Commit Graph

18 Commits

  • Densify and version memory summaries (#23148)
    ## Why
    
    `memory_summary.md` is injected into every session, so its value depends
    on staying compact, navigational, and easy to regenerate when the
    expected shape changes. The previous consolidation prompt encouraged a
    broad actionable inventory and allowed older summary structures to be
    patched in place, which makes it easier for stale or overly verbose
    summaries to keep accumulating.
    
    This change makes the summary format explicitly versioned and biases
    Phase 2 memory consolidation toward denser prompt-loaded context.
    
    ## What changed
    
    - Require `memory_summary.md` to begin with an exact `v1` header.
    - Teach consolidation to regenerate `memory_summary.md` from scratch
    when the header is missing or incompatible, while still allowing
    incremental updates to `MEMORY.md`.
    - Tighten the `memory_summary.md` instructions so it acts as a compact
    routing/index layer instead of a second handbook.
    - Lower `MEMORY_TOOL_DEVELOPER_INSTRUCTIONS_SUMMARY_TOKEN_LIMIT` from
    `5_000` to `2_500` so the runtime prompt budget matches the denser
    summary target.
    
    ## Verification
    
    Not run; this is a prompt/template update plus a prompt budget constant
    change.
  • [codex] request desktop attestation from app (#20619)
    ## 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.
    
    ![DeviceCheck attestation
    interface](https://raw.githubusercontent.com/openai/codex/dev/jm/devicecheck-diagram-assets/pr-assets/devicecheck-attestation-interface.png)
    
    ## 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>
  • nit: comment (#21763)
    Because of an async discussion
  • Disable empty Cargo test targets (#21584)
    ## Summary
    
    `cargo test` has entails both running standard Rust tests and doctests.
    It turns out that the doctest discovery is fairly slow, and it's a cost
    you pay even for crates that don't include any doctests.
    
    This PR disables doctests with `doctest = false` for crates that lack
    any doctests.
    
    For the collection of crates below, this speeds up test execution by
    >4x.
    
    E.g., before this PR:
    
    ```
    Benchmark 1: cargo test     -p codex-utils-absolute-path     -p codex-utils-cache     -p codex-utils-cli     -p codex-utils-home-dir     -p codex-utils-output-truncation     -p codex-utils-path     -p codex-utils-string     -p codex-utils-template     -p codex-utils-elapsed     -p codex-utils-json-to-toml
      Time (mean ± σ):      1.849 s ±  4.455 s    [User: 0.752 s, System: 1.367 s]
      Range (min … max):    0.418 s … 14.529 s    10 runs
    ```
    
    And after:
    
    ```
    Benchmark 1: cargo test     -p codex-utils-absolute-path     -p codex-utils-cache     -p codex-utils-cli     -p codex-utils-home-dir     -p codex-utils-output-truncation     -p codex-utils-path     -p codex-utils-string     -p codex-utils-template     -p codex-utils-elapsed     -p codex-utils-json-to-toml
      Time (mean ± σ):     428.6 ms ±   6.9 ms    [User: 187.7 ms, System: 219.7 ms]
      Range (min … max):   418.0 ms … 436.8 ms    10 runs
    ```
    
    For a single crate, with >2x speedup, before:
    
    ```
    Benchmark 1: cargo test -p codex-utils-string
      Time (mean ± σ):     491.1 ms ±   9.0 ms    [User: 229.8 ms, System: 234.9 ms]
      Range (min … max):   480.9 ms … 512.0 ms    10 runs
    ```
    
    And after:
    
    ```
    Benchmark 1: cargo test -p codex-utils-string
      Time (mean ± σ):     213.9 ms ±   4.3 ms    [User: 112.8 ms, System: 84.0 ms]
      Range (min … max):   206.8 ms … 221.0 ms    13 runs
    ```
    
