Workflow Templates

This workflow contains community nodes that are only compatible with the self-hosted version of n8n. 🚀 Overview This workflow enables a powerful AI-driven virtual assistant that dynamically responds to website queries using webhook input, Pinecone vector search, and OpenAI agents — all smartly routed based on the source website. 🔧 How It Works Webhook Trigger The workflow starts with a Webhook node that receives query parameters: query: The user's question userId: Unique user identifier site: Website identifier (e.g., test_site) page: Page identifier (e.g., homepage, pricing) Smart Routing A Switch node directs the request to the correct AI agent based on the site value. Each AI agent uses: OpenAI GPT-4/3.5 model Pinecone vector store for context-aware answers SQL-based memory for consistent multi-turn conversation Contextual AI Agent Each agent is customized per website using: Site-specific Pinecone namespaces Predefined system prompts to stay in scope Webhook context including page, site, and userId Final Response The response is sent back to the originating website using the Respond to Webhook node. 🧠 Use Case Ideal for multi-site platforms that want to serve tailored AI chat experiences per domain or page — whether it’s support, content discovery, or interactive agents. ✅ Highlights 🧠 Vector search using Pinecone for contextual responses 🔀 Website-aware logic with Switch node routing 🔐 No hardcoded API keys 🧩 Modular agents for scalable multi-site support

🤖 This n8n workflow creates an intelligent Telegram bot that processes multiple types of messages and provides automated responses using AI capabilities. The bot serves as a personal assistant that can handle text, voice messages, and images through a sophisticated processing pipeline. Core Components Message Reception and Validation 📥 🔄 Implements webhook-based message reception for real-time processing 🔐 Features a robust user validation system that verifies sender credentials 🔀 Supports both testing and production webhook endpoints for development flexibility Message Processing Pipeline ⚡ 🔄 Uses a smart router to detect and categorize incoming message types 📝 Processes three main message formats: 💬 Text messages 🎤 Voice recordings 📸 Images with captions AI Integration 🧠 🤖 Leverages OpenAI's GPT-4 for message classification and processing 🗣️ Incorporates voice transcription capabilities for audio messages 👁️ Features image analysis using GPT-4 Vision API for processing visual content Technical Architecture Webhook Management 🔌 🌐 Maintains separate endpoints for testing and production environments 📊 Implements automatic webhook status monitoring ⚡ Provides real-time webhook configuration updates Error Handling ⚠️ 🔍 Features comprehensive error detection and reporting 🔄 Implements fallback mechanisms for unprocessable messages 💬 Provides user feedback for failed operations Message Classification System 📋 🏷️ Categorizes incoming messages into tasks and general conversation 🔀 Implements separate processing paths for different message types 🧩 Maintains context awareness across message processing Security Features User Authentication 🔒 ✅ Validates user credentials against predefined parameters 👤 Implements first name, last name, and user ID verification 🚫 Restricts access to authorized users only Response System Intelligent Responses 💡 🤖 Generates contextual responses based on message classification

What is this workflow? This n8n template automates the process of adding an AI-generated summary at the top of your WordPress posts. It retrieves, processes, and updates your posts dynamically, ensuring efficiency and flexibility without relying on a heavy WordPress plugin. Example of AI Summary Section How It Works Triggers → Runs on a scheduled interval or via a webhook when a new post is published. Retrieves posts → Fetches content from WordPress and converts HTML to Markdown for AI processing. AI Summary Generation → Uses OpenAI to create a concise summary. Post Update → Inserts the summary at the top of the post while keeping the original excerpt intact. Data Logging & Notifications → Saves processed posts to Google Sheets and notifies a Slack channel. Why use this workflow? ✅ No need for a WordPress plugin → Keeps your site lightweight. ✅ Highly flexible → Easily connect with Google Sheets, Slack, or other services. ✅ Customizable → Adapt AI prompts, formatting, and integrations to your needs. ✅ Smart filtering → Ensures posts are not reprocessed unnecessarily. 💡 Check the detailed sticky notes for setup instructions and customization options!

Send a simple JSON array via HTTP POST and get an Excel file. The default filename is Export.xlsx. By adding the (optional) request ?filename=xyz you can specify the filename. NOTE: do not forget to change the webhook path!

