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## Why While investigating `codex exec hi` startup latency, the useful questions were not "is startup slow?" but "which durable bucket is slow in production?" The path we observed has a few distinct stages: 1. `thread/start` creates the session 2. startup prewarm builds the turn context, tools, and prompt 3. startup prewarm warms the websocket 4. the first real turn resolves the prewarm 5. the model produces the first token Before this PR, production telemetry had some of the raw measurements already: - aggregate startup-prewarm duration / age-at-first-turn metrics - TTFT as a metric - websocket request telemetry But there was no coherent production event stream for the startup breakdown itself, and TTFT was metric-only. That made it hard to answer the same latency questions from OpenTelemetry-backed logs without adding one-off local instrumentation. ## What changed Add durable production telemetry on the existing `SessionTelemetry` path: - new `codex.startup_phase` OTel log/trace events plus `codex.startup.phase.duration_ms` - new `codex.turn_ttft` OTel log/trace events while preserving the existing TTFT metric The startup phase event is emitted for the coarse buckets we actually observed while running `exec hi`: - `thread_start_create_thread` - `startup_prewarm_total` - `startup_prewarm_create_turn_context` - `startup_prewarm_build_tools` - `startup_prewarm_build_prompt` - `startup_prewarm_websocket_warmup` - `startup_prewarm_resolve` These phases are intentionally low-cardinality so they remain safe as production telemetry tags. ## Why this shape This keeps the instrumentation on the same production path as the rest of the session telemetry instead of adding a local debug-only trace mode. It also avoids changing startup behavior: - prewarm still runs - no control flow changes - no extra remote calls - no user-visible behavior changes One boundary is intentional: very early process bootstrap that happens before a session exists is not included here, because this PR uses session-scoped production telemetry. The expensive buckets we were trying to understand after `thread/start` are now covered durably. ## Verification - `cargo test -p codex-otel` - `cargo test -p codex-core turn_timing` - `cargo test -p codex-core regular_turn_emits_turn_started_without_waiting_for_startup_prewarm` - `cargo test -p codex-core interrupting_regular_turn_waiting_on_startup_prewarm_emits_turn_aborted` - `cargo test -p codex-app-server thread_start` - `just fix -p codex-otel -p codex-core -p codex-app-server` I also ran `cargo test -p codex-core`; it built successfully and then hit an existing unrelated stack overflow in `tools::handlers::multi_agents::tests::tool_handlers_cascade_close_and_resume_and_keep_explicitly_closed_subtrees_closed`.
npm i -g @openai/codex
or brew install --cask codex
Codex CLI is a coding agent from OpenAI that runs locally on your computer.
If you want Codex in your code editor (VS Code, Cursor, Windsurf), install in your IDE.
If you want the desktop app experience, run
codex app or visit the Codex App page.
If you are looking for the cloud-based agent from OpenAI, Codex Web, go to chatgpt.com/codex.
Quickstart
Installing and running Codex CLI
Install globally with your preferred package manager:
# Install using npm
npm install -g @openai/codex
# Install using Homebrew
brew install --cask codex
Then simply run codex to get started.
You can also go to the latest GitHub Release and download the appropriate binary for your platform.
Each GitHub Release contains many executables, but in practice, you likely want one of these:
- macOS
- Apple Silicon/arm64:
codex-aarch64-apple-darwin.tar.gz - x86_64 (older Mac hardware):
codex-x86_64-apple-darwin.tar.gz
- Apple Silicon/arm64:
- Linux
- x86_64:
codex-x86_64-unknown-linux-musl.tar.gz - arm64:
codex-aarch64-unknown-linux-musl.tar.gz
- x86_64:
Each archive contains a single entry with the platform baked into the name (e.g., codex-x86_64-unknown-linux-musl), so you likely want to rename it to codex after extracting it.
Using Codex with your ChatGPT plan
Run codex and select Sign in with ChatGPT. We recommend signing into your ChatGPT account to use Codex as part of your Plus, Pro, Business, Edu, or Enterprise plan. Learn more about what's included in your ChatGPT plan.
You can also use Codex with an API key, but this requires additional setup.
Docs
This repository is licensed under the Apache-2.0 License.