## Summary
Remove js_repl/node test-skip paths and make Node setup explicit in CI
so js_repl tests always run instead of silently skipping.
## Why
We had multiple “expediency” skip paths that let js_repl tests pass
without actually exercising Node-backed behavior. This reduced CI signal
and hid runtime/environment regressions.
## What changed
### CI
- Added Node setup using `codex-rs/node-version.txt` in:
- `.github/workflows/rust-ci.yml`
- `.github/workflows/bazel.yml`
- Added a Unix PATH copy step in Bazel workflow to expose the setup-node
binary in common paths.
### js_repl test harness
- Added explicit js_repl sandbox test configuration helpers in:
- `codex-rs/core/src/tools/js_repl/mod.rs`
- `codex-rs/core/src/tools/handlers/js_repl.rs`
- Added Linux arg0 dispatch glue for js_repl tests so sandbox subprocess
entrypoint behavior is correct under Linux test execution.
### Removed skip behavior
- Deleted runtime guard function and early-return skips in js_repl tests
(`can_run_js_repl_runtime_tests` and related per-test short-circuits).
- Removed view_image integration test skip behavior:
- dropped `skip_if_no_network!(Ok(()))`
- removed “skip on Node missing/too old” branch after js_repl output
inspection.
## Impact
- js_repl/node tests now consistently execute and fail loudly when the
environment is not correctly provisioned.
- CI has stronger signal for js_repl regressions instead of false green
from conditional skips.
## Testing
- `cargo test -p codex-core` (locally) to validate js_repl
unit/integration behavior with skips removed.
- CI expected to surface any remaining environment/runtime gaps directly
(rather than masking them).
#### [git stack](https://github.com/magus/git-stack-cli)
- ✅ `1` https://github.com/openai/codex/pull/12300
- ✅ `2` https://github.com/openai/codex/pull/12275
- ✅ `3` https://github.com/openai/codex/pull/12205
- ✅ `4` https://github.com/openai/codex/pull/12407
- ✅ `5` https://github.com/openai/codex/pull/12372
- 👉 `6` https://github.com/openai/codex/pull/12185
- ⏳ `7` https://github.com/openai/codex/pull/10673
## Summary
- Fix `js_repl` so `await codex.tool("view_image", { path })` actually
attaches the image to the active turn when called from inside the JS
REPL.
- Restore the behavior expected by the existing `js_repl`
image-attachment test.
- This is a follow-up to
[#12553](https://github.com/openai/codex/pull/12553), which changed
`view_image` to return structured image content.
## Root Cause
- [#12553](https://github.com/openai/codex/pull/12553) changed
`view_image` from directly injecting a pending user image message to
returning structured `function_call_output` content items.
- The nested tool-call bridge inside `js_repl` serialized that tool
response back to the JS runtime, but it did not mirror returned image
content into the active turn.
- As a result, `view_image` appeared to succeed inside `js_repl`, but no
`input_image` was actually attached for the outer turn.
## What Changed
- Updated the nested tool-call path in `js_repl` to inspect function
tool responses for structured content items.
- When a nested tool response includes `input_image` content, `js_repl`
now injects a corresponding user `Message` into the active turn before
returning the raw tool result back to the JS runtime.
- Kept the normal JSON result flow intact, so `codex.tool(...)` still
returns the original tool output object to JavaScript.
## Why
- `js_repl` documentation and tests already assume that `view_image` can
be used from inside the REPL to attach generated images to the model.
- Without this fix, the nested call path silently dropped that
attachment behavior.
## Why
`codex-rs/core/src/lib.rs` re-exported a broad set of types and modules
from `codex-protocol` and `codex-shell-command`. That made it easy for
workspace crates to import those APIs through `codex-core`, which in
turn hides dependency edges and makes it harder to reduce compile-time
coupling over time.
This change removes those public re-exports so call sites must import
from the source crates directly. Even when a crate still depends on
`codex-core` today, this makes dependency boundaries explicit and
unblocks future work to drop `codex-core` dependencies where possible.
## What Changed
- Removed public re-exports from `codex-rs/core/src/lib.rs` for:
- `codex_protocol::protocol` and related protocol/model types (including
`InitialHistory`)
- `codex_protocol::config_types` (`protocol_config_types`)
- `codex_shell_command::{bash, is_dangerous_command, is_safe_command,
parse_command, powershell}`
- Migrated workspace Rust call sites to import directly from:
- `codex_protocol::protocol`
- `codex_protocol::config_types`
- `codex_protocol::models`
- `codex_shell_command`
- Added explicit `Cargo.toml` dependencies (`codex-protocol` /
`codex-shell-command`) in crates that now import those crates directly.
