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## Why Older persisted rollouts can contain `input_image.detail` values of `auto` or `low` from before `ImageDetail` was narrowed to `high`/`original`. Current deserialization rejects those values, which can make resume skip later compacted checkpoints and reconstruct an oversized raw suffix before the next compaction attempt. Confirmed Sentry reports fixed by this compatibility path: - [CODEX-1H3F](https://openai.sentry.io/issues/7500642496/) - [CODEX-1H6N](https://openai.sentry.io/issues/7501025347/) - [CODEX-1JDP](https://openai.sentry.io/issues/7504549065/) - [CODEX-1HW6](https://openai.sentry.io/issues/7503407986/) ## Background [openai/codex#20693](https://github.com/openai/codex/pull/20693) added image-detail plumbing for app-server `UserInput` so input images could explicitly request `detail: original`. The Slack discussion behind that PR was about ScreenSpot / bridge evals where user input images were resized, while tool output images already had MCP/code-mode ways to request image detail. In review, the intended new API surface was narrowed to `high` and `original`: default to `high`, allow `original` when callers need unchanged image handling, and avoid encouraging new `auto` or `low` usage. That policy still makes sense for newly emitted values. The missing compatibility piece is persisted history. Older rollouts can already contain `auto` and `low`, and resume reconstructs typed history by deserializing those rollout records. Rejecting old values at that boundary causes valid compacted checkpoints to be skipped. This PR restores `auto` and `low` as real variants so old records deserialize and round-trip without being rewritten as `high`, while product paths can continue to default to `high` and avoid emitting `auto` for new behavior. ## What changed - Restored `ImageDetail::Auto` and `ImageDetail::Low` as first-class protocol values. - Preserved `auto`/`low` through rollout deserialization, MCP image metadata, code-mode image output, and schema/type generation. - Kept local image byte handling conservative: only `original` switches to original-resolution loading; `auto`/`low`/`high` continue through the resize-to-fit path while retaining their detail value. - Added regression coverage for enum round-tripping and code-mode `low` detail handling. ## Testing - `just write-app-server-schema` - `just test -p codex-protocol` - `just test -p codex-tools` - `just test -p codex-code-mode` - `just test -p codex-app-server-protocol` - `just test -p codex-core suite::rmcp_client::stdio_image_responses_preserve_original_detail_metadata` - `just test -p codex-core suite::code_mode::code_mode_can_use_mcp_image_result_with_image_helper` - Loaded broken rollouts on local fixed builds, and started/completed new turns. I also attempted `just test -p codex-core`; the local broad run did not finish green: 2559 tests run, 2467 passed, 55 flaky, 91 failed, 1 timed out. The failures were broad timeout/deadline failures across unrelated areas; targeted changed-path core tests above passed.
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
Run the following on Mac or Linux to install Codex CLI:
curl -fsSL https://chatgpt.com/codex/install.sh | sh
Run the following on Windows to install Codex CLI:
powershell -ExecutionPolicy ByPass -c "irm https://chatgpt.com/codex/install.ps1 | iex"
Codex CLI can also be installed via the following package managers:
# 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.