(Experimental)
This PR adds a first MVP for hooks, with SessionStart and Stop
The core design is:
- hooks live in a dedicated engine under codex-rs/hooks
- each hook type has its own event-specific file
- hook execution is synchronous and blocks normal turn progression while
running
- matching hooks run in parallel, then their results are aggregated into
a normalized HookRunSummary
On the AppServer side, hooks are exposed as operational metadata rather
than transcript-native items:
- new live notifications: hook/started, hook/completed
- persisted/replayed hook results live on Turn.hookRuns
- we intentionally did not add hook-specific ThreadItem variants
Hooks messages are not persisted, they remain ephemeral. The context
changes they add are (they get appended to the user's prompt)
## Summary
- remove the remaining model-visible guardian-specific `on-request`
prompt additions so enabling the feature does not change the main
approval-policy instructions
- neutralize user-facing guardian wording to talk about automatic
approval review / approval requests rather than a second reviewer or
only sandbox escalations
- tighten guardian retry-context handling so agent-authored
`justification` stays in the structured action JSON and is not also
injected as raw retry context
- simplify guardian review plumbing in core by deleting dead
prompt-append paths and trimming some request/transcript setup code
## Notable Changes
- delete the dead `permissions/approval_policy/guardian.md` append path
and stop threading `guardian_approval_enabled` through model-facing
developer-instruction builders
- rename the experimental feature copy to `Automatic approval review`
and update the `/experimental` snapshot text accordingly
- make approval-review status strings generic across shell, patch,
network, and MCP review types
- forward real sandbox/network retry reasons for shell and unified-exec
guardian review, but do not pass agent-authored justification as raw
retry context
- simplify `guardian.rs` by removing the one-field request wrapper,
deduping reasoning-effort selection, and cleaning up transcript entry
collection
## Testing
- `just fmt`
- full validation left to CI
---------
Co-authored-by: Codex <noreply@openai.com>
Adds a built-in `request_permissions` tool and wires it through the
Codex core, protocol, and app-server layers so a running turn can ask
the client for additional permissions instead of relying on a static
session policy.
The new flow emits a `RequestPermissions` event from core, tracks the
pending request by call ID, forwards it through app-server v2 as an
`item/permissions/requestApproval` request, and resumes the tool call
once the client returns an approved subset of the requested permission
profile.
## Summary
- add the guardian reviewer flow for `on-request` approvals in command,
patch, sandbox-retry, and managed-network approval paths
- keep guardian behind `features.guardian_approval` instead of exposing
a public `approval_policy = guardian` mode
- route ordinary `OnRequest` approvals to the guardian subagent when the
feature is enabled, without changing the public approval-mode surface
## Public model
- public approval modes stay unchanged
- guardian is enabled via `features.guardian_approval`
- when that feature is on, `approval_policy = on-request` keeps the same
approval boundaries but sends those approval requests to the guardian
reviewer instead of the user
- `/experimental` only persists the feature flag; it does not rewrite
`approval_policy`
- CLI and app-server no longer expose a separate `guardian` approval
mode in this PR
## Guardian reviewer
- the reviewer runs as a normal subagent and reuses the existing
subagent/thread machinery
- it is locked to a read-only sandbox and `approval_policy = never`
- it does not inherit user/project exec-policy rules
- it prefers `gpt-5.4` when the current provider exposes it, otherwise
falls back to the parent turn's active model
- it fail-closes on timeout, startup failure, malformed output, or any
other review error
- it currently auto-approves only when `risk_score < 80`
## Review context and policy
- guardian mirrors `OnRequest` approval semantics rather than
introducing a separate approval policy
- explicit `require_escalated` requests follow the same approval surface
as `OnRequest`; the difference is only who reviews them
- managed-network allowlist misses that enter the approval flow are also
reviewed by guardian
- the review prompt includes bounded recent transcript history plus
recent tool call/result evidence
- transcript entries and planned-action strings are truncated with
explicit `<guardian_truncated ... />` markers so large payloads stay
bounded
- apply-patch reviews include the full patch content (without
duplicating the structured `changes` payload)
- the guardian request layout is snapshot-tested using the same
model-visible Responses request formatter used elsewhere in core
## Guardian network behavior
- the guardian subagent inherits the parent session's managed-network
allowlist when one exists, so it can use the same approved network
surface while reviewing
- exact session-scoped network approvals are copied into the guardian
session with protocol/port scope preserved
- those copied approvals are now seeded before the guardian's first turn
is submitted, so inherited approvals are available during any immediate
review-time checks
## Out of scope / follow-ups
- the sandbox-permission validation split was pulled into a separate PR
and is not part of this diff
- a future follow-up can enable `serde_json` preserve-order in
`codex-core` and then simplify the guardian action rendering further
---------
Co-authored-by: Codex <noreply@openai.com>
Previously, we could only configure whether web search was on/off.
