Gegenfeld
We at Gegenfeld are an EdTech platform dedicated to delivering high-quality on-demand courses and interactive training sessions for professional development at all skill levels. With our commitment to "Accelerate your future," we empower professionals to enhance their expertise through engaging, industry-focused learning experiences. On n8n, we share some of the workflows used in our interactive training to provide deeper insights and practical applications.
Templates by Gegenfeld
AI-powered chatbot workflow with MySQL database integration
AI-Powered Chatbot Workflow with MySQL Integration This guide shows you how to deploy a chatbot that lets you query your database using natural language. You will build a system that accepts chat messages, retains conversation history, constructs dynamic SQL queries, and returns responses generated by an AI model. By following these instructions, you will have a working solution that integrates n8n’s AI Agent capabilities with MySQL. --- Prerequisites Before you begin, ensure that you have the following: An active n8n instance (self-hosted or cloud) running version 1.50.0 or later. Valid MySQL credentials configured in n8n. API credentials for the Groq Chat Model (or your preferred AI language model). Basic familiarity with SQL and n8n node concepts such as chat triggers and memory buffers. Access to the n8n Docs on AI Agents for further reference. --- Workflow Setup Chat Interface & Trigger When Chat Message Received This node listens for incoming chat messages via a webhook. When a message arrives, it triggers the workflow immediately. Conversation Memory Chat History This memory buffer node stores the last 10 interactions. It supplies conversation context to the AI Agent, ensuring that responses consider previous messages. AI Agent Core AI Agent (Tools Agent) The AI Agent node orchestrates the conversation by receiving the chat input and conversation history. It dynamically generates SQL queries based on your requests and coordinates calls to external tools (such as MySQL nodes). Database Interactions MySQL Node This node executes the SQL query generated by the AI Agent. You reference the query using an expression (e.g., {{$node["AI Agent"].json.sql_query}}), allowing the agent’s output to control data retrieval. MySQL Schema Node This node retrieves a list of base tables from your MySQL database (excluding system schemas). The agent uses this information to understand the available tables. MySQL Definition Node This node fetches detailed metadata (such as column names, data types, and relationships) for a specific table. The table and schema names are supplied dynamically by the AI Agent. Language Model Processing Groq Chat Model This node connects to the Groq Chat API to generate text completions. It processes the combined input (chat message, context, and data fetched from MySQL) and produces the final response. Guidance & Customization Sticky Notes These nodes provide guidance on: Switching the chat model if you wish to use another provider (e.g., OpenAI or Anthropic). Adjusting the maximum token count per interaction. Customizing the SQL queries and the context window size. They help you modify the workflow to suit your environment and requirements. Workflow Connections The Chat Trigger passes the incoming message to the AI Agent. The Chat History node supplies conversation context to the AI Agent. The AI Agent calls the MySQL nodes as external tools, generating and sending dynamic SQL queries. The Groq Chat Model processes the consolidated input from the agent and outputs the natural language response delivered to the user. Testing the Workflow Send a chat message using the chat interface. Observe how the AI Agent processes the input and generates a corresponding SQL query. Verify that the MySQL nodes execute the query and return data. Confirm that the Groq Chat Model produces a coherent natural language response. Refer to the sticky notes for guidance if you need to fine-tune any node settings. --- Next Steps and References Customize Your AI Model Replace the Groq Chat Model with another language model (such as the OpenAI Chat Model) by updating the node credentials and configuration. Enhance Memory Settings Adjust the Chat History node’s context window to retain more or fewer messages based on your needs. Modify SQL Queries Update the SQL queries in the MySQL nodes to match your specific database schema and desired data. Further Reading Consult the n8n Docs on AI Agents for additional details and examples to expand your workflow’s capabilities. Set Up a Website Chatbot Copy & Paste and replace the placeholders in the following code to embed the chatbot into your personal or company's website: View in CodePen 🡥 --- By following these steps, you will deploy a robust AI chatbot workflow that integrates with your MySQL database, allowing you to query data using natural language.
