Workflow Templates

How it works This workflow automates the process of handling incoming emails by: Receiving emails via IMAP. Converting the email to Markdown for better AI understanding. Summarizing the email using an AI model. Drafting a professional reply with AI, based on the summary. Requesting human approval for the AI-generated response. Sending the approved reply back to the original sender. Set up steps Estimated time: 10–20 minutes (excluding credential setup) What you’ll need: IMAP credentials for your email inbox SMTP credentials for sending emails OpenAI (or compatible) API key for AI steps Setup outline: Add your IMAP and SMTP credentials to the workflow. Connect your OpenAI (or compatible) account for AI summarization and reply generation. Deploy the workflow in n8n and activate it. Test by sending an email to your connected inbox. Note: Detailed configuration tips and explanations are included as sticky notes inside the workflow for each step.

This workflow contains community nodes that are only compatible with the self-hosted version of n8n. Who is this for? IT teams and support organizations looking to automate Level 1 support with AI-powered assistance while maintaining proper ticket management workflows. What problem does this solve? Eliminates repetitive manual support tasks by providing instant, context-aware assistance that references organizational knowledge and creates structured tickets when needed. What this workflow does RAG Pipeline**: Processes PDF/CSV documents into searchable vector database Intelligent Slack Bot**: This AI-helpdesk assistant handles support requests with thread-aware conversations Vector Knowledge Search**: Searches embedded knowledge base articles and historical case data JIRA Integration**: Creates, searches, and manages support tickets automatically Emoji Reactions**: Users can trigger actions (create tickets, escalate) via emoji reactions Requirements Required Accounts: n8n Cloud or self-hosted instance Slack workspace with admin access Supabase account (vector database) JIRA Cloud instance OpenAI API key Technical Prerequisites: Basic n8n workflow knowledge Slack app creation experience Understanding of vector databases Setup Steps 1. Slack App Configuration Create new Slack app with Bot Token Scopes: app_mentions:read, channels:history, channels:read, groups:history, groups:read, im:history, im:read, mpim:history, mpim:read, users:read Configure Event Subscriptions: app_mention, message.channels, message.groups, reaction_added Set Request URL to your n8n Slack Trigger webhook 2. Supabase Vector Database Setup Create new Supabase project Enable pgvector extension Create documents table with vector column (1536 dimensions for OpenAI embeddings) Configure RLS policies for secure access 3. JIRA Configuration Generate API token from JIRA Cloud Create helpdesk project with appropriate issue types Note project ID and issue type IDs for workflow configuration 4. n8n Workflow Configuration Import workflow and configure credentials Update Slack channel IDs in trigger nodes Set OpenAI API key in all OpenAI nodes Configure Supabase connection in vector store nodes Update JIRA project settings in MCP server nodes 5. Knowledge Base Data Format Supported file formats: PDF, CSV CSV Structure: Structure your data with columns, but not limited to, Ticket#, Issue Description, Issue Summary, Resolution Provided, Case Status, Contact User PDF Content: Technical documentation, troubleshooting guides, policy documents Upload documents via the form trigger to automatically embed in vector database. Customization Options AI Agent Behavior Modify system prompt in AIHelpdesk Agent node Adjust conversation memory window (default: 20 messages) Change AI model (GPT-4o, GPT-3.5-turbo, etc.) Reaction Mappings Customize emoji-to-action mappings in Reaction Handler code Add new reaction types for department-specific workflows Configure escalation rules and priority levels JIRA Integration Customize ticket templates and fields Add auto-assignment rules based on issue type Configure SLA and priority mappings Vector Search Adjust similarity thresholds for knowledge retrieval Modify search result limits and relevance scoring Add metadata filtering for departmental knowledge bases Advanced Features Thread-aware conversation memory Automatic bot loop prevention Context-preserving ticket creation Multi-modal file processing (PDF + CSV) Scalable MCP architecture for tool integration Use Cases Level 1 IT Support**: Automate common troubleshooting workflows Employee Onboarding**: Answer policy and procedure questions Internal Help Desk**: Route and track internal service requests Knowledge Management**: Make organizational knowledge searchable and actionable Template includes Complete Slack integration with thread support RAG pipeline for document processing Vector similarity search implementation JIRA ticket lifecycle management Emoji reaction-based user interactions Comprehensive error handling and validation

