Workflow Templates

LintGuardian: Automated PR Linting with n8n & AI What It Does LintGuardian is an n8n workflow template that automates code quality enforcement for GitHub repositories. When a pull request is created, the workflow automatically analyzes the changed files, identifies linting issues, fixes them, and submits a new PR with corrections. This eliminates manual code style reviews, reduces back-and-forth comments, and lets your team focus on functionality rather than formatting. How It Works The workflow is triggered by a GitHub webhook when a PR is created. It fetches all changed files from the PR using the GitHub API, processes them through an AI-powered linting service (Google Gemini), and automatically generates fixes. The AI agent then creates a new branch with the corrected files and submits a "linting fixes" PR against the original branch. Developers can review and merge these fixes with a single click, keeping code consistently formatted with minimal effort. Prerequisites To use this template, you'll need: n8n instance: Either self-hosted or using n8n.cloud GitHub repository: Where you want to enforce linting standards GitHub Personal Access Token: With permissions for repo access (repo, workflow, admin:repo_hook) Google AI API Key: For the Gemini language model that powers the linting analysis GitHub webhook: Configured to send PR creation events to your n8n instance Setup Instructions Import the template into your n8n instance Configure credentials: Add your GitHub Personal Access Token under Credentials → GitHub API Add your Google AI API key under Credentials → Google Gemini API Update repository information: Locate the "Set Common Fields" code node at the beginning of the workflow Change the gitHubRepoName and gitHubOrgName values to match your repository const commonFields = { 'gitHubRepoName': 'your-repo-name', 'gitHubOrgName': 'your-org-name' } Configure the webhook: Create a file named .github/workflows/lint-guardian.yml in your repository replacing the Trigger n8n Workflow step with your webhook: name: Lint Guardian on: pull_request: types: [opened, synchronize] jobs: trigger-linting: runs-on: ubuntu-latest steps: name: Trigger n8n Workflow uses: fjogeleit/http-request-action@v1 with: url: 'https://your-n8n-instance.com/webhook/1da5a6e1-9453-4a65-bbac-a1fed633f6ad' method: 'POST' contentType: 'application/json' data: | { "pull_request_number": ${{ github.event.pull_request.number }}, "repository": "${{ github.repository }}", "branch": "${{ github.event.pull_request.head.ref }}", "base_branch": "${{ github.event.pull_request.base.ref }}" } preventFailureOnNoResponse: true Customize linting rules (optional): Modify the AI Agent's system message to specify your team's linting preferences Adjust file handling if you have specific file types to focus on or ignore Security Considerations When creating your GitHub Personal Access Token, remember to: Choose the minimal permissions needed (repo, workflow, admin:repo_hook) Set an appropriate expiration date Treat your token like a password and store it securely Consider using GitHub's fine-grained personal access tokens for more limited scope As GitHub documentation notes: "Personal access tokens are like passwords, and they share the same inherent security risks." Extending the Template You can enhance this workflow by: Adding Slack notifications when linting fixes are submitted Creating custom linting rules specific to your team's needs Expanding it to handle different types of code quality checks Adding approval steps for more controlled environments This template provides an excellent starting point that you can customize to fit your team's exact workflow and code style requirements.

This workflow automatically enriches company domain lists with comprehensive business information scraped from ZoomInfo, organizing the data in Google Sheets for sales teams and researchers. Who's it for Sales teams** building prospect databases with accurate company information Marketing professionals** researching target companies for outreach campaigns Business development teams** qualifying leads with revenue and employee data Researchers** collecting structured company data for market analysis Lead generation specialists** enriching domain lists with contact details How it works The workflow processes unprocessed domains from a Google Sheet, searches for their ZoomInfo profiles using Serper API, scrapes the company pages through Oxylabs proxy service, and extracts structured business data. Each domain is marked as processed to prevent duplicates, and the workflow includes proper rate limiting to respect API limits. What it does Loads unprocessed domains from your Google Sheets database Searches ZoomInfo using targeted queries via Serper API for each domain Validates search results and extracts relevant ZoomInfo profile URLs Scrapes company pages using Oxylabs to bypass anti-scraping protection Extracts structured data including company details, address, revenue, and employee count Updates Google Sheets with enriched company information Tracks processing status to prevent reprocessing the same domains Requirements Serper API account** with search credits (Get API key) Oxylabs subscription** for web scraping proxy service (Sign up here) Google Sheets API access** with OAuth2 authentication Google Sheets template** - Make a copy of this template sheet with pre-configured columns How to set up Make a copy of the Google Sheets template - Click here to copy the template to your Google Drive Configure API credentials in the respective HTTP Request nodes: Add Serper API key in the search node Set up Oxylabs username/password in the scraping node Set up Google Sheets authentication using OAuth2 Update the Google Sheets document ID in all Google Sheets nodes to point to your copied template Add your domain list to the sheet with 'processed' column empty or false Run the workflow using the manual trigger How to customize the workflow Search query modification**: Update the search query in the Serper node for different geographic focus (currently set for Czech Republic) Data extraction fields**: Modify the Google Sheets column mapping to include/exclude specific company data points Rate limiting**: Adjust wait times between requests to match your API rate limits Batch processing**: Configure the split batch size for processing domains in smaller groups Error handling**: Customize the continue-on-error settings based on your data quality requirements Scheduling**: Replace Manual Trigger with Schedule Trigger for automated daily/weekly runs Output data includes Complete company name and official address Phone numbers and contact information Revenue figures and employee headcount Industry classifications and business categories LinkedIn company profile URLs Geographic location details (city, state, country, postal code) Processing status tracking for workflow management Note: This workflow includes comprehensive error handling to ensure domains are always marked as processed, preventing infinite loops while maintaining data integrity. Rate limiting is built-in to respect API quotas and avoid service interruptions.