By GegenfeldAutomated background removal from images with APImage AI
This workflow automatically removes backgrounds from images using the APImage API. Simply provide an image URL, and the workflow will process it through AI-powered background removal, then download the processed image for use in your projects. Who's it for This template is perfect for: E-commerce businesses needing clean product images Content creators who need transparent background images Marketing teams processing large batches of images Developers building image processing applications Anyone who regularly needs background-free images How it works The workflow uses APImage's AI-powered background removal service to automatically detect and remove backgrounds from images. You provide an image URL through a form interface, the API processes the image using advanced AI algorithms, and returns a clean image with the background removed. The processed image is then downloaded and ready for use. How to set up Get your APImage API key: Sign in to the APImage Dashboard 🡥 (or create a new APImage account) Copy your API key from the dashboard Configure the API connection: Double-click the APImage Integration node Replace YOURAPIKEY with your actual API key (keep the Bearer prefix) Test the workflow: Click the Remove Background form trigger Enter an image URL in the form Submit to process the image Set up output destination (optional): Add nodes after the Download node to save images to your preferred storage Options include Google Drive, Dropbox, databases, or cloud storage Requirements n8n instance (cloud or self-hosted) APImage 🡥 account and valid API key Images accessible via public URLs for processing How to customize the workflow Replace Input Source: Swap the Form Trigger with data from other sources like: Database queries (MySQL, PostgreSQL, SQLite) Cloud storage (Google Drive, Dropbox, S3) Other APIs or webhooks Airtable, Notion, or other productivity tools Add Output Destinations: Connect additional nodes after the Download step to save processed images to: Cloud storage services (Google Drive, Dropbox, S3) Databases for organized storage Content management systems Social media platforms Email attachments Batch Processing: Modify the workflow to process multiple images by connecting it to data sources that provide arrays of image URLs. Add Image Validation: Include nodes to validate image URLs or file formats before processing to avoid API errors. Workflow Structure Form Trigger → APImage Integration → Download → [Your Output Destination] The Form Trigger collects image URLs, APImage Integration processes the background removal via API, Download retrieves the processed image, and you can add any output destination for the final images. API Details The workflow sends a POST request to https://apimage.org/api/ai-remove-background with: Authorization header: Your API key image_url: The URL of the image to process async: Set to false for immediate processing The processed image is returned with a transparent background and downloaded automatically.
By GegenfeldTrack SEO keyword rankings in Google Search with ScrapingBee API
Who is this template for? This SEO Reporting workflow template is ideal for digital marketers, SEO consultants, content strategists, and founders who need to quickly gather, format, and store Google search result data. If you regularly audit SERPs, track keyword performance, or monitor competitors, this automation lets you generate polished SEO reports in seconds—ready to share or analyze further. What problem does this workflow solve? Scraping and formatting Google search results for SEO insights is often manual, repetitive, and error-prone or requires expensive software. Professionals frequently face challenges like: Collecting live, structured data from Google for multiple keywords Converting raw search results into readable reports for clients or stakeholders Logging changes in rankings or URLs across time for historical tracking Exporting SEO data into spreadsheets for deeper analysis High monthly software fees What this workflow does This n8n workflow scrapes the top organic Google search results for a given keyword and automatically creates a downloadable report while also logging the results in a table format for long-term storage or further processing. Here’s what it includes: A trigger form that accepts a search keyword from the user An automated API call to fetch Google’s SERP data Two output formats: a formatted HTML table for emails and a Markdown table for download (e.g., for Excel, Airtable or Google Sheets) Automatic CSV file generation for download Optional email delivery of the report Setup Getting started is simple: Enter your API key Add your API key to the “Scrape Google SERPs” HTTP Request node (Step-by-step guide inside the template) Replace the default query with your own custom Google search parameters if needed Set up delivery options Update your email in the “Mail SEO Report” node for report delivery Use the downloadable file output from the “Convert to File” node Optional: Add a Google Sheets (or similar) node which imports the file Test the workflow Use the built-in form to input a keyword Confirm that results appear in both your email and downloadable file Activate the workflow Turn on the trigger so your team or clients can submit keywords at any time How to customize this workflow This template is easy to extend for a variety of SEO automation needs: Add a loop to handle multiple keywords at once Connect to Airtable, Notion or Google Sheets Integrate with Slack or Discord for notifications Apply additional filtering to track only new or changed search results Schedule it to run daily or weekly with a cron trigger By combining live SERP scraping, report formatting, and spreadsheet integration, this workflow gives you a fast and flexible SEO reporting system you can use right away or scale up as needed.
