- Split the feature system into a new `codex-features` crate.
- Cut `codex-core` and workspace consumers over to the new config and
warning APIs.
Co-authored-by: Ahmed Ibrahim <219906144+aibrahim-oai@users.noreply.github.com>
Co-authored-by: Codex <noreply@openai.com>
## Summary
request_permissions flows should support persisting results for the
session.
Open Question: Still deciding if we need within-turn approvals - this
adds complexity but I could see it being useful
## Testing
- [x] Updated unit tests
---------
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.
## Why
After `#13440` and `#13445`, macOS Seatbelt policy generation was still
deriving filesystem and network behavior from the legacy `SandboxPolicy`
projection.
That projection loses explicit unreadable carveouts and conflates split
network decisions, so the generated Seatbelt policy could still be wider
than the split policy that Codex had already computed.
## What changed
- added Seatbelt entrypoints that accept `FileSystemSandboxPolicy` and
`NetworkSandboxPolicy` directly
- built read and write policy stanzas from access roots plus excluded
subpaths so explicit unreadable carveouts survive into the generated
Seatbelt policy
- switched network policy generation to consult `NetworkSandboxPolicy`
directly
- failed closed when managed-network or proxy-constrained sessions do
not yield usable loopback proxy endpoints
- updated the macOS callers and test helpers that now need to carry the
split policies explicitly
## Verification
- added regression coverage in `core/src/seatbelt.rs` for unreadable
carveouts under both full-disk and scoped-readable policies
- verified the current PR state with `just clippy`
---
[//]: # (BEGIN SAPLING FOOTER)
Stack created with [Sapling](https://sapling-scm.com). Best reviewed
with [ReviewStack](https://reviewstack.dev/openai/codex/pull/13448).
* #13453
* #13452
* #13451
* #13449
* __->__ #13448
* #13445
* #13440
* #13439
---------
Co-authored-by: viyatb-oai <viyatb@openai.com>
## 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.
- 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
## Why
`PermissionProfile` should describe filesystem roots as absolute paths
at the type level. Using `PathBuf` in `FileSystemPermissions` made the
shared type too permissive and blurred together three different
deserialization cases:
- skill metadata in `agents/openai.yaml`, where relative paths should
resolve against the skill directory
- app-server API payloads, where callers should have to send absolute
paths
- local tool-call payloads for commands like `shell_command` and
`exec_command`, where `additional_permissions.file_system` may
legitimately be relative to the command `workdir`
This change tightens the shared model without regressing the existing
local command flow.
## What Changed
- changed `protocol::models::FileSystemPermissions` and the app-server
`AdditionalFileSystemPermissions` mirror to use `AbsolutePathBuf`
- wrapped skill metadata deserialization in `AbsolutePathBufGuard`, so
relative permission roots in `agents/openai.yaml` resolve against the
containing skill directory
- kept app-server/API deserialization strict, so relative
`additionalPermissions.fileSystem.*` paths are rejected at the boundary
- restored cwd/workdir-relative deserialization for local tool-call
payloads by parsing `shell`, `shell_command`, and `exec_command`
arguments under an `AbsolutePathBufGuard` rooted at the resolved command
working directory
- simplified runtime additional-permission normalization so it only
canonicalizes and deduplicates absolute roots instead of trying to
recover relative ones later
- updated the app-server schema fixtures, `app-server/README.md`, and
the affected transport/TUI tests to match the final behavior
Summary is a required parameter on UserTurn. Ideally we'd like the core
to decide the appropriate summary level.
Make the summary optional and don't send it when not needed.
This PR replaces the old `additional_permissions.fs_read/fs_write` shape
with a shared `PermissionProfile`
model and wires it through the command approval, sandboxing, protocol,
and TUI layers. The schema is adopted from the
`SkillManifestPermissions`, which is also refactored to use this unified
struct. This helps us easily expose permission profiles in app
server/core as a follow-up.
## 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
Ahmed Ibrahim ·
Jack Mousseau ·
Dylan Hurd ·
Michael Bolin ·
pash-openai ·
pakrym-oai ·
Celia Chen ·