mirror of
https://github.com/pchuan98/codex.git
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Ahmed Ibrahim
eb1cc3824c
## Why The initial public `openai-codex` beta should read and install like a normal published Python package before a release tag is created. This follows merged PR #24828, which establishes the independent SDK beta release plumbing and exact runtime dependency. ## What changed - Rewrote `sdk/python/README.md` as a compact PyPI-facing beta package page: published installation, one quickstart, short login examples, built-in help, and links to deeper guides. - Updated the getting-started guide, API reference, FAQ, and examples index to present the published beta consistently without repeating onboarding in the package landing page or reference page. - Made `pip install openai-codex` the primary install path while beta releases are the only published SDK releases, with `--pre` documented for opting into prereleases after a stable release exists. - Added curated `help()` / `pydoc` docstrings across the public API and generated public convenience methods through `scripts/update_sdk_artifacts.py`. - Declared the repository `Apache-2.0` license expression and Documentation URL in package metadata, without introducing a duplicated SDK-local license file. - Kept the source distribution focused on installable package material (`src/openai_codex`, `README.md`, and `pyproject.toml`); the repository docs and runnable examples remain linked from the PyPI README. - Built release artifacts in an Alpine container on the Ubuntu runner, matching Python SDK CI and allowing type generation to install the published `musllinux` runtime wheel. - Added `twine check --strict` to the release workflow so malformed PyPI metadata or rendered README content fails before publishing. - Added focused SDK assertions for beta metadata, the exact runtime pin, source distribution contents, and the built-in Python documentation surface. ## Validation - Ran `uv run --frozen --extra dev ruff check scripts/update_sdk_artifacts.py src/openai_codex tests/test_public_api_signatures.py tests/test_artifact_workflow_and_binaries.py` before the final README-only reductions and review-fix follow-ups. - Built `openai_codex-0.1.0b1-py3-none-any.whl` and `openai_codex-0.1.0b1.tar.gz` before the final README-only reductions and review-fix follow-ups. - Ran `python -m twine check --strict` on both built artifacts before the final README-only reductions and review-fix follow-ups. - Verified artifact metadata reports `Apache-2.0` without a duplicated SDK-local license file. - Verified `inspect.getdoc(...)` resolves documentation for the package, `Codex`, `CodexConfig`, and key generated thread methods. - Rebased the documentation/readiness change onto merged PR #24828 without changing the intended SDK or workflow file contents. - Final verification is delegated to online CI for this PR.
124 lines
4.6 KiB
Markdown
124 lines
4.6 KiB
Markdown
# FAQ
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## Is the Python SDK stable?
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`openai-codex` is a public beta. Install it with
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`pip install openai-codex`; public APIs may change before `1.0`. While beta
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releases are the only published SDK releases, pip selects the latest beta.
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After a stable release exists, pass `--pre` to opt into newer prereleases.
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## Why does the SDK install a runtime package?
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The SDK and runtime packages are versioned independently. Each SDK release
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pins one compatible runtime dependency, so `openai-codex==0.1.0b1` installs
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`openai-codex-cli-bin==0.132.0` automatically.
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## Thread vs turn
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- A `Thread` is conversation state.
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- A `Turn` is one model execution inside that thread.
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- Multi-turn chat means multiple turns on the same `Thread`.
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## `run()` vs `stream()`
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- `Thread.run(...)` starts a turn and returns `TurnResult`.
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- `TurnHandle.run()` / `AsyncTurnHandle.run()` consumes events for an existing turn handle and returns the same `TurnResult` shape.
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- `TurnHandle.stream()` / `AsyncTurnHandle.stream()` yields raw notifications (`Notification`) so you can react event-by-event.
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Choose `run()` for most apps. Choose `stream()` for progress UIs, custom timeout logic, or custom parsing.
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## Sync vs async clients
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- `Codex` is the sync public API.
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- `AsyncCodex` is an async replica of the same public API shape.
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- Prefer `async with AsyncCodex()` for async code. It is the standard path for
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explicit startup/shutdown, and `AsyncCodex` initializes lazily on context
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entry or first awaited API use.
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If your app is not already async, stay with `Codex`.
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## How do I log in?
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- `login_api_key(...)` authenticates immediately with an API key.
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- `login_chatgpt()` starts browser login and returns a handle with `auth_url`.
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- `login_chatgpt_device_code()` starts device-code login and returns a handle
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with `verification_url` and `user_code`.
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- Interactive handles expose `wait()` for the matching
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`account/login/completed` notification and `cancel()` to stop that attempt.
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- `account()` reads the current account state, and `logout()` clears it.
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## Public kwargs are snake_case
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Public API keyword names are snake_case. The SDK still maps them to wire camelCase under the hood.
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If you are migrating older code, update these names:
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- `approvalPolicy` -> `approval_policy`
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- `baseInstructions` -> `base_instructions`
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- `developerInstructions` -> `developer_instructions`
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- `modelProvider` -> `model_provider`
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- `modelProviders` -> `model_providers`
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- `sortKey` -> `sort_key`
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- `sourceKinds` -> `source_kinds`
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- `outputSchema` -> `output_schema`
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## How do I choose sandbox access?
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Use the same `sandbox=` keyword for threads and turns:
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```python
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from openai_codex import Sandbox
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thread = codex.thread_start(sandbox=Sandbox.workspace_write)
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result = thread.run("Review only.", sandbox=Sandbox.read_only)
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```
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The presets are:
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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 turn
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sandbox override applies to that turn and subsequent turns.
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## Why only `thread_start(...)` and `thread_resume(...)`?
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The public API keeps only explicit lifecycle calls:
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- `thread_start(...)` to create new threads
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- `thread_resume(thread_id, ...)` to continue existing threads
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This avoids duplicate ways to do the same operation and keeps behavior explicit.
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## Why does constructor fail?
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`Codex()` is eager: it starts transport and calls `initialize` in `__init__`.
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Common causes:
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- installation is incomplete and the pinned `openai-codex-cli-bin` dependency is missing
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- local `codex_bin` override points to a missing file
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- a custom local Codex executable does not support the SDK operation being used
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## Why does a turn "hang"?
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A turn is complete only when `turn/completed` arrives for that turn ID.
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- `run()` waits for this automatically.
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- With `stream()`, keep consuming notifications until completion.
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## How do I retry safely?
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Use `retry_on_overload(...)` for transient overload failures (`ServerBusyError`).
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Do not blindly retry all errors. For `InvalidParamsError` or
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`MethodNotFoundError`, fix the input or use the runtime pinned by the SDK.
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## Common pitfalls
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- Starting a new thread for every prompt when you wanted continuity.
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- Forgetting to `close()` (or not using context managers).
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- Reading `Turn.items` from live start/completed payloads instead of using `TurnResult.items`.
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- Mixing SDK input classes with raw dicts incorrectly.
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