PR Review Bots for GitHub: 6 Compared With Pricing (2026)

A PR review bot is a GitHub App that comments on pull requests automatically. For AI review, CodeRabbit and Greptile are the main picks, and Copilot code review is easiest if you already pay for Copilot. For repeatable, rule-based comments, use Reviewdog or the repowise bot, which stays silent on clean pull requests.

The rest of this post explains the reasoning behind each of those picks. If you are deciding between AI and rule-based review in general, read deterministic vs LLM code review first.

The number of comments a bot writes says little about its value. The good ones catch real problems, stay out of the way on clean PRs, and keep review threads readable a month later. So I judge them on output quality, comment discipline, repo context, price at your team size, and whether the bot fits your existing review flow without turning every pull request into a wall of text. Installing as a GitHub App also helps, because GitHub Apps get scoped repository access and a cleaner install than older OAuth setups. (docs.github.com)

The growth of PR bots and the noise problem

The category grew fast because teams want a second set of eyes on every PR before a human reviewer spends time on something mechanical, such as style issues, security smells, missing tests, dependency risk and obvious logic mistakes. Many bots fall into the same trap: they produce comments even when they have nothing useful to say. The result is review fatigue, where people stop reading the bot, so the useful bots are strict about commenting only when they have a real finding. (codacy.com)

Teams also now expect a PR review bot to understand the repository around the diff, and vendors advertise context-aware PR feedback, linked repository analysis and codebase-aware review engines to meet that. Codacy says its AI reviewer combines deterministic analysis with context-aware reasoning, while CodeRabbit positions itself around PR reviews plus linked repo analysis and MCP support. (codacy.com)

PR Bot Signal vs Noise

Traits of a useful bot

A good automated PR review tool stays quiet when the diff is clean, comments on the changed lines, avoids repeating the same feedback across pushes, and gives reviewers a concrete path from the problem to the fix. (docs.coderabbit.ai)

Silence-on-green default

Silence on green (no comment when every check passes) is the first test, because a bot that comments on every PR no matter what is a bot your team stops reading. CodeRabbit's docs say it re-reviews after each new push and focuses on commits added since the last review, which keeps comment volume under control. Codacy's docs say its AI-enhanced comments are added to new pull requests and code reviews according to configured scope, and that scope setting is how you stop a bot from reaching beyond the PR. (docs.coderabbit.ai)

Deterministic vs LLM-generated comments

Deterministic bots take rules, linters or static analysis results and map them to PR feedback. Reviewdog is the clearest example: it turns linter output into PR comments or checks, and it supports github-pr-review comments directly. LLM-based bots read more of the codebase and can explain a bug in plain English, but they need guardrails. A hybrid model combines the two, with deterministic checks for known classes of issues and an LLM for context and hidden coupling. (github.com)

Editing comments instead of adding new ones

A bot should update or resolve its own feedback when a follow-up commit fixes the issue, because stale comments pile up fast when a bot cannot manage its own threads. Reviewdog has open issues about resolving review threads across runs, which shows how common that problem is. Greptile's config exposes statusCheck and triggerOnUpdates settings for the same reason: they tie review behavior to the PR lifecycle, so the bot reacts to each update. (github.com)

1. repowise PR bot: deterministic by default, free for public repos

I build repowise, so weigh this section accordingly. repowise's PR bot reads from a codebase intelligence layer, so each review draws on file docs, ownership, dependency paths and git history as well as the patch itself. That context matters because bad reviews often come from what the reviewer could not see: a change that touches a hotspot, crosses a hidden dependency boundary or edits code owned by another team. The bot itself is a hosted GitHub App. The engine underneath ships as open source under AGPL-3.0, and its coverage, doc-drift, security and change-risk gates run as a GitHub Action in your own CI. (gnu.org)

The main advantage is predictability. In its default Signals mode the comment is built from the index with no model call, so the same diff always gets the same comment, a clean pull request gets no comment at all, and it is free on public repositories. It keeps one comment per pull request up to date, can mark findings on diff lines, and can add a health check that branch protection can require. Private repositories and the two optional AI review modes need Pro ($15 a month) or Teams ($20 per seat per month). That makes it fit teams that want automated PR review without per-seat AI review fees or comments that change from run to run. The silent-by-default write-up explains why it says nothing on most pull requests. (repowise.dev)

The same index also powers docs, graphs and MCP tools beyond PR comments. See what repowise generates on real repos in our live examples, or check repowise's architecture and how the MCP server fits in. If you want to see the output shape before you install anything, the FastAPI architecture view and generated FastAPI docs show the layer the bot is reading from.

The repowise bot on a repowise-dev/repowise pull request: three things to check and the health change of the files it touches, with no model in the loop.

Deterministic Bot Architecture

2. CodeRabbit

CodeRabbit is the most polished general-purpose AI code review bot in this group. Reviews are free forever on public repositories. Paid plans, billed yearly, are Essentials at $24 per developer per month (AI reviews on PRs and in the CLI, 5 PR reviews per developer per hour), Team at $48 (custom pre-merge checks, generated unit tests, merge conflict help) and Advanced at $72 (blast radius and architectural impact analysis, security review of every PR). Enterprise adds self-hosting, SSO and audit logs. (coderabbit.ai)

The pricing shows where the value sits: the cross-file context, which is what makes a review bot useful on a large repo, is on the most expensive tier.

