Command-approval clients currently infer which choices to show from
side-channel fields like `networkApprovalContext`,
`proposedExecpolicyAmendment`, and `additionalPermissions`. That makes
the request shape harder to evolve, and it forces each client to
replicate the server's heuristics instead of receiving the exact
decision list for the prompt.
This PR introduces a mapping between `CommandExecutionApprovalDecision`
and `codex_protocol::protocol::ReviewDecision`:
```rust
impl From<CoreReviewDecision> for CommandExecutionApprovalDecision {
fn from(value: CoreReviewDecision) -> Self {
match value {
CoreReviewDecision::Approved => Self::Accept,
CoreReviewDecision::ApprovedExecpolicyAmendment {
proposed_execpolicy_amendment,
} => Self::AcceptWithExecpolicyAmendment {
execpolicy_amendment: proposed_execpolicy_amendment.into(),
},
CoreReviewDecision::ApprovedForSession => Self::AcceptForSession,
CoreReviewDecision::NetworkPolicyAmendment {
network_policy_amendment,
} => Self::ApplyNetworkPolicyAmendment {
network_policy_amendment: network_policy_amendment.into(),
},
CoreReviewDecision::Abort => Self::Cancel,
CoreReviewDecision::Denied => Self::Decline,
}
}
}
```
And updates `CommandExecutionRequestApprovalParams` to have a new field:
```rust
available_decisions: Option<Vec<CommandExecutionApprovalDecision>>
```
when, if specified, should make it easier for clients to display an
appropriate list of options in the UI.
This makes it possible for `CoreShellActionProvider::prompt()` in
`unix_escalation.rs` to specify the `Vec<ReviewDecision>` directly,
adding support for `ApprovedForSession` when approving a skill script,
which was previously missing in the TUI.
Note this results in a significant change to `exec_options()` in
`approval_overlay.rs`, as the displayed options are now derived from
`available_decisions: &[ReviewDecision]`.
## What Changed
- Add `available_decisions` to
[`ExecApprovalRequestEvent`](https://github.com/openai/codex/blob/de00e932dd9801de0a4faac0519162099753f331/codex-rs/protocol/src/approvals.rs#L111-L175),
including helpers to derive the legacy default choices when older
senders omit the field.
- Map `codex_protocol::protocol::ReviewDecision` to app-server
`CommandExecutionApprovalDecision` and expose the ordered list as
experimental `availableDecisions` in
[`CommandExecutionRequestApprovalParams`](https://github.com/openai/codex/blob/de00e932dd9801de0a4faac0519162099753f331/codex-rs/app-server-protocol/src/protocol/v2.rs#L3798-L3807).
- Thread optional `available_decisions` through the core approval path
so Unix shell escalation can explicitly request `ApprovedForSession` for
session-scoped approvals instead of relying on client heuristics.
[`unix_escalation.rs`](https://github.com/openai/codex/blob/de00e932dd9801de0a4faac0519162099753f331/codex-rs/core/src/tools/runtimes/shell/unix_escalation.rs#L194-L214)
- Update the TUI approval overlay to build its buttons from the ordered
decision list, while preserving the legacy fallback when
`available_decisions` is missing.
- Update the app-server README, test client output, and generated schema
artifacts to document and surface the new field.
## Testing
- Add `approval_overlay.rs` coverage for explicit decision lists,
including the generic `ApprovedForSession` path and network approval
options.
- Update `chatwidget/tests.rs` and app-server protocol tests to populate
the new optional field and keep older event shapes working.
## Developers Docs
- If we document `item/commandExecution/requestApproval` on
[developers.openai.com/codex](https://developers.openai.com/codex), add
experimental `availableDecisions` as the preferred source of approval
choices and note that older servers may omit it.
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
Previous to this change, `determine_action()` would
1. check if `program` is associated with a skill
2. if so, check if `program` is in `execve_session_approvals` to see
whether the user needs to be prompted
This PR flips the order of these checks to try to set us up so that
"session approvals" are always consulted first (which should soon extend
to include session approvals derived from `prefix_rule()`s, as well).
