codex/sdk/python/docs/getting-started.md

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
uv sync
source .venv/bin/activate

Requirements:

• Python >=3.10
• uv
• installed openai-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

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"})
    result = thread.run("Say hello in one sentence.")

    print("Thread:", thread.id)
    print("Text:", result.final_response)
    print("Items:", len(result.items))

What happened:

• Codex() started and initialized codex app-server.
• thread_start(...) created a thread.
• thread.run("...") started a turn, consumed events until completion, and returned the final assistant response plus collected items and usage.
• result.final_response is None when no final-answer or phase-less assistant message item completes for the turn.
• use thread.turn(...) when you need a TurnHandle for streaming, steering, interrupting, or turn IDs/status
• one client can have only one active turn consumer (thread.run(...), TurnHandle.stream(), or TurnHandle.run()) at a time in the current experimental build

3) Continue the same thread (multi-turn)

from codex_app_server import Codex

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

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

    print("first:", first.final_response)
    print("second:", second.final_response)

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


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


asyncio.run(main())

5) Resume an existing thread

from codex_app_server import Codex

THREAD_ID = "thr_123"  # replace with a real id

with Codex() as codex:
    thread = codex.thread_resume(THREAD_ID)
    result = thread.run("Continue where we left off.")
    print(result.final_response)

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