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To call the OpenAI API from Python, create an API key, install OpenAI’s official Python SDK, and send a request to the Responses API. Keep the key secret, and check OpenAI’s live documentation for current model names, request options, and data controls before building an application.

What you need to make an API request

Your Python program sends a request over the network to OpenAI’s hosted API. It needs an API key to authenticate. OpenAI describes the API as providing an interface to models for text generation, natural language processing, computer vision, and other capabilities in its Developer quickstart.

  • An OpenAI API key, created through the API platform.
  • Python installed in your development environment.
  • The official openai Python package.

Create and protect an API key

Follow the API-key instructions in the OpenAI Developer quickstart. Treat the key like a password: anyone who obtains it may be able to make API requests using your access.

  • Do not paste the key into public code, commit it to version control, or include it in a client-side application.
  • For local development, store it in an environment variable or a secure secret store rather than hard-coding it in a script.
  • If a key is exposed, revoke it and create a replacement using the controls in your account.

Install the SDK and send a first request

OpenAI’s quickstart provides the current installation command and first-request example. Use the command and code shown there rather than copying an old snippet from another tutorial, since SDK methods and recommended model identifiers can change.

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  1. Install the package: use the Python installation command in the quickstart for the environment you are using.
  2. Set the key: make the API key available to your process through the environment variable or secret mechanism described in the quickstart.
  3. Run the first request: copy the current Python example from the quickstart, including its model value and request shape. The example uses the Responses API for generated output.
  4. Check the result: inspect the returned response object and use the SDK’s documented text helper where appropriate to retrieve generated text.

The live quickstart is the source of truth for exact code, package installation syntax, and the currently demonstrated model. Avoid treating a model ID from a saved snippet as a permanent recommendation.

Understand the response before building on it

A request returns a structured response object, not necessarily a single plain-text field with a fixed layout. Use the SDK’s documented helper for generated text when you need a convenient text result, and consult the Responses API reference when you need other output, metadata, or control over request parameters. Do not assume that every response has the same output-array structure or ordering.

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Use tools when the request needs more than generation

The Responses API can be extended with tools. A tool-enabled request requires more than asking the model for text: your application may need to provide the tool definition, inspect the returned events or output, execute the corresponding operation, and send results back as appropriate. Available tools and their exact parameters evolve, so follow the current tools guide and API reference for the particular capability you plan to use.

Stream output as server-sent events

For incremental output, enable streaming using the documented request option. The API then delivers a server-sent event (SSE) flow rather than waiting to return the entire result at once. Your Python code must consume and handle the event stream.

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Process the documented event types and their meaning; do not assume that streaming is simply a sequence of text fragments in one unchanging format. Start with OpenAI’s streaming guide and the current SDK examples, especially if your application uses tools or needs to distinguish completed output from intermediate events.

Choose a model for the task, then verify its current details

Model availability and capabilities change. Begin with what the application needs—such as text, image, or another supported task—then compare the models currently available for that use case. Check the live model catalog for capabilities, input and output constraints, and pricing before choosing an identifier. There is no universally suitable model choice independent of task and cost.

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Check data controls before production

Before sending real user or business data, review OpenAI’s current data controls documentation. Retention and storage behavior may depend on the endpoint, feature, and account or configuration settings. The documentation search result available for this article described a 30-day default application-state retention period for the Responses API and at least 30 days of storage under a particular setting; because that result may not reflect current controls, do not treat those figures as a guarantee for your configuration. Verify the current endpoint-specific terms and settings directly in the live documentation.

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Where to look when your code changes

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