## TL;DR WIP esp the examples Thin the Python SDK public surface so the wrapper layer returns canonical app-server generated models directly. - keeps `Codex` / `AsyncCodex` / `Thread` / `Turn` and input helpers, but removes alias-only type layers and custom result models - `metadata` now returns `InitializeResponse` and `run()` returns the generated app-server `Turn` - updates docs, examples, notebook, and tests to use canonical generated types and regenerates `v2_all.py` against current schema - keeps the pinned runtime-package integration flow and real integration coverage ## Validation - `PYTHONPATH=sdk/python/src python3 -m pytest sdk/python/tests` - `GH_TOKEN="$(gh auth token)" RUN_REAL_CODEX_TESTS=1 PYTHONPATH=sdk/python/src python3 -m pytest sdk/python/tests -rs` --------- Co-authored-by: Codex <noreply@openai.com>
3.2 KiB
3.2 KiB
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-binruntime package, or an explicitcodex_binoverride - 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 initializedcodex app-server.thread_start(...)created a thread.turn(...).run()consumed events untilturn/completedand returned the canonical generated app-serverTurnmodel.- 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