OpenAI provides a few ways to manage conversation state, which is important for preserving information across multiple messages or turns in a conversation.

While each text generation request is independent and stateless, you can still implement multi-turn conversations by providing additional messages as parameters to your text generation request. Consider a knock-knock joke:

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from openai import OpenAI

client = OpenAI()

response = client.responses.create(
    model="gpt-5.5",
    input=[
        {"role": "user", "content": "knock knock."},
        {"role": "assistant", "content": "Who's there?"},
        {"role": "user", "content": "Orange."},
    ],
)

print(response.output_text)

By using alternating user and assistant messages, you capture the previous state of a conversation in one request to the model.

To manually share context across generated responses, include the model’s previous response output as input, and append that input to your next request.

In the following example, we ask the model to tell a joke, followed by a request for another joke. Appending previous responses to new requests in this way helps ensure conversations feel natural and retain the context of previous interactions.

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from openai import OpenAI

client = OpenAI()

history = [
    {
        "role": "user",
        "content": "tell me a joke"
    }
]

response = client.responses.create(
    model="gpt-5.5",
    input=history,
    store=False,
    include=["reasoning.encrypted_content"],
)

print(response.output_text)

# Add all response output items, including encrypted reasoning items, to the conversation
history += response.output

history.append({ "role": "user", "content": "tell me another" })

second_response = client.responses.create(
    model="gpt-5.5",
    input=history,
    store=False,
    include=["reasoning.encrypted_content"],
)

print(second_response.output_text)

Our APIs make it easier to manage conversation state automatically, so you don’t have to pass inputs manually with each turn of a conversation.

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conversation = openai.conversations.create()

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response = openai.responses.create(
  model="gpt-5.5",
  input=[{"role": "user", "content": "What are the 5 Ds of dodgeball?"}],
  conversation="conv_689667905b048191b4740501625afd940c7533ace33a2dab"
)

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from openai import OpenAI
client = OpenAI()

response = client.responses.create(
    model="gpt-5.5",
    input="tell me a joke",
)
print(response.output_text)

second_response = client.responses.create(
    model="gpt-5.5",
    previous_response_id=response.id,
    input=[{"role": "user", "content": "explain why this is funny."}],
)
print(second_response.output_text)

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from openai import OpenAI
client = OpenAI()

response = client.responses.create(
    model="gpt-5.5",
    input="tell me a joke",
)
print(response.output_text)

second_response = client.responses.create(
    model="gpt-5.5",
    previous_response_id=response.id,
    input=[{"role": "user", "content": "explain why this is funny."}],
)
print(second_response.output_text)

Managing the context window

Understanding context windows will help you successfully create threaded conversations and manage state across model interactions.

The context window is the maximum number of tokens that can be used in a single request. This max tokens number includes input, output, and reasoning tokens. To learn your model’s context window, see model details.

Managing context for text generation

As your inputs become more complex, or you include more turns in a conversation, you’ll need to consider both output token and context window limits. Model inputs and outputs are metered in tokens, which are parsed from inputs to analyze their content and intent and assembled to render logical outputs. Models have limits on token usage during the lifecycle of a text generation request.

• Output tokens are the tokens generated by a model in response to a prompt. Each model has different limits for output tokens. For example, gpt-4o-2024-08-06 can generate a maximum of 16,384 output tokens.
• A context window describes the total tokens that can be used for both input and output tokens (and for some models, reasoning tokens). Compare the context window limits of our models. For example, gpt-4o-2024-08-06 has a total context window of 128k tokens.

If you create a very large prompt—often by including extra context, data, or examples for the model—you run the risk of exceeding the allocated context window for a model, which might result in truncated outputs.

Use the tokenizer tool, built with the tiktoken library, to see how many tokens are in a particular string of text.

Tokens generated in excess of the context window limit may be truncated in API responses.

You can estimate the number of tokens your messages will use with the tokenizer tool.

Compaction

Detailed compaction guidance now lives in Compaction.

• For /responses with context_management and compact_threshold, see Server-side compaction.
• For explicit compaction control, see Standalone compact endpoint and the /responses/compact API reference.

For more specific examples and use cases, visit the OpenAI Cookbook, or learn more about using the APIs to extend model capabilities:

• Receive JSON responses with Structured Outputs
• Extend the models with function calling
• Enable streaming for real-time responses
• Build a computer-using agent