This workflow processes emails received in Gmail and saves detailed information about each email to a MySQL database. Before using, you need to have: Gmail credentials MySQL database credentials A table in your database with the following columns: messageId (Gmail message ID) threadId snippet sender_name (nullable) sender_email recipient_name (nullable) recipient_email subject (nullable) How it works: The Gmail Trigger listens for new emails (checked every minute). A Code Node extracts the following fields from each email: Sender's name and email Recipient's name and email The MySQL Node inserts the extracted data into your database. If an entry with the same sender email already exists, it updates the record with the new details. How to use: Make sure your database table has all required columns listed above. Select the appropriate table and configure the matching column (e.g., id) to avoid duplicates. Customizing this Workflow: You can further modify the workflow to store attachments, timestamps, labels, or any other Gmail metadata as needed.

This workflow contains community nodes that are only compatible with the self-hosted version of n8n. How it works? This workflow is an intelligent SEO analysis pipeline that ethically scrapes blog content and performs comprehensive SEO evaluation using AI. It receives blog URLs via webhook, validates permissions through robots.txt compliance, extracts content, and generates detailed SEO insights across four strategic dimensions: Content Optimization, Keyword Strategy, Technical SEO, and Backlink Building potential. The system prioritizes ethical web scraping by checking robots.txt permissions before proceeding, ensuring compliance with website policies. Upon successful analysis, it returns a structured JSON report with actionable SEO recommendations, performance scores, and optimization strategies. Technical Specifications Trigger: HTTP POST webhook Processing Time: 30-60 seconds depending on content size AI Model: GPT-4.1 minimum with specialized SEO analysis prompt. Output Format: Structured JSON Error Handling: Graceful failure with informative messages Compliance: Respects website robots.txt policies

This workflow is designed to analyze YouTube videos by extracting their transcripts, summarizing the content using AI models, and sending the analysis via email. This workflow is ideal for content creators, marketers, or anyone who needs to quickly analyze and summarize YouTube videos for research, content planning, or educational purposes. How It Works: Trigger: The workflow starts with a manual trigger, allowing you to test it by clicking "Test workflow." You can also set a YouTube video URL manually or dynamically. YouTube Video ID Extraction: The workflow extracts the YouTube video ID from the provided URL using a custom JavaScript function. This ID is necessary for fetching the transcript. Transcript Generation: The video ID is sent via an HTTP request to generate the transcript. You need to replace APIKEY with a free API key from the service. Transcript Validation: The workflow checks if a transcript exists for the video. If a transcript is available, it proceeds; otherwise, it stops. Full Text Extraction: If a transcript exists, the workflow combines all transcript segments into a single text variable for further analysis. AI-Powered Analysis: The full transcript is passed to an AI model (DeepSeek, OpenAI, or OpenRouter) for analysis. The AI generates a structured summary, including a title and key points, formatted in markdown. Email Notification: The analysis results (title and summary) are sent via email using SMTP credentials. The email contains the structured summary of the video. Set Up Steps: YouTube Transcript API: Obtain a free API key from youtube-transcript.io and replace APIKEY in the "Generate transcript" node with your key. AI Model Configuration: Configure the AI model nodes (DeepSeek, OpenAI, or OpenRouter) with the appropriate API credentials. You can choose one or multiple models depending on your preference. Email Setup: Configure the "Send Email" node with your SMTP credentials (e.g., Gmail, Outlook, or any SMTP service). Ensure the email settings are correct to send the analysis results. Key Features: Free Tools: Uses **youtube-transcript.io for free transcript generation. AI Models**: Supports multiple AI models (DeepSeek, OpenAI, OpenRouter) for flexible analysis. Email Notifications**: Sends the analysis results directly to your inbox. Customizable**: Easily adapt the workflow to analyze different videos or use different AI models.

