Workflow Templates

Image Creation with OpenAI and Telegram Check this channel: AutoTechAi_bot Description: In the realm of automation and artificial intelligence, n8n offers a sophisticated platform for seamlessly integrating AI algorithms to enhance image creation and communication processes. This innovative workflow leverages the capabilities of OpenAI and Telegram to facilitate creative image generation and streamline communication channels, ultimately enhancing user engagement and interaction. How to Use: Set Up Credentials: Configure credentials for the Telegram account and OpenAI API to enable seamless integration. Configure Nodes: Telegram Trigger Node: Set up the node to initiate the workflow based on incoming messages from users on Telegram. OpenAI Node: Utilize advanced AI algorithms to analyze text content from messages and generate intelligent responses. Telegram Node: Send processed data, including images and responses, back to users on Telegram for seamless communication. Merge Node: Organize and combine processed data for efficient handling and integration within the workflow. Aggregate Node: Aggregate all item data, including binaries if specified, for comprehensive reporting and analysis purposes. Run Workflow: Initiate the workflow to leverage AI-enhanced image processing and communication capabilities for enhanced user interactions. Monitor Execution: Keep an eye on the workflow execution for any errors or issues that may occur during processing. Customize Workflow: Tailor the workflow nodes, parameters, or AI models to align with specific business objectives and user engagement strategies. Experience Benefits: Embrace the power of AI-driven image processing and interactive communication on Telegram to elevate user engagement and satisfaction levels. By following these steps, businesses can unlock the transformative potential of AI integration in image creation and communication workflows using n8n. Elevate your user engagement strategies and deliver exceptional experiences to your audience through innovative AI-driven solutions. Embark on a journey of innovation and efficiency with AI integration in image creation and communication workflows using n8n!

Tags: Marketing, Image Processing, Automation Context Hey! I’m Samir, a Data Scientist from Paris and the founder of LogiGreen Consulting. We use AI, automation, and data to support sustainable business practices for small, medium and large companies. I implemented this workflow to support an event agency to automate image processing like background removal using Photoroom API. > Automate your photos processing with n8n! This n8n workflow collects all images in a Google Drive folder shared with multiple photographers. For each image, it calls the Photoroom API: A processed image w/o a background is saved in a subfolder Remove Background The original pictures are saved in the subfolder Original This workflow, triggered every morning, will process the backlog of images. 📬 For business inquiries, feel free to connect with me on LinkedIn Who is this template for? This workflow is useful for: Digital Marketing** teams that use images for content creation Photographs* or *Event Organisers** that collect large amounts of photos that need processing What does it do? This n8n workflow: ⏰ Triggers automatically every morning 🖼️ Collects the names and IDs of all images in the folder 🧹 HTTP POST request to Photoroom API to remove the background 📄 Stores the processed image and the original image in two separate sub-folders What do I need to get started? You’ll need: A Google Drive Account connected to your n8n instance with credentials A Photoroom API key that you can get for free (trial) here: Photoroom API Follow the Guide! Follow the sticky notes inside the workflow or check out my step-by-step tutorial on how to configure and deploy it. 🎥 Watch My Tutorial This workflow was built using n8n version 1.93.0 Submitted: May 26, 2025

Title: Automatic Strava Titles & Descriptions Generation with AI Description: This n8n workflow connects your Strava account to an AI to automatically generate personalized titles and descriptions for every new cycling activity. It leverages the native Strava trigger to detect new activities, extracts and formats ride data, then queries an AI agent (OpenRouter, ChatGPT, etc.) with an optimized prompt to get a catchy title and inspiring description. The workflow then updates the Strava activity in real time, with zero manual intervention. Key Features: Secure connection to the Strava API (OAuth2) Automatic triggering for every new activity Intelligent data preparation and formatting AI-powered generation of personalized content (title + description) Instant update of the activity on Strava Use Cases: Cyclists wanting to automatically enhance their Strava rides Sports content creators Community management automation for sports groups Prerequisites: Strava account Strava OAuth2 credentials set up in n8n Access to a compatible AI agent (OpenRouter, ChatGPT, etc.) Benefits: Saves time Advanced personalization Boosts the appeal of every ride to your community

This workflow analyzes the sentiments of the feedback provided by users and sends them to a Mattermost channel. Typeform Trigger node: Whenever a user submits a response to the Typeform, the Typeform Trigger node will trigger the workflow. The node returns the response that the user has submitted in the form. Google Cloud Natural Language node: This node analyses the sentiment of the response the user has provided and gives a score. IF node: The IF node uses the score provided by the Google Cloud Natural Language node and checks if the score is negative (smaller than 0). If the score is negative we get the result as True, otherwise False. Mattermost node: If the score is negative, the IF node returns true and the true branch of the IF node is executed. We connect the Mattermost node with the true branch of the IF node. Whenever the score of the sentiment analysis is negative, the node gets executed and a message is posted on a channel in Mattermost. NoOp: This node here is optional, as the absence of this node won't make a difference to the functioning of the workflow. This workflow can be used by Product Managers to analyze the feedback of the product. The workflow can also be used by HR to analyze employee feedback. You can even use this node for sentiment analysis of Tweets. To perform a sentiment analysis of Tweets, replace the Typeform Trigger node with the Twitter node. Note:* You will need a Trigger node or Start node to start the workflow. Instead of posting a message on Mattermost, you can save the results in a database or a Google Sheet, or Airtable. Replace the Mattermost node with (or add after the Mattermost node) the node of your choice to add the result to your database. You can learn to build this workflow on the documentation page of the Google Cloud Natural Language node.

