Workflow Templates

This n8n template demonstrates how to calculate the evaluation metric "Summarization" which in this scenario, measures the LLM's accuracy and faithfulness in producing summaries which are based on an incoming Youtube transcript. The scoring approach is adapted from https://cloud.google.com/vertex-ai/generative-ai/docs/models/metrics-templates#pointwise_summarization_quality How it works This evaluation works best for an AI summarization workflows. For our scoring, we simple compare the generated response to the original transcript. A key factor is to look out information in the response which is not mentioned in the documents. A high score indicates LLM adherence and alignment whereas a low score could signal inadequate prompt or model hallucination. Requirements n8n version 1.94+ Check out this Google Sheet for a sample data https://docs.google.com/spreadsheets/d/1YOnu2JJjlxd787AuYcg-wKbkjyjyZFgASYVV0jsij5Y/edit?usp=sharing

Are you a cord-cutter? Do you find yourself looking through the many titles of videos uploaded to Youtube, just to find the ones you want to watch? Even when you subscribe to the channels you like, do you find that you want to watch the news now and my tech/n8n videos later? Well, now you can have n8n grab the last 8 videos, posted in the last 24 hours, and put them in a playlist for the day; and, each day the old playlist is deleted. Are you tired of a channel filling your subscriptions with tons of videos a day; this workflow can be used for any channel, whether you are subscribed to the channel or not. It's a YouTube playlist automation. How it works: Create your list of prefered Youtube Channels in a Google Sheet and it will create you a daily playlist; and, it will delete the playlist created yesterday. Instructions To set this up, you need to create a Google Sheet with the following headings in line 1: Channel User Name Channel Name Channel Link Channel ID Copy the 'Create your Channel List' into it's own workflow and link the Sheets links to your new sheet. To get the 'Create your Channel List' to work, you need to visit each channel's page that you want included in your playlist; you need to get the "@" name of the channel and add it to the 'Channel User Name' column of your Google Sheet. For example: if you wanted to include this channel: Recruit Training Videos - Corporal Stock, you would search for the name, to add to the next available row of the 'Channel User Name' column: @CorporalStock Once you add all Channel User Names, run the 'Create your Channel list workflow, and it will fill in the remaining details. Now the 'YT Playlist Creator' can be run. Note: The first time the workflow us run, disconnect the 'Delete Yesterday's Playlist' leg, or the workflow will error and stop (because there is no 'Yesterday's Playlist'. Note: this was made to create a playlist every day, delete yesterday's playlist, and only get the last 8 videos posted within the last 24 hours. I choose to put the date (YYMMDD format) in front of the playlist, to ensure that it doesn't conflict with another playlist. Also, I have it notifying me in Telegram, so I know that the new playlist is posted.

🧑‍⚖️ AI Legal Assistant Agent — AI-Powered Legal Q&A with Document Retrieval Category: LegalTech / AI Agent / RAG / Chatbot Description: This no-code AI agent acts as a legal assistant chatbot that can answer user queries by retrieving information from a pre-indexed legal document library. It’s powered by OpenAI + Pinecone + Telegram and designed for law firms, compliance teams, or anyone who needs instant answers from contracts, policies, or regulatory documents. For more of such builds and step-by-step video tutorial, check out: https://www.youtube.com/@Automatewithmarc 🔍 How it Works: Telegram Trigger – Starts when a user sends a message via Telegram. AI Agent (Open AI Model) – Uses a retrieval-augmented generation (RAG) setup to understand the question and pull relevant context. Pinecone Vector Store – Searches across a vectorized legal contract library for relevant clauses or documents. OpenAI Embeddings – Converts uploaded documents into vector embeddings for efficient search. Memory Buffer – Maintains conversation flow and context for follow-up questions. Telegram Response – Sends the final AI-generated answer directly to the user. 🎯 Use Cases: In-house legal teams automating internal policy Q&A Law firms building client-facing legal bots Startups offering legal tech services with document-based queries Compliance teams monitoring contract terms and obligations ✅ Key Features: Real-time legal Q&A via Telegram Pinecone + OpenAI-powered vector search Retrieval-Augmented Generation (RAG) setup Factual, memory-aware assistant with fallback if info is unavailable Fully customizable and extendable ⚙️ Setup Instructions: Connect OpenAI, Pinecone, and Telegram credentials Upload your contracts or policy docs into Pinecone Customize the system prompt or expand document sources as needed Activate and test via Telegram This workflow is a solid foundation for any AI-powered legal assistant or chatbot solution—highly relevant for modern LegalOps and knowledge management teams.

