Workflow Templates

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.

Advanced n8n Error Handling: Automated Email Alerts & Global Error Workflow Configuration In any automated environment, n8n workflows, while powerful, can sometimes encounter unexpected issues or fail during execution. Without a dedicated error handling strategy, these failures might go unnoticed, leading to incomplete processes, data inconsistencies, or critical operational disruptions. Manually monitoring every workflow execution or sifting through logs for error details is inefficient and can significantly delay crucial fixes. This is where a centralized, automated error management system becomes essential to maintain reliability and quickly address any problems. The "Advanced n8n Error Handling: Automated Email Alerts & Global Error Workflow Configuration" template provides a robust solution to proactively manage and respond to errors within your n8n instance. For more powerful n8n templates, visit our website or contact us at AI Automation Pro. We help your business build custom AI workflow automation and apps. Highlight features Automated Email Notifications:** Sends detailed HTML emails via Gmail for both execution and trigger failures, ensuring you're promptly informed. Centralized Error Management:** Acts as a single, dedicated workflow to catch and process errors from multiple other n8n workflows. Proactive Global Error Handler Configuration:** A scheduled task automatically scans and updates other active n8n workflows to use this workflow as their default error handler, ensuring consistent error management. Comprehensive Error Reporting:** Notification emails are rich with information, including error messages, stack traces, the last executed node, direct links to failed executions, and detailed trigger failure context. Dynamic Email Content:** The subject line and body of the notification email are dynamically adjusted based on whether the failure was an execution error or a trigger failure. Highly Customizable:** Offers flexibility to modify email content (HTML), change the notification channel (e.g., Slack, other email providers), and adjust the logic for updating other workflows' error handlers. Scheduled Operation:** The global configuration part runs on a user-definable schedule (e.g., daily, hourly) for proactive and automated error handling setup across your n8n instance. Who is this for? This workflow is designed for n8n users and administrators who want to: Establish a resilient and centralized error handling mechanism across their n8n instance. Receive immediate and detailed email notifications for any workflow failures. Automate the process of assigning a default error handling workflow to all their active n8n workflows. Save time on manually configuring error handlers for each individual workflow and ensure comprehensive error coverage. What problem is this workflow solving? / use case In an n8n environment with multiple workflows, errors can occur without immediate visibility. This can lead to: Unnoticed failures, potentially causing data loss or incomplete automated processes. Time-consuming diagnosis of issues due to a lack of readily available, detailed error information. Inefficiency and oversight from manually setting an error workflow for every new or existing workflow. This template tackles these issues by providing a proactive error management system. It not only alerts you to failures with comprehensive details but also ensures that your other workflows are automatically configured to use this centralized handler. What this workflow does This workflow operates in two distinct yet complementary parts: 1\. Scheduled Global Error Handler Configuration: Trigger:** Initiates based on a configurable schedule (e.g., daily, hourly). Identify Self:** Retrieves its own workflow ID to use as the designated error handler. Scan Workflows:** Fetches a list of all other workflows within your n8n instance. Conditional Update Logic:** For each active workflow found, it checks if: An error workflow (errorWorkflow setting) is not currently set, OR The currently set errorWorkflow is different from this central error handling workflow. The workflow is active. Apply Default Handler:** If the above conditions are met, it automatically updates the target workflow's settings. This sets the current workflow as its default error handler, ensuring that any future errors in those workflows are routed here. The callerPolicy setting is also removed during this update. 