by M Sayed
The Problem 😫 Tired of manually logging every coffee and cab ride? Stop wrestling with spreadsheets! This template automates your expense tracking so you can manage your finances effortlessly. It's perfect for freelancers, small business owners, and anyone who wants a simple, chat-based way to track spending. How It Works ✨ Just send a message to your personal Telegram bot like "5 usd for coffee with my card" and this workflow will automatically: 📲 Get your message from Telegram. 🤖 Use AI to understand the amount, category, currency, and payment method. 💱 Convert currencies automatically using live exchange rates. ✍️ Log everything neatly into a new row in your Google Sheet. 🛠️ Quick Setup Guide Google Sheets 📝 Create a new Google Sheet. Make sure your first row has these exact column names: date, amount, category, description, user_id, payment_method, currency, exchange_rate, amount_converted Copy the Sheet ID from the browser's URL bar. Telegram Bot 🤖 Chat with @BotFather on Telegram, use the /newbot command, and get your API Token. Chat with @userinfobot to get your personal Chat ID. n8n Workflow 🔗 Add your credentials for Google Sheets, Telegram, and your AI model. Paste your Chat ID into the Telegram Trigger node. Paste your Sheet ID into the Append row in sheet node. Activate the workflow and start tracking! ✅
by InfraNodus
Using the knowledge graphs instead of RAG vector stores This workflow creates an AI 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 (no complex data import workflows needed) A knowledge graph has a holistic view of your knowledge base 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 AI chatbot (n8n interface, in this case, which can be accessed via a URL or embedded to any website) 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 user's chat (or a webhook endpoint) 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 and launch the workflow Requirements An InfraNodus account and API key An OpenAI (or any other LLM) API key Customizing this workflow You can use this same workflow with a Telegram bot, so you can interact with it using Telegram. 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:
by Rudi Afandi
Description This n8n workflow enables users to send an image to a Telegram bot and receive the extracted text using Tesseract OCR (via the n8n-nodes-tesseractjs Community Node). It's a quick and straightforward way to convert images into readable text directly through chat. How it Works The workflow listens for new image messages coming in via the Telegram bot. Once an image is received, it downloads the image file from Telegram (which initially arrives as application/octet-stream). The image data, now properly identified, is then sent to the Tesseract OCR node to extract the text. Finally, the recognized text is sent back as a reply to the Telegram user. Setup Steps Install Community Node: Ensure you have installed n8n-nodes-tesseractjs in your n8n instance. Connect Telegram Bot: Configure the Telegram Trigger node with your Telegram bot. Bot Token: Add your Telegram bot token to the Send Message node to send replies. Deploy & Test: Activate (deploy) the workflow and send an image to your Telegram bot to test.
by Rahul Joshi
Description: Automate your AI newsletter creation and delivery using this ready-to-deploy n8n workflow template. Powered by GPT (OpenAI/Azure) and integrated with Gmail, this workflow generates rich, structured, and engaging AI-focused newsletters and sends them out daily or weekly—completely hands-free. What It Does: 📰 Fetches the latest AI trends and updates using GPT ✍️ Automatically formats news into structured newsletter sections: headlines, tools, stats, tips, and more 📧 Sends HTML email newsletters via Gmail 🕘 Runs automatically at your chosen schedule (default: 9 AM daily) Setup Includes: Connect your OpenAI or Azure GPT API Add Gmail SMTP or OAuth credentials Customize categories, schedule, and email styling Perfect for: Tech bloggers, content marketers, AI influencers, and automation enthusiasts who want to send curated AI content to their audience without manual effort.
