Workflow Templates

Submission Overview for Voiceflow Demo Workflow View the YouTube video for this workflow here. Who is this for? This workflow is ideal for businesses and developers using Voiceflow to power AI voice chatbots. It benefits teams that want to enhance chatbot functionality through integrations with platforms like Zendesk, Google Calendar, and Airtable. What problem is this workflow solving? The workflow addresses the need for seamless integration of chatbot interactions with backend systems. It automates customer service tasks such as ticket creation, meeting scheduling, and data reporting, reducing manual effort and enhancing efficiency. What does this workflow do? Customer Lookup:** Checks the database for existing customers and returns relevant details or a "NOT_FOUND" status. Zendesk Ticket Creation:** Automates the creation of support tickets for customer issues. Meeting Scheduling:** Integrates with Google Calendar to provide availability and schedule meetings. Transcript Reporting:** Aggregates interaction data and sends it to Airtable for analysis by the product team. Setup Configure your Voiceflow chatbot to connect to this workflow via a webhook. Set up the required integrations: Zendesk API: For ticket creation. Google Calendar API: For scheduling. Airtable API: For storing transcripts. Customize the workflow's nodes to match your use case, such as database fields or API endpoints. Deploy the workflow on your n8n instance and test the integrations. How to customize this workflow to your needs Adjust database queries to match your customer data schema. Modify the Zendesk ticket payload to include additional fields or custom formats. Update Google Calendar configurations for different scheduling requirements. Add or remove Airtable fields based on the product team's analysis needs. This template adheres to n8n’s submission guidelines, ensuring clarity, relevance, and broad applicability for users in customer service, product development, and automation.

Document Chat Bot with Automated RAG System This workflow creates a conversational assistant that can answer questions based on your Google Drive documents. It automatically processes various file types and uses Retrieval-Augmented Generation (RAG) to provide accurate answers based on your document content. How It Works Monitors Google Drive for New Documents: Automatically detects when files are created or updated in designated folders Processes Multiple File Types: Handles PDFs, Excel spreadsheets, and Google Docs Builds a Knowledge Base: Converts documents into searchable vector embeddings stored in Supabase Provides Chat Interface: Users can ask questions about their documents through a web interface Retrieves Relevant Information: Uses advanced RAG techniques to find and present the most relevant information Setup Steps (Estimated time: 25-30 minutes) API Credentials: Connect your OpenAI API key for text processing and embeddings Google Drive Integration: Set up Google Drive triggers to monitor specific folders Supabase Configuration: Configure Supabase vector database for document storage Chat Interface Setup: Deploy the web-based chat interface using the provided webhook The workflow automatically chunks documents into manageable segments, generates embeddings, and stores them in a vector database for efficient retrieval. When users ask questions, the system finds the most relevant document sections and uses them to generate accurate, contextual responses.

Who is it for This workflow is for anyone who is using N8N. It's especially helpful if you are a DevOps and your N8N instance is self hosted. If you carea lot about security and number of failed executions and at the same time you are using InfluxDB to monitor status of your systems, this will perfectly fit in your stack. How it works This automation is fairly simple. It uses native N8N nodes to gather data from itself. Then it is parsing this data to be compatible with InfluxDB input. And finally it is sending this data to InfluxDB for further processing. Remember to set up Setup is really simple and you just need to provide just three variables. First is your InfluxDB URL, second is your InfluxDB organization, and third is your InfluxDB bucket name. Of course, to set up N8N nodes and gather data from them, you will need your instance API key. And that's all. How it looks in InfluxDB? See below Schedule Audits Audits don't need to be run often, but I would recommend it to be run on regular basis. This way you can see real data series in InfluxDB. I think that once a day should be enough, but it depends on your N8N usage of course Thank you, perfect! Glad I could help. Visit my profile for other automations for businesses. And if you are looking for dedicated software development, do not hesitate to reach out! You can also see automations on my Sailing Byte's GitHub N8N repository.