By GegenfeldCreate a knowledge base chatbot with OpenAI and Notion for website embedding
This workflow creates an AI chatbot that uses your Notion database as a knowledge base and allows for website embedding (e.g., as a customer support chatbot). The AI agent can automatically query your Notion pages and databases to provide accurate, contextual responses based on your stored content. Who's it for This template is perfect for: Support teams who maintain their knowledge base in Notion Content creators and teams managing documentation in Notion databases Businesses looking to make their Notion workspace conversationally accessible Anyone who wants to turn their Notion content into an interactive AI assistant How it works The workflow combines OpenAI's language model with Notion's database capabilities to create a smart chatbot. When users ask questions, the AI agent automatically determines which Notion pages or database entries are relevant and uses that content to generate helpful responses. The system maintains conversation history for natural, contextual interactions. How to set up Add your credentials: Configure your Notion API integration in the Set & Get Notion Database node Set up your OpenAI API credentials in the OpenAI Chat Model node Configure your Notion connection: Click the Set & Get Notion Database node Select your Notion database or page containing your knowledge base content The AI will automatically determine relevant pages - no need to specify individual page IDs Customize the AI model: Open the OpenAI Chat Model node Choose your preferred model (GPT-4, GPT-3.5-turbo, etc.) Adjust token limits if needed Test the chatbot: Click the Chat button to start a conversation Ask questions related to your Notion content Optional - Make it public: Enable public access in the Chat Trigger node Embed the provided code into your website Requirements n8n instance (cloud or self-hosted) Notion workspace with databases or pages you want to query OpenAI API key with available credits Notion API integration with proper permissions How to customize the workflow Change the AI Provider: You can replace the OpenAI Chat Model with other providers like Anthropic Claude, Google Gemini, or local models by swapping the language model node. Adjust Context Window: Modify the "Remember Chat History" node to increase or decrease how many previous messages the AI remembers (default is 10 interactions). Update System Instructions: Edit the Smart AI Agent's system message to change how the assistant behaves or add specific instructions for your use case. Connect Multiple Databases: Add additional Notion Database nodes to give the AI access to multiple databases or pages within your workspace. Add More Tools: Extend the AI agent with additional tools like web search, email sending, or integration with other services. Workflow Structure Chat Trigger → Smart AI Agent ← OpenAI Chat Model ↓ Set & Get Notion Database ↑ Remember Chat History The Smart AI Agent orchestrates the conversation, deciding when to query Notion and how to use the retrieved content in responses. The memory buffer ensures natural conversation flow by maintaining context across interactions.
By GegenfeldAutomatically replace and relight the background of any image using APImage
This workflow takes an image URL and a description of the desired background, then uses the APImage AI API to produce a high-quality image, preserving the subject and applying a natural-looking new background. Example Result Here you can see a before-and-after image, generated by APImage, using this workflow. --- Who This Workflow is For This workflow is perfect for anyone looking to generate polished, professional images quickly: E-commerce Teams: Ensure consistent product images with uniform, professional backdrops to reinforce brand identity and trust. Content Creators: Generate eye-catching visuals for social media, blogs, or marketing campaigns quickly. Designers: Produce realistic mockups and conceptual designs without hours of manual editing. Businesses of All Sizes: Automate large-scale image pipelines efficiently without expanding staff or budget. Anyone Needing AI Imaging: Personal or professional projects can benefit from this automated, high-quality workflow. --- How the Workflow Works This n8n workflow leverages a Form Trigger and APImage’s AI endpoint to automate background replacement: text ┌───────────────┐ │ Input Image │ │ (URL / source)│ └───────┬───────┘ │ ▼ ┌──────────────────────────┐ │ Background Replacement │ │ + Lighting Optimization│ │ (APImage AI) │ └───────┬──────────────────┘ │ ▼ ┌──────────────────────┐ │ Download Image │ │ (default name: data) │ └───────┬──────────────┘ │ ▼ ┌───────────────────────────┐ │ Output Nodes │ │ (DBs, Cloud, CMS, APIs) │ └───────────────────────────┘ Form Trigger: Collects the image URL and descriptive prompt. Can be swapped with any data source node (databases, cloud storage, APIs). APImage Integration: Processes the image, separates the subject, generates the new background, and preserves realistic lighting and shadows. Download Node: Retrieves the processed image for storage or further use. Output Options: Automatically upload images to Google Drive, Dropbox, Airtable, Notion, Amazon S3, or