Workflow Templates

This n8n workflow automates the import of your Google Keep notes into a structured Google Sheet, using Google Drive, OpenAI for AI-powered processing, and JSON file extraction. It's perfect for users who want to turn exported Keep notes into a searchable, filterable spreadsheet – optionally enhanced by AI summarization or transformation. Who is this for? Researchers, knowledge workers, and digital minimalists who rely on Google Keep and want to better organize or analyze their notes. Anyone who regularly exports Google Keep notes and wants a clean, automated workflow to store them in Google Sheets. Users looking to apply AI to process, summarize, or extract insights from raw notes. What problem is this workflow solving? Exporting Google Keep notes via Google Takeout gives you unstructured .json files that are hard to read and manage. This workflow solves that by: Filtering relevant .json files Extracting note content (Optionally) applying AI to analyze or summarize each note Writing the result into a structured Google Sheet What this workflow does Google Drive Search: Looks for .json files inside a specified "Keep" folder. Loop: Processes files in batches of 10. File Filtering: Filters by .json extension. Download + Extract: Downloads each file and extracts note content from JSON. Optional Filtering: Only keeps non-archived notes or those meeting content criteria. AI Processing (optional): Uses OpenAI to summarize or transform the note content. Prepare for Export: Maps note fields to be written. Google Sheets: Appends or updates the target sheet with the note data. Setup Export your Google Keep notes using Google Takeout: Deselect all, then choose only Google Keep. Choose “Send download link via email”. Unzip the downloaded archive and upload the .json files to your Google Drive. Connect Google Drive, OpenAI, and Google Sheets in n8n. Set the correct folder path for your notes in the “Search in ‘Keep’ folder” node. Point the Google Sheet node to your spreadsheet How to customize this workflow to your needs Skip AI processing: If you don't need summaries or transformations, remove or disable the OpenAI Chat Model node. Filter criteria: Customize the Filter node to extract only recent notes, or those containing specific keywords. AI prompts: Edit the Tools Agent or Chat Model node to instruct the AI to summarize, extract tasks, categorize notes, etc. Field mapping: Adjust the “Set fields for export” node to control what gets written to the spreadsheet. Use this template to build a powerful knowledge extraction tool from your Google Keep archive – ideal for backups, audits, or data-driven insights.

Notice Community nodes can only be installed on self-hosted instances of n8n. Who this is for The Automated Resume Job Matching Engine is an intelligent workflow designed for career platforms, HR tech startups, recruiting firms, and AI developers who want to streamline job-resume matching using real-time data from LinkedIn and job boards. This workflow is tailored for: HR Tech Founders** - Building next-gen recruiting products Recruiters & Talent Sourcers** - Seeking automated candidate-job fit evaluation Job Boards & Portals** - Enriching user experience with AI-driven job recommendations Career Coaches & Resume Writers** - Offering personalized job fit analysis AI Developers** - Automating large-scale matching tasks using LinkedIn and job data What problem is this workflow solving? Manually matching a resume to job description is time-consuming, biased, and inefficient. Additionally, accessing live job postings and candidate profiles requires overcoming web scraping limitations. This workflow solves: Automated LinkedIn profile and job post data extraction using Bright Data MCP infrastructure Semantic matching between job requirements and candidate resume using OpenAI 4o mini Pagination handling for high-volume job data End-to-end automation from scraping to delivery via webhook and persisting the job matched response to disk What this workflow does Bright Data MCP for Job Data Extraction Uses Bright Data MCP Clients to extract multiple job listings (supports pagination) Pulls job data from LinkedIn with the pre-defined filtering criteria's OpenAI 4o mini LLM Matching Engine Extracts paginated job data from the Bright Data MCP extracted info via the MCP scrape_as_html tool. Extracts textual job description information via the scraped job information by leveraging the Bright Data MCP scrape_as_html tool. AI Job Matching node handles the job description and the candidate resume compare to generate match scores with insights Data Delivery Sends final match report to a Webhook Notification endpoint Persistence of AI matched job response to disk 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. Navigate to Proxies & Scraping and create a new Web Unlocker zone by selecting Web Unlocker API under Scraping Solutions. Create a Web Unlocker proxy zone called mcp_unlocker on Bright Data control panel. In n8n, configure the OpenAi account credentials. 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>. Update the Set input fields for candidate resume, keywords and other filtering criteria's. Update the Webhook HTTP Request node with the Webhook endpoint of your choice. Update the file name and path to persist on disk. How to customize this workflow to your needs Target Different Job Boards Set input fields with the sites like Indeed, ZipRecruiter, or Monster Customize Matching Criteria Adjust the prompt inside the AI Job Match node Include scoring metrics like skills match %, experience relevance, or cultural fit Automate Scheduling Use a Cron Node to periodically check for new jobs matching a profile Set triggers based on webhook or input form submissions Output Customization Add Markdown/PDF formatting for report summaries Extend with Google Sheets export for internal analytics Enhance Data Security Mask personal info before sending to external endpoints

