Agents & Automations

Retrieve data with the HTTP app, convert HTML to text, create a message with Anthropic Claude, and add it to Google Sheets effortlessly.

This automation template is designed for content creators, digital marketers, and social media managers looking to simplify their video posting workflow. It automates the process of generating engaging video descriptions and uploading content to both Instagram and TikTok, making your social media management more efficient and error-free. Who Is This For? - Content Creators & Influencers: Streamline your video uploads and focus more on creating content. - Digital Marketers: Ensure consistent posting across multiple platforms with minimal manual intervention. - Social Media Managers: Automate repetitive tasks and maintain a steady online presence. What Problem Does This Workflow Solve? Manually creating descriptions and uploading videos to different platforms can be time-consuming and error-prone. This workflow addresses these challenges by: - Automating Video Uploads: Monitors a designated Google Drive folder for new videos. - Generating Descriptions: Uses OpenAI to transcribe video audio and generate engaging, customized social media descriptions. - Ensuring Multi-Platform Consistency: Simultaneously posts your video with the generated description to Instagram and TikTok. - Error Notifications: Optional Telegram integration sends alerts in case of issues, ensuring smooth operations. How It Works 1. Video Upload: Place your video in the designated Google Drive folder. 2. Description Generation: The automation triggers OpenAI to transcribe your video’s audio and generate a captivating description. 3. Content Distribution: Automatically uploads the video and description to both Instagram and TikTok. 4. Error Handling: Sends Telegram notifications if any issues arise during the process. Setup - Generate an API token at upload-post.com and configure it in both the Upload to TikTok and Upload to Instagram nodes. - Google Cloud Project: Create a project in Google Cloud Platform, enable the Google Drive API, and generate the necessary OAuth credentials to connect to your Google Drive account. - Set up your Google Drive folder in the Google Drive Trigger node. - Customize the OpenAI prompt in the Generate Social Description node to match your brand’s tone. - (Optional) Configure Telegram credentials for error notifications. Requirements - Accounts: upload-post.com, Google Drive, and (optionally) Telegram. - API Keys & Credentials: Upload-post.com API token, OpenAI API key, and (optional) Telegram bot token. - Google Cloud: A project with the Google Drive API enabled and valid OAuth credentials. Use this template to enhance your productivity, maintain consistency across your social media channels, and engage your audience with high-quality video content.

Split Test Agent Prompts with Supabase and OpenAIUse Case Oftentimes, it's useful to test different settings for a large language model in production against various metrics. Split testing is a good method for doing this. What it Does This workflow randomly assigns chat sessions to one of two prompts, the baseline and the alternative. The agent will use the same prompt for all interactions in that chat session. How it Works When messages arrive, a table containing information regarding session ID and which prompt to use is checked to see if the chat already exists. If it does not, the session ID is added to the table and a prompt is randomly assigned. These values are then used to generate a response. Setup Create a table in Supabase called split_test_sessions. It needs to have the following columns: session_id (text) and show_alternative (bool). Add your Supabase and OpenAI credentials. Modify the Define Path Values node to set the baseline and alternative prompt values. Activate the workflow and test by sending messages through n8n's inbuilt chat. Experiment with different chat sessions to test both prompts in action. Next Steps Modify the workflow to test different LLM settings such as temperature. Add a method to measure the efficacy of the two alternative prompts.