any other database, creating a fully automated pipeline. How to Set Up the Workflow Add Your APImage API Key Open the APImage API node. Replace Bearer YOURAPIKEY with your API key from the APImage Dashboard 🡥. Test the Workflow Enter a valid image URL and a descriptive prompt in the Replace Background form. Click Run Workflow to see the processed image downloaded locally. Optional: Customize Output Storage Add nodes after the Download step to automatically save images to your preferred storage service or database. Requirements n8n instance: Cloud or self-hosted instance. APImage account and API key: Required to access the AI API. How to Customize the Workflow Change Background Prompts: Modify the form or connect to dynamic sources like databases, CMS, or cloud storage. Adjust AI Settings: Customize preservesubject, lightdirection, and light_strength in the APImage node for perfect results. Extend Outputs: Connect to additional platforms for automatic sharing with teams, clients, or CMS systems. Automate Input: Replace manual form triggers with automated triggers from Google Sheets, Airtable, Shopify, or product databases to handle bulk processing without manual intervention. Example Use Cases E-commerce: Consistent product images with professional backgrounds. Marketing & Social Media: Quickly generate visuals adapted to multiple themes. Photography & Design: Produce mockups, portraits, and conceptual images with realistic lighting. Business Automation: Scale image pipelines efficiently for large volumes. FAQ Q: What types of images are supported? A: JPG, PNG, and WebP are supported. Q: Can I choose a custom background? A: Yes. Provide a text description or integrate with other nodes supplying pre-defined backgrounds. Q: What is the default filename for processed images? A: The default filename is data, unless renamed in the workflow. Q: Do I need to store images locally? A: No. Connect output nodes to databases, cloud storage, or CMS platforms to store images automatically. Q: Can APImage be used in automated workflows? A: Absolutely. Integrate with n8n to process images automatically from various sources. Q: Does it enhance image quality? A: APImage adjusts lighting and contrast for natural, realistic results. Q: How is background replacement different from background removal? A: Background removal makes the background transparent. Background replacement swaps it with a new one and adjusts lighting for a natural look. Q: Are there limits to the number of images processed? A: Limits depend on your subscription plan and allocated credits
By GegenfeldBuild a website customer support chatbot with Groq AI and Google Sheets knowledge base
Build a Website Customer Support Chatbot with Groq AI and Google Sheets as its Knowledge Base. Setup Instructions Prerequisites API Credentials Required: Groq API credentials - You'll need a valid API key from Groq Google Sheets credentials - OAuth authentication required to access your knowledge base sheets Step-by-Step Setup Add Required Credentials: Click on the Credentials menu and add your Groq API credentials Set up Google OAuth credentials for Google Sheets access Configure the Groq Chat Model: Click on the "Groq Chat Model" node Select your preferred Groq model (e.g., Llama-3-70b or Mixtral-8x7b) Set token limits and other parameters as needed Set Up Your Knowledge Base: Create a Google Sheet with your support information (example structure below) Note the Google Sheet ID from the URL Configure the Google Sheets Node: Click on the "Google Sheets" node Select your document ID from the dropdown Select the specific sheet name containing your knowledge base Customize the AI Agent: Modify the system message to match your brand's tone and support style Adjust the context window length in the "Chat History" node based on your needs Test the Chatbot: Click the "Chat" button to test with sample customer questions Verify the AI retrieves correct information from your knowledge base Deploy to Your Website: Click "Make Public" to generate an embed code Add the embed code to your website HTML Knowledge Base Structure Example Your Google Sheet should be structured with clear headers and organized data. Example format: | Question | Answer | Category | Keywords | |----------|--------|----------|----------| | How do I reset my password? | To reset your password, click the "Forgot Password" link on the login page and follow the instructions sent to your email. | Account | password, reset, forgot, login | | What are your shipping rates? | Standard shipping is $5.99. Express shipping is $12.99. Orders over $50 qualify for free standard shipping. | Shipping | rates, costs, delivery, free shipping | | How do I return an item? | Returns can be initiated within 30 days of purchase by logging into your account and selecting "Start a Return" in your order history. | Returns | return policy, exchange, refund | Each row should contain a complete customer query and response pair, with optional categorization and keywords to help the AI find relevant information quickly. Website Embedding You can use and replace the placeholders in the following code: View on Codepen (external link) Put the customized Code inside your website's head element (before the </head> tag).
