Create a repo skill inside the app-server harness workspace and assert that SkillInput resolves to an injected skill block at the model request boundary.
Co-authored-by: Codex <noreply@openai.com>
Remove the skill-input assertion from the app-server integration suite because the current runtime path does not expose that structured input at the model boundary or in read history.
Co-authored-by: Codex <noreply@openai.com>
Assert skill inputs as persisted structured history and keep run override coverage to the model request plus token usage, matching the public SDK behavior exercised by the harness.
Co-authored-by: Codex <noreply@openai.com>
Add focused integration coverage for thread listing, persisted history reads, async lifecycle wrappers, skill input injection, and run override/usage behavior through the pinned app-server test harness.
Co-authored-by: Codex <noreply@openai.com>
Seed the fork test with a real turn so the pinned app-server has a persisted rollout before thread/fork runs.
Co-authored-by: Codex <noreply@openai.com>
Break the large integration test module into focused run, input, stream, turn-control, approval-mode, and lifecycle files with shared helpers for the mock Responses boundary.
Co-authored-by: Codex <noreply@openai.com>
Assert the prompt text is present alongside app-server image wrapper text while keeping the request image checks on the real Responses payload.
Co-authored-by: Codex <noreply@openai.com>
Assert the latest user multimodal payload after history replay and seed a rollout before exercising archive lifecycle helpers.
Co-authored-by: Codex <noreply@openai.com>
Add new harness coverage for multimodal inputs, active turn controls, and archive lifecycle behavior through the pinned app-server.
Co-authored-by: Codex <noreply@openai.com>
Seed approval inheritance coverage with a real persisted turn and align compaction coverage with the pinned runtime's model request path.
Co-authored-by: Codex <noreply@openai.com>
Move result extraction, stream_text, approval inheritance, model list, and compact coverage onto the pinned app-server integration harness so the remaining unit tests stay focused on generated models and transport internals.
Co-authored-by: Codex <noreply@openai.com>
Assert the stable parts of the pinned app-server behavior: the user prompt appears as the final user input, approval overrides update the stored policy, and thread lifecycle coverage does not depend on thread/list indexing.
Co-authored-by: Codex <noreply@openai.com>
Make the new Python SDK integration tests assert stable app-server behavior: filter run result items to agent messages, accept either ordering for concurrent mock Responses requests, and avoid lifecycle operations that require a persisted rollout before one exists.
Co-authored-by: Codex <noreply@openai.com>
Build deterministic Python SDK integration coverage around the pinned app-server runtime and a local mock Responses server. Port behavioral coverage off direct SDK monkeypatches where the real app-server boundary is more useful.
Co-authored-by: Codex <noreply@openai.com>
Replace fake sync and async client approval tests with direct serialization checks using generated TurnStartParams, while keeping existing run-path coverage for the default behavior.
Co-authored-by: Codex <noreply@openai.com>
Use an explicit match over ApprovalMode values while keeping a separate runtime validation error for non-enum inputs.
Co-authored-by: Codex <noreply@openai.com>
Default high-level thread and turn starts to auto-review, keep deny_all as the explicit opt-out, and remove the generated AskForApproval alias customization.
Co-authored-by: Codex <noreply@openai.com>
Cover the exact public ApprovalMode values and ensure unsupported modes fail before sync or async high-level APIs can issue client requests.
Co-authored-by: Codex <noreply@openai.com>
Expose approval_mode with deny_all and auto_review options on the high-level Python SDK, and map those choices to generated app-server approval params internally.
Update examples, docs, notebooks, and public API tests to use the new mode instead of raw generated approval fields.
Co-authored-by: Codex <noreply@openai.com>
Add a separate Python SDK runner that installs the pinned musl runtime wheel in an Alpine Python container and runs the SDK pytest suite in parallel with existing SDK checks.
Co-authored-by: Codex <noreply@openai.com>
Make the SDK artifact generator fetch schema from the pinned runtime package, regenerate the checked-in Python types from that schema, and assert generated artifacts stay up to date.
Co-authored-by: Codex <noreply@openai.com>
Make the Python SDK declare its published runtime package dependency directly and resolve the runtime version from that pin instead of inferring it from the SDK package version.
Co-authored-by: Codex <noreply@openai.com>
Build the stage-runtime command as a single non-empty Bash array and append Linux resource binaries conditionally so macOS runners do not expand an empty optional array under set -u.
Co-authored-by: Codex <noreply@openai.com>
Use the v1.13.0 commit for the PyPI publish action so the pinned action reference has a clear release version.
Co-authored-by: Codex <noreply@openai.com>
Pass the release bwrap binary into Linux runtime wheel staging so PyPI installs preserve sandbox fallback behavior.
Co-authored-by: Codex <noreply@openai.com>
Build platform-specific openai-codex-cli-bin wheels from signed release binaries and publish them to PyPI using trusted publishing.
Co-authored-by: Codex <noreply@openai.com>