CodeRabbit stands out on review cadence. Its docs say it re-reviews a PR after each new push and focuses on the commits added since the last review, so feedback arrives in small increments and old comments are not repeated on the same line. It supports GitHub, GitHub Enterprise Cloud and GitHub Enterprise Server. (docs.coderabbit.ai)

This is the bot I would pick when a team wants broad language support, a strong UI and onboarding it can do without a sales call. Self-hosting needs the Enterprise plan, and teams that want a deterministic-only workflow should look elsewhere. For teams with strict review rules, the useful question is whether the bot stays quiet and predictable, and CodeRabbit gets part of the way there by tying feedback to new commits. For a fuller side-by-side, read the CodeRabbit comparison. (docs.coderabbit.ai)

3. Greptile PR review

Greptile is the strongest "codebase-aware" competitor in this set. Its code review bot docs say it reviews pull requests in GitHub and GitLab "with full context of your codebase," and it supports on-demand review by comment trigger as well as repo-level enablement. Pricing is credit-based: a free Starter plan for one active developer with 50 credits a month, Pro at $30 per seat per month, and $1 per extra credit. A base review costs 1 credit and the deeper review modes cost 3 or 10. Qualified non-commercial projects with an OSI-approved license get it free. (greptile.com)

Deployment is the other reason to evaluate Greptile. Its Enterprise plan can be self-hosted, and its docs say that includes air-gapped environments and GitHub Cloud, GitHub Enterprise Cloud and GitHub Enterprise Server, which matters for teams that cannot send source code off their own servers. It also has custom instructions and trigger rules, so reviewers can shape its behavior without forking the product. (greptile.com)

The tradeoff is cost predictability. Credit pricing means a busy month with many large pull requests costs more than a quiet one, so model it against your PR volume before you commit (more in the Greptile comparison). (greptile.com)

4. GitHub Copilot code review

If your team already pays for Copilot, this is the PR bot you already have. You can request Copilot as a reviewer on a pull request, or set a rule so it reviews every PR. It is available on Copilot Pro, Pro+ and Max for individuals and on Business and Enterprise for organizations, not on Copilot Free. (docs.github.com)

Billing changed in June 2026. Each review now spends AI credits, which GitHub estimates at $0.05 to $1 per review on its "Lite" effort setting and $0.25 to $5 on "Balanced", and the agentic part (gathering context, calling tools) also uses GitHub Actions minutes. (docs.github.com)

Copilot code review needs no setup and no new vendor, but the bill now scales with how many pull requests you open and how big they are, which is harder to predict than a per-seat price. It is a reasonable first bot to try before buying a separate one.

5. Codacy

Codacy is a code quality platform that has added AI review. Its AI reviewer page says it provides context-aware pull request feedback through a hybrid code review engine, and its docs say AI-enhanced comments are added to new pull requests and code reviews. The AI Reviewer is included in the Team plan at $18 per developer per month billed yearly ($21 monthly), which also covers up to 100 private repos, coverage reports and merge rules. Open-source projects are free. (codacy.com)

Codacy is worth a look if your team wants code quality gates, security scans and PR feedback in one system. The product is broader than a PR bot, which helps if you want one vendor for scans and review comments, but if you mainly want a concise code review bot, the platform can feel heavier than necessary. Our Codacy comparison looks at when that platform weight pays off. (codacy.com)

In March 2026, Codacy said it was deprecating AI-enhanced comments beta in favor of its AI Reviewer and consolidating coverage data into pull request review comments. The change points toward cleaner review output, with fewer scattered notes and a single review thread. (docs.codacy.com)

6. Reviewdog

Reviewdog is an open-source tool that connects linters and analyzers to GitHub PR comments or checks, with no AI model involved. The project supports github-pr-review, GitHub Checks, GitHub annotations, GitLab discussions and more. If you already have static analysis in CI, reviewdog can turn its findings into PR review comments without adding a new intelligence layer. (github.com)

That makes reviewdog a good fit when you want full control over the findings and the delivery channel. It also supports code suggestions with the github-pr-review reporter, so a comment can include the suggested fix next to the warning. Its limit is that it only knows what your analyzers know: if the bug is architectural, spans files or comes from a dependency, reviewdog has no extra context to add. (github.com)

Side-by-Side Bot Comparison

Side-by-side feature table

How to pick

1. Pick repowise if you want deterministic PR comments and a review bot that can see ownership, dependency paths, and hotspot data before it speaks. It fits teams that want consistent comments and a bot that stays quiet unless it has something specific to say. Install it on a public repo and the next pull request shows what it does; repowise for teams covers org-wide installs on private code. (repowise.dev)

2. Pick CodeRabbit if you want the strongest general-purpose AI reviewer, with a well-built product and broad integrations. It is the easiest choice for teams that want a hosted tool, and it is free on public repos. (coderabbit.ai)

3. Pick Greptile if you want AI review with whole-codebase context, or need it self-hosted. Its documentation is clear about on-prem deployment and GitHub Enterprise support. (greptile.com)

4. Pick Copilot code review if you already pay for Copilot and want to try a bot without adding a vendor. Watch the AI credit spend for a month before you turn it on for every PR. (docs.github.com)

5. Pick Codacy if you already use Codacy for quality and security and want AI review inside that workflow instead of in a separate bot. (codacy.com)

6. Pick reviewdog if your team already trusts its linters and SAST (static application security testing) tools and wants a simple way to show their findings in PRs. It is the cleanest open-source path for rule-based automated PR review. (github.com)

A simple rule works well in practice: choose an AI reviewer for judgment, a deterministic bot for repeatability, and a hybrid setup that keeps the deterministic layer visible when you need both. That is why many teams end up mixing tools instead of standardizing on one. (codacy.com)

Final pick

For the least noise, start with a deterministic bot: Reviewdog if your linters already know what matters, the repowise bot if you want comments built from the repository's graph and history. If you want an AI reader on every change, CodeRabbit and Greptile lead the hosted pack, and Copilot code review is the cheapest experiment for a team already on Copilot. Codacy fits if you already live inside a quality platform. In every case, the deciding question is whether the bot keeps its comments sparse, current and tied to the code you run, and the model behind it matters less.