Workflow Templates

Workflow Description Your workflow is an intelligent chatbot, using ++OpenAI assistant++, integrated with a backend that supports WhatsApp Business, designed to handle various use cases such as sales and customer support. Below is a breakdown of its functionality and key components: Workflow Structure and Functionality Chat Input (Chat Trigger) The flow starts by receiving messages from customers via WhatsApp Business. Collects basic information, such as session_id, to organize interactions. Condition Check (If Node) Checks if additional customer data (e.g., name, age, dependents) is sent along with the message. If additional data is present, a customized prompt is generated, which includes this information. The prompt specifies that this data is for the assistant's awareness and doesn’t require a response. Data Preparation (Edit Fields Nodes) Formats customer data and the interaction details to be processed by the AI assistant. Compiles the customer data and their query into a single text block. AI Responses (OpenAI Nodes) The assistant’s prompt is carefully designed to guide the AI in providing accurate and relevant responses based on the customer’s query and data provided. Prompts describe the available functionalities, including which APIs to call and their specific purposes, helping to prevent “hallucinated” or irrelevant responses. Memory and Context (Postgres Chat Memory) Stores context and messages in continuous sessions using a database, ensuring the chatbot maintains conversation history. API Calls The workflow allows the use of APIs with any endpoints you choose, depending on your specific use case. This flexibility enables integration with various services tailored to your needs. The OpenAI assistant understands JSON structures, and you can define in the prompt how the responses should be formatted. This allows you to structure responses neatly for the client, ensuring clarity and professionalism. Make sure to describe the purpose of each endpoint in the assistant’s prompt to help guide the AI and prevent misinterpretation. Customer Response Delivery After processing and querying APIs, the generated response is sent to the backend and ultimately delivered to the customer through WhatsApp Business. Best Practices Implemented Preventing Hallucinations** Every API has a clear description in its prompt, ensuring the AI understands its intended use case. Versatile Functionality** The chatbot is modular and flexible, capable of handling both sales and general customer inquiries. Context Persistence** By utilizing persistent memory, the flow maintains continuous interaction context, which is crucial for longer conversations or follow-up queries. Additional Recommendations Include practical examples in the assistant’s prompt, such as frequently asked questions or decision-making flows based on API calls. Ensure all responses align with the customer’s objectives (e.g., making a purchase or resolving technical queries). Log interactions in detail for future analysis and workflow optimization. This workflow provides a solid foundation for a robust and multifunctional virtual assistant 🚀

This flow monitors a file for changes of its content. If changed, an alert is sent out and you receive it as push, SMS or voice call on SIGNL4. User cases: Log-file monitoring Monitoring of production data Integration with third-party systems via file interface Etc. Sample file "alert-data.json": { "Body": "Alert in building A2.", "Done": false, "eventId": "2518088743722201372_4ee5617b-2731-4d38-8e16-e4148b8fb8a0" } Body: The alert text to be sent. Done: If false this is a new alert. If true this indicated the alert has been closed. eventId: Last SIGNL4 event ID written by SIGNL4. This flow can be easily adapted for database monitoring as well.

Overview: Automate Your Google OAuth2 Credential Creation This workflow template streamlines the process of creating and naming individual Google OAuth2 credentials for multiple Google services within n8n. By automating tasks typically done manually, it saves significant time and reduces the risk of errors. Who Is This For? This template is perfect for: Marketers who frequently use Google Docs, Google Sheets, Google Slides, and Google Drive for document automation. Developers or technical users who manage numerous integrations and need to minimize credential creation overhead. Teams or solo users with multiple Google accounts who want to eliminate repetitive Google OAuth2 credential creation tasks. Prerequisite Knowledge • Basic familiarity with n8n, including how to configure nodes and connect services. • A Google OAuth JSON file (client ID, client secret, etc.) already set up in your Google Cloud Console. • An understanding of Google services (Docs, Sheets, Slides, Drive, Gmail, Calendar, Contacts and others) and which you may need for your use cases What Problem Does It Solve? Manually creating and naming separate OAuth2 credentials for each Google service can be time-consuming and prone to errors. For instance, if you manage 16 Google accounts, you might need to create 112 credentials manually. This workflow automates that process by using your Google OAuth JSON file and email address, generating uniquely named OAuth2 credentials for each Google service in just a single run per account. Key Features Manual Trigger – Start the workflow on demand to generate new credentials. Set Node for Google OAuth Details – Store your client ID, client secret, and other JSON info securely in one place. Automated Naming Convention – Use your Google email address to create distinct names for each credential, simplifying organization. Array of Google Services – Customize which services to create credentials for, such as Docs, Sheets, Slides, Drive, Gmail, Calendar, or Contacts. Batch Credential Creation – Split the array of Google services and automatically create separate OAuth2 credentials for each. Seamless OAuth2 Integration – Effortlessly authenticate all generated credentials within n8n. How to Set Up Insert Your JSON Details – Paste your Google OAuth 2.0 JSON data into the “Google JSON” node. Add Your Google Email Address – Specify your email address in the “Google Email” node to personalize the naming convention. Configure n8n Account Credentials – In the “n8n Create Credentials” node, select or add proper credentials (e.g., n8n API credentials). Execute the Workflow – Run the workflow to automatically generate individual OAuth2 credentials. Finalize in n8n – Visit your n8n Credentials dashboard to complete the OAuth sign-in for each newly created credential. Customization Options Modify the Services Array** – Easily add or remove Google products based on your workspace needs by editing the array in the “Services” node. Adjust Naming Logic** – Tailor the credential naming convention in the “n8n Create Credentials” node to match your organization’s style or best practices. Set Role-Based Access** – In n8n, control who can view and use these automatically generated credentials by adjusting user permissions. Time-Saving Example I initially had 16 Google accounts, requiring 112 credentials if done manually. With this workflow, I needed just one run per account—saving around two hours of repetitive data entry. Yes, I spent three hours building and documenting this, but it pays off quickly for anyone managing multiple accounts or large teams.

