4 Commits

  • Remove CODEX_RS_SSE_FIXTURE test hook (#22413)
    ## Why
    
    `CODEX_RS_SSE_FIXTURE` let integration-style CLI, exec, and TUI tests
    bypass the normal Responses transport by reading SSE from local files.
    That kept test-only behavior wired through production client code. The
    affected tests can stay hermetic by using the existing
    `core_test_support::responses` mock server and passing `openai_base_url`
    instead.
    
    ## What Changed
    
    - Removed the `CODEX_RS_SSE_FIXTURE` flag,
    `codex_api::stream_from_fixture`, the `env-flags` dependency, and the
    checked-in SSE fixture files.
    - Repointed the affected core, exec, and TUI tests at `MockServer` with
    the existing SSE event constructors.
    - Removed the Bazel test data plumbing for the deleted fixtures and
    refreshed cargo/Bazel lock state.
    
    ## Verification
    
    - `cargo build -p codex-cli`
    - `cargo test -p codex-api`
    - `cargo test -p codex-core --test all responses_api_stream_cli`
    - `cargo test -p codex-core --test all
    integration_creates_and_checks_session_file`
    - `cargo test -p codex-exec --test all ephemeral`
    - `cargo test -p codex-exec --test all resume`
    - `cargo test -p codex-tui --test all
    resume_startup_does_not_consume_model_availability_nux_count`
    - `just bazel-lock-update`
    - `just bazel-lock-check`
    - `just fix -p codex-api -p codex-core -p codex-exec -p codex-tui`
    - `git diff --check`
  • Propagate cache key and service tiers in compact (#21249)
    ## Why
    
    `/responses/compact` should preserve the request-affinity fields that
    apply to the active auth mode. ChatGPT-auth compact requests need the
    effective `service_tier`, and compact requests for every auth mode need
    the stable `prompt_cache_key`, so compaction does not quietly lose
    routing or cache behavior that normal sampling already has.
    
    This follows the request-parity direction from #20719, but keeps the net
    change focused on the compact payload fields needed here.
    
    ## What changed
    
    - Add `service_tier` and `prompt_cache_key` to the compact endpoint
    input payload.
    - Build the remote compact payload from the existing responses request
    builder output so `Fast` still maps to `priority` when compact sends a
    service tier.
    - Pass the turn service tier into remote compaction, but only include it
    in compact payloads for ChatGPT-backed auth.
    - Keep `prompt_cache_key` on compact payloads for all auth modes.
    - Add request-body diff snapshot coverage in
    `core/tests/suite/compact_remote.rs` for:
    - API-key auth reusing `prompt_cache_key` while omitting `service_tier`
    even when `Fast` is configured.
      - ChatGPT auth reusing both `service_tier` and `prompt_cache_key`.
    - Drive the snapshot coverage through five varied turns: plain text,
    multi-part text, tool-call continuation, image+text input, local-shell
    continuation, and final-turn reasoning output.
    
    ## Verification
    
    - Added insta snapshots for compact request-body parity against the last
    normal `/responses` request after five varied turns.
    - Not run locally per repo guidance; relying on GitHub CI for test
    execution.
    
    ---------
    
    Co-authored-by: Codex <noreply@openai.com>
  • config: enforce enterprise feature requirements (#13388)
    ## Why
    
    Enterprises can already constrain approvals, sandboxing, and web search
    through `requirements.toml` and MDM, but feature flags were still only
    configurable as managed defaults. That meant an enterprise could suggest
    feature values, but it could not actually pin them.
    
    This change closes that gap and makes enterprise feature requirements
    behave like the other constrained settings. The effective feature set
    now stays consistent with enterprise requirements during config load,
    when config writes are validated, and when runtime code mutates feature
    flags later in the session.
    
    It also tightens the runtime API for managed features. `ManagedFeatures`
    now follows the same constraint-oriented shape as `Constrained<T>`
    instead of exposing panic-prone mutation helpers, and production code
    can no longer construct it through an unconstrained `From<Features>`
    path.
    