    Co-authored-by: Codex <noreply@openai.com>
  • 2- Use string service tiers in session protocol (#20971)
    ## Summary
    - break service tier session/op/app-server protocol fields from the
    closed enum to string tier ids
    - send the service tier string directly through model requests, prewarm,
    compaction, memories, and TUI/app-server turn starts
    - regenerate app-server protocol JSON/TypeScript schemas, removing the
    standalone ServiceTier TS enum
    
    ## Verification
    - just fmt
    - cargo check -p codex-core -p codex-app-server -p codex-tui
    - just write-app-server-schema
    
    ---------
    
    Co-authored-by: Codex <noreply@openai.com>
  • feat: add session_id (#20437)
    ## Summary
    
    Related to
    https://openai.slack.com/archives/C095U48JNL9/p1777537279707449
    TLDR:
    We update the meaning of session ids and thread ids:
    * thread_id stays as now
    * session_id become a shared id between every thread under a /root
    thread (i.e. every sub-agent share the same session id)
    
    This PR introduces an explicit `SessionId` and threads it through the
    protocol/client boundary so `session_id` and `thread_id` can diverge
    when they need to, while preserving compatibility for older serialized
    `session_configured` events.
    
    ---------
    
    Co-authored-by: Codex <noreply@openai.com>
  • [codex-analytics] rework thread_source for thread analytics (#20949)
    ## Summary
    - make `thread_source` an explicit optional thread-level field on
    `thread/start`, `thread/fork`, and returned thread payloads
    - persist `thread_source` in rollout/session metadata so resumed live
    threads retain the original value
    - replace the old best-effort `session_source` -> `thread_source`
    mapping with an explicit caller-supplied analytics classification
    
    ## Why
    Before this change, analytics `thread_source` was populated by a
    best-effort mapping from `session_source`. `session_source` describes
    the runtime/client surface, not the actual thread-level origin, so that
    projection was not accurate enough to distinguish cases such as `user`,
    `subagent`, `memory_consolidation`, and future thread origins reliably.
    
    Making `thread_source` explicit keeps one thread-level analytics field
    while letting callers provide the real classification directly instead
    of recovering it indirectly from `session_source`.
    
    ## Impact
    For new analytics events, `thread_source` now reflects the explicit
    thread-level classification supplied by the caller rather than an
    inferred value derived from `session_source`. Existing protocol fields
    remain optional; callers that omit `threadSource` now produce `null`
    instead of a best-effort inferred value.
    
    ## Validation
    - `just write-app-server-schema`
    - `cargo test -p codex-analytics -p codex-core -p
    codex-app-server-protocol --no-run`
    - `cargo test -p codex-app-server-protocol
    generated_ts_optional_nullable_fields_only_in_params`
    - `cargo test -p codex-analytics
    thread_initialized_event_serializes_expected_shape`
    - `cargo test -p codex-core
    resume_stopped_thread_from_rollout_preserves_thread_source`
  • feat: seed ad-hoc memory extension instructions (#20606)
    ## Summary
    
    Ad-hoc memory notes are written under `memories/extensions/ad_hoc/`, but
    the consolidation agent only knows how to interpret an extension when
    the extension folder has an `instructions.md`. Seed those instructions
    from the memories write pipeline so an enabled memories startup creates
    the expected ad-hoc extension layout automatically.
    
    This also moves extension-specific write behavior behind a dedicated
    `memories/write/src/extensions/` module. `ad_hoc` owns the seeded
    instructions template, while the existing resource-retention cleanup
    lives in its own `prune` module so future memory extensions can add
    their own write-side setup without growing a flat helper file.
    
    ## Changes
    
    - Seed `memories/extensions/ad_hoc/instructions.md` during eligible
    memory startup without overwriting an existing file.
    - Store the ad-hoc instructions template under
    `memories/write/templates/extensions/ad_hoc/`, keeping ownership in
    `codex-memories-write`.
    - Split memory extension support into `extensions::ad_hoc` and
    `extensions::prune`.
    - Keep the existing old-resource pruning behavior unchanged.
    