Complete MCP server exposing all Hacker News Tool operations to AI agents. Zero configuration needed - all 3 operations pre-built. ⚡ Quick Setup Need help? Want access to more workflows and even live Q&A sessions with a top verified n8n creator.. All 100% free? Join the community Import this workflow into your n8n instance Activate the workflow to start your MCP server Copy the webhook URL from the MCP trigger node Connect AI agents using the MCP URL 🔧 How it Works • MCP Trigger: Serves as your server endpoint for AI agent requests • Tool Nodes: Pre-configured for every Hacker News Tool operation • AI Expressions: Automatically populate parameters via $fromAI() placeholders • Native Integration: Uses official n8n Hacker News Tool tool with full error handling 📋 Available Operations (3 total) Every possible Hacker News Tool operation is included: 🔧 All (1 operations) • Get many items 🔧 Article (1 operations) • Get an article 👤 User (1 operations) • Get a user 🤖 AI Integration Parameter Handling: AI agents automatically provide values for: • Resource IDs and identifiers • Search queries and filters • Content and data payloads • Configuration options Response Format: Native Hacker News Tool API responses with full data structure Error Handling: Built-in n8n error management and retry logic 💡 Usage Examples Connect this MCP server to any AI agent or workflow: • Claude Desktop: Add MCP server URL to configuration • Custom AI Apps: Use MCP URL as tool endpoint • Other n8n Workflows: Call MCP tools from any workflow • API Integration: Direct HTTP calls to MCP endpoints ✨ Benefits • Complete Coverage: Every Hacker News Tool operation available • Zero Setup: No parameter mapping or configuration needed • AI-Ready: Built-in $fromAI() expressions for all parameters • Production Ready: Native n8n error handling and logging • Extensible: Easily modify or add custom logic > 🆓 Free for community use! Ready to deploy in under 2 minutes.

Who is this for? Content creators, social media managers, digital marketers, and businesses looking to automate video production without expensive equipment or technical expertise. What problem is this workflow solving? Traditional video creation requires cameras, editing software, voice recording equipment, and hours of post-production work. This workflow eliminates all these barriers by automatically generating professional videos with audio using just text prompts. What this workflow does This automated workflow takes video ideas from Google Sheets, generates optimized prompts using AI, creates videos through Google's V3 model via Fal AI, monitors the generation progress, and saves the final video URLs back to your spreadsheet for easy access and management. Setup Sign up for Fal AI account and obtain API key Create Google Sheet with video ideas and status columns Configure n8n with required credentials (Google Sheets, Fal AI API) Import the workflow template Set up authentication for all connected services Test with sample video idea How to customize this workflow to your needs Modify the AI prompts to match your brand voice, adjust video styles and camera movements, change polling intervals for video generation status, customize Google Sheet column mappings, and add additional processing steps like thumbnail generation or social media posting.

This workflow demonstrates three distinct approaches to chaining LLM operations using Claude 3.7 Sonnet. Connect to any section to experience the differences in implementation, performance, and capabilities. What you'll find: 1️⃣ Naive Sequential Chaining The simplest but least efficient approach - connecting LLM nodes in a direct sequence. Easy to set up for beginners but becomes unwieldy and slow as your chain grows. 2️⃣ Agent-Based Processing with Memory Process a list of instructions through a single AI Agent that maintains conversation history. This structured approach provides better context management while keeping your workflow organized. 3️⃣ Parallel Processing for Maximum Speed Split your prompts and process them simultaneously for much faster results. Ideal when you need to run multiple independent tasks without shared context. Setup Instructions: API Credentials: Configure your Anthropic API key in the credentials manager. This workflow uses Claude 3.7 Sonnet, but you can modify the model in each Anthropic Chat Model node, or pick an entirely different LLM. For Cloud Users: If using the parallel processing method (section 3), replace {{ $env.WEBHOOK_URL }} in the "LLM steps - parallel" HTTP Request node with your n8n instance URL. Test Data: The workflow fetches content from the n8n blog by default. You can modify this part to use a different content or a data source. Customization: Each section contains a set of example prompts. Modify the "Initial prompts" nodes to change the questions asked to the LLM. Compare these methods to understand the trade-offs between simplicity, speed, and context management in your AI workflows! Follow me on LinkedIn for more tips on AI automation and n8n workflows!

