PR-AF is an open-source code reviewer built around a pipeline rather than a prompt: turn each pull request into a task-specific review plan, spawn focused reviewer agents, ground findings in code evidence, then challenge the results. Its stated aim is to squeeze more useful review out of cheaper models, and it maps model class to review stakes — DeepSeek-class models for routine pull requests, GLM-5.2 for deep open-model reviews, frontier Opus-class models for major ones.
The reported numbers are unusually specific for this category: 0.706 golden recall, described as first among 42 compared open-source and commercial tools, and 595 independently valid findings, roughly three times the leading commercial tools in an adjusted comparison. It publishes public results, per-pull-request judge verdicts and reproduction scripts, runs locally in Docker, and can be triggered by CLI, curl, CI or another agent.
Challenging your own findings is the part that matters, because an unchallenged reviewer optimises for volume and a human then pays for every false positive. That is the constraint recorded in verification bandwidth being the scarce resource and observed where a three-person team shipping hundreds of pull requests moved the bottleneck into review. The coding agents hub tracks the review layer.
The benchmark is Code-Review-Bench and the results are self-reported by the tool's authors, with the adjusted comparison defined by them. The condition to watch is whether an independent run reproduces the recall figure on unseen pull requests, because a reviewer evaluated on a public benchmark faces exactly the optimisation problem its own challenge step is meant to catch.