## Why
The prompt image cache was bounded to 32 entries, but not by the size of
those entries. A set of large encoded images could therefore retain
substantially more memory than intended. Cache hits also cloned the full
encoded payload.
## What changed
- cap the cache at 64 MiB of encoded image data while preserving its
existing 32-entry limit
- skip caching an image that exceeds the entire byte budget
- evict least-recently-used entries until the cache is back within its
byte budget
- share cached encoded bytes with `Arc<[u8]>` so cache hits do not
deep-clone image payloads
## Validation
- `just test -p codex-utils-image`
## Summary
- Preserve ICC profiles and EXIF metadata when resizing and re-encoding
prompt images.
- Retain EXIF orientation metadata without rotating or otherwise
modifying the pixel data locally.
- Support metadata preservation for PNG, JPEG, and WebP outputs.
- Continue returning the original bytes when an image does not require
re-encoding.
This intentionally preserves the metadata most important for rendering
prompt images faithfully. Other format-specific metadata is not copied.
## Motivation
Client-side resizing previously discarded image metadata during
re-encoding. This could lose color-profile information and EXIF
orientation needed by downstream image consumers.
#### [git stack](https://github.com/magus/git-stack-cli)
- ✅ `1` https://github.com/openai/codex/pull/27245
- ✅ `2` https://github.com/openai/codex/pull/27247
- ✅ `3` https://github.com/openai/codex/pull/27246
- 👉 `4` https://github.com/openai/codex/pull/27266
## Summary
Adds complete client-side image preparation behind the default-off
`resize_all_images` feature flag.
When enabled, local image producers defer decoding and resizing. Images
are prepared centrally before insertion into conversation history,
covering user input, `view_image`, and structured tool-output images.
## Behavior
- Processes base64 `data:` images in messages and function/custom tool
outputs.
- Leaves non-data URLs, including HTTP(S) URLs, unchanged.
- Applies image-detail budgets:
- `high` and omitted: 2048px maximum dimension and 2.5K 32px patches.
- `original`: 6000px maximum dimension and 10K 32px patches.
- `auto`: uses the same 2048px / 2.5K-patch budget as high.
- `low`: unsupported and replaced with an actionable placeholder.
- Preserves original image bytes when no resize or format conversion is
needed.
- Enforces the shared 1 GiB encoded and decoded data-URL sanity limits.
- Replaces only an image that fails preparation, preserving sibling
content and tool-output metadata.
- Uses bounded placeholders distinguishing generic processing failures,
oversized images, and unsupported `low` detail.
- Prepares resumed and forked history before installing it as live
history without modifying persisted rollouts.
## Flag-Off Behavior
When `resize_all_images` is disabled:
- Existing local user-input and `view_image` processing remains
unchanged.
- Existing decoding and error behavior remains unchanged.
- Arbitrary tool-output images are not processed.
- HTTP(S) image URLs continue to be forwarded unchanged.
#### [git stack](https://github.com/magus/git-stack-cli)
- ✅ `1` https://github.com/openai/codex/pull/27245
- 👉 `2` https://github.com/openai/codex/pull/27247
- ⏳ `3` https://github.com/openai/codex/pull/27246
- ⏳ `4` https://github.com/openai/codex/pull/27266
## Summary
Add shared image-processing primitives needed for centralized image
preparation in a follow-up PR.
- Add `load_data_url_for_prompt` for decoding and preparing base64 image
data URLs.
- Add configurable maximum-dimension and 32px patch-budget resizing.
- Enforce a 1 GiB sanity limit on both encoded and decoded data-URL
representations.
- Preserve original PNG, JPEG, and WebP bytes when resizing is
unnecessary.
- Preserve the existing GIF-to-PNG behavior.
- Move image utility tests into the existing sidecar test module.
## Behavior
This PR is intended to be runtime behavior-preserving.
Existing production callers continue using
`PromptImageMode::ResizeToFit` and `PromptImageMode::Original` with
their existing semantics. The new data-URL entrypoint and configurable
resize mode have no production callers in this PR; they are used by the
next PR in the stack.
This PR does not change user-input handling, `view_image`, history
insertion, request construction, HTTP image URL forwarding, or
app-server behavior.
#### [git stack](https://github.com/magus/git-stack-cli)
- 👉 `1` https://github.com/openai/codex/pull/27245
- ⏳ `2` https://github.com/openai/codex/pull/27247
- ⏳ `3` https://github.com/openai/codex/pull/27246
- ⏳ `4` https://github.com/openai/codex/pull/27266
## Summary
- add Divan benchmarks for prompt image re-encoding paths
- wire the image benchmark smoke test into Rust CI workflows
## Why
Image prompt handling includes re-encoding work that benefits from
repeatable benchmark coverage so changes can be measured in CI and
locally.
This already helped identify a potential regression from changing compiler flags.
## Impact
Developers can run and compare the new image re-encoding benchmarks, and
CI exercises the benchmark target via the Rust benchmark smoke test.