Who is this for? This template is designed for anyone who wants to integrate MCP with their AI Agents. Whether you're a developer, a data analyst, or an automation enthusiast, if you're looking to leverage the power of MCP and Google Calendar in your n8n workflows, this template is for you. What problem is this workflow solving? This template caters to MCP beginners seeking a hands-on example and developers looking to integrate the Google Calendar MCP service. When integrating MCP with Google Calendar, manually updating AI Agents after changes to Google Calendar tools on the MCP Server is time-consuming and error-prone. This template automates the process, enabling the AI Agent to instantly recognize changes made to Google Calendar on the MCP Server. In project management, for example, it ensures that task schedule updates in Google Calendar are automatically detected by the AI Agent. With detailed steps, it simplifies the integration process for all users. What this workflow does This workflow focuses on integrating MCP with Google Calendar within n8n. Specifically, it allows you to build an MCP Server and Client using Google Calendar nodes in n8n. Any changes made to the Google Calendar tools on the MCP Server are automatically recognized by the MCP Client in the workflow. This means that you can make changes to your Google Calendar (such as adding, deleting, or modifying events) on the MCP Server, and the MCP Client in the n8n workflow will immediately detect these changes without any manual intervention. SetupRequirements - An active n8n account. - Access to Google Calendar API: You need to enable the Google Calendar API and create the necessary credentials (OAuth 2.0 client ID). - Basic knowledge of n8n workflows and MCP concepts. Step-by-step guide 1. Create a new workflow in n8n: Log in to your n8n account and create a new workflow. 2. Add Google Calendar nodes: Search for and add the Google Calendar nodes to your workflow. Configure the nodes with your Google Calendar API credentials. 3. Set up the MCP Server and Client: Use the appropriate nodes in n8n to set up the MCP Server and Client. Connect the Google Calendar nodes to the MCP nodes as required. 4. Test the workflow: Make some changes to your Google Calendar on the MCP Server and check if the MCP Client in the n8n workflow can detect these changes. How to customize this workflow to your needs If you want to customize this workflow, you can: - Modify the triggers: You can change the conditions under which the MCP Client detects changes. For example, you can set it to detect only specific types of events in Google Calendar. - Integrate with other services: You can add more nodes to the workflow to integrate with other services, such as sending notifications to Slack or saving data to a database when a change is detected.

Automatically share new articles on Discord by generating messages with ChatGPT and logging details in Google Sheets. Ideal for content creators and marketers.

Automate Audio Transcription, AI Summarization, and Google Drive Storage Who is this for? Content Teams, Researchers, and Administrators who need to automatically process voice memos, meeting recordings, or interview audio into structured, searchable documents. What problem does this solve? Eliminates manual transcription work by automatically converting audio files into organized text documents with AI analysis, while maintaining human oversight through approval workflows. What this workflow doesSmart Audio Processing: - Triggers when new .m4a files appear in Google Drive - Uses OpenAI's Whisper for accurate transcription - Implements dual-format reporting (JSON + Markdown) Human Oversight (optional): - Requires email approval before processing - 45-minute response window with escalation options AI-Powered Analysis: - Generates structured JSON reports with: - Key points & action items - Sentiment analysis - Technical terminology glossary - Creates Markdown versions for easy reading Document Management: - Stores raw transcripts + reports in Google Drive - Automatic file naming with timestamps - Sends completion alerts via Email/Telegram - Workflow visualization showing audio file processing path SetupCredentials Needed: - Google Drive API access - OpenAI API key (GPT-4o-mini) - Gmail & Telegram integrations Configuration: - Set your Google Drive folder ID in 3 nodes - Update email addresses in Gmail nodes - Customize approval timeout in "Gmail User for Approval" Customization Points: - File extension filters (.m4a) - AI report templates and prompts - Notification channels (Email/Telegram) How to customizeApproval Process: Add SMS/Teams notifications via additional nodes File Types: Modify filter node for .mp3/.wav support Analysis Depth: Adjust GPT-4 prompts in "Summarize to JSON" nodes Storage: Connect to Notion/Airtable instead of Google Drive

