## Why
Guardian review selection was hard-coded in `core`, which worked for the
default OpenAI path but did not give provider implementations a way to
choose backend-specific reviewer model IDs. That matters for Amazon
Bedrock: guardian review should run through the Bedrock/Mantle provider
using Bedrock's `openai.gpt-5.4` model ID, instead of accidentally
selecting a reviewer model that implies the OpenAI backend.
## What Changed
- Added provider-owned approval review model selection via
`ModelProvider::approval_review_model_selection`.
- Moved the existing default selection policy into the provider
abstraction: prefer the requested reviewer model when it is available,
otherwise fall back to the active turn model, preferring `Low` reasoning
when supported.
- Added an Amazon Bedrock override that pins guardian review to
`openai.gpt-5.4` with `Low` reasoning.
## Why
`apply_patch` is now a freeform/custom tool. Keeping the old
JSON/function-style registration and parsing path left another way for
models and tests to invoke `apply_patch`, which made the tool surface
harder to reason about.
## What changed
- Removed the `ApplyPatchToolType::Function` variant, JSON `apply_patch`
spec, and handler support for function payloads.
- Kept `apply_patch_tool_type = freeform` as the supported model
metadata path, including Bedrock catalog metadata.
- Migrated `apply_patch` tests and SSE fixtures to custom/freeform tool
calls.
## Verification
- `cargo test -p codex-tools -p codex-protocol -p codex-model-provider`
- `cargo test -p codex-core tools::handlers::apply_patch --lib`
- `cargo test -p codex-core --test all
apply_patch_tool_executes_and_emits_patch_events`
- `cargo test -p codex-core --test all
apply_patch_reports_parse_diagnostics`
- `cargo test -p codex-exec test_apply_patch_tool`
- `just fix -p codex-core`
- `just fix -p codex-tools -p codex-protocol -p codex-model-provider -p
codex-exec`
## Summary
TL;DR: teaches `codex-rs` / app-server to request a desktop-provided
attestation token and attach it as `x-oai-attestation` on the scoped
ChatGPT Codex request paths.
## Details
This PR teaches the Codex app-server runtime how to request and attach
an attestation token. It does not generate DeviceCheck tokens directly;
instead, it relies on the connected desktop app to advertise that it can
generate attestation and then asks that app for a fresh header value
when needed.
The flow is:
1. The Codex desktop app connects to app-server.
2. During `initialize`, the app can advertise that it supports
`requestAttestation`.
3. Before app-server calls selected ChatGPT Codex endpoints, it sends
the internal server request `attestation/generate` to the app.
4. app-server receives a pre-encoded header value back.
5. app-server forwards that value as `x-oai-attestation` on the scoped
outbound requests.
The code in this repo is mostly protocol and runtime plumbing: it adds
the app-server request/response shape, introduces an attestation
provider in core, wires that provider into Responses / compaction /
realtime setup paths, and covers the intended scoping with tests. The
signed macOS DeviceCheck generation remains owned by the desktop app PR.
## Related PR
- Codex desktop app implementation:
https://github.com/openai/openai/pull/878649
## Validation
<details>
<summary>Tests run</summary>
```sh
cargo test -p codex-app-server-protocol
cargo test -p codex-core attestation --lib
cargo test -p codex-app-server --lib attestation
```
Also ran:
```sh
just fix -p codex-core
just fix -p codex-app-server
just fix -p codex-app-server-protocol
just fmt
just write-app-server-schema
```
</details>
<details>
<summary>E2E DeviceCheck validation</summary>
First validated the signed desktop app boundary directly: launched a
packaged signed `Codex.app`, sent `attestation/generate`, decoded the
returned `v1.` attestation header, and validated the extracted
DeviceCheck token with `personal/jm/verify_devicecheck_token.py` using
bundle ID `com.openai.codex`. Apple returned `status_code: 200` and
`is_ok: true`.
