HumationHuman + Automation
Back to Catalog

Automated PR code reviews with GitHub, GPT-4, and Google Sheets best practices

JiheneJihene
11180 views
2/3/2026
AIWebhookImage AnalysisGoogle GeminiVision
Official Page

AI-Agent Code Review for GitHub Pull Requests

Description:

This n8n workflow automates the process of reviewing code changes in GitHub pull requests using an OpenAI-powered agent.

It connects your GitHub repo, extracts modified files, analyzes diffs, and uses an AI agent to generate a code review based on your internal code best practices (fed from a Google Sheet).

It ends by posting the review as a comment on the PR and tagging it with a visual label like ✅ Reviewed by AI.

🔧 What It Does

  1. Triggered on PR creation

  2. Extracts code diffs from the PR

  3. Formats and feeds them into an OpenAI prompt

  4. Enriches the prompt using a Google Sheet of Swift best practices

  5. Posts an AI-generated review as a comment on the PR

  6. Applies a PR label to visually mark reviewed PRs

✅ Prerequisites

Before deploying this workflow, ensure you have the following:

  • n8n Instance (Self-hosted or Cloud)
  • GitHub Repository with PR activity
  • OpenAI API Key for GPT-4o, GPT-4-turbo, or GPT-3.5
  • GitHub OAuth App (or PAT) connected to n8n to post comments and access PR diffs
  • (Optional) Google Sheets API credentials if using the code best practices lookup node.

⚙️ Setup Instructions

1. Import the Workflow in n8n, click on Workflows → Import from file or JSON Paste or upload the JSON code of this template

2. Configure Triggers and Connections

🔁 GitHub Trigger

  • Node: PR Trigger
  • Repository: Select the GitHub repo(s) to monitor
  • Events: Set to pull_request
  • Auth: Use GitHub OAuth2 credentials

📥 HTTP Request

Node: Get file's Diffs from PR

No authentication needed; it uses dynamic path from trigger

🧠 OpenAI Model

  • Node: OpenAI Chat Model

  • Model: Select gpt-4o, gpt-4-turbo, or gpt-3.5-turbo

  • Credential: Provide your OpenAI API Key

🧑‍💻 Code Review Agent

Node : Code Review Agent Connected to OpenAI and optionally to tools like Google Sheets

💬 GitHub Comment Poster

Uses GitHub API to post review comments back on PR Node: GitHub Robot Credential: Use the agent Github account (OAuth or PAT) Repo : Pick your owen Github Repository

🏷️ PR Labeler (optional)

Adds label ReviewedByAI after successful comment

Node: Add Label to PR Label : you ca customize the label text of your owen tag.

📊 Google Sheet Best Practices config (optional)

Connects to a Google Sheet for coding guideline lookups, we can replace Google sheet by another tool or data base

  • First prepare your best practices list with the clear description and the code bad/good examples
  • Add al the best practices in your Google Sheet
  • Configure the Code Best Practices node in the template :

Credential : Use your Google Sheet account by OAuth2

URL : Add your Google Sheet document URL

Sheet : Add the name of the best practices sheet

Automated PR Code Reviews with GitHub, GPT-4, and Google Sheets

This n8n workflow automates the process of generating code reviews for GitHub Pull Requests using an AI agent (powered by OpenAI's GPT-4) and recording these reviews in a Google Sheet. It streamlines the code review process, providing quick, AI-generated feedback and maintaining a centralized log of all reviews.

What it does

This workflow simplifies and automates the following steps:

  1. Triggers on GitHub Pull Request: Listens for new or updated pull requests in a specified GitHub repository.
  2. Fetches Pull Request Details: Retrieves the full details of the triggered pull request, including its title, body, and the files changed.
  3. Extracts Code Changes: Uses a Code node to parse the GitHub webhook data and extract the relevant code changes (diffs) from the pull request.
  4. Generates AI Code Review: Sends the extracted code changes to an AI Agent (configured with an OpenAI Chat Model) to generate a comprehensive code review.
  5. Records Review in Google Sheets: Appends the pull request details and the AI-generated review to a Google Sheet, creating a historical log.
  6. Posts Review as GitHub Comment: Publishes the AI-generated code review as a comment on the respective GitHub Pull Request.

