🎓 Learn JSON basics with an interactive step-by-step tutorial for beginners
How it works This workflow is an interactive, hands-on tutorial designed to teach you the absolute basics of JSON (JavaScript Object Notation) and, more importantly, how to use it within n8n. It's perfect for beginners who are new to automation and data structures. The tutorial is structured as a series of simple steps. Each node introduces a new, fundamental concept of JSON: Key/Value Pairs: The basic building block of all JSON. Data Types: It then walks you through the most common data types one by one: String (text) Number (integers and decimals) Boolean (true or false) Null (representing "nothing") Array (an ordered list of items) Object (a collection of key/value pairs) Using JSON with Expressions: The most important step! It shows you how to dynamically pull data from a previous node into a new one using n8n's expressions ({{ }}). Final Exam: A final node puts everything together, building a complete JSON object by referencing data from all the previous steps. Each node has a detailed sticky note explaining the concept in simple terms. Set up steps Setup time: 0 minutes! This is a tutorial workflow, so there is no setup required. Simply click the "Execute Workflow" button to run it. Follow the instructions in the main sticky note: click on each node in order, from top to bottom. For each node, observe the output in the right-hand panel and read the sticky note next to it to understand what you're seeing. By the end, you'll have a solid understanding of what JSON is and how to work with it in your own n8n workflows.
By Lucas PeyrinCustomer support WhatsApp bot with Google Docs knowledge base and Gemini AI
Document-Aware WhatsApp AI Bot for Customer Support Google Docs-Powered WhatsApp Support Agent 24/7 WhatsApp AI Assistant with Live Knowledge from Google Docs 📝Description Template Smart WhatsApp AI Assistant Using Google Docs Help customers instantly on WhatsApp using a smart AI assistant that reads your company’s internal knowledge from a Google Doc in real time. Built for clubs, restaurants, agencies, or any business where clients ask questions based on a policy, FAQ, or services document. ⚙️ How it works Users send free-form questions to your WhatsApp Business number (e.g. “What are the gym rules?” or “Are you open today?”) The bot automatically reads your company’s internal Google Doc (policy, schedule, etc.) It merges the document content with today’s date and the user’s question to craft a custom AI prompt The AI (Gemini or ChatGPT) then replies back on WhatsApp using natural, helpful language All conversations are logged to Google Sheets for reporting or audit > 💡Bonus: The AI even understands dates inside the document and compares them to today’s date — e.g. if your document says “Closed May 25 for 30 days,” it will say “We're currently closed until June 24. 🧰 Set up steps Connect your WhatsApp Cloud API account (Meta) Add your Google account and grant access to the Doc containing your company info Choose your AI model (ChatGPT/OpenAI or Gemini) Paste your document ID into the Google Docs node Connect your WhatsApp webhook to Meta (only takes 5 minutes) Done — start receiving and answering customer questions! > 📄 Works best with free-tier OpenAI/Gemini, Google Docs, and Meta's Cloud API (no phone required). Everything is modular, extensible, and low-code. 🔄 Customization Tips Change the Google Doc anytime to update answers — no retraining needed Add your logo and business name in the AI agent’s “System Prompt” Add fallback routes like “Escalate to human” if the bot can't help Clone for multiple brands by duplicating the workflow and swapping in new docs 🤝 Need Help Setting It Up? If you'd like help connecting your WhatsApp Business API, setting up Google Docs access, or customizing this AI assistant for your business or clients… 📩 I offer setup, branding, and customization services: WhatsApp Cloud API setup & verification Google OAuth & Doc structure guidance AI model configuration (OpenAI / Gemini) Branding & prompt tone customization Logging, reporting, and escalation logic Just send a message via: Email: tharwat.elsayed2000@gmail.com WhatsApp: +20 106 180 3236
By Tharwat MohamedDeep Research - Sales Lead Magnet Agent
Want to check out all my flows, follow me on: https://maxmitcham.substack.com/ https://www.linkedin.com/in/max-mitcham/ This automation flow is designed to generate comprehensive, research-backed lead magnet articles based on a user-submitted topic, conduct deep research across multiple sources, and automatically create a professional Google Doc ready for LinkedIn sharing. ⚙️ How It Works (Step-by-Step): 📝 Chat Input (Entry Point) A user submits a topic through the chat interface: Topic for lead magnet content Target audience (automatically detected) Company context (when relevant) 🔍 Query Builder Agent An AI agent refines the input by: Converting the topic into 5 targeted research queries Determining if topic relates to *company for specialized research Using structured output parsing for consistent results 📚 Research Leader Agent Conducts comprehensive research that: Uses