This PR enables sending along a web search config, which includes all
the stuff responsesapi supports: filters, location, etc.
## Summary
This is a purely mechanical refactor of `OtelManager` ->
`SessionTelemetry` to better convey what the struct is doing. No
behavior change.
## Why
`OtelManager` ended up sounding much broader than what this type
actually does. It doesn't manage OTEL globally; it's the session-scoped
telemetry surface for emitting log/trace events and recording metrics
with consistent session metadata (`app_version`, `model`, `slug`,
`originator`, etc.).
`SessionTelemetry` is a more accurate name, and updating the call sites
makes that boundary a lot easier to follow.
## Validation
- `just fmt`
- `cargo test -p codex-otel`
- `cargo test -p codex-core`
## 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.
## Summary
Add original-resolution support for `view_image` behind the
under-development `view_image_original_resolution` feature flag.
When the flag is enabled and the target model is `gpt-5.3-codex` or
newer, `view_image` now preserves original PNG/JPEG/WebP bytes and sends
`detail: "original"` to the Responses API instead of using the legacy
resize/compress path.
## What changed
- Added `view_image_original_resolution` as an under-development feature
flag.
- Added `ImageDetail` to the protocol models and support for serializing
`detail: "original"` on tool-returned images.
- Added `PromptImageMode::Original` to `codex-utils-image`.
- Preserves original PNG/JPEG/WebP bytes.
- Keeps legacy behavior for the resize path.
- Updated `view_image` to:
- use the shared `local_image_content_items_with_label_number(...)`
helper in both code paths
- select original-resolution mode only when:
- the feature flag is enabled, and
- the model slug parses as `gpt-5.3-codex` or newer
- Kept local user image attachments on the existing resize path; this
change is specific to `view_image`.
- Updated history/image accounting so only `detail: "original"` images
use the docs-based GPT-5 image cost calculation; legacy images still use
the old fixed estimate.
- Added JS REPL guidance, gated on the same feature flag, to prefer JPEG
at 85% quality unless lossless is required, while still allowing other
formats when explicitly requested.
- Updated tests and helper code that construct
`FunctionCallOutputContentItem::InputImage` to carry the new `detail`
field.
## Behavior
### Feature off
- `view_image` keeps the existing resize/re-encode behavior.
- History estimation keeps the existing fixed-cost heuristic.
### Feature on + `gpt-5.3-codex+`
- `view_image` sends original-resolution images with `detail:
"original"`.
- PNG/JPEG/WebP source bytes are preserved when possible.
- History estimation uses the GPT-5 docs-based image-cost calculation
for those `detail: "original"` images.
#### [git stack](https://github.com/magus/git-stack-cli)
- 👉 `1` https://github.com/openai/codex/pull/13050
- ⏳ `2` https://github.com/openai/codex/pull/13331
- ⏳ `3` https://github.com/openai/codex/pull/13049
- add a local Fast mode setting in codex-core (similar to how model id
is currently stored on disk locally)
- send `service_tier=priority` on requests when Fast is enabled
- add `/fast` in the TUI and persist it locally
- feature flag
Support loading plugins.
