## Why
The MCP tool path had accumulated a few core-owned special cases: a
dedicated payload variant, resolver plumbing, a legacy `AfterToolUse`
translation path, and a side channel for parallel-call metadata. That
made `ToolRegistry` and the spec builder know more about MCP than they
needed to.
This change moves MCP-specific execution details back onto `ToolInfo`
and `McpHandler` so `codex-core` can treat MCP calls like normal
function calls while still preserving MCP-specific dispatch and
telemetry behavior where it belongs.
## What changed
- removed `resolve_mcp_tool_info`, `ToolPayload::Mcp`, `ToolKind`, and
the remaining registry-side MCP resolver path
- stored MCP routing metadata directly on `McpHandler` and `ToolInfo`,
including `supports_parallel_tool_calls`
- deleted the legacy `AfterToolUse` consumer in `core`, which removes
the need for handler-specific `after_tool_use_payload` implementations
- switched tool-result telemetry to handler-provided tags and kept
MCP-specific dispatch payload construction inside the handler
- simplified tool spec planning/building by passing `ToolInfo` directly
and dropping the direct/deferred MCP wrapper structs and the
parallel-server side table
## Testing
- `cargo check -p codex-core -p codex-mcp -p codex-otel`
- `cargo test -p codex-core
mcp_parallel_support_uses_exact_payload_server`
- `cargo test -p codex-core
direct_mcp_tools_register_namespaced_handlers`
- `cargo test -p codex-core
search_tool_description_lists_each_mcp_source_once`
- `cargo test -p codex-mcp
list_all_tools_uses_startup_snapshot_while_client_is_pending`
- `just fix -p codex-core -p codex-mcp -p codex-otel`
## Why
`codex exec-server` should keep the existing public `ws://IP:PORT` URL
shape while serving that websocket connection through an HTTP upgrade
path internally. That keeps the client-facing configuration simple and
allows the listener to work through intermediate HTTP-aware
infrastructure.
## What changed
- keep the emitted and configured exec-server URL as `ws://IP:PORT`
- serve that websocket endpoint through Axum HTTP upgrade handling on
`/`
- expose `GET /readyz` from the same listener for readiness checks
- route upgraded Axum websocket streams through the shared JSON-RPC
connection machinery
- initialize the rustls crypto provider before websocket client
connections
- preserve inbound binary websocket JSON-RPC parsing for compatibility
with the prior transport behavior
## Verification
- `cargo test -p codex-exec-server --test health --test process --test
websocket --test initialize --test exec_process`
## Why
While investigating `codex exec hi` startup latency, the useful
questions were not "is startup slow?" but "which durable bucket is slow
in production?"
The path we observed has a few distinct stages:
1. `thread/start` creates the session
2. startup prewarm builds the turn context, tools, and prompt
3. startup prewarm warms the websocket
4. the first real turn resolves the prewarm
5. the model produces the first token
Before this PR, production telemetry had some of the raw measurements
already:
- aggregate startup-prewarm duration / age-at-first-turn metrics
- TTFT as a metric
- websocket request telemetry
But there was no coherent production event stream for the startup
breakdown itself, and TTFT was metric-only. That made it hard to answer
the same latency questions from OpenTelemetry-backed logs without adding
one-off local instrumentation.
## What changed
Add durable production telemetry on the existing `SessionTelemetry`
path:
- new `codex.startup_phase` OTel log/trace events plus
`codex.startup.phase.duration_ms`
- new `codex.turn_ttft` OTel log/trace events while preserving the
existing TTFT metric
The startup phase event is emitted for the coarse buckets we actually
observed while running `exec hi`:
- `thread_start_create_thread`
- `startup_prewarm_total`
- `startup_prewarm_create_turn_context`
- `startup_prewarm_build_tools`
- `startup_prewarm_build_prompt`
- `startup_prewarm_websocket_warmup`
- `startup_prewarm_resolve`
These phases are intentionally low-cardinality so they remain safe as
production telemetry tags.
