Workflow Templates

🐦 Twitter Profile Scraper via Bright Data API with Google Sheets Output A comprehensive n8n automation that scrapes Twitter profile data using Bright Data's Twitter dataset and stores comprehensive tweet analytics, user metrics, and engagement data directly into Google Sheets. 📋 Overview This workflow provides an automated Twitter data collection solution that extracts profile information and tweet data from specified Twitter accounts within custom date ranges. Perfect for social media analytics, competitor research, brand monitoring, and content strategy analysis. ✨ Key Features 🔗 Form-Based Input: Easy-to-use form for Twitter URL and date range selection 🐦 Twitter Integration: Uses Bright Data's Twitter dataset for accurate data extraction 📊 Comprehensive Data: Captures tweets, engagement metrics, and profile information 📈 Google Sheets Storage: Automatically stores all data in organized spreadsheet format 🔄 Progress Monitoring: Real-time status tracking with automatic retry mechanisms ⚡ Fast & Reliable: Professional scraping with built-in error handling 📅 Date Range Control: Flexible time period selection for targeted data collection 🎯 Customizable Fields: Advanced data field selection and mapping 🎯 What This Workflow Does Input Twitter Profile URL**: Target Twitter account for data scraping Date Range**: Start and end dates for tweet collection period Custom Fields**: Configurable data points to extract Processing Form Trigger: Collects Twitter URL and date range from user input API Request: Sends scraping request to Bright Data with specified parameters Progress Monitoring: Continuously checks scraping job status until completion Data Retrieval: Downloads complete dataset when scraping is finished Data Processing: Formats and structures extracted information Sheet Integration: Automatically populates Google Sheets with organized data Output Data Points | Field | Description | Example | |-------|-------------|---------| | user_posted | Username who posted the tweet | @elonmusk | | name | Display name of the user | Elon Musk | | description | Tweet content/text | "Exciting updates coming soon..." | | date_posted | When the tweet was posted | 2025-01-15T10:30:00Z | | likes | Number of likes on the tweet | 1,234 | | reposts | Number of retweets | 567 | | replies | Number of replies | 89 | | views | Total view count | 12,345 | | followers | User's follower count | 50M | | following | Users they follow | 123 | | is_verified | Verification status | true/false | | hashtags | Hashtags used in tweet | #AI #Technology | | photos | Image URLs in tweet | image1.jpg, image2.jpg | | videos | Video content URLs | video1.mp4 | | user_id | Unique user identifier | 12345678 | | timestamp | Data extraction timestamp | 2025-01-15T11:00:00Z | 🚀 Setup Instructions Prerequisites n8n instance (self-hosted or cloud) Bright Data account with Twitter dataset access Google account with Sheets access Valid Twitter profile URLs to scrape 10-15 minutes for setup Step 1: Import the Workflow Copy the JSON workflow code from the provided file In n8n: Workflows → + Add workflow → Import from JSON Paste JSON and click Import Step 2: Configure Bright Data Set up Bright Data credentials: In n8n: Credentials → + Add credential → HTTP Header Auth Enter your Bright Data API credentials Test the connection Configure dataset: Ensure you have access to Twitter dataset (gd_lwxkxvnf1cynvib9co) Verify dataset permissions in Bright Data dashboard Step 3: Configure Google Sheets Integration Create a Google Sheet: Go to Google Sheets Create a new spreadsheet named "Twitter Data" or similar Copy the Sheet ID from URL: https://docs.google.com/spreadsheets/d/SHEET_ID_HERE/edit Set up Google Sheets credentials: In n8n: Credentials → + Add credential → Google Sheets OAuth2 API Complete OAuth setup and test connection Prepare your data sheet with columns: Use the column headers from the data points table above The workflow will automatically populate these fields Step 4: Update Workflow Settings Update Bright Data nodes: Open "🚀 Trigger Twitter Scraping" node Replace BRIGHT_DATA_API_KEY with your actual API token Verify dataset ID is correct Update Google Sheets node: Open "📊 Store Twitter Data in Google Sheet" node Replace YOUR_GOOGLE_SHEET_ID with your Sheet ID Select your Google Sheets credential Choose the correct sheet/tab name Step 5: Test & Activate Add test data: Use the form trigger to input a Twitter profile URL Set a small date range for testing (e.g., last 7 days) Test the workflow: Submit the form to trigger the workflow Monitor progress in n8n