Workflow Templates

This n8n workflow template lets you easily generate comprehensive FAQ (Frequently Asked Questions) content for multiple services (or any items or pages you need to add the FAQs to). Simply provide the Google Sheets document containing the items to scrape, and the workflow automatically creates detailed, AI-enhanced FAQ documents. How it works The workflow reads data from a Google Sheets document containing information about different services and categories (again, in your case - whatever objects you need). For each service and category, it generates a set of standard questions and answers covering setup, permissions, integrations, use cases, and pricing benefits. An AI model (OpenAI's GPT) is used to enhance or complete some of the answers, making the content more comprehensive and natural-sounding. The workflow formats the Q&A pairs, combining AI-generated content with predefined answers where applicable. It creates a text file (JSON) for each service or category, containing the formatted Q&A pairs. The generated files are saved to specific folders in Google Drive, organized by the type of integration (native, credential-only, non-native) or category. After processing each service or category, it updates the status in the original Google Sheets document to mark it as completed. Ideal for: Marketing teams: Rapidly create comprehensive FAQ documents for multiple products or services. Customer support: Generate consistent and detailed answers for common customer queries. Product managers: Easily maintain up-to-date documentation as products evolve. Content creators: Streamline the process of creating informative content about various offerings. Accounts required Google account (for Google Sheets and Google Drive) OpenAI API account (for AI-enhanced content generation) n8n.io account (for workflow execution) Set up instructions Set up the required credentials for Google Sheets, Google Drive, and OpenAI when you first open the workflow. Prepare your Google Sheets document with the service/category information. Here's an example of Google Sheet. Fill the "Define Sheets" node with your sheets Adjust the folder IDs in the "Prepare Job" node to match your Google Drive structure. Configure the OpenAI model settings in the "OpenAI Chat Model" node if needed. Test the workflow with a small subset of data before running it on your entire dataset. Adjust the questions asked in the "Create your Q&A templates" section After testing, activate your workflow for automated FAQ generation. 🙏 Big, big kudos to Jim Le for his ideas, input and support when building this workflow. Your approach to AI workflows is always super helpful!

🚀 Local Multi-LLM Testing & Performance Tracker This workflow is perfect for developers, researchers, and data scientists benchmarking multiple LLMs with LM Studio. It dynamically fetches active models, tests prompts, and tracks metrics like word count, readability, and response time, logging results into Google Sheets. Easily adjust temperature 🔥 and top P 🎯 for flexible model testing. Level of Effort: 🟢 Easy – Minimal setup with customizable options. Setup Steps: Install LM Studio and configure models. Update IP to connect to LM Studio. Create a Google Sheet for result tracking. Key Outcomes: Benchmark LLM performance. Automate results in Google Sheets for easy comparison. Version 1.0

Purpose Use a lightweight Voice Interface, for you and your entire organization, to interact with an AI Supervisor, a personal AI Assistant, which has access to your custom workflows. You can also connect the supervisor to your already existing Agents. Demo & Explanation How it works After recording a message in the Vagent App, it gets transcribed and sent in combination with a session ID to the registered webhook The Main Agent acts as a router. I interprets the message while using the stored chat history (bound to the session ID) and chooses which tool to use to perform the required action and. Tools on this level are workflows, which contain subordinated Agents. Since the Main Agent interprets the original message, the raw input is passed to the Tools/Sub-Agents as a separate parameter Within the Sub-Agents the actual processing takes place. Each of those has it’s separate chat memory (with a suffix to the main session ID), to achieve a clear separation of concerns Depending on the required action an HTTP Request Tool is called. The result is being formatted in Markdown and returned to the Main Agent with an additional short prompt, so it does not get interpreted by the Main Agent. Drafts are separated from a short message by added indentation (angle brackets). If some information is missing, no tool is called just yet, instead a message is returned back to the user The Main Agent then outputs the result from the called Sub-Agent. If a draft is included, it gets separated from the spoken output Finally the formatted output is returned as response to the webhook. The message is split into a spoken and a text version, which enables the App to read out loud unnecessary information like drafts in this example See the full documentation of Vagent: https://vagent.io/docs Setup Import this workflow into your n8n instance Follow the instructions given in the sticky notes on the canvas Setup your credentials. OpenAI can be replaced by another LLM in the workflow, but is required for the App to work. Google Calendar and Notion are required for all scenarios to work Copy the Webhook URL from the Webhook node of the main workflow Download the Vagent App from https://vagent.io In the settings paste your OpenAI API Token, the Webhook URL and the password defined for Header Auth Now you can use the App to interact with the Multi-Agent using your Voice by tapping the Mic symbol in the App to record your message. To use the chat trigger (for testing) properly, temporarily disable the nodes after the Tools Agent.

