Commit Graph

59 Commits

  • feat: let model providers own model discovery (#18950)
    ## 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.
  • [rollout_trace] Add debug trace reduction command (#18880)
    ## Summary
    
    Adds the debug CLI entry point for reducing recorded rollout traces.
    This gives developers a direct way to inspect whether the emitted trace
    stream reduces into the expected conversation/runtime model.
    
    ## Stack
    
    This is PR 5/5 in the rollout trace stack.
    
    - [#18876](https://github.com/openai/codex/pull/18876): Add rollout
    trace crate
    - [#18877](https://github.com/openai/codex/pull/18877): Record core
    session rollout traces
    - [#18878](https://github.com/openai/codex/pull/18878): Trace tool and
    code-mode boundaries
    - [#18879](https://github.com/openai/codex/pull/18879): Trace sessions
    and multi-agent edges
    - [#18880](https://github.com/openai/codex/pull/18880): Add debug trace
    reduction command
    
    ## Review Notes
    
    This PR is intentionally last: it depends on the trace crate, core
    recorder, runtime/tool events, and session/agent edge data all existing.
    The command should remain a debug/developer tool and avoid adding new
    runtime behavior.
    
    The useful review question is whether the CLI exposes the reducer in the
    smallest practical way for local inspection without turning the debug
    command into a supported user-facing workflow.
  • [codex] Route live thread writes through ThreadStore (#18882)
    Begin migrating the thread write codepaths to ThreadStore.
    
    This starts using ThreadStore inside of core session code, not only in
    the app server code.
    
    Rework the interfaces around thread recording/persistence. We're left
    with the following:
    
    * `ThreadManager`: owns the process-level registry of loaded threads and
    handles cross-thread orchestration: start, resume, fork, lookup, remove,
    and route ops to running CodexThreads.
    * `CodexThread`: represents one loaded/running thread from the outside.
    It is the handle app-server and callers use to submit ops, inspect
    session metadata, and shut the thread down.
    * `LiveThread`: session-owned persistence lifecycle handle for one
    active thread. Core session code uses it to append rollout items,
    materialize lazy persistence, flush, shutdown, discard init-failed
    writers, and load that thread’s persisted history.
    * `ThreadStore`: storage backend abstraction. It answers “how are
    threads persisted, read, listed, updated, archived?” Local and remote
    implementations live behind this trait.
    * `LocalThreadStore`: local ThreadStore implementation. It owns the
    file/sqlite-specific details and keeps RolloutRecorder as a local
    implementation detail.
    
    This is a few too many Thread abstractions for my liking, but they do
    all represent different concepts / needs / layers.
    
    Migration note: in places where the core code explicitly requires a
    path, rather than a thread ID, throw an error if we're running with a
    remote store.
    
    Cover the new local live-writer lifecycle with focused tests and
    preserve app-server thread-start behavior, including ephemeral pathless
    sessions.
  • feat(auto-review) short-circuit (#18890)
    ## Summary
    Short circuit the convo if auto-review hits too many denials
    
    ## Testing
    - [x] Added unit tests
    
    ---------
    
    Co-authored-by: Codex <noreply@openai.com>
  • [rollout_trace] Record core session rollout traces (#18877)
    ## Summary
    
    Wires rollout trace recording into `codex-core` session and turn
    execution. This records the core model request/response, compaction, and
    session lifecycle boundaries needed for replay without yet tracing every
    nested runtime/tool boundary.
    
    ## Stack
    
    This is PR 2/5 in the rollout trace stack.
    
    - [#18876](https://github.com/openai/codex/pull/18876): Add rollout
    trace crate
    - [#18877](https://github.com/openai/codex/pull/18877): Record core
    session rollout traces
    - [#18878](https://github.com/openai/codex/pull/18878): Trace tool and
    code-mode boundaries
    - [#18879](https://github.com/openai/codex/pull/18879): Trace sessions
    and multi-agent edges
    - [#18880](https://github.com/openai/codex/pull/18880): Add debug trace
    reduction command
    
    ## Review Notes
    
    This layer is the first live integration point. The important review
    question is whether trace recording is isolated from normal session
    behavior: trace failures should not become user-visible execution
    failures, and recording should preserve the existing turn/session
    lifecycle semantics.
    
