Workflow Templates

This workflow restores all n8n instance credentials from GitHub backups using the n8n API node. It complements the Backup Your Credentials to GitHub template by allowing users to seamlessly restore previously saved credentials. How It Works The workflow fetches credentials stored in a GitHub repository and imports them into your n8n instance. Setup Instructions To configure the workflow, update the Globals node with the following values: repo.owner** – Your GitHub username repo.name** – The name of your GitHub repository storing the credentials repo.path** – The folder path within the repository where credentials are stored For example, if your GitHub username is john-doe, your repository is named n8n-backups, and credentials are stored in a credentials/ folder, you would set: repo.owner → john-doe repo.name → n8n-backups repo.path → credentials/ Required Credentials GitHub API** – Access to your repository n8n API** – To import credentials into your n8n instance Who Is This For? This template is ideal for users who want to restore their credentials from GitHub backups, ensuring easy migration and recovery in case of data loss. Check out my other templates: 👉 My n8n Templates

Automated pipeline that extracts job listings from Upwork and exports them to Google Sheets for better organization, analysis, and team collaboration. 🚀 What It Does Fetches job postings based on saved searches Extracts key job details (title, budget, description) Organizes data in Google Sheets Updates in real-time Supports multiple search criteria 🎯 Perfect For Freelancers tracking opportunities Teams managing multiple projects Agencies monitoring client needs Market researchers Business analysts ⚙️ Key Benefits ✅ Centralized job board ✅ Easy sharing with team members ✅ Advanced filtering and sorting ✅ Historical data tracking ✅ Customizable data points 🔧 What You Need Upwork account Google account n8n instance Google Sheets setup 📊 Data Exported Job title and description Budget and hourly rate Client information Posted date Required skills Job URL 🛠️ Setup & Support Quick Setup Get started in 15 minutes with our step-by-step guide 📺 Watch Tutorial 💼 Get Expert Support 📧 Direct Help Streamline your job search and opportunity tracking with automated data collection and organization.

Boost engagement on your Discord server by automatically sharing new YouTube videos along with AI generated summaries of their content. This workflow is ideal for content creators and community managers looking to provide value and spark interest through summarized content, making it easier for community members to decide if a video is of interest to them. Watch this video tutorial to learn more about the template. How it works RSS Feed Trigger**: Monitors your YouTube channel for new uploads using the RSS feed. Video Captions Retrieval**: Fetches video captions using the YouTube API to get detailed content data. AI Summary Generation**: Uses an AI model to generate concise summaries from the video captions, highlighting key points. Discord Notification**: Posts video announcements along with their AI generated summaries to a specified Discord channel using a webhook. Set up steps Configure YouTube RSS Feed: Set up the RSS feed node to detect new video uploads. Add your YouTube channel ID to the URL in the first node: https://www.youtube.com/feeds/videos.xml?channel_id=YOUR_CHANNEL_ID. Connect OpenAI Account: To enable AI summary generation, connect your OpenAI account in n8n. Set Up Discord Webhook: Create a webhook in your Discord server and configure it in the Discord node. Design the Message: Format the Discord message as you like to include the video title, link, and the AI generated summary. Example This template empowers you to maintain a highly engaging Discord community, ensuring members receive not only regular updates but also valuable insights into each video's content without needing to watch immediately.