Telegram Bot Starter template workflow + n8n AI Agent Chatbot provides a foundational setup for creating powerful Telegram bots with n8n. It handles incoming messages, photos, files, and voice notes, making it an excellent starting point for developers looking to create bots for customer engagement, support, or interactive services. Sign up to n8n now → and try it! Key Features: Dynamic Message Handling: Respond to text messages, photos, files, and more. Modular Design: Easily integrate additional workflows such as user registration, payment modules, or custom commands. Error Handling: Ensure the bot gracefully manages errors and user inputs. Extensibility: This workflow is the base for building any Telegram bot. Additional modules, such as a user registration module, payment integration, and user profile management, are available for easy connection to expand the bot’s functionality. ✍🏻Use the Telegram user registration workflow → 💵Use the Telegram Payment, Invoicing and Refund Workflow for Stars → Who Can Use This Workflow? Developers looking for a quick way to build and customize Telegram bots. Businesses and service providers who need customer interaction automation. Setup Instructions: Replace Telegram credentials with your own API credentials. Customize responses for different message types (text, photo, file). If integrating with external services (like Google Sheets), update the necessary credentials and links. UPDATES: 🔥 Get the most up-to-date and expanded version → June 25: New! AI Agent + Setup Instructions Simple setup instructions and examples are included inside the workflow as sticky notes. Sep 24: Improved message handler: Updated logic to handle various types of messages using Switch (text, photo, file, voice, and callback). Payment processing: Added new nodes for sending invoices and handling payments via Telegram Aug 24: Changed processing of system events: “new user” and ‘user who blocked bot’ events Please reach out to Victor if you need further assistance with your n8n workflows and automations! Sign up to n8n →, you have to try it!