Automated QuickBooks Invoice to Custom PDF & Email Tired of the standard, boring invoices from QuickBooks Online? This workflow completely automates the process of creating beautiful, custom-branded PDF invoices and emailing them directly to your clients, saving you time and elevating your brand's professionalism. The moment you create an invoice in QuickBooks, this workflow triggers, fetches all the necessary data, and generates a lavish, multi-page-aware PDF invoice complete with your company logo and signature. Key Features Fully Automated:** Runs instantly when a new invoice is created in QuickBooks. Custom Branding:** Automatically fetches your company logo and signature from a URL to place on the invoice. Modern & Professional Design:** Uses a premium, multi-column HTML template that is clean, easy to read, and far superior to the default QBO templates. Multi-Page Ready:** If an invoice has many line items, the template will intelligently create multiple pages and add a "Page X of Y" footer automatically. Smart Layout:** The totals and summary block are designed to never break across pages, ensuring a professional look no matter the length. Automatic Emailing:** The final PDF is attached to a beautifully formatted email and sent directly to the customer's email address on file. Prerequisites Before you start, you will need a few things: A running n8n instance. A QuickBooks Online account with API access. A running Gotenberg instance. This is a powerful, open-source tool for converting HTML to PDF. This workflow is designed to connect to its API. You can learn more about it here. Publicly accessible URLs for your company logo and signature image (e.g., hosted on your website or a service like Imgur). Setup Guide Follow these steps carefully to configure the workflow for your own use. Nodes that need your attention are marked with a [!!] prefix. Step 1: Configure the QuickBooks Webhook The workflow starts with a webhook. You need to tell QuickBooks to send information to this webhook. Open the [!!] Listen for New QuickBooks Invoice node. You will see a Webhook URL. Copy the Production URL. Go to your QuickBooks Developer dashboard, select your app, and navigate to the Webhooks section. Paste the n8n URL into the Endpoint URL field and select the Invoice event to subscribe to. Step 2: Connect Your QuickBooks Account Open the [!!] Get Invoice Data from QuickBooks node. In the "Credentials" field, select your existing QuickBooks Online credentials or create a new set. Step 3: Add Your Branding Open the [!!] Fetch Company Logo Image node. In the URL field, replace the placeholder with the public URL of your company's logo. Open the [!!] Fetch Company Signature Image node. In the URL field, replace the placeholder with the public URL of your signature image. Step 4: Update the PDF Generation Service Open the [!!] Generate PDF via Gotenberg node. In the URL field, replace the placeholder http://YourGotenBergInstanceURL/... with the real URL of your running Gotenberg instance. Step 5: Configure Your Email Open the [!!] Email PDF Invoice to Customer node. In the "Credentials" field, select your SMTP or email service credentials. Customize the From Email and Subject fields. You can also edit the beautiful HTML email body to match your company's tone of voice. Step 6: Activate Your Workflow You're all set! Save the workflow and activate it using the toggle at the top-right of the screen. Now, when you create a new invoice in QuickBooks, this automation will handle the rest. A Note from the Creator Thank you for using this workflow! I believe that professional and automated invoicing is a cornerstone of a great business. This tool was designed to save you time and help you put your best foot forward with every client interaction. If you have any questions or need assistance, feel free to reach out. Website:** https://www.elegantbiztech.com/ Email:** sales@elegantbiztech.com

🧑‍💼 Who is this for? This workflow is for anyone with two YouTube channels who wants to copy playlists from one to the other — no technical skills required. Whether you're a content creator, hobbyist, educator, or just someone managing multiple channels, this workflow helps you save time and avoid the manual work of recreating playlists video by video. 🧠 What problem is this workflow solving? YouTube doesn't provide an option to transfer or duplicate playlists between accounts or channels. That means if you want the same playlists in two places, you're stuck: Creating new playlists manually Searching for each video again Copy-pasting links one by one This workflow automates the entire process for you — accurately, quickly, and with no manual work. ⚙️ What this workflow does Retrieves all playlists from a source YouTube channel (excluding private ones) For each playlist: Gets all its videos Filters out private or unavailable videos Creates a new playlist in the target channel with the same title Adds the videos to the new playlist Continues smoothly even if some videos fail to copy (e.g., if they’re restricted or deleted) 🛠️ Setup Create two YouTube OAuth2 credentials in n8n: One for your source channel One for your target channel Assign the credentials to the correct nodes as indicated in the sticky notes: Source nodes → source credentials Target nodes → target credentials Click “Test workflow” to run it. > ⚠️ Note: If you have many playlists or videos, you may hit YouTube’s API quota. You can request a quota increase in your Google Cloud Console if needed. 🧩 How to customize this workflow to your needs ✂️ Copy only specific playlists Use a Filter node after the playlist fetch to include only certain titles or IDs. 📝 Change the title of the copied playlists Modify the title in the Create playlist node (e.g., add “(Copy)” or a prefix). 🔄 Automate it regularly Replace the Manual Trigger with a Cron node if you want to run this periodically. 🧪 Test safely If you're unsure, use a secondary channel as your test target before applying changes to your main account.

