Workflow Templates

Automated Content Idea Generation and Expansion with Google Gemini and Google Sheets This n8n workflow automates the process of generating content ideas based on a user-defined topic, then expands each idea into a more detailed content piece (like a blog post) using Google Gemini, and finally saves all the generated data (idea title, description, and full content) into a Google Sheet. It's a powerful tool for streamlining content creation workflows. This workflow includes: Generation of multiple content ideas from a single topic. Expansion of each idea into detailed content using AI. Storage of ideas and generated content in a structured Google Sheet. Sticky Notes within the workflow for inline documentation and setup guidance. Prerequisites n8n Instance: You need a running n8n instance (self-hosted or cloud). Google AI Account: Access to Google AI (Gemini). You will need an API key. Google Account: Access to Google Sheets. You will need to create or use an existing spreadsheet with specific column headers. Installation and Setup Import the Workflow: Copy the entire JSON code provided. In your n8n instance, go to "Workflows". Click "New" -> "Import from JSON". Paste the JSON code and click "Import". Configure Credentials: Google AI (Gemini): Find the "Google Gemini Chat Model for Content Idea Generator" node and the "Google Gemini Chat Model for Content Generation" node. Click on the "Credentials" field in both nodes (it will likely show a placeholder name like "Google Gemini(PaLM) Api account"). Click "Create New". Select "Google AI API". Enter your Google AI API Key. Save the credential. (You can reuse the same credential for both nodes). Google Sheets: Find the "Google Sheets" node. Click on the "Credentials" field (it will likely show a placeholder name like "Google Sheets account"). Click "Create New". Select "Google Sheets OAuth2 API". Follow the steps to connect your Google Account and grant n8n access to Google Sheets. Save the credential. Configure Google Sheets Node: Open the "Google Sheets" node settings. Spreadsheet ID: Replace the placeholder value with the actual ID of your Google Sheet. You can find the Spreadsheet ID in the URL of your Google Sheet (it's the long string of characters between /d/ and /edit). Sheet Name: Select or enter the name or GID of the sheet within your spreadsheet where you want to save the data (e.g., Sheet1 or gid=0). Columns: Ensure your Google Sheet has columns named title, description, and content. The node is configured to map the generated data to these specific column headers. Save the node settings. Review Sticky Notes: Look at the Sticky Notes placed around the workflow canvas. They provide helpful context and reminders for setup, required Google Sheet columns, and the AI models used. How to Use Activate the Workflow: Toggle the workflow switch to "Active". Trigger the Workflow: Since this workflow uses a "When clicking ‘Execute workflow’" node as the trigger, you can run it directly from the n8n editor. Click the "Execute Workflow" button. The workflow will start automatically. Set the Topic: Open the "Set the input fields" node. Modify the topic value to the subject you want to generate content ideas about. Save the node settings. Monitor Execution: Watch the workflow execute. The nodes will light up as they process. The "Loop Over Items" node will show multiple executions as it processes each generated idea. Check Results: The generated content ideas (title, description) and the expanded content will be written as new rows in the Google Sheet you configured. Each row will correspond to one generated idea and its content. This workflow provides a robust starting point for AI-assisted content creation. You can customize the AI prompts in the "Content Idea Generator" and "LLM Content Generator" nodes to refine the output style and format, or integrate additional steps like sending notifications or further processing the generated content.

