Workflow Templates

Monitor X for Relevant Posts Use Case This automation monitors X (formerly Twitter) search pages in real time and extracts high-signal posts that match your categories of interest. It’s ideal for community engagement, lead discovery, thought leadership tracking, or competitive analysis. What This Automation Does Given a search URL and a list of categories, it: Logs into X using Airtop Opens the specified search URL Scrolls through the results Extracts up to 10 valid, English-language posts Filters and classifies each post by category (or marks as [NA] if unrelated) Returns the structured results as JSON Input parameters: airtop_profile** — An Airtop browser profile authenticated on X x_url** — X search URL (e.g., https://x.com/search?q=ai agents&f=live) relevant_categories** — Text-based list of categories to classify posts (e.g., "Web automation use cases", "Thought leadership") Output: A JSON array of posts, each with: writer time text url category How It Works Trigger: This workflow is triggered by another workflow (e.g., a community engagement pipeline). Input Setup: Accepts the Airtop profile, search URL, and categories to use for classification. Session: Starts a browser session using the Airtop profile. Window Navigation: Opens the provided X search URL. Extraction: Scrapes up to 10 posts with /status/ in the URL and text in English. Classification: Each post is labeled with a category if relevant, or [NA] otherwise. Filtering: Discards [NA] posts. Output: Returns the list of classified posts. Setup Requirements Airtop profile with an active X login. Airtop API key connected in n8n. List of category definitions to guide post classification (used in prompt). Next Steps Feed into Engagement Workflows**: Pass the results to workflows that reply, retweet, or track posts. Use in Slack Alerts**: Push classified posts into Slack channels for review and reaction. Customize Classifier**: Refine the categorization logic to include sentiment or company mentions. Read more about Monitoring X for Relevant Posts

✉️ Telegram Email Agent with GPT + Gmail Category: Messaging / AI Agent Level: Beginner-Friendly Tags: Telegram, Email Automation, AI Agent, Gmail, GPT Model Watch Step-by-step video guide here: https://www.youtube.com/watch?v=nyI40s9QOuw&t=420s&pp=0gcJCb4JAYcqIYzv 🤖 What This Workflow Does This workflow turns your Telegram bot into a personal email assistant powered by AI. With just a message on Telegram, users can: Send an email via Gmail Automatically generate the email content using OpenAI Models. Get confirmation or responses directly in Telegram It's like ChatGPT meets Gmail, inside your Telegram chat. 🔧 How It Works Telegram Trigger – Listens for incoming messages from your bot. AI Agent – Processes the input using OpenAI Model and converts it into structured email content (To, Subject, Body). Memory Node – Stores short-term context per user (via chat ID), so the agent can hold simple conversations. Gmail Node – Sends the generated email using your Gmail account. Telegram Node – Replies to the user confirming the output or status. 🧠 Why This is Useful Ever wanted to send an email while on the go, without typing the whole thing out in Gmail? This is a fast, intuitive, and AI-powered way to: Dictate or draft emails from anywhere Create an AI-powered virtual assistant via Telegram Integrate n8n's Langchain Agent with real-world productivity use cases 🪜 Setup Instructions Connect your Telegram bot via BotFather and add the credentials in n8n. Set up your OpenAI API key (GPT-4o-mini recommended). Add your Gmail OAuth credentials. Activate the workflow and start messaging your bot!