Who this is for The Crunchbase B2B Lead Discovery Pipeline is designed for sales teams, B2B marketers, business analysts, and data operations teams who need a reliable way to extract, structure, and summarize company information from Crunchbase to fuel lead generation and market intelligence. This workflow is ideal for: Sales Development Reps (SDRs) - Needing structured leads from Crunchbase Marketing Analysts - Generating segmented outreach lists Growth Teams - Identifying trending B2B startups RevOps Teams - Automating company research pipelines Data Teams - Consolidating insights into Google Sheets for dashboards What problem is this workflow solving? Manual extraction of company data from Crunchbase is time-consuming, inconsistent, and often lacks the contextual summary required for sales enablement or growth targeting. This workflow automates the extraction, transformation, summarization, and delivery of Crunchbase company data into structured formats, making it instantly usable for B2B targeting and analysis. It solves: The difficulty of scaling lead discovery from Crunchbase The need to summarize raw textual content for quick insights The lack of integration between web scraping, LLM processing, and storage What this workflow does Markdown to Textual Data Extractor**: Takes raw scraped markdown from Crunchbase and converts it into readable plain text using a basic LLM chain Structured Data Extraction**: Applies a parsing model (OpenAI) to extract structured fields such as company name, funding rounds, industry tags, location, and founding year Summarization Chain**: Generates an executive summary from the raw Crunchbase text using a summarization prompt template Send to Google Sheets**: Adds the structured data and summary into a Google Sheet for team access and further processing Persist to Disk**: Saves both raw and structured data files locally for archiving or further use Webhook Notification**: Sends a structured payload to a webhook endpoint (e.g., Slack, CRM, internal tools) with lead insights Pre-conditions You need to have a Bright Data account and do the necessary setup as mentioned in the "Setup" section below. You need to have an OpenAI Account. Setup Sign up at Bright Data. Navigate to Proxies & Scraping and create a new Web Unlocker zone by selecting Web Unlocker API under Scraping Solutions. In n8n, configure the Header Auth account under Credentials (Generic Auth Type: Header Authentication). The Value field should be set with the Bearer XXXXXXXXXXXXXX. The XXXXXXXXXXXXXX should be replaced by the Web Unlocker Token. In n8n, Configure the Google Sheet Credentials with your own account. Follow this documentation - Set Google Sheet Credential In n8n, configure the OpenAi account credentials. Ensure the URL and Bright Data zone name are correctly set in the Set URL, Filename and Bright Data Zone node. Set the desired local path in the Write a file to disk node to save the responses. How to customize this workflow to your needs LLM Prompt Customization : Modify the extraction prompt to include additional fields like revenue, social links, leadership team Adjust summarization tone (e.g., executive summary, sales-focused snapshot or marketing digest) File Persistence Store raw markdown, extracted JSON, and summary text separately for audit/debug Webhook Notification Connect to CRM (e.g., HubSpot, Salesforce) via webhook to automatically create leads Send Slack notifications to alert sales reps when a new high-potential company is discovered

🎙️ VoiceScribe AI: Telegram Audio Message Auto Transcription with OpenAI Whisper > Automatically transcribe Telegram voice messages and store them as structured logs in Google Sheets, while backing up the audio in Google Drive. 🧑‍💼 Who’s it for Journalists, content creators, or busy professionals who often record voice memos or short interviews on the go. Anyone who wants to turn voice recordings into searchable, structured notes. ⚙️ How it works / What it does User sends a voice message to a Telegram bot. n8n checks if the message is an audio voice note. If valid, it downloads the audio file and: Transcribes it using OpenAI Whisper (or your LLM of choice). Uploads the original audio to Google Drive for safekeeping. The transcript and audio metadata are merged. The workflow: Logs the data into a Google Sheet. Sends a formatted confirmation message to the user via Telegram. If the input is not audio, the bot politely informs the user that only voice messages are accepted. ✅ Features Accepts only Telegram voice messages. Transcribes via OpenAI Whisper. Logs DateTime, Duration, Transcript, and Audio URL to Google Sheets. Sends user feedback message via Telegram with download + transcript link. 🚀 How to set up Prerequisites Telegram Bot connected to n8n (via Telegram Trigger) Google Drive & Google Sheets credentials configured OpenAI or Whisper API credentials (for transcription) Steps Telegram Trigger Start the flow when a new message is sent to your bot. Check Message Type Use a conditional node to confirm it's a voice message. Download Voice Message Download the .oga file from Telegram. Transcribe Audio Send the binary audio to OpenAI Whisper or your transcription service. Upload to Google Drive Backup the original audio file. Merge Outputs Combine transcription with Drive metadata. Transform to Row Format Prepare structured JSON for Google Sheets. Append to Google Sheet Store the transcript log (DateTime, Duration, Transcript, AudioURL). Send Confirmation to User Inform the user via Telegram with their transcript and download link. Unsupported Message Handler Reply to users who send non-audio messages. 📄 Example Output in Google Sheet | DateTime | Duration | Transcript | AudioURL | |-----------------------|----------|--------------------------------------------|------------------------------------------------------------| | 2025-08-07T13:12:19Z | 27 | Dự án Outlet Activation là... | https://drive.google.com/uc?id=xxxx&export=download | 🧠 How to customize the workflow Swap Whisper with Deepgram, AssemblyAI, or other providers. Add speaker name detection or prompt-based tagging via GPT. Route transcripts into Notion, Airtable, or CRM systems. Add multi-language support or summarization steps. 📦 Requirements | Component | Required | |---------------------|----------| | Telegram API | ✅ | | Google Drive API | ✅ | | Google Sheets API | ✅ | | OpenAI Whisper API | ✅ | | n8n Cloud or Self-hosted | ✅ | Created with ❤️ using n8n

