reasoning:

Set store: false in the store field.
Preserve and replay every returned reasoning item. Each item includes encrypted_content by default when you create a response.

The API will then return an encrypted version of the reasoning tokens, which you can pass back in future requests just like regular reasoning items. For ZDR organizations, OpenAI enforces store: false automatically. When a request includes encrypted_content, it is decrypted in memory, used for generating the next response, and then securely discarded. Any new reasoning tokens are immediately encrypted and returned to you, ensuring no intermediate state is persisted.

5. Update function definitions and outputs

There are two minor, but notable, differences in how functions are defined between Chat Completions and Responses.

In Chat Completions, function definitions are externally tagged. In Responses, they are internally tagged.
In Chat Completions, functions are non-strict by default. In Responses, omitting strict attempts strict mode; if the schema cannot be made compatible, Responses falls back to non-strict, best-effort function calling and returns the resolved tool with strict: false. To keep non-strict behavior in Responses explicitly, set strict: false.

The Responses API function example on the right is functionally equivalent to the Chat Completions example on the left.

Chat Completions API

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20{
  "type": "function",
  "function": {
    "name": "get_weather",
    "description": "Determine weather in my location",
    "strict": true,
    "parameters": {
      "type": "object",
      "properties": {
        "location": {
          "type": "string"
        }
      },
      "additionalProperties": false,
      "required": [
        "location"
      ]
    }
  }
}

Responses API

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17{
  "type": "function",
  "name": "get_weather",
  "description": "Determine weather in my location",
  "parameters": {
    "type": "object",
    "properties": {
      "location": {
        "type": "string"
      }
    },
    "additionalProperties": false,
    "required": [
      "location"
    ]
  }
}

Follow function-calling best practices

In Responses, tool calls and their outputs are two distinct types of Items that are correlated using a call_id. See the function calling docs for more detail on how function calling works in Responses.

6. Update Structured Outputs definitions

In the Responses API, Structured Outputs definitions have moved from response_format to text.format:

Structured Outputs

JavaScript

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33const completion = await openai.chat.completions.create({
  model: "gpt-5.6",
  messages: [
    {
      role: "user",
      content: "Jane, 54 years old",
    },
  ],
  response_format: {
    type: "json_schema",
    json_schema: {
      name: "person",
      strict: true,
      schema: {
        type: "object",
        properties: {
          name: {
            type: "string",
            minLength: 1,
          },
          age: {
            type: "number",
            minimum: 0,
            maximum: 130,
          },
        },
        required: ["name", "age"],
        additionalProperties: false,
      },
    },
  },
  reasoning_effort: "medium",
});

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30from openai import OpenAI

client = OpenAI()

response = client.chat.completions.create(
    model="gpt-5.6",
    messages=[
        {
            "role": "user",
            "content": "Jane, 54 years old",
        }
    ],
    response_format={
        "type": "json_schema",
        "json_schema": {
            "name": "person",
            "strict": True,
            "schema": {
                "type": "object",
                "properties": {
                    "name": {"type": "string", "minLength": 1},
                    "age": {"type": "number", "minimum": 0, "maximum": 130},
                },
                "required": ["name", "age"],
                "additionalProperties": False,
            },
        },
    },
    reasoning_effort="medium",
)

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39package main

import (
	"context"
	"fmt"

	"github.com/openai/openai-go/v3"
	"github.com/openai/openai-go/v3/shared"
)

func main() {
	client := openai.NewClient()
	schema := map[string]any{
		"type": "object",
		"properties": map[string]any{
			"name": map[string]any{"type": "string", "minLength": 1},
			"age":  map[string]any{"type": "number", "minimum": 0, "maximum": 130},
		},
		"required":             []string{"name", "age"},
		"additionalProperties": false,
	}

	completion, err := client.Chat.Completions.New(context.Background(), openai.ChatCompletionNewParams{
		Model:           "gpt-5.6",
		ReasoningEffort: openai.ReasoningEffortMedium,
		Messages: []openai.ChatCompletionMessageParamUnion{
			openai.UserMessage("Jane, 54 years old"),
		},
		ResponseFormat: openai.ChatCompletionNewParamsResponseFormatUnion{
			OfJSONSchema: &shared.ResponseFormatJSONSchemaParam{JSONSchema: shared.ResponseFormatJSONSchemaJSONSchemaParam{
				Name: "person", Strict: openai.Bool(true), Schema: schema,
			}},
		},
	})
	if err != nil {
		panic(err)
	}
	fmt.Println(completion.Choices[0].Message.Content)
}