Though to make the new ordering work, we need to record any relevant
metadata to associate with the approval, which in the case of a
skill-based approval is the `SkillMetadata` so that we can derive the
`PermissionProfile` to include with the escalation. (Though as noted by
the `TODO`, this `PermissionProfile` is not honored yet.)
The new `ExecveSessionApproval` struct is used to retain the necessary
metadata.
## What Changed
- Replace the `execve_session_approvals` `HashSet` with a map that
stores an `ExecveSessionApproval` alongside each approved `program`.
- When a user chooses `ApprovedForSession` for a skill script, capture
the matched `SkillMetadata` in the session approval entry.
- Consult that cache before re-running `find_skill()`, and reuse the
originally approved skill metadata and permission profile when allowing
later execve callbacks in the same session.
## 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`
This reverts commit daf0f03ac8.
# External (non-OpenAI) Pull Request Requirements
Before opening this Pull Request, please read the dedicated
"Contributing" markdown file or your PR may be closed:
https://github.com/openai/codex/blob/main/docs/contributing.md
If your PR conforms to our contribution guidelines, replace this text
with a detailed and high quality description of your changes.
Include a link to a bug report or enhancement request.
# External (non-OpenAI) Pull Request Requirements
Before opening this Pull Request, please read the dedicated
"Contributing" markdown file or your PR may be closed:
https://github.com/openai/codex/blob/main/docs/contributing.md
If your PR conforms to our contribution guidelines, replace this text
with a detailed and high quality description of your changes.
Include a link to a bug report or enhancement request.
---------
Co-authored-by: Codex <noreply@openai.com>
**PR Summary**
This PR adds embedded-only OTEL policy audit logging for
`codex-network-proxy` and threads audit metadata from `codex-core` into
managed proxy startup.
### What changed
- Added structured audit event emission in `network_policy.rs` with
target `codex_otel.network_proxy`.
- Emitted:
- `codex.network_proxy.domain_policy_decision` once per domain-policy
evaluation.
- `codex.network_proxy.block_decision` for non-domain denies.
- Added required policy/network fields, RFC3339 UTC millisecond
`event.timestamp`, and fallback defaults (`http.request.method="none"`,
`client.address="unknown"`).
- Added non-domain deny audit emission in HTTP/SOCKS handlers for
mode-guard and proxy-state denies, including unix-socket deny paths.
- Added `REASON_UNIX_SOCKET_UNSUPPORTED` and used it for unsupported
unix-socket auditing.
- Added `NetworkProxyAuditMetadata` to runtime/state, re-exported from
`lib.rs` and `state.rs`.
- Added `start_proxy_with_audit_metadata(...)` in core config, with
`start_proxy()` delegating to default metadata.
- Wired metadata construction in `codex.rs` from session/auth context,
including originator sanitization for OTEL-safe tagging.
- Updated `network-proxy/README.md` with embedded-mode audit schema and
behavior notes.
- Refactored HTTP block-audit emission to a small local helper to reduce
duplication.
- Preserved existing unix-socket proxy-disabled host/path behavior for
responses and blocked history while using an audit-only endpoint
override (`server.address="unix-socket"`, `server.port=0`).
### Explicit exclusions
- No standalone proxy OTEL startup work.
- No `main.rs` binary wiring.
- No `standalone_otel.rs`.
- No standalone docs/tests.
### Tests
- Extended `network_policy.rs` tests for event mapping, metadata
propagation, fallbacks, timestamp format, and target prefix.
- Extended HTTP tests to assert unix-socket deny block audit events.
- Extended SOCKS tests to cover deny emission from handler deny
branches.
- Added/updated core tests to verify audit metadata threading into
managed proxy state.