Who this is for The TrustPilot SaaS Product Review Tracker is designed for product managers, SaaS growth teams, customer experience analysts, and marketing teams who need to extract, summarize, and analyze customer feedback at scale from TrustPilot. This workflow is tailored for: Product Managers** - Monitoring feedback to drive feature improvements Customer Support & CX Teams** - Identifying sentiment trends or recurring issues Marketing & Growth Teams** - Leveraging testimonials and market perception Data Analysts** - Tracking competitor reviews and benchmarking Founders & Executives** - Wanting aggregated insights into customer satisfaction What problem is this workflow solving? Manually monitoring, extracting, and summarizing TrustPilot reviews is time-consuming, fragmented, and hard to scale across multiple SaaS products. This workflow automates that process from unlocking the data behind anti-bot layers to summarizing and storing customer insights enabling teams to respond faster, spot trends, and make data-backed product decisions. This workflow solves: The challenge of scraping protected review data (using Bright Data Web Unlocker) The need for structured insights from unstructured review content The lack of automated delivery to storage and alerting systems like Google Sheets or webhooks What this workflow does Extract TrustPilot Reviews: Uses Bright Data Web Unlocker to bypass anti-bot protections and pull markdown-based content from product review pages Convert Markdown to Text: Leverages a basic LLM chain to clean and convert scraped markdown into plain text Structured Information Extraction: Uses OpenAI GPT-4o via the Information Extractor node to extract fields like product name, review date, rating, and reviewer sentiment Summarization Chain: Generates concise summaries of overall review sentiment and themes using OpenAI Merge & Aggregate Output: Consolidates individual extracted records into a structured batch output Outbound Data Delivery: Google Sheets – Appends summary and structured review data Write to Disk – Persists raw and processed content locally Webhook Notification – Sends a real-time alert with summarized insights Pre-conditions You need to have a Bright Data account and do the necessary setup as mentioned in the "Setup" section below. You need to have an OpenAI Account. Setup Sign up at Bright Data. Navigate to Proxies & Scraping and create a new Web Unlocker zone by selecting Web Unlocker API under Scraping Solutions. In n8n, configure the Header Auth account under Credentials (Generic Auth Type: Header Authentication). The Value field should be set with the Bearer XXXXXXXXXXXXXX. The XXXXXXXXXXXXXX should be replaced by the Web Unlocker Token. In n8n, Configure the Google Sheet Credentials with your own account. Follow this documentation - Set Google Sheet Credential In n8n, configure the OpenAi account credentials. Ensure the URL and Bright Data zone name are correctly set in the Set URL, Filename and Bright Data Zone node. Set the desired local path in the Write a file to disk node to save the responses. How to customize this workflow to your needs Target Multiple Products : Configure the Bright Data input URL dynamically for different SaaS product TrustPilot URLs Loop through a product list and run parallel jobs for each Customize Extraction Fields : Update the prompt in the Information Extractor to include: Review title Response from company Specific feature mentions Competitor references Tune Summarization Style Change tone**: executive summary, customer pain-point focus, or marketing quote extract Enable sentiment aggregation** (e.g., 30% negative, 50% neutral, 20% positive) Expand Output Destinations Push to Notion, Airtable, or CRM tools using additional webhook nodes Generate and send PDF reports (via PDFKit or HTML-to-PDF nodes) Schedule summary digests via Gmail or Slack

A n8n workflow designed to shorten URLs using Dub.co API. How it works: It shortens a url using Dub.co API, with the ability to use custom domains and projects. It updates the current shortened url if the slug has been already used. Estimated Time: Around 15 minutes. Requirements: A Dub.co account. Configuration: Configure the "API Auth" node to add your Dub.co API key, project slug, and the long URL. There some extras that you're able to configure too. You will be able to do that by clicking the "API Auth" node and filling the fields. Detailed Instructions: Sticky notes within the workflow provide extensive setup information and guidance. Keywords: n8n workflow, dub.co, dub.sh, url shortener, short urls, short links

📊 Real-Time Flight Data Analytics Bot with Dynamic Chart Generation via Telegram 🚀 Template Overview This advanced n8n workflow creates an intelligent Telegram bot that transforms raw CSV flight data into stunning, interactive visualizations. Users can generate professional charts on-demand through a conversational interface, making data analytics accessible to anyone via messaging. Key Innovation: Combines real-time data processing, Chart.js visualization engine, and Telegram's messaging platform to deliver instant business intelligence insights. 🎯 What This Template Does Transform your flight booking data into actionable insights with four powerful visualization types: 📈 Bar Charts**: Top 10 busiest airlines by flight volume 🥧 Pie Charts**: Flight duration distribution (Short/Medium/Long-haul) 🍩 Doughnut Charts**: Price range segmentation with average pricing 📊 Line Charts**: Price trend analysis across flight durations Each chart includes auto-generated insights, percentages, and key business metrics delivered instantly to users' phones. 🏗️ Technical Architecture Core Components Telegram Webhook Trigger: Captures user interactions and button clicks Smart Routing Engine: Conditional logic for command detection and chart selection CSV Data Pipeline: File reading → parsing → JSON transformation Chart Generation Engine: JavaScript-powered data processing with Chart.js Image Rendering Service: QuickChart API for high-quality PNG generation Response Delivery: Binary image transmission back to Telegram Data Flow Architecture User Input → Command Detection → CSV Processing → Data Aggregation → Chart Configuration → Image Generation → Telegram Delivery 🛠️ Setup Requirements Prerequisites n8n instance** (self-hosted or cloud) Telegram Bot Token** from @BotFather CSV dataset** with flight information Internet connectivity** for QuickChart API Dataset Source This template uses the Airlines Flights Data dataset from GitHub: 🔗 Dataset: Airlines Flights Data by Rohit Grewal Required Data Schema Your CSV file should contain these columns: airline,flight,source_city,departure_time,arrival_time,duration,price,class,destination_city,stops File Structure /data/ └── flights.csv (download from GitHub dataset above) ⚙️ Configuration Steps 1. Telegram Bot Setup Create a new bot via @BotFather on Telegram Copy your bot token Configure the Telegram Trigger node with your token Set webhook URL in your n8n instance 2. Data Preparation Download the dataset from Airlines Flights Data Upload the CSV file to /data/flights.csv in your n8n instance Ensure UTF-8 encoding Verify column headers match the dataset schema Test file accessibility from n8n 3. Workflow Activation Import the workflow JSON Configure all Telegram nodes with your bot token Test the /start command Activate the workflow 🔧 Technical Implementation Details Chart Generation Process Bar Chart Logic: // Aggregate airline counts const airlineCounts = {}; flights.forEach(flight => { const airline = flight.airline || 'Unknown'; airlineCounts[airline] = (airlineCounts[airline] || 0) + 1; }); // Generate Chart.js configuration const chartConfig = { type: 'bar', data: { labels, datasets }, options: { responsive: true, plugins: {...