    The PR depends on the reducer/data model from the first stack entry and
    only introduces the core recorder surface that later PRs use for richer
    runtime and relationship events.
  • Support multiple managed environments (#18401)
    ## Summary
    - refactor EnvironmentManager to own keyed environments with
    default/local lookup helpers
    - keep remote exec-server client creation lazy until exec/fs use
    - preserve disabled agent environment access separately from internal
    local environment access
    
    ## Validation
    - not run (per Codex worktree instruction to avoid tests/builds unless
    requested)
    
    ---------
    
    Co-authored-by: Codex <noreply@openai.com>
  • fix: fully revert agent identity runtime wiring (#18757)
    ## 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.
  • [codex] Add local thread store listing (#17824)
    Builds on top of #17659 
    
    Move the filesystem + sqlite thread listing-related operations inside of
    a local ThreadStore implementation and call ThreadStore from the places
    that used to perform these filesystem/sqlite operations.
    
    This is the first of a series of PRs that will implement the rest of the
    local ThreadStore.
    
    Testing:
    - added unit tests for the thread store implementation
    - adjusted some unit tests in the realtime + personality packages whose
    callsites changed. Specifically I'm trying to hide ThreadMetadata inside
    of the local implementation and make ThreadMetadata a sqlite
    implementation detail concern rather than a public interface, preferring
    the more generate StoredThread interface instead
    - added a corner case test for the personality migration package that
    wasn't covered by the existing test suite
    - adjust the behavior of searched thread listing to run the existing
    local rollout repair/backfill pass _before_ querying SQLite results, so
    callers using ThreadStore::list_threads do not miss matches after a
    partial metadata warm-up
  • Register agent identities behind use_agent_identity (#17386)
    ## Summary
    
    Stack PR 2 of 4 for feature-gated agent identity support.
    
    This PR adds agent identity registration behind
    `features.use_agent_identity`. It keeps the app-server protocol
    unchanged and starts registration after ChatGPT auth exists rather than
    requiring a client restart.
    
    ## Stack
    
    - PR1: https://github.com/openai/codex/pull/17385 - add
    `features.use_agent_identity`
    - PR2: https://github.com/openai/codex/pull/17386 - this PR
    - PR3: https://github.com/openai/codex/pull/17387 - register agent tasks
    when enabled
    - PR4: https://github.com/openai/codex/pull/17388 - use `AgentAssertion`
    downstream when enabled
    
    ## Validation
    
    Covered as part of the local stack validation pass:
    
    - `just fmt`
    - `cargo test -p codex-core --lib agent_identity`
    - `cargo test -p codex-core --lib agent_assertion`
    - `cargo test -p codex-core --lib websocket_agent_task`
    - `cargo test -p codex-api api_bridge`
    - `cargo build -p codex-cli --bin codex`
    
    ## Notes
    
    The full local app-server E2E path is still being debugged after PR
    creation. The current branch stack is directionally ready for review
    while that follow-up continues.
  • fix(guardian, app-server): introduce guardian review ids (#17298)
    ## Description
    
    This PR introduces `review_id` as the stable identifier for guardian
    reviews and exposes it in app-server `item/autoApprovalReview/started`
    and `item/autoApprovalReview/completed` events.
    
    Internally, guardian rejection state is now keyed by `review_id` instead
    of the reviewed tool item ID. `target_item_id` is still included when a
    review maps to a concrete thread item, but it is no longer overloaded as
    the review lifecycle identifier.
    
    ## Motivation
    
    We'd like to give users the ability to preempt a guardian review while
    it's running (approve or decline).
    