Are you looking to create a counseling chatbot that provides emotional support and mental health guidance through the LINE messaging platform? This guide will walk you through connecting LINE with powerful AI language models like GPT-4 to build a chatbot that supports users in navigating their emotions, offering 24/7 conversational therapy and accessible mental health resources. By leveraging LINE's webhook integration and Azure OpenAI, this template allows you to design a chatbot that is both empathetic and efficient, ensuring users receive timely and professional responses. Whether you're a developer, counselor, or business owner, this guide will help you create a customizable counseling chatbot tailored to your audience's needs. ### Who Is This Template For? - Developers who want to integrate AI-powered chatbots into the LINE platform for mental health applications. - Counselors & Therapists looking to expand their reach and provide automated emotional support to clients outside of traditional sessions. - Businesses & Organizations focused on improving mental health accessibility and offering innovative solutions to their users. - Educators & Nonprofits seeking tools to provide free or low-cost counseling services to underserved communities. ### How Does This Work? 1. Line Webhook to receive new message 2. Send loading animation in Line 3. Check if the input is text or not 4. Send the text as prompt in chat model (GPT-4) 5. Reply to the message to the user (you'll need 'edit field' to format it before the reply) ### Pre-Requisites - You have access to the LINE Developers Console. - An Azure OpenAI account with necessary credentials. ### Set-up To receive messages from LINE, configure your webhook: - Set up a webhook in LINE Developer Console. - Copy the Webhook URL from the Line Chatbot node and paste it into the LINE Console. - Ensure to remove any 'test' part when moving to production. The loading animation reassures users that the system is processing their request. ### Message Handling Use the Check Message Type IsText? node to verify if the incoming message is text. If the message type is text, proceed with ChatGPT processing; otherwise, send a reply indicating non-text inputs are not supported. ### AI Agent Configuration Define the system message within the AI Agent node to guide the conversation based on desired interaction principles. Connect the Azure OpenAI Chat Model to the AI Agent. ### Formatting Responses Ensure responses are properly formatted before sending them back to the user. ### Reply Message Use the ReplyMessage - Line node to send the formatted response. Ensure proper header authorization using Bearer tokens.

This n8n workflow builds an appointment scheduling AI agent which can take enquiries from prospective customers and help them book an appointment by checking appointment availability. Where no appointment is booked, the agent is able to send follow-up messages to re-engage leads. After an appointment is booked, the agent can reschedule or even cancel the booking for the user without human intervention. For small outfits, this workflow could contribute the necessary "man-power" required to increase business sales. How it works: The customer sends an enquiry via SMS to trigger our workflow. For this trigger, we'll use a Twilio webhook. The prospective or existing customer's number is logged in an Airtable Base which we'll be using to track all our enquiries. Next, the message is sent to our AI Agent who can reply to the user and decide if an appointment booking can be made. The reply is made via SMS using Twilio. A scheduled trigger which runs every day checks our chat logs for a list of prospective customers who have yet to book an appointment but still show interest. This list is sent to our AI Agent to formulate a personalised follow-up message to each lead and ask them if they want to continue with the booking. The follow-up interaction is logged so as to not send too many messages to the customer. Requirements: - A Twilio account to receive customer messages. - An Airtable account and Base to use as our datastore for enquiries. - Cal.com account to use as our scheduling service. - OpenAI account for our AI model. Customising this workflow: Not using Airtable? Swap this out for your CRM of choice such as HubSpot or your own service. Not using Cal.com? Swap this out for API-enabled services such as Acuity Scheduling or your own service.

Who is this for? This workflow is built for anyone who works with YouTube content, whether you're: - A learner looking to understand a video’s key points - A content creator repurposing video material - A YouTube manager looking to update titles and descriptions - A social media strategist searching for the most shareable clips Don't just ask questions about what's said. Find out what's going on in a video too. What problem does this solve? YouTube videos hold valuable insights, but watching and processing them manually takes time. This workflow automates: - Quick content extraction: Summarize key ideas without watching full videos - Visual analysis: Understand what’s happening beyond spoken words - Clip discovery: Identify the best moments for social sharing How the workflow works This n8n-powered automation: - Uses Google’s Gemini 1.5 Flash AI for intelligent video analysis - Provides multiple content analysis templates tailored to different needs What makes this workflow powerful? The easiest place to start is by requesting a summary or transcript. From there, you can refine the prompts to match your specific use case and the type of video content you’re working with. But what's even more amazing? You can ask questions about what’s happening in the video — and get detailed insights about the people, objects, and scenes. It's jaw-dropping. This workflow is versatile — the actions adapt based on the values set. That means you can use a single workflow to: - Extract transcripts - Generate an extended YouTube description - Write a summary blog post You can also modify the trigger based on how you want to run the workflow — use a webhook, connect it to an event in Airtable, or leave it as-is for on-demand use. The output can then be sent anywhere: Notion, Airtable, CMS platforms, or even just stored for reference. How to set it up - Connect your Google API key - Paste a YouTube video URL - Select an analysis method - Run the workflow and get structured results Analysis Templates - Basic & Timestamped Transcripts: Extract spoken content - Summaries: Get concise takeaways - Visual Scene Analysis: Detect objects, settings, and people - Clip Finder: Locate shareable moments - Actionable Insights: Extract practical information Customization Options - Modify templates to fit your needs - Connect with external platforms - Adjust formatting preferences Advanced Configuration This workflow is designed for use with gemini-1.5-flash. In the future, you can update the flow to work with different models or even modify the HTTP request node to define which API endpoint should be used. It's also been designed so you can use this flow on its own or add it to a new/existing workflow. This workflow helps you get the most out of YouTube content — quickly and efficiently.