This n8n template automatically creates and publishes high-quality LinkedIn posts using your brand brief, AI-generated ideas, and structured feedback loops — all powered by OpenAI. Perfect for solo creators, marketers, and startup teams building a consistent presence on LinkedIn. Who's it for Creators and freelancers building a personal brand Social media managers at startups or agencies Marketing teams that want to scale LinkedIn content Anyone tired of manually ideating, writing, and posting daily What it does Triggers daily at a chosen time (default: 9 PM) Fetches new content ideas from your idea-generation workflow Loads your brand brief and previous post feedback Uses OpenAI to craft a branded, engaging LinkedIn post Publishes directly to LinkedIn — no manual copy-paste needed How the AI logic works The AI agent follows a consistent, looped prompt strategy to ensure brand alignment: ++Prompt++ You are a helpful content creator for Nabin Bhandari's personal brand. Use the below steps to create content: Always start by getting the brand brief using the Get_Brand_Brief tool. Create a post on the requested topic that aligns with the brand brief. Get feedback and a score on the post using the Get_Content_Feedback tool. If the score is below 0.8, use the feedback to refine the post and repeat. The final output should be the approved post. Bonus: You can fine-tune OpenAI on your own brand tone and style by uploading at least 50 examples of approved posts. This will help ensure even more accurate, on-brand outputs. Requirements OpenAI API Key LinkedIn API credentials set in the LinkedIn node A Notion page (or any storage) with your Brand Brief clearly described Optional: Fine-tuned OpenAI model for higher fidelity How to customize Adjust the AI prompt for different tones (e.g., witty, professional) Swap the idea source (Airtable, Notion, Webhook, etc.) Add manual approval steps before publishing Replace the LinkedIn node with other social media APIs (Twitter/X, Threads) Tips Store a clear and specific brand brief in your Notion page — it directly shapes the AI's tone Add a database of previously approved posts for fine-tuning OpenAI Use additional feedback metrics (likes, engagement) for future iterations

Ghost + Sendy Integration Está es una integración del CMS Ghost hacia Sendy Sendy ( www.sendy.co ) Ghost ( www.ghost.org ) Con esta integración podrás importar los miembros del CMS Ghost en su nueva versión que incluye la parte de Membresía hacía el Software de newsletter sendy. Está integración además nos avisa si se ha registrado un nuevo miembro via telegram. Para realizar esta customización es necesaria la creación de una custom integration en Ghost. Para ello desde el panel de Administración vamos a CUSTOM INTEGRATIONS / + Add custom Integration Una vez allí nos solicitará un nombre le ponemos el que queramos y añadimos un nuevo Hook: En Target URL debe ir La url que nos genera nuestro webhook dentro de n8n: Pegamos la URL y acamos de rellenar los datos del HTTP REQUEST1 con los datos de nuestra lista rellenando los campos. api_key list Que encontaras en tú instalación de Sendy Por último faltara añadir las credenciales de Telegram de Nuestro BOT ( https://docs.n8n.io/credentials/telegram/ ) e indicar el grupo o usuario donde queremos que notifique. Saludos,

This workflow creates an automatic self-hosted URL shortener. It consists of three sub-workflows: Short URL creation for extracting the provided long URL, generating an ID, and saving the record in the database. It returns a short link as a result. Redirection for extracting the ID value, validating the existence of its correspondent record in the database, and returning a redirection page after updating the visits (click) count. Dashboard for calculating simple statistics about the saved record and displaying them on a dashboard. Read more about this use case and how to set up the workflow in the blog post How to build a low-code, self-hosted URL shortener in 3 steps. Prerequisites A local proxy set up that redirects the n8n.ly domain to your n8n instance An Airtable account and credentials Basic knowledge of JavaScript, HTML, and CSS Nodes Webhook nodes trigger the sub-workflows on calls to a specified link. IF nodes route the workflows based on specified query parameters. Set nodes set the required values returned by the previous nodes (id, longUrl, and shortUrl). Airtable nodes retrieve records (values) from or append records to the database. Function node calculates statistics on link clicks to be displayed on the dashboard, as well as its design. Crypto node generates a SHA256 hash.

