You don’t need [insert expensive code review agent here]. All you need is Pi.

NobodyWho built an in-house AI code review agent on the open-source Pi coding agent and GitHub Actions instead of paying per-seat for services such as CodeRabbit, Greptile and Macroscope, triggered by a `/ai-review` comment on a pull request. The workflow installs Pi, runs the review with Z.ai's GLM 5.3 model via OpenRouter using a skill file stored at `.agents/skills/ai-review/SKILL.md` on the main branch, posts the review as a PR comment and saves the full transcript as a GitHub Action artifact. NobodyWho said the setup gives it control over model and prompt, bills per review or token rather than per seat, and keeps the whole configuration to one YAML file.

← Back to blog https://www.nobodywho.ai/posts/ You don’t need insert expensive code review agent here . All you need is Pi. Our code review action powered by Pi and GLM 5.3 As we’re shipping out more and more code, it’s becoming increasingly hard to keep up. There’s a whole economy of code-review-as-a-service companies that are tackling that space. CodeRabbit https://www.coderabbit.ai , Greptile https://www.greptile.com , Macroscope https://macroscope.io , are just some of them. These are great, but they are also expensive. Additionally - in most of them, you don’t actually pay per review or lines of code, or even tokens, you pay per seat At NobodyWho https://www.nobodywho.ai , we are all about keeping control. And if we can avoid a fat bill while we are at it, we will. Instead of adopting an expensive and fancy code review service, we just use Pi https://github.com/earendil-works/pi-coding-agent . Need a review in your PR? Just comment /ai-review and an agent will review your code. We control the model, we control the prompt, and we control when and how it runs. Here’s how it works. The core: Code review skill We stole the original review skill from the Earendil folks and their pi-review extension https://github.com/earendil-works/pi-review we are big fans . In our repo it sits right under .agents/skills/ai-review/SKILL.md https://github.com/nobodywho-ooo/nobodywho/blob/main/.agents/skills/ai-review/SKILL.md . Anyone can run that skill locally before they open a Pull Request. It’s a single file with code review instructions that our team can iterate on. One of the things we like the most about it is that it categorizes issues according to priority: P0 fix this now , P1 you should fix it , etc. We are completely in control of what the agent reviews, and how it reviews it. Great - now how do we run it? The engine: GitHub Actions Our GitHub Action https://github.com/nobodywho-ooo/nobodywho/blob/main/.github/workflows/ai-review.yml is also straightforward: 1. It runs whenever someone posts /ai-review as a comment in a PR 2. Adds an ai-review label and acknowledges that it started reviewing the code 3. It gets the details and diff of the PR 4. It installs Pi 5. The code review launches using the Pi CLI, our selected model from OpenRouter https://openrouter.ai we like GLM 5.3 https://openrouter.ai/z-ai/glm-5.3 and using the code review skill from the main branch to avoid prompt injection shenanigans 6. When it’s done it posts the review to the PR https://github.com/nobodywho-ooo/nobodywho/pull/810 issuecomment-6145687451 as a comment 7. Finally, it saves the review as a GitHub Action artifact we can inspect and debug https://github.com/nobodywho-ooo/nobodywho/actions/runs/38407626876 - which uses Pi’s export functionality Inspecting the transcript from an AI code review run Why do we prefer this? - Control : We know what model ran, we know what prompt ran, and we can change those whenever we want according to what we need. - Price: We pay per review or per token. We don’t pay per seat. No subscriptions, no surprises, no rug pulls. - Customizability: Want to test that new model? Go for it. Too verbose? Adjust the skill. Swarm of agents? Go nuts. Want to make it more fancy and have it comment the diff and suggest? Possible - Debuggability: Thanks to GitHub Actions and Pi, we save a full transcript every time we run it. This means we can see exactly what the agent inspected and how it reasoned about it. We could even train our own code review agent in the future given the team’s feedback. - Simple: One YAML file, that’s all you need. We hope some day that these can even run on local models in the future. Some argue we are almost there. And we are all about local models at NobodyWho https://github.com/nobodywho-ooo/nobodywho . Everything NobodyWho does is open-source, please leave a star on GitHub https://github.com/nobodywho-ooo/nobodywho to support us ❤️