### Validation run
- `just fmt`
- `cargo test -p codex-network-proxy` ✅
- `cargo test -p codex-core` ran with one unrelated flaky timeout
(`shell_snapshot::tests::snapshot_shell_does_not_inherit_stdin`), and
the test passed when rerun directly ✅
---------
Co-authored-by: viyatb-oai <viyatb@openai.com>
Add a stream parser to extract citations (and others) from a stream.
This support cases where markers are split in differen tokens.
Codex never manage to make this code work so everything was done
manually. Please review correctly and do not touch this part of the code
without a very clear understanding of it
## Why
`unix_escalation.rs` checks a session-scoped approval cache before
prompting again for an execve-intercepted skill script. Without also
recording `ReviewDecision::ApprovedForSession`, that cache never gets
populated, so the same skill script can still trigger repeated approval
prompts within one session.
## What Changed
- Add `execve_session_approvals` to `SessionServices` so the session can
track approved skill script paths.
- Record the script path when a skill-script prompt returns
`ReviewDecision::ApprovedForSession`, but only for the skill-script path
rather than broader prefix-rule approvals.
- Reuse the cached approval on later execve callbacks by treating an
already-approved skill script as `Decision::Allow`.
---
[//]: # (BEGIN SAPLING FOOTER)
Stack created with [Sapling](https://sapling-scm.com). Best reviewed
with [ReviewStack](https://reviewstack.dev/openai/codex/pull/12756).
* #12758
* __->__ #12756
Add service name to the app-server so that the app can use it's own
service name
This is on thread level because later we might plan the app-server to
become a singleton on the computer
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
- Fix `js_repl` so `await codex.tool("view_image", { path })` actually
attaches the image to the active turn when called from inside the JS
REPL.
- Restore the behavior expected by the existing `js_repl`
image-attachment test.
- This is a follow-up to
[#12553](https://github.com/openai/codex/pull/12553), which changed
`view_image` to return structured image content.
## Root Cause
- [#12553](https://github.com/openai/codex/pull/12553) changed
`view_image` from directly injecting a pending user image message to
returning structured `function_call_output` content items.
- The nested tool-call bridge inside `js_repl` serialized that tool
response back to the JS runtime, but it did not mirror returned image
content into the active turn.
- As a result, `view_image` appeared to succeed inside `js_repl`, but no
`input_image` was actually attached for the outer turn.
## What Changed
- Updated the nested tool-call path in `js_repl` to inspect function
tool responses for structured content items.
- When a nested tool response includes `input_image` content, `js_repl`
now injects a corresponding user `Message` into the active turn before
returning the raw tool result back to the JS runtime.
- Kept the normal JSON result flow intact, so `codex.tool(...)` still
returns the original tool output object to JavaScript.
## Why
- `js_repl` documentation and tests already assume that `view_image` can
be used from inside the REPL to attach generated images to the model.
- Without this fix, the nested call path silently dropped that
attachment behavior.
## Why
`codex-rs/core/src/tools/runtimes/shell/unix_escalation.rs` previously
located `codex-execve-wrapper` by scanning `PATH` and sibling
directories. That lookup is brittle and can select the wrong binary when
the runtime environment differs from startup assumptions.
We already pass `codex-linux-sandbox` from `codex-arg0`;
`codex-execve-wrapper` should use the same startup-driven path plumbing.
## What changed
- Introduced `Arg0DispatchPaths` in `codex-arg0` to carry both helper
executable paths:
- `codex_linux_sandbox_exe`
- `main_execve_wrapper_exe`
- Updated `arg0_dispatch_or_else()` to pass `Arg0DispatchPaths` to
top-level binaries and preserve helper paths created in
`prepend_path_entry_for_codex_aliases()`.
- Threaded `Arg0DispatchPaths` through entrypoints in `cli`, `exec`,
`tui`, `app-server`, and `mcp-server`.
- Added `main_execve_wrapper_exe` to core configuration plumbing
(`Config`, `ConfigOverrides`, and `SessionServices`).