Who is this for? This template is designed for internal support teams, product specialists, and knowledge managers in technology companies who want to automate ingestion of product documentation and enable AI-driven, retrieval-augmented question answering. What problem is this workflow solving? Support agents often spend too much time manually searching through lengthy documentation, leading to inconsistent or delayed answers. This solution automates importing, chunking, and indexing product manuals, then uses retrieval-augmented generation (RAG) to answer user queries accurately and quickly with AI. What these workflows do Workflow 1: Document Ingestion & Indexing Manually triggered to import product documentation from Google Docs. Automatically splits large documents into chunks for efficient searching. Generates vector embeddings for each chunk using OpenAI embeddings. Inserts the embedded chunks and metadata into a MongoDB Atlas vector store, enabling fast semantic search. Workflow 2: AI-Powered Query & Response Listens for incoming user questions (can be extended to webhook). Converts questions to vector embeddings and performs similarity search on MongoDB vector store. Uses OpenAI’s GPT-4o-mini model with retrieval-augmented generation to produce direct, context-aware answers. Maintains short-term conversation context using a memory buffer node. Setup Setting up vector embeddings Authenticate Google Docs and connect your Google Docs URL containing the product documentation you want to index. Authenticate MongoDB Atlas and connect the collection where you want to store the vector embeddings. Create a search index on this collection to support vector similarity queries. Ensure the index name matches the one configured in n8n (data_index). See the example MongoDB search index template below for reference. Setting up chat Configure the AI system prompt in the “Knowledge Base Agent” node to reflect your company’s tone, answering style, and any business rules. Update the workflow description and instructions to help users understand the chat’s purpose and capabilities. Connect the MongoDB collection used for vector search in the chat workflow and update the vector search index if needed to match your setup. Make sure Both MongoDB nodes (in ingestion and chat workflows) are connected to the same collection, with: An embedding field storing vector data, Relevant metadata fields (e.g., document ID, source), and The same vector index name configured (e.g., data_index). Search Index Example: { "mappings": { "dynamic": false, "fields": { "_id": { "type": "string" }, "text": { "type": "string" }, "embedding": { "type": "knnVector", "dimensions": 1536, "similarity": "cosine" }, "source": { "type": "string" }, "doc_id": { "type": "string" } } } }

Company Website Chatbot Agent Overview This workflow implements a modular Website AI Chatbot Assistant capable of handling multiple types of customer interactions autonomously. Instead of relying on a single large agent to handle all logic and tools, this system routes user queries to specialized sub-agents—each dedicated to a specific function. By using a manager-style orchestration layer, this approach prevents overloading a single AI model with excessive context, leading to cleaner routing, faster execution, and easier scaling as your automation needs grow. How It Works 1. Chat Trigger The flow is initiated when a chat message is received via the website widget. 2. Manager Agent (Ultimate Website AI Assistant) The central LLM-based agent is responsible for parsing the message and deciding which specialized sub-agent to route it to. It uses an OpenAI GPT model for natural language understanding and a lightweight memory system to preserve recent context. 3. Sub-Agent Routing calendarAgent: Handles availability checks and books meetings on connected calendars. RAGAgent: Searches company documentation or FAQs to provide accurate responses from your internal knowledge base. ticketAgent: Forwards requests to human support by generating and sending support tickets to a designated email. Setup Instructions Embed the Chatbot Use a custom HTML widget or script to embed the chatbot interface on your website. Connect the frontend to the webhook that triggers the When chat message received node. Configure Your OpenAI Key Insert your API key in the OpenAI Chat Model node. Adjust the model parameters for temperature, max tokens, etc., based on how formal or creative you want the bot to be. Customize Sub-Agents calendarAgent: Connect to your Google or Outlook calendar. RAGAgent: Link to a vector store or document database via API or native integration. ticketAgent: Set the destination email and format for ticket generation (e.g. via SendGrid or SMTP). Deploy in Production Host on n8n Cloud or your self-hosted instance. Monitor usage through the Executions tab and refine prompts based on user behavior. Benefits Modular system with dedicated logic per function Reduces token bloat by offloading complexity to sub-agents Easy to scale by adding more tools (e.g. CRM, analytics) Fast and responsive user experience for customers on your site Cleaner code structure and easier debugging

