Agents & Automations

This workflow employs OpenAI's language models and SerpAPI to create a responsive, intelligent conversational agent. It comes equipped with manual chat triggers and memory buffer capabilities to ensure seamless interactions. To use this template, you need to be on n8n version 1.50.0 or later.

Automatically Add Captions to Your Video Who Is This For? This workflow is ideal for content creators, marketers, educators, and businesses that regularly produce video content and want to enhance accessibility and viewer engagement by effortlessly adding subtitles. What Problem Does This Workflow Solve? Manually adding subtitles or captions to videos can be tedious and time-consuming. Accurate captions significantly boost viewer retention, accessibility, and SEO rankings. What Does This Workflow Do? This automated workflow quickly adds accurate subtitles to your video content by leveraging the Json2Video API. It accepts a publicly accessible video URL as input. It makes an HTTP request to Json2Video, where AI analyzes the video, generates captions, and applies them seamlessly. The workflow returns a URL to the final subtitled video. The second part of the workflow periodically checks the Json2Video API to monitor the processing status at intervals of 10 seconds. Key Features - Automatic & Synced Captions: Captions are generated automatically and synchronized perfectly with your video. - Fully Customizable Design: Easily adjust fonts, colors, sizes, and more to match your unique style. - Word-by-Word Display: Supports precise, word-by-word captioning for improved clarity and viewer engagement. - Super Fast Processing: Rapid caption generation saves time, allowing you to focus more on creating great content. Preconditions To use this workflow, you must have: - A Json2Video API account. - A video hosted at a publicly accessible URL. Why You Need This Workflow Adding subtitles to your videos significantly enhances their reach and effectiveness by: - Improving SEO visibility, enabling search engines to effectively index your video content. - Enhancing viewer engagement and accessibility, accommodating viewers who watch without sound or who have hearing impairments. - Streamlining your content production process, allowing more focus on creativity. Specific Use Cases - Social Media Content: Boost viewer retention by adding subtitles. - Educational Videos: Enhance understanding and improve learning outcomes. - Marketing Videos: Reach broader and more diverse audiences.

This workflow automates the process of discovering and extracting APIs from various services, followed by generating custom schemas. It works in three distinct stages: research, extraction, and schema generation, with each stage tracking progress in a Google Sheet. 🙏 Jim Le deserves major kudos for helping to build this sophisticated three-stage workflow that cleverly automates API documentation processing using a smart combination of web scraping, vector search, and LLM technologies. How it worksStage 1 - Research: - Fetches pending services from a Google Sheet - Uses Google search to find API documentation - Employs Apify for web scraping to filter relevant pages - Stores webpage contents and metadata in Qdrant (vector database) - Updates progress status in Google Sheet (pending, ok, or error) Stage 2 - Extraction: - Processes services that completed research successfully - Queries vector store to identify products and offerings - Further queries for relevant API documentation - Uses Gemini (LLM) to extract API operations - Records extracted operations in Google Sheet - Updates progress status (pending, ok, or error) Stage 3 - Generation: - Takes services with successful extraction - Retrieves all API operations from the database - Combines and groups operations into a custom schema - Uploads final schema to Google Drive - Updates final status in sheet with file location Ideal for: - Development teams needing to catalog multiple APIs - API documentation initiatives - Creating standardized API schema collections - Automating API discovery and documentation Accounts required: - Google account (for Sheets and Drive access) - Apify account (for web scraping) - Qdrant database - Gemini API access Set up instructions: - Prepare your Google Sheets document with the services information. Here's an example of a Google Sheet – you can copy it and change or remove the values under the columns. Also, make sure to update Google Sheets nodes with the correct Google Sheet ID. - Configure Google Sheets OAuth2 credentials, required third-party services (Apify, Qdrant) and Gemini. - Ensure proper permissions for Google Drive access.