This n8n template demonstrates how to automate comprehensive web research using multiple AI models to find, analyze, and extract insights from authoritative sources. Use cases are many: Try automating competitive analysis research, finding latest regulatory guidance from official sources, gathering authoritative content for reports, or conducting market research on industry developments! Good to know Each research query typically costs $0.08-$0.34 depending on the number of sources found and processed. The workflow includes smart filtering to minimize unnecessary API calls. The workflow requires multiple AI services and may need additional setup time compared to simpler templates. Qdrant storage is optional and can be removed without affecting performance. How it works Your research question gets transformed into optimized Google search queries that target authoritative sources while filtering out low-quality sites. Apify's RAG Web Browser scrapes the content and converts pages to clean markdown format. Claude Sonnet 4 evaluates each article for relevance and quality before full processing. Articles that pass the filter get analyzed in parallel - one pipeline creates focused summaries while another extracts specific claims and evidence. GPT-4.1 Mini ranks all findings and presents the top 3 most valuable insights and summaries. All processed content gets stored in your Qdrant vector database to prevent duplicate processing and enable future reference. How to use The manual trigger node is used as an example but feel free to replace this with other triggers such as webhook, form submissions, or scheduled research. You can modify the configuration variables in the Set Node to customize Qdrant URLs, collection names, and quality thresholds for your specific needs. Requirements OpenAI API account for GPT-4.1 Mini (query optimization, summarization, ranking) Anthropic API account for Claude Sonnet 4 (content filtering) Apify account for web scraping capabilities Qdrant vector database instance (local or cloud) Ollama with nomic-embed-text model for embeddings Customizing this workflow Web research automation can be adapted for many specialized use cases. Try focusing on specific domains like legal research (targeting .gov and .edu sites), medical research (PubMed and health authorities), or financial analysis (SEC filings and analyst reports).

Google Drive Duplicate File Manager 🧹📁 Purpose: Automate the process of finding and managing duplicate files in your Google Drive. Who's it for? Individuals and teams aiming to streamline their Google Drive. Anyone tired of manual duplicate file cleanup. What it Solves: Saves storage space 💾. Reduces file confusion 😕➡️🙂. Automates tedious cleanup tasks 🤖. How it works: Trigger: Monitors a Google Drive folder for new files. Configuration: Sets rules for keeping and handling duplicates. Find Duplicates: Identifies duplicate files based on their content (MD5Checksum). Action: Either moves duplicates to trash or renames them. Setup Guide: Google Drive Trigger ⏰: Set up the trigger to watch a specific folder or your entire drive (use caution with the root folder! ⚠️). Configure the polling interval (default: every 15 minutes). Config Node ⚙️: keep: Choose whether to keep the "first" or "last" uploaded file (default: "last"). action: Select "trash" to delete duplicates or "flag" to rename them with "DUPLICATE-" (default: "flag"). owner & folder: Taken from the trigger. Only change if needed. Key Considerations: Google Drive API limits:** Be mindful of API usage. Folder Scope:* The workflow handles one folder depth by default. (WARNING: If configured to work with the Root folder / all files in all sub-directories are processed so *USE THIS OPTION WITH CAUTION** since the workflow might trash/rename important files) Google Apps:** Google docs are ignored since they are not actual binary-files and their content can't be compared. Enjoy your clean Google Drive! ✨

How it works This template automates the conversion of CAD and BIM files Revit, AutoCAD, IFC, MicroStation (e.g. .rvt, .ifc, .dwg, .dgn) into structured Excel databases and lightweight 3D geometry .dae files using the DataDrivenConstruction open-source converter. 📦 High-level steps: Set file paths and converter path in the Set node Trigger conversion via Execute Command (runs .exe converter offline) Output includes .xlsx (data) and .dae (3D model) files Includes sticky note instructions for troubleshooting and GitHub repo info Set up steps 🕒 Setup time: ~10 minutes You’ll need: Windows machine (offline or airgapped OK) Path to the converter .exe file Path to a sample .rvt (or .ifc, .dwg, .dgn) file 🧷 Setup paths in the Set node: path_to_converter = "C:\\...\\RvtExporter.exe" path_project_file = "C:\\...\\project.rvt" Docs & Issues: Full Readme on GitHub

This workflow pushes Stripe charges to HubSpot contacts. It uses the Stripe API to get all charges and the HubSpot API to update the contacts. The workflow will create a new HubSpot property to store the total amount charged. If the property already exists, it will update the property. Prerequisites Stripe credentials. HubSpot credentials. How it works On a schedule, check if the property exists in HubSpot. If it doesn't exist, create it. The default schedule is once a day at midnight. Once property is acertained, the first Stripe node gets all charges. Once the charges are returned, the second Stripe node gets extra customer information. Once the customer information is returned, Merge data node will merge the customer information with the charges so that the next node Aggregate totals can calculate the total amount charged per contact. Once we have the total amount charged per contact, the Create or update customer node will create a new HubSpot contact if it doesn't exist or update the contact if it does exist with the total amount charged.

