Workflow Templates

How it works This workflow automates the conversion of various document formats (such as PDF, Word, and PPT) into Markdown. It connects to the MinerU API service, which leverages OCR, formula, and table recognition to produce high-quality output. Users can initiate the process by simply uploading a document through an n8n chat interface. Set up steps Ensure you have a local n8n instance running. Set up and run the MinerU MCP (MinerU Computing Platform) server locally. Import this workflow into your n8n instance. Configure your AI model credentials (e.g., for OpenAI, add your API Key and Base URL). Click the "Write Files from Disk" node and edit the file path to your desired local save location. Click the "MCP Client" node and input your MinerU MCP server address (e.g., http://localhost:8000/sse). Click the "Open Chat" button to upload a file, send a message, and test the workflow.

Who is this for? This workflow is perfect for marketers, SEO specialists, product teams, and competitive analysts who want to monitor and summarize public reviews of their competitors. It’s especially helpful for small teams who want fast insights from Google reviews without spending hours manually reading and sorting them. What problem is this workflow solving? Manually going through competitor reviews is time-consuming and repetitive. You risk missing patterns or insights, and it’s hard to share summaries with your team quickly. This workflow automatically scrapes reviews from Google and generates a structured summary of pain points and positive feedback. That way, you can focus on strategy instead of sorting through dozens of reviews. What this workflow does This automation watches for new competitor entries in a Google Sheet, then: Uses Dumpling AI to scrape the latest Google reviews (up to 20) for each business. Splits and cleans the reviews for analysis. Sends them to GPT-4o, which summarizes the most common complaints and praises. Saves the structured result back to the same Google Sheet. You’ll instantly get an overview of what people are saying about any competitor. Setup Google Sheet Setup Create a Google Sheet with at least one column: Business Add names or search queries for the competitors you want to analyze Optional: Add columns for Summary of Reviews and Pain Points Connect Dumpling AI Sign up at Dumpling AI Create an agent using the get-google-reviews endpoint Copy your agent key Use it in the HTTP Request node in this workflow OpenAI Setup Use your API key with GPT-4o access The prompt is already structured to generate grouped summaries from reviews Run the Workflow Trigger it manually or schedule it Make sure your Google Sheets, OpenAI, and Dumpling AI connections are active How to customize this workflow to your needs You can expand the number of reviews retrieved by changing the Dumpling AI agent config Replace Google Sheets with Airtable if you want more robust data views Add more fields like star ratings or review dates in your agent for richer analysis Change the GPT prompt to highlight emotional tone, urgency, or feature mentions 🧠 Node Details Google Sheets Trigger**: Watches for new competitor names HTTP Request (Dumpling AI)**: Scrapes 20 recent reviews from Google SplitOut Node**: Breaks review array into individual items Code Node**: Extracts and combines review text Edit Fields Node**: Structures the review content before GPT GPT-4o Node**: Analyzes and summarizes top pain points and praise Google Sheets Output**: Saves the summary back to the same sheet Dependencies Dumpling AI account and review scraping agent setup OpenAI API key with GPT-4o access Google Sheets OAuth2 credentials

WordPress Post Auto-Categorization Workflow 💡 Click here to read detailed case study 📺 Click here to watch youtube tutorial 🎯 Purpose Automatically categorize WordPress blog posts using AI, saving hours of manual work. This workflow analyzes your post titles and assigns them to predefined categories using artificial intelligence. 🔄 What This Workflow Does • Connects to your WordPress site • Retrieves all uncategorized posts • Uses AI to analyze post titles • Automatically assigns appropriate category IDs • Updates posts with new categories • Processes dozens of posts in minutes ⚙️ Setup Requirements WordPress site with admin access Predefined categories in WordPress OpenAI API credentials (or your preferred AI provider) n8n with WordPress credentials 🛠️ Configuration Steps Add your WordPress categories (manually in WordPress) Note down category IDs Update the AI prompt with your category IDs Configure WordPress credentials in n8n Set up AI API connection 🔧 Customization Options • Modify AI prompts for different categorization criteria • Adjust for multiple category assignments • Add tag generation functionality • Customize for different content types • Add additional metadata updates ⚠️ Important Notes • Backup your WordPress database before running • Test with a few posts first • Review AI categorization results initially • Categories must be created manually first 🎁 Bonus Features • Can be modified for tag generation • Works with scheduled posts • Handles bulk processing • Maintains categorization consistency Perfect for content managers, bloggers, and website administrators looking to organize their WordPress content efficiently. #n8n #WordPress #ContentManagement #Automation #AI Created by rumjahn

This workflow automatically adds closed deals from Pipedrive as new customers into Stripe. Prerequisites Pipedrive account and Pipedrive credentials Stripe account and Stripe credentials How it works Pipedrive trigger node starts the workflow when a deal gets updated in Pipedrive. IF node checks that the current won time is not equal to the previuos one in the deal and continues the workflow if it's true. Pipedrive node extracts the organization's details to pass it further. HTTP Request node searches for the same organization's details within Stripe. If a customer doesn't exist within Stripe, Merge node passes a new customer details to Stripe. Stripe node creates a new customer.