    ## Verification
    
    - `cargo test -p codex-memories-write`
    - `bazel build //codex-rs/memories/write:write`
    
    ---------
    
    Co-authored-by: chatgpt-codex-connector[bot] <199175422+chatgpt-codex-connector[bot]@users.noreply.github.com>
  • Make thread store process-scoped (#19474)
    - Build one app-server process ThreadStore from startup config and share
    it with ThreadManager and CodexMessageProcessor.
    - Remove per-thread/fork store reconstruction so effective thread config
    cannot switch the persistence backend.
    - Add params to ThreadStore create/resume for specifying thread
    metadata, since otherwise the metadata from store creation would be used
    (incorrectly).
  • fix: handle deferred network proxy denials (#19184)
    ## Why
    
    This bug is exposed by Guardian/auto-review approvals. With the managed
    network proxy enabled, a blocked network request can be reported back
    through the network approval service as an approval denial after the
    command has already started. Before this change, the shell and unified
    exec runtimes registered those network approval calls, but did not have
    a way to observe an async proxy denial as a cancellation/failure signal
    for the running process.
    
    The result was confusing: Guardian/auto-review could correctly deny
    network access, but the command path could keep running or unregister
    the approval without surfacing the denial as the command failure.
    
    ## What Changed
    
    - `NetworkApprovalService` now attaches a cancellation token to active
    and deferred network approvals.
    - Proxy-denial outcomes are recorded only for active registrations,
    cancel the owning token, and are consumed when the approval is
    finalized.
    - The shell runtime combines the normal command timeout with the
    network-denial cancellation token.
    - Unified exec stores the deferred network approval object, terminates
    tracked processes when the proxy denial arrives, and returns the denial
    as a process failure while polling or completing the process.
    - Tool orchestration passes the active network approval cancellation
    token into the sandbox attempt and preserves deferred approval errors
    instead of silently unregistering them.
    - App-server `command/exec` now handles the combined
    timeout-or-cancellation expiration variant used by the runtime.
    
    ## Verification
    
    - `cargo test -p codex-core network_approval --lib`
    - `cargo clippy -p codex-app-server --all-targets -- -D warnings`
    - `cargo clippy -p codex-core --all-targets -- -D warnings`
    
    ---------
    
    Co-authored-by: Codex <noreply@openai.com>
  • Add ThreadManager sample crate (#20141)
    Summary:
    - Add codex-thread-manager-sample, a one-shot binary that starts a
    ThreadManager thread, submits a prompt, and prints the final assistant
    output.
    - Pass ThreadStore into ThreadManager::new and expose
    thread_store_from_config for existing callsites.
    - Build the sample Config directly with only --model and prompt inputs.
    
    Verification:
    - just fmt
    - cargo check -p codex-thread-manager-sample -p codex-app-server -p
    codex-mcp-server
    - git diff --check
    
    Tests: Not run per request.
  • feat: house-keeping memories 3 (#20005)
    Move stuff in memories, no behavioural change expected
  • feat: house-keeping memories 2 (#20000)
    Just move metrics in a dedicated file
  • feat: skip memory startup when Codex rate limits are low (#19990)
    ## Why
    
    Memory startup runs in the background after an eligible turn, but it can
    consume Codex backend quota at exactly the wrong time: when the user is
    already near a rate-limit boundary. This PR adds a guard so the memory
    pipeline backs off when the Codex rate-limit snapshot says the remaining
    budget is too low.
    
    ## What Changed
    
    - Added `memories.min_rate_limit_remaining_percent` with a default of
    `25`, clamped to `0..=100`, and regenerated `core/config.schema.json`.
    - Added `codex-rs/memories/write/src/guard.rs`, which fetches Codex
    backend rate limits before memory startup and skips phase 1 / phase 2
    when the Codex limit is reached or either tracked window is above the
    configured usage ceiling.
    - Keeps startup best-effort: non-Codex auth or rate-limit fetch/client
    failures preserve the existing memory startup behavior.
    - Records a `codex.memory.startup` counter with
    `status=skipped_rate_limit` when startup is skipped.
    - Added config parsing/clamping coverage and guard unit tests.
    