2\. Error Notification via Email: Trigger:** Activates whenever an error occurs in any n8n workflow that has this workflow designated as its errorWorkflow. Gather Error Context:** Collects vital information about the failure, such as: The base URL of your n8n instance. Specific details of the workflow that failed (name, ID). The nature of the error: whether it's an "execution error" (occurring mid-workflow) or a "trigger failure" (occurring at the start). Format Detailed Error Message:** Constructs a comprehensive HTML email tailored to the error type: For Execution Errors: The email includes a direct link to the failed execution's page, the timestamp of the error, the name of the last node that successfully executed, the error message, and the full error stack trace. For Trigger Failures: The email includes the timestamp, operational mode, error message, error name and description, relevant context data, details about the cause (message, name, code, status), and the stack trace. Send Email Notification:** Dispatches the formatted HTML email using Gmail to a predefined recipient. The email subject line dynamically indicates the name of the failing workflow and the type of error, providing a quick overview. Setup Import Workflow: Import the JSON file into your n8n instance. Configure Credentials: n8n API Access: Locate the nodes: "N8n Get Error Handler", "N8n Get All Workflows", and "N8n Update Workflow". For each, select or create new n8n API credentials. These credentials must have permissions to read all workflows (workflows.read) and update workflows (workflows.update). Gmail Access: Locate the "Gmail Send Notification" node. Select or create new Gmail OAuth2 credentials to authorize n8n to send emails on your behalf. Set Email Recipient and Sender Details: Navigate to the "Settings" node, which is connected directly after the "Error Trigger" node. Modify the value for the Email Receiver variable to the email address where error notifications should be sent. Optionally, update the Email Sender Name variable. Configure Schedule (Optional): Select the "Schedule Trigger" node. Adjust the "Trigger Interval" (e.g., Every Day, Every Hour) according to how frequently you want the workflow to scan and update the error handler settings for other workflows in your n8n instance. Activate Workflow: Ensure this workflow is toggled to "Active". Once active, its scheduled component will begin operating, and it will be ready to process and notify on errors from other linked workflows. Manual Configuration (Optional): While this workflow automates the assignment, you can also manually set this workflow as the errorWorkflow in the settings of any critical existing workflows for immediate protection. How to customize this workflow to your needs Email Content & Formatting:** Modify the HTML content within the "HTML For Execution Error" and "HTML For Trigger Error" nodes to alter the appearance, structure, or information included in the notification emails. Alternative Notification Channels:** Replace the "Gmail Send Notification" node with a different email service node (e.g., Microsoft Outlook, SendGrid) or integrate other notification platforms like Slack, Microsoft Teams, or Discord. Remember to adjust the input data mappings for the new node. Refine Global Update Logic:** Adjust the conditions within the "If No Default Error Handler Set" node if you need more granular control over which workflows are automatically updated (e.g., filter by workflow tags, names, or explicitly exclude certain workflows). Enrich Error Data:** Insert additional nodes after the "Error Trigger" but before the "Settings" node if you need to fetch more context about the error or the workflow that failed (e.g., look up related information from a database or API). Advanced Notification Routing:** Implement more complex logic prior to sending notifications. For example, you could use a Switch node to route error alerts to different email addresses or channels based on the name of the failing workflow or the severity of the error. Handling of callerPolicy:** The "Set Data" node is configured to remove the callerPolicy setting from workflows it updates. If your workflows rely on this setting, you may need to modify or remove this part of the "Set Data" node's code. Adjust Scheduled Task:** Change the frequency or timing of the "Schedule Trigger" to better suit your operational needs for the global error handler update.