This n8n workflow was developed to collect and summarize feedback from an event that was collected via a Google Form and saved in a Google Sheets document. The workflow is triggered manually by clicking on the "Test workflow" button. The Google Sheets node retrieves the responses from the feedback form. The Aggregate node then combines all responses for each question into arrays and prepares the data for analysis. The OpenAI node processes the aggregated feedback data. The System Prompt instructs the model to analyze the responses and generate a summary report that includes the overall sentiment regarding the event and constructive suggestions for improvement. The Markdown node converts the summary report, which is in Markdown format, into HTML. Finally, the Gmail node sends an HTML-formatted email to the specified email address.

Ever wanted to build your own RAG search over Youtube videos? Well, now you can! This n8n template shows how you can build a very capable Youtube search engine powered by Apify, Qdrant, and your LLM of choice to quickly and efficiently browse over many videos for research. I originally started this template to ask questions on the "n8n @ scale office-hours" livestream videos but then extended it to include the latest videos on the official channel. Check out a demo here: [Demo Link] How it worksStage 1 is to collect the Youtube video transcripts and push them into a vector database. For this, I've used Apify to scrape Youtube and Qdrant to store the embeddings. Transcripts are broken down into smaller chunks and carefully tagged with metadata to assist in later search and filtering. Stage 2 is to build a web frontend for the user to query the vectorized transcripts. I'm using a webhook to serve a simple web app and API to dynamically fetch the results. When searching for a video, I've opted to use Qdrant's search groups API which, in this use-case, performs better as it returns a wider range of video results. In the web frontend, when the user clicks on the results, the matching Youtube video plays in an embedded video player. How to use Once credentials are all set, first run steps 1 - 3 to populate your vector store. Next, set the workflow to active to expose the web frontend. Visit the webhook URL in your browser to use it. If only for personal use, you may want to remove the rate limiting mechanism in step 4. Customizing the template Not interested in official n8n videos? Swap to a different channel - this template will work on many as long as videos are not private or set to prevent embeds. Technically any vector store should work but may not have the same grouping API. Use the simple vector store node and revert back to basic searching instead.

Use Case Transform web pages into AI-friendly markdown format: - You need to process webpage content for LLM analysis. - You want to extract both content and links from web pages. - You need clean, formatted text without HTML markup. - You want to respect API rate limits while crawling pages. What this Workflow Does The workflow uses Firecrawl.dev API to process webpages: - Converts HTML content to markdown format. - Extracts all links from each webpage. - Handles API rate limiting automatically. - Processes URLs in batches from your database. Setup 1. Create a Firecrawl.dev account and get your API key. 2. Add your Firecrawl API key to the HTTP Request node's Authorization header. 3. Connect your URL database to the input node (column name must be "Page") or edit the array in Example fields from data source. 4. Configure your preferred output database connection. How to Adjust it to Your Needs - Modify input source to pull URLs from different databases. - Adjust rate limiting parameters if needed. - Customize output format for your specific use case.

Easily translate Zoom recordings (to English) using OpenAI's Whisper. Create audio summaries and share them through Slack to enhance team communication.