👤 Who is this for? This workflow is ideal for social media managers, personal brand strategists, ghostwriters, and founders who want to post regularly on LinkedIn without spending hours writing from scratch. It’s also useful for marketing agencies and assistants looking to automate consistent post creation using curated articles as source material. 🧩 What problem does this workflow solve? Manually reading multiple articles, extracting key insights, and writing a clean, professional LinkedIn post is a time-consuming process. This workflow automates everything: from pulling topics, finding related articles, summarizing them using AI, and even generating a matching image to accompany the post. It ensures faster content turnaround, more consistency, and less manual effort. 🔁 What this workflow does This workflow starts manually and retrieves one topic marked as “To do” from a Google Sheet. That topic is used as a search term for Dumpling AI’s search endpoint, which scrapes and returns the top three article contents related to the topic. These articles are sent to a LangChain agent powered by GPT-4o, which analyzes and summarizes the content into a LinkedIn post in a friendly, insightful tone. It also generates an image prompt for the post. After generating the post and image prompt, the data is extracted using a Set node. The prompt is sent to Dumpling AI’s image generation endpoint, which returns an image URL. Finally, the post text, image prompt, image URL, and status update (“created”) are saved back to the original row in Google Sheets. 🛠️ Workflow Breakdown Manual Trigger – Starts the automation. Google Sheets (Get Topic) – Searches for the first row in your content pipeline sheet where the “status” is “To do”. HTTP Request (Dumpling AI Search) – Uses the topic as a search query to pull 3 article contents using Dumpling AI’s API. Set LangChain GPT Model – Defines GPT-4o as the LLM for the LangChain Agent. LangChain Agent (Summarize & Generate) – Summarizes all 3 articles and generates a LinkedIn post and a related image prompt. Set (Extract Data) – Extracts postText and imagePrompt from the LangChain agent output. HTTP Request (Dumpling Image Gen) – Sends imagePrompt to Dumpling AI’s image generation endpoint. Update Google Sheets – Writes the post, image prompt, and image URL back to the sheet and changes the row status to “created”. ⚙️ Setup Instructions Dumpling AI Sign up at Dumpling AI Get your API key and connect it in the HTTP Request nodes (Search and Image endpoints) Use the /search endpoint to retrieve article content Use the /generate-image endpoint to create the image Google Sheets Create a spreadsheet with columns: topic, status, postText, imagePrompt, imageURL Add sample topics and set their status to To do LangChain (GPT-4o) Connect your OpenAI credentials to n8n Make sure GPT-4o is available in your OpenAI account Use the LangChain node to process multi-input summarization and generate a social media caption Customize the Prompt (Optional) Adjust the Set node to tweak the input format sent to the LangChain agent Add constraints like tone, hashtags, or emojis to fit your brand style 🧠 How to Customize This Workflow Change the content source (RSS feed, Notion DB, etc.) instead of Google Sheets Add a scheduler node to run this automatically every morning or weekly Use Airtable instead of Google Sheets for more control and filtering Send the final post to LinkedIn using the Buffer or LinkedIn API Add a Telegram or Slack notification when new content is ready for approval

🧾 Generate Project Summary from meeting transcript Who’s it for 🤝 Project managers looking to automate client meeting summaries Client success teams needing structured deliverables from transcripts Agencies and consultants who want consistent, repeatable documentation How it works / What it does ⚙️ Trigger: Manual or webhook trigger kicks off the workflow. Get meeting transcript: Reads the raw transcript from a specified Google Docs file. Generate summary: Sends transcript + instructions to OpenAI (gpt-4.1-mini) to produce a structured project summary. Convert to HTML: Transforms the LLM-generated Markdown into styled HTML. Prepare request: Wraps HTML and metadata into a multipart request body. Create Google Doc: Uploads the new “Project Summary” document into your Drive folder. How to set up 🛠️ Credentials Google Docs & Drive OAuth2 credentials OpenAI API key (gpt-4.1-mini) Nodes configuration Manual Trigger / webhook node Google Docs “Get meeting transcript” node: set documentURL AI Chat Model node: select gpt-4.1-mini Markdown node: enable tables & emoji Google Drive “CreateGoogleDoc” node: set target folder ID Paste in your IDs Update documentURL to your transcript doc Update google_drive_folder_id in the Set node Execute Click “Execute Workflow” or call via webhook Requirements 📋 n8n Google OAuth2 scopes for Docs & Drive OpenAI account with GPT-4.1-mini access A Google Drive folder to store summaries How to customize ✨ Output format**: Edit the Markdown prompt in the ChainLlm node to adjust headings or tone Timeline section**: Extend LLM prompt template with your own phase table Styling**: Tweak inline CSS in the Code node (Prepare_Request) for fonts or margins Trigger**: Swap Manual Trigger for HTTP/Webhook trigger to integrate with other tools Language model**: Upgrade to a different model by changing model.value in the AI node