    ## Verification
    
    - Added `codex-rs/memories/write/src/guard_tests.rs` for threshold,
    primary/secondary window, and reached-limit behavior.
    - Added config tests for TOML parsing and clamping.
  • feat: trigger memories from user turns with cooldown (#19970)
    ## Why
    
    Memory startup was tied to thread lifecycle events such as create, load,
    and fork. That can run memory work before a thread receives real user
    input, and it makes startup cost scale with thread management instead of
    actual turns. Moving the trigger to `thread/sendInput` keeps memory
    startup aligned with the first real user turn and lets it use the
    current thread config at turn time.
    
    The idea is to prevent ghost cost due to pre-warm triggered by the app
    
    Turn-based startup can also make global phase-2 consolidation easier to
    request repeatedly, so this adds a success cooldown and tightens the
    default startup scan window.
    
    ## What Changed
    
    - Start `codex_memories_write::start_memories_startup_task` after a
    non-empty `thread/sendInput` turn is submitted, instead of from thread
    create/load/fork paths:
    https://github.com/openai/codex/blob/d4a6885b7829e2fd2ec7a09355e4f75ebe1d1fe3/codex-rs/app-server/src/codex_message_processor.rs#L6477-L6487
    - Expose `CodexThread::config()` so app-server can pass the live config
    into memory startup at turn time.
    - Add a six-hour successful-run cooldown for global phase-2
    consolidation via `SkippedCooldown`:
    https://github.com/openai/codex/blob/d4a6885b7829e2fd2ec7a09355e4f75ebe1d1fe3/codex-rs/state/src/runtime/memories.rs#L963-L966
    - Reduce memory startup defaults to at most 2 rollouts over 10 days:
    https://github.com/openai/codex/blob/d4a6885b7829e2fd2ec7a09355e4f75ebe1d1fe3/codex-rs/config/src/types.rs#L31-L34
    
    ## Verification
    
    Updated the memory runtime coverage around phase-2 reclaim behavior,
    including `phase2_global_lock_respects_success_cooldown`.
    
    ---------
    
    Co-authored-by: Codex <noreply@openai.com>
  • Stabilize memory Phase 2 input ordering (#19967)
    ## Why
    
    Phase 2 still needs to choose the most relevant stage-1 memory outputs
    by usage and recency, but exposing that ranking as the rendered
    `raw_memories.md` order creates unnecessary large diff. Usage-count or
    timestamp changes can reshuffle otherwise unchanged memories, making the
    workspace diff noisy and giving the consolidation prompt a misleading
    recency signal from file position.
    This fix will reduce token consumption
    
    ## What Changed
    
    - Keep the existing top-N Phase 2 selection ranking by `usage_count`,
    `last_usage`, `source_updated_at`, and `thread_id`.
    - Return the selected rows in stable ascending `thread_id` order before
    syncing Phase 2 filesystem inputs.
    - Update the memory README, raw memories header, and consolidation
    prompt so they describe the stable order and tell the prompt to use
    metadata and workspace diffs instead of file order as the recency
    signal.
    - Adjust the memory runtime tests to use deterministic thread IDs and
    assert the stable return order separately from the ranked selection
    semantics.
    
    ## Test Coverage
    
    - Existing memory runtime tests in
    `codex-rs/state/src/runtime/memories.rs` now cover the stable returned
    ordering for Phase 2 inputs.
    
    ---------
    
    Co-authored-by: Codex <noreply@openai.com>
  • feat: split memories part 2 (#19860)
    Keep extracting memories out of core and moving the write trigger in the
    app-server
    This is temporary and it should move at the client level as a follow-up
    This makes core fully independant from `codex-memories-write`
    
    ---------
    
    Co-authored-by: Codex <noreply@openai.com>
  • chore: split memories part 1 (#19818)
    Extract memories into 2 different crates