WhatsApp AI Agent: Auto-Train Product Data & Handle Customer SupportWho Is This For This workflow is ideal for eCommerce founders, product managers, customer support teams, and automation builders who rely on WhatsApp to manage product information and interact with clients. It’s perfect for businesses that want to automate product data entry and support responses directly from WhatsApp messages using GPT-4 and Google Sheets. What Problem Does This Workflow SolveManual Product Data Entry: Collecting and organizing product data from links is tedious and error-prone. Slow Customer Response Times: Responding to client questions manually leads to delays and inconsistent support. No Logging System for Issues: Without automation, support issues often go undocumented, making it harder to learn and improve. What This Workflow DoesStep 1 – Incoming Message Detection Listens for incoming messages via WhatsApp. If the message starts with train:, it routes to the product training process. Otherwise, it routes to the customer support assistant. Step 2 – Product Data Training Extracts URL from the message using a regex script. Fetches HTML content from the URL. Cleans HTML data to extract readable product description. Saves raw data (URL + description) into Google Sheets. Uses GPT-4 to enhance product data: → Name, price (one-time or subscription), topic, and FAQs. Updates the product row in Google Sheets with structured information. Step 3 – Customer Support Flow Analyzes user messages with GPT-4 to understand the request or issue. Looks up relevant product info in Google Sheets. Detects potential problems (e.g. payment, login, delivery). Suggests an appropriate solution. Logs the problem, solution, and category to the Customer Issues sheet. Sends a response back to the client via WhatsApp. Step 4 – Client Response Sends the AI-generated response to the client via WhatsApp. Keeps the communication fast, clear, and professional. Setup GuidePrerequisites - WhatsApp Business API access - OpenAI API Key - Google Account with Google Sheets access - A hosted instance of n8n (Cloud or self-hosted)Setup Steps - Import the Workflow into your n8n instance. - Connect your credentials for WhatsApp, OpenAI, and Google Sheets. - Customize Google Sheet IDs and names as needed. - Test by sending a train: message or a regular customer message to WhatsApp. - Activate the workflow to make it live.How to Customize This Workflow - Edit AI prompts to reflect your product type, language style, or tone. - Change the trigger keyword (e.g. from train: to add: or anything else). - Add integrations like Notion, Airtable, or CRM tools. - Expand the Sheets structure with more product fields (e.g. stock status, image link). - Add notifications to Slack or email after product updates or issue logging.

Multi-AI Agent Chatbot for Postgres/Supabase Databases and QuickChart GenerationWho is this for? This workflow is ideal for data analysts, developers, and business intelligence teams who need an AI-powered chatbot to query Postgres/Supabase databases and generate dynamic charts for data visualization. What problem does this solve? It simplifies data exploration by combining conversational AI with database querying and chart generation. Users can interact with their database using natural language, retrieve insights, and visualize data without manual SQL queries or chart configuration. What this workflow doesAI-Powered Chat Interface: - Accepts natural language prompts to query databases or generate charts. - Routes user requests through a tool agent system to determine the appropriate action (query or chart). Database Querying: - Executes SQL queries on Postgres/Supabase databases based on user input. - Retrieves schema information, table definitions, and specific data records. Dynamic Chart Generation: - Uses QuickChart to create bar charts, line charts, or other visualizations from database records. - Outputs a shareable chart URL or JSON configuration for further customization. Memory Integration: - Maintains chat history using Postgres memory nodes, enabling context-aware interactions. Workflow diagram showcasing AI agents, database querying, and chart generation paths.SetupPrerequisites: - A Postgres-compatible database (e.g., Supabase). - API credentials for OpenAI. Configuration Steps: - Add your database connection credentials in the Postgres nodes. - Set up OpenAI credentials for GPT-4o-mini in the language model nodes. - Adjust the QuickChart schema in the "QuickChart Object Schema" node to fit your use case. Testing: - Trigger the chat workflow via the "When chat message received" node. - Test with prompts like "Generate a bar chart of sales data" or "Show me all users in the database." How to customize this workflow - Modify AI Prompts - Add Chart Types - Integrate Other Tools