- Updated zsh-fork shell escalation to consume the configured
`main_execve_wrapper_exe` and removed path-sniffing fallback logic.
- Updated app-server config reload paths so reloaded configs keep the
same startup-provided helper executable paths.
## References
- [`Arg0DispatchPaths`
definition](https://github.com/openai/codex/blob/e355b43d5c2a771f045296a6deae10d7c9c36ec6/codex-rs/arg0/src/lib.rs#L20-L24)
- [`arg0_dispatch_or_else()` forwarding both
paths](https://github.com/openai/codex/blob/e355b43d5c2a771f045296a6deae10d7c9c36ec6/codex-rs/arg0/src/lib.rs#L145-L176)
- [zsh-fork escalation using configured wrapper
path](https://github.com/openai/codex/blob/e355b43d5c2a771f045296a6deae10d7c9c36ec6/codex-rs/core/src/tools/runtimes/shell/unix_escalation.rs#L109-L150)
## Testing
- `cargo check -p codex-arg0 -p codex-core -p codex-exec -p codex-tui -p
codex-mcp-server -p codex-app-server`
- `cargo test -p codex-arg0`
- `cargo test -p codex-core tools::runtimes::shell::unix_escalation:: --
--nocapture`
Rename `SkillMetadata.path` to `SkillMetadata.path_to_skills_md` for
clarity.
Would ideally change the type to `AbsolutePathBuf`, but that can be done
later.
Summary
- detect skill-invoking shell commands based on the original command
string, request approvals when needed, and cache positive decisions per
session
- keep implicit skill invocation emitted after approval and keep skill
approval decline messaging centralized to the shell handler
- expand and adjust skill approval tests to cover shell-based skill
scripts while matching the new detection expectations
Testing
- Not run (not requested)
## Why
This PR switches the `shell_command` zsh-fork path over to
`codex-shell-escalation` so the new shell tool can use the shared
exec-wrapper/escalation protocol instead of the `zsh_exec_bridge`
implementation that was introduced in
https://github.com/openai/codex/pull/12052. `zsh_exec_bridge` relied on
UNIX domain sockets, which is not as tamper-proof as the FD-based
approach in `codex-shell-escalation`.
## What Changed
- Added a Unix zsh-fork runtime adapter in `core`
(`core/src/tools/runtimes/shell/unix_escalation.rs`) that:
- runs zsh-fork commands through
`codex_shell_escalation::run_escalate_server`
- bridges exec-policy / approval decisions into `ShellActionProvider`
- executes escalated commands via a `ShellCommandExecutor` that calls
`process_exec_tool_call`
- Updated `ShellRuntime` / `ShellCommandHandler` / tool spec wiring to
select a `shell_command` backend (`classic` vs `zsh-fork`) while leaving
the generic `shell` tool path unchanged.
- Removed the `zsh_exec_bridge`-based session service and deleted
`core/src/zsh_exec_bridge/mod.rs`.
- Moved exec-wrapper entrypoint dispatch to `arg0` by handling the
`codex-execve-wrapper` arg0 alias there, and removed the old
`codex_core::maybe_run_zsh_exec_wrapper_mode()` hooks from `cli` and
`app-server` mains.
- Added the needed `codex-shell-escalation` dependencies for `core` and
`arg0`.
## Tests
- `cargo test -p codex-core
shell_zsh_fork_prefers_shell_command_over_unified_exec`
- `cargo test -p codex-app-server turn_start_shell_zsh_fork --
--nocapture`
- verifies zsh-fork command execution and approval flows through the new
backend
- includes subcommand approve/decline coverage using the shared zsh
DotSlash fixture in `app-server/tests/suite/zsh`
- To test manually, I added the following to `~/.codex/config.toml`:
```toml
zsh_path = "/Users/mbolin/code/codex3/codex-rs/app-server/tests/suite/zsh"
[features]
shell_zsh_fork = true
```
Then I ran `just c` to run the dev build of Codex with these changes and
sent it the message:
```
run `echo $0`
```
And it replied with:
```
echo $0 printed:
/Users/mbolin/code/codex3/codex-rs/app-server/tests/suite/zsh
In this tool context, $0 reflects the script path used to invoke the shell, not just zsh.