execution logs Verify data appears in Google Sheet Check all expected columns are populated 📖 Usage Guide Running the Workflow Access the workflow form trigger URL (available when workflow is active) Enter the Twitter profile URL you want to scrape Set the start and end dates for tweet collection Submit the form to initiate scraping Monitor progress - the workflow will automatically check status every minute Once complete, data will appear in your Google Sheet Understanding the Data Your Google Sheet will show: Real-time tweet data** for the specified date range User engagement metrics** (likes, replies, retweets, views) Profile information** (followers, following, verification status) Content details** (hashtags, media URLs, quoted tweets) Timestamps** for each tweet and data extraction Customizing Date Ranges Recent data**: Use last 7-30 days for current activity analysis Historical analysis**: Select specific months or quarters for trend analysis Event tracking**: Focus on specific date ranges around events or campaigns Comparative studies**: Use consistent time periods across different profiles 🔧 Customization Options Modifying Data Fields Edit the custom_output_fields array in the "🚀 Trigger Twitter Scraping" node to add or remove data points: "custom_output_fields": [ "id", "user_posted", "name", "description", "date_posted", "likes", "reposts", "replies", "views", "hashtags", "followers", "is_verified" ] Changing Google Sheet Structure Modify the column mapping in the "📊 Store Twitter Data in Google Sheet" node to match your preferred sheet layout and add custom formulas or calculations. Adding Multiple Recipients To process multiple Twitter profiles: Modify the form to accept multiple URLs Add a loop node to process each URL separately Implement delays between requests to respect rate limits 🚨 Troubleshooting Common Issues & Solutions "Bright Data connection failed" Cause: Invalid API credentials or dataset access Solution: Verify credentials in Bright Data dashboard, check dataset permissions "No data extracted" Cause: Invalid Twitter URLs or private/protected accounts Solution: Verify URLs are valid public Twitter profiles, test with different accounts "Google Sheets permission denied" Cause: Incorrect credentials or sheet permissions Solution: Re-authenticate Google Sheets, check sheet sharing settings "Workflow timeout" Cause: Large date ranges or high-volume accounts Solution: Use smaller date ranges, implement pagination for high-volume accounts "Progress monitoring stuck" Cause: Scraping job failed or API issues Solution: Check Bright Data dashboard for job status, restart workflow if needed Advanced Troubleshooting Check execution logs in n8n for detailed error messages Test individual nodes by running them separately Verify data formats and ensure consistent field mapping Monitor rate limits if scraping multiple profiles consecutively Add error handling and implement retry logic for robust operation 📊 Use Cases & Examples 1. Social Media Analytics Goal: Track engagement metrics and content performance Monitor tweet engagement rates over time Analyze hashtag effectiveness and reach Track follower growth and audience interaction Generate weekly/monthly performance reports 2. Competitor Research Goal: Monitor competitor social media activity Track competitor posting frequency and timing Analyze competitor content themes and strategies Monitor competitor engagement and audience response Identify trending topics and hashtags in your industry 3. Brand Monitoring Goal: Track brand mentions and sentiment analysis Monitor specific Twitter accounts for brand mentions Track hashtag campaigns and user-generated content Analyze sentiment trends and audience feedback Identify influencers and brand advocates 4. Content Strategy Development Goal: Analyze successful content patterns Identify high-performing tweet formats and topics Track optimal posting times and frequencies Analyze hashtag performance and reach Study audience engagement patterns 5. Market Research Goal: Collect social media data for market analysis Gather consumer opinions and feedback Track industry trends and discussions Monitor product launches and market reactions Support product development with social insights ⚙ Advanced Configuration Batch Processing Multiple Profiles To monitor multiple Twitter accounts efficiently: Create a master sheet with profile URLs and date ranges Add a loop node to process each profile separately Implement delays between requests to respect rate limits Use separate sheets or tabs for different profiles Adding Data Analysis Enhance