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## Summary - use generated image data URLs in the Python SDK examples and notebook - document HTTP and HTTPS image URLs as deprecated and recommend `LocalImageInput` - replace the remote-URL integration test with data-URL coverage `ImageInput` remains available for data URLs. The SDK does not duplicate app-server URL validation. ## Testing - `uv run --frozen --no-sync ruff check --output-format=full .` - `uv run --frozen --no-sync ruff format --check .` - full Python SDK test suite with an isolated writable `CODEX_SQLITE_HOME` (119 passed, 38 skipped)
297 lines
10 KiB
Markdown
297 lines
10 KiB
Markdown
# OpenAI Codex Python SDK (Beta) - API Reference
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Public surface of `openai_codex` for Codex workflows.
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This SDK is in beta. Public APIs may change before `1.0`. Turn streams are routed by turn ID so one client can consume multiple active turns concurrently.
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Thread starts default to `ApprovalMode.auto_review`; turn starts accept an optional `approval_mode` override.
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## Package Entry
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```python
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from openai_codex import (
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Codex,
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AsyncCodex,
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CodexConfig,
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ApprovalMode,
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Sandbox,
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ChatgptLoginHandle,
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DeviceCodeLoginHandle,
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AsyncChatgptLoginHandle,
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AsyncDeviceCodeLoginHandle,
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Thread,
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AsyncThread,
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TurnHandle,
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AsyncTurnHandle,
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TurnResult,
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Input,
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InputItem,
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RunInput,
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TextInput,
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ImageInput,
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LocalImageInput,
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SkillInput,
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MentionInput,
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)
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from openai_codex.types import (
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Account,
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AccountLoginCompletedNotification,
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CancelLoginAccountResponse,
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CancelLoginAccountStatus,
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GetAccountResponse,
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InitializeResponse,
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ThreadItem,
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ThreadTokenUsage,
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TurnError,
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TurnStatus,
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)
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```
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- Version: `openai_codex.__version__`
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- Requires Python >= 3.10
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- Public Codex protocol value and event types live in `openai_codex.types`
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## Codex (sync)
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```python
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Codex(config: CodexConfig | None = None)
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```
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Properties/methods:
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- `metadata -> InitializeResponse`
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- `close() -> None`
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- `login_api_key(api_key: str) -> None`
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- `login_chatgpt() -> ChatgptLoginHandle`
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- `login_chatgpt_device_code() -> DeviceCodeLoginHandle`
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- `account(*, refresh_token: bool = False) -> GetAccountResponse`
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- `logout() -> None`
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- `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) -> Thread`
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- `thread_list(*, archived=None, cursor=None, cwd=None, limit=None, model_providers=None, sort_key=None, source_kinds=None) -> ThreadListResponse`
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- `thread_resume(thread_id: str, *, approval_mode=ApprovalMode.auto_review, base_instructions=None, config=None, cwd=None, developer_instructions=None, model=None, model_provider=None, personality=None, sandbox: Sandbox | None = None) -> Thread`
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- `thread_fork(thread_id: str, *, approval_mode=ApprovalMode.auto_review, base_instructions=None, config=None, cwd=None, developer_instructions=None, model=None, model_provider=None, sandbox: Sandbox | None = None) -> Thread`
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- `thread_archive(thread_id: str) -> ThreadArchiveResponse`
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- `thread_unarchive(thread_id: str) -> Thread`
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- `models(*, include_hidden: bool = False) -> ModelListResponse`
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Context manager:
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```python
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with Codex() as codex:
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...
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```
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## AsyncCodex (async parity)
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```python
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AsyncCodex(config: CodexConfig | None = None)
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```
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Preferred usage:
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```python
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async with AsyncCodex() as codex:
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...
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```
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`AsyncCodex` initializes lazily. Context entry is the standard path because it
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ensures startup and shutdown are paired explicitly.