Turn BBC News Articles into Podcasts using Hugging Face and Google Gemini Effortlessly transform BBC news articles into engaging podcasts with this automated n8n workflow. Who is this for? This template is perfect for: Content creators** who want to quickly produce podcasts from current events. Students** looking for an efficient way to create audio content for projects or assignments. Individuals** interested in generating their own podcasts without technical expertise. Setup Information Install n8n: If you haven't already, download and install n8n from n8n.io. Import the Workflow: Copy the JSON code for this workflow and import it into your n8n instance. Configure Credentials: Gemini API: Set up your Gemini API credentials in the workflow's LLM nodes. Hugging Face Token: Obtain an access token from Hugging Face and add it to the HTTP Request node for the text-to-speech model. Customize (Optional): Filtering Criteria: Adjust the News Classifier node to fine-tune the selection of news articles based on your preferences. Output Options: Modify the workflow to save the generated audio file to a cloud storage service or publish it to a podcast hosting platform. Prerequisites An active n8n instance. Basic understanding of n8n workflows (no coding required). API credentials for Gemini and a Hugging Face account with an access token. What problem does it solve? This workflow eliminates the manual effort involved in creating podcasts from news articles. It automates the entire process, from fetching and filtering news to generating the final audio file. What are the benefits? Time-saving:** Create podcasts in minutes, not hours. Easy to use:** No coding or technical skills required. Customizable:** Adapt the workflow to your specific needs and preferences. Cost-effective:** Leverage free or low-cost services like Gemini and Hugging Face. How does it work? The workflow fetches news articles from the BBC website. It filters articles based on their suitability for a podcast. It extracts the full content of the selected articles. It uses Gemini LLM to create a podcast script. It converts the script to speech using Hugging Face's text-to-speech model. The final podcast audio is ready for use. Nodes in the Workflow Fetch BBC News Page: Retrieves the main BBC News page. News Classifier: Categorizes news articles using Gemini LLM. Fetch BBC News Detail: Extracts detailed content from suitable articles. Basic Podcast LLM Chain: Generates a podcast script using Gemini LLM. HTTP Request: Converts the script to speech using Hugging Face. Add Story I'm excited to share this workflow with the n8n community and help content creators and students easily produce engaging podcasts! Additional Tips Explore the n8n documentation and community resources for more advanced customization options. Experiment with different filtering criteria and LLM prompts to achieve your desired podcast style.

Search LinkedIn companies and add them to Airtable CRM Who is this for? This template is ideal for sales teams, business development professionals, and marketers looking to build a robust prospect database without manual LinkedIn research. Perfect for agencies, consultants, and B2B companies targeting specific business profiles. What problem does this workflow solve? Manually researching companies on LinkedIn and adding them to your CRM is time-consuming and error-prone. This automation eliminates the tedious process of finding, qualifying, and importing prospects into your database. What this workflow does This workflow automatically searches for companies on LinkedIn based on your criteria (keywords, size, location), retrieves detailed information about each company, filters them based on quality indicators (follower count and website availability), and adds new companies to your Airtable CRM while preventing duplicates. Setup Create a Ghost Genius API account and get your API key Configure HTTP Request nodes with Header Auth credentials (Name: "Authorization", Value: "Bearer your_api_key") Create an Airtable base named "CRM" with columns: name, website, LinkedIn, id, etc. Set up your Airtable credentials following n8n documentation Add your company search selection criteria to the “Set Variables” node. How to customize this workflow Modify search parameters in the "Set Variables" node to target different industries, locations, or company sizes Adjust the follower count threshold in the "Filter Valid Companies" node based on your qualification criteria Customize the Airtable fields mapping in the "Add Company to CRM" node to match your database structure Add notification nodes (Slack, Email) to alert you when new companies are added

