This article provides a complete, practical walkthrough of the Splitout Code Automation Webhook n8n agent. It connects HTTP Request, Webhook across approximately 1 node(s). Expect a Intermediate setup in 15-45 minutes. One‑time purchase: €29.

What This Agent Does

This agent orchestrates a reliable automation between HTTP Request, Webhook, handling triggers, data enrichment, and delivery with guardrails for errors and rate limits.

It streamlines multi‑step processes that would otherwise require manual exports, spreadsheet cleanup, and repeated API requests. By centralizing logic in n8n, it reduces context switching, lowers error rates, and ensures consistent results across teams.

Typical outcomes include faster lead handoffs, automated notifications, accurate data synchronization, and better visibility via execution logs and optional Slack/Email alerts.

How It Works

The workflow uses standard n8n building blocks like Webhook or Schedule triggers, HTTP Request for API calls, and control nodes (IF, Merge, Set) to validate inputs, branch on conditions, and format outputs. Retries and timeouts improve resilience, while credentials keep secrets safe.

Third‑Party Integrations

• HTTP Request
• Webhook

Import and Use in n8n

1. Open n8n and create a new workflow or collection.
2. Choose Import from File or Paste JSON.
3. Paste the JSON below, then click Import.
4. Show n8n JSON

   Title:
   Automating GitLab Code Reviews with AI Using n8n and OpenAI
   
   Meta Description:
   Discover how to automate and streamline GitLab code reviews using an n8n workflow, OpenAI’s language model, and GitLab’s REST API. Increase your code quality and reduce manual overhead with smart, markdown-formatted suggestions.
   
   Keywords:
   n8n automation, GitLab API, AI code review, OpenAI, LangChain, webhook automation, DevOps productivity, Git diff parser, AI code reviewer, automated merge request feedback, software QA automation
   
   Third-party APIs used:
   - GitLab REST API v4
   - OpenAI API (via LangChain integration in n8n)
   
   Article:
   
   How to Automate GitLab Code Reviews Using n8n and OpenAI
   
   Modern software development thrives on efficient collaboration. With teams pushing frequent updates and merge requests (MRs) across a distributed codebase, ensuring consistent and high-quality reviews can be demanding. What if your team could automate intelligent, senior-developer-quality code reviews?
   
   Using n8n, a powerful node-based workflow automation tool, developers can integrate GitLab’s merge request API with OpenAI’s Large Language Models (LLMs) via LangChain to automatically perform AI-powered code reviews. This article explores such a workflow, demonstrating how to eliminate bottlenecks in code review pipelines and boost code quality with minimal manual input.
   
   👷‍♂️ Overview of the Workflow
   
   The provided n8n workflow listens for GitLab MR comment events via a webhook. When a merge request receives a “+0” comment (a custom trigger indicating the need for review), the workflow kicks off. Here's how it unfolds:
   
   1. Trigger via Webhook (GitLab)
   The workflow begins with a Webhook node configured to listen for POST requests from GitLab when a comment is posted on a merge request. It uses a unique webhook path that you'll need to configure in your GitLab integration settings.
   
   2. Conditional Trigger for “+0”
   An If node ("Need Review1") checks if the posted comment is exactly "+0". This acts as the manual flag to signal a request for AI-driven code analysis.
   
   3. Fetch MR Changes from GitLab
   Once triggered, the workflow uses the HTTP Request node to fetch the full JSON diff of the associated merge request via:
   https://gitlab.com/api/v4/projects/:project_id/merge_requests/:iid/changes
   
   This request requires a valid GitLab private token (configured in the headers) to authenticate the API calls.
   
   4. Extract and Filter Changes
   The workflow next splits the `changes` array using a Split Out node, allowing parallel processing of each changed file. An If node called “Skip File Change1” filters out file deletions, renames, or irrelevant diffs (i.e., ones starting with “@@”, indicating metadata-only diffs).
   
   5. Parse Diff Details
   Next, a Code node analyzes the last hunk of the git diff for each file. It calculates the last line numbers affected in both the old and new versions using regex-based parsing from the unified diff format.
   
   6. Distill Code Differences
   Another Code node separates the original code and new code based on each line's prefix:
   - Lines starting with `-` are old
   - Lines starting with `+` are new
   - Neutral lines are unchanged context
   
   This prepares a clear before-and-after snapshot for the language model to evaluate.
   
