## Why
Memory startup used hardcoded OpenAI model slugs for extraction and
consolidation. That works for the default OpenAI-compatible path, but
provider-specific backends can require different model identifiers. In
particular, Amazon Bedrock should use its Bedrock model ID for these
background memory requests instead of the OpenAI `gpt-5.4-mini` /
`gpt-5.4` slugs.
## What Changed
- Added provider-owned preferred memory model methods alongside
`approval_review_preferred_model`.
- Updated memory extraction and consolidation to resolve their default
model through the active `ModelProvider`.
- Added Amazon Bedrock overrides so both memory stages use
`openai.gpt-5.4` through Bedrock’s provider-specific model ID.
- Kept explicit `memories.extract_model` and
`memories.consolidation_model` config overrides taking precedence.
- Added startup coverage for default OpenAI and Bedrock memory model
selection.
#closes #26288
## 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>
## Why
Memory startup runs in the background after an eligible turn, but it can
consume Codex backend quota at exactly the wrong time: when the user is
already near a rate-limit boundary. This PR adds a guard so the memory
pipeline backs off when the Codex rate-limit snapshot says the remaining
budget is too low.
## What Changed
- Added `memories.min_rate_limit_remaining_percent` with a default of
`25`, clamped to `0..=100`, and regenerated `core/config.schema.json`.
- Added `codex-rs/memories/write/src/guard.rs`, which fetches Codex
backend rate limits before memory startup and skips phase 1 / phase 2
when the Codex limit is reached or either tracked window is above the
configured usage ceiling.
- Keeps startup best-effort: non-Codex auth or rate-limit fetch/client
failures preserve the existing memory startup behavior.
- Records a `codex.memory.startup` counter with
`status=skipped_rate_limit` when startup is skipped.
- Added config parsing/clamping coverage and guard unit tests.
## Verification
- Added `codex-rs/memories/write/src/guard_tests.rs` for threshold,
primary/secondary window, and reached-limit behavior.
- Added config tests for TOML parsing and clamping.
Keep extracting memories out of core and moving the write trigger in the
app-server
This is temporary and it should move at the client level as a follow-up
This makes core fully independant from `codex-memories-write`
---------
Co-authored-by: Codex <noreply@openai.com>
Celia Chen ·
Charlie Marsh ·
jif-oai ·