Automated Code Review with AI A first pass on every pull request
Every pull request gets a first review within minutes — concrete bugs and risks on the exact lines, not style notes.
- Installed for you · one-time$249
- Run for you$247/month
- Custom build · this process$3,500–6,000 one-time
- Starts whenGitHub / GitLab event (pull request opened or updated)
Covers up to 500 PRs a month across one organisation's repositories. Monorepo-specific context strategies, self-hosted models or custom standards encoded as checks are a custom build. Custom builds start at $2,000 site-wide; this band is for this process and its integrations.
Automated Code Review with AI is an automation Agentmelt installs in CI (GitHub Actions, GitLab CI). The free template is a file you import yourself; the kit ($49) adds the setup guide and prompts. Installed for you costs $249 one-time and is live within two working days of access, with a recorded walkthrough and 30 days of fixes. Run for you costs $247/month, with setup and maintenance included. A custom build of this process is $3,500–6,000 one-time; custom builds start at $2,000 site-wide. No call is needed for any option. Written by Max Zeshut; last revised 2026-09-17.
Reference stack: Claude + n8n. Node names are on each step; the file is under the template.
Founder at Agentmelt
Reviews every build before it goes live; the workflow escalates to a person, never past one.
What is automated code review?
Automated code review adds a consistent first pass to every pull request: a workflow reads the diff with its surrounding context and the linked issue, checks it against your written standards, posts inline comments only for concrete problems — bugs, security issues, missing tests, breaking changes — classifies the risk, and leaves a summary for the human reviewer, who still approves. It shortens the wait for a first review from hours to minutes and keeps the human review for design and judgement.
- Pull requests wait a day for a first review, and the reviewer's first comments are style and missing tests.
- Review quality depends on who is on call; the standards live in people's heads.
- Security and breaking-change checks happen at the end, when they are expensive.
- Junior developers get slow, inconsistent feedback; seniors spend their week reviewing.
- The CI passes and the bug still ships, because nobody read the diff in context.
If two of these are yours, this is the process to look at first. Free audit — or read on.
What it looks like once it runs.
Automated code review adds a consistent first pass to every pull request: the workflow reads the diff with the surrounding context, checks it against your written standards and the linked issue, posts inline comments only for concrete problems (bugs, security, missing tests, breaking changes), classifies the PR's risk, and writes the summary a human reviewer reads before opening the files. Reviewers spend their time on design, not on the things a checklist catches.
- 14:02 A pull request opens. The diff, the changed files and the linked issue are read; CI's lint and type results are collected so nothing is repeated.
- 14:04 The change is analysed: does it do what the issue asks, where could it break, what did it miss.
- 14:05 Only confident findings are posted as inline comments, with a summary of what the PR does and its risk.
- For high-risk changes A human reviewer is required before merge.
Review is the bottleneck and the quality of a review depends on who is free. Small PRs wait a day for a rubber stamp; large PRs get skimmed. Standards live in a wiki nobody reads during review, and the same mistakes are caught — or missed — repeatedly.
Every PR gets a substantive first review within minutes of opening: specific comments with the reasoning, a risk label, and a summary of what changed and what to look at. Human reviewers pick up PRs already triaged, review time per PR falls, and the standards document is finally enforced on every change.
Trigger, then 8 steps.
All eight steps run without anyone; a person sees only what the guardrails escalate.
GitHub / GitLab event (pull request opened or updated)
Receive the PR event
Repository, PR number, author, base and head, changed files, linked issue. PRs from bots or with a skip label are ignored.
Fetch the diff and context
The unified diff plus the full content of changed files and the definitions they reference, the linked issue or ticket text, and the repo's CONTRIBUTING and standards docs.
Run static checks
Linters, type checks and secret scanning results from CI are collected so the model does not repeat what tooling already reported.
Analyse the change
With the diff, context, standards and issue: does the change do what the issue asks; concrete bugs or edge cases; security issues (injection, auth, secrets, unsafe deserialisation); breaking API or schema changes; missing or weak tests; deviations from the written standards. Output is structured: findings with file, line, severity, explanation and a suggested fix.
Filter to what matters
Findings below a confidence threshold or classed as style-only are dropped or folded into the summary; only concrete, explained issues become inline comments. Comment volume per PR is capped.
Post inline comments and summary
Review comments on the exact lines via the GitHub/GitLab API, plus a summary comment: what the PR does, risk level, test coverage of the change, and the two or three things a human should look at.
Label and route
Risk label (low/medium/high), area labels, and — for high-risk changes touching auth, payments or data — a required reviewer from the CODEOWNERS group.
Learn from resolutions
Weekly: which comments were resolved, dismissed or replied 'not an issue'; precision per finding type is reported and thresholds adjusted; recurring findings become proposals for a lint rule.
No step waits for a person.
What the workflow hands to someone, by rule:
- The workflow never approves or merges; it reviews and labels. A human approval remains required.
- High-risk areas require a named human reviewer regardless of the automated verdict.
Every PR gets a substantive first review within minutes of opening: specific comments with the reasoning, a risk label, and a summary of what changed and what to look at
Every PR gets a substantive first review within minutes of opening: specific comments with the reasoning, a risk label, and a summary of what changed and what to look at.