by Tharwat Mohamed
💡 What It Is SmartReserve is a flexible, automated Telegram chatbot built in n8n that allows users to request and confirm reservations for any kind of resource—training sessions, equipment, appointments, event slots, or more. It connects with Google Sheets for live availability tracking and automatically sends confirmation emails to your users. ⚙️ How It Works Telegram Chatbot Interface Users interact with a friendly bot to submit their reservation request. The bot collects: Date Name Email Resource / Service Start Time & End Time Final confirmation All in one seamless message. Conflict-Free Booking System The bot checks your existing reservation sheet to avoid time overlaps before confirming. Google Sheets Integration Two spreadsheets are used: Resource Info: Define available services, resources, or assets. Reservation Log: Store confirmed reservations in structured rows. Confirmation via Email Once a reservation is accepted, the bot sends a detailed confirmation email to the user. 🚀 Setup Steps Import the n8n Workflow Use the provided .json template inside your n8n workspace. Create Your Google Sheets Sheet 1: Resource Info (e.g., rooms, courts, sessions, etc.) Sheet 2: Reservation Log with these headers: CopyEditDate | Name | Email | Resource | Start Time | End Time | Status Set Telegram Bot Token Create a Telegram bot and paste the token into n8n credentials. Connect Google Sheets Add your Google account to n8n and allow spreadsheet access. Customize for Your Use Case Rename “Resource” to anything (e.g., Room, Coach, Equipment). Edit confirmation text and branding inside the “Set” and “Email” nodes. Go Live! Enable the workflow, and you’re ready to accept real-time reservations. 📦 What You Get ✅ One-click Telegram reservation system ✅ Conflict checker with Google Sheets ✅ Auto email confirmation ✅ User-friendly one-shot data collection ✅ Fully editable & extendable workflow ✅ Future updates and support options 🙋 Need Help Setting It Up? If you'd like help customizing or deploying this workflow, I offer quick setup assistance and extended support.📧 Contact: tharwat.elsayed.hamad@gmail.com 💬 Whatsapp: +201061803236 Whether you're setting it up for your team, your club, or your business—I’m here to help!
by damo
Overview This workflow leverages the KIE. AI Veo3 model to generate AI videos from simple text descriptions. Users interact via a form interface, inputting a prompt (e.g., a scene description), and the system automatically submits the request to the KIE. AI API, monitors the generation status in real time, and retrieves the final video output. It's ideal for content creators, marketers, or developers exploring text-to-video AI creation, supporting intelligent video generation with minimal setup. Prerequisites A KIE. AI account and API key: Sign up at KIE.AI to obtain your free or paid API key. An active n8n instance (cloud or self-hosted) with HTTP Request and form submission capabilities. Basic knowledge of AI prompts for video generation to achieve optimal results. Setup Instructions Obtain API Key: Register at KIE. AI and generate your API key. Store it securely—do not share it publicly. Configure the Form: In the "On Form Submission" node, ensure fields like "prompt" (for video description) and "api_key" are set up. Example prompt: "A serene mountain landscape at sunset with birds flying." Test the Workflow: Click "Execute Workflow" in n8n. Access the generated form URL, submit your prompt and API key. The workflow will poll the API every 10 seconds until the video is ready, then display the results. Handle Outputs: The final node formats and displays the video file URL for download or embedding. Customization Tips Enhance Prompts**: Include specifics like duration, style (e.g., realistic, animated), actions, and visual elements to improve AI video quality. Keywords for SEO**: This template focuses on AI video generation, text-to-video models, Veo3 API integration, and automated workflows.