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.

This automated n8n workflow checks daily class schedules, syncs upcoming classes to Google Calendar, and sends reminder notifications to students via email or SMS. Perfect for educational institutions to keep students informed about their daily classes and schedule changes. What This Workflow Does: Automatically checks class schedules every day Identifies today's classes and upcoming sessions Syncs class information to Google Calendar Sends personalized reminders to enrolled students Tracks reminder delivery status and logs activities Handles both email and SMS notification preferences Main Components Daily Schedule Check** - Triggers daily to check class schedules Read Class Schedule** - Retrieves today's class schedule from database/Excel Filter Today's Classes** - Identifies classes happening today Has Classes Today?** - Checks if there are any classes scheduled Read Student Contacts** - Gets student contact information for enrolled classes Sync to Google Calendar** - Creates/updates events in Google Calendar Create Student Reminders** - Generates personalized reminder messages Split Into Batches** - Processes reminders in manageable batches Email or SMS?** - Routes based on student communication preferences Prepare Email Reminders** - Creates email reminder content Prepare SMS Reminders** - Creates SMS reminder content Read Reminder Log** - Checks previous reminder history Update Reminder Log** - Records sent reminders Save Reminder Log** - Saves updated log data Essential Prerequisites Class schedule database/Excel file with student enrollments Student contact database with email and phone numbers Google Calendar API access and credentials SMTP server for email notifications SMS service provider (Twilio, etc.) for text reminders Reminder log file for tracking sent notifications Required Data Files: class_schedule.xlsx: Class ID | Class Name | Date | Time | Duration Instructor | Room | Students Enrolled | Status student_contacts.xlsx: Student ID | Name | Email | Phone | Preferred Contact Program | Class IDs | Active Status reminder_log.xlsx: Log ID | Date | Student ID | Class ID | Contact Method Status | Sent Time | Response Key Features ⏰ Daily Automation:** Runs automatically every day 📅 Calendar Sync:** Syncs classes to Google Calendar 📧 Smart Reminders:** Sends email or SMS based on preference 👥 Batch Processing:** Handles multiple students efficiently 📊 Activity Logging:** Tracks all reminder activities 🔄 Duplicate Prevention:** Avoids sending multiple reminders 📱 Multi-Channel:** Supports both email and SMS notifications Quick Setup Import workflow JSON into n8n Configure daily trigger schedule Set up class schedule and student contact files Connect Google Calendar API credentials Configure SMTP server for emails Set up SMS service provider (Twilio) Test with sample class data Activate workflow Parameters to Configure schedule_file_path: Path to class schedule file contacts_file_path: Path to student contacts file google_calendar_id: Google Calendar ID for syncing google_api_credentials: Google Calendar API credentials smtp_host: Email server settings smtp_user: Email username smtp_password: Email password sms_api_key: SMS service API key sms_phone_number: SMS sender phone number Sample Reminder Messages Email:** "Hi [Name], reminder: [Class Name] starts at [Time] in [Room]. See you there!" SMS:** "[Name], your [Class Name] class starts at [Time] in [Room]. Don't miss it!" Use Cases Daily class reminders for students Schedule change notifications Exam and assignment deadline alerts Teacher absence notifications Room change announcements

A short-term technical analysis agent for 15-minute candles on Binance Spot Market pairs. Calculates and interprets key trading indicators (RSI, MACD, BBANDS, ADX, SMA/EMA) and returns structured summaries, optimized for Telegram or downstream AI trading agents. This tool is designed to be triggered by another workflow (such as the Binance SM Financial Analyst Tool or Binance Quant AI Agent) and is not intended for standalone use. 🔧 Key Features ⏱️ Uses 15-minute kline data (last 100 candles) 📈 Calculates: RSI, MACD, Bollinger Bands, SMA/EMA, ADX 🧠 Interprets numeric data using GPT-4.1-mini 📤 Outputs concise, formatted analysis like: • RSI: 72 → Overbought • MACD: Cross Up • BB: Expanding • ADX: 34 → Strong Trend 🧠 AI Agent Purpose > You are a short-term analysis tool for spotting volatility, early breakouts, and scalping setups. Used by higher agents to determine: Entry/exit precision Momentum shifts Scalping opportunities ⚙️ How it Works Triggered externally by another workflow Accepts input: { "message": "BTCUSDT", "sessionId": "123456789" } Sends POST request to backend endpoint: https://treasurium.app.n8n.cloud/webhook/15m-indicators Fetches last 100 candles and calculates indicators Passes data to GPT for interpretation Returns summary with indicator tags for human readability 🔗 Dependencies This tool is triggered by: ✅ Binance SM Financial Analyst Tool ✅ Binance Spot Market Quant AI Agent 🚀 Setup Instructions Import into your n8n instance Make sure /15m-indicators webhook is active and calculates indicators correctly Connect your OpenAI GPT-4.1-mini credentials Trigger from upstream agent with Binance symbol and session ID Ensure all external calls (to Binance + webhook) are working 🧪 Example Use Cases | Use Case | Result | | ------------------------------------- | --------------------------------------- | | Short-term trade decision for ETHUSDT | Receives 15m signal indicators summary | | Input from Financial Analyst Tool | Returns real-time volatility snapshot | | Telegram bot asks for “DOGE update” | Returns momentum indicators in 15m view | 🎥 Watch Tutorial: 🧾 Licensing & Attribution © 2025 Treasurium Capital Limited Company Architecture, prompts, and trade report structure are IP-protected. No unauthorized rebranding or resale permitted. 🔗 For support: Don Jayamaha – LinkedIn

