### Summary
Propagate trace context originating at app-server RPC method handlers ->
codex core submission loop (so this includes spans such as `run_turn`!).
This implements PR 2 of the app-server tracing rollout.
This also removes the old lower-level env-based reparenting in core so
explicit request/submission ancestry wins instead of being overridden by
ambient `TRACEPARENT` state.
### What changed
- Added `trace: Option<W3cTraceContext>` to codex_protocol::Submission
- Taught `Codex::submit()` / `submit_with_id()` to automatically capture
the current span context when constructing or forwarding a submission
- Wrapped the core submission loop in a submission_dispatch span
parented from Submission.trace
- Warn on invalid submission trace carriers and ignore them cleanly
- Removed the old env-based downstream reparenting path in core task
execution
- Stopped OTEL provider init from implicitly attaching env trace context
process-wide
- Updated mcp-server Submission call sites for the new field
Added focused unit tests for:
- capturing trace context into Submission
- preferring `Submission.trace` when building the core dispatch span
### Why
PR 1 gave us consistent inbound request spans in app-server, but that
only covered the transport boundary. For long-running work like turns
and reviews, the important missing piece was preserving ancestry after
the request handler returns and core continues work on a different async
path.
This change makes that handoff explicit and keeps the parentage rules
simple:
- app-server request span sets the current context
- `Submission.trace` snapshots that context
- core restores it once, at the submission boundary
- deeper core spans inherit naturally
That also lets us stop relying on env-based reparenting for this path,
which was too ambient and could override explicit ancestry.
### Overview
This PR adds the first piece of tracing for app-server JSON-RPC
requests.
There are two main changes:
- JSON-RPC requests can now take an optional W3C trace context at the
top level via a `trace` field (`traceparent` / `tracestate`).
- app-server now creates a dedicated request span for every inbound
JSON-RPC request in `MessageProcessor`, and uses the request-level trace
context as the parent when present.
For compatibility with existing flows, app-server still falls back to
the TRACEPARENT env var when there is no request-level traceparent.
This PR is intentionally scoped to the app-server boundary. In a
followup, we'll actually propagate trace context through the async
handoff into core execution spans like run_turn, which will make
app-server traces much more useful.
### Spans
A few details on the app-server span shape:
- each inbound request gets its own server span
- span/resource names are based on the JSON-RPC method (`initialize`,
`thread/start`, `turn/start`, etc.)
- spans record transport (stdio vs websocket), request id, connection
id, and client name/version when available
- `initialize` stores client metadata in session state so later requests
on the same connection can reuse it
**PR Summary**
This PR adds the OpenTelemetry `host.name` resource attribute to Codex
OTEL exports so every OTEL log (and trace, via the shared resource)
carries the machine hostname.
**What changed**
- Added `host.name` to the shared OTEL `Resource` in
`/Users/michael.mcgrew/code/codex/codex-rs/otel/src/otel_provider.rs`
- This applies to both:
- OTEL logs (`SdkLoggerProvider`)
- OTEL traces (`SdkTracerProvider`)
- Hostname is now resolved via `gethostname::gethostname()`
(best-effort)
- Value is trimmed
- Empty values are omitted (non-fatal)
- Added focused unit tests for:
- including `host.name` when present
- omitting `host.name` when missing/empty
**Why**
- `host.name` is host/process metadata and belongs on the OTEL
`resource`, not per-event attributes.
- Attaching it in the shared resource is the smallest change that
guarantees coverage across all exported OTEL logs/traces.
**Scope / Non-goals**
- No public API changes
- No changes to metrics behavior (this PR only updates log/trace
resource metadata)
**Dependency updates**
- Added `gethostname` as a workspace dependency and `codex-otel`
dependency
- `Cargo.lock` updated accordingly
- `MODULE.bazel.lock` unchanged after refresh/check
**Validation**
- `just fmt`
- `cargo test -p codex-otel`
- `just bazel-lock-update`
- `just bazel-lock-check`
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
Summary
- capture the origin for each configured MCP server and expose it via
the connection manager
- plumb MCP server name/origin into tool logging and emit
codex.tool_result events with those fields
- add unit coverage for origin parsing and extend OTEL tests to assert
empty MCP fields for non-MCP tools
- currently not logging full urls or url paths to prevent logging
potentially sensitive data
Testing
- Not run (not requested)
Add per-turn notice when a request is downgraded to a fallback model due
to cyber safety checks.
**Changes**
- codex-api: Emit a ServerModel event based on the openai-model response
header and/or response payload (SSE + WebSocket), including when the
model changes mid-stream.