Monthly Spotify Track Archiving and Playlist Classification This n8n workflow allows you to automatically archive your monthly Spotify liked tracks in a Google Sheet, along with playlist details and descriptions. Based on this data, Claude 3.5 is used to classify each track into multiple playlists and add them in bulk. Who is this template for? This workflow template is perfect for Spotify users who want to systematically archive their listening history and organize their tracks into custom playlists. What problem does this workflow solve? It automates the monthly process of tracking, storing, and categorizing Spotify tracks into relevant playlists, helping users maintain well-organized music collections and keep a historical record of their listening habits. Workflow Overview Trigger Options**: Can be initiated manually or on a set schedule. Spotify Playlists Retrieval**: Fetches the current playlists and filters them by owner. Track Details Collection**: Retrieves information such as track ID and popularity from the user’s library. Audio Features Fetching**: Uses Spotify's API to get audio features for each track. Data Merging**: Combines track information with their audio features. Duplicate Checking**: Filters out tracks that have already been logged in Google Sheets. Data Logging**: Archives new tracks into a Google Sheet. AI Classification**: Uses an AI model to classify tracks into suitable playlists. Playlist Updates**: Adds classified tracks to the corresponding playlists. Setup Instructions Credentials Setup: Make sure you have valid Spotify OAuth2 and Google Sheets access credentials. Trigger Configuration: Choose between manual or scheduled triggers to start the workflow. Google Sheets Preparation: Set up a Google Sheet with the necessary structure for logging track details. Spotify Playlists Setup: Have a diverse range of playlists and exhaustive description (see example) ready to accommodate different music genres and moods. Customization Options Adjust Playlist Conditions**: Modify the AI model’s classification criteria to align with your personal music preferences. Enhance Track Analysis**: Incorporate additional audio features or external data sources for more refined track categorization. Personalize Data Logging**: Customize which track attributes to log in Google Sheets based on your archival preferences. Configure Scheduling**: Set a preferred schedule for periodic track archiving, e.g., monthly or weekly. Cost Estimate For 300 tracks, the token usage amounts to approximately 60,000 tokens (58,000 for input and 2,000 for completion), costing around 20 cents with Claude 3.5 Sonnet (as of October 2024). Playlists' Description Examples | Playlist Name | Playlist Description | |-------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------| | Classique | Indulge in the timeless beauty of classical music with this refined playlist. From baroque to romantic periods, this collection showcases renowned compositions. | | Poi | Find your flow with this dynamic playlist tailored for poi, staff, and ball juggling. Featuring rhythmic tracks that complement your movements. | | Pro Sound | Boost your productivity and focus with this carefully selected mix of concentration-enhancing music. Ideal for work or study sessions. | | ChillySleep | Drift off to dreamland with this soothing playlist of sleep-inducing tracks. Gentle melodies and ambient sounds create a peaceful atmosphere for restful sleep. | | To Sing | Warm up your vocal cords and sing your heart out with karaoke-friendly tracks. Featuring popular songs, perfect for solo performances or group sing-alongs. | | 1990s | Relive the diverse musical landscape of the 90s with this eclectic mix. From grunge to pop, hip-hop to electronic, this playlist showcases defining genres. | | 1980s | Take a nostalgic trip back to the era of big hair and neon with this 80s playlist. Packed with iconic hits and forgotten gems, capturing the energy of the decade.| | Groove Up | Elevate your mood and energy with this upbeat playlist. Featuring a mix of feel-good tracks across various genres to lift your spirits and get you moving. | | Reggae & Dub | Relax and unwind with the laid-back vibes of reggae and dub. This playlist combines classic reggae tunes with deep, spacious dub tracks for a chilled-out vibe. | | Psytrance | Embark on a mind-bending journey with this collection of psychedelic trance tracks. Ideal for late-night dance sessions or intense focus. | | Cumbia | Sway to the infectious rhythms of Cumbia with this lively playlist. Blending traditional Latin American sounds with modern interpretations for a danceable mix. | | Funky Groove | Get your body moving with this collection of funk and disco tracks. Featuring irresistible basslines and catchy rhythms, perfect for dance parties. | | French Chanson | Experience the romance and charm of France with this mix of classic and modern French songs, capturing the essence of French musical culture. | | Workout Motivation | Push your limits and power through your exercise routine with this high-energy playlist. From warm-up to cool-down, these tracks will keep you motivated. | | Cinematic Instrumentals | Immerse yourself in a world of atmospheric sounds with this collection of cinematic instrumental tracks, perfect for focus, relaxation, or contemplation. |