🔧 How It Works Telegram Trigger – Listens for incoming messages from users via your Telegram bot. Watch Full Step-by-step Guide Video here: https://www.youtube.com/watch?v=GzWO7_1lyI8 AI Agent – Processes the message to determine the user's intent (booking or canceling) and extracts necessary details like date, time, and participant names. Google Calendar Node – Depending on the intent: Booking: Creates a new event in Google Calendar with the extracted details. Canceling: Searches for the specified event and deletes it from the calendar. Telegram Node – Sends a confirmation message back to the user, informing them of the successful booking or cancellation. 🧠 Why This is Useful Managing appointments can be time-consuming. This workflow automates the process, allowing users to schedule or cancel meetings effortlessly through a simple chat interface. It's ideal for: Solopreneurs managing their own schedules. Small businesses coordinating meetings with clients. Anyone looking to streamline their appointment management process. 🪜 Setup Instructions Set Up Telegram Bot: Create a new bot using BotFather on Telegram. Obtain the API token and set up the Telegram Trigger node in n8n with this token. OpenAI Platform API required for OpenAI Chat Model Connect to Google Calendar For the full video tutorial, watch here: https://youtu.be/GzWO7_1lyI8

Overview Transform your LLM into a powerful GitHub automation specialist with this n8n workflow template. In a world where multiple MCP servers can overwhelm LLMs with context, this streamlined solution provides a dedicated GitHub Agent that handles all GitHub API operations through a single, specialized tool. When you need GitHub operations like creating repositories, managing issues, or handling pull requests, your LLM can make one simple call to the GitHub Agent. This agent specializes exclusively in GitHub MCP server operations, offloading all contextual complexity and providing clean, efficient GitHub automation. ✨ Features Single MCP Server Trigger** - One tool and one parameter to handle all GitHub API interactions Specialized GitHub Agent** - Dedicated AI agent with direct GitHub MCP Server connection Self-Executing Workflow** - "When Executed by Another Workflow" trigger enables seamless workflow chaining Scalable Architecture** - Ready to integrate with unlimited GitHub tools and operations Context Optimization** - Reduces LLM token usage by delegating GitHub complexity to a specialized agent Flexible Request Processing** - Handles any GitHub operation through natural language requests 🎯 Use Cases Repository Management** - Create, clone, and manage repositories programmatically Issue Tracking** - Automate issue creation, updates, and management workflows Pull Request Automation - Streamline code review and merge processes GitHub Actions Integration** - Trigger and monitor CI/CD workflows Team Collaboration** - Automate notifications and team management tasks Documentation Updates** - Automatically update README files and documentation 🏗️ Workflow Architecture Node Breakdown: MCP Server Trigger - Receives requests with GitHub operation parameters Set GitHub Username - Configures GitHub user context for API calls OpenAI Chat Model - Powers the intelligent GitHub agent with contextual understanding Simple Memory - Maintains conversation context and operation history GitHub AI Agent - Specialized Tools Agent with direct GitHub MCP Server access [MCP Server Trigger] → [Set GitHub Username] → [GitHub AI Agent] ↓ [OpenAI Chat Model] ← [Simple Memory] ← [GitHub API Operations] 📋 Requirements Essential Prerequisites: ✅ OpenAI API Key - For AI Agent and Chat Model functionality ✅ GitHub Username Configuration - Edit the "Set GitHub Username" node with your GitHub username for API calls ✅ n8n Version - Compatible with n8n 2024+ releases ✅ MCP Server Setup - Existing GitHub MCP server configuration Recommended Setup: GitHub Personal Access Token with appropriate permissions Basic understanding of n8n workflow configuration Familiarity with GitHub API operations 🚀 Setup Instructions Step 1: Import and Configure Import the workflow template into your n8n instance Navigate to the Set GitHub Username node Replace the placeholder with your actual GitHub username Step 2: API Keys Setup Configure your OpenAI API key in the Chat Model node Ensure your GitHub credentials are properly configured in n8n Test the connection to verify API access Step 3: MCP Server Integration Connect your existing GitHub MCP server to the workflow Verify the MCP Server Trigger is properly configured Test with a simple GitHub operation (e.g., "List my repositories") Step 4: Deploy and Test Activate the workflow in your n8n instance Test with various GitHub operations to ensure functionality Monitor execution logs for any configuration issues 🔧 Customization Options Agent Behavior Modify the Chat Model prompt** to adjust agent personality and response style Configure memory settings** to control conversation context retention Adjust timeout settings** for long-running GitHub operations GitHub Operations Extend supported operations** by adding new GitHub API endpoints Configure repository filters** to limit scope of operations Set up notification preferences** for important GitHub events Integration Points Webhook triggers** for real-time GitHub event processing Scheduled operations** for regular repository maintenance Cross-workflow triggers** for complex automation chains 💡 Pro Tips Start Simple**: Begin with basic operations like repository listing before attempting complex workflows Monitor Token Usage**: The specialized agent approach significantly reduces OpenAI API costs Batch Operations**: Group related GitHub operations in single requests for efficiency Error Handling**: The agent provides detailed error messages for troubleshooting 🤝 Support and Community Documentation**: Official n8n Documentation Community Forum**: n8n Community Issues & Contributions**: Feel free to suggest improvements or report issues 📄 License This workflow template is provided under the MIT License. You're free to use, modify, and redistribute with attribution. Created by: William Lettieri Version: 1.0 Last Updated: May 28, 2025 Compatibility: n8n 2024+

