Every pull request your team opens, Codeflash benchmarks the changed code, catches regressions before they merge, and posts a faster rewrite with the numbers to prove it. Your engineers see it in the same PR they're already reviewing. No new workflow.
"After installing Codeflash in the GitHub Pull Request code review stage, it tries to optimize every new code we write. With that, I can be more confident that our engineers are shipping more optimized code every time."
A one-time optimization engagement cuts your bill. But new code ships every week, and most of it has never been profiled. Within a year, the gains are gone. Continuous Optimization closes that loop. The same agent that found your bottlenecks now watches every PR going forward.
The agent identifies which functions changed in the diff and selects representative inputs based on prior execution traces.
Runs the old and new version on isolated hardware. The result is only reported if it's statistically significant.
If a faster equivalent exists, the agent writes it and verifies correctness against your test suite before surfacing it.
Posts directly on the PR with before/after numbers and a one-click patch. Your engineers decide whether to apply it.
Check runs and inline comments on every pull request. You can configure it to block merges on regressions, or keep it advisory.
The plugin surfaces optimization suggestions as your engineers write code, before a PR is even opened.
Regressions and rewrites surface without leaving the editor. The feedback loop tightens to the moment of authorship.
If your team uses Codex as their primary agent, the Codeflash plugin runs the same benchmarking and rewrite loop inside it.
No. Benchmarks run on our hardware, out-of-band. Your CI pipeline just reads the result from us. There's no compute overhead on your side.
Only if you configure it to. The default is advisory: it posts the benchmark and patch as a comment, and your engineers decide whether to apply it. You can enable merge-blocking on regressions for critical paths.
Python, Java, JavaScript, TypeScript, Go, and more.
On Pro, never. Not ours, not any third party's. On the free plan your code is public by definition, so we make no training restriction on public code.
AI coding tools suggest changes based on what they can see in the file. Codeflash runs an actual benchmark before and after, verifies correctness against your test suite, and only surfaces a rewrite if the numbers prove it's faster. The difference is measurement versus intuition.
Continuous Optimization pairs with an Optimization Engagement. The engagement cuts the bill; this keeps it there.