This workflow is a modification of the previous template on how to create an SQL agent with LangChain and SQLite. The key difference – the agent has access only to the database schema, not to the actual data. To achieve this, SQL queries are made outside the AI Agent node, and the results are never passed back to the agent. This approach allows the agent to generate SQL queries based on the structure of tables and their relationships, without having to access the actual data. This makes the process more secure and efficient, especially in cases where data confidentiality is crucial. 🚀 Setup To get started with this workflow, you’ll need to set up a free MySQL server and import your database (check Step 1 and 2 in this tutorial). Of course, you can switch MySQL to another SQL database such as PostgreSQL, the principle remains the same. The key is to download the schema once and save it locally to avoid repeated remote connections. Run the top part of the workflow once to download and store the MySQL chinook database schema file on the server. With this approach, we avoid the need to repeatedly connect to a remote db4free database and fetch the schema every time. As a result, we reach greater processing speed and efficiency. 🗣️ Chat with your data Start a chat: send a message in the chat window. The workflow loads the locally saved MySQL database schema, without having the ability to touch the actual data. The file contains the full structure of your MySQL database for analysis. The Langchain AI Agent receives the schema, your input and begins to work. The AI Agent generates SQL queries and brief comments based solely on the schema and the user’s message. An IF node checks whether the AI Agent has generated a query. When: Yes: the AI Agent passes the SQL query to the next MySQL node for execution. No: You get a direct answer from the Agent without further action. The workflow formats the results of the SQL query, ensuring they are convenient to read and easy to understand. Once formatted, you get both the Agent answer and the query result in the chat window. 🌟 Example queries Try these sample queries to see the schema-driven AI Agent in action: Would you please list me all customers from Germany? What are the music genres in the database? What tables are available in the database? Please describe the relationships between tables. - In this example, the AI Agent does not need to create the SQL query. And if you prefer to keep the data private, you can manually execute the generated SQL query in your own environment using any database client or tool you trust 🗄️ 💭 The AI Agent memory node does not store the actual data as we run SQL-queries outside the agent. It contains the database schema, user questions and the initial Agent reply. Actual SQL query results are passed to the chat window, but the values are not stored in the Agent memory.

This n8n template combines an AI agent with n8n's multi-page forms to create a novel interaction which allows automated question-and-answer sessions. One of the more obvious use-cases of this interaction is what I'm calling the AI interviewer. You can read the full post here: https://community.n8n.io/t/build-your-own-ai-interview-agents-with-n8n-forms/62312 Live demo here: https://jimleuk.app.n8n.cloud/form/driving-lessons-survey How it works A form trigger is used to start the interview and a new session is created in redis to capture the transcript. An AI agent is then tasked to ask questions to the user regarding the topic of the interview. This is setup as a loop so the questions never stop unless the user wishes to end the interview. Each answer is recorded in our session set up earlier between questions. When the user requests to end the interview we break the loop and show the interview completion screen. Finally, the session is then saved in a Google Sheet which can then be shared with team members and for the purpose of data analysis. How to use You'll need to be on a n8n instance that is accessible to your target audience. Not technical enough to setup your own server? Try out n8n cloud and instantly deploy template! Remember to activate the workflow so the form trigger is published and available for users to use. Requirements Groq LLM for AI agent. Feel free to swap this out for any other LLM. Redis(-compatible) storage for capturing sessions Customising this workflow The next step would be adding tools! AI interviews with knowledge retrieval could definitely open up other possibilities. Eg. An onboarding wizard generating questions by pulling facts from internal knowledgebase.

This n8n template scrapes a list of AI grants from grants.gov and qualifies them using AI; determining interest and eligibility for the business. It then sends an email alert of interesting items to team members in an email. The template also shows how you can use the "Remove Duplicates" node to simplify deduplication of external listings without the need to manage this yourself. Not particularly interested in AI Grants? This template works for other tender websites as long as you're able to scrape them. How it works A scheduled trigger is set to fetch a list of AI grants listed on the grants.gov website in the past day. A Remove Duplicates node is used to track Grant IDs to filter out those already processed by the workflow. New grants are summarized and analysed by AI nodes to determine eligibility and interest which is then saved to an Airtable database. Another scheduled trigger starts a little later than the first to collect and summarize the new grants The results are then compiled into an email template using the HTML node, in the form of a newsletter designed to alert and brief team members of new AI grants. This email is then sent to a list of subscribers using the gmail node. How to use Make a copy of sample Airtable here: https://airtable.com/appiNoPRvhJxz9crl/shrRdP6zstgsxjDKL The filters for fetching the grants is currently set to the "AI" category. Feel free to change this to include more categories. Not interested in grants, this template can works for other sources of leads just change the endpoint and how you're defining the item ID to track. Requirements Airtable for database OpenAI for LLM Note: These are not hard requirements and can be exchanged for services available to you. customising the workflow "Eligibility" criteria at this stage may be better served by identifying hard blockers instead ie. certifications, geographical considerations or certain legal checks. Be sure to mention any hard blockers into the Eligibility prompt. Not particularly interested in AI prompts? This template works for other tender websites as long as you're able to scrape them.