} } }; Dynamic Color Schemes: Bar Charts: Professional blue gradient palette Pie Charts: Duration-based color coding (light→dark blue) Doughnut Charts: Price-tier specific colors (green→purple) Line Charts: Trend-focused red gradient with smooth curves Performance Optimizations Efficient Data Processing: Single-pass aggregations with O(n) complexity Smart Caching: QuickChart handles image caching automatically Minimal Memory Usage: Stream processing for large datasets Error Handling: Graceful fallbacks for missing data fields Advanced Features Auto-Generated Insights: Statistical calculations (percentages, averages, totals) Trend analysis and pattern detection Business intelligence summaries Contextual recommendations User Experience Enhancements: Reply keyboards for easy navigation Visual progress indicators Error recovery mechanisms Mobile-optimized chart dimensions (800x600px) 📈 Use Cases & Business Applications Airlines & Travel Companies Fleet Analysis**: Monitor airline performance and market share Pricing Strategy**: Analyze competitor pricing across routes Operational Insights**: Track duration patterns and efficiency Data Analytics Teams Self-Service BI**: Enable non-technical users to generate reports Mobile Dashboards**: Access insights anywhere via Telegram Rapid Prototyping**: Quick data exploration without complex tools Business Intelligence Executive Reporting**: Instant charts for presentations Market Research**: Compare industry trends and benchmarks Performance Monitoring**: Track KPIs in real-time 🎨 Customization Options Adding New Chart Types Create new Switch condition Add corresponding data processing node Configure Chart.js options Update user interface menu Data Source Extensions Replace CSV with database connections Add real-time API integrations Implement data refresh mechanisms Support multiple file formats Visual Customizations // Custom color palette backgroundColor: ['#your-colors'], // Advanced styling borderRadius: 8, borderSkipped: false, // Animation effects animation: { duration: 2000, easing: 'easeInOutQuart' } 🔒 Security & Best Practices Data Protection Validate CSV input format Sanitize user inputs Implement rate limiting Secure file access permissions Error Handling Graceful degradation for API failures User-friendly error messages Automatic retry mechanisms Comprehensive logging 📊 Expected Outputs Sample Generated Insights "✈️ Vistara leads with 350+ flights, capturing 23.4% market share" "📈 Long-haul flights dominate at 61.1% of total bookings" "💰 Budget category (₹0-10K) represents 47.5% of all bookings" "📊 Average prices peak at ₹14K for 6-8 hour duration flights" Performance Metrics Response Time**: <3 seconds for chart generation Image Quality**: 800x600px high-resolution PNG Data Capacity**: Handles 10K+ records efficiently Concurrent Users**: Scales with n8n instance capacity 🚀 Getting Started Download the workflow JSON Import into your n8n instance Configure Telegram bot credentials Upload your flight data CSV Test with /start command Deploy and share with your team 💡 Pro Tips Data Quality**: Clean data produces better insights Mobile First**: Charts are optimized for mobile viewing Batch Processing**: Handles large datasets efficiently Extensible Design**: Easy to add new visualization types Ready to transform your data into actionable insights? Import this template and start generating professional charts in minutes! 🚀

Notice Community nodes can only be installed on self-hosted instances of n8n. Who this is for The Legal Case Research Extractor is a powerful automated workflow designed for legal tech teams, researchers, law firms, and data scientists focused on transforming unstructured legal case data into actionable, structured insights. This workflow is tailored for: Legal Researchers automating case law data mining Litigation Support Teams handling large volumes of case records LawTech Startups building AI-powered legal research assistants Compliance Analysts extracting case-specific insights AI Developers working on legal NLP, summarization, and search engines What problem is this workflow solving? Legal case data is often locked in semi-structured or raw HTML formats, scattered across jurisdiction-specific websites. Manually extracting and processing this data is tedious and inefficient. This workflow automates: Extraction of legal case data via Bright Data's powerful MCP infrastructure Parsing of HTML into clean, readable text using Google Gemini LLM Structuring and delivering the output through webhook and file storage What this workflow does Input Set the Legal Case Research URL node is responsible for setting the legal case URL for the data extraction. Bright Data MCP Data Extractor Bright Data MCP Client For Legal Case Research node is responsible for the legal case extraction via the Bright Data MCP tool - scrape_as_html Case Extractor Google Gemini based Case Extractor is responsible for producing a paginated list of cases Loop through Legal Case URLs Receives a collection of legal case links to process Each URL represents a different case from a target legal website Bright Data MCP Scraping Utilizes Bright Data’s scrape_as_html MCP mode Retrieves raw HTML content of each legal case Google Gemini LLM Extraction Transforms raw HTML into clean, structured text Performs additional information extraction if required (e.g., case summary, court, jurisdiction etc.) Webhook Notification Sends extracted legal case content to a configurable webhook URL Enables downstream processing or storage in legal databases Binary Conversion & File Persistence Converts the structured text to binary format Saves the final response to disk for archival or further processing Pre-conditions