Prerequisites/Requirements

Before running this workflow, you will need:

  • n8n Instance: A running instance of n8n.
  • GitHub Account: With access to the repository you want to monitor.
  • GitHub Credential: Configured in n8n to allow the workflow to interact with your GitHub repository (trigger, fetch PRs, post comments).
  • OpenAI API Key: To use the OpenAI Chat Model for generating code reviews.
  • Google Sheets Account: To store the generated code reviews.
  • Google Sheets Credential: Configured in n8n to allow the workflow to write to your Google Sheet.

Setup/Usage

  1. Import the Workflow: Import the provided JSON into your n8n instance.
  2. Configure GitHub Trigger:
    • Select your GitHub credential.
    • Specify the Owner and Repository you want to monitor.
    • Choose Pull Request as the Resource and Opened and Synchronize as the Events to trigger on.
  3. Configure GitHub Node (Get PR):
    • Select your GitHub credential.
    • Ensure the Resource is set to Pull Request and the Operation to Get.
    • Map the ID field to the pull request number from the GitHub Trigger node (e.g., {{ $json.pull_request.number }}).
    • Set Owner and Repository using expressions from the trigger data (e.g., {{ $json.repository.owner.login }} and {{ $json.repository.name }}).
  4. Configure Code Node: No specific configuration needed, it processes the incoming data.
  5. Configure AI Agent:
    • Select your OpenAI Chat Model credential.
    • The prompt for the AI Agent will instruct it to act as a senior software engineer and review the provided code changes for various aspects like bugs, security, performance, and best practices.
  6. Configure Google Sheets Node:
    • Select your Google Sheets credential.
    • Specify the Spreadsheet ID and Sheet Name where you want to store the reviews.
    • Map the data fields to the columns in your Google Sheet (e.g., PR Title, PR URL, Review Content, etc.) using expressions from previous nodes.
  7. Configure GitHub Node (Create Comment):
    • Select your GitHub credential.
    • Ensure the Resource is set to Pull Request and the Operation to Create Comment.
    • Map the ID field to the pull request number from the GitHub Trigger node.
    • Set Owner and Repository using expressions from the trigger data.
    • Map the Body field to the AI-generated review content from the AI Agent node.
  8. Activate the Workflow: Once all credentials and configurations are set, activate the workflow. It will now automatically trigger whenever a pull request is opened or synchronized in your specified GitHub repository.

Related Templates

Automate Dutch Public Procurement Data Collection with TenderNed

TenderNed Public Procurement What This Workflow Does This workflow automates the collection of public procurement data from TenderNed (the official Dutch tender platform). It: Fetches the latest tender publications from the TenderNed API Retrieves detailed information in both XML and JSON formats for each tender Parses and extracts key information like organization names, titles, descriptions, and reference numbers Filters results based on your custom criteria Stores the data in a database for easy querying and analysis Setup Instructions This template comes with sticky notes providing step-by-step instructions in Dutch and various query options you can customize. Prerequisites TenderNed API Access - Register at TenderNed for API credentials Configuration Steps Set up TenderNed credentials: Add HTTP Basic Auth credentials with your TenderNed API username and password Apply these credentials to the three HTTP Request nodes: "Tenderned Publicaties" "Haal XML Details" "Haal JSON Details" Customize filters: Modify the "Filter op ..." node to match your specific requirements Examples: specific organizations, contract values, regions, etc. How It Works Step 1: Trigger The workflow can be triggered either manually for testing or automatically on a daily schedule. Step 2: Fetch Publications Makes an API call to TenderNed to retrieve a list of recent publications (up to 100 per request). Step 3: Process & Split Extracts the tender array from the response and splits it into individual items for processing. Step 4: Fetch Details For each tender, the workflow makes two parallel API calls: XML endpoint - Retrieves the complete tender documentation in XML format JSON endpoint - Fetches metadata including reference numbers and keywords Step 5: Parse & Merge Parses the XML data and merges it with the JSON metadata and batch information into a single data structure. Step 6: Extract Fields Maps the raw API data to clean, structured fields including: Publication ID and date Organization name Tender title and description Reference numbers (kenmerk, TED number) Step 7: Filter Applies your custom filter criteria to focus on relevant tenders only. Step 8: Store Inserts the processed data into your database for storage and future analysis. Customization Tips Modify API Parameters In the "Tenderned Publicaties" node, you can adjust: offset: Starting position for pagination size: Number of results per request (max 100) Add query parameters for date ranges, status filters, etc. Add More Fields Extend the "Splits Alle Velden" node to extract additional fields from the XML/JSON data, such as: Contract value estimates Deadline dates CPV codes (procurement classification) Contact information Integrate Notifications Add a Slack, Email, or Discord node after the filter to get notified about new matching tenders. Incremental Updates Modify the workflow to only fetch new tenders by: Storing the last execution timestamp Adding date filters to the API query Only processing publications newer than the last run Troubleshooting No data returned? Verify your TenderNed API credentials are correct Check that you have setup youre filter proper Need help setting this up or interested in a complete tender analysis solution? Get in touch 🔗 LinkedIn – Wessel Bulte