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44import com.openai.client.OpenAIClient;
import com.openai.client.okhttp.OpenAIOkHttpClient;
import com.openai.core.JsonValue;
import com.openai.models.ReasoningEffort;
import com.openai.models.chat.completions.ChatCompletionCreateParams;
import java.util.List;
import java.util.Map;

ChatCompletionCreateParams params =
    ChatCompletionCreateParams.builder()
        .model("gpt-5.6")
        .reasoningEffort(ReasoningEffort.MEDIUM)
        .addUserMessage("Jane, 54 years old")
        .putAdditionalBodyProperty(
            "response_format",
            JsonValue.from(
                Map.of(
                    "type",
                    "json_schema",
                    "json_schema",
                    Map.of(
                        "name",
                        "person",
                        "strict",
                        true,
                        "schema",
                        Map.of(
                            "type",
                            "object",
                            "properties",
                            Map.of(
                                "name",
                                Map.of("type", "string", "minLength", 1),
                                "age",
                                Map.of("type", "number", "minimum", 0, "maximum", 130)),
                            "required",
                            List.of("name", "age"),
                            "additionalProperties",
                            false)))))
        .build();

client.chat().completions().create(params).choices().stream()
    .flatMap(choice -> choice.message().content().stream())
    .forEach(System.out::println);

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24require "openai"

client = OpenAI::Client.new
schema = {
  type: "object",
  properties: {
    name: {type: "string", minLength: 1},
    age: {type: "number", minimum: 0, maximum: 130}
  },
  required: ["name", "age"],
  additionalProperties: false
}

completion = client.chat.completions.create(
  model: "gpt-5.6",
  reasoning_effort: :medium,
  messages: [{role: :user, content: "Jane, 54 years old"}],
  response_format: {
    type: :json_schema,
    json_schema: {name: "person", strict: true, schema: schema}
  }
)

puts(completion.choices.fetch(0).message.content)

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39curl https://api.openai.com/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $OPENAI_API_KEY" \
  -d '{
  "model": "gpt-5.6",
  "messages": [
    {
      "role": "user",
      "content": "Jane, 54 years old"
    }
  ],
  "response_format": {
    "type": "json_schema",
    "json_schema": {
      "name": "person",
      "strict": true,
      "schema": {
        "type": "object",
        "properties": {
          "name": {
            "type": "string",
            "minLength": 1
          },
          "age": {
            "type": "number",
            "minimum": 0,
            "maximum": 130
          }
        },
        "required": [
          "name",
          "age"
        ],
        "additionalProperties": false
      }
    }
  },
  "reasoning_effort": "medium"
}'

Structured Outputs

JavaScript

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27const response = await openai.responses.create({
  model: "gpt-5.6",
  input: "Jane, 54 years old",
  text: {
    format: {
      type: "json_schema",
      name: "person",
      strict: true,
      schema: {
        type: "object",
        properties: {
          name: {
            type: "string",
            minLength: 1,
          },
          age: {
            type: "number",
            minimum: 0,
            maximum: 130,
          },
        },
        required: ["name", "age"],
        additionalProperties: false,
      },
    },
  },
});

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20response = client.responses.create(
    model="gpt-5.6",
    input="Jane, 54 years old",
    text={
        "format": {
            "type": "json_schema",
            "name": "person",
            "strict": True,
            "schema": {
                "type": "object",
                "properties": {
                    "name": {"type": "string", "minLength": 1},
                    "age": {"type": "number", "minimum": 0, "maximum": 130},
                },
                "required": ["name", "age"],
                "additionalProperties": False,
            },
        }
    },
)

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34package main

import (
	"context"
	"fmt"

