## Summary - enforce a 10 MiB cap per `thread_id` in state log storage - enforce a 10 MiB cap per `process_uuid` for threadless (`thread_id IS NULL`) logs - scope pruning to only keys affected by the current insert batch - add a cheap per-key `SUM(...)` precheck so windowed prune queries only run for keys that are currently over the cap - add SQLite indexes used by the pruning queries - add focused runtime tests covering both pruning behaviors ## Why This keeps log growth bounded by the intended partition semantics while preserving a small, readable implementation localized to the existing insert path. ## Local Latency Snapshot (No Truncation-Pressure Run) Collected from session `019c734f-1d16-7002-9e00-c966c9fbbcae` using local-only (uncommitted) instrumentation, while not specifically benchmarking the truncation-heavy regime. ### Percentiles By Query (ms) | query | count | p50 | p90 | p95 | p99 | max | |---|---:|---:|---:|---:|---:|---:| | `insert_logs.insert_batch` | 110 | 0.332 | 0.999 | 1.811 | 2.978 | 3.493 | | `insert_logs.precheck.process` | 106 | 0.074 | 0.152 | 0.206 | 0.258 | 0.426 | | `insert_logs.precheck.thread` | 73 | 0.118 | 0.206 | 0.253 | 1.025 | 1.025 | | `insert_logs.prune.process` | 58 | 0.291 | 0.576 | 0.607 | 1.088 | 1.088 | | `insert_logs.prune.thread` | 44 | 0.318 | 0.467 | 0.728 | 0.797 | 0.797 | | `insert_logs.prune_total` | 110 | 0.488 | 0.976 | 1.237 | 1.593 | 1.684 | | `insert_logs.total` | 110 | 1.315 | 2.889 | 3.623 | 5.739 | 5.961 | | `insert_logs.tx_begin` | 110 | 0.133 | 0.235 | 0.282 | 0.412 | 0.546 | | `insert_logs.tx_commit` | 110 | 0.259 | 0.689 | 0.772 | 1.065 | 1.080 | ### `insert_logs.total` Histogram (ms) | bucket | count | |---|---:| | `<= 0.100` | 0 | | `<= 0.250` | 0 | | `<= 0.500` | 7 | | `<= 1.000` | 33 | | `<= 2.000` | 40 | | `<= 5.000` | 28 | | `<= 10.000` | 2 | | `<= 20.000` | 0 | | `<= 50.000` | 0 | | `<= 100.000` | 0 | | `> 100.000` | 0 | ## Local Latency Snapshot (Truncation-Heavy / Cap-Hit Regime) Collected from a run where cap-hit behavior was frequent (`135/180` insert calls), using local-only (uncommitted) instrumentation and a temporary local cap of `10_000` bytes for stress testing (not the merged `10 MiB` cap). ### Percentiles By Query (ms) | query | count | p50 | p90 | p95 | p99 | max | |---|---:|---:|---:|---:|---:|---:| | `insert_logs.insert_batch` | 180 | 0.524 | 1.645 | 2.163 | 3.424 | 3.777 | | `insert_logs.precheck.process` | 171 | 0.086 | 0.235 | 0.373 | 0.758 | 1.147 | | `insert_logs.precheck.thread` | 100 | 0.105 | 0.251 | 0.291 | 1.176 | 1.622 | | `insert_logs.prune.process` | 109 | 0.386 | 0.839 | 1.146 | 1.548 | 2.588 | | `insert_logs.prune.thread` | 56 | 0.253 | 0.550 | 1.148 | 2.484 | 2.484 | | `insert_logs.prune_total` | 180 | 0.511 | 1.221 | 1.695 | 4.548 | 5.512 | | `insert_logs.total` | 180 | 1.631 | 3.902 | 5.103 | 8.901 | 9.095 | | `insert_logs.total_cap_hit` | 135 | 1.876 | 4.501 | 5.547 | 8.902 | 9.096 | | `insert_logs.total_no_cap_hit` | 45 | 0.520 | 1.700 | 2.079 | 3.294 | 3.294 | | `insert_logs.tx_begin` | 180 | 0.109 | 0.253 | 0.287 | 1.088 | 1.406 | | `insert_logs.tx_commit` | 180 | 0.267 | 0.813 | 1.170 | 2.497 | 2.574 | ### `insert_logs.total` Histogram (ms) | bucket | count | |---|---:| | `<= 0.100` | 0 | | `<= 0.250` | 0 | | `<= 0.500` | 16 | | `<= 1.000` | 39 | | `<= 2.000` | 60 | | `<= 5.000` | 54 | | `<= 10.000` | 11 | | `<= 20.000` | 0 | | `<= 50.000` | 0 | | `<= 100.000` | 0 | | `> 100.000` | 0 | ### `insert_logs.total` Histogram When