Then ran the fuller app + app-server flow. The packaged `Codex.app`
launched a current-branch app-server via `CODEX_CLI_PATH`, and a local
MITM proxy intercepted outbound `chatgpt.com` traffic. The app-server
requested `attestation/generate` from the real Electron app process, and
the intercepted `/backend-api/codex/responses` traffic included
`x-oai-attestation` on both routes:
```text
GET /backend-api/codex/responses Upgrade: websocket x-oai-attestation: present
POST /backend-api/codex/responses Upgrade: none x-oai-attestation: present
```
The captured header decoded to a DeviceCheck token that also validated
with Apple for `com.openai.codex` (`status_code: 200`, `is_ok: true`,
team `2DC432GLL2`).
</details>
---------
Co-authored-by: Codex <noreply@openai.com>
## Why
`/fast` was wired as a one-off slash command even though model metadata
now exposes service tiers as catalog data. That meant adding another
tier, such as a slower/cheaper tier, would require more hardcoded TUI
plumbing instead of letting the model catalog drive the available
commands.
This change makes service-tier commands data-driven: each advertised
`service_tiers` entry becomes a `/name` command using the catalog
description, while the request path sends the tier `id` only when the
selected model supports it.
## What Changed
- Removed the hardcoded `/fast` slash-command variant and introduced
dynamic service-tier command items in the composer and command popup.
- Added toggle behavior for service-tier commands: invoking `/name`
selects that tier, and invoking it again clears the selection.
- Preserved the existing Fast-mode keybinding/status affordances by
resolving the current model tier whose name is `fast`, while still
sending the tier request value such as `priority`.
- Persisted service-tier selections as raw request strings so non-fast
tiers can round-trip through config.
- Updated the Bedrock catalog entry to advertise fast support through
`service_tiers` with `id: "priority"` and `name: "fast"`.
- Added defensive filtering in core so unsupported selected service
tiers are omitted from `/responses` requests.
## Validation
- Added/updated coverage for dynamic service-tier slash command lookup,
popup descriptions, composer dispatch, TUI fast toggling, and
unsupported-tier omission in core request construction.
- Local tests were not run per request.
---------
Co-authored-by: Codex <noreply@openai.com>
## Summary
Codex's Amazon Bedrock provider signs Mantle requests with SigV4 using
credentials resolved by the AWS SDK. That worked for standard AWS
profiles and environment credentials, but AWS CLI console-login profiles
created by `aws login` require the SDK's `credentials-login` feature to
resolve `login_session` credentials.
This change enables that credential provider so Bedrock can use AWS
console-login credentials through the existing provider-owned AWS auth
path.
While testing the console-login path, we also hit a Mantle-specific
SigV4 regression from the new split between `session_id` and
`thread_id`. Mantle does not preserve legacy OpenAI compatibility
headers that use `snake_case` before SigV4 verification, so signing
those headers can make the server reconstruct a different canonical
request. The Bedrock auth path now removes that header class before
signing, keeping preserved hyphenated Codex/AWS headers such as
`x-codex-turn-metadata` signed normally.
## Changes
- Enable `aws-config`'s `credentials-login` feature in
`codex-rs/aws-auth`.
- Add a compile-time regression test for
`aws_config::login::LoginCredentialsProvider`.
- Strip `snake_case` compatibility headers from Bedrock Mantle SigV4
requests before signing.
- Expand the Bedrock auth regression test to cover `session_id`,
`thread_id`, and future headers of the same shape.
- Refresh Cargo and Bazel lockfiles for the added `aws-sdk-signin`
dependency.
## Tests
- tested with `aws login` locally and verified that it works as
intended.
## Why
The model list needs to carry display-ready service tier metadata so
clients can render tier choices with stable IDs, names, and
descriptions. A raw speed-tier string list is not enough for richer UI
copy or future tier labels.
## What changed
- Added `ModelServiceTier` to shared model metadata with string `id`,
`name`, and `description` fields.
- Added `service_tiers` to `ModelInfo` and `ModelPreset`, preserving
empty defaults for older cached model payloads.
- Exposed `serviceTiers` on app-server v2 `Model` responses and threaded
it through TUI app-server model conversion.