the workflow with analytical capabilities: Create additional sheets for processed data and insights Add formulas to calculate engagement rates and trends Implement data visualization with charts and graphs Generate automated reports and summaries Integration with Business Tools Connect the workflow to your existing systems: CRM Integration**: Update customer records with social media data Slack Notifications**: Send alerts when data collection is complete Database Storage**: Store data in PostgreSQL/MySQL for advanced analysis BI Tools**: Connect to Tableau/Power BI for comprehensive visualization 📈 Performance & Limits Expected Performance Single profile**: 30 seconds to 5 minutes (depending on date range) Data accuracy**: 95%+ for public Twitter profiles Success rate**: 90%+ for accessible accounts Daily capacity**: 10-50 profiles (depends on rate limits and data volume) Resource Usage Memory**: ~200MB per execution Storage**: Minimal (data stored in Google Sheets) API calls**: 1 Bright Data call + multiple Google Sheets calls per profile Bandwidth**: ~5-10MB per profile scraped Execution time**: 2-10 minutes for typical date ranges Scaling Considerations Rate limiting**: Add delays for high-volume scraping Error handling**: Implement retry logic for failed requests Data validation**: Add checks for malformed or missing data Monitoring**: Track success/failure rates over time Cost optimization**: Monitor API usage to control costs 🤝 Support & Community Getting Help n8n Community Forum**: community.n8n.io Documentation**: docs.n8n.io Bright Data Support**: Contact through your dashboard GitHub Issues**: Report bugs and feature requests Contributing Share improvements with the community Report issues and suggest enhancements Create variations for specific use cases Document best practices and lessons learned 📋 Quick Setup Checklist Before You Start ☐ n8n instance running (self-hosted or cloud) ☐ Bright Data account with Twitter dataset access ☐ Google account with Sheets access ☐ Valid Twitter profile URLs ready for scraping ☐ 10-15 minutes available for setup Setup Steps ☐ Import Workflow - Copy JSON and import to n8n ☐ Configure Bright Data - Set up API credentials and test ☐ Create Google Sheet - New sheet with proper column structure ☐ Set up Google Sheets credentials - OAuth setup and test ☐ Update workflow settings - Replace API keys and sheet IDs ☐ Test with sample data - Add 1 Twitter URL and small date range ☐ Verify data flow - Check data appears in Google Sheet correctly ☐ Activate workflow - Enable form trigger for production use Ready to Use! 🎉 Your workflow URL: Access form trigger when workflow is active 🎯 Happy Twitter Scraping! This workflow provides a solid foundation for automated Twitter data collection. Customize it to fit your specific social media analytics and research needs. For any questions or support, please contact: info@incrementors.com or fill out this form: https://www.incrementors.com/contact-us/

This is a simple webpage scraper that specifically grabs today's newest 4K Bluray Preorders as listed on the Blu-ray.com website. This is a scheduled workflow that can run every day and will post a formatted summary message of links to a Discord channel of your choice. Minimal setup required: Just create a webhook for the channel you want posted to in Discord and provide that in the final step. The timezone format step is set to East Coast (NYC) by default, feel free to change. No API keys or any special configuration needed (beyond your Discord webhook) Feel free to customize the formatting of the message that gets posted 👍 How it works: First format todays date to match the formatting used on the website Grab the HTML for the preorders page at www.blu-ray.com Filter only the hyperlinks for each Bluray on the page Then further filter only those with an html header matching today's date Format how you want the message to be sent to your Discord channel (in this case a simple list of Hyperlinks for each Title) Send to Discord! Disclaimer: This should be only for personal use.** The links go back to the blu-ray.com website, which is a good thing! Don't abuse this by slamming their site with some crazy level of automation frequency. Support the blu-ray.com website by using their affiliate links whenever you do want to preorder a title ;) This is one of my first shared templates, so it may not be super optimal or perfect but it works for my needs and hopefully you'll find some use out of it! Discord currently has a 2000 character limit on webhook messages. Some of the messages may get truncated as a result.