Retrieve 20 Latest TechCrunch Articles Who is this for? This workflow is designed for developers, content creators, and data analysts who need to scrape recent articles from TechCrunch. It’s perfect for anyone looking to aggregate news articles or create custom feeds for analysis, reporting, or integration into other systems. What problem is this workflow solving? This workflow automates the process of scraping recent articles from TechCrunch. Manually collecting article data can be time-consuming and inefficient, but with this workflow, you can quickly gather up-to-date news articles with relevant metadata, saving time and effort. What this workflow does This workflow retrieves the latest 20 news articles from TechCrunch’s “Recent” page. It extracts the article URLs, metadata (such as titles and publication dates), and main content for each article, allowing you to access the information you need without any manual effort. Setup Clone or download the workflow template. Ensure you have a working n8n environment. Configure the HTTP Request nodes with your desired parameters to connect to the TechCrunch API. (Optional) Customize the workflow to target specific sections or topics of interest. Run the workflow to retrieve the latest 20 articles. How to customize this workflow to your needs Modify the HTTP request to pull articles from different pages or sections of TechCrunch. Adjust the number of articles to retrieve by changing the selection criteria. Add additional processing steps to further filter or analyze the article data. Workflow Steps Send an HTTP request to the TechCrunch "Recent" page. Parse a posts box that holds the list of articles. Parse all posts to extract all articles. spilt out posts for each article. Extract the URL and metadata from each article. Send an HTTP request for each article using its URL. Locate and parse the main content of each article. Note: Be sure to update the HTTP Request nodes with any necessary headers or authentication to work with TechCrunch’s website.

Building agentic AI workflows often requires multiple moving parts: memory management, document retrieval, vector similarity, and orchestration. Until now, these pieces had to be custom-wired. But with the new native n8n nodes for MongoDB Atlas, we reduce that overhead dramatically. With just a few clicks: Store and recall long-term memory from MongoDB Query vector embeddings stored in Atlas Vector Search Use these results in your LLM chains and automation logic In this example we present an ingestion and AI Agent flows that focus around Travel Planning. The different interest points that we want the agent to know about can be ingested into the vector store. The AI Agent will use the vector store tool to get relevant context about those points of interest if it needs to. Prerequisites MongoDB Atlas project and Cluster OpenAI Valid API Key for embeddings (can be other provider) Gemini API Key for the LLM (can be other provider) How it works: There are 2 main flows. One is ingesting flow: Gets a document from a webhook and use MongoDB Vector Atlas to embed the document title and description into points_of_interest collection. Embeddings are stored in a field named embedding Embeddings used are OpenAI's but it can be any type of supported embedders. Second flow is an AI Agent node with Chat Memory Stored in MongoDB Atlas and a Vector Search node as a tool: Chat Message Trigger**: Chatting with the AI Agent will trigger the conversation store in the MongoDB Chat Memory node. When data is necessary like a location search or details it will go to the "Vector Search" tool. Vector Search Tool** - uses Atlas Vector Search index created on the points_of_interest collection: // index name : "vector_index" // If you change an embedding provider make sure the numDimensions correspond to the model. { "fields": [ { "type": "vector", "path": "embedding", "numDimensions": 1536, "similarity": "cosine" } ] } Additional Resources MongoDB Atlas Vector Search n8n Atlas Vector Search docs

How it works This workflow demonstrates a fundamental pattern for securing a webhook by requiring an API key. It acts as a gatekeeper, checking for a valid key in the request header before allowing the request to proceed. Incoming Request: The Secured Webhook node receives an incoming POST request. It expects an API key to be sent in the x-api-key header. API Key Verification: The Check API Key node takes the key from the incoming request's header. It then makes an internal HTTP request to a second webhook (Get API Key) which acts as a mock database. This second webhook retrieves a list of registered API keys (from the Registered API Keys node) and filters it to find a match for the key that was provided. Conditional Response: If a match is found, the API Key Identified node routes the execution to the "success" path, returning a 200 OK response with the identified user's ID. If no match is found, it routes to the "unauthorized" path, returning a 401 Unauthorized error. This pattern separates the public-facing endpoint from the data source, which is a good security practice. Set up steps Setup time: ~2 minutes This workflow is designed to be a self-contained example. Set up Credentials: This workflow uses "Header Auth" for its internal communication. Go to Credentials and create a new Header Auth credential. You can use any name and value (e.g., Name: X-N8N-Auth, Value: my-secret-password). Select this credential in all four webhook/HTTP Request nodes. Add Your API Keys: Open the Registered API Keys node. This is your mock database. Edit the array to include the user_id and api_key pairs you want to authorize. Activate the workflow. Test it: Use the Test Secure Webhook node to send a request. Try it with a valid key from your list to see the success response. Change the x-api-key header to an invalid key to see the 401 Unauthorized error. For Production: Replace the mock database part of this workflow (the Get API Key webhook and Registered API Keys node) with a real database node like Supabase, Postgres, or Baserow to look up keys.