Check Online Version ! [https://n8n-tools.streamlit.app/](https://n8n-tools.streamlit.app/ ) Who is it for? This workflow is perfect for n8n users who want to maintain clean and organized workflows without manually repositioning nodes. Whether you're building complex workflows or sharing them with a team, maintaining visual clarity is essential for efficiency and collaboration. This template automates the positioning process, saving time and ensuring consistent layout standards. How does it work? The template is divided into two parts: Positioning Engine: A webhook node kicks off the process by receiving a workflow ID. Using the provided workflow ID, an n8n API node fetches the workflow details. The fetched workflow is sent to a processing webhook that calculates optimized positions for the nodes. Finally, an n8n API node updates the workflow with the newly positioned nodes, ensuring a clean and professional layout. Reusable Positioning Block: This is an HTTP Request node that can be seamlessly integrated into any workflow you create. When triggered, it sends the current workflow for automatic positioning via the first part of this template. How to set it up? Enable n8n API Access: Ensure that your n8n instance has API access enabled with the appropriate credentials. Input Your n8n API URL and Credentials: Open the template, locate the n8n API nodes, and update them with your instance API key. Update the URL of the 'Magic Positioning' Http Request node to point to your n8n instance webhook URL. Embed the Reusable Block: Add the provided HTTP Request node to any of your workflows to instantly connect to the auto-positioning engine.

Video Guide I prepared a detailed guide showcasing the process of building an AI agent that interacts with a Snowflake database using n8n. This setup enables conversational querying, secure execution of SQL queries, and dynamic report generation with rich visualization capabilities. Youtube Link Who is this for? This workflow is designed for developers, data analysts, and business professionals who want to interact with their Snowflake data conversationally. It suits users looking to automate SQL query generation with AI, manage large datasets efficiently, and produce interactive reports without deep technical knowledge. What problem does this workflow solve? Querying Snowflake databases typically requires SQL proficiency and can lead to heavy token usage if large datasets are sent to AI models directly. This workflow addresses these challenges by: Guiding AI to generate accurate SQL queries based on user chat input while referencing live database schema to avoid errors. Executing queries safely on Snowflake with proper credential management. Aggregating large result sets to reduce token consumption. Offering a user-friendly report link with pagination, filtering, charts, and CSV export instead of returning overwhelming raw data. Providing an error-resilient environment that prompts regenerations for SQL errors or connectivity issues. What this workflow does The scenario consists of multiple focused n8n workflows orchestrated for smooth, secure, and scalable interactions: Agent Workflow Starts with a chat node and sets the system role as "Snowflake SQL assistant." AI generates SQL after verifying database schema and table definitions to avoid hallucinations. Reinforcement rules ensure schema validation before query creation. Data Retrieval Workflow Receives SQL queries from the agent workflow. Executes them against the Snowflake database using user-provided credentials (hostname, account, warehouse, database, schema, username, password). Optionally applies safety checks on SQL to prevent injection attacks. Aggregation and Reporting Decision Aggregates returned data into arrays for efficient processing. Applies a threshold (default 100 records) to decide whether to return raw data or generate a dynamic report link. Prepares report links embedding URL-encoded SQL queries to securely invoke a separate report workflow. Report Viewing Workflow Triggered via webhook from the report link. Re-executes SQL queries to fetch fresh data. Displays data with pagination, column filtering, and selectable chart visualizations. Supports CSV export and custom HTML layouts for tailored user experience. Provides proper error pages in case of SQL or data issues. Schema and Table Definition Retrieval Tools Two helper workflows that fetch the list of tables and column metadata from Snowflake. Require the user to replace placeholders with actual database and data source names. Crucial for AI to maintain accurate understanding of the database structure. N8N Workflow Preparation Create your Snowflake credentials in n8n with required host and account details, warehouse (e.g., "computer_warehouse"), database, schema, username, and password. Replace placeholder variables in schema retrieval workflows with your actual database and data source names. Verify the credentials by testing the connection; reset passwords if needed. Workflow Logic The Agent Workflow listens to user chats, employs system role "Snowflake SQL assistant," and ensures schema validation before generating SQL queries. Generated SQL queries pass to the Data Retrieval Workflow, which executes them against Snowflake securely. Retrieved data is aggregated and evaluated against a configurable threshold to decide between returning raw data or creating a report link. When a report link is generated, the Report Viewing Workflow renders a dynamic interactive HTML-based report webpage, including pagination, filters, charts, and CSV export options. Helper workflows periodically fetch or update the current database schema and table definitions to maintain AI accuracy and prevent hallucinations in SQL generation. Error handling mechanisms provide user-friendly messages both in the agent chat and report pages when issues arise with SQL or connectivity. This modular, secure, and extensible setup empowers you to build intelligent AI-driven data interactions with Snowflake through n8n automations and custom reporting.