- Kept `codex-core` internal modules compiling by using `pub(crate)`
aliases in `core/src/lib.rs` (internal-only, not part of the public
API).
- Updated the two utility crates that can already drop a `codex-core`
dependency edge entirely:
- `codex-utils-approval-presets`
- `codex-utils-cli`
## Verification
- `cargo test -p codex-utils-approval-presets`
- `cargo test -p codex-utils-cli`
- `cargo check --workspace --all-targets`
- `just clippy`
rm `remote_models` feature flag.
We see issues like #11527 when a user has `remote_models` disabled, as
we always use the default fallback `ModelInfo`. This causes issues with
model performance.
Builds on #11690, which helps by warning the user when they are using
the default fallback. This PR will make that happen much less frequently
as an accidental consequence of disabling `remote_models`.
### What
It's currently unclear when the harness falls back to the default,
generic `ModelInfo`. This happens when the `remote_models` feature is
disabled or the model is truly unknown, and can lead to bad performance
and issues in the harness.
Add a user-facing warning when this happens so they are aware when their
setup is broken.
### Tests
Added tests, tested locally.
## Summary
This PR adds host-integrated helper APIs for `js_repl` and updates model
guidance so the agent can use them reliably.
### What’s included
- Add `codex.tool(name, args?)` in the JS kernel so `js_repl` can call
normal Codex tools.
- Keep persistent JS state and scratch-path helpers available:
- `codex.state`
- `codex.tmpDir`
- Wire `js_repl` tool calls through the standard tool router path.
- Add/align `js_repl` execution completion/end event behavior with
existing tool logging patterns.
- Update dynamic prompt injection (`project_doc`) to document:
- how to call `codex.tool(...)`
- raw output behavior
- image flow via `view_image` (`codex.tmpDir` +
`codex.tool("view_image", ...)`)
- stdio safety guidance (`console.log` / `codex.tool`, avoid direct
`process.std*`)
## Why
- Standardize JS-side tool usage on `codex.tool(...)`
- Make `js_repl` behavior more consistent with existing tool execution
and event/logging patterns.
- Give the model enough runtime guidance to use `js_repl` safely and
effectively.
## Testing
- Added/updated unit and runtime tests for:
- `codex.tool` calls from `js_repl` (including shell/MCP paths)
- image handoff flow via `view_image`
- prompt-injection text for `js_repl` guidance
- execution/end event behavior and related regression coverage
#### [git stack](https://github.com/magus/git-stack-cli)
- ✅ `1` https://github.com/openai/codex/pull/10674
- 👉 `2` https://github.com/openai/codex/pull/10672
- ⏳ `3` https://github.com/openai/codex/pull/10671
- ⏳ `4` https://github.com/openai/codex/pull/10673
- ⏳ `5` https://github.com/openai/codex/pull/10670
Summary
- add a `prefer_websockets` field to `ModelInfo`, defaulting to `false`
in all fixtures and constructors
- wire the new flag into websocket selection so models that opt in
always use websocket transport even when the feature gate is off
Testing
- Not run (not requested)
- Keep `view_image` in the advertised tool list for all models.
- Return a clear error when the current model does not support image
inputs, and cover it with a unit test.
## Summary
Support updating Personality mid-Thread via UserTurn/OverwriteTurn. This
is explicitly unused by the clients so far, to simplify PRs - app-server
and tui implementations will be follow-ups.
## Testing
- [x] added integration tests
Agent wouldn't "see" attached images and would instead try to use the
view_file tool:
<img width="1516" height="504" alt="image"
src="https://github.com/user-attachments/assets/68a705bb-f962-4fc1-9087-e932a6859b12"
/>
In this PR, we wrap image content items in XML tags with the name of
each image (now just a numbered name like `[Image #1]`), so that the
model can understand inline image references (based on name). We also
put the image content items above the user message which the model seems
to prefer (maybe it's more used to definitions being before references).