The 🌐🤖 AI Agent Chatbot with Jina.ai Webpage Scraper workflow is a powerful automation designed to integrate real-time web scraping capabilities into an AI-driven chatbot. Here's how it works and why it's important: How It Works 💬 Chat Trigger: The workflow begins when a user sends a chat message, triggering the "When chat message received" node. 🧠 AI Agent Processing: The input is passed to the "Jina.ai Web Scraping Agent," which uses advanced AI logic to interpret the user’s query and determine the information needed. 🌐 Web Scraping: The agent utilizes the "HTTP Request" node to scrape real-time data from a user-provided URL, enabling the chatbot to fetch and analyze live website content. 🗂️ Memory Management: The "Window Buffer Memory" node ensures context retention by storing and managing conversational history, allowing for seamless interactions. 🤖 Language Model Integration: The scraped data is processed using the "gpt-4o-mini" language model, which generates clear, accurate, and contextually relevant responses for the user. Why It's Cool ⏱️ Real-Time Information Retrieval**: This workflow empowers users to access up-to-date web content directly through a chatbot, eliminating manual web searches. ✨ Enhanced User Experience**: By combining web scraping with conversational AI, it delivers precise answers tailored to user queries in real time. 🔄 Versatility**: It can be applied across various domains, such as customer support, research, or data analysis, making it a valuable tool for businesses and individuals alike. ⚙️ Automation Efficiency**: Automating web scraping and response generation saves time and effort while ensuring accuracy.

Dessix Moss Ttsd Text Generator Description MOSS-TTSD (text to spoken dialogue) is an open-source bilingual spoken dialogue synthesis model that supports both Chinese and English. It can transform dialogue scripts between two speakers into natural, expressive conversational speech. Overview This n8n workflow integrates with the Replicate API to use the dessix/moss-ttsd model. This powerful AI model can generate high-quality text content based on your inputs. Features Easy integration with Replicate API Automated status checking and result retrieval Support for all model parameters Error handling and retry logic Clean output formatting Parameters Optional Parameters seed** (integer, default: 42): Random seed for reproducibility text** (string, default: [S1]你好[S2]你好，最近怎么样[S1]还不错，你呢[S2]我也挺好的，谢谢关心): Dialogue text, format: [S1]Speaker 1 content[S2]Speaker 2 content[S1]... use_normalize** (boolean, default: True): Whether to use text normalization (recommended for better handling of numbers, punctuation, etc.) reference_text_speaker1** (string, default: 周一到周五每天早晨七点半到九点半的直播片段,言下之意呢就是废话有点多,大家也别嫌弃,因为这都是直播间最真实的状态了): Reference text for speaker 1 (corresponding to reference audio) reference_text_speaker2** (string, default: 如果大家想听到更丰富更及时的直播内容,记得在周一到周五准时进入直播间,和大家一起畅聊新消费新科技新趋势): Reference text for speaker 2 (corresponding to reference audio) reference_audio_speaker1** (string, default: None): Reference audio file for speaker 1 (optional, for voice cloning) reference_audio_speaker2** (string, default: None): 说话者2的参考音频文件（可选，用于声音克隆）/ Reference audio file for speaker 2 (optional, for voice cloning) How to Use Set up your Replicate API key in the workflow Configure the required parameters for your use case Run the workflow to generate text content Access the generated output from the final node API Reference Model: dessix/moss-ttsd API Endpoint: https://api.replicate.com/v1/predictions Requirements Replicate API key n8n instance Basic understanding of text generation parameters