Smart Customer Support AI Agent with Gmail and Telegram Who is this for? This workflow is perfect for: Small to medium businesses** looking to automate customer support E-commerce stores** handling order inquiries and customer questions SaaS companies** providing technical support to users Service providers** managing appointment bookings and general inquiries Startups** wanting to provide 24/7 customer service without hiring full-time staff Agencies** managing client communications across multiple channels What problem is this workflow solving? Customer support is essential but resource-intensive. Common challenges include: Slow response times** leading to frustrated customers Repetitive questions** consuming valuable staff time Inconsistent responses** across different support agents Limited availability** outside business hours Scaling support costs** as business grows Context loss** when customers switch between channels This workflow eliminates these pain points by providing instant, consistent, and intelligent responses 24/7. What this workflow does Core Functionality Multi-Channel Monitoring: Simultaneously watches Gmail and Telegram for customer inquiries Intelligent Processing: Uses AI to understand customer intent and context Knowledge Base Integration: Accesses your company's FAQ and support information Contextual Responses: Provides personalized, helpful replies maintaining conversation history Smart Escalation: Automatically escalates complex issues to human agents Comprehensive Logging: Tracks all interactions for analytics and improvement AI Agent Capabilities Natural Language Understanding**: Comprehends customer questions in plain English Context Awareness**: Remembers previous conversations with each customer Knowledge Retrieval**: Searches your knowledge base for accurate information Response Generation**: Creates professional, brand-appropriate responses Escalation Decision**: Identifies when human intervention is needed Multi-Channel Support**: Handles Gmail and Telegram with channel-specific formatting Automation Features Auto-Response**: Replies to customers within seconds Email Management**: Marks processed emails as read Conversation Threading**: Maintains context in email threads and Telegram chats Error Handling**: Gracefully handles failures with admin notifications Analytics Tracking**: Logs interactions for performance monitoring Setup Prerequisites Active Google Workspace or Gmail account Telegram account for bot creation OpenAI API access Google Sheets access n8n instance (cloud or self-hosted) Step 1: Credential Setup Gmail OAuth2 Configuration Go to Google Cloud Console Create new project or select existing one Enable Gmail API Create OAuth 2.0 credentials Add authorized redirect URIs for n8n In n8n: Settings → Credentials → Add Gmail OAuth2 Enter Client ID and Client Secret Complete OAuth flow Telegram Bot Setup Message @BotFather on Telegram Create new bot with /newbot command Choose bot name and username Copy the bot token In n8n: Settings → Credentials → Add Telegram Enter bot token Set webhook URL in bot settings OpenAI API Configuration Sign up at OpenAI Platform Generate API key in API Keys section In n8n: Settings → Credentials → Add OpenAI Enter API key Choose appropriate model (gpt-4o-mini recommended) Google Sheets Setup Use existing Google account from Gmail setup In n8n: Settings → Credentials → Add Google Sheets OAuth2 Complete authorization flow Step 2: Google Sheets Preparation Create three Google Sheets in your Google Drive: Knowledge Base Sheet Sheet Name**: "Knowledge Base" Columns**: ID, Category, Question/Topic, Answer/Response, Keywords, Last_Updated Import sample data from the Knowledge Base example Customize with your company's FAQs and policies Escalation Tracker Sheet Sheet Name**: "Escalations" Columns**: Timestamp, Customer_Name, Customer_Contact, Inquiry_Summary, Escalation_Reason, Priority, Status, Assigned_To This will be auto-populated by the AI agent Interaction Log Sheet Sheet Name**: "Interaction Log" Columns**: Timestamp, Channel, Customer_Name, Customer_Contact, Inquiry_Subject, Customer_Message, AI_Response, Response_Time, Status This tracks all customer interactions for analytics Step 3: Workflow Configuration Import Template Copy the workflow JSON from the template In n8n: Import workflow from JSON Replace placeholder Sheet IDs with your actual Google Sheet IDs Update Sheet References Open each Google Sheets node Select your created sheets from the dropdown Verify column mappings match your sheet structure Customize AI Prompts Edit the "Customer Support AI Agent" node Update system message with: Your company name and description Brand voice and tone guidelines Specific policies and procedures Escalation criteria Configure Error Notifications (Optional) Set up Slack webhook or email notifications Update error notification node with your webhook URL Customize error message format Step 4: Testing Test Gmail Integration Send test email to your support Gmail account Check workflow execution in n8n Verify response is sent and email marked as read Check interaction logging in Google Sheets Test Telegram Integration Send message to your Telegram bot Verify bot responds appropriately Test conversation memory with follow-up messages Check escalation functionality with complex request Test Knowledge Base Ask questions covered in your knowledge base Verify AI retrieves and uses correct information Test with variations of the same question Ensure responses are consistent and helpful How to customize this workflow to your needs Brand Voice Customization Update the AI system prompt to include: Your company's tone (formal, casual, friendly) Key phrases and terminology you use Brand personality traits Communication style preferences Knowledge Base Expansion Add industry-specific FAQs Include product documentation Add troubleshooting guides Create category-specific responses Escalation Rules Customize when to escalate by modifying the AI agent instructions: Billing disputes over $X amount Technical issues requiring developer help Angry or dissatisfied customers Requests outside standard services Legal or compliance questions Additional Channels Extend the workflow