- Marked legacy `additional_speed_tiers` / `additionalSpeedTiers`
metadata as deprecated in source and generated schema output.
- Regenerated app-server protocol JSON schema and TypeScript fixtures,
including `ModelServiceTier.ts`.
## Verification
- Ran `just write-app-server-schema`.
- Did not run local tests per repo instruction; relying on PR CI.
---------
Co-authored-by: Codex <noreply@openai.com>
## Why
`/status` was showing the configured `ModelProviderInfo.base_url` for
Amazon Bedrock, which can be stale or misleading because the actual
Bedrock Mantle endpoint is derived at runtime from the resolved AWS
region. This made sessions report the wrong provider endpoint even
though requests used the correct runtime URL.
## What changed
- Added `ModelProvider::runtime_base_url()` so provider implementations
can expose the request-time base URL through the shared runtime provider
abstraction.
- Moved Bedrock region-to-Mantle URL resolution into
`amazon_bedrock::mantle::runtime_base_url()`, keeping region resolution
private to the Mantle module.
- Overrode `runtime_base_url()` for Amazon Bedrock so it returns the
resolved Mantle endpoint instead of the configured default.
- Resolved and cached the runtime provider base URL during TUI startup,
then used that cached value when rendering `/status`.
- Added status coverage that verifies Bedrock displays the runtime URL
and ignores the configured Bedrock `base_url` when they differ.
## Verification
model provider is resolved correctly in local build:
<img width="696" height="245" alt="Screenshot 2026-04-29 at 5 01 36 PM"
src="https://github.com/user-attachments/assets/a13c10a5-3720-41ab-8ace-3c4bc573f971"
/>
## Summary
Amazon Bedrock Mantle's OpenAI-compatible endpoint now lives under
`/openai/v1`, and the GPT-5.4 Mantle model ID no longer uses the `-cmb`
suffix. This updates Codex's built-in Bedrock provider configuration so
generated providers and the static Bedrock catalog use the current
endpoint and model ID.
## Changes
- Update the Bedrock Mantle base URL from
`https://bedrock-mantle.{region}.api.aws/v1` to
`https://bedrock-mantle.{region}.api.aws/openai/v1`.
- Update the Amazon Bedrock default base URL in
`codex-model-provider-info`.
- Change the Bedrock GPT-5.4 catalog slug from `openai.gpt-5.4-cmb` to
`openai.gpt-5.4`.
- Align provider and catalog tests with the new URL and model ID.
## Test Plan
- Manual smoke test:
```shell
target/debug/codex \
-m openai.gpt-5.4 \
-c 'model_provider="amazon-bedrock"' \
-c 'model_providers.amazon-bedrock.aws.region="us-west-2"'
```
## Why
Unsupported features must fail closed and Codex must not expose
OpenAI-hosted fallback paths when the active provider cannot support
them. In practice, Bedrock should not surface app connectors, MCP
servers, tool search/suggestions, image generation, web search, or JS
REPL until those paths are explicitly supported for that provider.
This PR moves that decision into provider-owned capability metadata
instead of scattering Bedrock-specific checks across callers.
## What changed
- Adds `ProviderCapabilities` to `codex-model-provider`, with default
support for existing providers and a Bedrock override that disables
unsupported launch surfaces.
- Adds `ToolCapabilityBounds` to `codex-tools` so provider capability
limits can clamp otherwise-enabled tool config.
- Applies capability bounds when building session and review-thread tool
config.
- Routes MCP/app connector configuration through
`McpManager::mcp_config`, which filters configured MCP servers and app
connectors based on the active provider.
- Updates app-server MCP list/read paths to use the filtered MCP config.
- Adds coverage for default provider capabilities, Bedrock disabled
capabilities, and optional tool-surface clamping.
## Testing
built locally and verified that bedrock responses api now return without
errors calling unsupported tools.
Right now, if Codex winds up in a state with auth but it can't refresh
the token, the user is left with an unhelpful message that says to log
out and log back in again.
