## Why Python callers need control over response history loading and a way to override the service tier for one turn. These options also need runtime compatibility checks to prevent older CLIs from silently ignoring them. ## What changed - Add `include_turns` to sync and async thread resume/fork methods. Omission preserves server defaults; `False` skips response history loading without changing model context. - Add `turn_service_tier` and `source` to sync and async `run()` and `turn()`, and generate both methods together to keep their options aligned. - Require CLI `0.151.0` or newer when sending the new options, with lazy schema checks for unversioned local builds. - Pin the bundled runtime dependency to `0.153.4` and reject unsupported runtime versions during SDK packaging. ## Testing Add coverage for option forwarding, history flag omission and inversion, runtime version checks, cached schema probing, and packaging compatibility. Extend app-server and installed SDK smoke tests to exercise the new options. GitOrigin-RevId: 4bcc9cff687b0651e67852e7c080df7fadac6d76
13 KiB
OpenAI Codex Python SDK - API Reference
Public surface of openai_codex for Codex workflows.
Turn streams are routed by turn ID so one client can consume multiple active turns concurrently.
Thread starts default to ApprovalMode.auto_review; turn starts accept an optional approval_mode override.
Package Entry
from openai_codex import (
Codex,
AsyncCodex,
CodexConfig,
ApprovalMode,
Sandbox,
ChatgptLoginHandle,
DeviceCodeLoginHandle,
AsyncChatgptLoginHandle,
AsyncDeviceCodeLoginHandle,
Thread,
AsyncThread,
TurnHandle,
AsyncTurnHandle,
TurnResult,
Input,
InputItem,
RunInput,
TextInput,
ImageInput,
LocalImageInput,
SkillInput,
MentionInput,
)
from openai_codex.types import (
Account,
AccountLoginCompletedNotification,
CancelLoginAccountResponse,
CancelLoginAccountStatus,
GetAccountResponse,
InitializeResponse,
ThreadItem,
ThreadTokenUsage,
TurnError,
TurnStatus,
)
- Version:
openai_codex.__version__ - Requires Python >= 3.10
- Public Codex protocol value and event types live in
openai_codex.types
Codex (sync)
Codex(config: CodexConfig | None = None)
Properties/methods:
metadata -> InitializeResponseclose() -> Nonelogin_api_key(api_key: str) -> Nonelogin_chatgpt() -> ChatgptLoginHandlelogin_chatgpt_device_code() -> DeviceCodeLoginHandleaccount(*, refresh_token: bool = False) -> GetAccountResponselogout() -> Nonethread_start(*, approval_mode=ApprovalMode.auto_review, base_instructions=None, config=None, cwd=None, developer_instructions=None, ephemeral=None, model=None, model_provider=None, personality=None, sandbox: Sandbox | None = None) -> Threadthread_list(*, archived=None, cursor=None, cwd=None, limit=None, model_providers=None, sort_key=None, source_kinds=None) -> ThreadListResponsethread_resume(thread_id: str, *, approval_mode=None, base_instructions=None, config=None, cwd=None, developer_instructions=None, include_turns: bool | None = None, model=None, model_provider=None, personality=None, sandbox: Sandbox | None = None, service_tier=None) -> Threadthread_fork(thread_id: str, *, approval_mode=None, base_instructions=None, config=None, cwd=None, developer_instructions=None, ephemeral=None, include_turns: bool | None = None, model=None, model_provider=None, sandbox: Sandbox | None = None, service_tier=None) -> Threadthread_archive(thread_id: str) -> ThreadArchiveResponsethread_unarchive(thread_id: str) -> Threadmodels(*, include_hidden: bool = False) -> ModelListResponse
Context manager:
with Codex() as codex:
...
thread_resume(...) and thread_fork(...) accept include_turns to control
whether the server loads turn history into its response. False skips that
work; True requests it. Omitting the option, or passing None, preserves the
server's default behavior. This does not remove history from the model's
context. Both methods return a thread handle; use thread.read(include_turns=True)
to retrieve its history.
AsyncCodex (async parity)
AsyncCodex(config: CodexConfig | None = None)
Preferred usage:
async with AsyncCodex() as codex:
...
AsyncCodex initializes lazily. Context entry is the standard path because it
ensures startup and shutdown are paired explicitly.
