Workflow Templates

Who is this for? This workflow is ideal for HR teams, startups, and enterprises that want to handle employee interactions through WhatsApp and automate responses using LLM (OpenAI) and intelligent routing. What problem is this workflow solving? Managing WhatsApp messages manually can be time-consuming and error-prone. This workflow solves that by: Auto-classifying messages using LLM Routing them to the right AI-powered agent Automating leave approvals, attendance, HR FAQs, complaints, and candidate shortlisting Delivering final responses interactively via WhatsApp What this workflow does WhatsApp Trigger captures incoming messages LLM Classification analyzes message intent and outputs category (1–5) Switch Node routes the message to the correct agent: 1 → Leave Agent 2 → HR FAQ Chatbot 3 → Attendance Agent 4 → Complaint/Request Agent 5 → Shortlisting Agent Each agent performs specific tasks using tools like: Google Sheets (fetch dept head emails, JD/applicants, logs) Google Calendar (schedule meetings) Vector Search (for policy embeddings) OpenAI (transcription, classification, chatbot) Final WhatsApp Response node sends updates and interactive options to the user Setup Connect WhatsApp API (e.g., via Twilio or WhatsApp Business Cloud API) Configure OpenAI credentials Set up Google Sheets with: Employee data JD and applicants info Policy documents (for embedding) Prepare Google Calendar access Create a vector store with embedded company policy docs How to customize this workflow to your needs Update the LLM prompt to suit your company’s categories or expand to more intents Replace sample sheets with your organization’s actual data Train your own policy embeddings if needed Add/modify agents (e.g., Payroll Bot, IT Support Bot) by cloning an existing pattern Adjust the Switch Node if you add more classifications With this modular and intelligent setup, you can turn your WhatsApp into a smart HR & operations assistant powered by AI, accessible 24/7.

This workflow sends a OpenAI GPT reply when an email is received from specific email recipients. It then saves the initial email and the GPT response to an automatically generated Google spreadsheet. Subsequent GPT responses will be added to the same spreadsheet. Additionally, when feedback is given for any of the GPT responses, it will be recorded to the spreasheet, which can then be used later to fine-tune the GPT model. Prerequisites OpenAI credentials Google credentials How it works This workflow is essentially a two-in-one workflow. It triggers off from two different nodes and have very different functionality from each trigger. The flow triggered from On email received node is as follows: Triggers off on the On email received node. Extract the email body from the email. Generate a response from the email body using the OpenAI node. Reply to the email sender using the Send reply to recipient node. A feedback link is also included in the email body which will trigger the On feedback given node. This is used to fine-tune the GPT model. Save the email body and OpenAI response to a Google Sheet. If a sheet does not exist, it will be created. The flow triggered from On feedback given node is as follows: Triggers off when a feedback link is clicked in the emailed GPT response. The feedback, either positive or negative, for that specific GPT response is then recorded to the Google Sheet.

This n8n template is designed to assist and improve customer support team member capacity by automating the resolution of long-lived and forgotten JIRA issues. How it works Schedule Trigger runs daily to check for long-lived unresolved issues and imports them into the workflow. Each Issue is handled as a separate subworkflow by using an execute workflow node. This allows parallel processing. A report is generated from the issue using its comment history allowing the issue to be classified by AI - determining the state and progress of the issue. If determined to be resolved, sentiment analysis is performed to track customer satisfaction. If negative, a slack message is sent to escalate, otherwise the issue is closed automatically. If no response has been initiated, an AI agent will attempt to search and resolve the issue itself using similar resolved issues or from the notion database. If a solution is found, it is posted to the issue and closed. If the issue is blocked and waiting for responses, then a reminder message is added. How to use This template searches for JIRA issues which are older than 7 days which are not in the "Done" status. Ensure there are some issues that meet this criteria otherwise adjust the search query to suit. Works best if you frequently have long-lived issues that need resolving. Ensure the notion tool is configured as to not read documents you didn't intend it to ie. private and/or internal documentation. Requirements JIRA for issues management OpenAI for LLM Slack for notifications Customising this workflow Why not try classifying issues as they are created? One use-case may be for quality control such as ensuring reporting criteria is adhered to, summarising and rephrasing issue for easier reading or adjusting priority.