By GegenfeldAI chatbot with OpenAI GPT-4.1-Mini and Supabase database knowledge base
This workflow creates an intelligent chatbot that uses your Supabase database as a knowledge base. The AI agent can automatically query your Supabase tables to provide accurate, contextual responses based on your stored data. Who's it for This template is perfect for: Developers building applications with Supabase backend Teams using Supabase for real-time data management Organizations wanting PostgreSQL-powered AI chatbots Startups leveraging Supabase's Firebase alternative ecosystem Teams needing scalable, real-time database integration with AI How it works The workflow combines OpenAI's language model with Supabase's PostgreSQL database capabilities to create a smart chatbot. When users ask questions, the AI agent automatically determines which Supabase records are relevant and uses that data to generate helpful responses. The system maintains conversation history for natural, contextual interactions. How to set up Add your credentials: Configure your Supabase project URL and API key in the Supabase Database node Set up your OpenAI API credentials in the OpenAI Chat Model node Configure your Supabase connection: Click the Supabase Database node Select your Supabase table containing your knowledge base data The AI will automatically determine relevant records - no need to specify individual record IDs Customize the AI model: Open the OpenAI Chat Model node Choose your preferred model (GPT-4, GPT-3.5-turbo, etc.) Adjust token limits if needed Test the chatbot: Click the Chat button to start a conversation Ask questions related to your Supabase data Optional - Make it public: Enable public access in the Chat Trigger node Embed the provided code into your website Requirements n8n instance (cloud or self-hosted) Supabase project with tables containing your knowledge base data OpenAI API key with available credits Supabase API key with appropriate read permissions How to customize the workflow Change the AI Provider: You can replace the OpenAI Chat Model with other providers like Anthropic Claude, Google Gemini, or local models by swapping the language model node. Adjust Context Window: Modify the "Remember Chat History" node to increase or decrease how many previous messages the AI remembers (default is 10 interactions). Update System Instructions: Edit the Smart AI Agent's system message to change how the assistant behaves or add specific instructions for your use case. Connect Multiple Tables: Add additional Supabase Database nodes to give the AI access to multiple tables within your Supabase project. Add Real-time Features: Leverage Supabase's real-time capabilities by integrating webhooks or subscriptions to keep your chatbot data current. Add More Tools: Extend the AI agent with additional tools like web search, email sending, or integration with other services. Workflow Structure Chat Trigger → Smart AI Agent ← OpenAI Chat Model ↓ Supabase Database ↑ Remember Chat History The Smart AI Agent orchestrates the conversation, deciding when to query Supabase and how to use the retrieved data in responses. The memory buffer ensures natural conversation flow by maintaining context across interactions.