## Why this shape
This keeps the instrumentation on the same production path as the rest
of the session telemetry instead of adding a local debug-only trace
mode. It also avoids changing startup behavior:
- prewarm still runs
- no control flow changes
- no extra remote calls
- no user-visible behavior changes
One boundary is intentional: very early process bootstrap that happens
before a session exists is not included here, because this PR uses
session-scoped production telemetry. The expensive buckets we were
trying to understand after `thread/start` are now covered durably.
## Verification
- `cargo test -p codex-otel`
- `cargo test -p codex-core turn_timing`
- `cargo test -p codex-core
regular_turn_emits_turn_started_without_waiting_for_startup_prewarm`
- `cargo test -p codex-core
interrupting_regular_turn_waiting_on_startup_prewarm_emits_turn_aborted`
- `cargo test -p codex-app-server thread_start`
- `just fix -p codex-otel -p codex-core -p codex-app-server`
I also ran `cargo test -p codex-core`; it built successfully and then
hit an existing unrelated stack overflow in
`tools::handlers::multi_agents::tests::tool_handlers_cascade_close_and_resume_and_keep_explicitly_closed_subtrees_closed`.
## Summary
- add multi-environment apply_patch routing for both freeform and
function-call tool flows
- parse and reconcile the optional environment selector in the main
apply_patch parser, then verify against the selected environment in the
handler
- carry environment_id through runtime and approval surfaces so
remote-targeted patches stay explicit end to end
## Testing
- just fmt
- remote exec-server e2e: `cargo test -p codex-core --test all
apply_patch_multi_environment_uses_remote_executor -- --nocapture` on
dev via `scripts/test-remote-env.sh`
---------
Co-authored-by: Codex <noreply@openai.com>
## Why
Update `codex remote-control` to use the new app server daemon commands
instead.
- if the updater loop is not running, bootstrap the daemon with remote
control enabled (`codex app-server daemon bootstrap --remote-control`)
- otherwise, enable the persisted remote-control setting and start the
daemon normally
# Why
Managed hook configs need a shared cross-platform shape without making
the existing `command` field polymorphic. The common case is still one
command string, with Windows needing a different entrypoint only when
the runtime is actually Windows.
Keeping `command` as the portable/default path and adding an optional
Windows override keeps the config easier to read, preserves the existing
scalar shape for non-Windows users, and avoids forcing every caller into
a `{ unix, windows }` object when only one platform needs special
handling.
# What
- Add optional `command_windows` / `commandWindows` alongside the
existing hook `command` field.
- Resolve `command_windows` only on Windows during hook discovery; other
platforms continue to use `command` unchanged.
- Keep trust hashing aligned to the effective command selected for the
current runtime.
# Docs
The Codex hooks/config reference should document `command_windows` as
the Windows-only override for command hooks.
## Why
Review telemetry should describe reviews as first-class events, not only
as counters denormalized onto terminal tool-item events. That lets us
analyze guardian and user reviews consistently across command execution,
file changes, permissions, and network access, while still preserving
the terminal item summaries that existing tool analytics need.
To make those review events accurate, analytics also needs the observed
completion time for each review and enough command metadata to
distinguish `shell` from `unified_exec` reviews.
## What changed
- emit generic `codex_review_event` rows for completed user and guardian
reviews, with review subjects, reviewer, trigger, terminal status,
resolution, and observed duration
- reduce approval request / response / abort facts into review events
for command execution, file change, and permissions flows
- keep denormalized review counts, final approval outcome, and
permission-request flags on terminal tool-item events for
item-associated reviews
- plumb review completion timing so user-review responses and aborts use
app-server-observed completion times, while guardian analytics reuse the
same terminal timestamps emitted on guardian assessment events
- carry command approval `source` through the protocol and app-server
layers so review analytics can distinguish `shell` from `unified_exec`
- add analytics coverage for user-review emission, guardian-review
emission, permission reviews that should not denormalize onto tool
items, item-summary isolation across threads, and the serialized
review-event shape
## Verification
- `cargo test -p codex-analytics`
---
[//]: # (BEGIN SAPLING FOOTER)
Stack created with [Sapling](https://sapling-scm.com). Best reviewed
with [ReviewStack](https://reviewstack.dev/openai/codex/pull/18748).