by Zacharia Kimotho
This workflow is designed to generate prompts for AI agents and store them in Airtable. It starts by receiving a chat message, processes it to create a structured prompt, categorizes the prompt, and finally stores it in Airtable. 2. Setup Instructions Prerequisites AI model eg Gemini, openAI etc** Airtable base and table or other storage tool** Step-by-Step Guide Clone the Workflow Copy the provided workflow JSON and import it into your n8n instance. Configure Credentials Set up the Google Gemini(PaLM) API account credentials. Set up the Airtable Personal Access Token account credentials. Map Airtable Base and Table Create a copy of the Prompt Library in Airtable. Map the Airtable base and table in the Airtable node. Customize Prompt Template Edit the 'Create prompt' node to customize the prompt template as needed. Configuration Options Prompt Template:** Customize the prompt template in the 'Create prompt' node to fit your specific use case. Airtable Mapping:** Ensure the Airtable base and table are correctly mapped in the Airtable node. 4. Running and Troubleshooting Running the Workflow Trigger the Workflow: Send a chat message to trigger the workflow. Monitor Execution: Use the n8n interface to monitor the workflow execution. Check Completion: Verify that the prompt is stored in Airtable and check the chat interface for the result. Troubleshooting Tips API Issues:** Ensure that the APIs and Airtable credentials are correctly configured. Data Mapping:** Verify that the Airtable base and table are correctly mapped. Prompt Template:** Check the prompt template for any errors or inconsistencies. Use Case Examples This workflow is particularly useful in scenarios where you want to automate the generation and management of AI agent prompts. Here are some examples: Rapid Prototyping of AI Agents: Quickly generate and test different prompts for AI agents in various applications. Content Creation:** Generate prompts for AI models that create blog posts, articles, or social media content. Customer Service Automation:** Develop prompts for AI-powered chatbots to handle customer inquiries and support requests. Educational Tools:** Create prompts for AI tutors or learning assistants. Industries/Professionals: Software Development:** Developers building AI-powered applications. Marketing:** Marketers automating content creation and social media management. Customer Service:** Customer service managers implementing AI-driven chatbots. Education:** Educators creating AI-based learning tools. Practical Value: Time Savings:** Automates the prompt generation process, saving significant time and effort. Improved Prompt Quality:** Leverages Google Gemini and structured prompt engineering principles to generate more effective prompts. Centralized Prompt Management:** Stores prompts in Airtable for easy access, organization, and reuse. 4. Running and Troubleshooting Running the Workflow:** Activate the workflow in n8n. Send a chat message to the webhook URL configured in the "When chat message received" node. Monitor the workflow execution in the n8n editor. Monitoring Execution:** Check the execution log in n8n to see the data flowing through each node and identify any errors. Checking for Successful Completion:** Verify that a new record is created in your Airtable base with the generated prompt, name, and category. Confirm that the "Return results" node sends back confirmation of the prompt in the chat interface. Troubleshooting Tips:** Error:** 400: Bad Request in the Google Gemini nodes: Cause:** Invalid API key or insufficient permissions. Solution:** Double-check your Google Gemini API key and ensure that the API is enabled for your project. Error:** Airtable node fails to create a record: Cause:** Invalid Airtable credentials, incorrect Base ID or Table ID, or mismatched column names. Solution:** Verify your Airtable API key, Base ID, Table ID, and column names. Ensure that the data types in n8n match the data types in your Airtable columns. Follow me on Linkedin for more
by Ranjan Dailata
Who this is for? Extract Amazon Best Seller Electronic Info is an automated workflow that extracts best seller data from Amazon's Electronics section using Bright Data Web Unlocker, transform it into structured JSON using Google Gemini's LLM, and forwards a fully structured JSON response to a specified webhook for downstream use. This workflow is tailored for: eCommerce Analysts** Who need to monitor Amazon best-seller trends in the Electronics category and track changes in real-time or on a schedule. Product Intelligence Teams** Who want structured insights on competitor offerings, including rankings, prices, ratings, and promotions. AI-powered Chatbot Developers** Who are building assistants capable of answering product-related queries with fresh, structured data from Amazon. Growth Hackers & Marketers** Looking to automate competitive research and surface trending product data to inform pricing strategies. Data Aggregators and Price Trackers** Who need reliable and smart scraping of Amazon data enriched with AI-driven