Perplexity API for real-time web research Integrates *company knowledge base when relevant Creates detailed table of contents with research insights Identifies key trends, expert opinions, and case studies 📋 Project Planner Agent Structures the content by: Generating professional title and subtitle Creating 8-10 logical chapter outlines Developing detailed writing prompts for each section Ensuring step-by-step actionable guidance ✍️ Research Assistant Team Multiple AI agents write simultaneously: Each agent writes one chapter with proper citations Maintains consistent voice across all sections Includes real-world examples and implementation steps Uses both web research and *company knowledge 📝 Editor Agent Professional content polishing: Refines tone for authenticity and engagement Adds image placeholders where appropriate Ensures proper flow between chapters Optimizes for LinkedIn lead magnet format 📄 Google Docs Creation Automated document generation: Creates new Google Doc with formatted content Sets proper sharing permissions (public link) Organizes in designated company folder Returns shareable URL for immediate use 🛠️ Tools Used: n8n: Workflow orchestration platform Anthropic Claude: Primary AI model for content generation OpenRouter: Backup AI model options Perplexity API: Real-time research capabilities *Company Knowledge Hub: Internal documentation access Google Docs API: Document creation and formatting Google Drive API: File management and sharing 📦 Key Features: End-to-end automation from topic to published document Multi-agent approach ensures comprehensive coverage Real-time research with proper citations Company-specific knowledge integration Professional editing and formatting Automatic Google Docs creation with sharing Scalable content generation (3-5 minutes per article) 🚀 Ideal Use Cases: B2B companies building thought leadership content Sales teams creating industry-specific lead magnets Marketing departments scaling content production Consultants developing expertise-demonstrating resources SaaS companies creating feature-focused educational content Startups establishing market presence without content teams
By Max MitchamGenerate Funny AI Videos with Sora 2 and Auto-Publish to TikTok
This automation creates a fully integrated pipeline to generate AI-powered videos, store them, and publish them on TikTok — all automatically. It connects OpenAI Sora 2, and Postiz (for TikTok publishing) to streamline content creation. --- Key Benefits ✅ Full Automation – From text prompt to TikTok upload, everything happens automatically with no manual intervention once set up. ✅ Centralized Control – Google Sheets acts as a simple dashboard to manage prompts, durations, and generated results. ✅ AI-Powered Creativity – Uses OpenAI Sora 2 for realistic video generation and GPT-5 for optimized titles. ✅ Social Media Integration – Seamlessly posts videos to TikTok via Postiz, ready for your audience. ✅ Scalable & Customizable – Can easily be extended to other platforms like YouTube, Instagram, or LinkedIn. ✅ Time-Saving – Eliminates repetitive steps like manual video uploads or caption writing. --- How it works This workflow automates the end-to-end process of generating AI videos and publishing them to TikTok. It is triggered either manually or on a recurring schedule. Trigger & Data Fetch: The workflow starts by checking a specified Form for new entries. It looks for rows where a video has been requested (a "PROMPT" is filled) but not yet generated (the "VIDEO" column is empty). AI Video Generation: For each new prompt found, the workflow sends a request to the Fal.ai Sora 2 model to generate a video. It then enters a polling loop, repeatedly checking the status of the generation request every 60 seconds until the video is "COMPLETED". Post-Processing & Upload: Once the video is ready, the workflow performs several actions in parallel: Fetch Video & Store: It retrieves the final video URL, downloads the video file Generate Title: It uses the OpenAI GPT-4o-mini model to analyze the original prompt and generate an optimized, engaging title for the video. Publish to TikTok: The video file is uploaded to Postiz, a social media scheduling tool, which then automatically publishes it to a connected TikTok channel, using the AI-generated title as the post's caption. --- Set up steps To make this workflow functional, you need to complete the following configuration steps: Prepare the Google Sheet: Create a Form with at least "PROMPT", "DURATION", and "VIDEO" fields. Configure Fal.ai for Video Generation: Create an account at Fal.ai and obtain your API key. In both the "Create Video" and "Get status" HTTP Request nodes, set up the "Header Auth" credential. Set the Name to Authorization and the Value to Key YOURAPIKEY. Set up TikTok Publishing via Postiz: Create an account on Postiz and connect your TikTok account to get a Channel ID. Obtain your Postiz API key. In the "Upload Video to Postiz" and "TikTok" (Postiz) nodes, configure the API credentials. In the "TikTok" node, replace the placeholder "XXX" in the integrationId field with your actual TikTok Channel ID from Postiz. (Optional) Configure AI Title Generation: The "Generate title" node uses OpenAI. Ensure you have valid OpenAI API credentials configured in n8n for this node to work. --- Need help customizing? Contact me for consulting and support or add me on Linkedin. Header 2