Properties/methods:
metadata -> InitializeResponseclose() -> Awaitable[None]login_api_key(api_key: str) -> Awaitable[None]login_chatgpt() -> Awaitable[AsyncChatgptLoginHandle]login_chatgpt_device_code() -> Awaitable[AsyncDeviceCodeLoginHandle]account(*, refresh_token: bool = False) -> Awaitable[GetAccountResponse]logout() -> Awaitable[None]thread_start(*, approval_mode=ApprovalMode.auto_review, base_instructions=None, config=None, cwd=None, developer_instructions=None, ephemeral=None, model=None, model_provider=None, personality=None, sandbox: Sandbox | None = None) -> Awaitable[AsyncThread]thread_list(*, archived=None, cursor=None, cwd=None, limit=None, model_providers=None, sort_key=None, source_kinds=None) -> Awaitable[ThreadListResponse]thread_resume(thread_id: str, *, approval_mode=None, base_instructions=None, config=None, cwd=None, developer_instructions=None, include_turns: bool | None = None, model=None, model_provider=None, personality=None, sandbox: Sandbox | None = None, service_tier=None) -> Awaitable[AsyncThread]thread_fork(thread_id: str, *, approval_mode=None, base_instructions=None, config=None, cwd=None, developer_instructions=None, ephemeral=None, include_turns: bool | None = None, model=None, model_provider=None, sandbox: Sandbox | None = None, service_tier=None) -> Awaitable[AsyncThread]thread_archive(thread_id: str) -> Awaitable[ThreadArchiveResponse]thread_unarchive(thread_id: str) -> Awaitable[AsyncThread]models(*, include_hidden: bool = False) -> Awaitable[ModelListResponse]
Async context manager:
async with AsyncCodex() as codex:
...
Login handles
ChatgptLoginHandle / AsyncChatgptLoginHandle
login_id: strauth_url: strwait() -> AccountLoginCompletedNotificationcancel() -> CancelLoginAccountResponse
Async handle methods return awaitables.
DeviceCodeLoginHandle / AsyncDeviceCodeLoginHandle
login_id: strverification_url: struser_code: strwait() -> AccountLoginCompletedNotificationcancel() -> CancelLoginAccountResponse
Async handle methods return awaitables.
wait() consumes only the completion notification for its matching login
attempt. API-key login completes synchronously and does not return a handle.
Thread / AsyncThread
Thread and AsyncThread share the same shape and intent.
Thread
run(input: RunInput, *, approval_mode=None, cwd=None, effort=None, model=None, output_schema=None, personality=None, sandbox: Sandbox | None = None, service_tier=None, source=None, summary=None, turn_service_tier=None) -> TurnResultturn(input: RunInput, *, approval_mode=None, cwd=None, effort=None, model=None, output_schema=None, personality=None, sandbox: Sandbox | None = None, service_tier=None, source=None, summary=None, turn_service_tier=None) -> TurnHandleread(*, include_turns: bool = False) -> ThreadReadResponseset_name(name: str) -> ThreadSetNameResponsecompact() -> ThreadCompactStartResponse
AsyncThread
run(input: RunInput, *, approval_mode=None, cwd=None, effort=None, model=None, output_schema=None, personality=None, sandbox: Sandbox | None = None, service_tier=None, source=None, summary=None, turn_service_tier=None) -> Awaitable[TurnResult]turn(input: RunInput, *, approval_mode=None, cwd=None, effort=None, model=None, output_schema=None, personality=None, sandbox: Sandbox | None = None, service_tier=None, source=None, summary=None, turn_service_tier=None) -> Awaitable[AsyncTurnHandle]read(*, include_turns: bool = False) -> Awaitable[ThreadReadResponse]set_name(name: str) -> Awaitable[ThreadSetNameResponse]compact() -> Awaitable[ThreadCompactStartResponse]
run(...) is the common-case convenience path. It accepts the same input and
options as turn(...), consumes notifications until completion, and returns a
small result object with:
id: strstatus: TurnStatuserror: TurnError | Nonestarted_at: int | Nonecompleted_at: int | Noneduration_ms: int | Nonefinal_response: str | Noneitems: list[ThreadItem]usage: ThreadTokenUsage | None
final_response is None when the turn finishes without a final-answer or
phase-less assistant message item.
Use turn(...) when you need low-level turn control (stream(), steer(),
interrupt()) before collecting the turn result.