This n8n workflow demonstrates how to transform natural language date and time expressions into structured data with 96%+ accuracy. Parse complex expressions like "early next July", "2 weeks after project launch", or "end of Q3" into precise datetime objects with confidence scoring, timezone intelligence, and business rules validation for any automation workflow. Good to know Achieves 96%+ accuracy on complex natural language date expressions At time of writing, this is the most advanced open-source date parser available Includes AI learning that improves over time with user corrections Supports 6 languages with auto-detection (English, Spanish, French, German, Italian, Portuguese) Sub-millisecond response times with intelligent caching Enterprise-grade with business intelligence and timezone handling How it works Natural Language Input**: Receives date expressions via webhook, form, email, or chat AI-Powered Parsing**: Your world-class date parser processes the text through: 50+ custom rule patterns for complex expressions Multi-language auto-detection and smart translation Confidence scoring (0.0-1.0) for AI decision-making Ambiguity detection with helpful suggestions Business Intelligence**: Applies enterprise rules automatically: Holiday calendar awareness (US + International) Working hours validation and warnings Business day auto-adjustment Timezone normalization (IANA format) Smart Scheduling**: Creates calendar events with: Structured datetime objects (start/end times) Confidence metadata for workflow decisions Alternative interpretations for ambiguous inputs Rich context for follow-up actions Integration Ready**: Outputs connect seamlessly to: Google Calendar, Outlook, Apple Calendar CRM systems (HubSpot, Salesforce) Project management tools (Notion, Asana) Communication platforms (Slack, Teams) How to use The webhook trigger receives natural language date requests from any source Replace the MCP server URL with your deployed date parser endpoint Configure timezone preferences for your organization Customize business rules (working hours, holidays) in the parser settings Connect calendar integration nodes for automatic event creation Add notification workflows for scheduling confirmations Use Cases Meeting Scheduling**: "Schedule our quarterly review for early Q3" Project Management**: "Set deadline 2 weeks after product launch" Event Planning**: "Book venue for the weekend before Labor Day" Personal Assistant**: "Remind me about dentist appointment next Tuesday morning" International Teams**: "Team standup tomorrow morning" (auto-timezone conversion) Seasonal Planning**: "Launch campaign in late spring 2025" Requirements Natural Language Date Parser MCP server (provided code) Webhook endpoint or form trigger Calendar integration (Google Calendar, Outlook, etc.) Optional: Slack/Teams for notifications Optional: Database for learning pattern storage Customizing this workflow Multi-language Support**: Enable auto-detection for global teams Business Rules**: Configure company holidays and working hours Learning System**: Enable AI learning from user corrections Integration Depth**: Connect to your existing calendar and CRM systems Confidence Thresholds**: Set minimum confidence levels for auto-scheduling Ambiguity Handling**: Route unclear dates to human review or clarification requests Sample Input/Output Input Examples: "early next July" "2 weeks after Thanksgiving" "next Wednesday evening" "Q3 2025" "mañana por la mañana" (Spanish) "first thing Monday" Rich Output: { "parsed": [{ "start": "2025-07-01T00:00:00Z", "end": "2025-07-10T23:59:59Z", "timezone": "America/New_York" }], "confidence": 0.95, "method": "custom_rules", "business_insights": [{ "type": "business_warning", "message": "Selected date range includes July 4th holiday" }], "predictions": [{ "type": "time_preference", "suggestion": "You usually schedule meetings at 10 AM" }], "ambiguities": [], "alternatives": [{ "interpretation": "Early July 2026", "confidence": 0.15 }], "performance": { "cache_hit": true, "response_time": "0.8ms" } } Why This Workflow is Unique World-Class Accuracy**: 96%+ success rate on complex expressions AI Learning**: Improves over time with user feedback Global Ready**: Multi-language and timezone intelligence Business Smart**: Enterprise rules and holiday awareness Performance Optimized**: Sub-millisecond cached responses Context Aware**: Provides confidence scores and alternatives for AI decision-making Transform your scheduling workflows from rigid form inputs to natural, conversational date requests that your users will love!