Convert PostgreSQL table to CSV CSV is a super useful and universal way to transfer data between different tools. This workflow gives an example of how to take data from PostgreSQL and convert it easily into a CSV. What you need Before running the workflow, please make sure you have access to a remote PostgreSQL server and have table data: book_title,book_author,read_date Demons,Fyodor Dostoyevsky,2022-09-08 Ulysses,James Joyce,2022-05-06 Catch-22,Joseph Heller,2023-01-04 The Bell Jar,Sylvia Plath,2023-01-21 Frankenstein,Mary Shelley,2023-02-14 How it works Trigger the workflow on click Declare the name of the Excel file and sheet names Remotely connect to the PostgreSQL database and specify query execution Write the query data to CSV The detailed process is explained further in the tutorial: https://blog.n8n.io/postgres-export-to-csv/

This workflow contains community nodes that are only compatible with the self-hosted version of n8n. Fetch SEO and traffic information from ahref for a list of domains in a Google Sheet. This is great for marketing research and SEO workflow optimizations and saves tons of time. How it works We'll import domains from the Google sheet We use an SEO MCP server to fetch data from ahref free tooling The fetched data is stored in the Google sheet Set up steps Copy Google Sheet template and add it in all Google Sheet nodes Make sure that n8n has read & write permissions for your Google sheet. Add your list of domains in the first column in the Google sheet Add MCP credentials for seo-mcp

This workflow automates invoice generation from form submissions, ensuring unique order IDs, creating PDF invoices, storing files, emailing customers, and logging invoice data — all seamlessly integrated. 🔹 Workflow Overview Trigger (Webhook) Starts when an order form is submitted, capturing customer and order details. Generate Random Order ID A Function node creates a unique alphanumeric invoice ID (e.g., INV-X92B7D). Check for Duplicate Order ID Google Sheets looks up the generated order ID in your invoice log sheet to prevent duplicates. Conditional Check (IF Node) If the ID already exists → regenerates a new ID (loops back) If unique → proceeds to invoice creation Prepare Invoice Data A Set node formats customer info, date, order items, and the unique order ID to fit your invoice template. Convert HTML to PDF HTTP Request node sends your invoice HTML to the RapidAPI HTML-to-PDF service and receives the PDF file. Upload PDF to Cloud Storage Save the PDF in Google Drive or Dropbox with a clear file name like Invoice-INV-X92B7D.pdf. Send Invoice Email to Customer Email node attaches the PDF and includes the order ID in the email subject/body. Log Invoice Details Append invoice data (customer info, order ID, total, PDF link) to your Google Sheet for tracking. ⚙️ Node Details & Setup 1. Webhook Trigger Configure to receive form submissions (order details like name, email, items, total). 2. Function: Generate Random Order ID Sample JS code generates unique IDs prefixed by INV-. 3. Google Sheets: Lookup Row Set up connection to your invoice log sheet. Search for existing order ID to avoid duplicates. 4. IF Node: Check Order ID Existence Condition: If order ID found → loop to regenerate. Else → continue workflow. 5. Set Node: Prepare Invoice HTML Define variables like customer name, date, items, and order ID. This data populates your HTML invoice template. 6. HTTP Request: Convert HTML to PDF API URL to get your key Send invoice HTML in the request body. Receive PDF file blob or download URL. 7. Google Drive (or Dropbox) Upload Upload the PDF file. Use file name format: Invoice-{{$json["order_id"]}}.pdf 8. Email Node Recipient: customer email from the form data. Attach generated PDF. Include order ID in email subject or body for reference. 9. Google Sheets: Append Row Log invoice metadata to keep records updated. 📁 Google Sheets Template You can make a copy of the invoice log template here This sheet includes columns for order\_id, customer name, email, total, and invoice PDF link. Customize it as needed. 📌 Additional Notes Customize the invoice HTML template inside the Set node to match your branding. Ensure API credentials for RapidAPI, Google Drive/Dropbox, and email are properly set up in your n8n credentials. You can expand this workflow by adding payment processing or SMS notifications. Need help or want a custom workflow? Reach out via email at joseph@uppfy.com.