Knowledge of Model Context Protocol (MCP) is highly essential. Please read this blog post - model-context-protocol You need to have the Bright Data account and do the necessary setup as mentioned in the Setup section below. You need to have the Google Gemini API Key. Visit Google AI Studio You need to install the Bright Data MCP Server @brightdata/mcp You need to install the n8n-nodes-mcp Setup Please make sure to setup n8n locally with MCP Servers by navigating to n8n-nodes-mcp Please make sure to install the Bright Data MCP Server @brightdata/mcp on your local machine. Sign up at Bright Data. Create a Web Unlocker proxy zone called mcp_unlocker on Bright Data control panel. Navigate to Proxies & Scraping and create a new Web Unlocker zone by selecting Web Unlocker API under Scraping Solutions. In n8n, configure the Google Gemini(PaLM) Api account with the Google Gemini API key (or access through Vertex AI or proxy). In n8n, configure the credentials to connect with MCP Client (STDIO) account with the Bright Data MCP Server as shown below. Make sure to copy the Bright Data API_TOKEN within the Environments textbox above as API_TOKEN=<your-token> How to customize this workflow to your needs Target New Legal Portals Modify the legal case input URLs to scrape from different state or federal case databases Customize LLM Extraction Modify the prompt to extract specific fields: case number, plaintiff, case summary, outcome, legal precedents etc. Add a summarization step if needed Enhance Loop Handling Integrate with a Google Sheet or API to dynamically fetch case URLs Add error handling logic to skip failed cases and log them Improve Security & Compliance Redact sensitive information before sending via webhook Store processed case data in encrypted cloud storage Output Formats Save as PDF, JSON, or Markdown Enable output to cloud storage (S3, Google Drive) or legal document management systems

Tesseract - Money Mate Workflow Description Disclaimer: This template requires the n8n-nodes-tesseractjs community node, which is only available on self-hosted n8n instances. You’ll need a self-hosted n8n setup to use this workflow. Who is this for? This workflow is designed for individuals, freelancers, or small business owners who want an easy way to track expenses using Telegram. It’s ideal for anyone looking to digitize receipts—whether from photos or text messages—using free tools, without needing advanced technical skills. What problem does this workflow solve? Manually entering receipt details into a spreadsheet or app is time-consuming and prone to mistakes. This workflow automates the process by extracting information from receipt images or text messages sent via Telegram, categorizing expenses, and sending back a clear, formatted summary. It saves time, reduces errors, and makes expense tracking effortless. What this workflow does The workflow listens for messages sent to a Telegram bot, which can be either text descriptions of expenses or photos of receipts. If a photo is sent, Tesseract (an open-source text recognition tool) extracts the text. If text is sent, it’s processed directly. An AI model (LLaMA via OpenRouter) analyzes the input, categorizes it into expense types (e.g., Food & Beverages, Household, Transport), and creates a structured summary including store name, date, items, total, and category. The summary is then sent back to the user’s Telegram chat. Setup Instructions Follow these step-by-step instructions to set up the workflow. No advanced technical knowledge is required, but you’ll need a self-hosted n8n instance. Set Up a Self-Hosted n8n Instance: If you don’t have n8n installed, follow the n8n self-hosting guide to set it up. You can use platforms like Docker or a cloud provider (e.g., DigitalOcean, AWS). Ensure your n8n instance is running and accessible via a web browser. Install the Tesseract Community Node: In your n8n instance, go to Settings > Community Nodes in the sidebar. Click Install a Community Node, then enter n8n-nodes-tesseractjs in the search bar. Click Install and wait for confirmation. This node enables receipt image processing. If you encounter issues, check the n8n community nodes documentation for troubleshooting. Create a Telegram Bot: Open Telegram and search for @BotFather to start a new bot. Send /start to BotFather, then /newbot to create your bot. Follow the prompts to name your bot (e.g., “MoneyMateBot”). BotFather will provide a Bot Token (e.g., 23872837287:ExampleExampleExample). Copy this token. In n8n, go to Credentials > Add Credential, select Telegram API, and paste the token. Name the credential (e.g., “MoneyMateBot”) and save. Set Up OpenRouter for AI Processing: Sign up for a free account at OpenRouter. In your OpenRouter dashboard, generate an API Key under the API section. In n8n, go to Credentials > Add Credential, select OpenRouter API, and paste the API key. Name it (e.g., “OpenRouter Account”) and save. The free tier of OpenRouter’s LLaMA model is sufficient for this workflow. Import and Configure the Workflow: Download the workflow JSON file (provided separately or copy from the source). In n8n, go to Workflows > Import Workflow and upload the JSON file. Open the imported workflow (“Tesseract - Money Mate”). Ensure the Telegram Trigger and Send Expense Summary nodes use the Telegram credential you created. Ensure the AI Analyzer node uses the OpenRouter credential. Save the workflow. Test the Workflow: Activate the workflow by toggling the Active switch in n8n. In Telegram, find your bot (e.g., @MoneyMateBot) and send /start. Test with a sample input (see “Example Inputs” below). Check the n8n workflow execution panel to ensure data flows correctly. If errors occur, double-check credentials and node connections. Activate for Continuous Use: Once tested, keep the workflow active in n8n. Your bot will now process any text or image sent to it via Telegram. Example Inputs/Formats To help the workflow process your data accurately, use clear and structured inputs. Below are examples of valid inputs: Text Input Example: Send a message to your Telegram bot like this: Bought coffee at Starbucks, Jalan Sudirman, yesterday. Total Rp 50,000. 