Automate Customer Feedback Analysis with Google Sheets, WhatsApp, and Email Introduction: Drowning in Data, Starving for Insight? Imagine this: Your team launches a new feature. Feedback starts pouring in emails, support tickets, social media mentions, and survey responses. You know gold is buried in there, but manually reading, tagging, and summarising hundreds, maybe thousands, of comments? It takes days, maybe weeks. By the time you have a clear picture, the moment might have passed. Sounds exhausting, right? What if you could have an AI assistant tirelessly working 24/7, instantly analysing every piece of feedback the moment it arrives? This isn't science fiction anymore. AI-powered automation can transform this slow, manual chore into a real-time insight engine, giving you the pulse of your customer base almost instantly. Let's explore how. What's the Goal? Understanding the Workflow Objective The core challenge is transforming raw, unstructured customer feedback into actionable intelligence quickly and efficiently. The Problem: Manual Overload: Sifting through vast amounts of feedback manually is incredibly time-consuming and prone to human error or bias. Delayed Insights: The lag between receiving feedback and understanding it means missed opportunities and slow responses to critical issues. Inconsistent Analysis: Different team members might interpret or categorize feedback differently, leading to unreliable trend spotting. The AI Solution: Automated Data Collection: Connects directly to feedback sources (surveys, social media, review sites, helpdesks). AI-Powered Analysis: Uses Large Language Models (LLMs) like GPT-4 or Claude to analyze sentiment, extract key topics, and summarize comments. Intelligent Categorization: Automatically tags feedback based on predefined or dynamically identified themes (e.g., "bug report," "feature request," "pricing issue"). Real-time Reporting: Pushes structured insights into dashboards, databases, or triggers notifications for immediate awareness. Outcome: You move from reactive problem-solving based on stale data to proactive, strategic decisions driven by a near real-time understanding of customer sentiment and needs. Why Does It Matter? Achieving 100X Productivity and Efficiency Look, automating feedback isn't just about saving time; it's about scaling your ability to listen and respond smarter, not harder. When you leverage AI, the gains aren't incremental - they're exponential. Here’s why this is a game changer: Blazing Speed: Analyse feedback 100x Faster (or more!) than manual methods. Insights appear in minutes or hours, not days or weeks. Unhuman Scalability: Process virtually unlimited volumes of feedback without needing to scale your human team proportionally. AI doesn't get tired or bored. Consistent Accuracy: AI applies analysis rules consistently, reducing human bias and ensuring reliable categorisation and sentiment scoring over time. Proactive Trend Spotting: Identify emerging issues or popular requests much earlier by analysing aggregated data automatically. Spot patterns humans might miss. Free Up Your Team: Let your talented team focus on acting on insights – improving products, fixing issues, engaging customers – instead of drowning in data entry. How It Works: AI Automation Step by Step Getting this set up is more straightforward than you might think, especially with tools like n8n acting as the central hub. Automated Feedback Triggering CRM/Website Event Node Trigger feedback requests after: Purchases (eCommerce) Support ticket resolution Feature usage (SaaS) Time-Based Node Schedule recurring NPS surveys Customer health check-ups Chat App Node (WhatsApp/Telegram/Messenger) Send conversational feedback prompts: "How was your recent experience with [specific interaction]?" Multi-Channel Feedback Collection Email Node (SendGrid/Mailchimp) Send personalized feedback requests Embed 1-5 rating widgets SMS Node (Twilio) Short mobile surveys: "Reply 1-5: How satisfied with your purchase?" Webhook Node Capture in-app feedback Process chatbot responses Social Media Node Monitor Twitter/X, Instagram mentions Analyze comments for unsolicited feedback AI-Powered Real-Time Analysis OpenAI/ChatGPT Node (Sentiment Analysis) Prompt: "Analyze sentiment (positive/neutral/negative) and key themes from: [customer feedback]" Output fields: Sentiment score (1-5) Urgency flag (high/medium/low) Key topics (billing, support, product, etc.) Translation Node (Optional) Convert multilingual feedback into a consistent language Instant