! n8n Workflow: HTML to PDF Generator This n8n workflow converts HTML content into a styled PDF and returns it as a response via a webhook. The workflow receives HTML input, processes it using CustomJS's PDF toolkit, and sends back the resulting PDF to the original webhook requester. @custom-js/n8n-nodes-pdf-toolkit Features: Webhook Trigger**: Accepts incoming requests with HTML content. HTML to PDF Conversion**: Uses CustomJS to transform HTML into a PDF. Response**: Sends the generated PDF back to the webhook response. Requirements: Self-hosted** n8n instance A CustomJS API key for HTML to PDF conversion HTML content** to be converted into a PDF Workflow Steps: Webhook Trigger: Accepts incoming HTTP requests with HTML content. This data is passed to the next node for processing. HTML to PDF Conversion: Uses the CustomJS node to convert the incoming HTML into a PDF document. You can customize the HTML content to match the design requirements. Respond to Webhook: Sends the generated PDF as a binary response to the original webhook request. Setup Guide: 1. Configure CustomJS API Sign up at CustomJS. Retrieve your API key from the profile page. Add your API key as n8n credentials. 2. Design Workflow Create a Webhook: Set up a webhook to trigger the workflow when HTML content is received. Prepare HTML Content: The incoming request should include the HTML content you wish to convert into a PDF. Configure HTML to PDF Node: Use the HTML to PDF node to convert the provided HTML into a PDF. The node uses the HTML input to generate a PDF using the CustomJS API. Respond with the PDF: The Respond to Webhook node will send the generated PDF back to the original requester as a binary response. Example HTML Input: Hello CustomJS! CustomJS provides the missing toolset for your no-code projects Result PDF

Complete MCP server exposing all PagerDuty Tool operations to AI agents. Zero configuration needed - all 9 operations pre-built. ⚡ Quick Setup Need help? Want access to more workflows and even live Q&A sessions with a top verified n8n creator.. All 100% free? Join the community Import this workflow into your n8n instance Activate the workflow to start your MCP server Copy the webhook URL from the MCP trigger node Connect AI agents using the MCP URL 🔧 How it Works • MCP Trigger: Serves as your server endpoint for AI agent requests • Tool Nodes: Pre-configured for every PagerDuty Tool operation • AI Expressions: Automatically populate parameters via $fromAI() placeholders • Native Integration: Uses official n8n PagerDuty Tool tool with full error handling 📋 Available Operations (9 total) Every possible PagerDuty Tool operation is included: 🔧 Incident (4 operations) • Create an incident • Get an incident • Get many incidents • Update an incident 🔧 Incidentnote (2 operations) • Create an incident note • Get many incident notes 🔧 Logentry (2 operations) • Get a log entry • Get many log entries 👤 User (1 operations) • Get a user 🤖 AI Integration Parameter Handling: AI agents automatically provide values for: • Resource IDs and identifiers • Search queries and filters • Content and data payloads • Configuration options Response Format: Native PagerDuty Tool API responses with full data structure Error Handling: Built-in n8n error management and retry logic 💡 Usage Examples Connect this MCP server to any AI agent or workflow: • Claude Desktop: Add MCP server URL to configuration • Custom AI Apps: Use MCP URL as tool endpoint • Other n8n Workflows: Call MCP tools from any workflow • API Integration: Direct HTTP calls to MCP endpoints ✨ Benefits • Complete Coverage: Every PagerDuty Tool operation available • Zero Setup: No parameter mapping or configuration needed • AI-Ready: Built-in $fromAI() expressions for all parameters • Production Ready: Native n8n error handling and logging • Extensible: Easily modify or add custom logic > 🆓 Free for community use! Ready to deploy in under 2 minutes.

This workflow offers several additional features for time tracking with Awork: Check whether time has been tracked when closing a task. If not, the task is reopened and the user is notified. This can be restricted to specific tasks using tags. Enforce a minimum time entry for tasks to comply with "at least 15-minute intervals are billed" policies. This can also be limited to specific tasks by using tags. Clean up time entries to match billing intervals. Add a start time to time entries if it is missing. This workflow does not use the Awork community nodes package, as the package does not support all required API calls and is therefore not used here. If you prefer to use that package, you can find more information at awork integration guide and replace the HTTP nodes with the corresponding community nodes where applicable. How it works Triggered via Awork Webhook call on status change of tasks and new time entries Set up steps Add webhook call to Awork (please see in-workflow notes regarding webhook configuration) Configure Awork API credentials Set up workflow configuration via setup node, e.g. user notification text, tags, enabled features etc.

