## Why
Codex needs to manage Amazon Bedrock API key credentials through the
existing auth lifecycle instead of introducing a separate auth manager
or provider-specific credential file. Treating Bedrock API key login as
a primary auth mode gives it the same persistence, keyring, reload, and
logout behavior as the existing OpenAI API key and ChatGPT modes.
The credential is valid only for the `amazon-bedrock` model provider.
OpenAI-compatible providers must reject this auth mode rather than
treating the Bedrock key as an OpenAI bearer token.
## What changed
- Added `bedrockApiKey` as an app-server `AuthMode` and
`CodexAuth::BedrockApiKey` as a primary `AuthManager` mode.
- Added `BedrockApiKeyAuth`, containing the API key and AWS region, to
the existing `AuthDotJson` payload stored in `$CODEX_HOME/auth.json` or
the configured keyring backend.
- Added `login_with_bedrock_api_key(...)`, parallel to
`login_with_api_key(...)`, which replaces the current stored login with
Bedrock credentials.
- Reused generic auth reload and logout behavior instead of adding a
Bedrock-specific auth manager or logout path.
- Updated login restrictions, status reporting, diagnostics, telemetry
classification, generated app-server schemas, and auth fixtures for the
new mode.
- Added explicit errors when Bedrock API key auth is selected with an
OpenAI-compatible model provider.
This PR establishes managed storage and auth-mode behavior. Routing the
managed key and region into Amazon Bedrock requests will be in follow-up
PRs.
## Summary
- add v2 personal access token support for `codex login
--with-access-token` and `CODEX_ACCESS_TOKEN`
- classify opaque `at-` tokens separately from legacy Agent Identity
JWTs
- hydrate required ChatGPT account metadata through AuthAPI
`/v1/user-auth-credential/whoami`
- use PATs directly as bearer tokens while preserving existing ChatGPT
account surfaces
- expose PAT-backed auth as the explicit `personalAccessToken`
app-server auth mode
## Implementation
PAT auth is intentionally small and stateless. Loading a PAT performs
one AuthAPI metadata request, stores the hydrated metadata in the
in-memory auth object, and redacts the secret from debug output. Legacy
Agent Identity JWT handling remains unchanged. The shared access-token
classifier lives in a private neutral module because it dispatches
between both credential types.
PAT hydration fails closed when AuthAPI omits any required metadata,
including email. Hydrated metadata is intentionally not persisted:
startup performs a live `whoami` preflight so revoked tokens or changed
account metadata are not accepted from a stale cache.
## Workspace restriction scope
This change intentionally does **not** apply
`forced_chatgpt_workspace_id` to PAT authentication. The setting is a
client-side config guardrail, not an authorization boundary, and PAT
does not currently require workspace-ID parity. The PAT login and
`CODEX_ACCESS_TOKEN` paths therefore validate through AuthAPI without
threading workspace-restriction state through access-token loading.
Existing workspace checks for non-PAT auth remain on their established
paths.
## App-server compatibility
The public app-server `AuthMode` is shared across v1 and v2, and
PAT-backed auth reports `personalAccessToken` through both APIs.
Following human review, this intentionally removes the temporary v1
compatibility mapping that reported PATs as `chatgpt`; the deprecated v1
API is kept in parity with v2 rather than maintaining a separate closed
enum. Clients with exhaustive auth-mode handling in either API version
must add the new case and should generally treat it as ChatGPT-backed
unless they need PAT-specific behavior.
The v1 auth-status response still omits the raw PAT when `includeToken`
is requested because that response cannot carry the account metadata
needed to reuse the credential safely. Persisted PAT auth also omits the
new enum value so older Codex builds can deserialize `auth.json` and
infer PAT auth from the credential field after a rollback.