2 lattes, each Rp 25,000. Expected Output: hello [Your Name] Ini Rekap Belanjamu 📋 Store: Starbucks 📍 Location: Jalan Sudirman 📅 Date: 2025-05-26 🛒 Items: Latte: Rp 25,000 Latte: Rp 25,000 💸 Total: Rp 50,000 📌 Category: Food & Beverages Image Input Example: Upload a photo of a receipt to your Telegram bot. The receipt should contain: Store name (e.g., “Alfamart”) Address (e.g., “Jl. Gatot Subroto, Jakarta”) Date and time (e.g., “27/05/2025 14:00”) Items with prices (e.g., “Bread Rp 15,000”, “Milk Rp 20,000”) Total amount (e.g., “Total: Rp 35,000”) Expected Output: hello [Your Name] Ini Rekap Belanjamu 📋 Store: Alfamart 📍 Location: Jl. Gatot Subroto, Jakarta 📅 Date: 2025-05-27 14:00 🛒 Items: Bread: Rp 15,000 Milk: Rp 20,000 💸 Total: Rp 35,000 📌 Category: Household Tips for Images: Ensure the receipt is well-lit and text is readable. Avoid blurry or angled photos for better Tesseract accuracy. How to Customize This Workflow Change Expense Categories: In the **AI Categorizer node, edit the prompt to include custom categories (e.g., add “Entertainment” or “Utilities” to the list: Food & Beverages, Household, Transport). Modify Response Format: In the **Format Summary Message node, adjust the JavaScript code to change how the summary looks (e.g., add emojis, reorder fields). Save to a Database: Add a node (e.g., Google Sheets or PostgreSQL) after the **Format Summary Message node to store summaries. Support Other Languages: In the **AI Categorizer node, update the prompt to handle additional languages (e.g., Spanish, Mandarin) by specifying them in the instructions. Add Error Handling: Enhance the **Check Invalid Input node to catch more edge cases, like invalid dates. All Free, End-to-End This workflow is 100% free! It leverages: Telegram Bot API**: Free via BotFather. Tesseract**: Open-source text recognition. LLaMA via OpenRouter**: Free tier available for AI processing. Enjoy automating your expense tracking without any cost! Made by: khmuhtadin Need a custom? contact me on LinkedIn or Web

Template: Create an AI Knowledge Base Chatbot with Google Drive and OpenAI GPT (Venio/Salesbear) 📋 Template Overview This comprehensive n8n workflow template creates an intelligent AI chatbot that automatically transforms your Google Drive documents into a searchable knowledge base. The chatbot uses OpenAI's GPT models to provide accurate, context-aware responses based exclusively on your uploaded documents, making it perfect for customer support, internal documentation, and knowledge management systems. 🎯 What This Template Does Automated Knowledge Processing Real-time Document Monitoring**: Automatically detects when files are added or updated in your designated Google Drive folder Intelligent Document Processing**: Converts PDFs, text files, and other documents into searchable vector embeddings Smart Text Chunking**: Breaks down large documents into optimally-sized chunks for better AI comprehension Vector Storage**: Creates a searchable knowledge base that the AI can query for relevant information AI-Powered Chat Interface Webhook Integration**: Receives questions via HTTP requests from any external platform (Venio/Salesbear) Contextual Responses**: Maintains conversation history for natural, flowing interactions Source-Grounded Answers**: Provides responses based strictly on your document content, preventing hallucinations Multi-platform Support**: Works with any chat platform that can send HTTP requests 🔧 Pre-conditions and Requirements Required API Accounts and Permissions 1. Google Drive API Access Google Cloud Platform account Google Drive API enabled OAuth2 credentials configured Read access to your target Google Drive folder 2. OpenAI API Account Active OpenAI account with API access Sufficient API credits for embeddings and chat completions API key with appropriate permissions 3. n8n Instance n8n cloud account or self-hosted instance Webhook functionality enabled Ability to install community nodes (LangChain nodes) 4. Target Chat Platform (Optional) API credentials for your chosen chat platform Webhook capability or API endpoints for message sending Required Permissions Google Drive**: Read access to folder contents and file downloads OpenAI**: API access for text-embedding-ada-002 and gpt-4o-mini models External Platform**: API access for sending/receiving messages (if integrating with existing chat systems) 🚀 Detailed Workflow Operation Phase 1: Knowledge Base Creation File Monitoring: Two trigger nodes continuously monitor your Google Drive folder for new files or updates Document Discovery: When changes are detected, the workflow searches for and identifies the modified files Content Extraction: Downloads the actual file content from Google Drive Text Processing: Uses LangChain's document loader to extract text from various file formats Intelligent Chunking: Splits documents into overlapping chunks (configurable size) for optimal AI processing Vector Generation: Creates embeddings using OpenAI's text-embedding-ada-002 model Storage: Stores vectors in an in-memory vector store for instant retrieval Phase 2: Chat Interaction Question Reception: Webhook receives user questions in JSON format Data Extraction: Parses incoming data to extract chat content and session information AI Processing: AI Agent analyzes the question and determines relevant context Knowledge Retrieval: Searches the vector store for the most relevant document sections Response Generation: OpenAI generates responses based on found content and conversation history Authentication: Validates the request using token-based authentication Response Delivery: Sends the answer back to the originating platform 📚 Usage Instructions After Setup Adding Documents to Your Knowledge Base Upload Files: Simply drag and drop documents into your configured Google Drive folder Supported Formats: PDFs, TXT, DOC, DOCX, and other text-based formats Automatic Processing: The workflow will automatically detect and process new files within minutes Updates: Modify existing files, and the knowledge base will automatically update Integrating with Your Chat Platform Webhook URL: Use the generated webhook URL to send questions POST https://your-n8n-domain/webhook/your-custom-path Content-Type: application/json { "body": { "Data": { "ChatMessage": { "Content": "What are your business hours?", "RoomId": "user-123-session", "Platform": "web", "User": { "CompanyId": "company-456" } } } } } Response Format: The chatbot returns structured responses that your platform can display Testing Your Chatbot Initial Test: Send a simple question about content you know exists in your documents Context Testing: Ask follow-up questions to test conversation memory Edge Cases: Try questions about topics not in your documents to verify appropriate responses Performance: Monitor response times and accuracy 🎨 Customization Options System Message Customization Modify the AI Agent's system message to match your brand and use case: You are a [YOUR_BRAND] customer support specialist. You provide helpful, accurate information based on our documentation. Always maintain a [TONE] tone and [SPECIFIC_GUIDELINES]. Response