Who is it for? This workflow is for marketers, sales teams, and local businesses who want to quickly collect leads (business name, phone, website, and email) from Google Maps and store them in Airtable. You can use it for real estate agents, restaurants, therapists, or any local niche. How it works Scrape Google Maps with Apify Google Maps Extractor. Clean and structure the data (name, address, phone, website). Visit each website and retrieve the raw HTML. Use GPT to extract the most relevant email from the site content. Save everything to Airtable for easy filtering and future outreach. It works for any location or keyword – just adapt the input in Apify. Requirements Before running this workflow, you’ll need: ✅ Apify account (to use the Google Maps Extractor) ✅ OpenAI API key (for GPT email extraction) ✅ Airtable account & base with the following fields: Business Name Address Website Phone Number Email Google Maps URL Airtable Structure Your Airtable base should contain these columns: Airtable Structure | Title | Street | Website | Phone Number | Email | URL | |-------------------------|-------------------------|--------------------|-----------------|------------------------|----------------------| | Paris Real Estate Agency| 10 Rue de Rivoli, Paris | https://agency.fr | +33 1 23 45 67 | contact@agency.fr | maps.google.com/... | | Example Business 2 | 25 Avenue de l’Opéra | https://example.fr | +33 1 98 76 54 | info@example.fr | maps.google.com/... | | Example Business 3 | 8 Boulevard Haussmann | https://demo.fr | +33 1 11 22 33 | contact@demo.fr | maps.google.com/... | Error Handling Missing websites:** If a business has no website, the flow skips the scraping step. No email found:** GPT returns Null if no email is detected. API rate limits:** Add a Wait node between requests to avoid Apify/OpenAI throttling. Now let’s take a detailed look at how to set up this automation, using real estate agencies in Paris as an example. Step 1 – Launch the Google Maps Scraper Start with a When clicking Execute workflow trigger to launch the flow manually. Then, add an HTTP Request node with the method set to POST. 👉 Head over to Apify: Google Maps Extractor On the page: https://apify.com/compass/google-maps-extractor Enter your business keyword (e.g., real estate agency, hairdresser, restaurant) Set the location you want to target (e.g., Paris, France) Choose how many results to fetch (e.g., 50) Optionally, use filters (only places with a website, by category, etc.) ⚠️ No matter your industry, this works — just adapt the keyword and location. Once everything is filled in: Click Run to test. Then, go to the top right → click on API. Select the API endpoints tab. Choose Run Actor synchronously and get dataset items. Copy the URL and paste it into your HTTP Request (in the URL field). Then enable: ✅ Body Content Type → JSON ✅ Specify Body Using JSON` Go back to Apify, click on the JSON tab, copy the entire code, and paste it into the JSON body field of your HTTP Request. At this point, if you run your workflow, you should see a structured output similar to this: title subTitle price categoryName address neighborhood street city postalCode ........ Step 2 – Clean and structure the data Once the raw data is fetched from Apify, we clean it up using the Edit Fields node. In this step, we manually select and rename the fields we want to keep: Title → {{ $json.title }} Address → {{ $json.address }} Website → {{ $json.website }} Phone → {{ $json.phone }} URL → {{ $json.url }}* This node lets us keep only the essentials in a clean format, ready for the next steps. On the right: a clear and usable table, easy to work with. Step 3 – Loop Over Items Now that our data is clean (see step 2), we’ll go through it item by item to handle each contact individually. The Loop Over Items node does exactly that: it takes each row from the table (each contact pulled from Apify) and runs the next steps on them, one by one. 👉 Just set a Batch Size of 20 (or more, depending on your needs). Nothing tricky here, but this step is essential to keep the flow dynamic and scalable. Step 4 – Edit Field (again) After looping through each contact one by one (thanks to Loop Over Items), we're refining the data a bit more. This time, we only want to keep the website. We use the Edit Fields node again, in Manual Mapping mode, with just: Website → {{ $json.website }} The result on the right? A clean list with only the URLs extracted from Google Maps. 🔧 This simple step helps isolate the websites so we can scrape them one by one in the next part of the flow. Step 5 – Scrape Each Website with an HTTP Request Let’s continue the flow: in the previous step, we isolated the websites into a clean list. Now, we’re going to send a request to each URL to fetch the content of the site. ➡️ To do this, we add an HTTP Request node, using the GET method, and set the URL as: {{ $json.website }} This value comes from the previous Edit Fields input This node will simply “visit” each website automatically and return the raw HTML code (as shown on the right). 📄 That’s the material we’ll use in the next step to extract email addresses (and any other useful info). We’re not reading this code manually — we’ll scan through it line by line to detect patterns that matter to us. This is a technical but crucial step: it’s how we turn a URL into real, usable data. Step 6 – Extract the Email with GPT Now that we've retrieved all the raw HTML from the websites using the HTTP Request node, it's time to analyze it. 💡 Goal: detect the most relevant email address on each site (ideally the main contact or owner). 👉 To do that, we’ll use an OpenAI node (Message a Model). Here’s how to configure it: ⚙️ Key Parameters: Model: GPT-4-1-MINI (or any GPT-4+ model available) Operation: Message a Model Resource: Text Simplify Output: ON Prompt (message you provide): Look at this website content and extract only the email I can contact this business. In your output, provide only the email and nothing else. Ideally, this email should be of the business owner, so if you have 2 or more options, try for most authoritative one. If you don't find any email, output 'Null'. Exemplary output of yours: name@examplewebsite.com {{ $json.data }} Step 7 – Save the Data in Airtable Once we’ve collected everything — the business name, address, phone number, website… and most importantly the email extracted via ChatGPT — we need to store all of this somewhere clean and organized. 👉 The best place in this workflow is Airtable. 📦 Why Airtable? Because it allows you to: Easily view and sort the leads you've scraped Filter, tag, or enrich them later And most importantly… reuse them in future automations ⚙️ What we're doing here We add an Airtable → Create Record node to insert each lead into our database. Inside this node, we manually map each field with the data collected in the previous steps: | Airtable Field | Description | Value from n8n | | -------------- | ------------------------ | ------------------------------------------ | | Title | Business name | {{ $('Edit Fields').item.json.Title }} | | Street | Full address | {{ $('Edit Fields').item.json.Address }} | | Website | Website URL | {{ $('Edit Fields').item.json.Website }} | | Phone Number | Business phone number | {{ $('Edit Fields').item.json.Phone }} | | Email | Email found by ChatGPT | {{ $json.message.content }} | | URL | Google Maps listing link | {{ $('Edit Fields').item.json.URL }} | 🧠 Reminder: we’re keeping only clean, usable data — ready to be exported, analyzed, or used in cold outreach campaigns (email, CRM, enrichment, etc.). ➡️ And the best part? You can rerun this workflow automatically every week or month to keep collecting fresh leads 🔁.