By DavideAI-powered restaurant order chatbot with GPT-4o for POS integration
This workflow automates the restaurant POS (Point of Sale) data management process, facilitating seamless order handling, customer tracking, inventory management, and sales reporting. It retrieves order details, processes payment information, updates inventory, and generates real-time sales reports, all integrated into a centralized system that improves restaurant operations. The workflow integrates various systems, including a POS terminal to gather order data, payment gateways to process transactions, inventory management tools to update stock, and reporting tools like Google Sheets or an internal database for generating sales and performance reports. Who Needs Restaurant POS Automation? This POS automation workflow is ideal for restaurant owners, managers, and staff looking to streamline their operations: Restaurant Owners – Automate order processing, track sales, and monitor inventory to ensure smooth operations. Managers – Access real-time sales data and performance reports to make informed decisions. Staff – Reduce manual work, focusing on providing better customer service while the system handles orders and payments. Inventory Teams – Automatically update inventory levels based on orders and ingredient usage. If you need a reliable and automated POS solution to manage restaurant orders, payments, inventory, and reporting, this workflow minimizes human error, boosts efficiency, and saves valuable time. Why Use This Workflow? End-to-End Automation – Automates everything from order input to inventory updates and sales reporting. Seamless Integration – Connects POS, payment systems, inventory management, and reporting tools for smooth data flow.(if needed) Real-Time Data – Provides up-to-the-minute reports on sales, stock levels, and order statuses. Scalable & Efficient – Supports multiple locations, multiple users, and high order volumes. Step-by-Step: How This Workflow Manages POS Data Collect Orders – Retrieves order details from the POS system, including customer information, ordered items, and payment details. Update Inventory – Decreases inventory levels based on sold items, ensuring stock counts are always accurate. Generate Reports – Compiles sales, revenue, and inventory data into real-time reports and stores them in Google Sheets or an internal database. Track Customer Data – Keeps a log of customer details and order history for better service and marketing insights. Customization: Tailor to Your Needs Multiple POS Systems – Adapt the workflow to work with different POS systems or terminals based on your restaurant setup. Custom Reporting – Modify the reporting format or include specific sales metrics (e.g., daily totals, best-selling items, employee performance). Inventory Management – Adjust inventory updates to include alerts when stock reaches critical levels or needs reordering. Integration with Accounting Software – Connect with platforms like QuickBooks for automated financial tracking. 🔑 Prerequisites POS System Integration – Ensure the POS system can export order data in a compatible format. Payment Gateway API – Set up the necessary API keys for payment processing (e.g., Stripe, PayPal). Inventory Management Tools – Use inventory software or databases that can automatically update stock levels. Reporting Tools – Use Google Sheets or an internal database to store and generate sales and inventory reports. 🚀 Installation & Setup Configure Credentials Set up API credentials for payment gateways and inventory management tools. Import Workflow Import the workflow into your automation platform (e.g., n8n, Zapier). Link POS system, payment gateway, and inventory management systems. Test & Run Process a test order to ensure that data flows correctly through each step. Verify that inventory updates and reports are generated as expected. ⚠ Important Data Privacy – Ensure compliance with data protection regulations (e.g., GDPR, PCI DSS) when handling customer payment and order data. System Downtime – Monitor system performance to ensure that the workflow runs without disruptions during peak hours. Summary This restaurant POS automation workflow integrates order management, payment processing, inventory updates, and real-time reporting, enabling efficient restaurant operations. Whether you are running a single location or a chain of restaurants, this solution streamlines daily tasks, reduces errors, and provides valuable insights, saving time and improving customer satisfaction. 🚀
By Kumar ShivamQualify & reach out to B2B leads with Groq AI, Apollo, Gmail & Sheets