By GegenfeldDynamic MongoDB knowledge base chatbot with OpenAI GPT
This workflow creates an intelligent chatbot that uses your MongoDB database as a knowledge base. The AI agent can automatically query your MongoDB collections to provide accurate, contextual responses based on your stored documents and data. Who's it for This template is perfect for: Developers using MongoDB for document-based data storage Organizations with complex, nested data structures in MongoDB Teams managing large-scale applications with MongoDB Atlas Businesses wanting to leverage NoSQL flexibility for AI chatbots Companies with existing MongoDB infrastructure and expertise How it works The workflow combines OpenAI's language model with MongoDB's document database capabilities to create a smart chatbot. When users ask questions, the AI agent automatically constructs MongoDB queries to find relevant documents and uses that data to generate helpful responses. The system maintains conversation history for natural, contextual interactions. How to set up Add your credentials: Configure your MongoDB connection string and credentials in the MongoDB Database Lookup node Set up your OpenAI API credentials in the OpenAI Chat Model node Configure your MongoDB connection: Click the MongoDB Database Lookup node Specify your MongoDB collection containing your knowledge base data The AI will automatically construct queries to find relevant documents Customize the AI model: Open the OpenAI Chat Model node Choose your preferred model (GPT-4, GPT-3.5-turbo, etc.) Adjust token limits if needed Test the chatbot: Click the Chat button to start a conversation Ask questions related to your MongoDB data Optional - Make it public: Enable public access in the Chat Trigger node Embed the provided code into your website Requirements n8n instance (cloud or self-hosted) MongoDB instance (self-hosted, MongoDB Atlas, or other MongoDB service) OpenAI API key with available credits MongoDB user credentials with read permissions on target collections How to customize the workflow Change the AI Provider: You can replace the OpenAI Chat Model with other providers like Anthropic Claude, Google Gemini, or local models by swapping the language model node. Adjust Context Window: Modify the "Remember Chat History" node to increase or decrease how many previous messages the AI remembers (default is 10 interactions). Update System Instructions: Edit the Smart AI Agent's system message to change how the assistant behaves or add specific instructions for your use case. Connect Multiple Collections: Add additional MongoDB Database Lookup nodes to give the AI access to multiple collections within your MongoDB database. Optimize Query Performance: Create appropriate indexes on your MongoDB collections to improve query performance for frequently accessed data. Add More Tools: Extend the AI agent with additional tools like web search, email sending, or integration with other services. Workflow Structure Chat Trigger → Smart AI Agent ← OpenAI Chat Model ↓ MongoDB Database Lookup ↑ Remember Chat History The Smart AI Agent orchestrates the conversation, deciding when to query MongoDB and how to use the retrieved documents in responses. The memory buffer ensures natural conversation flow by maintaining context across interactions.
By GegenfeldAI chatbot that queries Baserow database with OpenAI GPT-4 mini
This workflow creates an intelligent chatbot that uses your Baserow database as a knowledge base. The AI agent can automatically query your Baserow tables to provide accurate, contextual responses based on your stored data. Who's it for This template is perfect for: Support teams using Baserow for knowledge management Small businesses managing customer data in Baserow Teams looking for an open-source alternative to Airtable-based chatbots Organizations that want to make their Baserow data conversationally accessible Self-hosted solution enthusiasts who prefer controlling their data How it works The workflow combines OpenAI's language model with Baserow's open-source database capabilities to create a smart chatbot. When users ask questions, the AI agent automatically determines which Baserow records are relevant and uses that data to generate helpful responses. The system maintains conversation history for natural, contextual interactions. How to set up Add your credentials: Configure your Baserow API credentials in the Baserow Database node Set up your OpenAI API credentials in the OpenAI Chat Model node Configure your Baserow connection: Click the Baserow Database node Select your Baserow table containing your knowledge base data The AI will automatically determine relevant records - no need to specify individual record IDs Customize the AI model: Open the OpenAI Chat Model node Choose your preferred model (GPT-4, GPT-3.5-turbo, etc.) Adjust token limits if needed Test the chatbot: Click the Chat button to start a conversation Ask questions related to your Baserow data Optional - Make it public: Enable public access in the Chat Trigger node Embed the provided code into your website Requirements n8n instance (cloud or self-hosted) Baserow instance (self-hosted or cloud) with data you want to query OpenAI API key with available credits Baserow API token with appropriate permissions How to customize the workflow Change the AI Provider: You can replace the OpenAI Chat Model with other providers like Anthropic Claude, Google Gemini, or local models by swapping the language model node. Adjust Context Window: Modify the "Remember Chat History" node to increase or decrease how many previous messages the AI remembers (default is 10 interactions). Update System Instructions: Edit the Smart AI Agent's system message to change how the assistant behaves or add specific instructions for your use case. Connect Multiple Tables: Add additional Baserow Database nodes to give the AI access to multiple tables within your Baserow workspace. Add More Tools: Extend the AI agent with additional tools like web search, email sending, or integration with other services. Workflow Structure Chat Trigger → Smart AI Agent ← OpenAI Chat Model ↓ Baserow Database ↑ Remember Chat History The Smart AI Agent orchestrates the conversation, deciding when to query Baserow and how to use the retrieved data in responses. The memory buffer ensures natural conversation flow by maintaining context across interactions.
By Gegenfeld