This n8n template uses a Telegram chatbot to conduct a Product Satisfaction Survey and fetches questions, storing answers in a Google sheet. It augments an AI Agent to ask follow-up questions to engage the user and uncover more insights in their responses. This template is intended to demonstrate how you'd realistically approach a workflow where there is structured conversation (static questions) but you still want to include a free-form element (follow-up questions) which can only be accomplished via AI. How it works A chat session is started with the user who needs to enter the bot command "/next" to start the survey. Once started, the template pulls in questions from a Google sheet to ask the user. Questions are asked in sequence from the left column to the right column. When the user answers the question, a text classifier node is used to determine if a follow-up question could be asked. If so, a mini conversation is initiated by the AI agent to get more details. If not, the survey proceeds to the next question. All answers and mini-conversations are recorded in the Google Sheet under the respective question. When all questions are answered, the template will stop the survey and give the user a chance to restart. How to use 1. You'll need to set up a Telegram bot (see docs). 2. Create a Google sheet with an ID column. Populate the rest of the columns with your survey questions (see sample). 3. Ensure you have a Redis instance to capture state. Either self-host or sign up to Upstash for a free account. 4. Update the "Set Variable" node with your Google sheet ID and survey title. 5. Share your bot to allow others to participate in your survey. Requirements - Telegram for Chatbot - Google Sheets for Survey questions and answers - Redis for State Management and Chat Memory - Community+ license and above for Execution data node - you can remove this node if you don't have this license. Customizing this workflow Not using Telegram? This template technically works with other chat apps such as WhatsApp, WeChat, and even n8n's hosted chat! This state management pattern can also be applied to other use-cases and scenarios. Try it for other types of surveys!

Who this is for? Extract & Summarize Yelp Business Review is an automated workflow that extracts the Yelp business reviews using Bright Data Web Unlocker, processes and formats the raw data, summarizes using Google Gemini's LLM, and forwards the concise summary with the review response to a specified webhook endpoint. This workflow is tailored for: - Local SEO Specialists who need structured insights from Yelp reviews to optimize listings. - Business Owners wanting quick summaries of what customers love or complain about. - Reputation Managers who monitor brand sentiment and identify customer pain points. - Data Analysts & Researchers extracting Yelp review patterns at scale. - AI Product Builders needing clean Yelp review data as input for their LLMs or recommender systems. What problem is this workflow solving? Yelp reviews are rich in customer sentiment but messy to work with manually. This workflow solves: - The pain of scraping Yelp review content manually. - The challenge of building structured data with the summary. - The need for structured outputs suitable for analysis, reports, or AI input. What this workflow does This automated pipeline does the following: - Bright Data Integration: Queries Yelp and scrapes business listing data using Bright Data's Web Unlocker. - Structured Data Formatting: Formats the Yelp review data to a structured response in JSON format. - Google Gemini Summarization: Sends the cleaned reviews to Google Gemini to summarize. - Output Delivery: Returns the structured response with the concise summary over the webhook endpoint. How to customize this workflow to your needs This workflow is built to be flexible - whether you’re a market researcher, entrepreneur, or data analyst. Here's how you can adapt it to fit your specific use case: - Target Specific Business Categories: Update the Yelp Business Review input to scrape different businesses like gyms, salons, etc. - Limit Reviews: Add filters by description, location, page range to get the top reviews. - Tweak the Data Extraction Node: Update the Structured Data Extractor node Output Parser for building the JSON response with the appropriate fields or attributes. - Tweak the Summarization Prompt: Modify the Gemini prompt to generate a comprehensive summary. - Send Output to Other Destinations: Replace the Webhook URL to forward output to Google Sheets, Airtable, Slack or Discord, or custom API endpoints.

