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## Why For reproducibility. A hand-written `config.toml` is not enough to recreate what a Codex session actually ran with because layered config, CLI overrides, defaults, feature aliases, resolved feature config, prompt setup, and model-catalog/session values can all affect the final runtime behavior. This PR adds an effective config lockfile path: one run can export the resolved session config, and a later run can replay that lockfile and fail early if the regenerated effective config drifts. ## What Changed - Add a dedicated `ConfigLockfileToml` wrapper with top-level lockfile metadata plus the replayable config: ```toml version = 1 codex_version = "..." [config] # effective ConfigToml fields ``` - Keep lockfile metadata out of regular `ConfigToml`; replay loads `ConfigLockfileToml` and then uses its nested `config` as the authoritative config layer. - Add `debug.config_lockfile.export_dir` to write `<thread_id>.config.lock.toml` when a root session starts. - Add `debug.config_lockfile.load_path` to replay a saved lockfile and validate the regenerated session lockfile against it. - Add `debug.config_lockfile.allow_codex_version_mismatch` to optionally tolerate Codex binary version drift while still comparing the rest of the lockfile. - Add `debug.config_lockfile.save_fields_resolved_from_model_catalog` so lock creation can either save model-catalog/session-resolved fields or intentionally leave those fields dynamic. - Build lockfiles from the effective config plus resolved runtime values such as model selection, reasoning settings, prompts, service tier, web search mode, feature states/config, memories config, skill instructions, and agent limits. - Materialize feature aliases and custom feature config into the lockfile so replay compares canonical resolved behavior instead of user-authored alias shape. - Strip profile/debug/file-include/environment-specific inputs from generated lockfiles so they contain replayable values rather than the inputs that produced those values. - Surface JSON-RPC server error code/data in app-server client and TUI bootstrap errors so config-lock replay failures include the actual TOML diff. - Regenerate the config schema for the new debug config keys. ## Review Notes The main flow is split across these files: - `config/src/config_toml.rs`: lockfile/debug TOML shapes. - `core/src/config/mod.rs`: loading `debug.config_lockfile.*`, replaying a lockfile as a config layer, and preserving the expected lockfile for validation. - `core/src/session/config_lock.rs`: exporting the current session lockfile and materializing resolved session/config values. - `core/src/config_lock.rs`: lockfile parsing, metadata/version checks, replay comparison, and diff formatting. ## Usage Export a lockfile from a normal session: ```sh codex -c 'debug.config_lockfile.export_dir="/tmp/codex-locks"' ``` Export a lockfile without saving model-catalog/session-resolved fields: ```sh codex -c 'debug.config_lockfile.export_dir="/tmp/codex-locks"' \ -c 'debug.config_lockfile.save_fields_resolved_from_model_catalog=false' ``` Replay a saved lockfile in a later session: ```sh codex -c 'debug.config_lockfile.load_path="/tmp/codex-locks/<thread_id>.config.lock.toml"' ``` If replay resolves to a different effective config, startup fails with a TOML diff. To tolerate Codex binary version drift during replay: ```sh codex -c 'debug.config_lockfile.load_path="/tmp/codex-locks/<thread_id>.config.lock.toml"' \ -c 'debug.config_lockfile.allow_codex_version_mismatch=true' ``` ## Limitations This does not support custom rules/network policies. ## Verification - `cargo test -p codex-core config_lock` - `cargo test -p codex-config` - `cargo test -p codex-thread-manager-sample`
0b04d1b3cc
·
2026-05-01 17:46:02 +02:00
6,074 Commits
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.