Plugins can now be enabled via [plugins.<name>] in config.toml. They are
loaded as first-class entities through PluginsManager, and their default
skills/ and .mcp.json contributions are integrated into the existing
skills and MCP flows.
## Summary
Lower the `js_repl` minimum Node version from `24.13.1` to `22.22.0`.
This updates the enforced minimum in `codex-rs/node-version.txt` and the
corresponding user-facing `/experimental` description for the JavaScript
REPL feature.
## Rationale
The previous `24.13.1` floor was stricter than necessary for `js_repl`.
I validated the REPL kernel behavior under Node `22.22.0` still works.
## Why `22.22.0`
`22.22.0` is a current, widely packaged Node 22 release across common
developer environments and distros, including Homebrew `node@22`, Fedora
`nodejs22`, Arch `nodejs-lts-jod`, and Debian testing. That makes it a
better exact floor than guessing at an older `22.x` patch we have not
validated.
`22.x` is also a maintenance branch that will be supported through April
2027, where the previous maintenance branch of `20.x` is only supported
through April of this year.
## Changes
- Update `codex-rs/node-version.txt` from `24.13.1` to `22.22.0`
- Update the `/experimental` JavaScript REPL description to say
`Requires Node >= v22.22.0 installed.`
This reverts commit https://github.com/openai/codex/pull/12633. We no
longer need this PR, because we favor sending normal exec command
approval server request with `additional_permissions` of skill
permissions instead
## Summary
- allow `request_user_input` in Default collaboration mode as well as
Plan
- update the Default-mode instructions to prefer assumptions first and
use `request_user_input` only when a question is unavoidable
- update request_user_input and app-server tests to match the new
Default-mode behavior
- refactor collaboration-mode availability plumbing into
`CollaborationModesConfig` for future mode-related flags
## Codex author
`codex resume 019c9124-ed28-7c13-96c6-b916b1c97d49`
## Summary
- Promote `js_repl` to an experimental feature that users can enable
from `/experimental`.
- Add `js_repl` experimental metadata, including the Node prerequisite
and activation guidance.
- Add regression coverage for the feature metadata and the
`/experimental` popup.
## What Changed
- Changed `Feature::JsRepl` from `Stage::UnderDevelopment` to
`Stage::Experimental`.
- Added experimental metadata for `js_repl` in `core/src/features.rs`:
- name: `JavaScript REPL`
- description: calls out interactive website debugging, inline
JavaScript execution, and the required Node version (`>= v24.13.1`)
- announcement: tells users to enable it, then start a new chat or
restart Codex
- Added a core unit test that verifies:
- `js_repl` is experimental
- `js_repl` is disabled by default
- the hardcoded Node version in the description matches
`node-version.txt`
- Added a TUI test that opens the `/experimental` popup and verifies the
rendered `js_repl` entry includes the Node requirement text.
## Testing
- `just fmt`
- `cargo test -p codex-tui`
- `cargo test -p codex-core` (unit-test phase passed; stopped during the
long `tests/all.rs` integration suite)
- Add a hidden `realtime_conversation` feature flag and `/realtime`
slash command for start/stop live voice sessions.
- Reuse transcription composer/footer UI for live metering, stream mic
audio, play assistant audio, render realtime user text events, and
force-close on feature disable.
---------
Co-authored-by: Codex <noreply@openai.com>
## Summary
Introduces the initial implementation of Feature::RequestPermissions.
RequestPermissions allows the model to request that a command be run
inside the sandbox, with additional permissions, like writing to a
specific folder. Eventually this will include other rules as well, and
the ability to persist these permissions, but this PR is already quite
large - let's get the core flow working and go from there!