This workflow contains community nodes that are only compatible with the self-hosted version of n8n. 🎯 How it works This workflow captures new lead information from a web form, enriches it with Apollo.io data, qualifies the lead using AI, and—if the lead is strong—automatically sends a personalized outreach email via Gmail and logs the result in Google Sheets. 🛠️ Key Features 📩 Lead form capture with validation 🔍 Enrichment via Apollo API 🤖 Lead scoring using AI (LangChain + Groq) 📧 Dynamic email generation & sending via Gmail 📊 Logging leads with job title & org into Google Sheets ✅ Conditional email sending (score ≥ 6 only) 🧪 Set up steps Estimated time: 15–20 minutes Add your Apollo API Key to the HTTP Header credential (never hardcode!) Connect your Gmail account for sending emails Connect your Google Sheets account and set up the correct spreadsheet & sheet name Enable LangChain/Groq credentials for lead scoring and AI-generated emails Update the form endpoint to your live webhook if needed 📌 Sticky Notes Add the following mandatory sticky notes inside your workflow: FormTrigger Node: "Collects lead info via form. Ensure your form is connected to this endpoint." HTTP Request Node: "Enrich lead using Apollo.io API. Add your API key via header-based authentication." AI Agent (Lead Score): "Scores lead from 1-10 based on job title and industry match. Only leads with score ≥ 6 proceed." AI Agent (Email Composer): "Generates a concise, polite email using lead’s job title & company. Modify tone if needed." Google Sheets Append: "Logs enriched lead with job title, org, and LinkedIn URL. Customize sheet structure if needed." Gmail Node: "Sends personalized outreach email if lead passes score threshold. Uses AI-generated content." 💸 Free or Paid? Free – No paid API services are required (Apollo has a free tier).
By VKAPS ITAI Assistant which answers questions with a RAG MCP and a Search Engine MCP
Build an AI Agent which accesses two MCP Servers: a RAG MCP Server and a Search Engine API MCP Server. This workflow contains community nodes that are only compatible with the self-hosted version of n8n. Tutorial Click here to watch the full tutorial on YouTube! How it works We build an AI Agent which has access to two MCP servers: An MCP Server with a RAG database (click here for the RAG MCP Server An MCP Server which can access a Search Engine, so the AI Agent also has access to data about more current events Installation In order to use the MCP Client, you also have to use MCP Server Template. Open the MCP Client "MCP Client: RAG" node and update the SSE Endpoint to the MCP Server workflow Install the "n8n-nodes-mcp" community node via settings > community nodes ONLY FOR SELF-HOSTING: In Docker, click on your n8n container. Navigate to "Exec" and execute the below command to allow community nodes: N8NCOMMUNITYPACKAGESALLOWTOOL_USAGE=true Navigate to Bright Data and create a new "Web Unlocker API" with the name "mcp_unlocker". Open the "MCP Client" and add the following credentials: How to use it Run the Chat node and start asking questions More detailed instructions Missed a step? Find more detailed instructions here: Personal Newsfeed With Bright Data and n8n What is Retrievel Augmented Generation (RAG)? Large Language Models (LLM's) are trained on data until a specific cutoff date. Imagine a model is trained in December 2023 based data until September 2023. This means the model doesn't have any knowledge about events which happened in 2024. So if you ask the LLM who was the Formula 1 World Champion of 2024, it doesn't know the answer. The solution? Retrieval Augmented Generation. When using Retrieval Augmented Generation, a user's question is being sent to a semantic database. The LLM will use the information retrieved from the semantic database to answer the user's question. What is Model Context Protocol (MCP)? MCP is a communication protocol which is used by AI agents to call tools hosted on external servers. When an MCP client communicates with an MCP server, the server will provide an overview of all its tools, prompts and resources. The MCP server can then choose which tools to execute (based on the user's request) and execute the tools. An MCP client can communicate with multiple MCP servers, which can all host multiple tools.
By Thomas JanssenUpload video, create playlist and add video to playlist
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By Harshil AgrawalAI: Ask questions about any data source (using the n8n workflow retriever)
This template aims to perform Q&A on data retrieved from another n8n workflow. Since that workflow can be used to retrieve any data from any service, this template can be used to ask questions about any data. It uses a manual trigger, various AI nodes, and an OpenAI Chat Model to extract and provide relevant information based on a specific query. Note that to use this template, you need to be on n8n version 1.19.4 or later.
By n8n TeamPush JSON data into an app or to spreadsheet file
This workflow template shows how to load JSON data into a workflow and push that data into an App or convert it into a Spreadsheet file. Specifically, this workflow shows how to make a generic API request that returns JSON. It then shows how to load that into a Google Sheets spreadsheet, or convert it to .CSV file format. However, you can use the general pattern to load data into any app or convert to any spreadsheet file format (such as .xlsx).