🧩 Jamf Smart Group Membership to Slack Automatically export Jamf smart group membership to Slack in CSV format. Perfect for IT and security teams who need fast visibility into device grouping—without manually logging into Jamf. Slack automatically parses the CSV, making it viewable directly in the chat—no download required. ✅ Prerequisites • A Jamf Pro API key with permissions to read smart groups and computer details • A Slack app or incoming webhook URL with permission to post messages to your desired channel 🔍 How it works • Manually trigger the flow or connect it to a webhook • Fetch the list of smart group IDs (set manually in the workflow) • Loop over each group to get its members • Use a sub-workflow to fetch detailed info for each device • Convert the member list to CSV • Post the CSV file to a Slack channel ⚙️ Set up steps • Takes ~5–10 minutes to configure • Set your Jamf BaseURL and group IDs in the Set nodes • Add your Jamf Pro API credentials to the HTTP Request nodes • Provide your Slack webhook token or channel ID in the Slack node • Optional: Customize CSV fields or formatting as needed

CallForge - AI Gong Transcript PreProcessor Transform your Gong.io call transcripts into structured, enriched, and AI-ready data for better sales insights and analytics. Who is This For? This workflow is designed for: ✅ Sales teams looking to automate call transcript formatting. ✅ Revenue operations (RevOps) professionals optimizing AI-driven insights. ✅ Businesses using Gong.io that need structured, enriched call transcripts for better decision-making. What Problem Does This Workflow Solve? Manually processing raw Gong call transcripts is inefficient and often lacks essential context for AI-driven insights. With CallForge, you can: ✔ Extract and format Gong call transcripts for structured AI processing. ✔ Enhance metadata using sales data from Salesforce. ✔ Classify speakers as internal (sales team) or external (customers). ✔ Identify external companies by filtering out free email domains (e.g., Gmail, Yahoo). ✔ Enrich customer profiles using PeopleDataLabs to identify company details and locations. ✔ Prepare transcripts for AI models by structuring conversations and removing unnecessary noise. What This Workflow Does 1. Retrieves Gong Call Data Calls the Gong API to extract call metadata, speaker interactions, and collaboration details. Fetches call transcripts for AI processing. 2. Processes and Cleans Transcripts Converts call transcripts into structured, speaker-based dialogues. Assigns each speaker as either Internal (Sales Team) or External (Customer). 3. Extracts Company Information Retrieves Salesforce data** to match customers with existing sales opportunities. Filters out free email domains* to determine the *customer’s actual company domain**. Calls the PeopleDataLabs API to retrieve additional company data and location details. 4. Merges and Enriches Data Combines Gong metadata, Salesforce customer details and insights**. Ensures all necessary data is available for AI-driven sales insights. 5. Final Formatting for AI Processing Merges all call transcript data into a single structured format for AI analysis. Extracts the final cleaned, enriched dataset for further AI-powered insights. How to Set Up This Workflow 1. Connect Your APIs 🔹 Gong API Access – Set up your Gong API credentials in n8n. 🔹 Salesforce Setup – Ensure API access if you want customer enrichment. 🔹 PeopleDataLabs API – Required to retrieve company and location details based on email domains. 🔹 Webhook Integration – Modify the webhook call to push enriched call data to an internal system. CallForge - 01 - Filter Gong Calls Synced to Salesforce by Opportunity Stage CallForge - 02 - Prep Gong Calls with Sheets & Notion for AI Summarization CallForge - 03 - Gong Transcript Processor and Salesforce Enricher CallForge - 04 - AI Workflow for Gong.io Sales Calls CallForge - 05 - Gong.io Call Analysis with Azure AI & CRM Sync CallForge - 06 - Automate Sales Insights with Gong.io, Notion & AI CallForge - 07 - AI Marketing Data Processing with Gong & Notion CallForge - 08 - AI Product Insights from Sales Calls with Notion How to Customize This Workflow 💡 Modify Data Sources – Connect different CRMs (e.g., HubSpot, Zoho) instead of Salesforce. 💡 Expand AI Analysis – Add another AI model (e.g., OpenAI GPT, Claude) for advanced conversation insights. 💡 Change Speaker Classification Rules – Adjust internal vs. external speaker logic to match your team’s structure. 💡 Filter Specific Customers – Modify the free email filtering logic to better fit your company’s needs. Why Use CallForge? 🚀 Automate Gong call transcript processing to save time. 📊 Improve AI accuracy with enriched, structured data. 🛠 Enhance sales strategy by extracting actionable insights from calls. Start optimizing your Gong transcript analysis today!