- core: When the server-reported model differs from the requested model,
emit a single per-turn warning explaining the reroute to gpt-5.2 and
directing users to Trusted
Access verification and the cyber safety explainer.
- app-server (v2): Surface these cyber model-routing warnings as
synthetic userMessage items with text prefixed by Warning: (and document
this behavior).
So that the rest of the codebase (like TUI) don't need to be concerned
whether ChatGPT auth was handled by Codex itself or passed in via
app-server's external auth mode.
calculated a hashed user ID from either auth user id or API key
Also correctly populates OS.
These will make our metrics more useful and powerful for analysis.
Summary
- expose websocket telemetry hooks through the responses client so
request durations and event processing can be reported
- record websocket request/event metrics and emit runtime telemetry
events that the history UI now surfaces
- improve tests to cover websocket telemetry reporting and guard runtime
summary updates
<img width="824" height="79" alt="Screenshot 2026-01-31 at 5 28 12 PM"
src="https://github.com/user-attachments/assets/ea9a7965-d8b4-4e3c-a984-ef4fdc44c81d"
/>
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
This PR configures Codex CLI so it can be built with
[Bazel](https://bazel.build) in addition to Cargo. The `.bazelrc`
includes configuration so that remote builds can be done using
[BuildBuddy](https://www.buildbuddy.io).
If you are familiar with Bazel, things should work as you expect, e.g.,
run `bazel test //... --keep-going` to run all the tests in the repo,
but we have also added some new aliases in the `justfile` for
convenience:
- `just bazel-test` to run tests locally
- `just bazel-remote-test` to run tests remotely (currently, the remote
build is for x86_64 Linux regardless of your host platform). Note we are
currently seeing the following test failures in the remote build, so we
still need to figure out what is happening here:
```
failures:
suite::compact::manual_compact_twice_preserves_latest_user_messages
suite::compact_resume_fork::compact_resume_after_second_compaction_preserves_history
suite::compact_resume_fork::compact_resume_and_fork_preserve_model_history_view
```
- `just build-for-release` to build release binaries for all
platforms/architectures remotely
To setup remote execution:
- [Create a buildbuddy account](https://app.buildbuddy.io/) (OpenAI
employees should also request org access at
https://openai.buildbuddy.io/join/ with their `@openai.com` email
address.)
- [Copy your API key](https://app.buildbuddy.io/docs/setup/) to
`~/.bazelrc` (add the line `build
--remote_header=x-buildbuddy-api-key=YOUR_KEY`)
- Use `--config=remote` in your `bazel` invocations (or add `common
--config=remote` to your `~/.bazelrc`, or use the `just` commands)
## CI
In terms of CI, this PR introduces `.github/workflows/bazel.yml`, which
uses Bazel to run the tests _locally_ on Mac and Linux GitHub runners
(we are working on supporting Windows, but that is not ready yet). Note
that the failures we are seeing in `just bazel-remote-test` do not occur
on these GitHub CI jobs, so everything in `.github/workflows/bazel.yml`
is green right now.
The `bazel.yml` uses extra config in `.github/workflows/ci.bazelrc` so
that macOS CI jobs build _remotely_ on Linux hosts (using the
`docker://docker.io/mbolin491/codex-bazel` Docker image declared in the
root `BUILD.bazel`) using cross-compilation to build the macOS
artifacts. Then these artifacts are downloaded locally to GitHub's macOS
runner so the tests can be executed natively. This is the relevant
config that enables this:
```
common:macos --config=remote
common:macos --strategy=remote
common:macos --strategy=TestRunner=darwin-sandbox,local
```
Because of the remote caching benefits we get from BuildBuddy, these new
CI jobs can be extremely fast! For example, consider these two jobs that
ran all the tests on Linux x86_64:
- Bazel 1m37s
https://github.com/openai/codex/actions/runs/20861063212/job/59940545209?pr=8875
- Cargo 9m20s
https://github.com/openai/codex/actions/runs/20861063192/job/59940559592?pr=8875
For now, we will continue to run both the Bazel and Cargo jobs for PRs,
but once we add support for Windows and running Clippy, we should be
able to cutover to using Bazel exclusively for PRs, which should still
speed things up considerably. We will probably continue to run the Cargo
jobs post-merge for commits that land on `main` as a sanity check.
Release builds will also continue to be done by Cargo for now.