	"github.com/openai/openai-go/v3"
	"github.com/openai/openai-go/v3/responses"
)

func main() {
	client := openai.NewClient()
	schema := map[string]any{
		"type": "object",
		"properties": map[string]any{
			"name": map[string]any{"type": "string", "minLength": 1},
			"age":  map[string]any{"type": "number", "minimum": 0, "maximum": 130},
		},
		"required":             []string{"name", "age"},
		"additionalProperties": false,
	}

	response, err := client.Responses.New(context.Background(), responses.ResponseNewParams{
		Model: "gpt-5.6",
		Input: responses.ResponseNewParamsInputUnion{OfString: openai.String("Jane, 54 years old")},
		Text: responses.ResponseTextConfigParam{Format: responses.ResponseFormatTextConfigUnionParam{
			OfJSONSchema: &responses.ResponseFormatTextJSONSchemaConfigParam{Name: "person", Schema: schema, Strict: openai.Bool(true)},
		}},
	})
	if err != nil {
		panic(err)
	}
	fmt.Println(response.OutputText())
}

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46import com.openai.client.OpenAIClient;
import com.openai.client.okhttp.OpenAIOkHttpClient;
import com.openai.core.JsonValue;
import com.openai.models.responses.ResponseCreateParams;
import com.openai.models.responses.ResponseFormatTextJsonSchemaConfig;
import com.openai.models.responses.ResponseTextConfig;
import java.util.List;
import java.util.Map;

ResponseCreateParams params =
    ResponseCreateParams.builder()
        .model("gpt-5.6")
        .input("Jane, 54 years old")
        .text(
            ResponseTextConfig.builder()
                .format(
                    ResponseFormatTextJsonSchemaConfig.builder()
                        .name("person")
                        .strict(true)
                        .schema(
                            ResponseFormatTextJsonSchemaConfig.Schema.builder()
                                .putAdditionalProperty("type", JsonValue.from("object"))
                                .putAdditionalProperty(
                                    "properties",
                                    JsonValue.from(
                                        Map.of(
                                            "name",
                                            Map.of("type", "string", "minLength", 1),
                                            "age",
                                            Map.of(
                                                "type", "number", "minimum", 0, "maximum",
                                                130))))
                                .putAdditionalProperty(
                                    "required", JsonValue.from(List.of("name", "age")))
                                .putAdditionalProperty(
                                    "additionalProperties", JsonValue.from(false))
                                .build())
                        .build())
                .build())
        .build();

client.responses().create(params).output().stream()
    .flatMap(item -> item.message().stream())
    .flatMap(message -> message.content().stream())
    .flatMap(content -> content.outputText().stream())
    .forEach(text -> System.out.println(text.text()));

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27require "openai"

client = OpenAI::Client.new
schema = {
  type: "object",
  properties: {
    name: {type: "string", minLength: 1},
    age: {type: "number", minimum: 0, maximum: 130}
  },
  required: ["name", "age"],
  additionalProperties: false
}

response = client.responses.create(
  model: "gpt-5.6",
  input: "Jane, 54 years old",
  text: {
    format: {
      type: :json_schema,
      name: "person",
      strict: true,
      schema: schema
    }
  }
)

puts(response.output_text)

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33curl https://api.openai.com/v1/responses \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $OPENAI_API_KEY" \
  -d '{
  "model": "gpt-5.6",
  "input": "Jane, 54 years old",
  "text": {
    "format": {
      "type": "json_schema",
      "name": "person",
      "strict": true,
      "schema": {
        "type": "object",
        "properties": {
          "name": {
            "type": "string",
            "minLength": 1
          },
          "age": {
            "type": "number",
            "minimum": 0,
            "maximum": 130
          }
        },
        "required": [
          "name",
          "age"
        ],
        "additionalProperties": false
      }
    }
  }
}'

7. Update streaming consumers

Chat Completions streaming returns incremental chunks with a delta field. Responses streaming uses typed server-sent events. Update stream consumers to branch on each event’s type and handle the events your UI or orchestration layer needs.

For text streaming, listen for events such as:

response.created
response.output_text.delta
response.completed
error

Function-calling streams can also emit events such as response.function_call_arguments.delta and response.function_call_arguments.done. See the streaming Responses guide and Responses streaming events reference.