This n8n demonstrates how to build your own Github MCP server to personalise it to your organisation's repositories, issues and pull requests. This n8n implementation, though not as fully featured as the official MCP server offered by Github, allows you to control precisely what access and/or functionality is granted to users which can make MCP use simpler and in some cases, more secure. The use-case in this template is to simply view and comment on issues within a specific repository but can be extended to meet the needs of your team. This MCP example is based off an official MCP reference implementation which can be found here https://github.com/modelcontextprotocol/servers/tree/main/src/github How it works A MCP server trigger is used and connected to 3 custom workflow tools. We're using custom workflow tools as there is quite a few nodes required for each task. Behind these tools are regular Github nodes although preconfigured with credentials and targeted repository. The "Get Issue Comments" and "Create Issue Comment" tools depend on obtaining an Issue Number first. The agent should call the "Get Latest Issues" tool for this. How to use This Github MCP server allows any compatible MCP client to view and comment on Github Issues. You will need to have a Github account and repository access available before you can use this server. Connect your MCP client by following the n8n guidelines here - https://docs.n8n.io/integrations/builtin/core-nodes/n8n-nodes-langchain.mcptrigger/#integrating-with-claude-desktop Try the following queries in your MCP client: "Can you get me the latest issues about MCP?" "What is the current progress on Issue 12345?" "Please can you add a comment to Issue 12345 that they should try installing the latest version and see if that works?" Requirements Github for account and repository access. The repository need not be your own but you'll still need to ensure you have the correct permissions. MCP Client or Agent for usage such as Claude Desktop - https://claude.ai/download Customising this workflow Extend this template to interactive with pull requests or workflows within your own company's Github repositories. Alternatively, pull in metrics and generate reports for programme managers. Remember to set the MCP server to require credentials before going to production and sharing this MCP server with others!

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.

Who is this template for? This template is for learners, researchers, students and professionals who want to quickly capture the essence of a YouTube video. Steps in the workflow: Gets the transcript from any YouTube video through Supadata. Process the result from Supadata to one text Process the text with AI (any LLM of your choice) Final result: Produces a summary accompanied with the most important lessons and interesting facts mentioned in the video. The workflow automatically creates a new Google Doc wiht this output, in a folder of your choice on your Google Drive. (If you want to convert the markdown text to real markup after the Google Doc is created: just select all text (Ctrl-A or CMD-A), Cut the text (Ctrl-X or CMD-X and then go to Edit > Paste from Markdown.) Setup Edit your Supadata credentials in the second node (you can start for free) Choose your favourite LLM for AI processing Edit your Google Drive credentials. How to adjust it to your needs If you want the outcome to be different, edit the Prompt in "Proces transcript to summary template". The file name is a combination of ‘transcript ‘ and the date and time. You can change this to whatever you need in the Google Drive node. Supadata offers more details and options (or even translation) when working with transcripts. Check the options here: https://supadata.ai/documentation/youtube/get-transcript

