Workflow Templates

This n8n workflow takes Slack conversations and turns them into Calendar events complete with accurate date and times and location information. Adding and removing attendees are also managed automatically. How it works Workflow monitors a Slack channel for invite messages with a "📅" reaction and sends this to the AI agent. AI agent parses the message determining the time, date and location. Using its Location tool, the AI agent searches for the precise location address from Google Maps. Using its Calendar tool, the AI agent creates a Google Calendar invite with the title, description and location address for the user. Back in the Slack channel, others can RSVP to the invite by reacting with the "✅" emjoi. The workflow polls the message after a while and adds the users who have reacted to the Calendar Invite as attendees. Conversely, removing any attendees who have since removed their reaction. Examples Jill: "Hey team, I'm organising a round of Laser Tag (Bunker 51) next Thursday around 6pm. Please RSVP with a ✅" AI: "I've helped you create an event in your calendar https://cal.google.com/..." Jack: "✅" AI: "I've added Jack to the event as an attendee". Requirements Slack channel to attach the workflow OpenAI account to use a GPT model Google Calendar to create and update events Customising the Workflow This workflow can work with other messaging platforms that support reactions or tagging like features such as discord. Don't use Google Calendar? Swap it out for Outlook or your own. Use any combinations of emjoi reactions and add new rules like "RSVP maybe" which could send reminder updates nearer the event date.

This n8n template automates triaging of newly opened support tickets and issue resolution via JIRA. If your organisation deals with a large number of support requests daily, automating triaging is a great use-case for introducing AI to your support teams. Extending the idea, we can also get AI to give a first attempt at resolving the issue intelligently. How it works A scheduled trigger picks up newly opened JIRA support tickets from the queue and discards any seen before. An AI agent analyses the open ticket to add labels, priority on the seriousness of the issue and simplifies the description for better readability and understanding for human support. Next, the agent attempts to address and resolve the issue by finding similar issues (by tags) which have been resolved. Each similar issue has its comments analysed and summarised to identify the actual resolution and facts. These summarises are then used as context for the AI agent to suggest a fix to the open ticket. How to use Simply connect your JIRA instance to the workflow and activate to start watching for open tickets. Depending on frequency, you may need to increase for decrease the intervals. Define labels to use in the agent's system prompt. Restrict to certain projects or issue types to suit your organisation. Requirements JIRA for issue management and support portal OpenAI for LLM Customising this workflow Not using JIRA? Try swapping out the nodes for Linear or your issue management system of choice. Try a different approach for issue resolution. You might want to try RAG approach where a knowledge base is used.

🔄 Reddit Content Operations via MCP Server 🧑‍💼 Who is this for? This workflow is built for content creators, marketers, Reddit automation enthusiasts, and AI agent developers who want structured, programmable access to Reddit content. If you're researching niche communities, tracking trends, or automating Reddit engagement — this is for you. 💡 What problem is this workflow solving? Reddit has valuable content scattered across subreddits, but manual analysis or engagement is inefficient. This workflow acts as a centralized API interface to: Query and manage Reddit posts Create, fetch, delete, and reply to comments Analyze subreddit metadata and behavior Enable AI agents to autonomously operate on Reddit data It does this using an MCP (Model Context Protocol) Server over Server-Sent Events (SSE). ⚙️ What this workflow does This template sets up a custom MCP Server that listens for JSON-based operation commands sent via SSE. Based on the operation, it routes the request to one of the following branches: 🟥 Post CRUD Create a new Reddit post Search posts across subreddits Fetch posts by ID Delete existing posts 🟩 Comment CRUD Create or reply to comments Fetch multiple comments from posts Delete specific comments 🟦 Subreddit Read Operations Get information about subreddits List subreddit posts Retrieve subreddit rules 🛠 Setup Import this workflow into your self-hosted n8n instance. Configure Reddit credentials (OAuth2). Connect your input system to the MCP Server Trigger node via SSE. Send operation payloads to the server like this: { "operation": "post_search", "params": { "query": "AI agents", "subreddit": "machinelearning" } } The workflow will route to the appropriate node based on operation type. 🧩 Supported Operations post_create post_get_many post_search post_delete post_get_by_id comment_create comment_reply comment_get_many comment_delete subreddit_get_about subreddit_get_many subreddit_get_rules 🧠 How to customize this workflow to your needs Add new operations to the operation_switch node for additional API functionality. Chain results into Notion, Slack, Airtable, or external APIs. Integrate with OpenAI/GPT to summarize posts or filter content. Add logic to score and sort content by engagement, sentiment, or keywords. 🟨 Sticky Notes Each operation group is color-coded (Posts, Comments, Subreddits). Sticky Notes explain the purpose and dependencies of each section. Easy to maintain and extend with clear logical separation. ⚠️ This template uses a custom MCP Server node and only works in self-hosted n8n. 🖼 Workflow Preview