    However, we can't implement the API that allows the user to override a
    running guardian review because we didn't have a unique `review_id` per
    guardian review. Using `target_item_id` is not correct since:
    - with execve reviews, there can be multiple execve calls (and therefore
    guardian reviews) per shell command
    - with network policy reviews, there is no target item ID
    
    The PR that actually implements user overrides will use `review_id` as
    the stable identifier.
  • Update guardian output schema (#17061)
    ## Summary
    - Update guardian output schema to separate risk, authorization,
    outcome, and rationale.
    - Feed guardian rationale into rejection messages.
    - Split the guardian policy into template and tenant-config sections.
    
    ## Validation
    - `cargo test -p codex-core mcp_tool_call`
    - `env -u CODEX_SANDBOX_NETWORK_DISABLED INSTA_UPDATE=always cargo test
    -p codex-core guardian::`
    
    ---------
    
    Co-authored-by: Owen Lin <owen@openai.com>
  • [codex] reduce module visibility (#16978)
    ## Summary
    - reduce public module visibility across Rust crates, preferring private
    or crate-private modules with explicit crate-root public exports
    - update external call sites and tests to use the intended public crate
    APIs instead of reaching through module trees
    - add the module visibility guideline to AGENTS.md
    
    ## Validation
    - `cargo check --workspace --all-targets --message-format=short` passed
    before the final fix/format pass
    - `just fix` completed successfully
    - `just fmt` completed successfully
    - `git diff --check` passed
  • Disable env-bound tools when exec server is none (#16349)
    ## Summary
    - make `CODEX_EXEC_SERVER_URL=none` map to an explicit disabled
    environment mode instead of inferring from a missing URL
    - expose environment capabilities (`exec_enabled`, `filesystem_enabled`)
    so tool building can gate behavior explicitly and future
    multi-environment work has a clearer seam
    - suppress env-backed tools when the relevant capability is unavailable,
    including exec tools, `js_repl`, `apply_patch`, `list_dir`, and
    `view_image`
    - keep handler/runtime backstops so disabled environments still reject
    execution if a tool path somehow bypasses registration
    
    ## Testing
    - `just fmt`
    - `cargo test -p codex-exec-server`
    - `cargo test -p codex-tools
    disabled_environment_omits_environment_backed_tools`
    - `cargo test -p codex-tools
    environment_capabilities_gate_exec_and_filesystem_tools_independently`
    - remote devbox Bazel build via `codex-applied-devbox`:
    `//codex-rs/cli:cli`
  • remove temporary ownership re-exports (#16626)
    Stacked on #16508.
    
    This removes the temporary `codex-core` / `codex-login` re-export shims
    from the ownership split and rewrites callsites to import directly from
    `codex-model-provider-info`, `codex-models-manager`, `codex-api`,
    `codex-protocol`, `codex-feedback`, and `codex-response-debug-context`.
    
    No behavior change intended; this is the mechanical import cleanup layer
    split out from the ownership move.
    
    ---------
    
    Co-authored-by: Codex <noreply@openai.com>
  • core: remove cross-crate re-exports from lib.rs (#16512)
    ## Why
    
    `codex-core` was re-exporting APIs owned by sibling `codex-*` crates,
    which made downstream crates depend on `codex-core` as a proxy module
    instead of the actual owner crate.
    
    Removing those forwards makes crate boundaries explicit and lets leaf
    crates drop unnecessary `codex-core` dependencies. In this PR, this
    reduces the dependency on `codex-core` to `codex-login` in the following
    files:
    
    ```
    codex-rs/backend-client/Cargo.toml
    codex-rs/mcp-server/tests/common/Cargo.toml
    ```
    