## Validation
Latest review-fix validation:
- `CARGO_INCREMENTAL=0 just test -p codex-login` (126 passed)
- `CARGO_INCREMENTAL=0 just test -p codex-cli` (263 passed)
- `CARGO_INCREMENTAL=0 just test -p codex-cli
stored_auth_validation_handles_personal_access_token`
- `CARGO_INCREMENTAL=0 just test -p codex-app-server-protocol` (226
passed)
- `CARGO_INCREMENTAL=0 just test -p codex-models-manager
refresh_available_models_uses_remote_only_catalog_for_chatgpt_auth`
- `CARGO_INCREMENTAL=0 just test -p codex-tui
existing_non_oauth_chatgpt_login_counts_as_signed_in`
- `CARGO_INCREMENTAL=0 just fix -p codex-login -p
codex-app-server-protocol -p codex-models-manager -p codex-tui -p
codex-cli`
- `just fmt`
- `git diff --check`
The broader `codex-tui` suite previously compiled and ran 2,834 tests.
Three unrelated environment-sensitive guardian/IDE-socket tests failed
after retries; the PAT-relevant TUI coverage passed.
Add Codex config for static trace span attributes and structured W3C
tracestate field upserts. The config flows through OtelSettings so
callers can attach trace metadata without touching every span call site.
Apply span attributes with an SDK span processor so every exported
trace span carries the configured metadata. Model tracestate as nested
member fields so configured keys can be upserted while unrelated
propagated state in the same member is preserved.
Validate configured tracestate before installing provider-global state,
including header-unsafe values the SDK does not reject by itself. This
keeps Codex from propagating malformed trace context from config.
Update the config schema, public docs, and OTLP loopback coverage for
config parsing, span export, propagation, and invalid-header rejection.
## Summary
This PR installs a first wave of WFP (Windows Filtering Platform)
filters that reduce the surface area of network egress vulnerabilities
for the Windows Sandbox.
- Add persistent Windows Filtering Platform provider, sublayer, and
filters for the Windows sandbox offline account.
- Install WFP filters during elevated full setup, log failures
non-fatally, and emit setup metrics when analytics are enabled.
- Bump the Windows sandbox setup version so existing users rerun full
setup and receive the new filters.
## What WFP is
Windows Filtering Platform (WFP) is the low-level Windows networking
policy engine underneath things like Windows Firewall. It lets
privileged code install persistent filtering rules at specific network
stack layers, with conditions like "only traffic from this Windows
account" or "only this remote port," and an action like block.
In this change, we create a Codex-owned persistent WFP provider and
sublayer, then install block filters scoped to the Windows sandbox's
offline user account via `ALE_USER_ID`. That means the filters are
targeted at sandboxed processes running as that account, rather than
globally affecting the host.
## Initial filter set
We are starting with 12 concrete WFP filters across a few high-value
bypass surfaces. The table below describes the filter families rather
than one filter per row:
| Area | Concrete filters | Purpose |
| --- | --- | --- |
| ICMP | 4 filters: ICMP v4/v6 on `ALE_AUTH_CONNECT` and
`ALE_RESOURCE_ASSIGNMENT` | Block direct ping-style network reachability
checks from the offline account. |
| DNS | 2 filters: remote port `53` on `ALE_AUTH_CONNECT_V4/V6` | Block
direct DNS queries that bypass our intended proxy/offline path. |
| DNS-over-TLS | 2 filters: remote port `853` on
`ALE_AUTH_CONNECT_V4/V6` | Block encrypted DNS attempts that could
bypass ordinary DNS interception. |
| SMB / NetBIOS | 4 filters: remote ports `445` and `139` on
`ALE_AUTH_CONNECT_V4/V6` | Block Windows file-sharing/network share
traffic from sandboxed processes. |
For IPv4/IPv6 coverage, the port-based filters are installed on both
`ALE_AUTH_CONNECT_V4` and `ALE_AUTH_CONNECT_V6`. ICMP also gets both
connect-layer and resource-assignment-layer coverage because ICMP
traffic is shaped differently from ordinary TCP/UDP port traffic.
## Validation
- `cargo fmt -p codex-windows-sandbox` (completed with existing
stable-rustfmt warnings about `imports_granularity = Item`)
- `cargo test -p codex-windows-sandbox wfp::tests`
- `cargo test -p codex-windows-sandbox` (fails in existing legacy
PowerShell sandbox tests because `Microsoft.PowerShell.Utility` could
not be loaded; WFP tests passed before that failure)
## Summary
This PR adds `CodexAuth::AgentIdentity` as an explicit auth mode.