Pizza Ordering Chatbot with OpenAI - Menu, Orders & Status Tracking Introduction This workflow template is designed to automate order processing for a pizza store using OpenAI and n8n. The chatbot acts as a virtual assistant to handle customer inquiries related to menu details, order placement, and order status tracking. Features The chatbot provides an interactive experience for customers by performing the following functions: Menu Inquiry: When a customer asks about the menu, the chatbot responds with a list of available pizzas, prices, and additional options. Order Placement: If a customer places an order, the chatbot confirms order details, provides a summary, informs the customer that the order is being processed, and expresses gratitude. Order Status Tracking: If a customer asks about their order status, the chatbot retrieves details such as order date, pizza type, and quantity, providing real-time updates. Prerequisites Before setting up the workflow, ensure you have the following: OpenAI account** (Sign up here) OpenAI API key** to interact with GPT-3.5 n8n instance** running locally or on a server (Installation Guide) Configuration Steps Step 1: Set Up OpenAI API Credentials Log in to OpenAI's website. Navigate to API Keys under your account settings. Click Create API Key and copy the key for later use. Step 2: Configure OpenAI Node in n8n Open n8n and create a new workflow. Click Add Node and search for OpenAI. Select OpenAI from the list. In the OpenAI node settings, click "Create New" under the Credentials section. Enter a name for the credentials (e.g., "PizzaBot OpenAI Key"). Paste your API Key into the field. Click Save. Step 3: Set Up the Chatbot Logic Connect the AI Agent Builder Node to the OpenAI Node and HTTP Request Node. Configure the OpenAI Node with the following settings: Model: gpt-3.5-turbo Prompt: Provide dynamic text based on customer inquiries (e.g., "List available pizzas," "Place an order for Margherita pizza," "Check my order status"). Temperature: Adjust based on desired creativity (recommended: 0.7). Max Tokens: Limit response length (recommended: 150). Add multiple HTTP Request Node: For Get Products: Fetch stored menu data and return details. For Order Product: Capture order details, generate an order ID, and confirm with the customer. For Get Order: Retrieve order details based on the order ID and display progress. Step 4: Testing and Deployment Click Execute Workflow to test the chatbot. Open the Chat Message node, then copy the chat URL to access the chatbot in your browser. Interact with the chatbot by asking different queries (e.g., "What pizzas do you have?" or "I want to order a Pepperoni pizza"). Verify responses and adjust prompts or configurations as needed. Deploy the workflow and integrate it with a messaging platform (e.g., Telegram, WhatsApp, or a website chatbot). Conclusion This n8n workflow enables a fully functional pizza ordering chatbot using OpenAI's GPT-3.5. Customers can view menus, place orders, and track their order status efficiently. You can further customize the chatbot by refining prompts, adding new features, or integrating with external databases for order management. 🚀 Happy automating!

Advanced Retry and Delay Logic This template provides a robust solution for handling API rate limits and temporary service outages in n8n workflows. It overcomes the limitations of the default node retry settings, which cap retries at 5 and delays at 5 seconds. By using a custom loop with a Set, If, and Wait node, this workflow gives you complete control over the number of retries and the delay between them. Instructions: Replace the placeholder HTTP Request node with your target node (the one that might fail). In the initial Set Fields node, modify the max_tries value to set the total number of attempts for your workflow. Adjust the delay_seconds value to define the initial delay between retries. Optionally, configure the Edit Fields node to implement exponential backoff by adjusting the delay_seconds expression (e.g., {{$json.delay_seconds * 2}}). For a more detailed breakdown and tutorial of this template, you can find additional information here.

Welcome to Ozki Your Data Analyst Agent V1. The Ozki Data Analyst Agent is designed to analyze data from Google Sheets. To use it, you'll need to provide the URL of your Google Sheet file. The agent will then process the data and provide you with analysis results, including key performance indicators (KPIs). Configuration: Configure your credentials on the OpenAI model or select the n8n free OpenAI credits. Set up your agent memory. Use Simple Memory as default. Set your credentials to Google Sheets. Log in with the Google Sheet tool. Instructions: Start with a "Hi" to get the instructions. Ozki needs your Google Sheet URL, which you can get from the address bar of your browser when you have the file open. Follow the conversation with Ozki for your data analysis results.