    The PR also hardens the `compact_resume_fork` integration coverage on
    Windows. After the feature-management changes,
    `compact_resume_after_second_compaction_preserves_history` was
    overflowing the libtest/Tokio thread stacks on Windows, so the test now
    uses an explicit larger-stack harness as a pragmatic mitigation. That
    may not be the ideal root-cause fix, and it merits a parallel
    investigation into whether part of the async future chain should be
    boxed to reduce stack pressure instead.
    
    ## What Changed
    
    Enterprises can now pin feature values in `requirements.toml` with the
    requirements-side `features` table:
    
    ```toml
    [features]
    personality = true
    unified_exec = false
    ```
    
    Only canonical feature keys are allowed in the requirements `features`
    table; omitted keys remain unconstrained.
    
    - Added a requirements-side pinned feature map to
    `ConfigRequirementsToml`, threaded it through source-preserving
    requirements merge and normalization in `codex-config`, and made the
    TOML surface use `[features]` (while still accepting legacy
    `[feature_requirements]` for compatibility).
    - Exposed `featureRequirements` from `configRequirements/read`,
    regenerated the JSON/TypeScript schema artifacts, and updated the
    app-server README.
    - Wrapped the effective feature set in `ManagedFeatures`, backed by
    `ConstrainedWithSource<Features>`, and changed its API to mirror
    `Constrained<T>`: `can_set(...)`, `set(...) -> ConstraintResult<()>`,
    and result-returning `enable` / `disable` / `set_enabled` helpers.
    - Removed the legacy-usage and bulk-map passthroughs from
    `ManagedFeatures`; callers that need those behaviors now mutate a plain
    `Features` value and reapply it through `set(...)`, so the constrained
    wrapper remains the enforcement boundary.
    - Removed the production loophole for constructing unconstrained
    `ManagedFeatures`. Non-test code now creates it through the configured
    feature-loading path, and `impl From<Features> for ManagedFeatures` is
    restricted to `#[cfg(test)]`.
    - Rejected legacy feature aliases in enterprise feature requirements,
    and return a load error when a pinned combination cannot survive
    dependency normalization.
    - Validated config writes against enterprise feature requirements before
    persisting changes, including explicit conflicting writes and
    profile-specific feature states that normalize into invalid
    combinations.
    - Updated runtime and TUI feature-toggle paths to use the constrained
    setter API and to persist or apply the effective post-constraint value
    rather than the requested value.
    - Updated the `core_test_support` Bazel target to include the bundled
    core model-catalog fixtures in its runtime data, so helper code that
    resolves `core/models.json` through runfiles works in remote Bazel test
    environments.
    - Renamed the core config test coverage to emphasize that effective
    feature values are normalized at runtime, while conflicting persisted
    config writes are rejected.
    - Ran `compact_resume_after_second_compaction_preserves_history` inside
    an explicit 8 MiB test thread and Tokio runtime worker stack, following
    the existing larger-stack integration-test pattern, to keep the Windows
    `compact_resume_fork` test slice from aborting while a parallel
    investigation continues into whether some of the underlying async
    futures should be boxed.
    
    ## Verification
    
    - `cargo test -p codex-config`
    - `cargo test -p codex-core feature_requirements_ -- --nocapture`
    - `cargo test -p codex-core
    load_requirements_toml_produces_expected_constraints -- --nocapture`
    - `cargo test -p codex-core
    compact_resume_after_second_compaction_preserves_history -- --nocapture`
    - `cargo test -p codex-core compact_resume_fork -- --nocapture`
    - Re-ran the built `codex-core` `tests/all` binary with
    `RUST_MIN_STACK=262144` for
    `compact_resume_after_second_compaction_preserves_history` to confirm
    the explicit-stack harness fixes the deterministic low-stack repro.
    - `cargo test -p codex-core`
    - This still fails locally in unrelated integration areas that expect
    the `codex` / `test_stdio_server` binaries or hit existing `search_tool`
    wiremock mismatches.
    