What it does Pulls up to 700 Amazon reviews per product (recent and top-rated) and writes them straight into a Google Sheet tab you choose. Perfect for • Brand and product managers tracking sentiment • Marketplace sellers analysing competitor feedback • Agencies building product-review dashboards Apps used RapidAPI Real-Time Amazon Data, Google Sheets, n8n Form Trigger How it works Form Trigger collects brand, product and sheet info. Code node extracts the ASIN and builds 70 API requests (10 pages × star ratings). Split-in-batches loops through the request list, throttled by two Wait nodes. HTTP Request fetches reviews from RapidAPI. IF node drops empty or error responses. Split Out breaks arrays into single reviews. Google Sheets appends every review to the target tab. Loop continues until all pages finish. Setup Fill in Brand name, Product / Model Name, Amazon Product URL, Tab URL to insert reviews in the form. Grab your X-RapidAPI-Key from RapidAPI → Add as httpHeaderAuth credential. Connect Google Sheets OAuth2 and make the spreadsheet Anyone with the link can edit. Open Workflow Settings → set timezone if you plan to schedule runs. Hit Execute workflow or share the form link. Credentials • Real-Time Amazon Data (RapidAPI HTTP Header Auth) • Google Sheets OAuth2 Limits and notes • \~100 RapidAPI calls for the free plan. Plan quota accordingly. • Assumes Amazon returns 10 pages per star rating; fewer pages skip silently. • Large sheets may hit Google API write quotas. If you have any questions in running the workflow, feel free to reach out to me at my youtube channel: https://www.youtube.com/@lifeofhunyao

RAG over a PDF with Weaviate This workflow allows you to upload a PDF file and ask questions about it using the Question and Answer Chain and the Weaviate Vector Store nodes. Who it's for This workflow is the simplest possible implementation of RAG with Weaviate in n8n. It's intended to act as an extendable template for RAG over your own documents. Prerequisites An existing Weaviate cluster. You can view instructions for setting up a local cluster with Docker here or a Weaviate Cloud cluster here. API keys to generate embeddings and power chat models. We use OpenAI, but feel free to switch out the models as you like. Self-hosted n8n instance. See this video for how to get set up in just three minutes. How it works Part 1: Manually upload data In this example, we manually upload a 100+ page article from arXiv called "A Survey of Large Language Models". But you can replace this with your own more advanced data pipeline, if you wish. Part 2: Embed and load data into Weaviate collection Here, we generate embeddings for the full-text of the article and store them in Weaviate. Part 3: Perform RAG over PDF file with Weaviate In this part of the workflow, you can enter your query by running the Chat Node and get a RAG response grounded in context via the Question and Answer Chain node. How to run the workflow Go through the prerequisites, creating a Weaviate cluster (can be local or cloud), downloading self-hosted n8n, and adding your API keys and other credentials. Select the embedding and chat models you'd like to use. Upload a PDF file you want to ask questions about. Execute the rest of the workflow.

MCP Tool — Replicate (Flux) Image Generator → WordPress/Twitter Generates images via Replicate Flux models and uploads to WordPress (and optionally Twitter/X). Built to act as an MCP module that other agents/workflows call for on-demand image creation. Models configured in this workflow:\ black-forest-labs/flux-schnell, black-forest-labs/flux-dev, black-forest-labs/flux-1.1-pro Switch rationale: lower cost 💰, broader model choice 🎯, full control of parameters ⚙️ Leonardo API credits cannot be used in the web UI 🙅‍♂️; separate spend for API vs UI Links: 📜 Prior Leonardo-based workflow: https://n8n.io/workflows/6363-generate-and-upload-images-with-leonardo-ai-wordpress-and-twitter/ 📰 Blog automation consuming these images: https://n8n.io/workflows/6734-ai-blog-automation-publish-hourly-seo-articles-to-wordpress-and-twitter-v3/ 📥 Inputs | Field | Type | Description | | ------ | ------ | --------------------------------- | | prompt | string | Text description for the image | | slug | string | Filename slug for WP media | | model | string | One of the configured Flux models | Example: { "prompt":"Joker watching a Batman movie on his laptop", "slug":"joker-watching-batman", "model":"black-forest-labs/flux-dev" } 📤 Output { "public_image_url": "https://your-wp.com/wp-content/uploads/2025/08/img-joker-watching-batman.webp", "wordpress": {...}, "twitter": {...