Wessel BulteBy Wessel Bulte
247

AI multi-agent executive team for entrepreneurs with Gemini, Perplexity and WhatsApp

This workflow is an AI-powered multi-agent system built for startup founders and small business owners who want to automate decision-making, accountability, research, and communication, all through WhatsApp. The “virtual executive team,” is designed to help small teams to work smarter. This workflow sends you market analysis, market and sales tips, It can also monitor what your competitors are doing using perplexity (Research agent) and help you stay a head, or make better decisions. And when you feeling stuck with your start-up accountability director is creative enough to break the barrier 🎯 Core Features 🧑‍💼 1. President (Super Agent) Acts as the main controller that coordinates all sub-agents. Routes messages, assigns tasks, and ensures workflow synchronization between the AI Directors. 📊 2. Sales & Marketing Director Uses SerpAPI to search for market opportunities, leads, and trends. Suggests marketing campaigns, keywords, or outreach ideas. Can analyze current engagement metrics to adjust content strategy. 🕵️‍♀️ 3. Business Research Director Powered by Perplexity AI for competitive and market analysis. Monitors competitor moves, social media engagement, and product changes. Provides concise insights to help the founder adapt and stay ahead. ⏰ 4. Accountability Director Keeps the founder and executive team on track. Sends motivational nudges, task reminders, and progress reports. Promotes consistency and discipline — key traits for early-stage success. 🗓️ 5. Executive Secretary Handles scheduling, email drafting, and reminders. Connects with Google Calendar, Gmail, and Sheets through OAuth. Automates follow-ups, meeting summaries, and notifications directly via WhatsApp. 💬 WhatsApp as the Main Interface Interact naturally with your AI team through WhatsApp Business API. All responses, updates, and summaries are delivered to your chat. Ideal for founders who want to manage operations on the go. ⚙️ How It Works Trigger: The workflow starts from a WhatsApp Trigger node (via Meta Developer Account). Routing: The President agent analyzes the incoming message and determines which Director should handle it. Processing: Marketing or sales queries go to the Sales & Marketing Director. Research questions are handled by the Business Research Director. Accountability tasks are assigned to the Accountability Director. Scheduling or communication requests are managed by the Secretary. Collaboration: Each sub-agent returns results to the President, who summarizes and sends the reply back via WhatsApp. Memory: Context is maintained between sessions, ensuring personalized and coherent communication. 🧩 Integrations Required Gemini API – for general intelligence and task reasoning Supabase- for RAG and postgres persistent memory Perplexity API – for business and competitor analysis SerpAPI – for market research and opportunity scouting Google OAuth – to connect Sheets, Calendar, and Gmail WhatsApp Business API – for message triggers and responses 🚀 Benefits Acts like a team of tireless employees available 24/7. Saves time by automating research, reminders, and communication. Enhances accountability and strategy consistency for founders. Keeps operations centralized in a simple WhatsApp interface. 🧰 Setup Steps Create API credentials for: WhatsApp (via Meta Developer Account) Gemini, Perplexity, and SerpAPI Google OAuth (Sheets, Calendar, Gmail) Create a supabase account at supabase Add the credentials in the corresponding n8n nodes. Customize the system prompts for each Director based on your startup’s needs. Activate and start interacting with your virtual executive team on WhatsApp. Use Case You are a small organisation or start-up that can not afford hiring; marketing department, research department and secretar office, then this workflow is for you 💡 Need Customization? Want to tailor it for your startup or integrate with CRM tools like Notion or HubSpot? You can easily extend the workflow or contact the creator for personalized support. Consider adjusting the system prompt to suite your business