Complete MCP server exposing 1 Image Moderation API operations to AI agents. ⚡ Quick Setup Need help? Want access to more workflows and even live Q&A sessions with a top verified n8n creator.. All 100% free? Join the community Import this workflow into your n8n instance Credentials Add Image Moderation credentials Activate the workflow to start your MCP server Copy the webhook URL from the MCP trigger node Connect AI agents using the MCP URL 🔧 How it Works This workflow converts the Image Moderation API into an MCP-compatible interface for AI agents. • MCP Trigger: Serves as your server endpoint for AI agent requests • HTTP Request Nodes: Handle API calls to https://api.moderatecontent.com/moderate/ • AI Expressions: Automatically populate parameters via $fromAI() placeholders • Native Integration: Returns responses directly to the AI agent 📋 Available Operations (1 endpoints) General (1 operation) Detect Nudity in Images 🤖 AI Integration Parameter Handling: AI agents automatically provide values for: • Path parameters and identifiers • Query parameters and filters • Request body data • Headers and authentication Response Format: Native Image Moderation API responses with full data structure Error Handling: Built-in n8n HTTP request error management 💡 Usage Examples Connect this MCP server to any AI agent or workflow: • Claude Desktop: Add MCP server URL to configuration • Cursor: Add MCP server SSE URL to configuration • Custom AI Apps: Use MCP URL as tool endpoint • API Integration: Direct HTTP calls to MCP endpoints ✨ Benefits • Zero Setup: No parameter mapping or configuration needed • AI-Ready: Built-in $fromAI() expressions for all parameters • Production Ready: Native n8n HTTP request handling and logging • Extensible: Easily modify or add custom logic > 🆓 Free for community use! Ready to deploy in under 2 minutes.

Complete MCP server exposing 4 BikeWise API v2 API operations to AI agents. ⚡ Quick Setup Need help? Want access to more workflows and even live Q&A sessions with a top verified n8n creator.. All 100% free? Join the community Import this workflow into your n8n instance Credentials Add BikeWise API v2 credentials Activate the workflow to start your MCP server Copy the webhook URL from the MCP trigger node Connect AI agents using the MCP URL 🔧 How it Works This workflow converts the BikeWise API v2 API into an MCP-compatible interface for AI agents. • MCP Trigger: Serves as your server endpoint for AI agent requests • HTTP Request Nodes: Handle API calls to https://bikewise.org/api • AI Expressions: Automatically populate parameters via $fromAI() placeholders • Native Integration: Returns responses directly to the AI agent 📋 Available Operations (4 total) 🔧 V2 (4 endpoints) • GET /v2/incidents: Paginated incidents matching parameters • GET /v2/incidents/{id}: GET /v2/incidents/{id} • GET /v2/locations: Unpaginated geojson response • GET /v2/locations/markers: Unpaginated geojson response with simplestyled markers 🤖 AI Integration Parameter Handling: AI agents automatically provide values for: • Path parameters and identifiers • Query parameters and filters • Request body data • Headers and authentication Response Format: Native BikeWise API v2 API responses with full data structure Error Handling: Built-in n8n HTTP request error management 💡 Usage Examples Connect this MCP server to any AI agent or workflow: • Claude Desktop: Add MCP server URL to configuration • Cursor: Add MCP server SSE URL to configuration • Custom AI Apps: Use MCP URL as tool endpoint • API Integration: Direct HTTP calls to MCP endpoints ✨ Benefits • Zero Setup: No parameter mapping or configuration needed • AI-Ready: Built-in $fromAI() expressions for all parameters • Production Ready: Native n8n HTTP request handling and logging • Extensible: Easily modify or add custom logic > 🆓 Free for community use! Ready to deploy in under 2 minutes.

This automated n8n workflow sets up a complete MERN Stack development environment on a Linux server by installing core technologies, development tools, package managers, global npm packages, deployment tools, build tools, and security configurations. It creates a dedicated developer user and configures essential settings for MERN projects. What is MERN Stack Setup? MERN Stack setup involves installing and configuring Node.js, MongoDB, Express.js, and React, along with additional tools and packages, to create a fully functional development environment for building MERN-based applications on a Linux system. Good to Know The workflow triggers manually and takes 10-15 minutes to complete A dedicated developer user with proper permissions is created Firewall configuration secures development ports The environment variables template is provided All tools are installed and ready for immediate use How It Works Set Parameters** - Configures server host, user, password, setup type, Node.js version, MongoDB version, username, and user password System Preparation** - Prepares the system for installation Install Node.js** - Installs Node.js (v20 by default) with npm Install MongoDB** - Installs MongoDB (v7.0 by default) with Compass & Shell Install Git & GitHub CLI** - Installs Git and GitHub CLI Install Development Tools** - Installs VS Code, Docker, Docker Compose, Postman, Nginx, Redis, and PostgreSQL Create Dev User** - Creates a development user account Install Additional Tools** - Installs package managers (npm, Yarn, pnpm), global npm packages, deployment tools, build tools, and security tools Final Configuration** - Configures firewall, SSH keys, and environment variables template Setup Complete** - Marks the completion of the setup process How to Use Import the workflow into n8n Configure parameters in the Set Parameters node (server_host, server_user, server_password, setup_type, node_version, mongodb_version, username, user_password) Run the workflow SSH into the server as the new developer user Start building MERN applications Requirements Linux server access with SSH Administrative privileges (root access) Customizing This Workflow Adjust the setup_type parameter to customize the installation scope Modify node_version or mongodb_version to use different versions Change the username and user_password for the developer account