Behavior Customization Tone and Voice**: Adjust from professional to casual, formal to friendly Response Length**: Configure for brief answers or detailed explanations Fallback Messages**: Customize what the bot says when it can't find relevant information Language Support**: Adapt for different languages or technical terminologies Technical Configuration Options Document Processing Chunk Size**: Adjust from 1000 to 4000 characters based on your document complexity Overlap**: Modify overlap percentage for better context preservation File Types**: Add support for additional document formats AI Model Configuration Model Selection**: Switch between gpt-4o-mini (cost-effective) and gpt-4 (higher quality) Temperature**: Adjust creativity vs. factual accuracy (0.0 to 1.0) Max Tokens**: Control response length limits Memory and Context Conversation Window**: Adjust how many previous messages to remember Session Management**: Configure session timeout and user identification Context Retrieval**: Tune how many document chunks to consider per query Integration Customization Authentication Methods Token-based**: Default implementation with bearer tokens API Key**: Simple API key validation OAuth**: Full OAuth2 implementation for secure access Custom Headers**: Validate specific headers or signatures Response Formatting JSON Structure**: Customize response format for your platform Markdown Support**: Enable rich text formatting in responses Error Handling**: Define custom error messages and codes 🎯 Specific Use Case Examples Customer Support Chatbot Scenario: E-commerce company with product documentation, return policies, and FAQ documents Setup: Upload product manuals, policy documents, and common questions to Google Drive Customization: Professional tone, concise answers, escalation triggers for complex issues Integration: Website chat widget, mobile app, or customer portal Internal HR Knowledge Base Scenario: Company HR department with employee handbook, policies, and procedures Setup: Upload HR policies, benefits information, and procedural documents Customization: Friendly but professional tone, detailed policy explanations Integration: Internal Slack bot, employee portal, or HR ticketing system Technical Documentation Assistant Scenario: Software company with API documentation, user guides, and troubleshooting docs Setup: Upload API docs, user manuals, and technical specifications Customization: Technical tone, code examples, step-by-step instructions Integration: Developer portal, support ticket system, or documentation website Educational Content Helper Scenario: Educational institution with course materials, policies, and student resources Setup: Upload syllabi, course content, academic policies, and student guides Customization: Helpful and encouraging tone, detailed explanations Integration: Learning management system, student portal, or mobile app Healthcare Information Assistant Scenario: Medical practice with patient information, procedures, and policy documents Setup: Upload patient guidelines, procedure explanations, and practice policies Customization: Compassionate tone, clear medical explanations, disclaimer messaging Integration: Patient portal, appointment system, or mobile health app 🔧 Advanced Customization Examples Multi-Language Support // In Edit Fields node, detect language and route accordingly const language = $json.body.Data.ChatMessage.Language || 'en'; const systemMessage = { 'en': 'You are a helpful customer support assistant...', 'es': 'Eres un asistente de soporte al cliente útil...', 'fr': 'Vous êtes un assistant de support client utile...' }; Department-Specific Routing // Route questions to different knowledge bases based on department const department = $json.body.Data.ChatMessage.Department; const vectorStoreKey = vector_store_${department}; Advanced Analytics Integration // Track conversation metrics const analytics = { userId: $json.body.Data.ChatMessage.User.Id, timestamp: new Date().toISOString(), question: $json.body.Data.ChatMessage.Content, response: $json.response, responseTime: $json.processingTime }; 📊 Performance Optimization Tips Document Management Optimal File Size**: Keep documents under 10MB for faster processing Clear Structure**: Use headers and sections for better chunking Regular Updates**: Remove outdated documents to maintain accuracy Logical Organization**: Group related documents in subfolders Response Quality System Message Refinement**: Regularly update based on user feedback Context Tuning**: Adjust chunk size and overlap for your specific content Testing Framework**: Implement systematic testing for response accuracy User Feedback Loop**: Collect and analyze user satisfaction data Cost Management Model Selection**: Use gpt-4o-mini for cost-effective responses Caching Strategy**: Implement response caching for frequently asked questions Usage Monitoring**: Track API usage and set up alerts Batch Processing**: Process multiple documents efficiently 🛡️ Security and Compliance Data Protection Document Security**: Ensure sensitive documents are properly secured Access Control**: Implement proper authentication and authorization Data Retention**: Configure appropriate data retention policies Audit Logging**: Track all interactions for compliance Privacy Considerations User Data**: Minimize collection and storage of personal information Session Management**: Implement secure session handling Compliance**: Ensure adherence to relevant privacy regulations Encryption**: Use HTTPS for all communications 🚀 Deployment and Scaling Production Readiness Environment Variables**: Use environment variables for sensitive configurations Error Handling**: Implement comprehensive error handling and logging Monitoring**: Set up monitoring for workflow health and performance Backup Strategy**: Ensure document and configuration backups Scaling Considerations Load Testing**: Test with expected user volumes Rate Limiting**: Implement appropriate rate limiting Database Scaling**: Consider external vector database for large-scale deployments Multi-Instance**: Configure for multiple n8n instances if needed 📈 Success Metrics and KPIs Quantitative Metrics Response Accuracy**: Percentage of correct answers Response Time**: Average time from question to answer User Satisfaction**: Rating scores and feedback Usage Volume**: Questions per day/week/month Cost Efficiency**: Cost per interaction Qualitative Metrics User Feedback**: Qualitative feedback on response quality Use Case Coverage**: Percentage of user needs addressed Knowledge Gaps**: Identification of missing information Conversation Quality**: Natural flow and context understanding