parsing. What problem is this workflow solving? Keeping up with Amazon's best sellers in Electronics is a time-consuming, error-prone task when done manually.This workflow automates the process, ensuring: Automating Data Extraction from Amazon Best Sellers using Bright Data, ensuring reliable access to real-time, structured data. Enhancing Raw Data with Google Gemini, turning product lists into structured JSON using the Google Gemini LLM. Sending Results to a Webhook, enabling seamless integration into dashboards, databases, or chatbots. What this workflow does The workflow performs the following steps: Extracts Amazon Best Seller Electronics page info using Bright Data's Web Unlocker API. Processes the unstructured content using Google Gemini's Flash Exp model to extract structured product data. Sends the structured information to a webhook endpoint. Setup Sign up at Bright Data. Navigate to Proxies & Scraping and create a new Web Unlocker zone by selecting Web Unlocker API under Scraping Solutions. In n8n, configure the Header Auth account under Credentials (Generic Auth Type: Header Authentication). The Value field should be set with the Bearer XXXXXXXXXXXXXX. The XXXXXXXXXXXXXX should be replaced by the Web Unlocker Token. In n8n, configure the Google Gemini(PaLM) Api account with the Google Gemini API key (or access through Vertex AI or proxy). Update the Amazon URL with the Bright Data zone by navigating to the Amazon URL with the Bright Data Zone node. Update the Webhook HTTP Request node with the Webhook endpoint of your choice. How to customize this workflow to your needs This workflow is built to be flexible - whether you're a market researcher, e-commerce entrepreneur, or data analyst. Here's how you can adapt it to fit your specific use case: Change the Amazon Category** Update the Amazon URL with the topic of your interest such as Computers & Accessories, Home Audio, etc. Customize the Gemini Prompt** Update the Gemini prompt to get different styles of output — comparison tables, summaries, feature highlights, etc. Send Output to Other Destinations** Replace the Webhook URL to forward output to: Google Sheets Airtable Slack or Discord Custom API endpoints
by Anurag
This workflow contains community nodes that are only compatible with the self-hosted version of n8n. Description This workflow automates document processing and structured table extraction using the Nanonets API. You can submit a PDF file via an n8n form trigger or webhook—the workflow then forwards the document to Nanonets, waits for asynchronous parsing to finish, retrieves the results (including header fields and line items/tables), and returns the output as an Excel file. Ideal for automating invoice, receipt, or order data extraction with downstream business use. How It Works A document is uploaded (via n8n form or webhook). The PDF is sent to the Nanonets Workflow API for parsing. The workflow waits until processing is complete. Parsed results are fetched. Both top-level fields and any table rows/line items are extracted and restructured. Data is exported to Excel format and delivered to the requester. Setup Steps Nanonets Account: Register for a Nanonets account and set up a workflow for your specific document type (e.g., invoice, receipt). Credentials in n8n: Add HTTP Basic Auth credentials in n8n for the Nanonets API (never store credentials directly in node parameters). Webhook/Form Configuration: Option 1: Configure and enable the included n8n Form Trigger node for document uploads. Option 2: Use the included Webhook node to accept external POSTs with a PDF file. Adjust Workflow: Update any HTTP nodes to use your credential profile. Insert your Nanonets Workflow ID in all relevant nodes. Test the Workflow: Enable the workflow and try with a sample document. Features Accepts documents via n8n Form Trigger or direct webhook POST. Securely sends files to Nanonets for document parsing (credentials stored in n8n credentials manager). Automatically waits for async processing, checking Nanonets until results are ready. Extracts both header data and all table/line items into a tabular format. Exports results as an Excel file download. Modular nodes allow easy customization or extension. Prerequisites Nanonets account** with workflow configured for your document type. n8n** instance with HTTP Request, Webhook/Form, Code, and Excel/Spreadsheet nodes enabled. Valid HTTP Basic Auth credentials** saved in n8n for API access. Example Use Cases | Scenario | Benefit | |-----------------------|--------------------------------------------------| | Invoice Processing | Automated extraction of line items and totals | | Receipt Digitization | Parse amounts and charges for expense reports | | Purchase Orders | Convert scanned POs into structured Excel sheets | Notes You must set up credentials in the n8n credentials manager—do not store API keys directly in nodes. All configuration and endpoints are clearly explained with inline sticky notes in the workflow editor. Easily adaptable for other document types or similar APIs—just modify endpoints and result mapping.