This n8n workflow template lets you easily generate comprehensive FAQ (Frequently Asked Questions) content for multiple services (or any items or pages you need to add the FAQs to). Simply provide the Google Sheets document containing the items to scrape, and the workflow automatically creates detailed, AI-enhanced FAQ documents. How it works The workflow reads data from a Google Sheets document containing information about different services and categories (again, in your case - whatever objects you need). For each service and category, it generates a set of standard questions and answers covering setup, permissions, integrations, use cases, and pricing benefits. An AI model (OpenAI's GPT) is used to enhance or complete some of the answers, making the content more comprehensive and natural-sounding. The workflow formats the Q&A pairs, combining AI-generated content with predefined answers where applicable. It creates a text file for each service or category, containing the formatted Q&A pairs. The generated files are saved to specific folders in Google Drive, organized by the type of integration (native, credential-only, non-native) or category. After processing each service or category, it updates the status in the original Google Sheets document to mark it as completed. Ideal for: - Marketing teams: Rapidly create comprehensive FAQ documents for multiple products or services. - Customer support: Generate consistent and detailed answers for common customer queries. - Product managers: Easily maintain up-to-date documentation as products evolve. - Content creators: Streamline the process of creating informative content about various offerings. Accounts required - Google account (for Google Sheets and Google Drive) - OpenAI API account (for AI-enhanced content generation) - n8n.io account (for workflow execution) Set up instructions 1. Set up the required credentials for Google Sheets, Google Drive, and OpenAI when you first open the workflow. 2. Prepare your Google Sheets document with the service/category information. 3. Fill the "Define Sheets" node with your sheets. 4. Adjust the folder IDs in the "Prepare Job" node to match your Google Drive structure. 5. Configure the OpenAI model settings in the "OpenAI Chat Model" node if needed. 6. Test the workflow with a small subset of data before running it on your entire dataset. 7. Adjust the questions asked in the "Create your Q&A templates" section. 8. After testing, activate your workflow for automated FAQ generation. 🙏 Big, big kudos to Jim Le for his ideas, input, and support when building this workflow. Your approach to AI workflows is always super helpful!

Who is this for? This workflow is designed for Customer Satisfaction Managers (CSM), sales professionals, and operations managers who need to automate the analysis of client transcripts, save summarized notes to HubSpot, and route relevant feedback to the appropriate departments via email. What problem is this workflow solving? / Use Case Manually processing client conversations, extracting key insights, and distributing them to the right teams is time-consuming and error-prone. This workflow automates: - Transcript analysis using AI (OpenAI) to identify relevant content. - HubSpot integration to log meeting notes against client records. - Email routing to ensure feedback reaches the correct departments (e.g., support, sales, product, admin). What this workflow does - Input Transcript: Accepts a client conversation transcript (e.g., from emails, calls, or chats). - HubSpot Sync: - Searches for the client’s HubSpot ID using their email. - Uploads a summarized version of the conversation as meeting notes. - AI-Powered Routing: - Uses an OpenAI model to analyze the transcript and categorize content by department. - Triggers emails (via Gmail) to route feedback to the relevant teams. - Form Completion: Ends the workflow with optional user confirmation. SetupPrerequisites: - n8n instance (cloud or self-hosted). - HubSpot API credentials (for contact lookup and notes upload). - OpenAI API key (for transcript analysis). - Gmail account (for sending emails). Configuration: - Replace placeholder nodes (e.g., HubSpot, OpenAI, Gmail) with your authenticated accounts. - Define email templates and recipient addresses for routing. - Adjust the OpenAI prompt to match your categorization criteria (e.g., "support," "billing"). How to customize this workflow to your needs - Transcript Sources: Extend the workflow to pull transcripts from other sources (e.g., Zoom, Slack). - Departments: Modify the routing logic to include additional teams or conditions. - Notifications: Add Slack/MS Teams alerts for urgent feedback. - Error Handling: Introduce retries or fallback actions for failed HubSpot/Gmail steps.