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Properties/methods:
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- `metadata -> InitializeResponse`
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- `close() -> Awaitable[None]`
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- `login_api_key(api_key: str) -> Awaitable[None]`
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- `login_chatgpt() -> Awaitable[AsyncChatgptLoginHandle]`
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- `login_chatgpt_device_code() -> Awaitable[AsyncDeviceCodeLoginHandle]`
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- `account(*, refresh_token: bool = False) -> Awaitable[GetAccountResponse]`
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- `logout() -> Awaitable[None]`
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- `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]`
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- `thread_list(*, archived=None, cursor=None, cwd=None, limit=None, model_providers=None, sort_key=None, source_kinds=None) -> Awaitable[ThreadListResponse]`
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- `thread_resume(thread_id: str, *, approval_mode=ApprovalMode.auto_review, base_instructions=None, config=None, cwd=None, developer_instructions=None, model=None, model_provider=None, personality=None, sandbox: Sandbox | None = None) -> Awaitable[AsyncThread]`
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- `thread_fork(thread_id: str, *, approval_mode=ApprovalMode.auto_review, base_instructions=None, config=None, cwd=None, developer_instructions=None, ephemeral=None, model=None, model_provider=None, sandbox: Sandbox | None = None) -> Awaitable[AsyncThread]`
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- `thread_archive(thread_id: str) -> Awaitable[ThreadArchiveResponse]`
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- `thread_unarchive(thread_id: str) -> Awaitable[AsyncThread]`
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- `models(*, include_hidden: bool = False) -> Awaitable[ModelListResponse]`
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Async context manager:
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```python
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async with AsyncCodex() as codex:
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...
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```
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## Login handles
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### ChatgptLoginHandle / AsyncChatgptLoginHandle
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- `login_id: str`
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- `auth_url: str`
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- `wait() -> AccountLoginCompletedNotification`
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- `cancel() -> CancelLoginAccountResponse`
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Async handle methods return awaitables.
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### DeviceCodeLoginHandle / AsyncDeviceCodeLoginHandle
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- `login_id: str`
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- `verification_url: str`
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- `user_code: str`
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- `wait() -> AccountLoginCompletedNotification`
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- `cancel() -> CancelLoginAccountResponse`
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Async handle methods return awaitables.
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`wait()` consumes only the completion notification for its matching login
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attempt. API-key login completes synchronously and does not return a handle.
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## Thread / AsyncThread
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`Thread` and `AsyncThread` share the same shape and intent.
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### Thread
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- `run(input: str | Input, *, approval_mode=None, cwd=None, effort=None, model=None, output_schema=None, personality=None, sandbox: Sandbox | None = None, service_tier=None, summary=None) -> TurnResult`
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- `turn(input: str | Input, *, approval_mode=None, cwd=None, effort=None, model=None, output_schema=None, personality=None, sandbox: Sandbox | None = None, service_tier=None, summary=None) -> TurnHandle`
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- `read(*, include_turns: bool = False) -> ThreadReadResponse`
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- `set_name(name: str) -> ThreadSetNameResponse`
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- `compact() -> ThreadCompactStartResponse`
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### AsyncThread
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- `run(input: str | Input, *, approval_mode=None, cwd=None, effort=None, model=None, output_schema=None, personality=None, sandbox: Sandbox | None = None, service_tier=None, summary=None) -> Awaitable[TurnResult]`
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- `turn(input: str | Input, *, approval_mode=None, cwd=None, effort=None, model=None, output_schema=None, personality=None, sandbox: Sandbox | None = None, service_tier=None, summary=None) -> Awaitable[AsyncTurnHandle]`
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- `read(*, include_turns: bool = False) -> Awaitable[ThreadReadResponse]`
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- `set_name(name: str) -> Awaitable[ThreadSetNameResponse]`
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- `compact() -> Awaitable[ThreadCompactStartResponse]`
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`run(...)` is the common-case convenience path. It accepts plain strings, starts
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the turn, consumes notifications until completion, and returns a small result
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object with:
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- `id: str`
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- `status: TurnStatus`
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- `error: TurnError | None`
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- `started_at: int | None`
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- `completed_at: int | None`
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- `duration_ms: int | None`
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- `final_response: str | None`
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- `items: list[ThreadItem]`
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- `usage: ThreadTokenUsage | None`
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`final_response` is `None` when the turn finishes without a final-answer or
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phase-less assistant message item.