to support: Slack**: Add Slack triggers and response nodes WhatsApp**: Integrate WhatsApp Business API Web Chat**: Add webhook triggers for website chat Discord**: Connect Discord bot integration Analytics Enhancement Add sentiment analysis to customer messages Implement customer satisfaction scoring Create automated reporting dashboards Set up alert thresholds for escalation rates Integration Opportunities CRM Integration**: Connect to HubSpot, Salesforce, or Pipedrive Ticketing System**: Link to Zendesk, Freshdesk, or Jira Service Desk E-commerce Platform**: Integrate with Shopify, WooCommerce, or Magento Calendar Booking**: Connect to Calendly or Acuity for appointment scheduling Advanced Features Multi-language Support**: Add translation capabilities Voice Messages**: Integrate speech-to-text for Telegram voice notes Image Recognition**: Process customer screenshots for technical support Proactive Outreach**: Send follow-up messages based on customer behavior Workflow Maintenance Daily Tasks Review escalation queue Monitor error notifications Check response quality in interaction log Weekly Reviews Analyze customer interaction patterns Update knowledge base with new common questions Review escalation reasons and optimize AI prompts Monthly Optimization Export interaction data for detailed analysis Calculate key metrics (response time, resolution rate, escalation rate) Update AI model parameters based on performance Expand knowledge base with seasonal or trending topics Key Metrics to Track Response Time**: Average time from customer message to AI response Resolution Rate**: Percentage of inquiries resolved without escalation Customer Satisfaction**: Based on follow-up surveys or sentiment analysis Escalation Rate**: Percentage of conversations requiring human intervention Channel Performance**: Effectiveness of Gmail vs Telegram vs other channels Knowledge Base Usage**: Which topics are accessed most frequently Peak Hours**: When customers contact support most often Troubleshooting Common Issues Gmail not triggering**: Check OAuth permissions and API quotas Telegram bot not responding**: Verify bot token and webhook configuration AI responses seem off**: Review and update system prompts Escalations not logging**: Check Google Sheets permissions and column mapping High escalation rate**: Expand knowledge base and refine AI instructions Performance Optimization Monitor OpenAI API usage and costs Adjust AI model temperature for response consistency Optimize knowledge base for faster searches Set appropriate conversation memory limits This workflow provides a solid foundation for automated customer support that can be extensively customized to match your specific business needs and grow with your company.

Using the knowledge graphs instead of RAG vector stores This workflow creates a Telegram chatbot agent that has access to several knowledge bases at the same time (used as "experts"). These knowledge bases are provided using the InfraNodus GraphRAG using the knowledge graphs and providing high-quality responses without the need to set up complex RAG vector store workflows. The advantages of using GraphRAG instead of the standard vector stores for knowledge are: Easy and quick to set up and update (no complex data import workflows or vector stores needed) A knowledge graph has a holistic view of your knowledge base and knows what it's about Better retrieval of relations between the document chunks = higher quality responses How it works This template uses the n8n AI agent node as an orchestrating agent that decides which tool (knowledge graph) to use based on the user's prompt. Here's a description step by step: The user submits a question using the Telegram bot, which is then received in the n8n workflow via the Telegram trigger node. The AI agent node checks a list of tools it has access to. Each tool has a description of the knowledge it has auto-generated by InfraNodus. The AI agent decides which tool should be used to generate a response. It may reformulate user's query to be more suitable for the expert. The query is then sent to the InfraNodus HTTP node endpoint, which will query the graph that corresponds to that expert. Each InfraNodus GraphRAG expert provides a rich response that takes the whole context into account and provides a response from each expert (graph) along with a list of relevant statements retrieved using a combination or RAG and GraphRAG. The n8n AI Agent node integrates the responses received from the experts to produce the final answer. The final answer is sent back to the Telegram bot who delivers it back to the private chat or a Telegram group. How to use You need an InfraNodus GraphRAG API account and key to use this workflow. Create an InfraNodus account Get the API key at https://infranodus.com/api-access and create a Bearer authorization key for the InfraNodus HTTP nodes. Create a separate knowledge graph for each expert (using PDF / content import options) in InfraNodus For each graph, go to the workflow, paste the name of the graph into the body name field. Keep other settings intact or learn more about them at the InfraNodus access points page. Once you add one or more graphs as experts to your flow, add the LLM key to the OpenAI node Create a Telegram bot (the instructions are in the workflow Post note) — it takes 30 seconds. Get its API key and create the Telegram credentials to use in the Telegram nodes in this workflow. Requirements An InfraNodus account and API key An OpenAI (or any other LLM) API key A Telegram account Customizing this workflow You can use this same workflow with a standard AI chatbot via a URL that can also be embedded to any website. You can also use it with ElevenLabs AI voice agent. There are many more customizations available. Check out the complete guide at https://support.noduslabs.com/hc/en-us/articles/20174217658396-Using-InfraNodus-Knowledge-Graphs-as-Experts-for-AI-Chatbot-Agents-in-n8n Also check out the video tutorial with a demo: Support If you have any questions, contact us via the support portal at https://support.noduslabs.com or via our Discord channel. More n8n workflows are available on our support portal: n8n x InfraNodus AI automation workflows.