AI Response System Conditional Node (Routing Logic) Positive feedback → Send thank-you + referral ask Neutral feedback → Follow-up question for details Negative feedback → Escalate to the human team AI Response Generator Node Prompt: "Create a personalized response to [feedback type] about [topic] with sentiment [score]" Adjust tone (professional/friendly/empathetic) Escalation Node Route critical issues to the support team with full context Automated Insights & Alerts Dashboard Node Real-time sentiment tracking Emerging issue detection Alert Node (Slack/Teams/Email) Notify teams of negative trends: "3+ complaints about checkout flow in the past hour!" Report Node Auto-generate weekly/monthly summaries: "Top 5 customer pain points this week" Product Board Integration Auto-create feature requests Prioritize based on feedback volume Tools of the Trade: AI & Automation Tech Stack You don't need a massive, complex tech stack. Focus on a few core, powerful tools: n8n: The workflow automation platform. This is the 'glue' that connects everything and orchestrates the process without needing deep coding knowledge. Honestly, it's incredibly versatile. OpenAI (GPT-4/GPT-4o): State-of-the-art LLM for high-quality text analysis, summarization, and classification. Great for complex understanding. Anthropic (Claude 3 Sonnet/Opus): Another top-tier LLM, known for strong performance in analysis and handling large contexts. Often, a great alternative or complement to GPT models. Feedback Sources APIs: Connectors for where your feedback lives (e.g., Typeform, SurveyMonkey, Twitter API, Zendesk API, Google Play/App Store review APIs). Data Storage/Destination: Where the processed insights go (e.g., Google Sheets, Airtable, Notion, PostgreSQL database, BigQuery). (Optional) Visualization Tool: Tools like Metabase, Grafana, Looker Studio, or Power BI to create dashboards from your structured feedback data. What's the Cost? Estimated Budget Let's talk investment. You're mainly looking at: Setup Costs: Primarily your time (or a consultant's) to design and build the initial workflow in n8n. Depending on complexity, this could range from a few hours to a few days. No major software licenses are usually needed upfront if using self-hosted n8n or starting with free/low-tier cloud plans. AI API Calls: You pay per usage to OpenAI/Anthropic. Costs depend heavily on volume but can start from $20-$50/month for moderate usage and scale up. Newer models are getting more cost-effective. n8n Hosting: Free if self-hosted (requires a server), or tiered cloud pricing starting around $20/month. Feedback Source APIs: Some platforms might have API access costs or rate limits on free tiers. Total Estimated Monthly Cost: For many businesses, ongoing costs can range from $50 - $500+ per month, highly dependent on feedback volume and AI model choice. The Return on Investment (ROI) is typically rapid. Consider the hours saved from manual analysis, the value of faster issue resolution, preventing churn, and the benefits of making product decisions based on real-time data. It often pays for itself very quickly. Who Benefits? Target Users and Industries This automated feedback loop isn't niche; it's valuable across many sectors and roles: Top Industries: SaaS (Software as a Service): Understanding user friction, feature requests, bug reports. E-commerce & Retail: Analyzing product reviews, post-purchase surveys, and support chats. Hospitality & Travel: Processing guest reviews, survey feedback. Mobile Apps: Monitoring app store reviews, in-app feedback. Financial Services: Gauging customer satisfaction with services, identifying pain points. Key Roles: Product Managers: Prioritizing features, understanding user needs, tracking launch reception. Customer Experience (CX) / Success Managers: Monitoring customer health, identifying churn risks, and improving support processes. Marketing Teams: Understanding brand perception, campaign feedback, and voice of the customer. Support Leads: Identifying recurring issues, measuring support quality, spotting training needs. This approach works for businesses of all sizes, from startups wanting to stay lean and agile to large enterprises needing to manage massive feedback volumes. How to use workflow? Importing a workflow in n8n is a straightforward process that allows you to use pre-built or shared workflows to save time. Below is a step-by-step guide to import a workflow in n8n, based on the official documentation and community resources. Steps to Import a Workflow in n8n 1. Obtain the Workflow JSON Source the Workflow:** Workflows are typically shared as JSON files or code snippets. You might receive them from: The n8n community (e.g., n8n.io workflows page). A colleague or tutorial (e.g., a .json file or copied JSON code). Exported from another n8n instance (see export instructions below if needed). Format:** Ensure you have the workflow in JSON format, either as a file (e.g., workflow.json) or as text copied to your clipboard. 