## Summary
Sets up an explicit Feature flag for `/personality`, so users can now
opt in to it via `/experimental`. #10114 also updates the config
## Testing
- [x] Tested locally
## Summary
Move `model_instructions_template` config to the experimental slug while
we iterate on this feature
## Testing
- [x] Tested locally, unit tests still pass
## Summary
Adds /personality selector in the TUI, which leverages the new core
interface in #9644
Notes:
- We are doing some of our own state management for model_info loading
here, but not sure if that's ideal. open to opinions on simpler
approach, but would like to avoid blocking on a larger refactor
- Right now, the `/personality` selector just hides when the model
doesn't support it. we can update this behavior down the line
## Testing
- [x] Tested locally
- [x] Added snapshot tests
Keep an unmasked base collaboration mode and apply the active mask on
demand. Simplify the TUI mask helpers and update tests/docs to match the
mask contract.
## Summary
#9555 is the start of a rename, so I'm starting to standardize here.
Sets up `model_instructions` templating with a strongly-typed object for
injecting a personality block into the model instructions.
## Testing
- [x] Added tests
- [x] Ran locally
We have `models.json` and `/models` response
Behavior:
1. New models from models endpoint gets added
2. Shared models get replaced by remote ones
3. Existing models in `models.json` but not `/models` are kept
4. Mark highest priority as default
## Summary
Introduces the concept of a config model_personality. I would consider
this an MVP for testing out the feature. There are a number of
follow-ups to this PR:
- More sophisticated templating with validation
- In-product experience to manage this
## Testing
- [x] Testing locally
## Summary
This PR consolidates base_instructions onto SessionMeta /
SessionConfiguration, so we ensure `base_instructions` is set once per
session and should be (mostly) immutable, unless:
- overridden by config on resume / fork
- sub-agent tasks, like review or collab
In a future PR, we should convert all references to `base_instructions`
to consistently used the typed struct, so it's less likely that we put
other strings there. See #9423. However, this PR is already quite
complex, so I'm deferring that to a follow-up.
## Testing
- [x] Added a resume test to assert that instructions are preserved. In
particular, `resume_switches_models_preserves_base_instructions` fails
against main.
Existing test coverage thats assert base instructions are preserved
across multiple requests in a session:
- Manual compact keeps baseline instructions:
core/tests/suite/compact.rs:199
- Auto-compact keeps baseline instructions:
core/tests/suite/compact.rs:1142
- Prompt caching reuses the same instructions across two requests:
core/tests/suite/prompt_caching.rs:150 and
core/tests/suite/prompt_caching.rs:157
- Prompt caching with explicit expected string across two requests:
core/tests/suite/prompt_caching.rs:213 and
core/tests/suite/prompt_caching.rs:222
- Resume with model switch keeps original instructions:
core/tests/suite/resume.rs:136
- Compact/resume/fork uses request 0 instructions for later expected
payloads: core/tests/suite/compact_resume_fork.rs:215
# External (non-OpenAI) Pull Request Requirements
Before opening this Pull Request, please read the dedicated
"Contributing" markdown file or your PR may be closed:
https://github.com/openai/codex/blob/main/docs/contributing.md
If your PR conforms to our contribution guidelines, replace this text
with a detailed and high quality description of your changes.
Include a link to a bug report or enhancement request.
Have only the following Methods:
- `list_models`: getting current available models
- `try_list_models`: sync version no refresh for tui use
- `get_default_model`: get the default model (should be tightened to
core and received on session configuration)
- `get_model_info`: get `ModelInfo` for a specific model (should be
tightened to core but used in tests)
- `refresh_if_new_etag`: trigger refresh on different etags
Also move the cache to its own struct
Historically we started with a CodexAuth that knew how to refresh it's
own tokens and then added AuthManager that did a different kind of
refresh (re-reading from disk).
I don't think it makes sense for both `CodexAuth` and `AuthManager` to
be mutable and contain behaviors.
Move all refresh logic into `AuthManager` and keep `CodexAuth` as a data
object.
We used to override truncation policy by comparing model info vs config
value in context manager. A better way to do it is to construct model
info using the config value
- Merge ModelFamily into ModelInfo
- Remove logic for adding instructions to apply patch
- Add compaction limit and visible context window to `ModelInfo`
With `config.toml`:
```
model = "gpt-5.1-codex"
```
(where `gpt-5.1-codex` has `show_in_picker: false` in
[`model_presets.rs`](https://github.com/openai/codex/blob/main/codex-rs/core/src/models_manager/model_presets.rs);
this happens if the user hasn't used codex in a while so they didn't see
the popup before their model was changed to `show_in_picker: false`)
The upgrade picker used to not show (because `gpt-5.1-codex` was
filtered out of the model list in code). Now, the filtering is done
downstream in tui and app-server, so the model upgrade popup shows:
<img width="1503" height="227" alt="Screenshot 2026-01-06 at 5 04 37 PM"
src="https://github.com/user-attachments/assets/26144cc2-0b3f-4674-ac17-e476781ec548"
/>
This isn't very useful parameter.
logic:
```
if model puts `**` in their reasoning, trim it and visualize the header.
if couldn't trim: don't render
if model doesn't support: don't render
```
We can simplify to:
```
if could trim, visualize header.
if not, don't render
```
# External (non-OpenAI) Pull Request Requirements
Before opening this Pull Request, please read the dedicated
"Contributing" markdown file or your PR may be closed:
https://github.com/openai/codex/blob/main/docs/contributing.md
If your PR conforms to our contribution guidelines, replace this text
with a detailed and high quality description of your changes.
Include a link to a bug report or enhancement request.
pakrym-oai