No decisions needed. Anything the guardrails flag is handed to a person; the rest ran alone.
Each pattern page explains it in plain words and lists every automation that uses it.
The template is built on n8n (how the n8n AI Agent node works); the same steps run on Make, Zapier or plain code. The tool is chosen to fit your accounts, you own the result, and the template comes in all three formats.
Data it touches
- Git host (GitHub, GitLab, Bitbucket) PR events and API
- Repository standards docs and CODEOWNERS
- Issue tracker (linked ticket text)
- CI results (lint, types, tests, secret scanning)
Guardrails
- The workflow never approves or merges; it reviews and labels. A human approval remains required.
- Comments are posted only for concrete findings with reasoning; style nits go to the summary or nowhere.
- Code is sent to the model provider under your data-processing terms; repositories can be excluded, and self-hosted models are an option for sensitive code.
- High-risk areas require a named human reviewer regardless of the automated verdict.
What an automated first pass should and should not do
It should catch the things that are checkable: the change does not match the issue, a null path is unhandled, an input reaches a query unescaped, a public function's signature changed without a version bump, a new branch has no test. It should not restyle code, argue about naming, or post fifteen comments on a ten-line PR. The filter step is the most important design element: precision beats recall for review comments, because a noisy reviewer is muted within a week.
Context is what separates useful from generic
A model looking at a diff alone produces generic comments. Given the full changed files, the definitions they call, the linked issue and the team's standards document, it produces comments a senior engineer would: 'this retries on a non-idempotent call', 'the issue asks for pagination and this returns everything'. Assembling that context is what the workflow does before the model sees anything, and it is why the same model performs very differently in a well-built workflow than in a browser tab.
Measuring it honestly
Track comment precision (resolved or acknowledged vs dismissed) per finding type, time from PR open to first human review, and defects found in review vs found in production for changes that went through the workflow. If precision on a finding type is below about 70%, raise its threshold or drop it. The weekly report surfaces recurring findings that should become lint rules — the best outcome is a finding type the workflow no longer needs to make.
What it connects to.
| Tool | Role in this workflow |
|---|---|
| n8n | Event handling, context assembly, posting, feedback loop |
| Claude | Change analysis with structured findings |
| GitHub / GitLab | PRs, comments, labels |
| CI (GitHub Actions, GitLab CI) | Static checks and tests |
| Linear / Jira | Linked issue context |
Want this running without building it?
$247/month
We set it up in your tools, run it on our servershost it on our n8n cloud (or deploy it to your own n8n, Make or Zapier), watch it, and fix it when something changes. Setup included, cancel monthly; the automation stays yours if you leave.
$3,500–6,000 one-time
Your systems, your rules, your edge cases. A one-off build delivered into your accounts to keepon Claude and n8n, delivered into your accounts to keep, with documentation and a recorded walkthrough.
Covers up to 500 PRs a month across one organisation's repositories. Monorepo-specific context strategies, self-hosted models or custom standards encoded as checks are a custom build.
Also sold one-time, installed in your own accounts, as part of the developers package and the engineering teams package — do it yourself $49 · installed for you $249 · done for you $490.
When this becomes a custom build.
The subscription covers the standard version — $247/month, setup and maintenance included. Three things move it to a custom build ($3,500–6,000 one-time), delivered into your accounts to keep. If one of them is you, say so on the audit and the plan prices both.
A monorepo that needs its own context strategy so reviews stay within the changed packages.
Self-hosted models because source code cannot leave your network.
Your engineering standards encoded as enforceable checks, not just review comments.
Running the standard version first is the usual path: the exceptions it logs in the first month are the scope of the custom build, with your own numbers. How custom builds work.
Frequently asked questions.
Will it approve pull requests?
No. It reviews, comments, labels and can request a specific human reviewer. Approval and merge remain human actions.
Is our code sent to a third party?
The diff and context are sent to the model provider under your data-processing agreement (Anthropic's API does not train on your data). Sensitive repositories can be excluded, and a self-hosted model is available as a custom option.
How is this different from GitHub Copilot review or CodeRabbit?
Those are good generic reviewers. The workflow is built around your standards, your issue tracker and your CI output, with a filter tuned on your team's dismissals, and it runs in your own account where you can change any step.
What does automated AI code review catch that linters and CI do not?
Things that need the surrounding context: a null path a test never hits, an API change that breaks a caller in another package, a query inside a loop, a secret in a config, a missing migration, a change that contradicts the linked issue. Linters see syntax; CI sees what the tests cover; the review reads the diff the way a careful colleague would.
How do you monitor an autonomous coding agent so it does not make things worse?
Give it comment rights, not merge rights; log every comment with the rule or evidence it cited; measure the accept rate per comment type and turn off the types reviewers keep dismissing; and keep the human approval on every merge. This workflow is built that way — it never approves, and its tuning report is part of the product.
What this blueprint is built on.
Vendor documentation the steps rely on, and the rules the guardrails are written against.
- GitHub — REST API: pull request reviews docs.github.com/en/rest/pulls/reviews
- GitHub — Actions documentation docs.github.com/en/actions
- n8n docs — AI Agent node docs.n8n.io/integrations/builtin/cluster-nodes/root-nodes/n8n-nodes-langchain.agent/