🚀 What this flow does • 🔎 Selects the least-used WordPress category (tracked in PostgreSQL) • 🤖 Uses GPT (4-mini or better) to generate a fully formatted SEO article with headings, TOC, lists, CTA, and Yoast blocks • 🖼️ Creates a placeholder cover image and uploads it to WordPress Media • 📬 Publishes the final post via /wp-json/wp/v2/posts with correct category + featured image • 🧠 Logs the used category for future rotation (zero duplicates!) ⚙️ Setup in 3 mins 🏷️ Add your WordPress domain with a simple Set node:     domain=https://yourdomain.com 🔐 Create these 3 credentials in n8n:     YOUR_WORDPRESS_CREDENTIAL — for /media, /posts     YOUR_POSTGRES_CREDENTIAL — for category tracking     YOUR_OPENAI_CREDENTIAL — GPT-4-mini or better 🧱 Run the SQL from docs to create the used_categories table ✅ Manually test first 3–5 nodes to check WP auth, OpenAI response, and DB connection 🕒 Then just schedule it and let the bot write for you. 🎯 Why it's awesome This is your personal AI content writer + publisher — perfect for: • 📰 SEO content farms • 📈 Affiliate blogs • 🧰 Micro niche sites • 🤫 PBNs with rotation-safe automation No more manual uploads, broken categories, or GPT spam. Every post is structured, beautiful, and intelligently categorized.

This workflow automates sentiment analysis of Reddit posts related to Apple's WWDC25 event. It extracts data, categorizes posts, analyzes sentiment of comments, and updates a Google Sheet with the results. Preliquisites Bright Data Account: You need a Bright Data account to scrape Reddit data. Ensure you have the correct permissions to use their API. https://brightdata.com/ Google Sheets API Credentials: Enable the Google Sheets API in your Google Cloud project and create credentials (OAuth 2.0 Client IDs). Google Gemini API Credentials: You need a Gemini API key to run the sentiment analysis. Ensure you have the correct permissions to use their API. https://ai.google.dev/". You can use any other models of choice Setup Import the Workflow: Import the provided JSON workflow into your n8n instance.", Configure Bright Data Credentials:, In the 'scrap reddit' and the 'get status' nodes, in Header Parameters find the Authorization field, replace Bearer 1234 with your Bright Data API key. Apply this to every node that utilizes your Bright Data API Key., Set up the Google Sheets API credentials, In the 'Append Sentiments' node, set up the Google Sheets API by connecting your Google Sheets account through oAuth 2 credentials. ", Configure the Google Gemini Credential ID, In the ' Sentiment Analysis per comment' node, set up the Google Gemini API by connecting your Google AI account through the API credentials. , Configure Additional Parameters:, In the 'scrap reddit' node, modify the JSON body to adjust the search term, date, or sort method., In the 'Wait' node, alter the 'Amount' to adjust the polling interval for scraping status, it is set to 15 seconds by default., In the 'Text Classifier' node, customize the categories and descriptions to suit the sentiment analysis needs. Review categories such as 'WWDC events' to ensure relevancy., In the 'Sentiment Analysis per comment' node, modify the system prompt template to improve context. customization_options Bright Data API parameters to adjust scraping behavior. Wait node duration to optimize polling. Text Classifier categories and descriptions. Sentiment Analysis system prompt. Use Case Examples Brand Monitoring:** Track public sentiment towards Apple during and after the WWDC25 event. Product Feedback Analysis:** Gather insights into user reactions to new product announcements. Competitive Analysis:** Compare sentiment towards Apple's announcements versus competitors. Event Impact Assessment:** Measure the overall impact of the WWDC25 event on various aspects of Apple's business. Target_audiences: Marketing professionals in the tech industry, Brand managers, Product managers, Market research analysts, Social media managers Troubleshooting: Workflow fails to start. Check that all necessary credentials (Bright Data and Google Sheets API) are correctly configured and that the Bright Data API key is valid. Data scraping fails. Verify the Bright Data API key, ensure the dataset ID is correct, and inspect the Bright Data dashboard for any issues with the scraping job. Sentiment analysis is inaccurate. Refine the categories and descriptions in the 'Text Classifier' node. Check that you have the correct Google Gemini API key, as the original is a placeholder. Google Sheets are not updating. Ensure the Google Sheets API credentials have the necessary permissions to write to the specified spreadsheet and sheet. Check API usage limits. Workflow does not produce the correct output. Check the data connections, by clicking the connections, and looking at which data is being produced. Check all formulas for errors. Happy productivity!