Video Guide I prepared a comprehensive guide detailing how to automate the parsing of invoices using n8n and LlamaParse, seamlessly capturing and storing vital billing information. Youtube Link Who is this for? This workflow is ideal for finance teams, accountants, and business operations managers who need to streamline invoice processing. It is particularly helpful for organizations seeking to reduce manual entry errors and improve efficiency in managing billing information. What problem does this workflow solve? Manually processing invoices can be time-consuming and error-prone. This automation eliminates the need for manual data entry by capturing invoice details directly from uploaded documents and storing structured data efficiently. This enhances productivity and accuracy across financial operations. What this workflow does The workflow leverages n8n and LlamaParse to automatically detect new invoices in a designated Google Drive folder, parse essential billing details, and store the extracted data in a structured format. The key functionalities include: Real-time detection of new invoices via Google Drive triggers. Automated HTTP requests to initiate parsing through Lama Cloud. Structured storage of invoice details and line items in a database for future reference. Google Drive Integration: Monitors a specific folder in Google Drive for new invoice uploads. Parsing with LlamaParse: Automatically sends invoices for parsing and processes results through webhooks. Data Storage in Airtable: Creates records for invoices and their associated line items, allowing for detailed tracking. Setup N8N Workflow Google Drive Trigger: Set up a trigger to detect new files in a specified folder dedicated to invoices. File Upload to LlamaParse: Create an HTTP request that sends the invoice file to LlamaParse for parsing, including relevant header settings and webhook URL. Webhook Processing: Establish a webhook node to handle parsed results from LlamaParse, extracting needed invoice details effectively. Invoice Record Creation: Create initial records for invoices in your database using the parsed details received from the webhook. Line Item Processing: Transform string data into structured line item arrays and create individual records for each item linked to the main invoice.

Phishing Email Detection and Reporting with n8n Who is this for? This workflow is designed for IT teams, security professionals, and managed service providers (MSPs) looking to automate the process of detecting, analyzing, and reporting phishing emails. What problem is this workflow solving? Phishing emails are a significant cybersecurity threat, and manually detecting and reporting them is time-consuming and prone to errors. This workflow streamlines the process by automating email analysis, generating detailed reports, and logging incidents in a centralized system like Jira. What this workflow does This workflow automates phishing email detection and reporting by integrating Gmail and Microsoft Outlook email triggers, analyzing the content and headers of incoming emails, and generating Jira tickets for flagged phishing emails. Here’s what happens: Email Triggers: Captures incoming emails from Gmail or Microsoft Outlook. Email Analysis: Extracts email content, headers, and metadata for analysis. HTML Screenshot: Converts the email’s HTML body into a visual screenshot. AI Phishing Detection: Leverages ChatGPT to analyze the email and detect potential phishing indicators. Jira Integration: Automatically creates a Jira ticket with detailed analysis and attaches the email screenshot for review by the security team. Customizable Reports: Includes options to customize ticket descriptions and adapt the workflow to organizational needs. Setup Authentication: Set up Gmail and Microsoft Outlook OAuth credentials in n8n to access your email accounts securely. API Keys: Add API credentials for the HTML screenshot service (hcti.io) and ChatGPT. Jira Integration: Configure your Jira project and issue types in the workflow. Workflow Configuration: Update sticky notes and nodes to include any additional setup or configuration details unique to your system. How to customize this workflow to your needs Email Filters**: Modify email triggers to filter specific subjects or sender addresses. Analysis Scope**: Adjust the ChatGPT prompt to refine phishing detection logic. Integration**: Replace Jira with your preferred ticketing system or modify the ticket fields to include additional information. This workflow provides an end-to-end automated solution for phishing email management, enhancing efficiency and reducing security risks. It’s perfect for teams looking to minimize manual effort and improve incident response times.