This template is designed for community managers, developers, and enthusiasts who want to monitor and capture discussions on the n8n community forum by specific keywords. By tracking new topics that match your provided keyword, the workflow automatically logs them into a Google Sheet, helping you keep a pulse on trending discussions and manage community insights effortlessly. How it Works Keyword Monitoring: The workflow continuously monitors the n8n community forum for new topics containing your specified keyword. Data Extraction: When a matching topic is found, relevant details such as id, title, URL, and posting time are extracted. Google Sheets Integration: The extracted information is automatically appended as a new row in your Google Sheet, providing an organized log of community discussions. Alert Option: Optionally, you can add notifications (e.g., Slack messages or emails) to alert you when a new topic is captured, ensuring you never miss an important update. Set Up Steps Import the Workflow: Download and import the workflow into your n8n instance. Configure Keyword Monitoring: In the HTTP Request node, set your desired keyword to filter the n8n community topics. Set Up Google Sheets: Connect your Google Sheets account and specify the target sheet where the new topics should be logged. Customize Data Fields: Adjust the data extraction node parameters if you wish to capture additional details from each topic. Deploy and Test: Deploy the workflow and perform a test run to confirm that new topics matching the keyword are correctly added to your Google Sheet. Set Up Messaging Channels (Optional): Connect to Slack, Email or other providers to get instant messages when new topics are added into your Google Sheet. This template streamlines the process of tracking community discussions, ensuring you have timely and organized insights to enhance your community engagement and strategic decision-making. Enjoy seamless monitoring and easy data management with this ready-to-use n8n workflow template!

This n8n workflow automates the process of converting a newly stored PDF file from Google Drive into an HTML file and saving it back to Google Drive. The workflow is triggered whenever a new PDF is uploaded to a specific folder, ensuring seamless conversion and storage without any manual intervention. This workflow provides an efficient, automated solution for converting PDFs to HTML, eliminating the need for manual file handling and ensuring a smooth document transformation process. It is particularly useful for scenarios where PDFs need to be dynamically converted and stored in an organised manner for web usage, archiving, or further processing. Prerequisites : Before setting up this workflow, ensure the following: PDF.co API Key: Sign up at PDF.co and obtain an API key for PDF to HTML conversion. Proper Authentication: Ensure authentication is configured for Google Drive in n8n. Customisation Options : Modify the API request to convert PDFs to other formats like Text, CSV, or XML. Extend the IF Node to reject files based on size or other properties. Send a notification once the conversion is complete using an Email or Telegram Node. Steps : Step 1: Google Drive Trigger Node (Watch for New Files) Click "Add Node" and search for Google Drive. Select "Google Drive Trigger" and add it to the workflow. Authenticate with your Google Account. Select the folder to monitor. Set the trigger to activate whenever a new file is added. Click "Execute Node" to test. Click "Save". Step 2: IF Node (Check if File is a PDF) Click "Add Node" and search for IF. Add a condition to check if the file extension is .pdf If true → Send the file to the next step. If false → Stop the workflow. Click "Execute Node" to test. Click "Save". Step 3: HTTP Request Node (Convert PDF to HTML) Click "Add Node" and search for HTTP Request. Set the Method to POST. Enter the PDF.co API endpoint for PDF to HTML conversion. In the Headers, add API key which we have get from pdf.co Send the binary PDF data as the request body. Click "Execute Node" to test. Click "Save". Step 4: Function Node (Convert Response to Binary) Click "Add Node" and search for Function. Write a JavaScript function to transform the API response into a binary file. Click "Execute Node" to test. Click "Save". Step 5: Google Drive Node (Save Converted HTML File) Click "Add Node" and search for Google Drive. Select "Upload File" as the action. Authenticate with your Google Account. Set the destination folder for storing the HTML file. Map the binary data from the Function Node. Click "Execute Node" to test. Click "Save". Step 6: Connect & Test the Workflow Link the nodes in this order (Google Drive Trigger → IF Node → HTTP Request → Function Node → Google Drive Upload) Run the workflow manually. Upload a test PDF to Google Drive. Check Google Drive for the converted HTML file. Who’s behind this? WeblineIndia’s AI development team. We've delivered 3500+ software projects across 25+ countries since 1999. From no-code automations to complex AI systems — our AI team builds tools that drive results. Looking to hire AI developers? Start with us.

This tutorial demonstrates the creation of the HTML report via Markdown node. The main idea is to prepare a very long gext variable via the Function Node and then convert it to the HTML file. The resulting report can be downloaded from the workflow canvas directly or send via email as an attachment.

This workflow illustrates how HTML reports can be created using Markdown Node. An example data consists of a Time Sheet table for 2 persons. Based on this table a markdown document is generated using Function Node. After that a final HTML report is created and is saved as binary file. This file can be either downloaded directly from the workflow canvas or sent as an email attachement.