This n8n workflow builds an appointment scheduling AI agent which can Take enquiries from prospective customers and help them book an appointment by checking appointment availability Where no appointment is booked, the Agent is able to send follow-up messages to re-engage leads. After an appointment is booked, the agent is able reschedule or even cancel the booking for the user without human intervention. For small outfits, this workflow could contribute the necessary "man-power" required to increase business sales. The sample Airtable can be found here: https://airtable.com/appO2nHiT9XPuGrjN/shroSFT2yjf87XAox 2024-10-22 Updated to Cal.com API v2. How it works The customer sends an enquiry via SMS to trigger our workflow. For this trigger, we'll use a Twilio webhook. The prospective or existing customer's number is logged in an Airtable Base which we'll be using to track all our enquries. Next, the message is sent to our AI Agent who can reply to the user and decide if an appointment booking can be made. The reply is made via SMS using Twilio. A scheduled trigger which runs every day, checks our chat logs for a list of prospective customers who have yet to book an appointment but still show interest. This list is sent to our AI Agent to formulate a personalised follow-up message to each lead and ask them if they want to continue with the booking. The follow-up interaction is logged so as to not to send too many messages to the customer. Requirements A Twilio account to receive customer messages. An Airtable account and Base to use as our datastore for enquiries. Cal.com account to use as our scheduling service. OpenAI account for our AI model. Customising this workflow Not using Airtable? Swap this out for your CRM of choice such as hubspot or your own service. Not using Cal.com? Swap this out for API-enabled services such as Acuity Scheduling or your own service.

This workflow automates the process of discovering and extracting APIs from various services, followed by generating custom schemas. It works in three distinct stages: research, extraction, and schema generation, with each stage tracking progress in a Google Sheet. 🙏 Jim Le deserves major kudos for helping to build this sophisticated three-stage workflow that cleverly automates API documentation processing using a smart combination of web scraping, vector search, and LLM technologies. How it works Stage 1 - Research: Fetches pending services from a Google Sheet Uses Google search to find API documentation Employs Apify for web scraping to filter relevant pages Stores webpage contents and metadata in Qdrant (vector database) Updates progress status in Google Sheet (pending, ok, or error) Stage 2 - Extraction: Processes services that completed research successfully Queries vector store to identify products and offerings Further queries for relevant API documentation Uses Gemini (LLM) to extract API operations Records extracted operations in Google Sheet Updates progress status (pending, ok, or error) Stage 3 - Generation: Takes services with successful extraction Retrieves all API operations from the database Combines and groups operations into a custom schema Uploads final schema to Google Drive Updates final status in sheet with file location Ideal for: Development teams needing to catalog multiple APIs API documentation initiatives Creating standardized API schema collections Automating API discovery and documentation Accounts required: Google account (for Sheets and Drive access) Apify account (for web scraping) Qdrant database Gemini API access Set up instructions: Prepare your Google Sheets document with the services information. Here's an example of a Google Sheet – you can copy it and change or remove the values under the columns. Also, make sure to update Google Sheets nodes with the correct Google Sheet ID. Configure Google Sheets OAuth2 credentials, required third-party services (Apify, Qdrant) and Gemini. Ensure proper permissions for Google Drive access.

Turn Reddit Questions into SEO Articles Automatically This workflow takes real user questions from Reddit and transforms them into fully structured blog posts — title, intro, steps, and conclusion — using AI. How it works Manually triggered when you want to run it Scrapes the latest posts from a specific subreddit (e.g. r/n8n) Filters only posts that are real questions (based on keywords like “how,” “what,” “why”) Logs relevant questions into a Google Sheet as raw input Enhances each question using AI (rephrases, creates a clean title and slug) Generates full-length blog content: ✏️ Intro paragraph ✅ Step-by-step guide 🧠 Clear conclusion Saves the final blog content to a second Google Sheet for publishing Set up steps You’ll need access to: Reddit API (OAuth) OpenAI API Google Sheets Takes around 15–20 minutes to connect all the credentials and tweak prompts Customize the subreddit or topic focus by changing the Reddit node config Perfect for content teams who want to scale content output using real community pain points — without ever starting from a blank page.