An AgentIdentity auth record is a standalone `auth.json` mode. When
`AuthManager::auth().await` loads that mode, it registers one
process-scoped task and stores it in runtime-only state on the auth
value. Header creation stays synchronous after that because the task is
initialized before callers receive the auth object.
This PR also removes the old feature flag path. AgentIdentity is
selected by explicit auth mode, not by a hidden flag or lazy mutation of
ChatGPT auth records.
Reference old stack: https://github.com/openai/codex/pull/17387/changes
## Design Decisions
- AgentIdentity is a real auth enum variant because it can be the only
credential in `auth.json`.
- The process task is ephemeral runtime state. It is not serialized and
is not stored in rollout/session data.
- Account/user metadata needed by existing Codex backend checks lives on
the AgentIdentity record for now.
- `is_chatgpt_auth()` remains token-specific.
- `uses_codex_backend()` is the broader predicate for ChatGPT-token auth
and AgentIdentity auth.
## Stack
1. https://github.com/openai/codex/pull/18757: full revert
2. https://github.com/openai/codex/pull/18871: isolated Agent Identity
crate
3. This PR: explicit AgentIdentity auth mode and startup task allocation
4. https://github.com/openai/codex/pull/18811: migrate Codex backend
auth callsites through AuthProvider
5. https://github.com/openai/codex/pull/18904: accept AgentIdentity JWTs
and load `CODEX_AGENT_IDENTITY`
## Testing
Tests: targeted Rust checks, cargo-shear, Bazel lock check, and CI.
## 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
- Move the auth implementation and token data into codex-login.
- Keep codex-core re-exporting that surface from codex-login for
existing callers.
---------
Co-authored-by: Codex <noreply@openai.com>
## Summary
- log guardian-reviewed tool approvals as `source=automated_reviewer` in
`codex.tool_decision`
- keep direct user approvals as `source=user` and config-driven
approvals as `source=config`
## Testing
-
`/Users/gabec/.codex/skills/codex-oss-fastdev/scripts/codex-rs-fmt-quiet.sh`
-
`/Users/gabec/.codex/skills/codex-oss-fastdev/scripts/codex-rs-test-quiet.sh
-p codex-otel` (fails in sandboxed loopback bind tests under
`otel/tests/suite/otlp_http_loopback.rs`)
- `cargo test -p codex-core guardian -- --nocapture` (original-tree run
reached Guardian tests and only hit sandbox-related listener/proxy
failures)
Co-authored-by: Codex <noreply@openai.com>
CXC-410 Emit Env Var Status with `/feedback` report
Add more observability on top of #14611
[Unset](https://openai.sentry.io/issues/7340419168/?project=4510195390611458&query=019cfa8d-c1ba-7002-96fa-e35fc340551d&referrer=issue-stream)
[Set](https://openai.sentry.io/issues/7340426331/?project=4510195390611458&query=019cfa91-aba1-7823-ab7e-762edfbc0ed4&referrer=issue-stream)
<img width="1063" height="610" alt="image"
src="https://github.com/user-attachments/assets/937ab026-1c2d-4757-81d5-5f31b853113e"
/>
###### Summary
- Adds auth-env telemetry that records whether key auth-related env
overrides were present on session start and request paths.
- Threads those auth-env fields through `/responses`, websocket, and
`/models` telemetry and feedback metadata.
- Buckets custom provider `env_key` configuration to a safe
`"configured"` value instead of emitting raw config text.
- Keeps the slice observability-only: no raw token values or raw URLs
are emitted.
###### Rationale (from spec findings)
- 401 and auth-path debugging needs a way to distinguish env-driven auth
paths from sessions with no auth env override.
- Startup and model-refresh failures need the same auth-env diagnostics
as normal request failures.
- Feedback and Sentry tags need the same auth-env signal as OTel events
so reports can be triaged consistently.
- Custom provider config is user-controlled text, so the telemetry
contract must stay presence-only / bucketed.
###### Scope
- Adds a small `AuthEnvTelemetry` bundle for env presence collection and
threads it through the main request/session telemetry paths.