Every time a new Google Doc is created, Make will automatically convert it to an audio file via the Google Text-to-Speech API.

This is the first version of a template for a RAG/GenAI App using WordPress content. As creating, sharing, and improving templates brings me joy 😄, feel free to reach out on LinkedIn if you have any ideas to enhance this template! How It Works This template includes three workflows: Workflow 1: Generate embeddings for your WordPress posts and pages, then store them in the Supabase vector store. Workflow 2: Handle upserts for WordPress content when edits are made. Workflow 3: Enable chat functionality by performing Retrieval-Augmented Generation (RAG) on the embedded documents. Why use this template? This template can be applied to various use cases: - Build a GenAI application that requires embedded documents from your website's content. - Embed or create a chatbot page on your website to enhance user experience as visitors search for information. - Gain insights into the types of questions visitors are asking on your website. - Simplify content management by asking the AI for related content ideas or checking if similar content already exists. Useful for internal linking. Prerequisites - Access to Supabase for storing embeddings. - Basic knowledge of Postgres and pgvector. - A WordPress website with content to be embedded. - An OpenAI API key. Ensure that your n8n workflow, Supabase instance, and WordPress website are set to the same timezone (or use GMT) for consistency. Workflow 1: Initial Embedding This workflow retrieves your WordPress pages and posts, generates embeddings from the content, and stores them in Supabase using pgvector. Step 0: Create Supabase tables Nodes: - Postgres - Create Documents Table: This table is structured to support OpenAI embedding models with 1536 dimensions. - Postgres - Create Workflow Execution History Table. These two nodes create tables in Supabase: - The documents table, which stores embeddings of your website content. - The n8n_website_embedding_histories table, which logs workflow executions for efficient management of upserts. This table tracks the workflow execution ID and execution timestamp. Step 1: Retrieve and Merge WordPress Pages and Posts Nodes: - WordPress - Get All Posts - WordPress - Get All Pages - Merge WordPress Posts and Pages These three nodes retrieve all content and metadata from your posts and pages and merge them. Important: Apply filters to avoid generating embeddings for all site content. Step 2: Set Fields, Apply Filter, and Transform HTML to Markdown Nodes: - Set Fields - Filter - Only Published & Unprotected Content - HTML to Markdown These three nodes prepare the content for embedding by: - Setting up the necessary fields for content embeddings and document metadata. - Filtering to include only published and unprotected content (protected=false), ensuring private or unpublished content is excluded from your GenAI application. - Converting HTML to Markdown, which enhances performance and relevance in Retrieval-Augmented Generation (RAG) by optimizing document embeddings. Step 3: Generate Embeddings, Store Documents in Supabase, and Log Workflow Execution Nodes: - Supabase Vector Store - Sub-nodes: - Embeddings OpenAI - Default Data Loader - Token Splitter - Aggregate - Supabase - Store Workflow Execution This step involves generating embeddings for the content and storing it in Supabase, followed by logging the workflow execution details. Generate Embeddings: The Embeddings OpenAI node generates vector embeddings for the content. Load Data: The Default Data Loader prepares the content for embedding storage. The metadata stored includes the content title, publication date, modification date, URL, and ID, which is essential for managing upserts. ⚠️ Important Note: Be cautious not to store any sensitive information in metadata fields, as this information will be accessible to the AI and may appear in user-facing answers. Token Management: The Token Splitter ensures that content is segmented into manageable sizes to comply with token limits. Aggregate: Ensure the last node is run only for 1 item. Store Execution Details: The Supabase - Store Workflow Execution node saves the workflow execution ID and timestamp, enabling tracking of when each content update was processed. This setup ensures that content embeddings are stored in Supabase for use in downstream applications, while workflow execution details are logged for consistency and version tracking. This workflow should be executed only once for the initial embedding. Workflow 2, described below, will handle all future upserts, ensuring that new