Remixed Backup your workflows to GitHub from Solomon's work. Check out his templates. How it works This workflow will backup your workflows to GitHub. It uses the n8n API node to export all workflows. It then loops over the data, checks in GitHub to see if a file exists that uses the credential's ID. Once checked it will: update the file on GitHub if it exists; create a new file if it doesn't exist; ignore if it's the same. In addition, it also checks if any workflows have been deleted from n8n. If a workflow no longer exists in n8n, the corresponding file will be removed from the repository to keep everything in sync. Who is this for? People wanting to backup their workflows outside the server for safety purposes or to migrate to another server.

Automate your email management with this workflow, designed for freelancers and business professionals who receive high volumes of emails. By leveraging AI-powered categorisation and dynamic email processing, this template helps you organise your inbox and streamline communication for better efficiency and productivity. Check out the YouTube video for step-by-step set up instructions! How it works: Fetch & Filter Emails: The workflow retrieves emails from your Microsoft Outlook account, filtering out flagged emails and those already categorised. Content Preparation: Each email is cleaned up and converted to a structured format using Markdown, making it easier for AI processing. AI Categorization: The content is analysed using an AI model, which categorises the emails into predefined categories (e.g., Action, Junk, Business, SaaS) based on the context and content. Email Categorization & Folder Management: The categorised emails are updated in Microsoft Outlook and moved to respective folders such as "Junk Email" or "Receipts" based on the AI's classification. Conditional Processing & Final Checks: Additional checks and conditions ensure that only unread emails are processed, and errors are gracefully managed to maintain workflow stability. Set up steps: Connect Microsoft Outlook: Link your Microsoft Outlook account using the built-in credentials node to enable email fetching, updating, and folder management. Configure AI Model (Ollama API): Set up the AI model by connecting to the Ollama API and choosing your desired language model for categorisation. Modify Email Categories (Optional): Customize the categories and subcategories within the workflow to suit your unique email management needs. Set Up Error Handling: Review the error handling node settings to ensure smooth workflow execution. This template offers a robust solution for managing and organising your inbox, helping you save time and keep your focus on important emails.

n8n Workflow: Automating Website Screenshots from Google Sheets This n8n workflow captures screenshots of websites listed in a Google Sheet and saves them to Google Drive using the CustomJS PDF Toolkit. @custom-js/n8n-nodes-pdf-toolkit Features Monitors** a Google Sheet for new rows with website URLs. Captures** screenshots of the websites using the CustomJS PDF Toolkit. Uploads** the screenshots to a specified Google Drive folder. Notice Community nodes can only be installed on self-hosted instances of n8n. Requirements Self-hosted** n8n instance A Google Sheets document containing website URLs and Titles. A Google Drive folder to store the screenshots. A CustomJS API key for website screenshots. n8n credentials** for Google Sheets and Google Drive. Workflow Steps Google Sheets Trigger Monitors a specified sheet for new rows. Extracts the URL and Title from the row. Website Screenshot Node Uses CustomJS PDF Toolkit to take a screenshot of the given URL. Google Drive Upload Saves the screenshot to a specific Google Drive folder. Uses the Title column as the filename. Setup Guide 1. Connect Google Sheets Ensure your Google Sheet has a column named Url for website URLs and Name for website names. Set up Google Sheets credentials in n8n. 2. Configure CustomJS API Sign up at CustomJS. Retrieve your API key from the profile page. Add your API key as n8n credentials. 3. Set Up Google Drive Create a folder in Google Drive to store screenshots. Copy the folder ID and set it in the Google Drive node in n8n. Perfect for: Website monitoring** Generating visual archives of web pages** Automating content curation** This workflow streamlines the process of capturing and organizing website screenshots efficiently.