8. Upgrade to native tools

If your application has use cases that would benefit from OpenAI’s native tools, you can update your tool calls to use OpenAI’s tools out of the box.

With Chat Completions, you cannot use OpenAI-hosted tools natively and have to write your own tool integration.

Web search tool

JavaScript

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24async function web_search(query) {
  const res = await fetch(`https://api.example.com/search?q=${query}`);
  const data = await res.json();
  return data.results;
}

const completion = await client.chat.completions.create({
  model: "gpt-5.6",
  messages: [
    { role: "system", content: "You are a helpful assistant." },
    { role: "user", content: "Who is the current president of France?" },
  ],
  functions: [
    {
      name: "web_search",
      description: "Search the web for information",
      parameters: {
        type: "object",
        properties: { query: { type: "string" } },
        required: ["query"],
      },
    },
  ],
});

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26import requests


def web_search(query):
    r = requests.get(f"https://api.example.com/search?q={query}")
    return r.json().get("results", [])


completion = client.chat.completions.create(
    model="gpt-5.6",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Who is the current president of France?"},
    ],
    functions=[
        {
            "name": "web_search",
            "description": "Search the web for information",
            "parameters": {
                "type": "object",
                "properties": {"query": {"type": "string"}},
                "required": ["query"],
            },
        }
    ],
)

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34package main

import (
	"context"
	"fmt"

	"github.com/openai/openai-go/v3"
	"github.com/openai/openai-go/v3/shared"
)

func main() {
	client := openai.NewClient()
	completion, err := client.Chat.Completions.New(context.Background(), openai.ChatCompletionNewParams{
		Model: "gpt-5.6",
		Messages: []openai.ChatCompletionMessageParamUnion{
			openai.SystemMessage("You are a helpful assistant."),
			openai.UserMessage("Who is the current president of France?"),
		},
		Functions: []openai.ChatCompletionNewParamsFunction{{
			Name:        "web_search",
			Description: openai.String("Search the web for information"),
			Parameters: map[string]any{
				"type":       "object",
				"properties": map[string]any{"query": map[string]any{"type": "string"}},
				"required":   []string{"query"},
			},
		}},
		ReasoningEffort: shared.ReasoningEffortNone,
	})
	if err != nil {
		panic(err)
	}
	fmt.Println(completion.Choices[0].Message)
}

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33import com.openai.client.OpenAIClient;
import com.openai.client.okhttp.OpenAIOkHttpClient;
import com.openai.core.JsonValue;
import com.openai.models.FunctionParameters;
import com.openai.models.ReasoningEffort;
import com.openai.models.chat.completions.ChatCompletionCreateParams;
import java.util.List;
import java.util.Map;

ChatCompletionCreateParams params =
    ChatCompletionCreateParams.builder()
        .model("gpt-5.6")
        .reasoningEffort(ReasoningEffort.NONE)
        .addSystemMessage("You are a helpful assistant.")
        .addUserMessage("Who is the current president of France?")
        .addFunction(
            ChatCompletionCreateParams.Function.builder()
                .name("web_search")
                .description("Search the web for information")
                .parameters(
                    FunctionParameters.builder()
                        .putAdditionalProperty("type", JsonValue.from("object"))
                        .putAdditionalProperty(
                            "properties",
                            JsonValue.from(Map.of("query", Map.of("type", "string"))))
                        .putAdditionalProperty("required", JsonValue.from(List.of("query")))
                        .build())
                .build())
        .build();

client.chat().completions().create(params).choices().stream()
    .map(choice -> choice.message())
    .forEach(System.out::println);

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25require "openai"

client = OpenAI::Client.new

completion = client.chat.completions.create(
  model: "gpt-5.6",
  reasoning_effort: :none,
  messages: [
    {role: :system, content: "You are a helpful assistant."},
    {role: :user, content: "Who is the current president of France?"}
  ],
  functions: [
    {
      name: "web_search",
      description: "Search the web for information",
      parameters: {
        type: "object",
        properties: {query: {type: "string"}},
        required: ["query"]
      }
    }
  ]
)

puts(completion.choices.fetch(0).message)

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4curl https://api.example.com/search \
  -G \
  --data-urlencode "q=your+search+term" \
  --data-urlencode "key=$SEARCH_API_KEY"

With Responses, you can specify the tools that you want the model to use.