    ## What
    
    - Remove `codex-rs/core/src/lib.rs` re-exports for symbols owned by
    `codex-login`, `codex-mcp`, `codex-rollout`, `codex-analytics`,
    `codex-protocol`, `codex-shell-command`, `codex-sandboxing`,
    `codex-tools`, and `codex-utils-path`.
    - Delete the `default_client` forwarding shim in `codex-rs/core`.
    - Update in-crate and downstream callsites to import directly from the
    owning `codex-*` crate.
    - Add direct Cargo dependencies where callsites now target the owner
    crate, and remove `codex-core` from `codex-rs/backend-client`.
  • Extract codex-core-skills crate (#15749)
    ## Summary
    - move skill loading and management into codex-core-skills
    - leave codex-core with the thin integration layer and shared wiring
    
    ## Testing
    - CI
    
    ---------
    
    Co-authored-by: Codex <noreply@openai.com>
  • Extract codex-analytics crate (#15748)
    ## Summary
    - move the analytics events client into codex-analytics
    - update codex-core and app-server callsites to use the new crate
    
    ## Testing
    - CI
    
    ---------
    
    Co-authored-by: Codex <noreply@openai.com>
  • core: Make FileWatcher reusable (#15093)
    ### Summary
    Make `FileWatcher` a reusable core component which can be built upon.
    Extract skills-related logic into a separate `SkillWatcher`.
    Introduce a composable `ThrottledWatchReceiver` to throttle filesystem
    events, coalescing affected paths among them.
    
    ### Testing
    Updated existing unit tests.
  • Move environment abstraction into exec server (#15125)
    The idea is that codex-exec exposes an Environment struct with services
    on it. Each of those is a trait.
    
    Depending on construction parameters passed to Environment they are
    either backed by local or remote server but core doesn't see these
    differences.
  • fix(subagents) share execpolicy by default (#13702)
    ## Summary
    If a subagent requests approval, and the user persists that approval to
    the execpolicy, it should (by default) propagate. We'll need to rethink
    this a bit in light of coming Permissions changes, though I think this
    is closer to the end state that we'd want, which is that execpolicy
    changes to one permissions profile should be synced across threads.
    
    ## Testing
    - [x] Added integration test
    
    ---------
    
    Co-authored-by: Codex <noreply@openai.com>
  • Add FS abstraction and use in view_image (#14960)
    Adds an environment crate and environment + file system abstraction.
    
    Environment is a combination of attributes and services specific to
    environment the agent is connected to:
    File system, process management, OS, default shell.
    
    The goal is to move most of agent logic that assumes environment to work
    through the environment abstraction.
  • Support waiting for code_mode sessions (#14295)
    ## Summary
    - persist the code mode runner process in the session-scoped code mode
    store
    - switch the runner protocol from `init` to `start` with explicit
    session ids
    - handle runner-side session processing without the init waiter queue
    
    ## Validation
    - just fmt
    - cargo check -p codex-core
    - node --check codex-rs/core/src/tools/code_mode_runner.cjs
  • Add store/load support for code mode (#14259)
    adds support for transferring state across code mode invocations.
  • chore(otel): rename OtelManager to SessionTelemetry (#13808)
    ## Summary
    This is a purely mechanical refactor of `OtelManager` ->
    `SessionTelemetry` to better convey what the struct is doing. No
    behavior change.
    
    ## Why
    
    `OtelManager` ended up sounding much broader than what this type
    actually does. It doesn't manage OTEL globally; it's the session-scoped
    telemetry surface for emitting log/trace events and recording metrics
    with consistent session metadata (`app_version`, `model`, `slug`,
    `originator`, etc.).
    
    `SessionTelemetry` is a more accurate name, and updating the call sites
    makes that boundary a lot easier to follow.
    
    ## Validation
    
    - `just fmt`
    - `cargo test -p codex-otel`
    - `cargo test -p codex-core`
  • feat: load from plugins (#12864)
    Support loading plugins.
    
