Continuation of breaking up this PR
https://github.com/openai/codex/pull/9116
## Summary
- Thread user text element ranges through TUI/TUI2 input, submission,
queueing, and history so placeholders survive resume/edit flows.
- Preserve local image attachments alongside text elements and rehydrate
placeholders when restoring drafts.
- Keep model-facing content shapes clean by attaching UI metadata only
to user input/events (no API content changes).
## Key Changes
- TUI/TUI2 composer now captures text element ranges, trims them with
text edits, and restores them when submission is suppressed.
- User history cells render styled spans for text elements and keep
local image paths for future rehydration.
- Initial chat widget bootstraps accept empty `initial_text_elements` to
keep initialization uniform.
- Protocol/core helpers updated to tolerate the new InputText field
shape without changing payloads sent to the API.
## 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
### Motivation
- Persist richer per-turn configuration in rollouts so resumed/forked
sessions and tooling can reason about the exact instruction inputs and
output constraints used for a turn.
### Description
- Extend `TurnContextItem` to include optional `base_instructions`,
`user_instructions`, and `developer_instructions`.
- Record the optional `final_output_json_schema` associated with a turn.
- Add an optional `truncation_policy` to `TurnContextItem` and populate
it when writing turn-context rollout items.
- Introduce a protocol-level `TruncationPolicy` representation and
convert from core truncation policy when recording.
### Testing
- `cargo test -p codex-protocol` (pass)
This reverts commit c2ec477d93.
# External (non-OpenAI) Pull Request Requirements
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"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.
- This PR is to make it on path for truncating by tokens. This path will
be initially used by unified exec and context manager (responsible for
MCP calls mainly).
- We are exposing new config `calls_output_max_tokens`
- Use `tokens` as the main budget unit but truncate based on the model
family by Introducing `TruncationPolicy`.
- Introduce `truncate_text` as a router for truncation based on the
mode.
In next PRs:
- remove truncate_with_line_bytes_budget
- Add the ability to the model to override the token budget.
This PR does the following:
- Add compact prefix to the summary
- Change the compaction prompt
- Allow multiple compaction for long running tasks
- Filter out summary messages on the following compaction
Considerations:
- Filtering out the summary message isn't the most clean
- Theoretically, we can end up in infinite compaction loop if the user
messages > compaction limit . However, that's not possible in today's
code because we have hard cap on user messages.
- We need to address having multiple user messages because it confuses
the model.
Testing:
- Making sure that after compact we always end up with one user message
(task) and one summary, even on multiple compaction.
charley-oai