This workflow automates the process of removing backgrounds from WooCommerce product images using the BackgroundCut API, and then updates the product images in both WooCommerce and a Google Sheet. Once set up, the workflow processes product images in bulk, removing backgrounds and updating WooCommerce seamlessly. This workflow is perfect for online stores that sell: Clothing and fashion items Jewelry and accessories General consumer products Any product that benefits from clean, background-free images for a professional storefront presentation will see improved visual appeal and potentially higher conversions. Benefits ⏱ Time-saving:** Automates what would otherwise be a manual and repetitive task of editing images and updating product listings. 🔄 Fully Integrated:** Connects Google Sheets, BackgroundCut API, FTP server, and WooCommerce in a seamless loop. 📦 Scalable:** Supports batch processing, making it suitable for stores with hundreds of products. 📁 Organized Tracking:** Updates the Google Sheet with the new image and a “DONE” flag for easy monitoring. 🔧 Customizable:** You can change the image processing API, storage server, or eCommerce platform if needed. How It Works Data Retrieval: The workflow starts by fetching product data (ID and IMAGE URL) from a Google Sheets document. Only rows without a "DONE" marker are processed to avoid duplicates. Background Removal: Each product image URL is sent to the BackgroundCut API, which removes the background and returns the edited image. File Handling: The processed image is uploaded to an FTP server with the original filename preserved. A new URL for the edited image is generated and assigned to the product. WooCommerce Update: The product in WooCommerce is updated with the new image URL. Sheet Update: The Google Sheet is marked as "DONE" for the processed row, and the new image URL is recorded. Batch Processing: The workflow loops through all rows in the sheet until all products are processed. Set Up Steps Prepare the Google Sheet: Clone the provided Google Sheet template. Fill in the ID (product ID) and IMAGE (original image URL) columns. API & Credentials Setup: Get an API key from BackgroundCut.co. Configure the HTTP Request node ("Remove from Image URL") with: Header Auth: Authorization = API_KEY. Set up WooCommerce API credentials in the "Update product" node. FTP Configuration: Replace YOUR_FTP_URL in the "New Image Url" node with your FTP/CDN base URL. Ensure FTP credentials are correctly set in the FTP node. Execution: Run the workflow manually via "When clicking ‘Execute workflow’". The process automatically handles background removal, file upload, and WooCommerce updates. Need help customizing? Contact me for consulting and support or add me on Linkedin.

This n8n workflow automates the discovery, enrichment, and comparative analysis of startups from the Crunchbase dataset via Bright Data, enhanced with AI, and exports structured results to Google Sheets. 🚀 What It Does Receives a keyword from the user that describes the area of interest — such as an industry, sector, technology, or trend (e.g., "AI in healthcare", "carbon capture", "edtech"). This keyword is used to filter relevant startups from the Crunchbase dataset via Bright Data. Fetches data from Bright Data's Crunchbase snapshot API. Extracts and cleans key fields from the JSON response. Sorts startups by most recent founding date. Selects the top 10 most recent companies. Sends these 10 companies to Google Gemini AI for comparative analysis. Embeds the AI-generated summary into the final export. Appends results to a Google Sheet for tracking and reporting. 🛠️ Step-by-Step Setup Get user keyword input from a form. Use 3 Bright Data requests: Start snapshot. Poll snapshot status until ready. Fetch snapshot data in JSON format. Use a Python Code node to: Parse and sort companies by founded_date. Clean and standardize data fields. Pass the top 10 companies into Gemini AI for comparative insight. Merge the AI output back with company data. Send everything to Google Sheets. 🧠 How It Works Snapshot Control: Polls every few seconds until the Bright Data snapshot is complete. Code Cleanup: Ensures consistent structure and formatting across all records. Comparative AI Analysis: Gemini compares all 10 companies at once and returns a unified analysis. Merging Output: AI analysis is merged into the first company’s record (to avoid duplication), while all 10 are exported. 📤 Google Sheet Output Each row includes: name, founded, about, num_employees, type, ipo_status, full_description, social_media_links, address, website, funding_total, num_investors, lead_investors, founders, products_and_services, monthly_visits, crunchbase_link, ai_analysis. AI comparative analysis summary (only once per batch – attached to the first company). All fields from above customizible through the python code (you can add additional ones from Bright Data output). 🔐 Required Credentials Bright Data* – Replace *YOUR_API_KEY** in 3 HTTP Request nodes. Google Gemini API** – For AI analysis. Google Sheets OAuth2** – For spreadsheet export. ⚠️ Notes AI output is shared once per batch of 10 companies, attached to the first company entry. You can configure the limit of batch size in the first "Code" node.