    Plugins can now be enabled via [plugins.<name>] in config.toml. They are
    loaded as first-class entities through PluginsManager, and their default
    skills/ and .mcp.json contributions are integrated into the existing
    skills and MCP flows.
  • feat: scope execve session approvals by approved skill metadata (#12814)
    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.
  • feat: record whether a skill script is approved for the session (#12756)
    ## 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
  • feat: pass helper executable paths via Arg0DispatchPaths (#12719)
    ## 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`
  • feat: run zsh fork shell tool via shell-escalation (#12649)
    ## 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.
  • feat(core): zsh exec bridge (#12052)
    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.
  • feat(core): add structured network approval plumbing and policy decision model (#11672)
    ### Description
    #### Summary
    Introduces the core plumbing required for structured network approvals
    
    #### What changed
    - Added structured network policy decision modeling in core.
    - Added approval payload/context types needed for network approval
    semantics.
    - Wired shell/unified-exec runtime plumbing to consume structured
    decisions.
    - Updated related core error/event surfaces for structured handling.
    - Updated protocol plumbing used by core approval flow.
    - Included small CLI debug sandbox compatibility updates needed by this
    layer.
    
    #### Why
    establishes the minimal backend foundation for network approvals without
    yet changing high-level orchestration or TUI behavior.
    
    #### Notes
    - Behavior remains constrained by existing requirements/config gating.
    - Follow-up PRs in the stack handle orchestration, UX, and app-server
    integration.
    
    ---------
    
    Co-authored-by: Codex <199175422+chatgpt-codex-connector[bot]@users.noreply.github.com>
  • Extract hooks into dedicated crate (#11311)
    Summary
    - move `core/src/hooks` implementation into a new `codex-hooks` crate
    with its own manifest
    - update `codex-rs` workspace and `codex-core` crate to depend on the
    extracted `hooks` crate and wire up the shared APIs
    - ensure references, modules, and lockfile reflect the new crate layout
    
    Testing
    - Not run (not requested)
  • feat: retain NetworkProxy, when appropriate (#11207)
    As of this PR, `SessionServices` retains a
    `Option<StartedNetworkProxy>`, if appropriate.
    
    Now the `network` field on `Config` is `Option<NetworkProxySpec>`
    instead of `Option<NetworkProxy>`.
    
    Over in `Session::new()`, we invoke `NetworkProxySpec::start_proxy()` to
    create the `StartedNetworkProxy`, which is a new struct that retains the
    `NetworkProxy` as well as the `NetworkProxyHandle`. (Note that `Drop` is
    implemented for `NetworkProxyHandle` to ensure the proxies are shutdown
    when it is dropped.)
    
    The `NetworkProxy` from the `StartedNetworkProxy` is threaded through to
    the appropriate places.
    
    
    ---
    [//]: # (BEGIN SAPLING FOOTER)
    Stack created with [Sapling](https://sapling-scm.com). Best reviewed
    with [ReviewStack](https://reviewstack.dev/openai/codex/pull/11207).
    * #11285
    * __->__ #11207
  • feat: tie shell snapshot to cwd (#11231)
    Fix for this: https://github.com/openai/codex/issues/11223
    
    Basically we tie the shell snapshot to a `cwd` to handle `cwd`-based env
    setups
  • Add hooks implementation and wire up to notify (#9691)
    This introduces a `Hooks` service. It registers hooks from config and
    dispatches hook events at runtime.
    
    N.B. The hook config is not wired up to this yet. But for legacy
    reasons, we wire up `notify` from config and power it using hooks now.
    Nothing about the `notify` interface has changed.
    
    I'd start by reviewing `hooks/types.rs`
    
    Some things to note:
      - hook names subject to change
      - no hook result yet
      - stopping semantics yet to be introduced
      - additional hooks yet to be introduced
  • Session-level model client (#10664)
    Make ModelClient a session-scoped object.
    Move state that is session level onto the client, and make state that is
    per-turn explicit on corresponding methods.
    Stop taking a huge Config object, instead only pass in values that are
    actually needed.
    