This workflow automates the process of gathering LinkedIn advice articles, extracting their content, and generating unique contributions for each article using an AI model. The contributions are then posted to a Slack channel and a NocoDB database for record-keeping. The workflow is triggered weekly to ensure new articles are continuously collected and responded to. Who is this for? This workflow is designed for professionals, marketers, and content creators looking to boost their LinkedIn presence by regularly engaging with LinkedIn advice articles. It’s especially useful for those who want to be seen as a "thought leader" or "top voice" in their niche by contributing relevant and unique advice to trending topics. What problem is this workflow solving? Manually searching for relevant LinkedIn articles, reading through them, and crafting thoughtful contributions can be time-consuming. This workflow solves that by automating the process of finding new articles, extracting key content, and generating AI-powered contributions. It helps users stay consistently active on LinkedIn, contributing value to trending discussions. What this workflow does Triggers Weekly: The workflow is set to run every Monday at 8:00 AM. Search Google for LinkedIn Advice Articles: Uses a predefined Google search URL to find the latest LinkedIn advice articles based on the user's area of expertise. Extract LinkedIn Article Links: A code node extracts all LinkedIn advice article links from the search results. Retrieve Article Content: For each article link, the workflow retrieves the HTML content and extracts the article title, topics, and existing contributions. Generate AI-Powered Contributions: The workflow sends the extracted article content to an AI model, which generates unique, helpful advice for each topic within the article. Post to Slack & NocoDB: The AI-generated contributions, along with the article links, are posted to a designated Slack channel and stored in a NocoDB database for future reference. Setup Google Search URL: Update the Google search URL with the relevant LinkedIn advice query for your field (e.g., "site:linkedin.com/advice 'marketing automation'"). Slack Integration: Connect your Slack account and specify the Slack channel where you want the contributions to be posted. NocoDB Integration: Set up your NocoDB project to store the generated contributions along with the article titles and links. How to customize this workflow Change Search Terms**: Modify the Google search URL to focus on a different LinkedIn topic or expertise area. Adjust Trigger Frequency**: The workflow is set to run weekly, but you can adjust the frequency by changing the schedule trigger. Enhance Contribution Quality**: Customize the AI model's prompt to generate contributions that align with your brand voice or content strategy. Workflow Summary This workflow helps users maintain a consistent presence on LinkedIn by automating the discovery of new advice articles and generating unique contributions using AI. It is ideal for professionals who want to engage with LinkedIn content regularly without spending too much time manually searching and drafting responses.