This n8n template allows you to use AI to generate logos or images which mimic visual styles of other logos or images. The model used to generate the images is Google's Imagen 3.0. With this template, users will be able to automate design and marketing tasks such as creating variants of existing designs, remixing existing assets to validate different styles and explore a range of designs which would have been otherwise too expensive and time-consming previously. How it works A form trigger is used to capture the source image to reference styles from and a prompt for the target image to generate. The source image is passed to Gemini 2.0 to be analysed and its visual style and tone extracted as a detailed description. This visual style description is then combined with the user's initial target image prompt. This final prompt is given to Imagen 3.0 to generate the images. A quick webpage is put together with the generated images to present back to the user. If the user provided an email address, a copy of this HTML page will be sent. How to use Ensure the workflow is live to share the form publicly. The source image must be accessible to your n8n instance - either a public image of the internet or within your network. For best results, select a source image which has strong visual identity as these will allow the LLM to better describe it. For your prompt, refer to the imagen prompt guide found here: https://ai.google.dev/gemini-api/docs/image-generation#imagen-prompt-guide Requirements Gemini for LLM and Imagen model. Cloudinary for image CDN. Gmail for email sending. Customising this workflow Feel free to swap any of these out for tools and services you prefer. Want to fully automate? Switch the form trigger for a webhook trigger!

Video Guide I prepared a detailed guide that demonstrates the complete process of building a trading agent automation using n8n and Telegram, seamlessly integrating various functions for stock analysis. Youtube Link Who is this for? This workflow is perfect for traders, financial analysts, and developers looking to automate stock analysis interactions via Telegram. It’s especially valuable for those who want to leverage AI tools for technical analysis without needing to write complex code. What problem does this workflow solve? Many traders desire real-time analysis of stock data but lack the technical expertise or tools to perform in-depth analysis. This workflow allows users to easily interact with an AI trading agent through Telegram for seamless stock analysis, chart generation, and technical evaluation, all while eliminating the need for manual interventions. What this workflow does This workflow utilizes n8n to construct an end-to-end automation process for stock analysis through Telegram communication. The setup involves: Receiving messages via a Telegram bot. Processing audio or text messages for trading queries. Transcribing audio using OpenAI API for interpretation. Gathering and displaying charts based on user-specified parameters. Performing technical analysis on generated charts. Sending back the analyzed results through Telegram. Setup Prepare Airtable: Create simple table to store tickers. Prepare Telegram Bot: Ensure your Telegram bot is set up correctly and listening for new messages. Replace Credentials: Update all nodes with the correct credentials and API keys for services involved. Configure API Endpoints: Ensure chart service URLs are correctly set to interact with the corresponding APIs properly. Start Interaction: Message your bot to initiate analysis; specify ticker symbols and desired chart styles as required.

How it works Transform your business with intelligent deal monitoring and automated customer engagement! This AI-powered coupon aggregator continuously tracks competitor deals and creates personalized marketing campaigns that convert. Key Steps 24/7 Deal Monitoring - Automatically scans competitor websites daily for the best deals and offers Smart Customer Segmentation - Uses AI to intelligently categorize and target your customer base Personalized Offer Generation - Creates tailored coupon campaigns based on customer behavior and preferences Automated Email Marketing - Sends targeted email campaigns with personalized deals to the right customers Performance Analytics - Tracks campaign performance and provides detailed insights and reports Daily Management Reports - Delivers comprehensive analytics to management team every morning Set up steps Setup time: 10-15 minutes Configure competitor monitoring - Add target websites and deal sources you want to track Set up customer database - Connect your customer data source for intelligent segmentation Configure email integration - Connect your email service provider for automated campaigns Customize deal criteria - Define what types of deals and offers to prioritize Set up analytics tracking - Configure Google Sheets or database for performance monitoring Test automation flow - Run a test cycle to ensure all integrations work smoothly Never miss a profitable deal opportunity - let AI handle the monitoring and targeting while you focus on growth!