This n8n workflow integrates Shopify order management with Telegram, allowing you to query open orders and order details directly through Telegram chat commands. It provides an interactive way to monitor your Shopify store orders using Telegram as an interface. Key Features Telegram Trigger: Listens for messages and callback queries from your Telegram bot. Switch Node: Routes incoming Telegram messages to different flows based on message content: /orders command to fetch all open orders Callback queries starting with /order_ to fetch details of a specific order Shopify Get Orders: Retrieves all open orders from your Shopify store using your Shopify API credentials. Conditional Check (If Node): Determines if there are any open orders; branches accordingly: If orders exist, prepare an interactive Telegram message with a list of orders.1 If no orders exist, send a “No Order” message. Orders Code Node: Formats the list of open orders into a Telegram message with inline buttons. Each button corresponds to an order and sends a callback data containing the order ID. Get Order Details: When a user selects an order button, the workflow extracts the order ID from the callback data, fetches detailed order information from Shopify, and formats the order items into a readable message. Send Messages to Telegram: Sends formatted messages back to Telegram: The list of open orders with clickable buttons. Detailed information about a selected order. “No Order” notification if there are no open orders. How It Works A Telegram user sends /orders to the bot. The workflow fetches open orders from Shopify and sends a message with buttons listing each order. When a user clicks an order button, the workflow fetches and displays detailed information about that specific order in Telegram. If there are no open orders, the bot replies accordingly. Setup Instructions Create a Telegram Bot: Use @BotFather on Telegram to create a bot and get the bot token. Obtain Shopify API Credentials: Create a private app in your Shopify admin dashboard with permission to read orders. Obtain the API key and access token. Configure n8n Credentials: Add your Telegram bot token as Telegram API credentials in n8n. Add your Shopify API credentials in n8n Shopify credentials. Import the Workflow: Import this workflow into your n8n instance. Update the Telegram and Shopify credential nodes to use your credentials. Set Webhook URLs: Ensure your Telegram bot webhook is set correctly to receive messages. n8n webhook URLs should be publicly accessible. Test the Workflow: Send /orders to your Telegram bot to verify it retrieves and lists open orders. Customization Guidance Modify Commands: Update the Switch node to add more Telegram commands or change existing ones. Change Message Formats: Edit the Code nodes to customize how order lists and details appear. Expand Shopify Integration: Add nodes to handle other Shopify operations like updating orders, managing products, etc. Multi-User Support: Adapt the workflow to handle multiple Telegram chat IDs dynamically. Security and Implementation Notes The native Telegram node in n8n has limitations: it does not support sending dynamic inline keyboard arrays in JSON format, which is essential for displaying a variable number of buttons depending on how many orders are retrieved from Shopify. To overcome this, this workflow uses the HTTP Request node to call Telegram’s API directly, allowing full flexibility to send dynamic inline keyboards as JSON objects. (I will make an update once Telegram Node support dynamic inline keyboards). Security Considerations:** Always store your Telegram bot token securely in n8n credentials and never expose it in the HTTP Request node’s URL or body directly. Use environment variables or n8n credentials to inject tokens safely. Be mindful of Telegram API rate limits and add error handling in your workflow. While using HTTP Request nodes increases flexibility, it also requires careful management of request payloads and authentication, as opposed to the built-in Telegram node which abstracts much of this complexity. Benefits Quickly access Shopify order data without leaving Telegram. Interactive inline buttons improve user experience. Automated, real-time integration between Shopify and Telegram.

