## 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>
6.3 KiB
6.3 KiB
Codex App Server SDK — API Reference
Public surface of codex_app_server for app-server v2.
This SDK surface is experimental. The current implementation intentionally allows only one active TurnHandle.stream() or TurnHandle.run() consumer per client instance at a time.
Package Entry
from codex_app_server import (
Codex,
AsyncCodex,
Thread,
AsyncThread,
TurnHandle,
AsyncTurnHandle,
InitializeResponse,
Input,
InputItem,
TextInput,
ImageInput,
LocalImageInput,
SkillInput,
MentionInput,
TurnStatus,
)
from codex_app_server.generated.v2_all import ThreadItem
- Version:
codex_app_server.__version__ - Requires Python >= 3.10
- Canonical generated app-server models live in
codex_app_server.generated.v2_all
Codex (sync)
Codex(config: AppServerConfig | None = None)
Properties/methods:
metadata -> InitializeResponseclose() -> Nonethread_start(*, approval_policy=None, base_instructions=None, config=None, cwd=None, developer_instructions=None, ephemeral=None, model=None, model_provider=None, personality=None, sandbox=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_policy=None, base_instructions=None, config=None, cwd=None, developer_instructions=None, model=None, model_provider=None, personality=None, sandbox=None) -> Threadthread_fork(thread_id: str, *, approval_policy=None, base_instructions=None, config=None, cwd=None, developer_instructions=None, model=None, model_provider=None, sandbox=None) -> Threadthread_archive(thread_id: str) -> ThreadArchiveResponsethread_unarchive(thread_id: str) -> Threadmodels(*, include_hidden: bool = False) -> ModelListResponse
Context manager:
with Codex() as codex:
...
AsyncCodex (async parity)
AsyncCodex(config: AppServerConfig | 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]thread_start(*, approval_policy=None, base_instructions=None, config=None, cwd=None, developer_instructions=None, ephemeral=None, model=None, model_provider=None, personality=None, sandbox=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_policy=None, base_instructions=None, config=None, cwd=None, developer_instructions=None, model=None, model_provider=None, personality=None, sandbox=None) -> Awaitable[AsyncThread]thread_fork(thread_id: str, *, approval_policy=None, base_instructions=None, config=None, cwd=None, developer_instructions=None, ephemeral=None, model=None, model_provider=None, sandbox=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:
...
Thread / AsyncThread
Thread and AsyncThread share the same shape and intent.
Thread
turn(input: Input, *, approval_policy=None, cwd=None, effort=None, model=None, output_schema=None, personality=None, sandbox_policy=None, summary=None) -> TurnHandleread(*, include_turns: bool = False) -> ThreadReadResponseset_name(name: str) -> ThreadSetNameResponsecompact() -> ThreadCompactStartResponse
AsyncThread
turn(input: Input, *, approval_policy=None, cwd=None, effort=None, model=None, output_schema=None, personality=None, sandbox_policy=None, summary=None) -> Awaitable[AsyncTurnHandle]read(*, include_turns: bool = False) -> Awaitable[ThreadReadResponse]set_name(name: str) -> Awaitable[ThreadSetNameResponse]compact() -> Awaitable[ThreadCompactStartResponse]
TurnHandle / AsyncTurnHandle
TurnHandle
steer(input: Input) -> TurnSteerResponseinterrupt() -> TurnInterruptResponsestream() -> Iterator[Notification]run() -> codex_app_server.generated.v2_all.Turn
Behavior notes:
stream()andrun()are exclusive per client instance in the current experimental build- starting a second turn consumer on the same
Codexinstance raisesRuntimeError
AsyncTurnHandle
steer(input: Input) -> Awaitable[TurnSteerResponse]interrupt() -> Awaitable[TurnInterruptResponse]stream() -> AsyncIterator[Notification]run() -> Awaitable[codex_app_server.generated.v2_all.Turn]
Behavior notes:
stream()andrun()are exclusive per client instance in the current experimental build- starting a second turn consumer on the same
AsyncCodexinstance raisesRuntimeError
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
Generated Models
The SDK wrappers return and accept canonical generated app-server models wherever possible:
from codex_app_server.generated.v2_all import (
AskForApproval,
ThreadReadResponse,
Turn,
TurnStartParams,
TurnStatus,
)
Retry + errors
from codex_app_server 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 codex_app_server import Codex, TextInput
with Codex() as codex:
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(completed_turn.id, completed_turn.status)