} } 🔄 Flow Trigger with prompt, slug, model Build model payload (quality/steps/ratio/output format) Call Replicate: POST /v1/models/{model}/predictions (Prefer: wait) Download the generated image URL Upload to WordPress (returns public URL) Optional: upload to Twitter/X Return URL + metadata 🤖 MCP Use at Scale (emp0.com) Operational pattern: I currently use this setup for my blog where i generate 300 posts/month, each with 4 images (banner + 2 to 3 inline images) → 1,000 images/month produced by this MCP. 💡 Hybrid Cost-Optimized Setup: High-priority images* (banners, main visuals): Generated using *Flux Dev** on Leonardo for slightly better prompt adherence. Low-priority images* (inline blog visuals): Generated using *Flux Schnell** on Replicate for maximum cost efficiency. 💰 Pricing Comparison (per image) Leonardo per-image cost uses API Basic math: $9 / 3,500 credits = $0.0025714 per credit. Flux Schnell (Leonardo)** = 7 credits Flux Dev (Leonardo)** = 7 credits Flux 1.1 Pro equivalent in Leonardo* = *Leonardo Phoenix** based on my experience = 10 credits | Flux Model | Replicate | Leonardo API* | | ------------------------ | ------------------------- | ------------------------------- | | flux-schnell | $0.0030 (=$3/1,000) | $0.0180 (7 × $0.0025714) | | flux-dev | $0.0250 | $0.0180 (7 × $0.0025714) | | flux-1.1-pro / Phoenix | $0.0400 | $0.0257 (10 × $0.0025714) | Replicate pricing: https://replicate.com/pricing\ Leonardo pricing: https://leonardo.ai/pricing/\ Leonardo API usage: https://docs.leonardo.ai/docs/commonly-used-api-values 📊 Monthly Cost Example (1,000 images/month) Mix: 300 ×flux-dev on Leonardo, 700 ×flux-schnell on Replicate. | Platform/Model | Images | Price per Image | Total | | ------------------------ | ------ | --------------- | ---------- | | Leonardo flux-dev | 300 | $0.0180 | $5.40 | | Replicate flux-schnell | 700 | $0.0030 | $2.10 | | Total Monthly Spend | 1000 | — | $7.50 | 💵 If using Leonardo for both: 300 × $0.0180 = $5.40 700 × $0.0180 = $12.60 Total = $18.00** Savings: $10.50/month (≈58% lower) with the hybrid setup. 📌 Notes More Replicate models can be added in Code1 node. Parameters tuned for aspect ratio, inference steps, quality, guidance. Leonardo credit model is API-only; credits are not spendable in Leonardo's web UI.

Introduction This workflow offers a streamlined solution for uploading multiple files to a GitHub repository simultaneously using GitHub's REST API. It addresses a significant limitation of n8n's native GitHub node, which only supports single-file uploads at a time. By leveraging GitHub's Git Data API, this workflow creates a new Git tree containing multiple files, commits this tree, and updates the target branch—all in a single automated process. The workflow is particularly valuable for automation scenarios that require batch file operations, such as deploying website updates, publishing documentation, or maintaining configuration files across repositories. It eliminates the need for multiple separate API calls when working with multiple files, making your automation more efficient and less prone to partial update issues. By abstracting the complexities of GitHub's Git Data API into a reusable workflow, it provides a practical solution for developers, content managers, and DevOps professionals who need to programmatically manage repository content at scale. Who is this for? This workflow is designed for: Developers and DevOps engineers who need to automate file updates in GitHub repositories Content managers who regularly publish multiple files to GitHub-hosted websites or documentation Automation specialists looking to integrate GitHub operations into larger workflows Teams using n8n for CI/CD processes who need to push code or configuration changes Users should have basic familiarity with GitHub concepts (repositories, branches, commits) and should be comfortable obtaining and using GitHub Personal Access Tokens. While the workflow handles the API complexity, users should understand the fundamentals of version control to effectively utilize and customize it. What problem is this workflow solving? This workflow addresses several key challenges: Limited batch operations: n8n's native GitHub node only supports uploading one file at a time, making multi-file operations cumbersome and inefficient. API complexity: GitHub's Git Data API requires multiple sequential calls with interdependent data to create commits with multiple files, which is complex to implement manually. Automation bottlenecks: Without this workflow, automating multi-file updates would require either multiple separate API calls (risking partial updates) or custom scripting outside of n8n. Consistency issues: When files need to be updated together (e.g., code and corresponding documentation), this workflow ensures they're committed in a single atomic operation. By solving these issues, the workflow enables reliable, atomic updates of multiple files, maintaining repository consistency