<img width="1279" height="541" alt="Screenshot 2026-02-15 at 2 26 22 PM"
src="https://github.com/user-attachments/assets/0ee3ec0f-02ec-4509-91a2-809ac80be368"
/>
## Testing
- [x] Added tests
- [x] Tested locally
- [x] Feature
## Why
Compiling `codex-rs/core` is a bottleneck for local iteration, so this
change continues the ongoing extraction of config-related functionality
out of `codex-core` and into `codex-config`.
The goal is not just to move code, but to reduce `codex-core` ownership
and indirection so more code depends on `codex-config` directly.
## What Changed
- Moved config diagnostics logic from
`core/src/config_loader/diagnostics.rs` into
`config/src/diagnostics.rs`.
- Updated `codex-core` to use `codex-config` diagnostics types/functions
directly where possible.
- Removed the `core/src/config_loader/diagnostics.rs` shim module
entirely; the remaining `ConfigToml`-specific calls are in
`core/src/config_loader/mod.rs`.
- Moved `CONFIG_TOML_FILE` into `codex-config` and updated existing
references to use `codex_config::CONFIG_TOML_FILE` directly.
- Added a direct `codex-config` dependency to `codex-cli` for its
`CONFIG_TOML_FILE` use.
Summary
- avoid emitting metrics for features marked as `Stage::Removed`
- keep feature metrics aligned with active and planned states only
Testing
- Not run (not requested)
Summary
- replace the stale `docs/config.md#feature-flags` reference in the
legacy feature notice with the canonical published URL
- align the deprecation notice test to expect the new link
This addresses #12123
zsh fork PR stack:
- https://github.com/openai/codex/pull/12051
- https://github.com/openai/codex/pull/12052👈
### Summary
This PR introduces a feature-gated native shell runtime path that routes
shell execution through a patched zsh exec bridge, removing MCP-specific
behavior from the shell hot path while preserving existing
CommandExecution lifecycle semantics.
When shell_zsh_fork is enabled, shell commands run via patched zsh with
per-`execve` interception through EXEC_WRAPPER. Core receives wrapper
IPC requests over a Unix socket, applies existing approval policy, and
returns allow/deny before the subcommand executes.
### What’s included
**1) New zsh exec bridge runtime in core**
- Wrapper-mode entrypoint (maybe_run_zsh_exec_wrapper_mode) for
EXEC_WRAPPER invocations.
- Per-execution Unix-socket IPC handling for wrapper requests/responses.
- Approval callback integration using existing core approval
orchestration.
- Streaming stdout/stderr deltas to existing command output event
pipeline.
- Error handling for malformed IPC, denial/abort, and execution
failures.
**2) Session lifecycle integration**
SessionServices now owns a `ZshExecBridge`.
Session startup initializes bridge state; shutdown tears it down
cleanly.
**3) Shell runtime routing (feature-gated)**
When `shell_zsh_fork` is enabled:
- Build execution env/spec as usual.
- Add wrapper socket env wiring.
- Execute via `zsh_exec_bridge.execute_shell_request(...)` instead of
the regular shell path.
- Non-zsh-fork behavior remains unchanged.
**4) Config + feature wiring**
- Added `Feature::ShellZshFork` (under development).
- Added config support for `zsh_path` (optional absolute path to patched
zsh):
- `Config`, `ConfigToml`, `ConfigProfile`, overrides, and schema.
- Session startup validates that `zsh_path` exists/usable when zsh-fork
is enabled.
- Added startup test for missing `zsh_path` failure mode.
**5) Seatbelt/sandbox updates for wrapper IPC**
- Extended seatbelt policy generation to optionally allow outbound
connection to explicitly permitted Unix sockets.
- Wired sandboxing path to pass wrapper socket path through to seatbelt
policy generation.
- Added/updated seatbelt tests for explicit socket allow rule and
argument emission.