🎥 AI Video Generator with HeyGen 🚀 Create AI-Powered Videos in n8n with HeyGen This workflow enables you to generate realistic AI videos using HeyGen, an advanced AI platform for video automation. Simply input your text, choose an AI avatar and voice, and let HeyGen generate a high-quality video for you – all within n8n! ✅ Ideal for: Content creators & marketers 🏆 Automating personalized video messages 📩 AI-powered video tutorials & training materials 🎓 🔧 How It Works 1️⃣ Provide a text script – This will be spoken in the AI-generated video. 2️⃣ Select an Avatar & Voice – Choose from a variety of AI-generated avatars and voices. 3️⃣ Run the workflow – HeyGen processes your request and generates a video. 4️⃣ Download your video – Get the direct link to your AI-powered video! ⚡ Setup Instructions 1️⃣ Get Your HeyGen API Key Sign up for a HeyGen account. Go to your account settings and retrieve your API Key. 2️⃣ Configure n8n Credentials In n8n, create new credentials and select "Custom Auth" as the authentication type. In the Name provide : X-Api-Key And in the value paste your API key from Heygen Update the 2 http node with the right credentials. 3️⃣ Select an AI Avatar & Voice Browse available avatars & voices in your HeyGen account. Copy the Avatar ID and Voice ID for your video. 4️⃣ Run the Workflow Enter your text, avatar ID, and voice ID. Execute the workflow – your video will be generated automatically! 🎯 Why Use This Workflow? ✔️ Fully Automated – No manual editing required! ✔️ Realistic AI Avatars – Choose from a variety of digital avatars. ✔️ Seamless Integration – Works directly within your n8n workflow. ✔️ Scalable & Fast – Generate multiple videos in minutes. 🔗 Start automating AI-powered video creation today with n8n & HeyGen!