* __->__ #18748
* #21434
* #18747
* #17090
* #17089
* #20514
## Why
The Python SDK needs the same tight formatter/lint loop as the rest of
the repo: a safe Ruff autofix pass, Ruff formatting, editor save
behavior, and CI checks that catch drift. Without that loop, SDK changes
can land with formatting or import ordering that differs from what
reviewers and CI expect.
## What
- Add Ruff configuration to `sdk/python/pyproject.toml`, excluding
generated protocol code and notebooks from the normal lint/format pass.
- Update `just fmt` so it still formats Rust and also runs Python SDK
Ruff autofix and formatting.
- Add Python SDK CI steps for `ruff check` and `ruff format --check`
before pytest.
- Recommend the Ruff VS Code extension and enable Python
format/fix/organize-on-save so Cmd+S uses the same tooling.
- Apply the resulting Ruff formatting to SDK Python files, examples, and
the checked-in generated `v2_all.py` output emitted by the pinned
generator.
- Add a guard test for the `just fmt` recipe so it keeps working from
both Rust and Python SDK working directories.
## Stack
1. #21891 `[1/8]` Pin Python SDK runtime dependency
2. #21893 `[2/8]` Generate Python SDK types from pinned runtime
3. #21895 `[3/8]` Run Python SDK tests in CI
4. #21896 `[4/8]` Define Python SDK public API surface
5. #21905 `[5/8]` Rename Python SDK package to `openai-codex`
6. #21910 `[6/8]` Add high-level Python SDK approval mode
7. #22014 `[7/8]` Add Python SDK app-server integration harness
8. This PR `[8/8]` Add Python SDK Ruff formatting
## Verification
- Added `test_root_fmt_recipe_formats_rust_and_python_sdk` for the
shared format recipe.
- Ran `just fmt` after the recipe update.
---------
Co-authored-by: Codex <noreply@openai.com>
## Why
The SDK had behavioral tests that replaced SDK client internals. Those
tests could catch wrapper mistakes, but they did not prove the pinned
app-server runtime, generated notification models, request routing, and
sync/async public clients worked together.
This PR adds deterministic integration coverage that starts the pinned
`codex app-server` process and mocks only the upstream Responses HTTP
boundary.
## What
- Add `AppServerHarness` and `MockResponsesServer` helpers for isolated
`CODEX_HOME`, mock-provider config, queued SSE responses, and captured
`/v1/responses` requests.
- Add shared helpers for SSE construction, stream assertions,
approval-policy inspection, and image fixtures.
- Split integration coverage into focused modules for run behavior,
inputs, streaming, turn controls, approvals, and thread lifecycle.
- Cover sync and async `Thread.run`, `TurnHandle.stream`, interleaved
streams, approval-mode persistence, lifecycle helpers, final-answer
phase handling, image inputs, loaded skill input injection, steering,
interruption, listing, history reads, run overrides, and token usage
mapping.
- Replace public-wrapper tests that duplicated integration-test behavior
with lower-level client tests only where direct client behavior is the
thing under test.
## Stack
1. #21891 `[1/8]` Pin Python SDK runtime dependency
2. #21893 `[2/8]` Generate Python SDK types from pinned runtime
3. #21895 `[3/8]` Run Python SDK tests in CI
4. #21896 `[4/8]` Define Python SDK public API surface
5. #21905 `[5/8]` Rename Python SDK package to `openai-codex`
6. #21910 `[6/8]` Add high-level Python SDK approval mode
7. This PR `[7/8]` Add Python SDK app-server integration harness
8. #22021 `[8/8]` Add Python SDK Ruff formatting
## Verification
- Added pinned app-server integration tests under
`sdk/python/tests/test_app_server_*.py` and
`test_real_app_server_integration.py`.