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Use `turn(...)` when you need low-level turn control (`stream()`, `steer()`,
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`interrupt()`) before collecting the turn result.
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## Sandbox
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Use `sandbox=` consistently on thread lifecycle methods and turns:
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```python
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from openai_codex import Codex, Sandbox
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with Codex() as codex:
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thread = codex.thread_start(sandbox=Sandbox.workspace_write)
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result = thread.run("Review the diff only.", sandbox=Sandbox.read_only)
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```
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Presets:
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- `Sandbox.read_only`: read files without allowing writes.
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- `Sandbox.workspace_write`: the normal default for projects with a recorded trust decision; read files and write inside the workspace and configured writable roots.
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- `Sandbox.full_access`: run without filesystem access restrictions.
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When `sandbox=` is omitted, Codex uses its configured default. A sandbox
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passed to `run(...)` or `turn(...)` applies to that turn and subsequent turns.
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## TurnHandle / AsyncTurnHandle
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### TurnHandle
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- `steer(input: str | Input) -> TurnSteerResponse`
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- `interrupt() -> TurnInterruptResponse`
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- `stream() -> Iterator[Notification]`
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- `run() -> TurnResult`
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Behavior notes:
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- `stream()` and `run()` consume only notifications for their own turn ID
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- one `Codex` instance can stream multiple active turns concurrently
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### AsyncTurnHandle
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- `steer(input: str | Input) -> Awaitable[TurnSteerResponse]`
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- `interrupt() -> Awaitable[TurnInterruptResponse]`
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- `stream() -> AsyncIterator[Notification]`
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- `run() -> Awaitable[TurnResult]`
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Behavior notes:
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- `stream()` and `run()` consume only notifications for their own turn ID
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- one `AsyncCodex` instance can stream multiple active turns concurrently
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## Inputs
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```python
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@dataclass class TextInput: text: str
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@dataclass class ImageInput: url: str
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@dataclass class LocalImageInput: path: str
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@dataclass class SkillInput: name: str; path: str
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@dataclass class MentionInput: name: str; path: str
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InputItem = TextInput | ImageInput | LocalImageInput | SkillInput | MentionInput
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Input = list[InputItem] | InputItem
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RunInput = Input | str
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```
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Use `ImageInput` with a base64-encoded `data:image/...` URL. HTTP and HTTPS image URLs are
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deprecated; download remote images and pass their local paths with `LocalImageInput` instead.
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Use a plain `str` as shorthand for `TextInput(...)` anywhere a turn input is accepted:
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`thread.run("...")`, `thread.turn("...")`, and `turn.steer("...")`.
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## Public Types
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The SDK wrappers return and accept public Codex protocol models wherever possible:
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```python
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from openai_codex.types import (
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Account,
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AccountLoginCompletedNotification,
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CancelLoginAccountResponse,
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CancelLoginAccountStatus,
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GetAccountResponse,
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ThreadReadResponse,
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Turn,
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TurnStatus,
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)
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```
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## Retry + errors
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```python
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from openai_codex import (
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retry_on_overload,
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JsonRpcError,
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MethodNotFoundError,
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InvalidParamsError,
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ServerBusyError,
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is_retryable_error,
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)
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```
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- `retry_on_overload(...)` retries transient overload errors with exponential backoff + jitter.
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- `is_retryable_error(exc)` checks if an exception is transient/overload-like.
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## Example
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```python
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from openai_codex import Codex
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with Codex() as codex:
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thread = codex.thread_start(model="gpt-5.4", config={"model_reasoning_effort": "high"})
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result = thread.run("Say hello in one sentence.")
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print(result.final_response)
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```
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