This template can be used to generate research questions from PDF documents (e.g. research papers, market reports) based on the content gaps found in text using the InfraNodus knowledge graph GraphRAG knowledge graph representation. Simply upload several PDF files (research papers, corporate or market reports, etc) and generate a research question / AI prompt in seconds. The template is useful for: generating research questions generating AI prompts that drive research further finding blind spots in any discourse and generating ideas that address them. avoiding the generic bias of LLM models and focusing on what's important in your particular context Using Content Gaps for Generating Research Questions Knowledge graphs represent any text as a network: the main concepts are the nodes, their co-occurrences are the connections between them. Based on this representation, we build a graph and apply network science metrics to rank the most important nodes (concepts) that serve as the crossroads of meaning and also the main topical clusters that they connect. Naturally, some of the clusters will be disconnected and will have gaps between them. These are the topics (groups of concepts) that exist in this context (the documents you uploaded) but that are not very well connected. Addressing those gaps can help you see which groups of concepts you could connect with your own ideas. This is exactly what InfraNodus does: builds the structure, finds the gaps, then uses the built-in AI to generate research questions that bridge those gaps. How it works 1) Step 1: First, you upload your PDF files using an online web form, which you can run from n8n or even make publicly available. 2) Steps 2-4: The documents are processed using the Code and PDF to Text nodes to extract plain text from them. 3) Step 5: This text is then sent to the InfraNodus GraphRAG node that creates a knowledge graph, identifies structural gaps in this graph, and then uses built-in AI to research questions / prompts. 4) Step 6: The ideas are then shown to the user in the same web form. Optionally, you can hook this template to your own workflow and send the question generated to an InfraNodus expert or your own AI model / agent for further processing. If you'd like to sync this workflow to PDF files in a Google Drive folder, you can copy our Google Drive PDF processing workflow for n8n. 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. Add this key into the InfraNodus GraphRAG HTTP node(s) you use in this workflow. You do not need any OpenAI keys for this to work. Optionally, you can change the settings in the Step 4 of this workflow and enforce it to always use the biggest gap it identifies. Requirements An InfraNodus account and API key Note: OpenAI key is not required. You will have direct access to the InfraNodus AI with the API key. Customizing this workflow You can use this same workflow with a Telegram bot or Slack (to be notified of the summaries and ideas). You can also hook up automated social media content creation workflows in the end of this template, so you can generate posts that are relevant (covering the important topics in your niche) but also novel (because they connect them in a new way). Check out our n8n templates for ideas at https://n8n.io/creators/infranodus/ Also check the full tutorial with a conceptual explanation at https://support.noduslabs.com/hc/en-us/articles/20454382597916-Beat-Your-Competition-Target-Their-Content-Gaps-with-this-n8n-Automation-Workflow Also check out the video introduction to InfraNodus to better understand how knowledge graphs and content gaps work: For support and help with this workflow, please, contact us at https://support.noduslabs.com

n8n Workflow: Invoice PDF Generator This n8n workflow captures invoice data and generates a PDF invoice, ready to be sent or saved. It uses a webhook to trigger the process, preprocesses the invoice data, and converts it to a PDF using HTML and custom styling. @custom-js/n8n-nodes-pdf-toolkit Features: Webhook Trigger**: Receives incoming data, including invoice details. Preprocessing**: Transforms the invoice data into HTML format. HTML to PDF Conversion**: Converts the preprocessed HTML into a styled PDF document. Response**: Sends the generated PDF back to the webhook response. Notice Community nodes can only be installed on self-hosted instances of n8n. Requirements Self-hosted** n8n instance A CustomJS API key for website screenshots. Invoice data** for PDF generation Workflow Steps: Webhook Trigger: Accepts incoming data (e.g., invoice number, recipient details, itemized list). This data is passed to the next node for processing. Set Data Node: Configures initial values for the invoice, including the recipient, sender, invoice number, and the items on the invoice. The invoice details include information like description, unit price, and quantity. Preprocess Node: Processes the raw data to format it correctly for HTML. This includes splitting addresses and converting the items into an HTML table format. HTML to PDF Conversion: Converts the generated HTML into a PDF document. The HTML includes a header, a detailed invoice table, and a footer with contact information. Respond to Webhook: Returns the generated PDF as a response to the initial webhook request. Setup Guide: 1. Configure CustomJS API Sign up at CustomJS. Retrieve your API key from the profile page. Add your API key as n8n credentials. 2. Design Workflow Create a Webhook: Set up a webhook to trigger the workflow when invoice data is received. Prepare Data: Ensure the incoming request contains fields like "Invoice No", "Bill To", "From", and "Details" (list of items with price and quantity). Customize the HTML: The HTML template for the invoice includes custom styling to give the invoice a professional look. Convert to PDF: The HTML to PDF node is configured with the data generated from the preprocessing step to convert the invoice HTML to a PDF format. Example Invoice Data: { "Invoice No": "1", "Bill To": "John Doe\n1234 Elm St, Apt 567\nCity, Country, 12345", "From": "ABC Corporation\n789 Business Ave\nCity, Country, 67890", "Details": [ { "description": "Web Hosting", "price": 150, "qty": 2 }, { "description": "Domain", "price": 15, "qty": 5 } ], "Email": "support@mycompany.com" } Result PDF File