**6) Runtime entrypoint hooks**
- This allows the same binary to act as the zsh wrapper subprocess when
invoked via `EXEC_WRAPPER`.
**7) Tool selection behavior**
- ToolsConfig now prefers ShellCommand type when shell_zsh_fork is
enabled.
- Added test coverage for precedence with unified-exec enabled.
rm `remote_models` feature flag.
We see issues like #11527 when a user has `remote_models` disabled, as
we always use the default fallback `ModelInfo`. This causes issues with
model performance.
Builds on #11690, which helps by warning the user when they are using
the default fallback. This PR will make that happen much less frequently
as an accidental consequence of disabling `remote_models`.
## Summary
This feature is now reasonably stable, let's remove it so we can
simplify our upcoming iterations here.
## Testing
- [x] Existing tests pass
Summary
- rename the collab feature key to multi_agent while keeping the Feature
enum unchanged
- add legacy alias support so both "multi_agent" and "collab" map to the
same feature
- cover the alias behavior with a new unit test
## Summary
- add a shared `codex-core` sleep inhibitor that uses native macOS IOKit
assertions (`IOPMAssertionCreateWithName` / `IOPMAssertionRelease`)
instead of spawning `caffeinate`
- wire sleep inhibition to turn lifecycle in `tui` (`TurnStarted`
enables; `TurnComplete` and abort/error finalization disable)
- gate this behavior behind a `/experimental` feature toggle
(`[features].prevent_idle_sleep`) instead of a dedicated `[tui]` config
flag
- expose the toggle in `/experimental` on macOS; keep it under
development on other platforms
- keep behavior no-op on non-macOS targets
<img width="1326" height="577" alt="image"
src="https://github.com/user-attachments/assets/73fac06b-97ae-46a2-800a-30f9516cf8a3"
/>
## Testing
- `cargo check -p codex-core -p codex-tui`
- `cargo test -p codex-core sleep_inhibitor::tests -- --nocapture`
- `cargo test -p codex-core
tui_config_missing_notifications_field_defaults_to_enabled --
--nocapture`
- `cargo test -p codex-core prevent_idle_sleep_is_ -- --nocapture`
## Semantics and API references
- This PR targets `caffeinate -i` semantics: prevent *idle system sleep*
while allowing display idle sleep.
- `caffeinate -i` mapping in Apple open source (`assertionMap`):
- `kIdleAssertionFlag -> kIOPMAssertionTypePreventUserIdleSystemSleep`
- Source:
https://github.com/apple-oss-distributions/PowerManagement/blob/PowerManagement-1846.60.12/caffeinate/caffeinate.c#L52-L54
- Apple IOKit docs for assertion types and API:
-
https://developer.apple.com/documentation/iokit/iopmlib_h/iopmassertiontypes
-
https://developer.apple.com/documentation/iokit/1557092-iopmassertioncreatewithname
- https://developer.apple.com/library/archive/qa/qa1340/_index.html
## Codex Electron vs this PR (full stack path)
- Codex Electron app requests sleep blocking with
`powerSaveBlocker.start("prevent-app-suspension")`:
-
https://github.com/openai/codex/blob/main/codex/codex-vscode/electron/src/electron-message-handler.ts
- Electron maps that string to Chromium wake lock type
`kPreventAppSuspension`:
-
https://github.com/electron/electron/blob/main/shell/browser/api/electron_api_power_save_blocker.cc
- Chromium macOS backend maps wake lock types to IOKit assertion
constants and calls IOKit:
- `kPreventAppSuspension -> kIOPMAssertionTypeNoIdleSleep`
- `kPreventDisplaySleep / kPreventDisplaySleepAllowDimming ->
kIOPMAssertionTypeNoDisplaySleep`
-
https://github.com/chromium/chromium/blob/main/services/device/wake_lock/power_save_blocker/power_save_blocker_mac.cc
## Why this PR uses a different macOS constant name
- This PR uses `"PreventUserIdleSystemSleep"` directly, via
`IOPMAssertionCreateWithName`, in
`codex-rs/core/src/sleep_inhibitor.rs`.