Ultimately, we should prevent that from happening but if it does,
returning None will allow the caller to redirect the user back to the
login page
## Summary
AgentIdentity auth previously registered the process task lazily behind
a `OnceCell`. That meant the auth object could be constructed before its
runtime task binding was known.
This PR makes AgentIdentity auth load the runtime task at auth load time
and stores the resulting process task id directly on the auth object.
The model-provider call path can then read a concrete task id instead of
handling a missing lazy value.
## Stack
1. [refactor: make auth loading
async](https://github.com/openai/codex/pull/19762) (merged)
2. **This PR:** [refactor: load AgentIdentity runtime
eagerly](https://github.com/openai/codex/pull/19763)
3. [fix: configure AgentIdentity AuthAPI base
URL](https://github.com/openai/codex/pull/19904)
4. [feat: verify AgentIdentity JWTs with
JWKS](https://github.com/openai/codex/pull/19764)
## Important call sites
| Area | Change |
| --- | --- |
| `AgentIdentityAuth::load` | Registers the process task during auth
loading and stores `process_task_id`. |
| `CodexAuth::from_agent_identity_jwt` | Awaits AgentIdentity auth
loading. |
| model-provider auth | Reads a concrete `process_task_id` instead of an
optional lazy value. |
| AgentIdentity auth tests | Mock task registration now covers eager
runtime allocation. |
## Design decisions
AgentIdentity auth now treats task registration as part of constructing
a usable auth object. That matches how callers use the value: once auth
is present, the model-provider path expects the task-scoped assertion
data to be ready.
## Testing
Tests: targeted Rust auth test compilation, formatter, scoped Clippy
fix, and Bazel lock check.
## Why
When using the Amazon Bedrock provider with `openai.gpt-5.4-cmb`, the
model picker allowed `xhigh` because the CMB catalog entry was derived
from the bundled `gpt-5.4` reasoning metadata. Bedrock rejects that
effort level, causing the request to fail before the turn can run:
```text
{"error":{"code":"validation_error","message":"Failed to deserialize the JSON body into the target type: Invalid 'reasoning': Invalid 'effort': unknown variant `xhigh`, expected one of `high`, `low`, `medium`, `minimal` at line 1 column 77239","param":null,"type":"invalid_request_error"}}
```
## What Changed
- Replace the runtime lookup of bundled `gpt-5.4` metadata for
`openai.gpt-5.4-cmb` with an explicit Bedrock CMB `ModelInfo` entry.
- Advertise only the Bedrock-supported CMB reasoning levels: `minimal`,
`low`, `medium`, and `high`.
- Keep the existing GPT OSS Bedrock model metadata and reasoning levels
unchanged.
- Add catalog coverage for the hardcoded CMB metadata and
Bedrock-compatible reasoning level list.
## Why
`openai.gpt-5.4-cmb` is served through the Amazon Bedrock provider,
whose request validator currently accepts `function` and `mcp` tool
specs but rejects Responses `custom` tools. The CMB catalog entry reuses
the bundled `gpt-5.4` metadata, which marks `apply_patch_tool_type` as
`freeform`. That causes Codex to include an `apply_patch` tool with
`type: "custom"`, so even heavily disabled sessions can fail before the
model runs with:
```text
Invalid tools: unknown variant `custom`, expected `function` or `mcp`
```
This is provider-specific: the model should still expose `apply_patch`,
but for Bedrock it needs to use the JSON/function tool shape instead of
the freeform/custom shape.
## What Changed
- Override the `openai.gpt-5.4-cmb` static catalog entry to set
`apply_patch_tool_type` to `function` after inheriting the rest of the
`gpt-5.4` model metadata.
- Update the catalog test expectation so the CMB entry continues to
track `gpt-5.4` metadata except for this Bedrock-specific tool shape
override.
## Verification
- `cargo test -p codex-model-provider`
- `just fix -p codex-model-provider`
## Why
`codex-models-manager` had grown to own provider-specific concerns:
constructing OpenAI-compatible `/models` requests, resolving provider
auth, emitting request telemetry, and deciding how provider catalogs
should be sourced. That made the manager harder to reuse for providers
whose model catalog is not fetched from the OpenAI `/models` endpoint,
such as Amazon Bedrock.