```
so the tool appears to be wired up correctly.
## Notes
- The zsh subcommand-decline integration test now uses `rm` under a
`WorkspaceWrite` sandbox. The previous `/usr/bin/true` scenario is
auto-allowed by the new `shell-escalation` policy path, which no longer
produces subcommand approval prompts.
## 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
Send a request with `generate: falls` but a full set of tools and
instructions to pre-warm inference.
---------
Co-authored-by: Codex <noreply@openai.com>
- use `skills_for_cwd` lookup to scope allowed skills and build
invocation context for downstream processing
- add detection in `stream_events_utils` to classify tool calls as
implicit skill invocations per the proposal (script runners, extensions,
`scripts` dirs, and SKILL.md reads)
- deduplicate invocations per turn and emit analytics/OTEL events on the
same background queue as explicit invokes
## Summary
Persist network approval allow/deny decisions as `network_rule(...)`
entries in execpolicy (not proxy config)
It adds `network_rule` parsing + append support in `codex-execpolicy`,
including `decision="prompt"` (parse-only; not compiled into proxy
allow/deny lists)
- compile execpolicy network rules into proxy allow/deny lists and
update the live proxy state on approval
- preserve requirements execpolicy `network_rule(...)` entries when
merging with file-based execpolicy
- reject broad wildcard hosts (for example `*`) for persisted
`network_rule(...)`
## 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
- move regular-turn context diff/full-context persistence into
`run_turn` so pre-turn compaction runs before incoming context updates
are recorded
- after successful pre-turn compaction, rely on a cleared
`reference_context_item` to trigger full context reinjection on the
follow-up regular turn (manual `/compact` keeps replacement history
summary-only and also clears the baseline)
- preserve `<model_switch>` when full context is reinjected, and inject
it *before* the rest of the full-context items
- scope `reference_context_item` and `previous_model` to regular user
turns only so standalone tasks (`/compact`, shell, review, undo) cannot
suppress future reinjection or `<model_switch>` behavior
- make context-diff persistence + `reference_context_item` updates
explicit in the regular-turn path, with clearer docs/comments around the
invariant
- stop persisting local `/compact` `RolloutItem::TurnContext` snapshots
(only regular turns persist `TurnContextItem` now)
- simplify resume/fork previous-model/reference-baseline hydration by
looking up the last surviving turn context from rollout lifecycle
events, including rollback and compaction-crossing handling
- remove the legacy fallback that guessed from bare `TurnContext`
rollouts without lifecycle events
- update compaction/remote-compaction/model-visible snapshots and
compact test assertions (including remote compaction mock response
shape)
## Why
We were persisting incoming context items before spawning the regular
turn task, which let pre-turn compaction requests accidentally include
incoming context diffs without the new user message. Fixing that exposed
follow-on baseline issues around `/compact`, resume/fork, and standalone
tasks that could cause duplicate context injection or suppress
`<model_switch>` instructions.
This PR re-centers the invariants around regular turns:
- regular turns persist model-visible context diffs/full reinjection and
update the `reference_context_item`
- standalone tasks do not advance those regular-turn baselines
- compaction clears the baseline when replacement history may have
stripped the referenced context diffs
## Follow-ups (TODOs left in code)
- `TODO(ccunningham)`: fix rollback/backtracking baseline handling more
comprehensively
- `TODO(ccunningham)`: include pending incoming context items in
pre-turn compaction threshold estimation
- `TODO(ccunningham)`: inject updated personality spec alongside
`<model_switch>` so some model-switch paths can avoid forced full
reinjection
- `TODO(ccunningham)`: review task turn lifecycle
(`TurnStarted`/`TurnComplete`) behavior and emit task-start context
diffs for task types that should have them (excluding `/compact`)
## Validation
- `just fmt`
- CI should cover the updated compaction/resume/model-visible snapshot
expectations and rollout-hydration behavior
- I did **not** rerun the full local test suite after the latest
resume-lookup / rollout-persistence simplifications
- Introduce `RealtimeConversationManager` for realtime API management
- Add `op::conversation` to start conversation, insert audio, insert
text, and close conversation.