and simplifying automation processes. What this workflow does Overview This workflow uses GitHub's REST API to push multiple files to a repository in a single operation. It follows Git's internal model by: Retrieving the current state of the repository Creating a new tree with the files to be added or updated Creating a new commit with this tree Updating the branch reference to point to the new commit Process Initialization: The workflow starts with a manual trigger and sets up GitHub credentials and repository information. File Content Definition: Two "Set" nodes define the content for the files to be uploaded. Repository State Retrieval: The workflow fetches the latest commit SHA for the specified branch It then retrieves the base tree SHA from this commit Tree Creation: A new Git tree is created that includes both files (file1.txt and file2.txt), specifying their paths and content. Commit Creation: A new commit is created with the specified commit message, referencing the new tree and the parent commit. Branch Update: Finally, the branch reference is updated to point to the new commit, making the changes visible in the repository. Setup To use this workflow: Import the workflow: Download the workflow JSON and import it into your n8n instance. Create a GitHub Personal Access Token: Go to GitHub Settings → Developer Settings → Personal Access Tokens → Fine-grained tokens Create a new token with "Contents" permission (Read and write) for your target repository Configure the workflow: Update the "Set Github Info" node with: Your GitHub Personal Access Token Your GitHub username Your repository name The target branch (default is "main") A commit message Define file content: Modify the "File 1" and "File 2" nodes with the content you want to upload Adjust file paths if needed: In the "Create new tree" node, update the file paths if you want to change where the files are stored in the repository Save and run the workflow: Click "Test workflow" to execute the process. How to customize this workflow to your needs This workflow can be adapted in several ways: Add more files: Create additional "Set" nodes for more file content In the "Create new tree" node, add more tree entries following the same pattern (path, mode, type, content) Change file locations: Modify the "path" parameters in the "Create new tree" node to place files in different directories Dynamic file content: Replace the static content in the "File" nodes with data from other sources Use previous nodes or HTTP requests to generate file content dynamically Conditional file updates: Add IF nodes to determine which files should be updated based on certain conditions Create separate branches in your workflow for different update scenarios Scheduled updates: Replace the manual trigger with a Schedule node to run the workflow at specific intervals Combine with other triggers like Webhook or database events to push files when certain events occur Error handling: Add Error Trigger nodes to handle potential API failures Implement notification nodes to alert you of successful pushes or failures

This n8n template demonstrates how to use OpenAI's Responses API with existing LLM and AI Agent nodes. Though I would recommend just waiting for official support, if you're impatient and would like a round-about way to integrate OpenAI's responses API into your existing AI workflows then this template is sure to satisfy! This approach implements a simple API wrapper for the Responses API using n8n's builtin webhooks. When the base url is pointed to these webhooks using a custom OpenAI credential, it's possible to intercept the request and remap for compatibility. How it works An OpenAI subnode is attached to our agent but has a special custom credential where the base_url is changed to point at this template's webhooks. When executing a query, the agent's request is forwarded to our mini chat completion workflow. Here, we take the default request and remap the values to use with a HTTP node which is set to query the Responses API. Once a response is received, we'll need to remap the output for Langchain compatibility. This just means the LLM or Agent node can parse it and respond to the user. There are two response formats, one for streaming and one for non-streaming responses. How to use You must activate this workflow to be able to use the webhooks. Create the custom OpenAI credential as instructed. Go to your existing AI workflows and replace the LLM node with the custom OpenAI credential. You do not need to copy anything else over to the existing template. Requirements OpenAI account for Responses API Customising this workflow Feel free to experiment with other LLMs using this same technique! Keep up to date with the Responses API announcements and make modifications as required.