This workflow template, "Chinese Translator via Line x OpenRouter (Text & Image)" is designed to provide seamless Chinese translation services directly within the LINE messaging platform. By integrating with OpenRouter.ai and advanced language models like Qwen, this workflow translates text or images containing Chinese characters into pinyin and English translations, making it an invaluable tool for language learners, travelers, and businesses operating in multilingual environments. This template is ideal for: - Language Learners: Who want to practice Chinese by receiving instant translations of text or images. - Travelers: Looking for quick translations of Chinese signs, menus, or documents while abroad. - Educators: Teaching Chinese language courses and needing tools to assist students with translations. - Businesses: Operating in multilingual markets and requiring efficient communication tools. - Automation Enthusiasts: Seeking to build intelligent chatbots that can handle language translation tasks. What Problem Does This Workflow Solve? Translating Chinese text or images into English and pinyin can be challenging, especially for beginners or those without access to reliable translation tools. This workflow solves that problem by: - Automatically detecting and translating text or images containing Chinese characters. - Providing accurate translations in both pinyin and English for better comprehension. - Supporting multiple input formats (text, images) to cater to diverse user needs. - Sending replies directly to users via the LINE messaging platform, ensuring accessibility and ease of use. What This Workflow Does - Receive Messages via LINE Webhook: The workflow is triggered when a user sends a message (text, image, or other types) to the LINE bot. - Display Loading Animation: A loading animation is displayed to reassure the user that their request is being processed. - Route Input Types: The workflow uses a Switch node to determine the type of input (text, image, or unsupported formats). If the input is text, it is sent to the OpenRouter.ai API for translation. If the input is an image, the workflow extracts the image content, converts it to base64, and sends it to the API for translation. Unsupported formats trigger a polite response indicating the limitation. - Translate Content Using OpenRouter.ai: The workflow leverages Qwen models from OpenRouter.ai to generate translations. For text inputs, it provides Chinese characters, pinyin, and English translations. For images, it extracts and translates using the qwen-VL model which can take images. - Reply with Translations: The translated content is formatted and sent back to the user via the LINE Reply API. How to Customize This Workflow to Your Needs - Add More Languages: Extend the workflow to support additional languages by modifying the API calls. - Enhance Image Processing: Integrate more advanced OCR tools to improve text extraction from complex images. - Customize Responses: Modify the reply format to include additional details, such as grammar explanations or cultural context. - Expand Use Cases: Adapt the workflow for specific industries, such as tourism or e-commerce, by tailoring the translations to relevant vocabulary. Why Use This Template? - Real-Time Translation: Provides instant translations of text and images, improving user experience and accessibility. - Multimodal Support: Handles both text and image inputs, catering to diverse user needs. - Scalable: Easily integrate into existing systems or scale to support multiple users and workflows. - Customizable: Tailor the workflow to suit your specific audience or industry requirements.

Automatically create and share Google My Business posts with Dropbox links using ChatGPT insights. Streamline content creation and sharing with Google Sheets, OpenAI, Dropbox, and Google My Business integration.

AI-Agent Code Review for GitHub Pull Requests This n8n workflow automates the process of reviewing code changes in GitHub pull requests using an OpenAI-powered agent. It connects your GitHub repo, extracts modified files, analyzes diffs, and uses an AI agent to generate a code review based on your internal code best practices (fed from a Google Sheet). It ends by posting the review as a comment on the PR and tagging it with a visual label like ✅ Reviewed by AI. --- 🔧 What It Does - Triggered on PR creation - Extracts code diffs from the PR - Formats and feeds them into an OpenAI prompt - Enriches the prompt using a Google Sheet of Swift best practices - Posts an AI-generated review as a comment on the PR - Applies a PR label to visually mark reviewed PRs --- ✅ Prerequisites Before deploying this workflow, ensure you have the following: - n8n Instance (Self-hosted or Cloud) - GitHub Repository with PR activity - OpenAI API Key for GPT-4o, GPT-4-turbo, or GPT-3.5 - GitHub OAuth App (or PAT) connected to n8n to post comments and access PR diffs - (Optional) Google Sheets API credentials if using the code best practices lookup node. --- ⚙️ Setup Instructions 1. Import the Workflow in n8n, click on Workflows → Import from file or JSON. Paste or upload the JSON code of this template. 2. Configure Triggers and Connections: - 🔁 GitHub Trigger - Node: PR Trigger - Repository: Select the GitHub repo(s) to monitor - Events: Set to pull_request - Auth: Use GitHub OAuth2 credentials - 📥 HTTP Request - Node: Get file's Diffs from PR - No authentication needed; it uses dynamic path from trigger - 🧠 OpenAI Model - Node: OpenAI Chat Model - Model: Select gpt-4o, gpt-4-turbo, or gpt-3.5-turbo - Credential: Provide your OpenAI API Key - 🧑‍💻 Code Review Agent - Node: Code Review Agent - Connected to OpenAI and optionally to tools like Google Sheets - 💬 GitHub Comment Poster - Uses GitHub API to post review comments back on PR - Node: GitHub Robot - Credential: Use the agent Github account (OAuth or PAT) - Repo: Pick your own Github Repository - 🏷️ PR Labeler (optional) - Adds label ReviewedByAI after successful comment - Node: Add Label to PR - Label: You can customize the label text of your own tag. - 📊 Google Sheet Best Practices config (optional) - Connects to a Google Sheet for coding guideline lookups. We can replace Google Sheet by another tool or database. - First prepare your best practices list with clear descriptions and the code bad/good examples. - Add all the best practices in your Google Sheet. - Configure the Code Best Practices node in the template: - Credential: Use your Google Sheet account by OAuth2 - URL: Add your Google Sheet document URL - Sheet: Add the name of the best practices sheet