    ---------
    
    Co-authored-by: Josh McKinney <joshka@openai.com>
  • Added support for live updates to skills (#10478)
    Add a centralized FileWatcher in codex-core (using notify) that watches
    skill roots from the config layer stack (recursive)
    
    Send `SkillsChanged` events when relevant file system changes are
    detected
    
    On `SkillsChanged`:
    * Invalidate the skills cache immediately in ThreadManager
    * Emit EventMsg::SkillsUpdateAvailable to active sessions
    ~~* Broadcast a new app-server notification:
    SkillsListUpdatedNotification~~
    
    This change does not inject new items into the event stream. That means
    the agent will not know about new skills, so it won't be able to
    implicitly invoke new skills. It also won't know about changes to
    existing skills, so if it has already read the contents of a modified
    skill, it will not honor the new behavior.
    
    This change also does not detect modifications to AGENTS.md.
    
    I plan to address these limitations in a follow-on PR modeled after
    #9985. Injection of new skills and AGENTS was deemed to risky, hence the
    need to split the feature into two stages. The changes in this PR were
    designed to easily accommodate the second stage once we have some other
    foundational changes in place.
    
    Testing: In addition to automated tests, I did manual testing to confirm
    that newly-created skills, deleted skills, and renamed skills are
    reflected in the TUI skill picker menu. Also confirmed that
    modifications to behaviors for explicitly-invoked skills are honored.
    
    ---------
    
    Co-authored-by: Xin Lin <xl@openai.com>
  • Fall back to http when websockets fail (#10139)
    I expect not all proxies work with websockets, fall back to http if
    websockets fail.
  • feat: sqlite 1 (#10004)
    Add a `.sqlite` database to be used to store rollout metatdata (and
    later logs)
    This PR is phase 1:
    * Add the database and the required infrastructure
    * Add a backfill of the database
    * Persist the newly created rollout both in files and in the DB
    * When we need to get metadata or a rollout, consider the `JSONL` as the
    source of truth but compare the results with the DB and show any errors
  • feat: hot reload mcp servers (#8957)
    ### Summary
    * Added `mcpServer/refresh` command to inform app servers and active
    threads to refresh mcpServer on next turn event.
    * Added `pending_mcp_server_refresh_config` to codex core so that if the
    value is populated, we reinitialize the mcp server manager on the thread
    level.
    * The config is updated on `mcpServer/refresh` command which we iterate
    through threads and provide with the latest config value after last
    write.
  • feat: metrics capabilities (#8318)
    Add metrics capabilities to Codex. The `README.md` is up to date.
    
    This will not be merged with the metrics before this PR of course:
    https://github.com/openai/codex/pull/8350
  • feat: agent controller (#8783)
    Added an agent control plane that lets sessions spawn or message other
    conversations via `AgentControl`.
    
    `AgentBus` (core/src/agent/bus.rs) keeps track of the last known status
    of a conversation.
    
    ConversationManager now holds shared state behind an Arc so AgentControl
    keeps only a weak back-reference, the goal is just to avoid explicit
    cycle reference.
    
    Follow-ups:
    * Build a small tool in the TUI to be able to see every agent and send
    manual message to each of them
    * Handle approval requests in this TUI
    * Add tools to spawn/communicate between agents (see related design)
    * Define agent types
  • Add ExecPolicyManager (#8349)
    Move exec policy management into services to keep turn context
    immutable.
  • Rename OpenAI models to models manager (#8346)
    # 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.
  • Reimplement skills loading using SkillsManager + skills/list op. (#7914)
    refactor the way we load and manage skills:
    1. Move skill discovery/caching into SkillsManager and reuse it across
    sessions.
    2. Add the skills/list API (Op::ListSkills/SkillsListResponse) to fetch
    skills for one or more cwds. Also update app-server for VSCE/App;
    3. Trigger skills/list during session startup so UIs preload skills and
    handle errors immediately.
  • Inject SKILL.md when it's explicitly mentioned. (#7763)
    1. Skills load once in core at session start; the cached outcome is
    reused across core and surfaced to TUI via SessionConfigured.
    2. TUI detects explicit skill selections, and core injects the matching
    SKILL.md content into the turn when a selected skill is present.