2. Access the n8n Workflow Editor Log in to n8n:** Open your n8n instance (via n8n Cloud or your - self-hosted instance). Navigate to the Workflows tab in the n8n dashboard. Open a New Workflow:** Click Add Workflow to create a blank workflow, or open an existing workflow if you want to merge the imported workflow. 3. Import the Workflow Option 1: Import via JSON Code (Clipboard): In the n8n editor, click the three dots (⋯) in the top-right corner to open the menu. Select Import from Clipboard. Paste the JSON code of the workflow into the provided text box. Click Import to load the workflow into the editor. Option 2: Import via JSON File: In the n8n editor, click the three dots (⋯) in the top-right corner. Select Import from File. Choose the .json file from your computer. Click Open to import the workflow. Note: If the workflow includes nodes for apps requiring credentials (e.g., Google Sheets), you’ll need to configure those credentials separately after importing.

Use case This automation is for teams working in Notion. When you have a lot of back and forth in the comment section, it’s easy to lose track of what is going on in the conversation. This automation relies on AI to generate a summary of the comment section. How it works Every hour (the trigger can be adapted to your need and usecase), the automation checks if new comments have been added to the pages of your Notion database. If there are new comments, the comments are sent to an AI model to write a summary. The summary is then added to a predefined page property. The automation also updates a “Last execution” property. This prevents to re-generate the AI summary when no new comments have been received. Setup Define your Notion variables: Notion database, property that will hold the AI summary, property that will hold the last execution date of the automation. Set up your Notion credentials. Set up your AI model credentials (API key). How to adjust it to your needs Use the LLM model of your choice. In this template, I used Gemini but you can easily replace it by ChatGPT, Claude, etc. Adapt the prompt to your use case to get better summaries: specify the maximum number of characters, give an example, etc. Adapt the trigger to your needs. You could use Notion webhooks as trigger in order to run the automation only when a new comment is added (this setup is advised if you’re on n8n cloud version).

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.

This workflow creates/updates/deletes a Notion database page when an issue is created/updated/deleted in Jira. Subsequent updates to the issue's title or status in Jira are updated in the Notion database. If you require more fields to send to Notion, this template is easily extendible which will be described in setup. The Notion database will require setup before the workflow can be used. Prerequisites Notion account and Notion credentials. Jira account and Jira credentials. How it works When a new issue is created in Jira, the workflow creates a new page in the Notion database will all the required fields. When the issue's title or status is updated in Jira, the workflow updates the specific Notion database page identified by the "Issue Key" field in Notion. If the status in Jira is set to "Done", the workflow will mark the Notion database page "Done" field as true. When the issue is deleted in Jira, the workflow archives the Notion database page. Setup This workflow requires that you set up a Notion database. To do so, follow the steps below: In Notion, create a new database. Add the following columns to the database: Done (with type "Checkbox") Title (renamed from "Name") Status (with the following options: "To Do", "In Progress", "Done") Link (with type "URL") Issue ID (with type "Number") Issue Key (with type "Text") Add any other fields you require to the database. Your database should look something like this Share the database to n8n. By default, the workflow will fill all the fields provided above, except for any other additional fields you add.

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.