🎯 Overview This n8n workflow automates the process of ingesting documents from multiple sources (Google Drive and web forms) into a Qdrant vector database for semantic search capabilities. It handles batch processing, document analysis, embedding generation, and vector storage - all while maintaining proper error handling and execution tracking. 🚀 Key Features Dual Input Sources**: Accepts files from both Google Drive folders and web form uploads Batch Processing**: Processes files one at a time to prevent memory issues and ensure reliability AI-Powered Analysis**: Uses Google Gemini to extract metadata and understand document context Vector Embeddings**: Generates OpenAI embeddings for semantic search capabilities Automated Cleanup**: Optionally deletes processed files from Google Drive (configurable) Loop Processing**: Handles multiple files efficiently with Split In Batches nodes Interactive Chat Interface**: Built-in chatbot for testing semantic search queries against indexed documents 📋 Use Cases Knowledge Base Creation**: Build searchable document repositories for organizations Document Compliance**: Process and index legal/regulatory documents (like Fair Work documents) Content Management**: Automatically categorize and store uploaded documents Research Libraries**: Create semantic search capabilities for research papers or reports Customer Support**: Enable instant answers to policy and documentation questions via chat interface 🔧 Workflow Components Input Methods Google Drive Integration Monitors a specific folder for new files Processes existing files in batch mode Supports automatic file conversion to PDF Web Form Upload Public-facing form for document submission Accepts PDF, DOCX, DOC, and CSV files Processes multiple file uploads in a single submission Processing Pipeline File Splitting: Separates multiple uploads into individual items Document Analysis: Google Gemini extracts document understanding Text Extraction: Converts documents to plain text Embedding Generation: Creates vector embeddings via OpenAI Vector Storage: Inserts documents with embeddings into Qdrant Loop Control: Manages batch processing with proper state handling Key Nodes Split In Batches**: Processes files one at a time with reset: false to maintain state Google Gemini**: Analyzes documents for context and metadata Langchain Vector Store**: Handles Qdrant insertion with embeddings HTTP Request**: Direct API calls for custom operations Chat Interface**: Interactive chatbot for testing vector search queries 🛠️ Technical Implementation Batch Processing Logic The workflow uses a clever looping mechanism: Split In Batches with batchSize: 1 ensures single-file processing reset: false maintains loop state across iterations Loop continues until all files are processed Error Handling All nodes include continueOnFail options where appropriate Execution logs are preserved for debugging File deletion only occurs after successful insertion Data Flow Form Upload → Split Files → Batch Loop → Analyze → Insert → Loop Back Google Drive → List Files → Batch Loop → Download → Analyze → Insert → Delete → Loop Back 📊 Performance Considerations Processing Time**: ~20-30 seconds per file Batch Size**: Set to 1 for reliability (configurable) Memory Usage**: Optimized for files under 10MB API Costs**: Uses OpenAI embeddings (text-embedding-3-large model) 🔐 Required Credentials Google Drive OAuth2: For file access and management OpenAI API: For embedding generation Qdrant API: For vector database operations Google Gemini API: For document analysis 💡 Implementation Tips Start Small: Test with a few files before processing large batches Monitor Costs: Track OpenAI API usage for embedding generation Backup First: Consider archiving instead of deleting processed files Check Collections: Ensure Qdrant collection exists before running 🎨 Customization Options Change Embedding Model**: Switch to text-embedding-3-small for cost savings Adjust Chunk Size**: Modify text splitting parameters for different document types Add Metadata**: Extend the Gemini prompt to extract specific fields Archive vs Delete**: Replace delete operation with move to "processed" folder 📈 Real-World Application This workflow was developed to process business documents and legal agreements, making them searchable through semantic queries. It's particularly useful for organizations dealing with large volumes of regulatory documentation that need to be quickly accessible and searchable. Chat Interface Testing The integrated chatbot interface allows users to: Query processed documents using natural language Test semantic search capabilities in real-time Verify document indexing and retrieval accuracy Ask questions about specific topics (e.g., "What are the pay rates for junior employees?") Get instant AI-powered responses based on the indexed content 🌟 Benefits Automation**: Eliminates manual document processing Scalability**: Handles individual files or bulk uploads Intelligence**: AI-powered understanding of document content Flexibility**: Multiple input sources and processing options Reliability**: Robust error handling and state management 👨‍💻 About the Creator Jeremy Dawes is the CEO of Jezweb, specializing in AI and automation deployment solutions. This workflow represents practical, production-ready automation that solves real business challenges while maintaining simplicity and reliability. 📝 Notes The workflow intelligently handles the n8n form upload pattern where multiple files create a single item with multiple binary properties (Files_0, Files_1, etc.) The Split In Batches pattern with reset: false is crucial for proper loop execution Direct API integration provides more control than pure Langchain implementations 🔗 Resources Qdrant Documentation OpenAI Embeddings n8n Documentation Jezweb - AI & Automation Solutions This workflow demonstrates practical automation that bridges document management with modern AI capabilities, creating intelligent document processing systems that scale with your needs.