---------
Co-authored-by: Codex <noreply@openai.com>
## Why
The high-level SDK should expose the approval behavior it actually
supports instead of leaking generated app-server routing fields. New
work should have two clear choices: default auto review, or explicitly
deny escalated permission requests. Existing threads and subsequent
turns should preserve their current approval behavior unless the caller
passes an override.
## What
- Add the public `ApprovalMode` enum with `auto_review` and `deny_all`.
- Default new thread creation to `ApprovalMode.auto_review`.
- Preserve existing approval settings by default for resume, fork, run,
and turn helpers.
- Remove raw `approval_policy` / `approvals_reviewer` kwargs from
high-level SDK wrappers.
- Update generated wrapper output, docs, examples, notebooks, and tests
for the high-level approval mode API.
## Stack
1. #21891 `[1/8]` Pin Python SDK runtime dependency
2. #21893 `[2/8]` Generate Python SDK types from pinned runtime
3. #21895 `[3/8]` Run Python SDK tests in CI
4. #21896 `[4/8]` Define Python SDK public API surface
5. #21905 `[5/8]` Rename Python SDK package to `openai-codex`
6. This PR `[6/8]` Add high-level Python SDK approval mode
7. #22014 `[7/8]` Add Python SDK app-server integration harness
8. #22021 `[8/8]` Add Python SDK Ruff formatting
## Verification
- Added approval-mode mapping/default tests for new threads, existing
threads, forks, resumes, and subsequent turns.
---------
Co-authored-by: Codex <noreply@openai.com>
## Why
The SDK should publish under the reserved public distribution name
`openai-codex`, and its import module should match that name in the
Python style. Since package names can contain hyphens but import modules
cannot, the public import path becomes `openai_codex`.
Keeping the rename separate from the public API surface change makes the
naming change easy to review and avoids mixing it with API curation.
## What
- Rename the SDK distribution from `openai-codex-app-server-sdk` to
`openai-codex`.
- Rename the import package from `codex_app_server` to `openai_codex`.
- Keep the runtime wheel as the separate `openai-codex-cli-bin`
dependency.
- Update docs, examples, notebooks, artifact scripts, lockfile metadata,
and tests for the new distribution/module names.
## Stack
1. #21891 `[1/8]` Pin Python SDK runtime dependency
2. #21893 `[2/8]` Generate Python SDK types from pinned runtime
3. #21895 `[3/8]` Run Python SDK tests in CI
4. #21896 `[4/8]` Define Python SDK public API surface
5. This PR `[5/8]` Rename Python SDK package to `openai-codex`
6. #21910 `[6/8]` Add high-level Python SDK approval mode
7. #22014 `[7/8]` Add Python SDK app-server integration harness
8. #22021 `[8/8]` Add Python SDK Ruff formatting
## Verification
- Updated package metadata and public API tests to assert the
distribution and import names.
Co-authored-by: Codex <noreply@openai.com>
## Why
The SDK package root should be the ergonomic public client API, not a
dump of every generated app-server schema type. Generated models still
need a supported import path, but callers should be able to tell which
names are high-level SDK entrypoints and which names are protocol value
models.
## What
- Define a curated root `__all__` for clients, handles, input helpers,
retry helpers, config, and public errors.
- Add a `types` module as the supported home for generated app-server
response, event, enum, and helper models.
- Update docs and examples to import protocol/value models from the type
module.
- Add tests that lock root exports, type-module exports, star-import
behavior, and example import hygiene.