- Does not add endpoint/base-url/provider-header/geo routing attribution
or broader telemetry API redesign.
###### Trade-offs
- `provider_env_key_name` is bucketed to `"configured"` instead of
preserving the literal configured env var name.
- `/models` is included because startup/model-refresh auth failures need
the same diagnostics, but broader parity work remains out of scope.
- This slice keeps the existing telemetry APIs and layers auth-env
fields onto them rather than redesigning the metadata model.
###### Client follow-up
- Add the separate endpoint/base-url attribution slice if routing-source
diagnosis is still needed.
- Add provider-header or residency attribution only if auth-env presence
proves insufficient in real reports.
- Revisit whether any additional auth-related env inputs need safe
bucketing after more 401 triage data.
###### Testing
- `cargo test -p codex-core emit_feedback_request_tags -- --nocapture`
- `cargo test -p codex-core
collect_auth_env_telemetry_buckets_provider_env_key_name -- --nocapture`
- `cargo test -p codex-core
models_request_telemetry_emits_auth_env_feedback_tags_on_failure --
--nocapture`
- `cargo test -p codex-otel
otel_export_routing_policy_routes_api_request_auth_observability --
--nocapture`
- `cargo test -p codex-otel
otel_export_routing_policy_routes_websocket_connect_auth_observability
-- --nocapture`
- `cargo test -p codex-otel
otel_export_routing_policy_routes_websocket_request_transport_observability
-- --nocapture`
- `cargo test -p codex-core --no-run --message-format short`
- `cargo test -p codex-otel --no-run --message-format short`
---------
Co-authored-by: Codex <noreply@openai.com>
## Summary
This PR fixes OTLP HTTP trace export in runtimes where the previous
exporter setup was unreliable, especially around app-server usage. It
also removes the old `codex_otel::otel_provider` compatibility shim and
switches remaining call sites over to the crate-root
`codex_otel::OtelProvider` export.
## What changed
- Use a runtime-safe OTLP HTTP trace exporter path for Tokio runtimes.
- Add an async HTTP client path for trace export when we are already
inside a multi-thread Tokio runtime.
- Make provider shutdown flush traces before tearing down the tracer
provider.
- Add loopback coverage that verifies traces are actually sent to
`/v1/traces`:
- outside Tokio
- inside a multi-thread Tokio runtime
- inside a current-thread Tokio runtime
- Remove the `codex_otel::otel_provider` shim and update remaining
imports.
## Why
I hit cases where spans were being created correctly but never made it
to the collector. The issue turned out to be in exporter/runtime
behavior rather than the span plumbing itself. This PR narrows that gap
and gives us regression coverage for the actual export path.
## 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`
## Summary
This is a structural cleanup of `codex-otel` to make the ownership
boundaries a lot clearer.
For example, previously it was quite confusing that `OtelManager` which
emits log + trace event telemetry lived under
`codex-rs/otel/src/traces/`. Also, there were two places that defined
methods on OtelManager via `impl OtelManager` (`lib.rs` and
`otel_manager.rs`).
What changed:
- move the `OtelProvider` implementation into `src/provider.rs`
- move `OtelManager` and session-scoped event emission into
`src/events/otel_manager.rs`
- collapse the shared log/trace event helpers into
`src/events/shared.rs`
- pull target classification into `src/targets.rs`
- move `traceparent_context_from_env()` into `src/trace_context.rs`
- keep `src/otel_provider.rs` as a compatibility shim for existing
imports
- update the `codex-otel` README to reflect the new layout
## Why
`lib.rs` and `otel_provider.rs` were doing too many different jobs at
once: provider setup, export routing, trace-context helpers, and session
event emission all lived together.
This refactor separates those concerns without trying to change the
behavior of the crate. The goal is to make future OTEL work easier to
reason about and easier to review.
## Notes
- no intended behavior change
- `OtelManager` remains the session-scoped event emitter in this PR
- the `otel_provider` shim keeps downstream churn low while the
internals move around
## Validation
- `just fmt`
- `cargo test -p codex-otel`
- `just fix -p codex-otel`
### Motivation
Today config.toml has three different OTEL knobs under `[otel]`:
- `exporter` controls where OTEL logs go
- `trace_exporter` controls where OTEL traces go
- `metrics_exporter` controls where metrics go
Those often (pretty much always?) serve different purposes.