We also tweak the view_file tool description which seemed to help a bit
Results on a simple eval set of images:
Before
<img width="980" height="310" alt="image"
src="https://github.com/user-attachments/assets/ba838651-2565-4684-a12e-81a36641bf86"
/>
After
<img width="918" height="322" alt="image"
src="https://github.com/user-attachments/assets/10a81951-7ee6-415e-a27e-e7a3fd0aee6f"
/>
```json
[
{
"id": "single_describe",
"prompt": "Describe the attached image in one sentence.",
"images": ["image_a.png"]
},
{
"id": "single_color",
"prompt": "What is the dominant color in the image? Answer with a single color word.",
"images": ["image_b.png"]
},
{
"id": "orientation_check",
"prompt": "Is the image portrait or landscape? Answer in one sentence.",
"images": ["image_c.png"]
},
{
"id": "detail_request",
"prompt": "Look closely at the image and call out any small details you notice.",
"images": ["image_d.png"]
},
{
"id": "two_images_compare",
"prompt": "I attached two images. Are they the same or different? Briefly explain.",
"images": ["image_a.png", "image_b.png"]
},
{
"id": "two_images_captions",
"prompt": "Provide a short caption for each image (Image 1, Image 2).",
"images": ["image_c.png", "image_d.png"]
},
{
"id": "multi_image_rank",
"prompt": "Rank the attached images from most colorful to least colorful.",
"images": ["image_a.png", "image_b.png", "image_c.png"]
},
{
"id": "multi_image_choice",
"prompt": "Which image looks more vibrant? Answer with 'Image 1' or 'Image 2'.",
"images": ["image_b.png", "image_d.png"]
}
]
```
This seems to be necessary to get the Bazel builds on ARM Linux to go
green on https://github.com/openai/codex/pull/8875.
I don't feel great about timeout-whack-a-mole, but we're still learning
here...
If an image can't be read by the API, it will poison the entire history,
preventing any new turn on the conversation.
This detect such cases and replace the image by a placeholder
## Summary
- add `TestCodex::submit_turn_with_policies` and extend the response
helpers with reusable tool-call utilities
- update the grep_files, read_file, list_dir, shell_serialization, and
tools suites to rely on the shared helpers instead of local copies
- make the list_dir helper return `anyhow::Result` so clippy no longer
warns about `expect`
## Testing
- `just fix -p codex-core`
- `cargo test -p codex-core --test all
suite::grep_files::grep_files_tool_collects_matches`
- `cargo test -p codex-core
suite::grep_files::grep_files_tool_collects_matches -- --ignored`
(filter requests ignored tests so nothing runs, but the build stays
clean)
------
[Codex
Task](https://chatgpt.com/codex/tasks/task_i_69112d53abac83219813cab4d7cb6446)
solves: https://github.com/openai/codex/issues/5675
Block non-image uploads in the view_image workflow. We now confirm the
file’s MIME is image/* before building the data URL; otherwise we emit a
“unsupported MIME type” error to the model. This stops the agent from
sending application/json blobs that the Responses API rejects with 400s.
<img width="409" height="556" alt="Screenshot 2025-10-28 at 1 15 10 PM"
src="https://github.com/user-attachments/assets/a92199e8-2769-4b1d-8e33-92d9238c90fe"
/>
Adds a new ItemStarted event and delivers UserMessage as the first item
type (more to come).
Renames `InputItem` to `UserInput` considering we're using the `Item`
suffix for actual items.
## Summary
- add a reusable `ev_response_created` helper that builds
`response.created` SSE events for integration tests
- update the exec and core integration suites to use the new helper
instead of repeating manual JSON literals
- keep the streaming fixtures consistent by relying on the shared helper
in every touched test
## Testing
- `just fmt`
------
https://chatgpt.com/codex/tasks/task_i_68e1fe885bb883208aafffb94218da61
## Summary
- replace manual event polling loops in several core test suites with
the shared wait_for_event helpers
- keep prior assertions intact by using closure captures for stateful
expectations, including plan updates, patch lifecycles, and review flow
checks
- rely on wait_for_event_with_timeout where longer waits are required,
simplifying timeout handling
## Testing
- just fmt
------
https://chatgpt.com/codex/tasks/task_i_68e1d58582d483208febadc5f90dd95e
# Tool System Refactor
- Centralizes tool definitions and execution in `core/src/tools/*`:
specs (`spec.rs`), handlers (`handlers/*`), router (`router.rs`),
registry/dispatch (`registry.rs`), and shared context (`context.rs`).
One registry now builds the model-visible tool list and binds handlers.
- Router converts model responses to tool calls; Registry dispatches
with consistent telemetry via `codex-rs/otel` and unified error
handling. Function, Local Shell, MCP, and experimental `unified_exec`
all flow through this path; legacy shell aliases still work.
- Rationale: reduce per‑tool boilerplate, keep spec/handler in sync, and
make adding tools predictable and testable.
Example: `read_file`
- Spec: `core/src/tools/spec.rs` (see `create_read_file_tool`,
registered by `build_specs`).
- Handler: `core/src/tools/handlers/read_file.rs` (absolute `file_path`,
1‑indexed `offset`, `limit`, `L#: ` prefixes, safe truncation).
- E2E test: `core/tests/suite/read_file.rs` validates the tool returns
the requested lines.
## Next steps:
- Decompose `handle_container_exec_with_params`
- Add parallel tool calls