Disclaimer: This workflow template uses the n8n-nodes-couchbase community package. Community nodes are unverified, and usage of them comes with some risks. This template is intended for those interested in learning more about Agentic AI workflow development, as well as how to use the Couchbase Search Vector Store node for practical applications. This workflow helps users decide on travel destinations based on descriptions of several points of interest loaded into Couchbase and retrieved using Vector Search. How it Works This template contains two workflows: 1. The Data Ingestion workflow uses the following nodes: - Webhook node (to listen for HTTP requests) - OpenAI Embeddings node (to generate embeddings on document insertion) - Note: You’ll need to configure OpenAI credentials for this node - Couchbase Vector node (configured for document insertion) - Default Data Loader and Recursive Character Text Splitter 2. The Chat Application workflow uses the following nodes: - Chat Trigger node - AI Tools Agent node connected to: - Gemini (as the Chat Model, for generating responses) - Note: You will have to configure Gemini credentials for this node - Simple Memory (as the Memory, to maintain conversation context) - Couchbase Search Vector node (as the Tool, for search) - OpenAI Embeddings node (as the Embedding model for the Couchbase Search Vector node, to convert queries to vectors) - Note: You’ll need to configure OpenAI credentials for this nodeSet up Setting up this workflow is easy and only takes around 10 minutes. Prerequisites - A Couchbase Cluster running the Search Service, and corresponding database access credentials - Ensure the Couchbase cluster allows the incoming IP address for n8n - Create a Vector Search Index using this index definition - Create a bucket (called travel-agent), scope (called vectors), and collection (called points-of-interest) in your Cluster - OpenAI API Key - Gemini API Key Steps 1. Configure all necessary credentials (Couchbase, OpenAI, and Gemini) 2. Select your bucket, scope, and collection for each of the Couchbase vector nodes 3. Ingest data, either using the cURL statements found on the sticky note within the workflow, or using this shell script to ingest 6 points of interest 4. Open the chat and test out your travel agent! Customization and Next Steps This workflow template can be made more robust by enhancing the data model to include more information about each point of interest. For example, the addition of price ranges, ideal seasons to visit, activity types, and accommodation options can help inform the LLM further about each destination, allowing it to provide a more tailored response and be more helpful for travel planning. Alternatively, the data model could be entirely reconfigured to suit a wide variety of other use cases. This template can serve as a building block for all sorts of AI Agent applications using RAG and is not limited to only travel recommendations.