ShadrackBy Shadrack
331

🎓 How to transform unstructured email data into structured format with AI agent

This workflow automates the process of extracting structured, usable information from unstructured email messages across multiple platforms. It connects directly to Gmail, Outlook, and IMAP accounts, retrieves incoming emails, and sends their content to an AI-powered parsing agent built on OpenAI GPT models. The AI agent analyzes each email, identifies relevant details, and returns a clean JSON structure containing key fields: From – sender’s email address To – recipient’s email address Subject – email subject line Summary – short AI-generated summary of the email body The extracted information is then automatically inserted into an n8n Data Table, creating a structured database of email metadata and summaries ready for indexing, reporting, or integration with other tools. --- Key Benefits ✅ Full Automation: Eliminates manual reading and data entry from incoming emails. ✅ Multi-Source Integration: Handles data from different email providers seamlessly. ✅ AI-Driven Accuracy: Uses advanced language models to interpret complex or unformatted content. ✅ Structured Storage: Creates a standardized, query-ready dataset from previously unstructured text. ✅ Time Efficiency: Processes emails in real time, improving productivity and response speed. *✅ Scalability: Easily extendable to handle additional sources or extract more data fields. --- How it works This workflow automates the transformation of unstructured email data into a structured, queryable format. It operates through a series of connected steps: Email Triggering: The workflow is initiated by one of three different email triggers (Gmail, Microsoft Outlook, or a generic IMAP account), which constantly monitor for new incoming emails. AI-Powered Parsing & Structuring: When a new email is detected, its raw, unstructured content is passed to a central "Parsing Agent." This agent uses a specified OpenAI language model to intelligently analyze the email text. Data Extraction & Standardization: Following a predefined system prompt, the AI agent extracts key information from the email, such as the sender, recipient, subject, and a generated summary. It then forces the output into a strict JSON structure using a "Structured Output Parser" node, ensuring data consistency. Data Storage: Finally, the clean, structured data (the from, to, subject, and summarize fields) is inserted as a new row into a specified n8n Data Table, creating a searchable and reportable database of email information. --- Set up steps To implement this workflow, follow these configuration steps: Prepare the Data Table: Create a new Data Table within n8n. Define the columns with the following names and string type: From, To, Subject, and Summary. Configure Email Credentials: Set up the credential connections for the email services you wish to use (Gmail OAuth2, Microsoft Outlook OAuth2, and/or IMAP). Ensure the accounts have the necessary permissions to read emails. Configure AI Model Credentials: Set up the OpenAI API credential with a valid API key. The workflow is configured to use the model, but this can be changed in the respective nodes if needed. Connect the Nodes: The workflow canvas is already correctly wired. Visually confirm that the email triggers are connected to the "Parsing Agent," which is connected to the "Insert row" (Data Table) node. Also, ensure the "OpenAI Chat Model" and "Structured Output Parser" are connected to the "Parsing Agent" as its AI model and output parser, respectively. Activate the Workflow: Save the workflow and toggle the "Active" switch to ON. The triggers will begin polling for new emails according to their schedule (e.g., every minute), and the automation will start processing incoming messages. --- Need help customizing? Contact me for consulting and support or add me on Linkedin.

DavideBy Davide
1616