This change moves provider-specific model discovery behind
provider-owned implementations, so the models manager can focus on
refresh policy, cache behavior, picker ordering, and model metadata
merging.
## What Changed
- Introduced a `ModelsManager` trait with separate `OpenAiModelsManager`
and `StaticModelsManager` implementations.
- Added `ModelsEndpointClient` so OpenAI-compatible HTTP fetching lives
outside `codex-models-manager`.
- Moved `/models` request construction, provider auth resolution,
timeout handling, and request telemetry into `codex-model-provider` via
`OpenAiModelsEndpoint`.
- Added provider-owned `models_manager(...)` construction so configured
OpenAI-compatible providers use `OpenAiModelsManager`, while
static/catalog-backed providers can return `StaticModelsManager`.
- Added an Amazon Bedrock static model catalog for the GPT OSS Bedrock
model IDs.
- Updated core/session/thread manager code and tests to depend on
`Arc<dyn ModelsManager>`.
- Moved offline model test helpers into
`codex_models_manager::test_support`.
## Metadata References
The Bedrock catalog metadata is based on the official Amazon Bedrock
OpenAI model documentation:
- [Amazon Bedrock OpenAI
models](https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-openai.html)
lists the Bedrock model IDs, text input/output modalities, and `128,000`
token context window for `gpt-oss-20b` and `gpt-oss-120b`.
- [Amazon Bedrock `gpt-oss-120b` model
card](https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-openai-gpt-oss-120b.html)
lists the `bedrock-runtime` model ID `openai.gpt-oss-120b-1:0`, the
`bedrock-mantle` model ID `openai.gpt-oss-120b`, text-only modalities,
and `128K` context window.
- [OpenAI `gpt-oss-120b` model
docs](https://developers.openai.com/api/docs/models/gpt-oss-120b)
document configurable reasoning effort with `low`, `medium`, and `high`,
plus text input/output modality.
The display names, default reasoning effort, and priority ordering are
Codex-local catalog choices.
## Test Plan
- Manually verified app-server model listing with an AWS profile:
```shell
CODEX_HOME="$(mktemp -d)" cargo run -p codex-app-server-test-client -- \
--codex-bin ./target/debug/codex \
-c 'model_provider="amazon-bedrock"' \
-c 'model_providers.amazon-bedrock.aws.profile="codex-bedrock"' \
-c 'model_providers.amazon-bedrock.aws.region="us-west-2"' \
model-list
```
The response returned the Bedrock catalog with `openai.gpt-oss-120b-1:0`
as the default model and `openai.gpt-oss-20b-1:0` as the second listed
model, both text-only and supporting low/medium/high reasoning effort.
## Why
AWS/Bedrock mode currently reports `account: null` with
`requiresOpenaiAuth: false` from `account/read`. That suppresses the
OpenAI-auth requirement, but it does not let app clients distinguish AWS
auth from any other non-OpenAI custom provider. For the prototype AWS
provider UX, clients need a simple provider-derived signal so they can
suppress ChatGPT/API-key login and token-refresh paths without
hardcoding Bedrock checks.
## What changed
- Adds an `aws` variant to the v2 `Account` protocol union.
- Adds `ProviderAccountKind` to `codex-model-provider` so the runtime
provider owns the app-visible account classification.
- Makes Amazon Bedrock return `ProviderAccountKind::Aws` from the
model-provider layer.
- Updates app-server `account/read` to map `ProviderAccountKind` to the
existing `GetAccountResponse` wire shape.
- Preserves the existing `account: null, requiresOpenaiAuth: false`
behavior for other non-OpenAI providers.
- Regenerates the app-server protocol schema fixtures.
- Adds coverage for provider account classification and for the Amazon
Bedrock `account/read` response.
## Testing
- `cargo test -p codex-model-provider`
- `cargo test -p codex-app-server-protocol`
- `cargo test -p codex-app-server get_account_with_aws_provider`
## Notes
I attempted `just bazel-lock-update` and `just bazel-lock-check`, but
both are blocked in my local environment because `bazel` is not
installed.