Voice Assistant Interface with n8n and OpenAI This workflow creates a voice-activated AI assistant interface that runs directly in your browser. Users can click on a glowing orb to speak with the AI, which responds with voice using OpenAI's text-to-speech capabilities. Who is it for? This template is perfect for: Developers looking to add voice interfaces to their applications Customer service teams wanting to create voice-enabled support systems Content creators building interactive voice experiences Anyone interested in creating their own "Alexa-like" assistant How it works The workflow consists of two main parts: Frontend Interface: A beautiful animated orb that users click to activate voice recording Backend Processing: Receives the audio transcription, processes it through an AI agent with memory, and returns voice responses The system uses: Web Speech API for voice recognition (browser-based) OpenAI GPT-4o-mini for intelligent responses OpenAI Text-to-Speech for voice synthesis Session memory to maintain conversation context Setup requirements n8n instance (self-hosted or cloud) OpenAI API key with access to: GPT-4o-mini model Text-to-Speech API Modern web browser with Web Speech API support (Chrome, Edge, Safari) How to set up Import the workflow into your n8n instance Add your OpenAI credentials to both OpenAI nodes Copy the webhook URL from the "Audio Processing Endpoint" node Edit the "Voice Assistant UI" node and replace YOUR_WEBHOOK_URL_HERE with your webhook URL Access the "Voice Interface Endpoint" webhook URL in your browser Click the orb and start talking! How to customize the workflow Change the AI personality**: Edit the system message in the "Process User Query" node Modify the visual style**: Customize the CSS in the "Voice Assistant UI" node Add more capabilities**: Connect additional tools to the AI Agent Change the voice**: Select a different voice in the "Generate Voice Response" node Adjust memory**: Modify the context window length in the "Conversation Memory" node Demo Watch the template in action: https://youtu.be/0bMdJcRMnZY