Who is this for? Security professionals Developers Individuals interested in data breach awareness Use Case Automated monitoring for new breaches Proactive identity protection Demonstration of simple cache mechanism What this workflow does Checks the Have I Been Pwned API every 15 minutes for the latest breaches. Compares new breach data against previously notified breaches. Demonstrates a simple cache mechanism to track previously seen breaches. How the Cache Functionality Works Read from Cache**: Retrieves the last known breach from cache.json to avoid redundant alerts for the same breach. Compare Against Current Breach**: The workflow checks if the latest fetched breach differs from the cached one. Update the Cache**: If a new breach is detected, it updates cache.json with the latest breach data. Setup instructions The endpoint used in this workflow does not require an API key. Add your desired alert mechanism in the red box attached to the New breach node. How to customize this workflow to your needs Modify Notification Settings**: Tailor where alerts are sent (email, Slack, etc.). Add the desired node after the New breach node. This node contains all the data from the breach so it is eaisily available. You can choose from a variety of n8n nodes to send alerts when a new breach is detected. Below are a few common options you might consider adding after the New breach node: Email Node What it does: Sends an email notification to one or more recipients. Use case: Great for simple alerts to your inbox or a team distribution list. Customization: You can include breach details in the subject or body of the email, using data from the New breach node. Slack Node What it does: Sends a message to a Slack channel or user. Use case: Perfect for real-time alerts to your team in Slack. Customization: You can post breach details directly in a channel or DM. You can also format the message (bold, code blocks, etc.). Microsoft Teams Node What it does: Sends a message to a Teams channel. Use case: For organizations that use Microsoft Teams for communication. Customization: Similar to Slack, you can customize the message content and include all relevant breach information. Discord Node What it does: Sends an alert message to a Discord channel. Use case: Useful for teams or communities that coordinate via Discord. Customization: Add formatted messages with breach details for easy viewing. Telegram Node What it does: Sends messages to a Telegram chat or group. Use case: Good for mobile notifications and fast alerts. Customization: You can include breach summaries or detailed information, and even use bots to automate this. Webhook Node (as a sender) What it does: Sends breach data to another service via a webhook. Use case: If you have an external system or app that handles alerts, you can push the data directly to it. Customization: Send JSON payloads with detailed breach information to trigger actions in other systems. SMS Nodes (like Twilio) What it does: Sends an SMS notification to one or more phone numbers. Use case: For urgent alerts that need to be seen immediately. Customization: Keep messages concise, including key breach details like the time, type of breach, and affected system. Adjust Check Frequency**: Change the interval in the Schedule Trigger node (e.g., hourly or daily).

Complete MCP server exposing 2 Wayback API operations to AI agents. ⚡ Quick Setup Need help? Want access to more workflows and even live Q&A sessions with a top verified n8n creator.. All 100% free? Join the community Import this workflow into your n8n instance Credentials Add Wayback API credentials Activate the workflow to start your MCP server Copy the webhook URL from the MCP trigger node Connect AI agents using the MCP URL 🔧 How it Works This workflow converts the Wayback API into an MCP-compatible interface for AI agents. • MCP Trigger: Serves as your server endpoint for AI agent requests • HTTP Request Nodes: Handle API calls to https://api.archive.org • AI Expressions: Automatically populate parameters via $fromAI() placeholders • Native Integration: Returns responses directly to the AI agent 📋 Available Operations (2 total) 🔧 Wayback (2 endpoints) • GET /wayback/v1/available: GET /wayback/v1/available • POST /wayback/v1/available: POST /wayback/v1/available 🤖 AI Integration Parameter Handling: AI agents automatically provide values for: • Path parameters and identifiers • Query parameters and filters • Request body data • Headers and authentication Response Format: Native Wayback API responses with full data structure Error Handling: Built-in n8n HTTP request error management 💡 Usage Examples Connect this MCP server to any AI agent or workflow: • Claude Desktop: Add MCP server URL to configuration • Cursor: Add MCP server SSE URL to configuration • Custom AI Apps: Use MCP URL as tool endpoint • API Integration: Direct HTTP calls to MCP endpoints ✨ Benefits • Zero Setup: No parameter mapping or configuration needed • AI-Ready: Built-in $fromAI() expressions for all parameters • Production Ready: Native n8n HTTP request handling and logging • Extensible: Easily modify or add custom logic > 🆓 Free for community use! Ready to deploy in under 2 minutes.

Based on Jonathan's work. Check out his templates. How it works This workflow will backup your workflows to GitHub. It uses the n8n API node to export all workflows. It then loops over the data, checks in GitHub to see if a file exists that uses the credential's ID. Once checked it will: update the file on GitHub if it exists; create a new file if it doesn't exist; ignore if it's the same. Who is this for? People wanting to backup their workflows outside the server for safety purposes or to migrate to another server. Check out my other templates 👉 https://n8n.io/creators/solomon/