   7. AI-Powered Review with OpenAI (via LangChain)
   This is where the magic happens. A “Basic LLM Chain1” node uses LangChain to call OpenAI’s chat model for code review.
   
   It passes a structured prompt that includes:
   - File path
   - Code before and after
   - A detailed instruction to simulate a senior developer bot who must:
     - Decide to “accept” or “reject” the change
     - Provide a numerical “Change Score”
     - Offer markdown-formatted suggestions in a strict, direct tone
   
   Example AI-generated output might look like:
   
   > **Reject**
   > **Change Score: 30**
   >
   > ❌ The added logic introduces a race condition under concurrent environments.
   > 🛠️ Suggest refactoring the function to make it atomic or adding a locking mechanism.
   >
   > ```suggestion
   > // Updated logic with mutex
   > ```
   
   8. Post AI Review Back to GitLab
   Finally, the review is posted back to GitLab as a discussion comment on the relevant section of the MR. It uses the start SHA, base SHA, and head SHA from the `diff_refs` provided in GitLab’s response. This ensures precise positioning of the comment in the code diff.
   
   ⚙️ Customization Options
   
   The workflow is designed to be plug-and-play with some light configuration:
   - Replace placeholder values with your GitLab Project ID and Token.
   - Adjust trigger comment value (`+0`) to suit your team's conventions.
   - Modify the AI prompt for tone, scoring criteria, or language preferences.
   - Tune the filtering logic to skip specific files or directories (e.g., auto-generated or vendor files).
   
   🏁 Benefits of Automated Code Review
   
   - ⏱️ Faster Code Reviews: Reduce review latency by providing immediate feedback upon request.
   - ✔️ Consistent Standards: AI applies the same scrutiny every time, without reviewer fatigue.
   - ✨ Markdown-Formatted Feedback: Easy for devs to follow, understand, and act upon.
   - 🔁 Reusable and Scalable: Easily clone and scale the automation across projects.
   
   🔐 Security and Considerations
   
   Be sure to secure the GitLab token and webhook access. Limit AI-powered code reviews to non-sensitive segments until you're satisfied with the output quality. Human oversight remains essential for high-risk code paths.
   
   🎯 Final Thoughts
   
   This n8n workflow is a blueprint for the future of augmented DevOps. By integrating GitLab’s robust merge request API with the intelligence of OpenAI via LangChain, developers can slash the overhead of manual code reviews without compromising quality.
   
   Whether you’re an engineering leader focused on scaling QA processes or a solo dev streamlining your project workflows—this integration is worth exploring. Embrace the future of automated code collaboration.
   
   Let AI be your code reviewer—and free your team for what matters most: building great software.

5. Set credentials for each API node (keys, OAuth) in Credentials.
6. Run a test via Execute Workflow. Inspect Run Data, then adjust parameters.
7. Enable the workflow to run on schedule, webhook, or triggers as configured.

Tips: keep secrets in credentials, add retries and timeouts on HTTP nodes, implement error notifications, and paginate large API fetches.

Validation: use IF/Code nodes to sanitize inputs and guard against empty payloads.

Why Automate This with AI Agents

AI‑assisted automations offload repetitive, error‑prone tasks to a predictable workflow. Instead of manual copy‑paste and ad‑hoc scripts, your team gets a governed pipeline with versioned state, auditability, and observable runs.

n8n’s node graph makes data flow transparent while AI‑powered enrichment (classification, extraction, summarization) boosts throughput and consistency. Teams reclaim time, reduce operational costs, and standardize best practices without sacrificing flexibility.

Compared to one‑off integrations, an AI agent is easier to extend: swap APIs, add filters, or bolt on notifications without rewriting everything. You get reliability, control, and a faster path from idea to production.

Best Practices

• Credentials: restrict scopes and rotate tokens regularly.
• Resilience: configure retries, timeouts, and backoff for API nodes.
• Data Quality: validate inputs; normalize fields early to reduce downstream branching.
• Performance: batch records and paginate for large datasets.
• Observability: add failure alerts (Email/Slack) and persistent logs for auditing.
• Security: avoid sensitive data in logs; use environment variables and n8n credentials.

FAQs

Can I swap integrations later? Yes. Replace or add nodes and re‑map fields without rebuilding the whole flow.

How do I monitor failures? Use Execution logs and add notifications on the Error Trigger path.

Does it scale? Use queues, batching, and sub‑workflows to split responsibilities and control load.

Is my data safe? Keep secrets in Credentials, restrict token scopes, and review access logs.