Earlier attempt at this PR: https://github.com/openai/codex/pull/8832
Earlier attempt to add support for Buck2, now abandoned:
https://github.com/openai/codex/pull/8504
---------
Co-authored-by: David Zbarsky <dzbarsky@gmail.com>
Co-authored-by: Michael Bolin <mbolin@openai.com>
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
This PR attempts to solve two problems by introducing a
`AbsolutePathBuf` type with a special deserializer:
- `AbsolutePathBuf` attempts to be a generally useful abstraction, as it
ensures, by constructing, that it represents a value that is an
absolute, normalized path, which is a stronger guarantee than an
arbitrary `PathBuf`.
- Values in `config.toml` that can be either an absolute or relative
path should be resolved against the folder containing the `config.toml`
in the relative path case. This PR makes this easy to support: the main
cost is ensuring `AbsolutePathBufGuard` is used inside
`deserialize_config_toml_with_base()`.
While `AbsolutePathBufGuard` may seem slightly distasteful because it
relies on thread-local storage, this seems much cleaner to me than using
than my various experiments with
https://docs.rs/serde/latest/serde/de/trait.DeserializeSeed.html.
Further, since the `deserialize()` method from the `Deserialize` trait
is not async, we do not really have to worry about the deserialization
work being spread across multiple threads in a way that would interfere
with `AbsolutePathBufGuard`.
To start, this PR introduces the use of `AbsolutePathBuf` in
`OtelTlsConfig`. Note how this simplifies `otel_provider.rs` because it
no longer requires `settings.codex_home` to be threaded through.
Furthermore, this sets us up better for a world where multiple
`config.toml` files from different folders could be loaded and then
merged together, as the absolutifying of the paths must be done against
the correct parent folder.
This fixes two issues with the OTEL HTTP exporter:
1. **Runtime panic with async reqwest client**
The `opentelemetry_sdk` `BatchLogProcessor` spawns a dedicated OS thread
that uses `futures_executor::block_on()` rather than tokio's runtime.
When the async reqwest client's timeout mechanism calls
`tokio::time::sleep()`, it panics with "there is no reactor running,
must be called from the context of a Tokio 1.x runtime".
The fix is to use `reqwest::blocking::Client` instead, which doesn't
depend on tokio for timeouts. However, the blocking client creates its
own internal tokio runtime during construction, which would panic if
built from within an async context. We wrap the construction in
`tokio::task::block_in_place()` to handle this.
2. **mTLS certificate handling**
The HTTP client wasn't properly configured for mTLS, matching the fixes
previously done for the model provider client:
- Added `.tls_built_in_root_certs(false)` when using a custom CA
certificate to ensure only our CA is trusted
- Added `.https_only(true)` when using client identity
- Added `rustls-tls` feature to ensure rustls is used (required for
`Identity::from_pem()` to work correctly)
this PR enables TUI to approve commands and add their prefixes to an
allowlist:
<img width="708" height="605" alt="Screenshot 2025-11-21 at 4 18 07 PM"
src="https://github.com/user-attachments/assets/56a19893-4553-4770-a881-becf79eeda32"
/>
note: we only show the option to whitelist the command when
1) command is not multi-part (e.g `git add -A && git commit -m 'hello
world'`)
2) command is not already matched by an existing rule
- Introduce `openai_models` in `/core`
- Move `PRESETS` under it
- Move `ModelPreset`, `ModelUpgrade`, `ReasoningEffortPreset`,
`ReasoningEffortPreset`, and `ReasoningEffortPreset` to `protocol`
- Introduce `Op::ListModels` and `EventMsg::AvailableModels`
Next steps:
- migrate `app-server` and `tui` to use the introduced Operation
* Removed sandbox risk categories; feedback indicates that these are not
that useful and "less is more"
* Tweaked the assessment prompt to generate terser answers
* Fixed bug in orchestrator that prevents this feature from being
exposed in the extension
This PR adds support for a model-based summary and risk assessment for
commands that violate the sandbox policy and require user approval. This
aids the user in evaluating whether the command should be approved.
The feature works by taking a failed command and passing it back to the
model and asking it to summarize the command, give it a risk level (low,
medium, high) and a risk category (e.g. "data deletion" or "data
exfiltration"). It uses a new conversation thread so the context in the
existing thread doesn't influence the answer. If the call to the model
fails or takes longer than 5 seconds, it falls back to the current
behavior.
For now, this is an experimental feature and is gated by a config key
`experimental_sandbox_command_assessment`.
Here is a screen shot of the approval prompt showing the risk assessment
and summary.
<img width="723" height="282" alt="image"
src="https://github.com/user-attachments/assets/4597dd7c-d5a0-4e9f-9d13-414bd082fd6b"
/>
Adds a new ItemStarted event and delivers UserMessage as the first item
type (more to come).
Renames `InputItem` to `UserInput` considering we're using the `Item`
suffix for actual items.