- emit conversation lifecycle and realtime events.
- Move shared realtime payload types into codex-protocol and add core
e2e websocket tests for start/replace/transport-close paths.
Things to consider:
- Should we use the same `op::` and `Events` channel to carry audio? I
think we should try this simple approach and later we can create
separate one if the channels got congested.
- Sending text updates to the client: we can start simple and later
restrict that.
- Provider auth isn't wired for now intentionally
## Summary
Simplify network approvals by removing per-attempt proxy correlation and
moving to session-level approval dedupe keyed by (host, protocol, port).
Instead of encoding attempt IDs into proxy credentials/URLs, we now
treat approvals as a destination policy decision.
- Concurrent calls to the same destination share one approval prompt.
- Different destinations (or same host on different ports) get separate
prompts.
- Allow once approves the current queued request group only.
- Allow for session caches that (host, protocol, port) and auto-allows
future matching requests.
- Never policy continues to deny without prompting.
Example:
- 3 calls:
- a.com (line 443)
- b.com (line 443)
- a.com (line 443)
=> 2 prompts total (a, b), second a waits on the first decision.
- a.com:80 is treated separately from a.com line 443
## Testing
- `just fmt` (in `codex-rs`)
- `cargo test -p codex-core tools::network_approval::tests`
- `cargo test -p codex-core` (unit tests pass; existing
integration-suite failures remain in this environment)
We now write MCP tools from installed apps to disk cache so that they
can be picked up instantly at startup. We still do a fresh fetch from
remote MCP server but it's non blocking unless there's a cache miss.
- [x] Store apps tool cache in disk to reduce startup time.
## Why
`McpConnectionManager` used a two-phase setup (`new()` followed by
`initialize()`), which forced call sites to construct placeholder state
and then mutate it asynchronously. That made MCP startup/refresh flows
harder to follow and easier to misuse, especially around cancellation
token ownership.
## What changed
- Replaced the two-phase initialization flow with a single async
constructor: `McpConnectionManager::new(...) -> (Self,
CancellationToken)`.
- Added `McpConnectionManager::new_uninitialized()` for places that need
an empty manager before async startup begins.
- Added `McpConnectionManager::new_mcp_connection_manager_for_tests()`
for test-only construction.
- Updated MCP startup and refresh call sites in
`codex-rs/core/src/codex.rs` to build a fresh manager via `new(...)`,
swap it in, and update the startup cancellation token consistently.
- Updated MCP snapshot/connector call sites in
`codex-rs/core/src/mcp/mod.rs` and `codex-rs/core/src/connectors.rs` to
use the consolidated constructor.
- Removed the now-obsolete `reset_mcp_startup_cancellation_token()`
helper in favor of explicit token replacement at the call sites.
## Testing
- Not run (refactor-only change; no new behavior was intended).
Summary
- expose `agents.max_depth` in config schema and toml parsing, with
defaults and validation
- thread-spawn depth guards and multi-agent handler now respect the
configured limit instead of a hardcoded value
- ensure documentation and helpers account for agent depth limits
TL;DR
Add top-level `model_catalog_json` config support so users can supply a
local model catalog override from a JSON file path (including adding new
models) without backend changes.
### Problem
Codex previously had no clean client-side way to replace/overlay model
catalog data for local testing of model metadata and new model entries.
### Fix
- Add top-level `model_catalog_json` config field (JSON file path).