Set up a chat with your documents without the complex vector store setup. This templates helps you ingest** your PDF / text / MD documents into a knowledge graph use the graph as the knowledge base for your AI chatbots (and other workflows) visualize the main topics* and *gaps** in your documents (good for observability and research) The knowledge base is provided using the InfraNodus GraphRAG with the knowledge graphs offering high-quality responses without the need to set up complex RAG vector store workflows. The advantages of using GraphRAG instead of the standard vector stores for knowledge are: Easy and quick to set up and update** — no complex data import workflows needed A knowledge graph offers a holistic and interactive view of your knowledge base (accessible via our API or a web interface — also shareable) Better retrieval of relations** between the document chunks = higher quality responses How it works This template uses the InfraNodus knowledge graph as a knowledge base for your n8n AI agent node. The knowledge graph contains the documents you can upload using this template from your Google Drive. When the user asks a question via the chat interface, the agent forwards this question to the InfraNodus knowledge graph, retrieves a response, a summary, and a list of matching statements (based advanced Graph RAG), then delivers the final response back the user. Here's a description step by step: Step 1: Upload your documents Put the PDF / text / MD files you want to chat with into a folder on your Google drive Authorize access to that folder using the Google drive node in the template. Add the InfraNodus API key to the InfraNodus Save to Graph HTTP node Optional: change the name of the graph you want to save the data to in the InfraNodus HTTP node (in the name field of the HTTP post request). Run the workflow to ingest all the files and save them into the graph Optional: check the link provided in the Step 1 workflow description to see the visualization of your knowledge base. It will look something like that: Note:* you can replace the PDF to Text convertor node with a better quality *PDF convertor* from ConvertAPI which respects the original file layout and doesn't split text into small chunks Step 2: Chat with your documents Deactive the trigger in the Step 1 Activate the chat trigger in the Step 2 Add your InfraNodus API credentials to Knowledge Base GraphRAG InfraNodus node Optional: change the graph name in the Knowledge Base node to match the name you provided in the step 1 above Run the chat and ask the question Watch the magic How to use You need an InfraNodus GraphRAG API account and key to use this workflow. Create an InfraNodus account Get the API key at https://infranodus.com/api-access and create a Bearer authorization key for the InfraNodus HTTP nodes. Requirements An InfraNodus account and API key An OpenAI (or any other LLM) API key A Google Drive OAuth access (follow the n8n instructions) Optional: ConvertAPI API key for better quality PDF conversion Customizing this workflow You can customize this workflow by adding several experts to your AI agent. Check out the complete guide at https://support.noduslabs.com/hc/en-us/articles/20174217658396-Using-InfraNodus-Knowledge-Graphs-as-Experts-for-AI-Chatbot-Agents-in-n8n Also check out the video tutorial with a demo: For support and feedback, please, contact us at https://support.noduslabs.com To learn more about InfraNodus: https://infranodus.com

🚀 How it works (Fonctionnement résumé) : Ce template permet de transformer un document (PDF, TXT, DocX...) en post LinkedIn engageant, prêt à être publié ou validé par email, le tout avec l’aide d’une IA spécialisée en copywriting LinkedIn. Voici les étapes clés : Formulaire de dépôt : L'utilisateur charge un fichier ou colle un texte. Détection du type de contenu : Un Switch analyse le type de fichier (PDF, DOCX, TXT, ou texte brut). Attention pour DocX nécessite un compte Make pour transformer le doc (mais cela fonctionne aussi sans docX) Extraction du contenu : Selon le format, le bon module d'extraction est utilisé. Génération d’un post LinkedIn : L'IA transforme le contenu en post LinkedIn selon une méthodologie de copywriting optimisée. Validation par email : Un email est envoyé à l’utilisateur pour approbation avec possibilité d’ajouter une image. Publication automatique : Si l'utilisateur valide, le post est publié sur LinkedIn. ⚙️ Setup Steps : Connecte tes comptes : Google Docs OAuth LinkedIn OAuth OpenAI (via gpt-4.1-mini ou un autre modèle) SMTP + IMAP pour l'envoi et la lecture d'emails Configure les champs du formulaire dans le nœud Form Trigger selon ton usage. Personnalise le prompt IA dans le nœud AI Agent si tu veux adapter le ton ou la méthodologie. Vérifie les emails dans le nœud d'envoi (Send Email) et de lecture (Email Trigger (IMAP)), pour que la validation fonctionne. Teste le workflow avec différents fichiers pour t'assurer que tous les types sont bien traités (PDF, DOCX, TXT, etc.). 🧩 Cas d’usage typiques : Créer des posts à partir de notes de réunion ou de rapports. Valoriser un article ou une publication professionnelle sous forme de contenu LinkedIn. Déléguer à l'IA le premier jet de ton contenu réseau. Bonus surveille une newsletter de ta messagerie pour proposer un post pertinent sur LinkedIn (vous pouvez supprimer il fonctionne en parallèle)