For example, for OpenAI internal usage, the **log exporter** is already
being used for IT/security telemetry, and that use case is intentionally
content-rich: tool calls, arguments, outputs, MCP payloads, and in some
cases user content are all useful there. `log_user_prompt` is a good
example of that distinction. When it’s enabled, we include raw prompt
text in OTEL logs, which is acceptable for the security use case.
The **trace exporter** is a different story. The goal there is to give
OpenAI engineers visibility into latency and request behavior when they
run Codex locally, without sending sensitive prompt or tool data as
trace event data. In other words, traces should help answer “what was
slow?” or “where did time go?”, not “what did the user say?” or “what
did the tool return?”
The complication is that Rust’s `tracing` crate does not make a hard
distinction between “logs” and “trace events.” It gives us one
instrumentation API for logs and trace events (via `tracing::event!`),
and subscribers decide what gets treated as logs, trace events, or both.
Before this change, our OTEL trace layer was effectively attached to the
general tracing stream, which meant turning on `trace_exporter` could
pick up content-rich events that were originally written with logging
(and the `log_exporter`) in mind. That made it too easy for sensitive
data to end up in exported traces by accident.
### Concrete example
In `otel_manager.rs`, this `tracing::event!` call would be exported in
both logs AND traces (as a trace event).
```
pub fn user_prompt(&self, items: &[UserInput]) {
let prompt = items
.iter()
.flat_map(|item| match item {
UserInput::Text { text, .. } => Some(text.as_str()),
_ => None,
})
.collect::<String>();
let prompt_to_log = if self.metadata.log_user_prompts {
prompt.as_str()
} else {
"[REDACTED]"
};
tracing::event!(
tracing::Level::INFO,
event.name = "codex.user_prompt",
event.timestamp = %timestamp(),
// ...
prompt = %prompt_to_log,
);
}
```
Instead of `tracing::event!`, we should now be using `log_event!` and
`trace_event!` instead to more clearly indicate which sink (logs vs.
traces) that event should be exported to.
### What changed
This PR makes the log and trace export distinct instead of treating them
as two sinks for the same data.
On the provider side, OTEL logs and traces now have separate
routing/filtering policy. The log exporter keeps receiving the existing
`codex_otel` events, while trace export is limited to spans and trace
events.
On the event side, `OtelManager` now emits two flavors of telemetry
where needed:
- a log-only event with the current rich payloads
- a tracing-safe event with summaries only
It also has a convenience `log_and_trace_event!` macro for emitting to
both logs and traces when it's safe to do so, as well as log- and
trace-specific fields.
That means prompts, tool args, tool output, account email, MCP metadata,
and similar content stay in the log lane, while traces get the pieces
that are actually useful for performance work: durations, counts, sizes,
status, token counts, tool origin, and normalized error classes.
This preserves current IT/security logging behavior while making it safe
to turn on trace export for employees.
### Full list of things removed from trace export
- raw user prompt text from `codex.user_prompt`
- raw tool arguments and output from `codex.tool_result`
- MCP server metadata from `codex.tool_result` (mcp_server,
mcp_server_origin)
- account identity fields like `user.email` and `user.account_id` from
trace-safe OTEL events
- `host.name` from trace resources
- generic `codex.tool_decision` events from traces
- generic `codex.sse_event` events from traces
- the full ToolCall debug payload from the `handle_tool_call` span
What traces now keep instead is mostly:
- spans
- trace-safe OTEL events
- counts, lengths, durations, status, token counts, and tool origin
summaries
This PR adds a durable trace linkage for each turn by storing the active
trace ID on the rollout TurnContext record stored in session rollout
files.
Before this change, we propagated trace context at runtime but didn’t
persist a stable per-turn trace key in rollout history. That made
after-the-fact debugging harder (for example, mapping a historical turn
to the corresponding trace in datadog). This sets us up for much easier
debugging in the future.
### What changed
- Added an optional `trace_id` to TurnContextItem (rollout schema).