- Apple’s IOKit header documents `kIOPMAssertionTypeNoIdleSleep` as
deprecated and recommends `kIOPMAssertPreventUserIdleSystemSleep` /
`kIOPMAssertionTypePreventUserIdleSystemSleep`:
-
https://github.com/apple-oss-distributions/IOKitUser/blob/IOKitUser-100222.60.2/pwr_mgt.subproj/IOPMLib.h#L1000-L1030
- So Chromium and this PR are using different constant names, but
semantically equivalent idle-system-sleep prevention behavior.
## Future platform support
The architecture is intentionally set up for multi-platform extensions:
- UI code (`tui`) only calls `SleepInhibitor::set_turn_running(...)` on
turn lifecycle boundaries.
- Platform-specific behavior is isolated in
`codex-rs/core/src/sleep_inhibitor.rs` behind `cfg(...)` blocks.
- Feature exposure is centralized in `core/src/features.rs` and surfaced
via `/experimental`.
- Adding new OS backends should not require additional TUI wiring; only
the backend internals and feature stage metadata need to change.
Potential follow-up implementations:
- Windows:
- Add a backend using Win32 power APIs
(`SetThreadExecutionState(ES_CONTINUOUS | ES_SYSTEM_REQUIRED)` as
baseline).
- Optionally move to `PowerCreateRequest` / `PowerSetRequest` /
`PowerClearRequest` for richer assertion semantics.
- Linux:
- Add a backend using logind inhibitors over D-Bus
(`org.freedesktop.login1.Manager.Inhibit` with `what="sleep"`).
- Keep a no-op fallback where logind/D-Bus is unavailable.
This PR keeps the cross-platform API surface minimal so future PRs can
add Windows/Linux support incrementally with low churn.
---------
Co-authored-by: jif-oai <jif@openai.com>
We've had a few cases recently where someone enabled a feature flag for
a feature that's still under development or experimental. This test
should prevent this.
Adds a new apps_mcp_gateway flag to route Apps MCP calls through
https://api.openai.com/v1/connectors/mcp/ when enabled, while keeping
legacy MCP routing as default.
## Summary
- Remove `Feature::SearchTool` and the `search_tool` config key from the
feature registry/schema.
- Gate `search_tool_bm25` exposure via `Feature::Apps` in
`core/src/tools/spec.rs`.
- Update MCP selection logic in `core/src/codex.rs` to use
`Feature::Apps` for search-tool behavior.
- Update `core/tests/suite/search_tool.rs` to enable `Feature::Apps`.
- Regenerate `core/config.schema.json` via `just write-config-schema`.
## Testing
- `just fmt`
- `cargo test -p codex-core --test all suite::search_tool::`
## Tickets
- None
1. Move Windows Sandbox NUX to right after trust directory screen
2. Don't offer read-only as an option in Sandbox NUX.
Elevated/Legacy/Quit
3. Don't allow new untrusted directories. It's trust or quit
4. move experimental sandbox features to `[windows]
sandbox="elevated|unelevatd"`
5. Copy tweaks = elevated -> default, non-elevated -> non-admin
Matthew Zeng ·
Andrei Eternal ·
pakrym-oai ·
Charley Cunningham ·
Jack Mousseau ·
Rohan Mehta ·
Owen Lin ·
jif-oai ·
pash-openai ·
Won Park ·
Michael Bolin ·
Curtis 'Fjord' Hawthorne ·
xl-openai ·
Celia Chen ·
Ahmed Ibrahim ·
Yaroslav Volovich ·
Dylan Hurd ·
Jeremy Rose ·
Eric Traut ·
gabec-openai ·
sayan-oai ·
canvrno-oai ·
Anton Panasenko ·
iceweasel-oai ·
viyatb-oai ·