## Summary
The Bedrock Mantle SigV4 auth provider currently looks like it can
lazily load `AwsAuthContext`, but the provider is only constructed after
`resolve_auth_method` has already loaded that context. Because
`with_context` always pre-populates the `OnceCell`, the
`get_or_try_init` fallback is unused in normal operation and makes the
provider lifecycle harder to reason about.
This change removes that dead lazy-loading path and makes the actual
behavior explicit:
- `BedrockAuthMethod::AwsSdkAuth` carries only the resolved
`AwsAuthContext`.
- `BedrockMantleSigV4AuthProvider` stores the resolved context directly.
- request signing uses the stored context without going through
`OnceCell`.
The existing eager AWS auth resolution behavior is unchanged; this is a
simplification of the provider state, not a behavior change.
## Testing
- `cargo shear`
- `cargo test -p codex-model-provider`
- `just bazel-lock-check`
## Summary
Add first-class Amazon Bedrock Mantle provider support so Codex can keep
using its existing Responses API transport with OpenAI-compatible
AWS-hosted endpoints such as AOA/Mantle.
This is needed for the AWS launch path, where provider traffic should
authenticate with AWS credentials instead of OpenAI bearer credentials.
Requests are authenticated immediately before transport send, so SigV4
signs the final method, URL, headers, and body bytes that `reqwest` will
send.
## What Changed
- Added a new `codex-aws-auth` crate for loading AWS SDK config,
resolving credentials, and signing finalized HTTP requests with AWS
SigV4.
- Added a built-in `amazon-bedrock` provider that targets Bedrock Mantle
Responses endpoints, defaults to `us-east-1`, supports region/profile
overrides, disables WebSockets, and does not require OpenAI auth.
- Added Amazon Bedrock auth resolution in `codex-model-provider`: prefer
`AWS_BEARER_TOKEN_BEDROCK` when set, otherwise use AWS SDK credentials
and SigV4 signing.
- Added `AuthProvider::apply_auth` and `Request::prepare_body_for_send`
so request-signing providers can sign the exact outbound request after
JSON serialization/compression.
- Determine the region by taking the `aws.region` config first (required
for bearer token codepath), and fallback to SDK default region.
## Testing
Amazon Bedrock Mantle Responses paths:
- Built the local Codex binary with `cargo build`.
- Verified the custom proxy-backed `aws` provider using `env_key =
"AWS_BEARER_TOKEN_BEDROCK"` streamed raw `responses` output with
`response.output_text.delta`, `response.completed`, and `mantle-env-ok`.
- Verified a full `codex exec --profile aws` turn returned
`mantle-env-ok`.
- Confirmed the custom provider used the bearer env var, not AWS profile
auth: bogus `AWS_PROFILE` still passed, empty env var failed locally,
and malformed env var reached Mantle and failed with `401
invalid_api_key`.
- Verified built-in `amazon-bedrock` with `AWS_BEARER_TOKEN_BEDROCK` set
passed despite bogus AWS profiles, returning `amazon-bedrock-env-ok`.
- Verified built-in `amazon-bedrock` SDK/SigV4 auth passed with
`AWS_BEARER_TOKEN_BEDROCK` unset and temporary AWS session env
credentials, returning `amazon-bedrock-sdk-env-ok`.
## Summary
This PR fully reverts the previously merged Agent Identity runtime
integration from the old stack:
https://github.com/openai/codex/pull/17387/changes
It removes the Codex-side task lifecycle wiring, rollout/session
persistence, feature flag plumbing, lazy `auth.json` mutation,
background task auth paths, and request callsite changes introduced by
that stack.
This leaves the repo in a clean pre-AgentIdentity integration state so
the follow-up PRs can reintroduce the pieces in smaller reviewable
layers.