This workflow contains community nodes that are only compatible with the self-hosted version of n8n. Description This workflow automatically collects daily trending topics from Twitter and YouTube, filters them for relevance, and uses an AI model (such as Mistral Cloud or another OpenAI-compatible API) to generate engaging social media hashtags. The final results, including source platform and date, are saved into a connected Google Sheet for easy access, tracking, or team collaboration. Ideal for content creators, marketers, and social media managers, this automation eliminates the manual effort of trend research and hashtag writing by combining real-time scraping with LLM-powered generation. The result is a scalable, daily strategy tool to stay aligned with what’s trending across major platforms. How It Works Daily Trigger Starts the workflow automatically on a daily schedule. Trend Scraping Scrapes current trending content from Twitter and YouTube using the Crawl and Scrape community node. Filtering & Slicing Removes irrelevant or duplicate entries and limits each platform’s list to top-performing trends. Merge Trends Combines Twitter and YouTube trends into a single dataset. AI Hashtag Generation Sends each trend topic to an AI model to generate relevant hashtags. Output to Google Sheets Loops through AI results and writes them to a Google Sheet, including trend, platform, hashtags, and timestamp. Setup Instructions Estimated time: 10–15 minutes Prerequisites A self-hosted instance of n8n (required for community nodes) API key for Mistral Cloud or any OpenAI-compatible LLM Google Sheets account connected via OAuth2 credentials Twitter and YouTube trend URLs (or scraping logic for target regions) Template Image: Example: Crawl and Scrape Node for Twitter Trends You can use the following configuration in the Crawl and Scrape node to extract Twitter trends from Trends24) { "parameters": { "url": "https://trends24.in/", "selectors": [ { "label": "Twitter Trends", "selector": ".trend-card__list li a", "type": "text" } ] }, "name": "Scrape Twitter Trends", "type": "n8n-nodes-crawl-and-scrape.crawlAndScrape", "typeVersion": 1, "position": [300, 200] } Google Sheet Column Format Column A: Generated Hashtags

This n8n workflow finds experts on any topic, scrapes their websites, and pulls out contact emails automatically. Core services used: SerpAPI (google search) · Apify (website crawler) · OpenAI (GPT-4o email extraction). 🛠️ Step-by-Step Setup & Execution 1️⃣ Run Workflow (Manual Trigger) | Node | Type | Purpose | |------|------|---------| | Run Workflow | Manual Trigger | Start the workflow on demand while you test. | 2️⃣ Set Your Topic | Node | Type | How to configure | |------|------|------------------| | Set Topic | Set | Add a string field Topic – e.g. "n8n". This keyword drives every subsequent step. | 3️⃣ Search Google (Results 1-10) | Node | Type | API Credential | |------|------|----------------| | Search Google (top 10) | SerpAPI | Create SerpAPI credential1. Sign up → copy API key → n8n → Credentials → New → SerpAPI → paste.2. Select the credential in this node. | | Key Params | | | | q | | ={{ $json.Topic }} Expert | | location | | Region code (ex 585069efee19ad271e9c9b36) | | additionalFields.start | | "10" (Google position 1-10)| 4️⃣ Search Google (Results 11-20) | Node | Type | Notes | |------|------|-------| | Search Google (11-20) | SerpAPI (same credential) | Remove start or set to 20+ to fetch next page. | 5️⃣ Extract URL Lists | Node | Type | Script Purpose | |------|------|----------------| | Extract Url & Extract Url 2 | Code | Loop data.organic_results → output { title, link, displayed_link } for each result. | 6️⃣ Combine Both Result Sets | Node | Type | Details | |------|------|---------| | Append Results | Merge (combineAll) | Merges arrays from steps 3 & 4 into a single list for processing. | 7️⃣ Loop Over Every URL | Node | Type | Configuration | |------|------|---------------| | Loop Over Items1 | Split In Batches | Default batch = 1 (process one page at a time).onError = continueRegularOutput keeps loop alive on failures. | 8️⃣ Scrape Webpage Content (Apify) | Node | Type | API Credential | |------|------|----------------| | Scrape URL with apify | HTTP Request | Create Apify credential1. Sign up at https://console.apify.com2. Account → API tokens → copy.3. n8n → Credentials → New → HTTP Query Auth → set query param token=YOUR_TOKEN. | | Request Details | | | | Method | POST | | URL | https://api.apify.com/v2/acts/6sigmag~fast-website-content-crawler/run-sync-get-dataset-items | | JSON Body | 9️⃣ Extract Email with OpenAI | Node | Type | API Credential | |------|------|----------------| | Extract Email from webpage | LangChain Agent | Create OpenAI credential1. Generate key at https://platform.openai.com/account/api-keys2. n8n → Credentials → New → OpenAI API → paste key. | | Prompt (system) | | Output Parser | Structured Output Parser2 expects → { "email": "address OR null" } | 🔟 Loop Continues & Final Data The extracted result returns to Loop Over Items1 until every URL is processed. Typical final item JSON**: { "title": "How to Build n8n Workflows", "link": "https://example.com", "email": "info@example.com" } 💡 Optional Enhancements Idea How Save Leads Add a Google Sheets or Airtable node after the loop. Validate Emails Chain a ZeroBounce / Hunter.io verification API before saving. Parallel Crawling Increase SplitInBatches size (watch Apify rate limits). 🙋‍♂️ Need More Help? Robert Breen – Automation Consultant & n8n Expert 📧 robert.j.breen@gmail.com 🔗 https://www.linkedin.com/in/robert-breen-29429625/ 🌐 https://ynteractive.com