Turn options
These options have the same behavior on sync and async run(...) and turn(...):
| Option | Behavior |
|---|---|
| `service_tier: str | None = None` |
| `turn_service_tier: str | None = None` |
| `source: str | None = None` |
turn_service_tier, source, and explicit include_turns
on resume/fork require Codex CLI 0.151.0 or newer. The SDK raises CodexError
before sending these options to an older runtime, which would otherwise ignore
them. Published SDK releases install a matching runtime automatically; when
using CodexConfig.codex_bin, choose a compatible executable. Unversioned local
builds are checked lazily against their experimental schema before these options
are sent. A custom launch_args_override must report a supported version.
Sandbox
Use sandbox= consistently on thread lifecycle methods and turns:
from openai_codex import Codex, Sandbox
with Codex() as codex:
thread = codex.thread_start(sandbox=Sandbox.workspace_write)
result = thread.run("Review the diff only.", sandbox=Sandbox.read_only)
Presets:
Sandbox.read_only: read files without allowing writes.Sandbox.workspace_write: the normal default for projects with a recorded trust decision; read files and write inside the workspace and configured writable roots.Sandbox.full_access: run without filesystem access restrictions.
When sandbox= is omitted, Codex uses its configured default. A sandbox
passed to run(...) or turn(...) applies to that turn and subsequent turns.
TurnHandle / AsyncTurnHandle
TurnHandle
steer(input: str | Input) -> TurnSteerResponseinterrupt() -> TurnInterruptResponsestream() -> Iterator[Notification]run() -> TurnResult
Behavior notes:
stream()andrun()consume only notifications for their own turn ID- one
Codexinstance can stream multiple active turns concurrently
AsyncTurnHandle
steer(input: str | Input) -> Awaitable[TurnSteerResponse]interrupt() -> Awaitable[TurnInterruptResponse]stream() -> AsyncIterator[Notification]run() -> Awaitable[TurnResult]
Behavior notes:
stream()andrun()consume only notifications for their own turn ID- one
AsyncCodexinstance can stream multiple active turns concurrently
Inputs
@dataclass class TextInput: text: str
@dataclass class ImageInput: url: str
@dataclass class LocalImageInput: path: str
@dataclass class SkillInput: name: str; path: str
@dataclass class MentionInput: name: str; path: str
InputItem = TextInput | ImageInput | LocalImageInput | SkillInput | MentionInput
Input = list[InputItem] | InputItem
RunInput = Input | str
Use ImageInput with a base64-encoded data:image/... URL. HTTP and HTTPS image URLs are
deprecated; download remote images and pass their local paths with LocalImageInput instead.
Use a plain str as shorthand for TextInput(...) anywhere a turn input is accepted:
thread.run("..."), thread.turn("..."), and turn.steer("...").
Public Types
The SDK wrappers return and accept public Codex protocol models wherever possible:
from openai_codex.types import (
Account,
AccountLoginCompletedNotification,
CancelLoginAccountResponse,
CancelLoginAccountStatus,
GetAccountResponse,
ThreadReadResponse,
Turn,
TurnStatus,
)
Notifications and generated models
Known notifications have typed Notification.payload values, including
authentication recovery, thread queue/project changes, thread reversion, and
realtime item updates. The Notification.payload type covers every registered
event. Unknown methods and payloads that fail validation still produce
UnknownNotification, with the raw data in
.params. When an event gains a typed payload, read its named fields instead
of .params.
Returned models include the current CLI's thread metadata, richer turn errors,
and functionCallOutput history items. Code that imports generated
HookMetadata directly must access the handler through .root, inspect its
handler_type, and then read the fields for that handler. For example, only a
"command" handler has a command field. This reflects the app-server's
separate command, MCP tool, prompt, and agent hook variants.
Retry + errors
from openai_codex import (
retry_on_overload,
JsonRpcError,
MethodNotFoundError,
InvalidParamsError,
ServerBusyError,
is_retryable_error,
)
retry_on_overload(...)retries transient overload errors with exponential backoff + jitter.is_retryable_error(exc)checks if an exception is transient/overload-like.
Example
from openai_codex import Codex
with Codex() as codex:
thread = codex.thread_start(model="gpt-5.4", config={"model_reasoning_effort": "high"})
result = thread.run("Say hello in one sentence.")
print(result.final_response)