Stay on top of the latest crypto news and market sentiment instantly, all inside Telegram! This workflow aggregates articles from the top crypto news sources, filters for your topic of interest, and summarizes key news and market sentiment using GPT-4o AI. Ideal for crypto traders, investors, analysts, and market watchers needing fast, intelligent news briefings. > 💬 Just type a coin name (e.g., "Bitcoin", "Solana", "DeFi") into your Telegram AI Agent—and get a smart news digest. How It Works Telegram Bot Trigger User sends a keyword (e.g., "Ethereum") of questions to the Telegram AI Agent. Keyword Extraction (AI-Powered) An AI agent identifies the main topic for better targeting. News Aggregation Pulls articles from 9 major crypto news RSS feeds: Cointelegraph Bitcoin Magazine CoinDesk Bitcoinist NewsBTC CryptoPotato 99Bitcoins CryptoBriefing Crypto.news Filtering Finds and merges articles relevant to the user's keyword. AI Summarization GPT-4o generates a 3-part summary: News Summary Market Sentiment Analysis List of Article Links Telegram Response Sends a structured, easy-to-read digest back to the user. 🔍 What You Can Do with This Workflow 🔹 Summarize breaking news for any crypto project or keyword 🔹 Monitor real-time market sentiment on Bitcoin, DeFi, NFTs, and more 🔹 Stay ahead of FUD, bullish trends, and major news events 🔹 Quickly brief yourself or your team via Telegram 🔹 Use it as a foundation for more advanced crypto alert bots ✅ Example User Inputs ✅ "Bitcoin" → Latest Bitcoin news and sentiment summary ✅ "Solana" → Updates on Solana projects, price movements, and community trends ✅ "NFT" → Aggregated news about NFT markets and launches ✅ "Layer 2" → Insights on Optimism, Arbitrum, and other L2s 🛠️ Setup Instructions Create a Telegram Bot Use @BotFather and obtain the Bot Token. Configure Telegram Credentials in n8n Add your bot token under Telegram API Credentials. Configure OpenAI API Add your OpenAI credentials for GPT-4o access. Update Telegram Send Node In the Telegram Send node, replace the placeholder chatId with your real Telegram user or group chat ID. Deploy and Test Start chatting with your bot: e.g., "Ethereum" or "DeFi". 📌 Workflow Highlights 9 major crypto news sources combined** Smart keyword matching** with AI query parsing Summarized insights** in human-readable format Reference links** included for deeper reading Instant delivery** via Telegram 🚀 Get ahead of the crypto market—automate your news and sentiment monitoring with AI inside Telegram!

AI evaluation in n8n This is a template for n8n's evaluation feature. Evaluation is a technique for getting confidence that your AI workflow performs reliably, by running a test dataset containing different inputs through the workflow. By calculating a metric (score) for each input, you can see where the workflow is performing well and where it isn't. How it works This template shows how to calculate a workflow evaluation metric: retrieved document relevance (i.e. whether the information retrieved from a vector store is relevant to the question). The workflow takes a question and checks whether the information retrieved to answer it is relevant. To run this workflow, you need to insert documents into a vector data store, so that they can be retrieved by the agent to answer questions. You can do this by running the top part of the workflow once. The main workflow works as follows: We use an evaluation trigger to read in our dataset It is wired up in parallel with the regular trigger so that the workflow can be started from either one. More info We make sure that the agent outputs the list data from the tools that it used If we’re evaluating (i.e. the execution started from the evaluation trigger), we calculate the relevance metric using AI to compare the retrieved documents with the question We pass this information back to n8n as a metric If we’re not evaluating we avoid calculating the metric, to reduce cost

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.

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.

🤖 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.