Who is this for? This template is ideal for sales teams, business development professionals, and marketers looking to build a targeted prospect database with automatic qualification. Perfect for agencies, consultants, and B2B companies wanting to identify and prioritize the most promising potential clients. What problem does this workflow solve? Manually researching companies on LinkedIn, evaluating their fit for your services, and tracking them in your CRM is time-consuming and subjective. This automation streamlines lead generation by automatically finding, scoring, and importing qualified prospects into your database. What this workflow does This workflow automatically searches for companies on LinkedIn based on your criteria, retrieves detailed information about each company, filters them based on quality indicators, uses AI to score how well they match your ideal customer profile, and adds them to your Google Sheet CRM while preventing duplicates. Setup - Create a Ghost Genius API account and get your API key - Configure HTTP Request nodes with Header Auth credentials - Create a copy of the provided Google Sheet template - Set up your Google Sheet and OpenAI credentials following n8n documentation - Customize the "Set Variables" node to match your target audience and scoring criteriaHow to customize this workflow - Modify search parameters to target different industries, locations, or company sizes - Adjust the follower count threshold based on your qualification criteria - Customize the AI scoring system to align with your specific product or service offering - Add notification nodes to alert you when high-scoring companies are identified

This workflow automates the process of analyzing emails and their attachments (PDFs and images) using AI models (DeepSeek, Gemini, and OpenRouter). It extracts and summarizes the content of emails and attachments, saves the summaries to Google Sheets, and sends a final consolidated summary via Telegram. This is a powerful tool for automating email analysis and summarization, saving time and ensuring that important information is easily accessible and actionable. Below is a breakdown of the workflow: 1. How It Works The workflow is designed to process incoming emails, analyze their content and attachments, and generate summaries. Here's how it works: Email Trigger: The workflow starts with the Email Trigger (IMAP) node, which monitors an email inbox for new emails. If an email contains attachments, the workflow processes them. Check for Attachments: The Contain Attachments? node checks if the email has attachments. If attachments are present, the workflow proceeds to process them. Process Attachments: The Get PDF and Images Attachments node extracts PDF and image attachments from the email. The Switch node separates PDFs and images for further processing: - PDFs: The Extract from PDF node extracts text from PDFs, and the PDF Analyzer node summarizes the content. - Images: The Analyze Image node uses AI to describe the content of images. Summarize Email Content: The Convert Text node converts the email's HTML content to plain text. The Email Summarization Chain node uses AI to generate a summary of the email's text content. Save Summaries: The Save Summary PDF, Save Summary Image, and Save Summary Text nodes save the summaries of PDFs, images, and email text, respectively, to Google Sheets. Consolidate Summaries: The All Summaries node aggregates the summaries of the email text, PDFs, and images. The Create Final Summary node uses AI to generate a unified summary of all the content. Send Final Summary: The Send Final Summary node sends the consolidated summary via Telegram to a specified chat ID. 2. Set Up Steps To set up and use this workflow in n8n, follow these steps: IMAP Configuration: Set up IMAP credentials in n8n for the Email Trigger (IMAP) node. Ensure the email account is accessible via IMAP. AI Model Configuration: Configure the DeepSeek, Gemini, and OpenRouter credentials in n8n for the Email Summarization Chain, PDF Analyzer, and Create Final Summary nodes. Ensure the AI models are set up to generate summaries. Google Sheets Integration: Set up Google Sheets credentials in n8n for the Save Summary PDF, Save Summary Image, and Save Summary Text nodes. Specify the Google Sheet and worksheet where the summaries will be saved. Telegram Integration: Set up Telegram credentials in n8n for the Send Final Summary node. Insert your Chat ID in the Telegram node to receive the final summary. Test the Workflow: Send an email with attachments (PDFs and images) to the monitored email account. The workflow will extract and summarize the email content and attachments, save the summaries to Google Sheets, and send a consolidated summary via Telegram. Optional Customization: - Replace IMAP trigger with Gmail or Outlook trigger. - Modify the workflow to include additional features, such as: - Adding more AI models for different types of analysis. - Sending notifications via other channels (e.g., Slack, email). - Integrating with other storage services (e.g., Dropbox, AWS S3). Need help customizing? Contact me for consulting and support or add me on LinkedIn.