## Stack
1. #21891 `[1/8]` Pin Python SDK runtime dependency
2. #21893 `[2/8]` Generate Python SDK types from pinned runtime
3. #21895 `[3/8]` Run Python SDK tests in CI
4. This PR `[4/8]` Define Python SDK public API surface
5. #21905 `[5/8]` Rename Python SDK package to `openai-codex`
6. #21910 `[6/8]` Add high-level Python SDK approval mode
7. #22014 `[7/8]` Add Python SDK app-server integration harness
8. #22021 `[8/8]` Add Python SDK Ruff formatting
## Verification
- Added public API signature tests for root exports, `types` exports,
and example imports.
---------
Co-authored-by: Codex <noreply@openai.com>
## Why
The Python SDK stack now depends on packaging metadata, pinned runtime
wheels, generated artifacts, async behavior, and stream interleaving.
Those checks need to run in CI so future changes cannot bypass the SDK
test suite.
## What
- Add a dedicated `python-sdk` job to `.github/workflows/sdk.yml`.
- Run the job in `python:3.12-alpine` so dependency resolution exercises
the pinned musl runtime wheel.
- Keep the Python SDK test job parallel to the existing SDK job instead
of serializing the full workflow.
## Stack
1. #21891 `[1/8]` Pin Python SDK runtime dependency
2. #21893 `[2/8]` Generate Python SDK types from pinned runtime
3. This PR `[3/8]` Run Python SDK tests in CI
4. #21896 `[4/8]` Define Python SDK public API surface
5. #21905 `[5/8]` Rename Python SDK package to `openai-codex`
6. #21910 `[6/8]` Add high-level Python SDK approval mode
7. #22014 `[7/8]` Add Python SDK app-server integration harness
8. #22021 `[8/8]` Add Python SDK Ruff formatting
## Verification
- The added workflow job installs the SDK with `uv sync --extra dev
--frozen` and runs the Python SDK pytest suite.
---------
Co-authored-by: Codex <noreply@openai.com>
## Why
Once the SDK declares its runtime package, generated Python artifacts
should come from that pinned runtime rather than whatever app-server
schema happens to be in the current checkout. That keeps the generated
API and model surface aligned with the runtime users install.
## What
- Teach `scripts/update_sdk_artifacts.py generate-types` to invoke the
pinned runtime package for schema generation.
- Regenerate `v2_all.py`, `notification_registry.py`, and generated
public wrapper methods from that schema.
- Add freshness coverage so regenerating from the pinned runtime must
leave checked-in artifacts unchanged.
## Stack
1. #21891 `[1/8]` Pin Python SDK runtime dependency
2. This PR `[2/8]` Generate Python SDK types from pinned runtime
3. #21895 `[3/8]` Run Python SDK tests in CI
4. #21896 `[4/8]` Define Python SDK public API surface
5. #21905 `[5/8]` Rename Python SDK package to `openai-codex`
6. #21910 `[6/8]` Add high-level Python SDK approval mode
7. #22014 `[7/8]` Add Python SDK app-server integration harness
8. #22021 `[8/8]` Add Python SDK Ruff formatting
## Verification
- Added `test_generated_files_are_up_to_date` for pinned-runtime
generation drift.
- Added generator-structure tests for schema annotation and notification
metadata generation.
---------
Co-authored-by: Codex <noreply@openai.com>
## Why
The Python SDK depends on the app-server runtime package for the bundled
`codex` binary and schema source of truth. That relationship should be
explicit in package metadata instead of inferred from matching version
numbers, so installers, lockfiles, and reviewers can see exactly which
runtime the SDK expects.
## What
- Declare `openai-codex-cli-bin==0.131.0a4` as a Python SDK dependency.
- Update runtime setup helpers to resolve the runtime version from the
declared dependency pin.
- Refresh the SDK lockfile for the pinned runtime wheel.
- Update package/runtime tests and docs that describe where the runtime
version comes from.