Web search tool

JavaScript

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7const answer = await client.responses.create({
  model: "gpt-5.6",
  input: "Who is the current president of France?",
  tools: [{ type: "web_search" }],
});

console.log(answer.output_text);

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7answer = client.responses.create(
    model="gpt-5.6",
    input="Who is the current president of France?",
    tools=[{"type": "web_search"}],
)

print(answer.output_text)

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24package main

import (
	"context"
	"fmt"

	"github.com/openai/openai-go/v3"
	"github.com/openai/openai-go/v3/responses"
)

func main() {
	client := openai.NewClient()
	response, err := client.Responses.New(context.Background(), responses.ResponseNewParams{
		Model: "gpt-5.6",
		Input: responses.ResponseNewParamsInputUnion{OfString: openai.String("Who is the current president of France?")},
		Tools: []responses.ToolUnionParam{
			responses.ToolParamOfWebSearch(responses.WebSearchToolTypeWebSearch),
		},
	})
	if err != nil {
		panic(err)
	}
	fmt.Println(response.OutputText())
}

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17import com.openai.client.OpenAIClient;
import com.openai.client.okhttp.OpenAIOkHttpClient;
import com.openai.models.responses.ResponseCreateParams;
import com.openai.models.responses.WebSearchTool;

ResponseCreateParams params =
    ResponseCreateParams.builder()
        .model("gpt-5.6")
        .input("Who is the current president of France?")
        .addTool(WebSearchTool.builder().type(WebSearchTool.Type.WEB_SEARCH).build())
        .build();

client.responses().create(params).output().stream()
    .flatMap(item -> item.message().stream())
    .flatMap(message -> message.content().stream())
    .flatMap(content -> content.outputText().stream())
    .forEach(text -> System.out.println(text.text()));

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11require "openai"

client = OpenAI::Client.new

response = client.responses.create(
  model: "gpt-5.6",
  input: "Who is the current president of France?",
  tools: [{type: :web_search}]
)

puts(response.output_text)

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8curl https://api.openai.com/v1/responses \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $OPENAI_API_KEY" \
  -d '{
    "model": "gpt-5.6",
    "input": "Who is the current president of France?",
    "tools": [{"type": "web_search"}]
  }'

9. Check common migration errors

Watch for these issues when moving code from Chat Completions to Responses:

Reading choices[0].message.content instead of response.output_text or response.output.
Treating every output entry as a message. Reasoning, tool, and function calls are separate Item types.
Dropping reasoning, function call, or function call output Items when manually carrying context into the next response.
Sending a function result without the matching call_id.
Using response_format in a Responses request instead of text.format.
Reusing Chat Completions streaming chunk handlers without handling typed Responses events.
Assuming previous_response_id removes billing for prior context. Previous input tokens in the response chain are still billed as input tokens.

Incremental rollout checklist

Chat Completions remains supported, so you can migrate one user flow at a time.

Start with a simple text-generation flow.
Update the endpoint, request body, and output handling.
Decide whether the flow uses previous_response_id, manual Item replay, or the Conversations API.
If the flow is stateless or ZDR, add store: false and include encrypted reasoning items when reasoning context must continue across turns.
Migrate function definitions and verify function call outputs include the correct call_id.
Move Structured Outputs schemas from response_format to text.format.
Update streaming consumers to handle typed Responses events.
Replace custom orchestration with OpenAI-hosted tools where they fit the workflow.
Compare behavior, latency, token usage, and errors before routing more traffic to Responses.

We recommend migrating all flows to the Responses API over time to take advantage of the latest OpenAI features and improvements.

Assistants API

Based on developer feedback from the Assistants API beta, we’ve incorporated key improvements into the Responses API to make it more flexible, faster, and easier to use. The Responses API represents the future direction for building agents on OpenAI.

We now have Assistant-like and Thread-like objects in the Responses API. Learn more in the migration guide. As of August 26, 2025, we’re deprecating the Assistants API, with a sunset date of August 26, 2026.

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