AI Recipe Generator from Pantry Items using FatSecret API This workflow creates an intelligent WhatsApp cooking assistant that transforms pantry ingredients into personalized recipe suggestions using AI and the FatSecret Recipes API What Makes This Different: AI-Powered Recipe Discovery** - Uses Google Gemini AI to understand user intent and dietary preferences Smart Ingredient Analysis** - Automatically extracts ingredients, dietary restrictions, and cooking constraints FatSecret API Integration** - Leverages comprehensive recipe database with nutritional information WhatsApp Native Experience** - Seamless chat interface for recipe discovery Contextual Memory** - Remembers conversation context for better user experience Intelligent Parameter Mapping** - AI automatically maps user requests to API parameters Key Benefits of AI-Driven Architecture: Natural Language Understanding** - Users can describe what they have in plain English Personalized Recommendations** - Considers dietary restrictions, time constraints, and preferences Eliminates Manual Search** - No need to manually input specific ingredients or filters Scalable Recipe Database** - Access to thousands of recipes through FatSecret API Conversational Interface** - Natural chat flow instead of form-based inputs Smart Context Management** - Remembers previous requests for better follow-up suggestions Who's it for This template is designed for food delivery services, meal planning apps, nutritionists, cooking enthusiasts, and businesses looking to provide intelligent recipe recommendations. It's perfect for companies who want to engage customers through WhatsApp with personalized cooking assistance, helping users discover new recipes based on available ingredients and preferences. How it works / What it does This workflow creates an intelligent WhatsApp cooking assistant that transforms simple ingredient lists into personalized recipe suggestions. The system: Receives WhatsApp messages through webhook triggers Analyzes user input using Google Gemini AI to extract ingredients, dietary needs, and preferences Maps user requests to FatSecret API parameters automatically Searches recipe database based on extracted criteria (ingredients, calories, time, cuisine, etc.) Processes API results to format recipe suggestions with images and nutritional info Maintains conversation context using memory buffer for better user experience Sends formatted responses back to users via WhatsApp Key Innovation: AI-Powered Parameter Extraction - Unlike traditional recipe apps that require users to fill out forms or select from predefined options, this system understands natural language requests and automatically maps them to the appropriate API parameters, making recipe discovery as simple as texting a friend. How to set up 1. Configure WhatsApp Business API Set up WhatsApp Business API credentials Configure webhook endpoints for message reception Set up phone number ID and recipient handling Ensure proper message sending permissions 2. Configure Google Gemini AI Set up Google Gemini (PaLM) API credentials Ensure proper API access and quota limits Configure the AI model for recipe-related conversations Test the AI's understanding of cooking terminology 3. Configure FatSecret API Set up FatSecret OAuth2 API credentials Ensure access to the Recipes Search v3 endpoint Configure proper authentication and rate limiting Test API connectivity and response handling 4. Set up Memory Management Configure the memory buffer for conversation context Set appropriate session key mapping for user identification Adjust context window length based on expected conversation depth Test memory persistence across multiple messages 5. Test the Integration Send test messages through WhatsApp to verify end-to-end functionality Test various ingredient combinations and dietary restrictions Verify recipe suggestions are relevant and properly formatted Check that context memory works across multiple interactions Requirements WhatsApp Business API** account with webhook capabilities Google Gemini AI** API access for natural language processing FatSecret API** credentials for recipe database access n8n instance** with proper webhook and HTTP request capabilities Active internet connection** for real-time API interactions How to customize the workflow Modify Recipe Search Parameters Adjust the number of results returned (currently set to 5) Add more filtering options (cuisine types, cooking methods, difficulty levels) Implement pagination for browsing through more recipe options Add sorting preferences (newest, oldest, calorie-based, popularity) Enhance AI Capabilities Train the AI on specific dietary restrictions or cuisine preferences Add support for multiple languages Implement recipe rating and review integration Add nutritional goal tracking and meal planning features Expand Recipe Sources Integrate with additional recipe APIs (Spoonacular, Edamam, etc.) Add support for user-generated recipes Implement recipe bookmarking and favorites Add shopping list generation from selected recipes Improve User Experience Add recipe step-by-step instructions Implement cooking timer and progress tracking Add recipe sharing capabilities Implement user preference learning over time Business Features Add recipe monetization options Implement affiliate marketing for ingredients Add restaurant delivery integration Implement meal kit subscription services Key Features Natural language processing** - Understands cooking requests in plain English Intelligent parameter mapping** - AI automatically extracts search criteria Comprehensive recipe database** - Access to thousands of recipes via FatSecret API WhatsApp native interface** - Seamless chat experience for recipe discovery Contextual memory** - Remembers conversation history for better recommendations Dietary restriction support** - Handles allergies, preferences, and special diets Nutritional information** - Provides calorie counts and macro details Image integration** - Shows recipe photos when available Technical Architecture Highlights AI-Powered Processing Google Gemini integration** - Advanced natural language understanding Smart parameter extraction** - Automatic mapping of user requests to API calls Contextual memory** - Conversation history management for better user experience Intelligent fallbacks** - Graceful handling of unclear or incomplete requests API Integration Excellence FatSecret Recipes API** - Comprehensive recipe database with nutritional data OAuth2 authentication** - Secure and reliable API access Parameter optimization** - Efficient API calls with relevant search criteria Response processing** - Clean formatting of recipe suggestions WhatsApp Integration Webhook-based triggers** - Real-time message reception Message formatting** - Clean, readable recipe presentations User identification** - Proper session management for multiple users Error handling** - Graceful fallbacks for failed operations Performance Optimizations Efficient API calls** - Single request per user message Memory management** - Optimized conversation context storage Response caching** - Reduced API calls for repeated requests Scalable architecture** - Handles multiple concurrent users Use Cases Food delivery platforms** requiring recipe recommendation engines Meal planning services** needing ingredient-based recipe discovery Nutrition and wellness apps** requiring dietary-specific suggestions Cooking schools** offering personalized recipe guidance Grocery stores** helping customers plan meals around available ingredients Restaurant chains** providing recipe inspiration for home cooking Health coaches** offering personalized meal suggestions Social cooking communities** sharing recipe ideas and inspiration Business Value Customer Engagement** - Interactive recipe discovery increases user retention Personalization** - AI-driven recommendations improve user satisfaction Operational Efficiency** - Automated recipe suggestions reduce manual support Revenue Generation** - Recipe recommendations can drive ingredient sales Brand Differentiation** - AI-powered cooking assistant sets services apart Data Insights** - User preferences provide valuable market intelligence Scalability** - Handles multiple users simultaneously without performance degradation This template revolutionizes recipe discovery by combining the power of AI natural language processing with comprehensive recipe databases, creating an intuitive WhatsApp experience that makes cooking inspiration as simple as having a conversation with a knowledgeable chef friend.

This workflow handles the incoming call from Twitter and sends the required response for verification. On registering the webhook with the Twitter Account Activity API, Twitter expects a signature in response. Twitter also randomly ping the webhook to ensure it is active and secure. Webhook node: Use the displayed URL to register with the Account Activity API. Crypto node: In the Secret field, enter your API Key Secret from Twitter. Set node: This node generates the response expected by the Twitter API. Learn more about connecting n8n with Twitter in the Getting Started with Twitter Webhook article.