## Stack
1. This PR `[1/8]` Pin Python SDK runtime dependency
2. #21893 `[2/8]` Generate Python SDK types from pinned runtime
3. #21895 `[3/8]` Run Python SDK tests in CI
4. #21896 `[4/8]` Define Python SDK public API surface
5. #21905 `[5/8]` Rename Python SDK package to `openai-codex`
6. #21910 `[6/8]` Add high-level Python SDK approval mode
7. #22014 `[7/8]` Add Python SDK app-server integration harness
8. #22021 `[8/8]` Add Python SDK Ruff formatting
## Verification
- Added coverage for the SDK runtime dependency pin and runtime
distribution naming.
---------
Co-authored-by: Codex <noreply@openai.com>
## Why
The permissions migration is making
`permissions.<profile>.network.enabled` the canonical sandbox network
bit, while proxy startup is a separate concern. Enabling network access
should not implicitly start the proxy, and users who are still on legacy
sandbox modes need a separate place to opt into proxy startup and
provide proxy-specific settings.
This follow-up to #19900 gives the network proxy its own feature surface
instead of overloading permission-profile network semantics.
## What changed
- Add an experimental `network_proxy` feature with a configurable
`[features.network_proxy]` table.
- Overlay `features.network_proxy` settings onto the configured proxy
state after permission-profile selection, so the proxy only starts when
the active `NetworkSandboxPolicy` already allows network access.
- Preserve `[experimental_network]` startup behavior independently of
the new feature flag.
## Behavior and examples
There are now three related knobs:
- `permissions.<profile>.network.enabled` controls whether the active
permission profile has network access at all.
- `features.network_proxy` enables proxy restrictions for an
already-network-enabled profile.
- Legacy `sandbox_mode` plus `[sandbox_workspace_write].network_access`
still control whether legacy `workspace-write` has network access at
all.
The rule is:
- network off + proxy flag on -> network stays off, proxy is a no-op
- network on + proxy flag off -> unrestricted direct network
- network on + proxy flag on -> network stays on, with proxy
restrictions applied
For permission profiles, the feature toggle adds proxy restrictions only
when network access is already enabled:
```toml
default_permissions = "workspace"
[permissions.workspace.filesystem]
":minimal" = "read"
[permissions.workspace.network]
enabled = true
[features]
network_proxy = true
```
If `network.enabled = false`, the same feature flag is a no-op: network
remains off and the proxy does not start.
For legacy sandbox config, `network_access` remains the master switch:
```toml
sandbox_mode = "workspace-write"
[sandbox_workspace_write]
network_access = true
[features]
network_proxy = true
```
That keeps legacy `workspace-write` network access on, but routes it
through the proxy policy. If `network_access = false`, the proxy feature
is a no-op and legacy `workspace-write` remains offline.
The same proxy opt-in can be supplied from the CLI:
```bash
codex -c 'features.network_proxy=true'
```
Additional proxy settings can be supplied when a table is needed:
```bash
codex \
-c 'features.network_proxy.enabled=true' \
-c 'features.network_proxy.enable_socks5=false'
```
The intended behavior matrix is:
| Config surface | Network setting | `features.network_proxy` | Direct
sandbox network | Proxy |
| --- | --- | --- | --- | --- |
| Permission profile | `network.enabled = false` | off | restricted |
off |
| Permission profile | `network.enabled = false` | on | restricted | off
|
| Permission profile | `network.enabled = true` | off | enabled | off |
| Permission profile | `network.enabled = true` | on | enabled | on |
| Legacy `workspace-write` | `network_access = false` | off | restricted
| off |
| Legacy `workspace-write` | `network_access = false` | on | restricted
| off |
| Legacy `workspace-write` | `network_access = true` | off | enabled |
off |
| Legacy `workspace-write` | `network_access = true` | on | enabled | on
|
`[experimental_network]` requirements remain separate from the user
feature toggle and still start the proxy on their own.