By n8n TeamEnrich company data from Google Sheet with OpenAI Agent and ScrapingBee
This workflow demonstrates how to enrich data from a list of companies in a spreadsheet. While this workflow is production-ready if all steps are followed, adding error handling would enhance its robustness. Important notes Check legal regulations: This workflow involves scraping, so make sure to check the legal regulations around scraping in your country before getting started. Better safe than sorry! Mind those tokens: OpenAI tokens can add up fast, so keep an eye on usage unless you want a surprising bill that could knock your socks off! 💸 Main Workflow Node 1 - Webhook This node triggers the workflow via a webhook call. You can replace it with any other trigger of your choice, such as form submission, a new row added in Google Sheets, or a manual trigger. Node 2 - Get Rows from Google Sheet This node retrieves the list of companies from your spreadsheet. here is the Google Sheet Template you can use. The columns in this Google Sheet are: Company: The name of the company Website: The website URL of the company These two fields are required at this step. Business Area: The business area deduced by OpenAI from the scraped data Offer: The offer deduced by OpenAI from the scraped data Value Proposition: The value proposition deduced by OpenAI from the scraped data Business Model: The business model deduced by OpenAI from the scraped data ICP: The Ideal Customer Profile deduced by OpenAI from the scraped data Additional Information: Information related to the scraped data, including: Information Sufficiency: Description: Indicates if the information was sufficient to provide a full analysis. Options: "Sufficient" or "Insufficient" Insufficient Details: Description: If labeled "Insufficient," specifies what information was missing or needed to complete the analysis. Mismatched Content: Description: Indicates whether the page content aligns with that of a typical company page. Suggested Actions: Description: Provides recommendations if the page content is insufficient or mismatched, such as verifying the URL or searching for alternative sources. Node 3 - Loop Over Items This node ensures that, in subsequent steps, the website in "extra workflow input" corresponds to the row being processed. You can delete this node, but you'll need to ensure that the "query" sent to the scraping workflow corresponds to the website of the specific company being scraped (rather than just the first row). Node 4 - AI Agent This AI agent is configured with a prompt to extract data from the content it receives. The node has three sub-nodes: OpenAI Chat Model: The model used is currently gpt4-o-mini. Call n8n Workflow: This sub-node calls the workflow to use ScrapingBee and retrieves the scraped data. Structured Output Parser: This parser structures the output for clarity and ease of use, and then adds rows to the Google Sheet. Node 5 - Update Company Row in Google Sheet This node updates the specific company's row in Google Sheets with the enriched data. Scraper Agent Workflow Node 1 - Tool Called from Agent This is the trigger for when the AI Agent calls the Scraper. A query is sent with: Company name Website (the URL of the website) Node 2 - Set Company URL This node renames a field, which may seem trivial but is useful for performing transformations on data received from the AI Agent. Node 3 - ScrapingBee: Scrape Company's Website This node scrapes data from the URL provided using ScrapingBee. You can use any scraper of your choice, but ScrapingBee is recommended, as it allows you to configure scraper behavior directly. Once configured, copy the provided "curl" command and import it into n8n. Node 4 - HTML to Markdown This node converts the scraped HTML data to Markdown, which is then sent to OpenAI. The Markdown format generally uses fewer tokens than HTML. Improving the Workflow It's always a pleasure to share workflows, but creators sometimes want to keep some magic to themselves ✨. Here are some ways you can enhance this workflow: Handle potential errors Configure the scraper tool to scrape other pages on the website. Although this will cost more tokens, it can be useful (e.g., scraping "Pricing" or "About Us" pages in addition to the homepage). Instead of Google Sheets, connect directly to your CRM to enrich company data. Trigger the workflow from form submissions on your website and send the scraped data about the lead to a Slack or Teams channel.
By DatakiAutomated Customer Service Ticket Creation & Notifications with Asana & WhatsApp
How it works: This workflow automates your customer service with built in notifications for your users & ticket creation with Asana. If a user submits a form, he gets send a confirmation message via WhatsApp a task is opened in Asana with his request in it. Setup: You need to add your credentials to the WhatsApp Business Cloud node. You need to add your credentials to the Asana node. Replace the placeholders with the correct phone number, id, and so on. Change the confirmation message to your liking. Optional Changes: You could extend this workflow to update your user on the progress of the ticket in Asana. You can change the messaging from WhatsApp to E-Mail. You can change the form submission service from n8n-native to Typeform or similar. You can change the task management software from Asana to the one you use. Click here to find a blog post with additional information.
By Bela