- Apply catalog entries when resolving `ModelInfo`:
1. Base resolved model metadata (remote/fallback)
2. Catalog overlay from `model_catalog_json`
3. Existing global top-level overrides (`model_context_window`,
`model_supports_reasoning_summaries`, etc.)
### Note
Will revisit per-field overrides in a follow-up
### Tests
Added tests
## Summary
- add `previous_context_item: Option<TurnContextItem>` to
`ContextManager`
- expose session/state accessors for reading and updating the stored
previous context item
- switch settings diffing to use `TurnContextItem` instead of
`TurnContext`
- remove submission-loop local `previous_context` and persist the
previous context item in history
## Testing
- `just fmt`
- `just fix -p codex-core`
- `cargo test -p codex-core --test all model_switching::`
- `cargo test -p codex-core --test all collaboration_instructions::`
- `cargo test -p codex-core --test all personality::`
- `cargo test -p codex-core --test all
permissions_messages::permissions_message_not_added_when_no_change`
## Summary
This change removes tool-list filtering in `js_repl_tools_only` mode and
relies on the normal model tool descriptions, while still enforcing that
tool execution must go through `js_repl` + `codex.tool(...)`.
## Motivation
The previous `js_repl_tools_only` filtering hid most tools from the
model request, which diverged from standard tool-list behavior and made
signatures less discoverable. I tested that this filtering is not
needed, and the model can follow the prompt to only call tools via
`js_repl`.
## What Changed
- `filter_tools_for_model(...)` in `core/src/tools/spec.rs` is now a
pass-through (no filtering when `js_repl_tools_only` is enabled).
- Updated tests to assert that model tools are not filtered in
`js_repl_tools_only` mode.
- Updated dynamic-tool test to assert dynamic tools remain visible in
model tool specs.
- Removed obsolete test helper used only by the old filtering
assertions.
## Safety / Behavior
- This commit does **not** relax execution policy.
- Direct model tool calls remain blocked in `js_repl_tools_only` mode
(except internal `js_repl` tools), and callers are instructed to use
`js_repl` + `codex.tool(...)`.
## Testing
- `cargo test -p codex-core js_repl_tools_only`
- Manual rollout validation showed the model can follow the `js_repl`
routing instructions without needing filtered tool lists.
#### [git stack](https://github.com/magus/git-stack-cli)
- 👉 `1` https://github.com/openai/codex/pull/12069
- ⏳ `2` https://github.com/openai/codex/pull/10673
- ⏳ `3` https://github.com/openai/codex/pull/10670
# External (non-OpenAI) Pull Request Requirements
In `js_repl` mode, module resolution currently starts from
`js_repl_kernel.js`, which is written to a per-kernel temp dir. This
effectively means that bare imports will not resolve.
This PR adds a new config option, `js_repl_node_module_dirs`, which is a
list of dirs that are used (in order) to resolve a bare import. If none
of those work, the current working directory of the thread is used.
For example:
```toml
js_repl_node_module_dirs = [
"/path/to/node_modules/",
"/other/path/to/node_modules/",
]
```
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.
zsh fork PR stack:
- https://github.com/openai/codex/pull/12051👈
- https://github.com/openai/codex/pull/12052
With upcoming support for a fork of zsh that allows us to intercept
`execve` and run execpolicy checks for each subcommand as part of a
`CommandExecution`, it will be possible for there to be multiple
approval requests for a shell command like `/path/to/zsh -lc 'git status
&& rg \"TODO\" src && make test'`.
To support that, this PR introduces a new `approval_id` field across
core, protocol, and app-server so that we can associate approvals
properly for subcommands.
### Summary
Ensure that we use the model value from the response header only so that
we are guaranteed with the correct slug name. We are no longer checking
against the model value from response so that we are less likely to have
false positive.
There are two different treatments - for SSE we use the header from the
response and for websocket we check top-level events.
Michael Bolin