Purpose The purpose of this automation is to help context switch from office to some side projects or passion gigs so you can be free of distracting thoughts and re-set your perspective. Benefits Anyone who works full time and also does something on the side (perhaps a side gig/being a mom/just follow your passion project) What you need N8N (lol) Any LLM API Key (I used OpenAI 4.1) IPhone (automations and shortcuts) Template Setup Setup LLM API key. Import template file to new workflow. On Iphone create a new shortcut as per video. Create automation steps. Resources Youtube

This n8n workflow operates as a two-agent system where each agent has a specialized task. The process flows from initial user input to a final analysis, with a seamless handoff between the agents. How it works The Chat Trigger The entire process begins when you send a message using n8n's chat interface. This message serves as the initial prompt or query for the system. The Research Agent Takes Over The user's message is first sent to the Research Agent. This agent's job is to understand the query and gather relevant information. To do this, it has access to: LLM: Google Gemini, which acts as the agent's "brain" to process language and make decisions. Tools: web_search: It uses this tool (powered by your self-hosted SearXNG instance) to perform live searches on the internet. get_current_date: It can access the current date, which is useful for context-aware or time-sensitive research. The Research Agent uses these tools to find the most relevant information related to your query and then compiles it into a concise summary. Handoff to the Sentiment Analysis Agent Once the Research Agent has completed its task, it passes its findings directly to the Sentiment Analysis Agent. The Final Analysis The Sentiment Analysis Agent receives the text from the Research Agent. Its sole purpose, as defined by its system prompt, is to analyze the sentiment of the provided information. It determines if the content is positive, negative, or neutral and formulates a final response. This final analysis is then sent back to you in the chat, completing the workflow. Set up steps Select the Language Model (LLM): This workflow is pre-configured with Google Gemini. You can select a different model for the agents as needed. Configure LLM Credentials: Ensure that valid credentials for your chosen LLM are correctly set up within your n8n instance. Set Up the SearXNG Connection: Configure the node to connect to your self-hosted SearXNG instance. This enables the agent's web search capabilities. Define the Research Agent's Task: Customize the system prompt for the "Research Agent" to define its role, instructions, and how it should conduct its research. Define the Sentiment Analysis Agent's Task: Adjust the system prompt for the "Sentiment Analysis Agent" to specify how it should analyze the information provided by the Research Agent. Test the Workflow: Use the built-in chat interface in the n8n canvas to send a message and verify that the agents are functioning correctly.

This workflow automatically syncs Shopify orders with your Zendesk tickets. Using this workflow, Shopify orders will be added or have their information updated straight to your Zendesk tickets. Prerequisites Shopify account and Shopify credentials Zendesk account and Zendesk credentials How it works Shopify Trigger starts the workflow whenever an order is updated. Zendesk node finds if the order already exists and has a ticket assigned. Set node keeps and passes only ticket ID. Merge by Key node combines the Shopify order data with the Zendesk ticket data. If node splits the workflow conditionally, checks if the ticket already exists or not. If order is new, Zendesk node creates a new ticket for the order.

This workflow adds a new product in Stripe whenever a new product has been added to Pipedrive. Prerequisites Stripe account and Stripe credentials Pipedrive account and Pipedrive credentials How it works Pipedrive trigger node starts the workflow when a new product is added. HTTP Request node creates a new product in Stripe using previuos input. Merge node combines data of both Pipedrive and Stripe inputs. The output will contain the data of Pipedrive input merged with the data of Stripe input. The merge occurs based on the index of the items. The Item Lists node splits prices to separate items. HTTP Request node creates price records in Stripe.