Analyze & Sort Suspicious Email Contents with ChatGPT and Jira Who is this for? This workflow is tailored for IT security teams, managed service providers (MSPs), and organizations aiming to streamline the detection and reporting of phishing emails. It's especially useful for teams handling high email volumes and requiring quick, automated analysis. What problem is this workflow solving? Phishing emails pose a significant cybersecurity threat, and manual review processes are time-consuming and prone to human error. This workflow automates the identification of malicious emails, provides AI-driven insights, and generates structured reports, enabling faster and more efficient responses to email-based threats. What this workflow does This workflow integrates Gmail or Microsoft Outlook to monitor and capture incoming emails. It processes the email content and headers, converts the email's body to a visual screenshot for clarity, and uses ChatGPT's advanced AI to analyze the email for phishing indicators. Based on the analysis, it categorizes emails as potentially malicious or benign, creating detailed Jira tickets for each case. Attachments, including the email body and screenshots, are automatically uploaded for comprehensive reporting. Key steps include: Email Integration: Captures emails from Gmail or Microsoft Outlook. Content Processing: Extracts and organizes email content and metadata. AI Analysis: Uses ChatGPT to evaluate email content and headers. Classification: Categorizes emails as malicious or benign. Automated Reporting: Creates Jira tickets with detailed analysis and attachments. Setup Authentication: Configure Gmail or Microsoft Outlook credentials in n8n. API Keys: Add credentials for the HTML screenshot service (hcti.io) and OpenAI. Jira Configuration: Set up project and issue types in the Jira nodes. Customization: Update sticky notes and nodes to fit your organizational requirements, such as modifying the AI prompt or Jira ticket fields. How to customize this workflow to your needs Adjust email triggers to include or exclude specific senders or subjects. Refine the AI prompt in the ChatGPT node to tailor phishing detection criteria. Modify Jira ticket content to include additional fields or match specific workflows. This workflow is ideal for automating email threat detection, reducing response times, and enhancing overall cybersecurity processes. By leveraging AI-powered insights, it helps organizations stay ahead of phishing attacks.

Video Guide I prepared a comprehensive guide detailing how to create a Smart Agent that automates meeting task management by analyzing transcripts, generating tasks in Airtable, and scheduling follow-ups when necessary. Youtube Link Who is this for? This workflow is ideal for project managers, team leaders, and business owners looking to enhance productivity during meetings. It is particularly helpful for those who need to convert discussions into actionable items swiftly and effectively. What problem does this workflow solve? Managing action items from meetings can often lead to missed tasks and poor follow-up. This automation alleviates that issue by automatically generating tasks from meeting transcripts, keeping everyone informed about their responsibilities and streamlining communication. What this workflow does The workflow leverages n8n to create a Smart Agent that listens for completed meeting transcripts, processes them using AI, and generates tasks in Airtable. Key functionalities include: Capturing completed meeting events through webhooks. Extracting relevant meeting details such as transcripts and participants using API calls. Generating structured tasks from meeting discussions and sending notifications to clients. Webhook Integration: Listens for meeting completion events to trigger subsequent actions. API Requests for Data: Pulls necessary details like transcripts and participant information from Fireflies. Task and Notification Generation: Automatically creates tasks in Airtable and notifies clients of their responsibilities. Setup N8N Workflow Configure the Webhook: Set up a webhook to capture meeting completion events and integrate it with Fireflies. Retrieve Meeting Content: Use GraphQL API requests to extract meeting details and transcripts, ensuring appropriate authentication through Bearer tokens. AI Processing Setup: Define system messages for AI tasks and configure connections to the AI chat model (e.g., OpenAI's GPT) to process transcripts. Task Creation Logic: Create structured tasks based on AI output, ensuring necessary details are captured and records are created in Airtable. Client Notifications: Use an email node to notify clients about their tasks, ensuring communications are client-specific. Scheduling Follow-Up Calls: Set up Google Calendar events if follow-up meetings are required, populating details from the original meeting context.