    ## Docs
    
    `developers.openai.com/codex` should document the requirements-side
    `[features]` table for enterprise and MDM-managed configuration,
    including that it only accepts canonical feature keys and that
    conflicting config writes are rejected.
  • feat: add support for building with Bazel (#8875)
    This PR configures Codex CLI so it can be built with
    [Bazel](https://bazel.build) in addition to Cargo. The `.bazelrc`
    includes configuration so that remote builds can be done using
    [BuildBuddy](https://www.buildbuddy.io).
    
    If you are familiar with Bazel, things should work as you expect, e.g.,
    run `bazel test //... --keep-going` to run all the tests in the repo,
    but we have also added some new aliases in the `justfile` for
    convenience:
    
    - `just bazel-test` to run tests locally
    - `just bazel-remote-test` to run tests remotely (currently, the remote
    build is for x86_64 Linux regardless of your host platform). Note we are
    currently seeing the following test failures in the remote build, so we
    still need to figure out what is happening here:
    
    ```
    failures:
        suite::compact::manual_compact_twice_preserves_latest_user_messages
        suite::compact_resume_fork::compact_resume_after_second_compaction_preserves_history
        suite::compact_resume_fork::compact_resume_and_fork_preserve_model_history_view
    ```
    
    - `just build-for-release` to build release binaries for all
    platforms/architectures remotely
    
    To setup remote execution:
    - [Create a buildbuddy account](https://app.buildbuddy.io/) (OpenAI
    employees should also request org access at
    https://openai.buildbuddy.io/join/ with their `@openai.com` email
    address.)
    - [Copy your API key](https://app.buildbuddy.io/docs/setup/) to
    `~/.bazelrc` (add the line `build
    --remote_header=x-buildbuddy-api-key=YOUR_KEY`)
    - Use `--config=remote` in your `bazel` invocations (or add `common
    --config=remote` to your `~/.bazelrc`, or use the `just` commands)
    
    ## CI
    
    In terms of CI, this PR introduces `.github/workflows/bazel.yml`, which
    uses Bazel to run the tests _locally_ on Mac and Linux GitHub runners
    (we are working on supporting Windows, but that is not ready yet). Note
    that the failures we are seeing in `just bazel-remote-test` do not occur
    on these GitHub CI jobs, so everything in `.github/workflows/bazel.yml`
    is green right now.
    
    The `bazel.yml` uses extra config in `.github/workflows/ci.bazelrc` so
    that macOS CI jobs build _remotely_ on Linux hosts (using the
    `docker://docker.io/mbolin491/codex-bazel` Docker image declared in the
    root `BUILD.bazel`) using cross-compilation to build the macOS
    artifacts. Then these artifacts are downloaded locally to GitHub's macOS
    runner so the tests can be executed natively. This is the relevant
    config that enables this:
    
    ```
    common:macos --config=remote
    common:macos --strategy=remote
    common:macos --strategy=TestRunner=darwin-sandbox,local
    ```
    
    Because of the remote caching benefits we get from BuildBuddy, these new
    CI jobs can be extremely fast! For example, consider these two jobs that
    ran all the tests on Linux x86_64:
    
    - Bazel 1m37s
    https://github.com/openai/codex/actions/runs/20861063212/job/59940545209?pr=8875
    - Cargo 9m20s
    https://github.com/openai/codex/actions/runs/20861063192/job/59940559592?pr=8875
    
    For now, we will continue to run both the Bazel and Cargo jobs for PRs,
    but once we add support for Windows and running Clippy, we should be
    able to cutover to using Bazel exclusively for PRs, which should still
    speed things up considerably. We will probably continue to run the Cargo
    jobs post-merge for commits that land on `main` as a sanity check.
    
    Release builds will also continue to be done by Cargo for now.
    
    Earlier attempt at this PR: https://github.com/openai/codex/pull/8832
    Earlier attempt to add support for Buck2, now abandoned:
    https://github.com/openai/codex/pull/8504
    
    ---------
    
    Co-authored-by: David Zbarsky <dzbarsky@gmail.com>
    Co-authored-by: Michael Bolin <mbolin@openai.com>