How it works This workflow changes the file name, and therefore the extension and MIME type, of any binary file passed to it. This is perfect for converting file formats on the fly, like turning a Telegram voice message (.oga) into an MP3 for an AI transcription service. Set New File Name: The SET OUTPUT FILE NAME node is where you define the desired output file name and extension (e.g., audio.mp3). It also dynamically captures the property name of the incoming binary (e.g., data). Extract Binary Data: The workflow temporarily converts the binary file into a Base64 text string to make it accessible in the next step. Rebuild Binary with New Name: A Code node takes the Base64 data and reconstructs it as a binary file, but this time, it assigns the new file name you specified. n8n automatically sets the MIME type based on the new file extension. Set up steps Setup time: < 1 minute This workflow is designed to be used as a sub-workflow. In your main workflow, add an Execute Sub-Workflow node where you need to change a file's type. In the Workflow parameter, select this "Change Binary MimeType/Extension" workflow. Open this workflow and go to the SET OUTPUT FILE NAME node. Modify the output_file_name value to your desired file name (e.g., voice_message.mp3 or document.pdf). Save this workflow. Now, any binary file you send to it from your main workflow will be returned with the new fileName and mimeType.

Idea The idea for app came since I wanted to build a unique gift for my niece because she gets excited for her birthday (which Im going to miss this year). The web app has a simple countdown (in html and JS) but more importantly, there is an AI agent that will answer some specific questions and know her preferences. How it works The questions from app are sent via web hook to N8N which has pulls preferences file (about her likes, dislikes, personality) from postgre and AI Agent that will answer questions/respond. The current status is stored back in postgre (especially about status of cat and universe happenings) before responding back. Features Integrated AI chatbot via N8N webhook Persistent conversation history Minimizable chat interface Fallback support for offline testing Features: -- Wheres Mittens - This is a query to track her lost cat in multiverse. -- Multiverse updates with recent update stored Pre Requisites Postgre SQL database is available. Alternatively, use any other database but change the N8N nodes. LLM Api Key. Step by Step Instructions Export this N8N Workflow. Modify LLM API Key, I used openAI, 4.1 For web app scofflding,you will need Node, HTML and Javascript. I've created a mini version using Node and JS with web app and N8N connection settings here: <https://github.com/productiser/FiBirthdayAgent> PostgreSQL Database Script (1 table for memory and context storage): CREATE TABLE fifi_world_context ( id TEXT PRIMARY KEY, -- e.g., 'agent_fifi' cat_location TEXT, -- e.g., "Bubble Nebula" cat_activity TEXT, -- e.g., "Playing laser tag with moon mice" fifi_preferences JSONB, -- e.g., likes/dislikes/foods/shows world_history TEXT, -- Summary of narrative events last_updated TIMESTAMP DEFAULT CURRENT_TIMESTAMP ); 5.Modify system prompt as per your needs. Built With N8N Self hosted Self hosted web app Hosted on Vercel Total spend = <£1 (AI costs only) Total Time = <1 day Support Watch this video for web app overview and how it looks. <https://youtu.be/e7PlrTdvwoM> Contact me on info@pankstr.com/ superllmuser@gmail.com for any queries Hope you enjoy!!

How it works Trigger the workflow manually via the n8n UI. Define key parameters like the image prompt, number of images, size, quality, and model. Send a POST request to OpenAI’s image generation API using those inputs. Split the API response to handle multiple images. Convert the base64 image data into downloadable binary files. Set up steps Initial setup takes around 5–10 minutes. You’ll need an OpenAI API key, a configured HTTP Request node with credentials, and to customize the prompt/parameter fields in the “Set Variables” node. No advanced config or external services needed. Important Note You have to make sure to complete OpenAI's new verification requirements to use their new image API: https://help.openai.com/en/articles/10910291-api-organization-verification It only takes a few minutes and does not cost any money.

This n8n workflow provides a ready-to-use API endpoint for extracting structured data from images. It processes an image URL using an AI-powered OCR model and returns the extracted details in a structured JSON format. Use Cases Document OCR** – Extract details from ID cards, invoices, receipts, etc. Text Extraction from Images** – Process screenshots, scanned documents, and photos. Automated Form Processing** – Digitize and capture information from paper forms. Business Card Data Extraction** – Extract names, emails, and phone numbers from business cards. How It Works Send a GET request with an image URL and define the required extraction parameters. The image is converted to base64 for processing. The AI model (Gemini API - Flash Lite) extracts relevant text. The response returns structured JSON data containing only the requested fields. Features ✔️ No-Code API Setup – Easily integrate into any application. ✔️ Customizable Extraction – Modify the request parameters to fit your needs. ✔️ AI-Powered OCR – Uses advanced models for accurate text recognition. ✔️ Automated Processing – Ideal for document processing and digitization. Integration Works with any frontend/backend system that supports API calls. Can be used for workflow automation in CRM, ERP, and document management solutions. Supports further customization based on specific OCR requirements.