## Summary
`cargo test` has entails both running standard Rust tests and doctests.
It turns out that the doctest discovery is fairly slow, and it's a cost
you pay even for crates that don't include any doctests.
This PR disables doctests with `doctest = false` for crates that lack
any doctests.
For the collection of crates below, this speeds up test execution by
>4x.
E.g., before this PR:
```
Benchmark 1: cargo test -p codex-utils-absolute-path -p codex-utils-cache -p codex-utils-cli -p codex-utils-home-dir -p codex-utils-output-truncation -p codex-utils-path -p codex-utils-string -p codex-utils-template -p codex-utils-elapsed -p codex-utils-json-to-toml
Time (mean ± σ): 1.849 s ± 4.455 s [User: 0.752 s, System: 1.367 s]
Range (min … max): 0.418 s … 14.529 s 10 runs
```
And after:
```
Benchmark 1: cargo test -p codex-utils-absolute-path -p codex-utils-cache -p codex-utils-cli -p codex-utils-home-dir -p codex-utils-output-truncation -p codex-utils-path -p codex-utils-string -p codex-utils-template -p codex-utils-elapsed -p codex-utils-json-to-toml
Time (mean ± σ): 428.6 ms ± 6.9 ms [User: 187.7 ms, System: 219.7 ms]
Range (min … max): 418.0 ms … 436.8 ms 10 runs
```
For a single crate, with >2x speedup, before:
```
Benchmark 1: cargo test -p codex-utils-string
Time (mean ± σ): 491.1 ms ± 9.0 ms [User: 229.8 ms, System: 234.9 ms]
Range (min … max): 480.9 ms … 512.0 ms 10 runs
```
And after:
```
Benchmark 1: cargo test -p codex-utils-string
Time (mean ± σ): 213.9 ms ± 4.3 ms [User: 112.8 ms, System: 84.0 ms]
Range (min … max): 206.8 ms … 221.0 ms 13 runs
```
Co-authored-by: Codex <noreply@openai.com>
Before this, the TUI was starting 2 app-server. One to check the login
status and one to actually start the session
This PR make only one app-server startup and defer the login check in
async, outside of the frame rendering path
---------
Co-authored-by: Codex <noreply@openai.com>
## 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
Cleanup image semantics in code mode.
`view_image` now returns `{image_url:string, details?: string}`
`image()` now allows both string parameter and `{image_url:string,
details?: string}`
Adds an environment crate and environment + file system abstraction.
Environment is a combination of attributes and services specific to
environment the agent is connected to:
File system, process management, OS, default shell.
The goal is to move most of agent logic that assumes environment to work
through the environment abstraction.
## Summary
Add original-resolution support for `view_image` behind the
under-development `view_image_original_resolution` feature flag.
When the flag is enabled and the target model is `gpt-5.3-codex` or
newer, `view_image` now preserves original PNG/JPEG/WebP bytes and sends
`detail: "original"` to the Responses API instead of using the legacy
resize/compress path.
## What changed
- Added `view_image_original_resolution` as an under-development feature
flag.
- Added `ImageDetail` to the protocol models and support for serializing
`detail: "original"` on tool-returned images.
- Added `PromptImageMode::Original` to `codex-utils-image`.
- Preserves original PNG/JPEG/WebP bytes.
- Keeps legacy behavior for the resize path.
- Updated `view_image` to:
- use the shared `local_image_content_items_with_label_number(...)`
helper in both code paths
- select original-resolution mode only when:
- the feature flag is enabled, and
- the model slug parses as `gpt-5.3-codex` or newer
- Kept local user image attachments on the existing resize path; this
change is specific to `view_image`.
- Updated history/image accounting so only `detail: "original"` images
use the docs-based GPT-5 image cost calculation; legacy images still use
the old fixed estimate.
- Added JS REPL guidance, gated on the same feature flag, to prefer JPEG
at 85% quality unless lossless is required, while still allowing other
formats when explicitly requested.
- Updated tests and helper code that construct
`FunctionCallOutputContentItem::InputImage` to carry the new `detail`
field.
## Behavior
### Feature off
- `view_image` keeps the existing resize/re-encode behavior.
- History estimation keeps the existing fixed-cost heuristic.
### Feature on + `gpt-5.3-codex+`
- `view_image` sends original-resolution images with `detail:
"original"`.
- PNG/JPEG/WebP source bytes are preserved when possible.
- History estimation uses the GPT-5 docs-based image-cost calculation
for those `detail: "original"` images.
#### [git stack](https://github.com/magus/git-stack-cli)
- 👉 `1` https://github.com/openai/codex/pull/13050
- ⏳ `2` https://github.com/openai/codex/pull/13331
- ⏳ `3` https://github.com/openai/codex/pull/13049
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>
jif ·
Curtis 'Fjord' Hawthorne ·
Adam Perry @ OpenAI ·
Charlie Marsh ·
pakrym-oai ·
natea-oai ·
zbarsky-openai ·
Josh McKinney ·