by Kevin
Monitor Postgres Data Freshness and Email Alert If Stale This template monitors a set of tables inside a Postgres database to ensure they're getting updated. If the table hasn't been updated in 3 days (configurable), an email alert is sent containing the tables that are stale. Requirements You must have a Postgres database containing one or more tables that you'd like to monitor. Each table to monitor must have a date or timestamp column that tracks when data was pushed. For example, this might be: A timestamp column if your table holds event/timeseries data A last_updated column if your rows are expected to be modified Usage Use this template Add your Postgres and email credentials Adjust the Produce tables + date columns node to produce pairs of [table, date_column] that should be monitored for freshness 💁♂️ Note that a timestamp column also works (Optional) Adjust the Remove fresh tables node for your desired staleness window (default is 3 days, but you can adjust as you please) (Optional) Customize the Send alerts node to call whichever alerting workflow you please (I recommend my alerting workflow for easiest plug-and-play) How it works This template works by: Pulling the most recent row for each table Calculating how out-of-date each table is, in days Dropping fresh tables that have been updated within the past 3 days Sending an email alert with the stale tables that haven't been updated within the past 3 days
by Airtop
Scoring LinkedIn Profiles Against Your ICP Use Case This automation scores individual LinkedIn profiles against your Ideal Customer Profile (ICP) based on interest in AI, technical depth, and seniority level. It's ideal for prioritizing leads and understanding how well a person fits your ICP criteria. What This Automation Does Given a LinkedIn profile and an Airtop profile, it: Extracts relevant data from the person's profile Determines levels of AI interest, seniority, and technical depth Calculates an ICP score based on weighted criteria Returns the full enriched profile with the score Input parameters: LinkedIn Profile URL** (e.g., https://linkedin.com/in/janedoe) Airtop Profile** connected to LinkedIn ICP scoring method** in the Airtop node prompt Output fields in JSON format: Full name, job title, employer, company LinkedIn URL, location, number of connections and followers, about section content and more Calculated ICP Score (out of 100) How It Works Form Trigger or Workflow Trigger: Accepts input from either a form or another workflow. Parameter Assignment: Ensures proper variable names for downstream nodes. Airtop Enrichment Tool: Extracts and scores the person based on a detailed prompt. Scoring: Uses this point system: AI Interest: beginner (5), intermediate (10), advanced (25), expert (35) Technical Depth: basic (5), intermediate (15), advanced (25), expert (35) Seniority Level: junior (5), mid-level (15), senior (25), executive (30) Output Formatting: Cleans and returns the result as JSON. Setup Requirements IMPORTANT: Enter your ICP scoring method in the prompt field of the Airtop node Airtop Profile connected to LinkedIn. Airtop API credentials configured in n8n. Optional: a front-end form to collect profile URLs and trigger the automation. Next Steps Embed in CRM**: Trigger this automation on new leads to auto-score them. Batch Process Leads**: Run it over a list of profile URLs for segmentation. Customize Scoring**: Adjust point weights based on your sales priorities. Read more about Scoring LinkedIn Profiles Against Your ICP
by Lucas Peyrin
How it works This workflow demonstrates a fundamental pattern for securing a webhook by requiring an API key. It acts as a gatekeeper, checking for a valid key in the request header before allowing the request to proceed. Incoming Request: The Secured Webhook node receives an incoming POST request. It expects an API key to be sent in the x-api-key header. API Key Verification: The Check API Key node takes the key from the incoming request's header. It then makes an internal HTTP request to a second webhook (Get API Key) which acts as a mock database. This second webhook retrieves a list of registered API keys (from the Registered API Keys node) and filters it to find a match for the key that was provided. Conditional Response: If a match is found, the API Key Identified node routes the execution to the "success" path, returning a 200 OK response with the identified user's ID. If no match is found, it routes to the "unauthorized" path, returning a 401 Unauthorized error. This pattern separates the public-facing endpoint from the data source, which is a good security practice. Set up steps Setup time: ~2 minutes This workflow is designed to be a self-contained example. Set up Credentials: This workflow uses "Header Auth" for its internal communication. Go to Credentials and create a new Header Auth credential. You can use any name and value (e.g., Name: X-N8N-Auth, Value: my-secret-password). Select this credential in all four webhook/HTTP Request nodes. Add Your API Keys: Open the Registered API Keys node. This is your mock database. Edit the array to include the user_id and api_key pairs you want to authorize. Activate the workflow. Test it: Use the Test Secure Webhook node to send a request. Try it with a valid key from your list to see the success response. Change the x-api-key header to an invalid key to see the 401 Unauthorized error. For Production: Replace the mock database part of this workflow (the Get API Key webhook and Registered API Keys node) with a real database node like Supabase, Postgres, or Baserow to look up keys.