This workflow takes two inputs, YouTube video URL (required) and a description of what information to extract from the video. If the description/"what you want" field is left empty, the default prompt will generate a detailed summary and description of the video's contents. However, you can ask for something more specific using this field/input. ++ Don't forget to make the workflow Active and use the production URL from the form node. Benefits Instant Summary Generation - Convert hours of watching YouTube videos to familiar, structured paragraphs and sentences in less than a minute Live Integration - Generate a summary or extract information on the contents of a YouTube video whenever, wherever Virtually Complete Automation - All that needs to be done is to add the video URL and describe what you want to know from the video Presentation - You can ask for a specific structure or tone to better help you understand or study the contents of the video How It Works Smart Form Interface: Simple N8N form captures video URL and description of what's to be extracted Designed for rapid and repeated completion anywhere and anytime Description Check: Uses JavaScript to determine if the description was filled in or left empty If the description field was left empty, the default prompt is, "Please be as descriptive as possible about the contents being spoken of in this video after giving a detailed summary." If the description field is filled, then the filled input will be used to describe what information to extract from the video HTTP Request: We're using Gemini API, specifically the video understanding endpoint We make a post HTTP request passing the video URL and the description of what information to extract Setup Instructions: HTTP Request Setup: Sign up for a Google Cloud account, join the Developer Program and get your Gemini API key Get curl for Gemini Video Understanding API The video understanding relies on the inputs from the form, code and HTTP request node, so correct mapping is essential for the workflow to function correctly. Feel free to reach out for additional help or clarification at my Gmail: terflix45@gmail.com, and I'll get back to you as soon as I can. Setup Steps: Code Node Setup: The code node is used as a filter to ensure a description prompt is always passed on. Use the JavaScript code below for that effect: // Loop over input items and add a new field called 'myNewField' to the JSON of each one for (const item of $input.all()) { item.json.myNewField = 1; if ($input.first().json['What u want?'].trim() == "") { $input.first().json['What do you want?'] = "Please be as descriptive as possible about the contents being spoken of this video after giving a detailed summary"; } } return $input.all(); // End of Code HTTP Request: To use Gemini Video Understanding, you'll need your Gemini API key Go to https://ai.google.dev/gemini-api/docs/video-understanding#youtube. This link will take you directly to the snippet. Just select REST programming language, copy that curl command, then paste it into the HTTP Request node Replace "Please summarize the video in 3 sentences." with the code node's output, which should either be the default description or the one entered by the user (second output field variable) Replace "https://www.youtube.com/watch?v=9hE5-98ZeCg" with the n8n form node's first output field, which should be the YouTube video URL variable Replace $GEMINI_API_KEY with your API key Redirect: Use n8n form node, page type "Final Ending" to redirect user to the initial n8n form for another analysis or preferred destination