Multi-AI Agent Chatbot for Postgres/Supabase Databases and QuickChart Generation Who is this for? This workflow is ideal for data analysts, developers, and business intelligence teams who need an AI-powered chatbot to query Postgres/Supabase databases and generate dynamic charts for data visualization. What problem does this solve? It simplifies data exploration by combining conversational AI with database querying and chart generation. Users can interact with their database using natural language, retrieve insights, and visualize data without manual SQL queries or chart configuration. What this workflow does AI-Powered Chat Interface: Accepts natural language prompts to query databases or generate charts. Routes user requests through a tool agent system to determine the appropriate action (query or chart). Database Querying: Executes SQL queries on Postgres/Supabase databases based on user input. Retrieves schema information, table definitions, and specific data records. Dynamic Chart Generation: Uses QuickChart to create bar charts, line charts, or other visualizations from database records. Outputs a shareable chart URL or JSON configuration for further customization. Memory Integration: Maintains chat history using Postgres memory nodes, enabling context-aware interactions. Workflow diagram showcasing AI agents, database querying, and chart generation paths. Setup Prerequisites: A Postgres-compatible database (e.g., Supabase). API credentials for OpenAI. Configuration Steps: Add your database connection credentials in the Postgres nodes. Set up OpenAI credentials for GPT-4o-mini in the language model nodes. Adjust the QuickChart schema in the "QuickChart Object Schema" node to fit your use case. Testing: Trigger the chat workflow via the "When chat message received" node. Test with prompts like "Generate a bar chart of sales data" or "Show me all users in the database." How to customize this workflow Modify AI Prompts** Add Chart Types** Integrate Other Tools**

