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Integrate Python with OpenAI’s cloud API using the official openai package: install the SDK, set an OPENAI_API_KEY environment variable, create an OpenAI client, and send a request with the Responses API. This connects your Python application to OpenAI models; it does not connect to the ChatGPT desktop app.
What you need to connect Python to OpenAI
- Python 3.10 or later. The official OpenAI Python library describes itself as providing access to the OpenAI REST API from Python 3.10+ applications.
- An OpenAI API key created in the OpenAI dashboard. API access and billing are separate from using the ChatGPT app.
- The official
openaiPython package, installed in the environment where your script runs.
For a new integration, the SDK documentation identifies the Responses API as its primary interface. Chat Completions remains documented for existing applications, but its message format and capabilities differ, so choose based on your application’s needs and model availability.
Install the SDK and make your first request
- Install the package in your active Python environment:
pip install openai - Create an API key in the OpenAI dashboard. Set it in the environment rather than embedding it in your Python file. On macOS or Linux, for example:
export OPENAI_API_KEY="your_api_key_here"In Windows, set the environment variable through your shell or system environment settings before launching Python.
- Save this as
example.py, replacing<current-model>with a model available to your account:from openai import OpenAI client = OpenAI() # reads OPENAI_API_KEY from the environment response = client.responses.create( model="<current-model>", input="Explain how Python decorators work in one paragraph.", ) print(response.output_text) - Run the script from the same environment where the SDK and API key are available:
python example.py
The result is printed from response.output_text. The SDK also supports passing an explicit api_key to OpenAI, but environment-based configuration avoids placing the credential in source code. For local development that needs a .env file, the SDK documentation describes using python-dotenv; ensure that file is excluded from source control.
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Keep the API key out of your code and repository
Do not commit an API key to Git or include it in a script, notebook, public issue, or client-side application. Anyone who obtains the key may use the associated API account. Read it from an environment variable, and store production credentials in the secrets mechanism provided by your deployment platform. If a key is exposed, revoke it and replace it.
Choose the API pattern that fits the application
For a new Python project, begin with client.responses.create(...). Responses supports a broader set of tools and multimodal inputs than the legacy message-based pattern. If an existing application uses Chat Completions, keep its current integration where appropriate or plan a deliberate migration rather than changing endpoints without checking how conversation state, inputs, and tool workflows are represented.
Before deployment, confirm that the model name you intend to use is available to your account and supports the capabilities your application needs. Model availability can change; the placeholder in the example is intentionally not a fixed model recommendation.
Use asynchronous requests when your application is asynchronous
In an async application, use AsyncOpenAI and await the request instead of making a synchronous call that blocks the event loop:
import asyncio
from openai import AsyncOpenAI
async def main():
client = AsyncOpenAI()
response = await client.responses.create(
model="<current-model>",
input="Explain how Python decorators work in one paragraph.",
)
print(response.output_text)
asyncio.run(main())Use this pattern when the surrounding framework or workload benefits from async I/O; a regular OpenAI client is simpler for ordinary scripts.
Stream output as it is generated
Set stream=True to receive events incrementally rather than waiting for the complete response. The SDK supports synchronous iteration and asynchronous iteration over streamed events. A minimal synchronous pattern is:
from openai import OpenAI
client = OpenAI()
stream = client.responses.create(
model="<current-model>",
input="Explain how Python decorators work in one paragraph.",
stream=True,
)
for event in stream:
if event.type == "response.output_text.delta":
print(event.delta, end="", flush=True)Handle the stream’s completion and error events as appropriate for your application; do not assume every event contains text.
Add tools or application-defined functions
Built-in tools such as web search and file search can extend a basic request. Function calling lets a model request that your application run a function; the model does not execute your Python code itself. Your program receives the requested function and arguments, validates them, runs only permitted operations, and returns the result to the model.
For function arguments, OpenAI’s guidance says strict: true constrains generated arguments to the supplied schema when that schema uses the supported JSON Schema subset and meets strict-mode requirements. Strict schema adherence does not replace application-level authorization, validation of business rules, or safeguards around consequential actions.
Handle API failures and support diagnostics
Production code should distinguish expected API errors from unexpected failures. The SDK documents typed exceptions for common HTTP status categories:
| Status | Meaning to check |
|---|---|
| 401 | Authentication failed; check that the key exists, is valid, and is being loaded by the running process. |
| 403 | The request is not permitted; check account permissions and access to the requested capability. |
| 404 | The requested resource or endpoint was not found; check the model name and request target. |
| 422 | The request failed validation; inspect input fields and schema requirements. |
| 429 | The request hit a rate limit; use appropriate retry handling and respect any retry guidance. |
| 500+ | A server-side failure occurred; handle transient errors without assuming the request succeeded. |
Capture the request ID associated with failed calls and include it when contacting support. Avoid logging API keys or sensitive prompt content. The API reference covers authentication, request schemas, streaming events, errors, rate limits, and request IDs.
Quick Recap
Common setup problems
- Authentication error: confirm that
OPENAI_API_KEYis set in the shell or service process that actually runs Python. A variable set in one terminal may not be available to another process. - Module not found: install
openaiusing the same Python environment or virtual environment used to run the script. - Model or permission error: verify the model name and account access; do not assume every model or tool is enabled for every account.
- Unexpected response format: check the endpoint and SDK documentation for the API pattern in use. Responses and Chat Completions are not interchangeable in every detail.
Official references
- OpenAI Python SDK README — installation, clients, Responses, Chat Completions, streaming, and errors.
- OpenAI API quickstart — API key setup and first request.
- OpenAI function-calling guide — tool handoffs, argument schemas, and strict mode.
- OpenAI API reference — shared API request and response details.
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