🤖 Universal E-Commerce AI Assistant (Shopify, WooCommerce & RAG) This powerful n8n workflow deploys a sophisticated, multi-talented AI chatbot designed to streamline your e-commerce and customer support operations. The AI assistant can intelligently understand user queries and route them to the correct specialized agent, whether it's for Shopify, WooCommerce, or general knowledge questions answered by a Retrieval-Augmented Generation (RAG) system. This template automates responses to a wide range of inquiries, from checking Shopify order statuses with GraphQL to fetching product lists from WooCommerce, and even answering general questions by looking up information in a Pinecone vector database. How It Works ⚙️ The workflow operates in a series of logical steps, starting from the moment a user sends a message. 💬 Chat Trigger: The workflow activates when a user sends a message in the n8n chat interface. It captures the user's input and a unique session ID to track the conversation. 🧠 Intelligent Routing: The user's query is first sent to a Router Agent powered by GPT-4o-mini. This agent's sole purpose is to classify the intent of the message and output one of three keywords: SHOPIFY, WOOCOMMERCE, or None of them. 🔀 Conditional Branching: Based on the Router's output, a series of IF nodes direct the conversation down one of three paths: General Queries Path Shopify Path WooCommerce Path 📚 General Queries (RAG): If the query is not about e-commerce, it's handled by a RAG agent. Embedding: The user's question is converted into a vector embedding using AWS Bedrock. Retrieval: The workflow searches a Pinecone Vector Store to find the most relevant information from your knowledge base. Generation: A GPT-4o-mini agent receives the context from Pinecone and generates a comprehensive, helpful answer. 🛍️ E-Commerce Specialists: If the query is about Shopify or WooCommerce, it's passed to a dedicated agent. Shopify Agent: This agent uses Google Gemini and has a suite of tools to manage Shopify tasks. It can Get Order info, Fetch All Products, or run complex queries using the powerful GraphQL tool. WooCommerce Agent: This agent also uses Google Gemini and is equipped with tools to Fetch Order Details and Fetch All Products from a WooCommerce store. 🗣️ Conversation Memory: Each agent (Router, General, Shopify, WooCommerce) is connected to its own Memory node. This allows the chatbot to remember previous parts of the conversation for a more natural and context-aware interaction. 🏁 Merge & Respond: All three paths converge at a final Merge node. This ensures that no matter which agent handled the request, the final answer is streamlined into a single output and sent back to the user in the chat. Nodes Used 🔗 Triggers: Chat Trigger: Starts the workflow when a chat message is received. AI & Agents: AI Agent: Four separate agents for Routing, Shopify, WooCommerce, and General Queries. OpenAI Chat Model: Uses GPT-4o-mini for the Router and General Queries agent. Google Gemini Chat Model: Uses Google Gemini for the Shopify and WooCommerce agents. Tools & Data: Shopify Tool: To get products and order information from Shopify. WooCommerce Tool: To get products and order information from WooCommerce. GraphQL Tool: For advanced, custom queries to the Shopify API. Pinecone Vector Store: To retrieve context for the RAG agent. AWS Bedrock Embeddings: To create vector embeddings for Pinecone. Logic & Memory: IF Node: To conditionally route the workflow. Merge Node: To consolidate the different branches before ending. Window Buffer Memory: Four nodes to provide conversational memory to each agent. Setup Guide 🛠️ To use this workflow, you'll need to configure several nodes with your own credentials and settings. 1\. AI Model Credentials OpenAI: Create an API key in your OpenAI Platform dashboard. Add this credential to the Router Model and GPT-4o-mini nodes. Google Gemini: Create an API key in your Google AI Studio dashboard. Add this credential to the Shopify Chat Model and WooCommerce Chat Model nodes. 2\. E-Commerce Platform Credentials Shopify: You will need a Shopify Access Token. Follow the n8n documentation to generate one. Add the credential to the Fetch All Products and Get Order info nodes. WooCommerce: Create API credentials from your WordPress dashboard. Add the credential to the Fetch All Products2 and Fetch Order Details nodes. 3\. RAG System Credentials (Pinecone & AWS) Pinecone: Sign up for a Pinecone account and create an API key. Add your Pinecone credentials in n8n. In the Pinecone Vector Store node, set the pineconeIndex to the name of your index. You must have a pre-existing index with data for the RAG to work. AWS: Create an AWS account and an IAM user with programmatic access to Amazon Bedrock. Add your AWS credentials in n8n. Select your AWS credentials in the AWS Bedrock Embeddings node. 4\. GraphQL Node Configuration In the GraphQL node, you must update the endpoint URL. Replace the placeholder https://{subdomain}.myshopify.com/admin/api/2025-04/graphql.json with your own Shopify store's GraphQL API endpoint.