AI-Powered Invoice Processing: from Email to Database & Chat Notifications Automatically process PDF invoices directly from your email inbox. This workflow uses AI to extract key data, saves it to a PostgreSQL database, and instantly notifies you about the new document in your preferred chat application. The workflow listens for new emails, fetches PDF attachments, and then passes their content to a Large Language Model (LLM) for intelligent recognition and data extraction. Finally, the information is securely archived in the database, and a summary of the invoice is sent as a notification. > 📝 This workflow is highly customizable. > It uses PostgreSQL, OpenAI (GPT), and Discord by default, but you can easily swap these components. > Feel free to use a different database like MySQL or Airtable, another AI model provider, or send notifications to Slack, MS Teams, or any other chat platform. > ⚠️ Note: If the workflow fails to extract data correctly from invoices issued by certain companies, you may need to adjust the prompt used in the Basic LLM Chain node to improve parsing accuracy. Use Case Automating accounts payable for small businesses and freelancers Centralizing financial documents without manual data entry Creating a searchable database of all incoming invoices Receiving real-time notifications for new financial commitments Features 📧 Email Trigger (IMAP):** Monitors a dedicated email inbox for new messages with attachments 📄 PDF Filtering:** Automatically identifies and processes only PDF attachments 🤖 AI-Powered Data Extraction:** Uses an LLM (e.g., GPT-4o-mini) to extract invoice number, buyer/seller details, amounts, currency, and due dates ⚙️ Structured Data Output:** Converts AI output to standardized JSON 🔍 Database Write Logic:** Prevents duplicates by checking invoice/company combo 🗄️ PostgreSQL Integration:** Stores extracted data into company and invoice tables 💬 Chat Notifications:** Sends invoice summary as message to a designated channel Setup Instructions ⚠️ API Access & Costs To use the AI extraction feature, you need an API key from a provider like OpenAI. Most providers charge for access to language models. You'll likely need a billing account. 1. PostgreSQL Database Configuration Ensure your database has the following tables: -- Table for companies (invoice issuers) CREATE TABLE company ( id SERIAL PRIMARY KEY, tax_number VARCHAR(255) UNIQUE NOT NULL, name VARCHAR(255), address TEXT, created_at TIMESTAMP WITH TIME ZONE DEFAULT CURRENT_TIMESTAMP ); -- Table for invoices CREATE TABLE invoice ( id SERIAL PRIMARY KEY, company_id INTEGER REFERENCES company(id), invoice_number VARCHAR(255) NOT NULL, -- Add other fields: total_to_pay, currency, due_date created_at TIMESTAMP WITH TIME ZONE DEFAULT CURRENT_TIMESTAMP, UNIQUE(company_id, invoice_number) ); Then, in n8n, create a credential for your PostgreSQL DB. 2. Email (IMAP) Configuration In n8n, add credentials for the email account that receives invoices: IMAP host IMAP port Username Password 3. AI Provider Configuration Log in to OpenAI (or similar provider) Generate API key In n8n, create credentials and paste the key 4. Chat Notification (Discord) Go to Discord > Server Settings > Integrations > Webhooks > New Webhook Select channel Copy Webhook URL In n8n, paste URL into the Discord node Placeholders and Fields to Fill | Placeholder | Description | Example | |---------------------------|-------------------------------------------|------------------------------------------| | YOUR_EMAIL_CREDENTIALS | Your IMAP email account in n8n | My Invoice Mailbox | | YOUR_OPENAI_CREDENTIALS | API credentials for AI model | My OpenAI Key | | YOUR_POSTGRES_CREDENTIALS| Your PostgreSQL DB credentials in n8n | My Production DB | | YOUR_DISCORD_WEBHOOK | Webhook URL for your chat system | https://discord.com/api/webhooks/... | Testing the Workflow Send a test invoice to the inbox as a PDF attachment Run the workflow manually in n8n and check if the IMAP node fetches the message Verify AI Extraction – inspect the LLM output (e.g., GPT node) and confirm structured JSON Check the DB – ensure new rows appear in company and invoice Check the chat – verify the invoice summary appears in the chosen channel Customization Tips Change the DB:** Use MySQL, Airtable, or Google Sheets instead of PostgreSQL Other notifications:** Swap Discord for Slack, MS Teams, Telegram, etc. Expand AI logic:** Extract line items, prices, etc. by customizing the prompt Add payment logic:** Allow marking invoices as paid via emoji or a separate webhook

🛠️ Pushover Tool MCP Server Complete MCP server exposing all Pushover Tool operations to AI agents. Zero configuration needed - 1 operation pre-built. ⚡ Quick Setup Need help? Want access to more workflows and even live Q&A sessions with a top verified n8n creator.. All 100% free? Join the community Import this workflow into your n8n instance Activate the workflow to start your MCP server Copy the webhook URL from the MCP trigger node Connect AI agents using the MCP URL 🔧 How it Works • MCP Trigger: Serves as your server endpoint for AI agent requests • Tool Nodes: Pre-configured for every Pushover Tool operation • AI Expressions: Automatically populate parameters via $fromAI() placeholders • Native Integration: Uses official n8n Pushover Tool tool with full error handling 📋 Available Operations (1 total) Every possible Pushover Tool operation is included: 💬 Message (1 operations) • Push a message 🤖 AI Integration Parameter Handling: AI agents automatically provide values for: • Resource IDs and identifiers • Search queries and filters • Content and data payloads • Configuration options Response Format: Native Pushover Tool API responses with full data structure Error Handling: Built-in n8n error management and retry logic 💡 Usage Examples Connect this MCP server to any AI agent or workflow: • Claude Desktop: Add MCP server URL to configuration • Custom AI Apps: Use MCP URL as tool endpoint • Other n8n Workflows: Call MCP tools from any workflow • API Integration: Direct HTTP calls to MCP endpoints ✨ Benefits • Complete Coverage: Every Pushover Tool operation available • Zero Setup: No parameter mapping or configuration needed • AI-Ready: Built-in $fromAI() expressions for all parameters • Production Ready: Native n8n error handling and logging • Extensible: Easily modify or add custom logic > 🆓 Free for community use! Ready to deploy in under 2 minutes.