A medium-term trend analyzer for the Binance Spot Market that leverages core technical indicators across 4-hour candle data to provide human-readable swing-trade signals via AI. 🎥 Watch Tutorial: 🎯 What It Does Accepts a Binance trading pair (e.g., AVAXUSDT) Sends the symbol to an internal webhook for technical indicator calculation Computes 4h RSI, MACD, Bollinger Bands, SMA, EMA, ADX Returns structured, GPT-analyzed signals ready for Telegram delivery 🧠 AI Agent Details Model:** GPT-4.1-mini (OpenAI Chat) Agent Role:** Translates raw indicator values into sentiment-labeled signals Memory:** Tracks session + symbol context for cleaner multi-turn logic 🔗 Required Backend Workflow To calculate indicators, this tool depends on: POST https://treasurium.app.n8n.cloud/webhook/4h-indicators { "symbol": "AVAXUSDT" } Returns a JSON object with the latest 40×4h candle-based calculations. 📥 Input Format { "message": "AVAXUSDT", "sessionId": "telegram_chat_id" } 📊 Sample Output 🕓 4h Technical Signals – AVAXUSDT • RSI: 64 → Slightly Bullish • MACD: Bullish Cross above baseline • BB: Upper band touch – volatility expanding • EMA > SMA → Confirmed Upside Momentum • ADX: 31 → Strengthening Trend 📚 Use Case Scenarios | Use Case | Result | | ----------------------------- | ---------------------------------------------------- | | Swing trend confirmation | Uses 4h indicators to validate or reject setups | | Breakout signal confluence | Helps assess if momentum is real or noise | | Inputs to Quant AI or Analyst | Supports higher-frame trade recommendation synthesis | 🛠️ Setup Instructions Import the JSON template into your n8n workspace. Set your OpenAI API credentials for the GPT node. Ensure the /webhook/4h-indicators backend tool is live and accessible. Connect this to your Binance Financial Analyst Tool or master Quant AI orchestrator. 🤖 Parent Workflows That Use This Tool Binance SM Financial Analyst Tool Binance Spot Market Quant AI Agent 📎 Sticky Notes & Annotations This workflow includes internal sticky notes describing: Node roles (GPT, webhook, memory) System behavior (reasoning agent logic) Telegram formatting guidance 🔐 Licensing & Attribution © 2025 Treasurium Capital Limited Company All architecture, prompt logic, and signal formatting are proprietary. Redistribution or rebranding is prohibited. 🔗 Connect with the creator: Don Jayamaha – LinkedIn

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)

GBP Exchange Rate Email Workflow Sends email with table that have GBP exchange rate for few money value every day, if user clicks the button to run it. How it works: Data come from API Changed into HTML table Before is send inside email

> 🛠️ Note: This workflow uses a custom Mastodon API request. Ensure your server supports bookmark access, and that your access token has the right permissions. OAuth or token-based credentials must be configured. 🧑‍💼 Who is this for? This workflow is ideal for digital researchers, social media users, and knowledge workers who want to automatically archive Mastodon bookmarks into their Raindrop.io collection for future reference and tagging. 🔧 What problem is this solving? Mastodon users often bookmark posts they want to read or save for later, but there's no native integration to archive them outside the app. This workflow solves that by syncing bookmarked posts from Mastodon to Raindrop, making them more accessible, organized, and searchable long-term. ⚙️ What this workflow does Triggers on schedule (or manually). Tracks the latest fetched min_id using workflow static data to avoid duplicates. Sends an HTTP GET request to the Mastodon bookmarks API, using bearer token authentication. Validates and processes the bookmarks if new entries exist. Parses pagination metadata (e.g. min_id) from response headers. Splits response array to handle individual bookmarks. Filters out entries with missing data. Saves each post to Raindrop.io, using its title and URL. Use the card URL if exist. Updates the min_id to remember where it left off. 🚀 Setup Create a Mastodon access token with access to bookmarks. Add a credential in n8n of type HTTP Bearer Auth with your token. Create and connect a Raindrop OAuth2 credential. Replace {VOTRE SERVEUR MASTODON} with your Mastodon server's base URL. (Optional) Adjust the scheduling interval under the "Schedule Trigger" node. Make sure the Raindrop collection ID is correct or leave it as default (-1) as this is the index for the `Unsorted` collection. 🧪 How to customize this workflow To save to a specific Raindrop collection, change the collectionId in both Raindrop nodes. You can extend the Code node to pull additional metadata like author, hashtags, or content excerpts. Add an Email or Slack node after Raindrop to notify you of saved bookmarks.

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.