English | 한국어

oauth-codex

OAuth PKCE-based Python SDK for the Codex backend.

Highlights

• Resource-style clients: Client and AsyncClient
• OAuth PKCE only, with interactive login and automatic token refresh
• OpenAI-style chat.completions.create(...)
• Lower-level responses.create(...), responses.parse(...), and responses.stream(...)
• Callable tool loop helpers via client.beta.chat.completions.run_tools(...)

Installation

pip install oauth-codex

Requires Python 3.11 or newer.

Quick Start

Synchronous Client

from oauth_codex import Client

client = Client()
client.authenticate()

completion = client.chat.completions.create(
    model="gpt-5.3-codex",
    messages=[{"role": "user", "content": "Hello from oauth-codex"}],
)
print(completion.choices[0].message.content)

Asynchronous Client

import asyncio

from oauth_codex import AsyncClient


async def main():
    client = AsyncClient()
    await client.authenticate()

    completion = await client.chat.completions.create(
        model="gpt-5.3-codex",
        messages=[{"role": "user", "content": "Hello async"}],
    )
    print(completion.choices[0].message.content)


asyncio.run(main())

Authentication

This SDK uses OAuth PKCE only. API keys are not supported.

client = Client()
client.authenticate()

On first authentication, the SDK prints an authorization URL, waits for the browser sign-in flow, and asks you to paste the localhost callback URL back into the terminal. Tokens are stored locally and refreshed automatically on later requests.

Main API Surface

Chat Completions

response = client.chat.completions.create(
    model="gpt-5.3-codex",
    messages=[{"role": "user", "content": "Write a sorting function"}],
)
print(response.choices[0].message.content)

Responses Resource

response = client.responses.create(
    model="gpt-5.3-codex",
    input=[{"role": "user", "content": "Analyze this code snippet."}],
)
print(response.output_text)

Streaming

for event in client.responses.stream(
    model="gpt-5.3-codex",
    input=[{"role": "user", "content": "Say hello in three words"}],
):
    if event.type == "text_delta" and event.delta:
        print(event.delta, end="", flush=True)

Structured Output

from pydantic import BaseModel


class Summary(BaseModel):
    title: str
    score: int


response = client.responses.parse(
    model="gpt-5.3-codex",
    input=[{"role": "user", "content": "Return JSON with title and score"}],
    response_format=Summary,
)
print(response.parsed)

Tool Execution

def add(a: int, b: int) -> int:
    return a + b


completion = client.beta.chat.completions.run_tools(
    model="gpt-5.3-codex",
    messages=[{"role": "user", "content": "What is 2 + 3?"}],
    tools=[add],
)
print(completion.choices[0].message.content)

Available Namespaces

• client.chat.completions
• client.responses
• client.files
• client.vector_stores
• client.vector_stores.files
• client.vector_stores.file_batches
• client.models
• client.beta.chat.completions

AsyncClient exposes the same namespaces with async methods.

Removed In 4.0

• authenticate_on_init
• generate, agenerate, stream, astream
• legacy OAuthCodexClient and AsyncOAuthCodexClient
• module-level proxy usage such as oauth_codex.responses.create(...)

Error Handling

The package exports OpenAI-style exception classes such as AuthenticationError, RateLimitError, APIConnectionError, and APIStatusError.

Documentation

• English: docs/en/index.md
• Korean: docs/ko/index.md

Development

pip install -e .[dev]
pytest -q
python -m build

Changelog

CHANGELOG.md