An automated backup solution designed for self-hosted n8n users to automatically backup their workflows to Bitbucket, leveraging Bitbucket's free private repository offering. Perfect for maintaining version control of your n8n workflows without additional costs. How it works: Runs on a regular schedule to check all workflows in your n8n instance Compares each workflow with its version in Bitbucket Only uploads workflows that are new or have changed Uses basic rate limiting to stay within Bitbucket's API limits Formats filenames for easy tracking and includes timestamps in commit messages Handles errors gracefully with automatic retries Set up steps (10-15 minutes): Create a free Bitbucket account and private repository Create a Bitbucket App Password with repository write access Add Bitbucket credentials to n8n (using your username and app password) Set up n8n API access (generate API key in your n8n instance) Configure your Bitbucket workspace and repository names in the Set node Optional: Adjust the backup schedule (default: 2 AM daily) Perfect for n8n self-hosters who want: Version control for their workflows Automated daily backups Free private repository storage Easy workflow recovery Change tracking over time The workflow includes basic error handling and rate limiting to ensure reliable backups even with larger numbers of workflows. Adjust your timing based on https://support.atlassian.com/bitbucket-cloud/docs/api-request-limits/.

What this workflow does Monitors Google Drive: The workflow triggers whenever a new CSV file is uploaded. Uses AI to Identify PII Columns: The OpenAI node analyzes the data and identifies PII-containing columns (e.g., name, email, phone). Removes PII: The workflow filters out these columns from the dataset. Uploads Cleaned File: The sanitized file is renamed and re-uploaded to Google Drive, ensuring the original data remains intact. How to customize this workflow to your needs Adjust PII Identification: Modify the prompt in the OpenAI node to align with your specific data compliance requirements. Include/Exclude File Types: Adjust the Google Drive Trigger settings to monitor specific file types (e.g., CSV only). Output Destination: Change the folder in Google Drive where the sanitized file is uploaded. Setup Prerequisites: A Google Drive account. An OpenAI API key. Workflow Configuration: Configure the Google Drive Trigger to monitor a folder for new files. Configure the OpenAI Node to connect with your API Set the Google Drive Upload folder to a different location than the Trigger folder to prevent workflow loops.

This workflow retrieves all Squarespace Orders and saves them into a Google Sheets spreadsheet using the Squarespace Commerce API. It uses pagination to ensure all orders are collected efficiently. How It Works The workflow queries your Squarespace Orders API. It fetches data in paginated batches and inserts them into Google Sheets. The Global node is used to configure API parameters dynamically, allowing users to set date filters, pagination, and fulfillment status. The workflow runs on demand or on a schedule, ensuring your data stays up to date. Parameters This workflow allows you to customize the API request using the Global node settings: api-version** (string, required) – The current API version (see Squarespace Orders API documentation). modifiedAfter**={a-datetime} (string, conditional) – Fetch orders modified after a specific date (ISO 8601 format). modifiedBefore**={b-datetime} (string, conditional) – Fetch orders modified before a specific date (ISO 8601 format). cursor**={c} (string, conditional) – Used for pagination, cannot be combined with other filters. fulfillmentStatus**={status} (optional, enum) – Filter by fulfillment status: PENDING, FULFILLED, or CANCELED. maxPage** – Set -1 to enables infinite pagination to fetch all available orders. Requirements Credentials To use this workflow, you need: Squarespace API Key – Retrieve from your Squarespace settings. Google Sheets API credentials – Required to insert data into a spreadsheet. Google Sheets Setup Use the Squarespace order export feature to create a reference sheet. Google Sheets template is available Who Is This For? This workflow is designed for: Squarespace store owners exporting orders for tax reports, analytics, or sales tracking. Businesses automating order data retrieval for external reporting. Anyone needing an efficient way to extract Squarespace order data without manual effort. Explore More Templates Get all orders in Shopify to Google Sheets Sync Shopify customers to Google Sheets + Squarespace compatible csv 👉 Check out my other n8n templates