📄 What this workflow does This workflow turns TikTok videos into high-quality marketing insights and social-ready posts using Dumpling AI and GPT-4. It takes a TikTok URL, keyword, and product name, then automatically extracts the video transcript, analyzes the content for key marketing insights (pain points, outcomes, triggers), and rewrites it as a social media post that positions your product as the solution. Everything is logged to Google Sheets for use by your content or product team. 👤 Who is this for Product marketers doing UGC research Copywriters repurposing TikTok into content Founders or VAs turning viral clips into assets Agencies building research-based social proof ⚙️ How to set up ✅ Requirements Dumpling AI**: For TikTok transcript extraction OpenAI GPT-4 or GPT-4o-mini**: For analysis and rewriting Google Sheets**: To log the results n8n Form Trigger**: To input TikTok URL, Keyword, and Product 🔧 Setup Instructions Google Sheets Create a sheet with the following columns: Video URL, Original Transcription, Pain points, Desired outcomes, Triggers or motivating events, Interesting direct quotes, New Script Update the sheet ID and tab in the Google Sheets node Credentials Add your Dumpling AI key using HTTP Header Auth Use GPT-4 via OpenAI credentials Connect your Google Sheets using OAuth2 Customization (Optional) You can modify the GPT-4 prompts in the LangChain nodes to change tone, output structure, or content depth 🧠 How it works A form is submitted with a TikTok URL, keyword, and product Dumpling AI fetches and returns the TikTok transcript The VTT format is cleaned into plain text GPT-4 (via LangChain agent) extracts: Pain points Desired outcomes Motivating events Direct quotes GPT-4 then rewrites the transcript into a compelling marketing post Results are saved to Google Sheets for further use 🛠️ Customization ideas Push insights to Notion or Airtable instead of Sheets Use Claude or Gemini instead of GPT-4 Automatically generate image prompts to pair with the rewritten script Add notification email or Slack post when draft is ready This workflow gives marketers and founders a fast way to convert real social content into reusable copy, backed by authentic user voice and GPT-powered insights.

Who is this template for? This template can be used by any automator who wants to create a workitem(incident/user story/bugs) in azure devops whenever an alert raised by systems. How it works Each time an alert raised in system( for ex: Elastic raises an alert for missing host or domain). Workflow reads an alert and creates a workitem in azure devops Workflow can be customized to send any required information as possible in azure devops Setup Instructions Azure DevOps Organization and Project:** Make sure you have access to an Azure DevOps organization and a project where the work item will be created. Personal Access Token (PAT):** You need a Personal Access Token with permissions to create work items. You can generate a PAT from the Azure DevOps user settings.

What this workflow does Runs automatically every Monday morning at 8 AM Collects your Meta Ads data from the last 7 days for a given account (date range is configurable) Formats the data, aggregating it at the campaign, ad set, and ad levels Generates AI-driven analysis and insights on your results, providing actionable recommendations Renders the report as a visually appealing PDF with charts and tables Sends the report via Slack (you can also add email or WhatsApp) A sample for the first page of the report: Setup Guide Create an account of pdforge and use the pre-made Meta Ads template. Connect Meta Ads, OpenAI and Slack to n8n Set your Ad Account Id and date range (choose from 'last_7d', 'last_14d', 'last30d') (opcional) Customize the scheduling date and time Requirements Meta Ads (via Facebook Graph API): Documentation pdforge access: Integration guide AI API access (e.g. via OpenAI, Anthropic, Google or Ollama) Slack acces (via OAuth2): Documentation Feel free to contact me via Linkedin, if you have any questions! 👋🏻