Relevant code:
-
[`features/src/feature_configs.rs`](https://github.com/openai/codex/blob/43785aff47/codex-rs/features/src/feature_configs.rs#L58-L117)
defines the feature-specific proxy config.
-
[`core/src/config/mod.rs`](https://github.com/openai/codex/blob/43785aff47/codex-rs/core/src/config/mod.rs#L1959-L1964)
reads the feature table, and [later applies it only when network access
is already
enabled](https://github.com/openai/codex/blob/43785aff47/codex-rs/core/src/config/mod.rs#L2448-L2458).
## Verification
Added focused coverage for:
- keeping the proxy off when `features.network_proxy` is enabled but
sandbox network access is disabled
- the full permission-profile and legacy `workspace-write` matrix above
- preserving `[experimental_network]` startup without the feature
- reusing profile-supplied proxy settings when the feature is enabled
Ran:
- `cargo test -p codex-features`
- `cargo test -p codex-core network_proxy_feature`
- `cargo test -p codex-core
experimental_network_requirements_enable_proxy_without_feature`
## Summary
- revoke previously stored managed ChatGPT tokens after a successful
re-login
- keep the new login successful even when revocation is unavailable or
fails
- cover the shared persistence path used by browser and device-code
login flows
## Why
A new `codex login` currently overwrites existing managed ChatGPT
credentials without attempting to revoke the superseded tokens, leaving
old credentials valid longer than necessary.
## Validation
- `just fmt`
- `CARGO_HOME=/tmp/cargo-home cargo test -p codex-login`
## Notes
- Initial local Cargo validation hit a corrupt existing crate cache in
the default `CARGO_HOME`; rerunning with a clean temporary `CARGO_HOME`
passed.
---------
Co-authored-by: Codex <noreply@openai.com>
## Why
`bootstrap` starts a detached pid-backed updater loop, but before this
change that updater could keep running an old executable image even
after `install.sh` replaced the managed standalone binary under
`CODEX_HOME`. That left the updater itself behind the binary it had just
rolled out, especially when the app-server was stopped or when the
managed binary changed without a version-string change.
## What changed
- Track updater identity from the executable contents rather than only
the reported CLI version.
- Force the managed app-server restart path when the managed binary
contents differ from the running updater image, then re-exec the updater
from the managed binary once the rollout is in a safe state.
- Distinguish a genuinely absent managed app-server from a managed
process that exists but is not yet probeable, so self-refresh does not
skip a required restart.
- Keep the restart/re-exec decision under the daemon operation lock so
`bootstrap` cannot race the handoff.
- Update `app-server-daemon/README.md` to document the resulting
standalone and out-of-band update behavior.
## Verification
- `cargo test -p codex-app-server-daemon`
- `just fix -p codex-app-server-daemon`
Added focused unit coverage for:
- content-based updater refresh decisions
- safe updater re-exec outcomes across restart states
## Summary
Fixes#22128.
The `/keymap` flow already persists the `-` key as `minus`, and the
runtime keymap parser already accepts that spelling. `codex-config` was
the missing leg: it rejected `minus` during config deserialization, so a
binding saved by Codex could fail on the next startup or config reload.
## What Changed
- Accept `minus` as a valid canonical key name in `tui.keymap` config
normalization.
- Update the config validation message so its supported-key list
includes `minus`.
- Add regression coverage that deserializes both `minus` and `alt-minus`
under `[tui.keymap.global]` and verifies the normalized config shape.
## How to Test
1. Start Codex TUI.
2. Run `/keymap`.
3. Assign the `-` key to an action and save the change.
4. Restart Codex or reload the config.
5. Confirm the config loads normally and the saved binding remains
usable instead of failing on `minus`.
6. As a focused regression check, repeat with a modifier form such as
`alt--` captured through `/keymap`, which persists as `alt-minus` and
should also reload successfully.
Targeted tests:
- `cargo test -p codex-config`