or updated content is embedded as needed. Workflow 2: Handle document upserts Content on a website follows a lifecycle—it may be updated, new content might be added, or, at times, content may be deleted. In this first version of the template, the upsert workflow manages: - Newly added content - Updated content Step 1: Retrieve WordPress Content with Regular CRON Nodes: - CRON - Every 30 Seconds - Postgres - Get Last Workflow Execution - WordPress - Get Posts Modified After Last Workflow Execution - WordPress - Get Pages Modified After Last Workflow Execution - Merge Retrieved WordPress Posts and Pages A CRON job (set to run every 30 seconds in this template, but you can adjust it as needed) initiates the workflow. A Postgres SQL query on the n8n_website_embedding_histories table retrieves the timestamp of the latest workflow execution. Next, the HTTP nodes use the WordPress API (update the example URL in the template with your own website’s URL and add your WordPress credentials) to request all posts and pages modified after the last workflow execution date. This process captures both newly added and recently updated content. The retrieved content is then merged for further processing. Step 2: Set fields, use filter Nodes: - Set fields2 - Filter - Only published and unprotected content The same that Step 2 in Workflow 1, except that HTML To Markdown is used in further Step. Step 3: Loop Over Items to Identify and Route Updated vs. Newly Added Content Here, I initially aimed to use 'update documents' instead of the delete + insert approach, but encountered challenges, especially with updating both content and metadata columns together. Any help or suggestions are welcome! :) Nodes: - Loop Over Items - Postgres - Filter on Existing Documents - Switch Route existing_documents (if documents with matching IDs are found in metadata): - Supabase - Delete Row if Document Exists: Removes any existing entry for the document, preparing for an update. - Aggregate2: Used to aggregate documents on Supabase with ID to ensure that Set Fields3 is executed only once for each WordPress content to avoid duplicate execution. - Set Fields3: Sets fields required for embedding updates. Route new_documents (if no matching documents are found with IDs in metadata): - Set Fields4: Configures fields for embedding newly added content. In this step, a loop processes each item, directing it based on whether the document already exists. The Aggregate2 node acts as a control to ensure Set Fields3 runs only once per WordPress content, effectively avoiding duplicate execution and optimizing the update process. Step 4: HTML to Markdown, Supabase Vector Store, Update Workflow Execution Table The HTML to Markdown node mirrors Workflow 1 - Step 2. Refer to that section for a detailed explanation on how HTML content is converted to Markdown for improved embedding performance and relevance. Following this, the content is stored in the Supabase vector store to manage embeddings efficiently. Lastly, the workflow execution table is updated. These nodes mirror the Workflow 1 - Step 3 nodes. Workflow 3: An example of GenAI App with WordPress Content: Chatbot to be embedded on your website Step 1: Retrieve Supabase Documents, Aggregate, and Set Fields After a Chat Input Nodes: - When Chat Message Received - Supabase - Retrieve Documents from Chat Input - Embeddings OpenAI1 - Aggregate Documents - Set Fields When a user sends a message to the chat, the prompt (user question) is sent to the Supabase vector store retriever. The RPC function match_documents (created in Workflow 1 - Step 0) retrieves documents relevant to the user’s question, enabling a more accurate and relevant response. In this step: - The Supabase vector store retriever fetches documents that match the user’s question, including metadata. - The Aggregate Documents node consolidates the retrieved data. - Finally, Set Fields organizes the data to create a more readable input for the AI agent. Directly using the AI agent without these nodes would prevent metadata from being sent to the language model (LLM), but metadata is essential for enhancing the context and accuracy of the AI’s response. By including metadata, the AI’s answers can reference relevant document details, making the interaction more informative. Step 2: Call AI Agent, Respond to User, and Store Chat Conversation History Nodes: - AI Agent - Sub-nodes: - OpenAI Chat Model - Postgres Chat Memories - Respond to Webhook This step involves calling the AI agent to generate an answer, responding to the user, and storing the conversation history. The model used is gpt4-o-mini, chosen for its cost-efficiency.