- Added a small OTEL helper to read the current span trace ID.
- Captured `trace_id` when creating `TurnContext` and included it in
`to_turn_context_item()`.
- Updated tests and fixtures that construct TurnContextItem so
older/no-trace cases still work.
### Why this approach
TurnContext is already the canonical durable per-turn metadata in
rollout. This keeps ownership clean: trace linkage lives with other
persisted turn metadata.
### 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
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
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.
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
### Title
## otel
Codex can emit [OpenTelemetry](https://opentelemetry.io/) **log events**
that
describe each run: outbound API requests, streamed responses, user
input,
tool-approval decisions, and the result of every tool invocation. Export
is
**disabled by default** so local runs remain self-contained. Opt in by
adding an
`[otel]` table and choosing an exporter.
```toml
[otel]
environment = "staging" # defaults to "dev"
exporter = "none" # defaults to "none"; set to otlp-http or otlp-grpc to send events
log_user_prompt = false # defaults to false; redact prompt text unless explicitly enabled
```
Codex tags every exported event with `service.name = "codex-cli"`, the
CLI
version, and an `env` attribute so downstream collectors can distinguish
dev/staging/prod traffic. Only telemetry produced inside the
`codex_otel`
crate—the events listed below—is forwarded to the exporter.
### Event catalog
Every event shares a common set of metadata fields: `event.timestamp`,
`conversation.id`, `app.version`, `auth_mode` (when available),
`user.account_id` (when available), `terminal.type`, `model`, and
`slug`.
With OTEL enabled Codex emits the following event types (in addition to
the
metadata above):
- `codex.api_request`
- `cf_ray` (optional)
- `attempt`
- `duration_ms`
- `http.response.status_code` (optional)
- `error.message` (failures)
- `codex.sse_event`
- `event.kind`
- `duration_ms`
- `error.message` (failures)
- `input_token_count` (completion only)
- `output_token_count` (completion only)
- `cached_token_count` (completion only, optional)
- `reasoning_token_count` (completion only, optional)
- `tool_token_count` (completion only)
- `codex.user_prompt`
- `prompt_length`
- `prompt` (redacted unless `log_user_prompt = true`)
- `codex.tool_decision`
- `tool_name`
- `call_id`
- `decision` (`approved`, `approved_for_session`, `denied`, or `abort`)
- `source` (`config` or `user`)
- `codex.tool_result`
- `tool_name`
- `call_id`
- `arguments`
- `duration_ms` (execution time for the tool)
- `success` (`"true"` or `"false"`)
- `output`
### Choosing an exporter
Set `otel.exporter` to control where events go:
- `none` – leaves instrumentation active but skips exporting. This is
the
default.
- `otlp-http` – posts OTLP log records to an OTLP/HTTP collector.
Specify the
endpoint, protocol, and headers your collector expects:
```toml
[otel]
exporter = { otlp-http = {
endpoint = "https://otel.example.com/v1/logs",
protocol = "binary",
headers = { "x-otlp-api-key" = "${OTLP_TOKEN}" }
}}
```
- `otlp-grpc` – streams OTLP log records over gRPC. Provide the endpoint
and any
metadata headers:
```toml
[otel]
exporter = { otlp-grpc = {
endpoint = "https://otel.example.com:4317",
headers = { "x-otlp-meta" = "abc123" }
}}
```
If the exporter is `none` nothing is written anywhere; otherwise you
must run or point to your
own collector. All exporters run on a background batch worker that is
flushed on
shutdown.
If you build Codex from source the OTEL crate is still behind an `otel`
feature
flag; the official prebuilt binaries ship with the feature enabled. When
the
feature is disabled the telemetry hooks become no-ops so the CLI
continues to
function without the extra dependencies.
---------
Co-authored-by: Anton Panasenko <apanasenko@openai.com>
Celia ChenandGitHub
cooper-oaiandGitHub
bbrown-oaiandGitHub
iceweasel-oaiandGitHub
efrazer-oaiandGitHub
pakrym-oaiandGitHub
Owen LinandMichael Bolin
jif-oaiandGitHub
alexsong-oaiandGitHub
Anton PanasenkoandGitHub