This workflow streamlines your WooCommerce product creation process by integrating directly with Google Sheets. Simply input product details into your spreadsheet, and the workflow takes care of the rest—automatically creating new products on your WooCommerce store with inventory management. But it doesn’t stop there. A dedicated SEO expert chain analyzes each product’s content and generates optimized meta titles and meta descriptions for the plugin Yoast SEO, enhancing visibility and ranking potential on search engines. Key Benefits: 🔄 Automation: No more manual uploads—save time and reduce errors by syncing Google Sheets directly with WooCommerce. ⚡ Speed: Instantly publish multiple products with just one action. 🧠 Built-in SEO Intelligence: Automatically generate SEO-friendly meta titles and descriptions tailored to each product. 📈 Improved Search Visibility: Boost your store's traffic with optimized product listings. 🧩 Customizable: Easily adapt the workflow to your specific needs or integrate with other platforms. How It Works This workflow automates the creation of WooCommerce products and generates optimized SEO meta tags (title and description) using AI. Here’s the step-by-step process: Data Retrieval: The workflow starts by fetching product details (title, category, description, price, etc.) from a Google Sheets document. Product Creation: Each product is created in WooCommerce using the retrieved data, including categories, pricing, stock details, and images. AI-Powered SEO Optimization: An AI model analyzes the product details and generates SEO-optimized meta titles (≤60 chars) and meta descriptions (≤160 chars). Meta Tag Assignment: The generated meta tags are saved back to the Google Sheets and applied to the WooCommerce product using Yoast SEO metadata. Completion Tracking: The workflow marks completed entries in Google Sheets and sends a Telegram notification upon finishing all products. Set Up Steps Before running the workflow, ensure the following steps are completed: Step 1: Install the Yoast SEO plugin on WordPress and add the provided PHP code to functions.php to enable meta tag API support. Step 2: Enable the WooCommerce REST API in WordPress and configure the Telegram node with a valid CHAT_ID for notifications. Step 3: Prepare a Google Sheet with product data (columns A-I in specific formats) and share its ID in the workflow. Ensure columns B, E, and F are in text format, and column I is numeric. Once set up, the workflow can be triggered manually or scheduled to run automatically, streamlining product creation and SEO optimization. Who is it useful for? Ideal for eCommerce managers, digital marketers, or anyone managing large product catalogs—this workflow turns your spreadsheet into a powerful product launcher. Need help customizing? Contact me for consulting and support or add me on LinkedIn.

This automation captures leads from your website, generates a personalized follow-up email using AI, logs the data, and notifies your team instantly — no manual work required. It’s designed for service-based businesses, nonprofits, and professionals who want to increase responsiveness, reduce drop-offs, and build trust from the first click.

This workflow allows you to ask questions about the data in a Google Sheet over a chat interface. It uses n8n's built-in chat, but could be modified to work with Slack, Teams, WhatsApp, etc. Behind the scenes, the workflow uses GPT-4, so you'll need to have an OpenAI API key that supports it. How it works The workflow uses an AI agent with custom tools that call a sub-workflow. That sub-workflow reads the Google Sheet and returns information from it. Because models have a context window (and therefore a maximum number of characters they can accept), we can't pass the whole Google Sheet to GPT - at least not for big sheets. So we provide three ways of querying less data, that can be used in combination to answer questions. Those three functions are: - List all the columns in the sheet - Get all values of a single column - Get all values of a single row Note that to use this template, you need to be on n8n version 1.19.4 or later.

