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Shaqayeq 0769ed5b5d python-sdk: clarify initialize metadata and async entrypoint (2026-03-16)

Normalize validated initialize metadata back onto InitializeResponse so successful metadata access exposes populated serverInfo fields even when they were derived from userAgent.

Also make async lifecycle guidance explicit in the public surface by documenting async with AsyncCodex() as the preferred entrypoint and aligning the AsyncCodex metadata error message with that model.

Co-authored-by: Codex <noreply@openai.com>

2026-03-16 15:10:12 -07:00

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Getting Started

This is the fastest path from install to a multi-turn thread using the public SDK surface.

The SDK is experimental. Treat the API, bundled runtime strategy, and packaging details as unstable until the first public release.

1) Install

From repo root:

cd sdk/python
python -m pip install -e .

Requirements:

Python >=3.10
installed codex-cli-bin runtime package, or an explicit codex_bin override
local Codex auth/session configured

2) Run your first turn (sync)

from codex_app_server import Codex, TextInput

with Codex() as codex:
    server = codex.metadata.serverInfo
    print("Server:", None if server is None else server.name, None if server is None else server.version)

    thread = codex.thread_start(model="gpt-5.4", config={"model_reasoning_effort": "high"})
    completed_turn = thread.turn(TextInput("Say hello in one sentence.")).run()

    print("Thread:", thread.id)
    print("Turn:", completed_turn.id)
    print("Status:", completed_turn.status)
    print("Items:", len(completed_turn.items or []))

What happened:

Codex() started and initialized codex app-server.
thread_start(...) created a thread.
turn(...).run() consumed events until turn/completed and returned the canonical generated app-server Turn model.
one client can have only one active TurnHandle.stream() / TurnHandle.run() consumer at a time in the current experimental build

3) Continue the same thread (multi-turn)

from codex_app_server import Codex, TextInput

with Codex() as codex:
    thread = codex.thread_start(model="gpt-5.4", config={"model_reasoning_effort": "high"})

    first = thread.turn(TextInput("Summarize Rust ownership in 2 bullets.")).run()
    second = thread.turn(TextInput("Now explain it to a Python developer.")).run()

    print("first:", first.id, first.status)
    print("second:", second.id, second.status)

4) Async parity

Use async with AsyncCodex() as the normal async entrypoint. AsyncCodex initializes lazily, and context entry makes startup/shutdown explicit.

import asyncio
from codex_app_server import AsyncCodex, TextInput


async def main() -> None:
    async with AsyncCodex() as codex:
        thread = await codex.thread_start(model="gpt-5.4", config={"model_reasoning_effort": "high"})
        turn = await thread.turn(TextInput("Continue where we left off."))
        completed_turn = await turn.run()
        print(completed_turn.id, completed_turn.status)


asyncio.run(main())

5) Resume an existing thread

from codex_app_server import Codex, TextInput

THREAD_ID = "thr_123"  # replace with a real id

with Codex() as codex:
    thread = codex.thread_resume(THREAD_ID)
    completed_turn = thread.turn(TextInput("Continue where we left off.")).run()
    print(completed_turn.id, completed_turn.status)

6) Generated models

The convenience wrappers live at the package root, but the canonical app-server models live under:

from codex_app_server.generated.v2_all import Turn, TurnStatus, ThreadReadResponse

7) Next stops

API surface and signatures: docs/api-reference.md
Common decisions/pitfalls: docs/faq.md
End-to-end runnable examples: examples/README.md

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