## Stack
1. This PR: full revert
2. https://github.com/openai/codex/pull/18871: move Agent Identity
business logic into a crate
3. https://github.com/openai/codex/pull/18785: add explicit
AgentIdentity auth mode and startup task allocation
4. https://github.com/openai/codex/pull/18811: migrate auth callsites
through AuthProvider
## Testing
Tests: targeted Rust checks, cargo-shear, Bazel lock check, and CI.
## Summary
This is the AgentAssertion downstream slice for feature-gated agent
identity support, replacing the oversized AgentAssertion slice from PR
#17807.
It isolates task-scoped downstream AgentAssertion wiring on top of the
merged PR3.1 work without re-carrying the earlier agent registration,
task registration, or task-state history.
This PR includes the task-scoped bug-fix call sites from the review:
generic file upload auth, MCP OpenAI file upload auth, and ARC monitor
auth. Broader user/control-plane calls move to PR4.1 and PR4.2.
## Stack
- PR1: https://github.com/openai/codex/pull/17385 - add
`features.use_agent_identity`
- PR2: https://github.com/openai/codex/pull/17386 - register agent
identities when enabled
- PR3: https://github.com/openai/codex/pull/17387 - register agent tasks
when enabled
- PR3.1: https://github.com/openai/codex/pull/17978 - persist and
prewarm registered tasks per thread
- PR4: this PR - use task-scoped `AgentAssertion` downstream when
enabled
- PR4.1: https://github.com/openai/codex/pull/18094 - introduce
AuthManager-owned background/control-plane `AgentAssertion` auth
- PR4.2: https://github.com/openai/codex/pull/18260 - use background
task auth for additional backend/control-plane calls
## What Changed
- add AgentAssertion envelope generation in `codex-core`
- route downstream HTTP and websocket auth through AgentAssertion when
an agent task is present
- extend the model-provider auth provider so non-bearer authorization
schemes can be passed through cleanly
- make generic file uploads attach the full authorization header value
- make MCP OpenAI file uploads use the cached thread agent task
assertion when present
- make ARC monitor calls use the cached thread agent task assertion when
present
## Why
The original PR had drifted ancestry and showed a much larger diff than
the semantic change actually required. Restacking it onto PR3.1 keeps
the reviewable surface down to the downstream assertion slice.
## Validation
- `just fmt`
- `cargo check -p codex-core -p codex-login -p codex-analytics -p
codex-app-server -p codex-cloud-requirements -p codex-cloud-tasks -p
codex-models-manager -p codex-chatgpt -p codex-model-provider -p
codex-mcp -p codex-core-skills`
- `cargo test -p codex-model-provider bearer_auth_provider`
- `cargo test -p codex-core agent_assertion`
- `cargo test -p codex-app-server remote_control`
- `cargo test -p codex-cloud-requirements fetch_cloud_requirements`
- `cargo test -p codex-models-manager manager::tests`
- `cargo test -p codex-chatgpt`
- `cargo test -p codex-cloud-tasks`
- `cargo test -p codex-login agent_identity`
- `just fix -p codex-core -p codex-login -p codex-analytics -p
codex-app-server -p codex-cloud-requirements -p codex-cloud-tasks -p
codex-models-manager -p codex-chatgpt -p codex-model-provider -p
codex-mcp -p codex-core-skills`
- `just fix -p codex-app-server`
- `git diff --check`
## Summary
- Add `codex-model-provider` as the runtime home for model-provider
behavior that does not belong in `codex-core`, `codex-login`, or
`codex-api`.
- The new crate wraps configured `ModelProviderInfo` in a
`ModelProvider` trait object that can resolve the API provider config,
provider-scoped auth manager, and request auth provider for each call.
- This centralizes provider auth behavior in one place today, and gives
us an extension point for future provider-specific auth, model listing,
request setup, and related runtime behavior.
## Tests
Ran tests manually to make sure that provider auth under different
configs still work as expected.
---------
Co-authored-by: pakrym-oai <pakrym@openai.com>
Celia Chen ·
pakrym-oai ·
Jiaming Zhang ·
Ahmed Ibrahim ·
Gabriel Peal ·
efrazer-oai ·
Adrian ·