Who's this for? If you own a website and need to analyze your Google Analytics data, If you need to create an SEO report on which pages are getting the most traffic or how your Google search terms are performing, If you want to grow your site based on suggestions from data. Use case Instead of hiring an SEO expert, I run this report weekly. It checks and compares the data from this week to the week before: - Views based on countries - The top performing pages - Google Search Console performance How it works The workflow gathers Google Analytics data for the past 7 days, then it gathers the data for the week before for comparison. It does this 3 times to get: views per country, engagement per page, and Google Search Console results for organic search results. The Google Analytics nodes have already chosen the correct dimensions and metrics. At the end, it passes the data to Openrouter.ai for A.I. analysis. Finally, it saves to Baserow. How to use this - Input your Google Analytics credentials - Input your property ID - Input your Openrouter.ai credentials - Input your Baserow credentials You will need to create a Baserow database with columns: Name, Country Views, Page Views, Search Report, Blog (name of your blog). Created by Rumjahn.

This workflow updates your Twitter profile banner when you have a new follower. To use this workflow: 1. Configure Header Auth in the Fetch New Followers to connect to your Twitter account. 2. Update the URL of the template image in the Fetch BG node. 3. Create and configure your Twitter OAuth 1.0 credentials in the last HTTP Request node. You can configure the size and position of the avatar images in the Edit Image nodes. Check out this video to learn how to build it from scratch.

Instantly receive and send messages to Claude and update Google Sheets cells with responses. Streamline communication and data management using Custom WebHook, Claude, and Google Sheets integration.

Use this template to transcribe audio data from Google Drive into the Eden AI Speech-to-text module. Then, extract sentiment from the transcribed text. The Datastore module is used to wait until the transcription is finished.

This workflow is for automating and centralizing your bookmarking process using AI-powered tagging and seamless integration between your Android device and a self-hosted Read Deck platform. This workflow eliminates manual entry, organizes links with smart AI-generated tags, and ensures your bookmarks are always accessible, searchable, and secure. ### How It Works 📱 Android Shortcut Integration Use the HTTP Shortcuts app to create a 1-tap trigger that sends URLs and titles from your Android phone directly to n8n. 🤖 AI-Powered Tagging & Processing Leverage ChatGPT-4 to analyze content context and auto-generate relevant tags (e.g., “Tech Tutorials,” “Productivity Tools”). Extract clean titles and URLs from messy shared data (even from apps like Twitter or Reddit). 🔗 Readeck Integration Automatically save processed bookmarks to your self-hosted Readeck-like platform with structured metadata (title, URL, tags). ⚡ Silent Automation It runs in the background—no pop-ups or interruptions. 🔒 Pro Security Optional authentication (API tokens, headers) to protect your data. ### Use Case Perfect for researchers, content creators, or anyone drowning in tabs who wants to: - Save articles, videos, or social posts in one click. - Organize bookmarks with AI-generated tags. - Build a personal knowledge base that’s always accessible. ### Tutorial 1️⃣ Set Up Android Shortcut Install "HTTP Shortcuts" and configure it to send data to your n8n webhook. Enable “Share Menu” to trigger bookmarks from any app. 2️⃣ Configure n8n Workflow Import the template and add your Read Deck API token (or similar service). 3️⃣ Test & Scale Share a link from your phone—watch it appear in Read Deck instantly! Add error handling or notifications for advanced use. Note: For self-hosted platforms, ensure your instance is publicly accessible (or use a VPN). ### Why Choose This Workflow? - Zero Manual Entry: Save hours of copying/pasting. - AI Organization: Say goodbye to chaotic bookmark folders. - Privacy First: Host your data on your terms. Transform your bookmarking chaos into a streamlined system—try “Save: Bookmark” today! 🚀

This Telegram bot is designed to send one random recipe a day. This specific bot has filtered out only vegan recipes, so you can choose your diet type and send only recipes for a specific diet. What credentials you need: - Set up a Telegram bot. - Use Airtable for listing who has joined your bot. This is needed to send one random recipe a day. - A recipe (or other) API. This one uses Spoonacular. I hope you enjoy your bot!