About The Product Hunt Automation Staying up-to-date with specific topics and launches on Product Hunt can be time-consuming. Manually checking the site multiple times a day interrupts your workflow and risks missing important launches. What if you could automatically get relevant launches delivered to your Slack workspace? How to Monitor Product Hunt In this guide, you'll learn how to create a Product Hunt monitoring system using Airtop's built-in node in n8n. This automation will scan Product Hunt for your chosen topics and deliver the most relevant launches directly to Slack. What You'll Need - A free Airtop API key - A Slack workspace with permissions to add incoming webhooks - Estimated setup time: 5 minutes Understanding the Process The Monitor Product Hunt automation uses Airtop's cloud browser capabilities to access Product Hunt and extract launch information. Here's what happens: - Airtop visits Product Hunt and navigates the page - It searches for and extracts up to 5 launches related to your chosen topic - The information is formatted and sent to your specified Slack channel This process can run on your preferred schedule, ensuring you never miss relevant launches. Setting Up Your Automation We've created a ready-to-use template that handles all the complex parts. Here's how to get started: 1. Connect your Airtop account by adding the API key you created 2. Connect your Slack account 3. Set your prompt in the Airtop node. For this example, we’ve set it to be “Extract up to 5 launches related to AI products” 4. Choose your preferred monitoring schedule. Customization Options While our template works immediately, you might want to customize it for your specific needs: - Adjust the prompt and the maximum number of launches to monitor - Customize the Slack message format - Change the monitoring frequency - Add filters for particular keywords or companies Real-World Applications Here's how teams can use this automation: - A startup's engineering team could track trends in other product’s tech stack, helping them stay informed about potential issues and improvements. - A product manager can track launches of competitor products, enabling them to gather valuable market insights and user feedback directly from the tech community on that launch. Best Practices To get the most out of this automation: - Choose Specific Search Terms: For more relevant results, instead of broad terms like "AI," use specific phrases like "machine learning for healthcare." - Optimize Scheduling: When setting the monitoring frequency, consider your team's workflow. Running the scenario every 4 hours during working hours often provides a good balance between staying updated and avoiding notification fatigue. - Set Up Error Handling: Enable n8n's error output to alert you if the automation encounters any issues with accessing Product Hunt or sending messages to Slack. - Regular Topic Review: Schedule a monthly review of your monitored topics to ensure they're still relevant to your needs and adjust as necessary. What's Next? Now that you've set up your Product Hunt monitor automation, you might be interested in: - Creating a similar monitor for other tech websites - Setting up automated content curation for your team's newsletter - Building a competitive intelligence dashboard using web monitoring Happy Automating!

This n8n template demonstrates an approach to image embeddings for the purpose of building a quick image contextual search. Use cases could include a personal photo library, product recommendations, or searching through video footage. How it works A photo is imported into the workflow via Google Drive. The photo is processed by the edit image node to extract colour information. This information forms part of our semantic metadata used to identify the image. The photo is also processed by a vision-capable model which analyses the image and returns a short description with semantic keywords. Both pieces of information about the image are combined with the metadata of the image to form a document describing the image. This document is then inserted into our vector store as a text embedding which is associated with our image. From here, the user can query the vector store as they would any document, and the relevant image references and/or links should be returned. Customise this workflow Text summarisation is just one of many techniques to generate image embeddings. If the results are unsatisfactory, there are dedicated image embedding models such as Google's Vertex AI multimodal embeddings.

Note: This template only works for self-hosted n8n. This n8n template demonstrates how to use the Langchain code node to track token usage and cost for every LLM call. This is useful if your templates handle multiple clients or customers and you need a cheap and easy way to capture how much of your AI credits they are using. How it works In our mock AI service, we're offering a data conversion API to convert Resume PDFs into JSON documents. A form trigger is used to allow for PDF upload and the file is parsed using the Extract from File node. An Edit Fields node is used to capture additional variables to send to our log. Next, we use the Information Extractor node to organise the Resume data into the given JSON schema. The LLM subnode attached to the Information Extractor is a custom one we've built using the Langchain Code node. With our custom LLM subnode, we're able to capture the usage metadata using lifecycle hooks. We've also attached a Google Sheet tool to our LLM subnode, allowing us to send our usage metadata to a Google sheet. Finally, we demonstrate how you can aggregate from the Google sheet to understand how much AI tokens/costs your clients are liable for. Requirements - Self-hosted version of n8n - OpenAI for LLM - Google Sheets to store usage metadata Customising this template Bring the custom LLM subnode into your own templates! In many cases, it can be a drop-in replacement for the regular OpenAI subnode. Not using Google Sheets? Try other databases or a HTTP call to pipe into your CRM.