Cap Was Hit (ms) | bucket | count | |---|---:| | `<= 0.100` | 0 | | `<= 0.250` | 0 | | `<= 0.500` | 0 | | `<= 1.000` | 22 | | `<= 2.000` | 51 | | `<= 5.000` | 51 | | `<= 10.000` | 11 | | `<= 20.000` | 0 | | `<= 50.000` | 0 | | `<= 100.000` | 0 | | `> 100.000` | 0 | ### Performance Takeaways - Even in a cap-hit-heavy run (`75%` cap-hit calls), `insert_logs.total` stays sub-10ms at p99 (`8.901ms`) and max (`9.095ms`). - Calls that did **not** hit the cap are materially cheaper (`insert_logs.total_no_cap_hit` p95 `2.079ms`) than cap-hit calls (`insert_logs.total_cap_hit` p95 `5.547ms`). - Compared to the earlier non-truncation-pressure run, overall `insert_logs.total` rose from p95 `3.623ms` to p95 `5.103ms` (+`1.48ms`), indicating bounded overhead when pruning is active. - This truncation-heavy run used an intentionally low local cap for stress testing; with the real 10 MiB cap, cap-hit frequency should be much lower in normal sessions. ## Testing - `just fmt` (in `codex-rs`) - `cargo test -p codex-state` (in `codex-rs`) |
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| .devcontainer | ||
| .github | ||
| .vscode | ||
| codex-cli | ||
| codex-rs | ||
| docs | ||
| patches | ||
| scripts | ||
| sdk/typescript | ||
| shell-tool-mcp | ||
| third_party | ||
| .bazelignore | ||
| .bazelrc | ||
| .bazelversion | ||
| .codespellignore | ||
| .codespellrc | ||
| .gitignore | ||
| .markdownlint-cli2.yaml | ||
| .npmrc | ||
| .prettierignore | ||
| .prettierrc.toml | ||
| AGENTS.md | ||
| announcement_tip.toml | ||
| BUILD.bazel | ||
| CHANGELOG.md | ||
| cliff.toml | ||
| defs.bzl | ||
| flake.lock | ||
| flake.nix | ||
| justfile | ||
| LICENSE | ||
| MODULE.bazel | ||
| MODULE.bazel.lock | ||
| NOTICE | ||
| package.json | ||
| pnpm-lock.yaml | ||
| pnpm-workspace.yaml | ||
| rbe.bzl | ||
| README.md | ||
npm i -g @openai/codex
or brew install --cask codex
Codex CLI is a coding agent from OpenAI that runs locally on your computer.
If you want Codex in your code editor (VS Code, Cursor, Windsurf), install in your IDE.
If you want the desktop app experience, run
codex app or visit the Codex App page.
If you are looking for the cloud-based agent from OpenAI, Codex Web, go to chatgpt.com/codex.
Quickstart
Installing and running Codex CLI
Install globally with your preferred package manager:
# Install using npm
npm install -g @openai/codex
# Install using Homebrew
brew install --cask codex
Then simply run codex to get started.
You can also go to the latest GitHub Release and download the appropriate binary for your platform.
Each GitHub Release contains many executables, but in practice, you likely want one of these:
- macOS
- Apple Silicon/arm64:
codex-aarch64-apple-darwin.tar.gz - x86_64 (older Mac hardware):
codex-x86_64-apple-darwin.tar.gz
- Apple Silicon/arm64:
- Linux
- x86_64:
codex-x86_64-unknown-linux-musl.tar.gz - arm64:
codex-aarch64-unknown-linux-musl.tar.gz
- x86_64:
Each archive contains a single entry with the platform baked into the name (e.g., codex-x86_64-unknown-linux-musl), so you likely want to rename it to codex after extracting it.
Using Codex with your ChatGPT plan
Run codex and select Sign in with ChatGPT. We recommend signing into your ChatGPT account to use Codex as part of your Plus, Pro, Team, Edu, or Enterprise plan. Learn more about what's included in your ChatGPT plan.
You can also use Codex with an API key, but this requires additional setup.
Docs
This repository is licensed under the Apache-2.0 License.