Notice Community nodes can only be installed on self-hosted instances of n8n. Who this is for? The Search Engine Intelligence Extractor is a powerful n8n automation that leverages Bright Data’s MCP based AI Agents to simulate human-like searches across Google, Bing, and Yandex, and then distills clean, structured insights using Google Gemini. This workflow is tailored for: SEO analysts researching competitors or market trends Market researchers needing real-time search visibility Journalists & content writers gathering contextual insights AI developers creating intelligent assistants Digital marketers tracking brand mentions or news What problem is this workflow solving? Traditional scraping of search engines is often blocked, cluttered, or filled with irrelevant information. Manually analyzing and cleaning this data for insight is time-consuming. This workflow solves the problem by: Simulating real user search behavior via Bright Data MCP based AI Agent Performing multi-platform search (Google, Bing, Yandex) in one unified flow Extracting clean, human-readable results (stripping ads, navigation, etc.) Structuring the content using Google Gemini LLM Automating delivery via Webhook or saving to disk What this workflow does Input Fields Node: Accepts the search query Accepts action for example - Perform a google search. Replace the action with bing, yandex etc. for other search providers Accepts Webhook notification URL Bright Data MCP Agent Execution: Triggers Bright Data’s intelligent search agent Handles search navigation, result loading, pagination Human Readable Data Extractor: Cleanses HTML, removes ads, footers, irrelevant links Produces a readable narrative of results Final Output Handling: Saves the processed response to disk Sends the structured data to a Webhook for real-time use Pre-conditions Knowledge of Model Context Protocol (MCP) is highly essential. Please read this blog post - model-context-protocol You need to have the Bright Data account and do the necessary setup as mentioned in the Setup section below. You need to have the Google Gemini API Key. Visit Google AI Studio You need to install the Bright Data MCP Server @brightdata/mcp You need to install the n8n-nodes-mcp Setup Please make sure to setup n8n locally with MCP Servers by navigating to n8n-nodes-mcp Please make sure to install the Bright Data MCP Server @brightdata/mcp on your local machine. Sign up at Bright Data. Create a Web Unlocker proxy zone called mcp_unlocker on Bright Data control panel. Navigate to Proxies & Scraping and create a new Web Unlocker zone by selecting Web Unlocker API under Scraping Solutions. In n8n, configure the Google Gemini(PaLM) Api account with the Google Gemini API key (or access through Vertex AI or proxy). In n8n, configure the credentials to connect with MCP Client (STDIO) account with the Bright Data MCP Server as shown below. Make sure to copy the Bright Data API_TOKEN within the Environments textbox above as API_TOKEN=<your-token> How to customize this workflow to your needs Add Scheduled Execution Add a Cron trigger to run this workflow on a set schedule (e.g., daily/weekly keyword tracking). Push Results to Custom Destinations Connect output to: Google Sheets (for analytics or dashboards) PostgreSQL or MySQL DBs (for structured storage) Notion or Airtable (for content pipelines) Slack or Email (for alerting teams) Customize Webhook Notifications Update the Webhook URL in the notification node to push processed results to external APIs, CRMs, or real-time dashboards.

⚠️ This template uses only official n8n nodes. No community nodes required. 🧑‍💼 Who is this for? This workflow is designed for: Legal tech founders Marketing freelancers or consultants Agencies supporting lawyers and small law firms Anyone doing outbound outreach in the legal niche ❓ What problem is this solving? LinkedIn is a goldmine for targeting legal professionals — but scraping and personalizing outreach is tedious and expensive. Most tools either: Require paid LinkedIn Sales Navigator Can’t personalize at scale Violate LinkedIn’s TOS This workflow solves that by using free Google Search, OpenRouter AI, and GPT-4o to find, enrich, and message up to 1,000 solo lawyers per day — without using browser automation or scrapers. ⚙️ What this workflow does Uses Google Programmable Search to find solo lawyers and small firm founders on LinkedIn Parses each profile’s name, title, profile URL, and snippet Saves raw lead data to Google Sheets Uses OpenRouter Sonar Pro to enrich each profile with external content Generates a personalized, 1-line message using GPT-4o Appends the final message into Google Sheets for outreach 🛠️ Setup Estimated time: 15–20 minutes ✅ Google Programmable Search Enable the Custom Search API on Google Cloud Create a programmable search engine set to search the full web Copy your API key and CX ID ✅ Google Sheets Create a sheet with columns: Name, Title, Profile URL, Outreach Message Share the sheet with your OAuth-connected Google account ✅ OpenRouter Sign up at openrouter.ai Fund with at least $5 and generate your API key Use the model perplexity/sonar-pro for real-time research ✅ GPT-4o (optional) You can use your OpenAI key or route GPT-4o via OpenRouter All setup-specific values are marked clearly in sticky notes and placeholders. 🛠️ How to customize this workflow to your needs Change the Google search query to match your industry (e.g., "founder" AND "therapist" site:linkedin.com/in) Modify the AI prompt to match your tone (formal, casual, humorous) Connect the final output to your CRM (like HubSpot, Airtable, etc.) Add a second outreach message variant to A/B test performance 📌 Sticky Notes & Annotations All nodes are clearly renamed for understandability (e.g., Find Lawyer Profiles, Parse LinkedIn Search Results) Color-coded sticky notes explain: Setup instructions Required credentials Use case 🗂 Category AI Sales Marketing