This workflow analyzes the sentiments of the feedback provided by users and sends them to a Mattermost channel. Typeform Trigger node: Whenever a user submits a response to the Typeform, the Typeform Trigger node will trigger the workflow. The node returns the response that the user has submitted in the form. AWS Comprehend node: This node analyses the sentiment of the response the user has provided and gives a score. IF node: The IF node uses the data provided by the AWS Comprehend node and checks if the sentiment is negative. If the sentiment is negative we get the result as true, otherwise false. Mattermost node: If the score is negative, the IF node returns true and the true branch of the IF node is executed. We connect the Mattermost node with the true branch of the IF node. Whenever the score of the sentiment analysis is negative, the node gets executed and a message is posted on a channel in Mattermost. NoOp: This node here is optional, as the absence of this node won't make a difference to the functioning of the workflow. This workflow can be used by Product Managers to analyze the feedback of the product. The workflow can also be used by HR to analyze employee feedback. You can even use this node for sentiment analysis of Tweets. To perform a sentiment analysis of Tweets, replace the Typeform Trigger node with the Twitter node. Note: You will need a Trigger node or Start node to start the workflow. Instead of posting a message on Mattermost, you can save the results in a database or a Google Sheet, or Airtable. Replace the Mattermost node with (or add after the Mattermost node) the node of your choice to add the result to your database.

🤖 Discord AI Workflow: Your Automated Assistant! 🚀 🌟 Workflow Overview Transforms your Discord server into an intelligent, responsive powerhouse of communication and automation! 🔧 Core Components 💬 AI-Powered Messaging 🤝 Multi-Channel Interaction 🧠 Smart Response Generation 🔗 Seamless Workflow Integration 🚦 Trigger Modes 1️⃣ Workflow Trigger 🔓 Activated by external workflows 📨 Processes incoming tasks 🌐 Supports complex automation scenarios 2️⃣ Chat Message Trigger 🗣️ Responds to direct Discord messages 🤔 Contextual understanding 🔍 Real-time interaction 🛠️ Key Features 🤖 AI-Driven Conversations 📊 Dynamic Message Handling 🔒 Secure Credential Management 🌈 Flexible Configuration 🚀 Use Cases 📢 Automated Announcements 🆘 Support Ticket Management 📝 Content Generation 🤝 Community Engagement 💡 Smart Capabilities 🧩 Modular Design 🔄 Seamless Data Flow 📝 Character Limit Management 🌐 Multi-Channel Support 🛡️ Security & Performance 🔐 OAuth Integration 🚧 Error Handling 📊 Performance Optimization 🛠️ Continuous Improvement 🎯 Workflow Magic User Input ➡️ AI Processing ➡️ Smart Response ➡️ Discord Channel 🌟 🤖 💬 📨 🔍 Customization Playground 🎨 Personalize AI Responses 🔧 Adjust Interaction Rules 📐 Fine-Tune Workflow Behavior 🚧 Troubleshooting Toolkit 🕵️ Credential Verification 🔬 Permissions Check 📋 Comprehensive Logging 🆘 Error Handling Strategies 🌈 Future Possibilities 🤖 Advanced AI Integration 🚀 Expanded Interaction Modes 🧠 Machine Learning Enhancements 🌐 Ecosystem Expansion

Workflow Description: This n8n workflow automates the drafting of email replies for Fastmail using OpenAI's GPT-4 model. Here’s the overall process: Email Monitoring: The workflow continuously monitors a specified IMAP inbox for new, unread emails. Email Data Extraction: When a new email is detected, it extracts relevant details such as the sender, subject, email body, and metadata. AI Response Generation: The extracted email content is sent to OpenAI's GPT-4, which generates a personalized draft response. Get Fastmail Session and Mailbox IDs: Connects to the Fastmail API to retrieve necessary session details and mailbox IDs. Draft Identification: Identifies the "Drafts" folder in the mailbox. Draft Preparation: Compiles all the necessary information to create the draft, including the generated response, original email details, and specified recipient. Draft Uploading: Uploads the prepared draft email to the "Drafts" folder in the Fastmail mailbox. Prerequisites: IMAP Email Account: You need to configure an IMAP email account in n8n to monitor incoming emails. Fastmail API Credentials: A Fastmail account with JMAP API enabled. You should set up HTTP Header authentication in n8n with your Fastmail API credentials. OpenAI API Key: An API key from OpenAI to access GPT-4. Make sure to configure the OpenAI credentials in n8n. Configuration Steps: Email Trigger (IMAP) Node: Provide your email server settings and credentials to monitor emails. HTTP Request Nodes for Fastmail: Set up HTTP Header authentication in n8n using your Fastmail API credentials. Replace the httpHeaderAuth credential IDs with your configured credential IDs. OpenAI Node: Configure the OpenAI API key in n8n. Replace the openAiApi credential ID with your configured credential ID. By following these steps and setting up the necessary credentials, you can seamlessly automate the creation of email drafts in response to new emails using AI-generated content. This workflow helps improve productivity and ensures timely, personalized communication.

Modern AI systems are powerful but pose privacy risks when handling sensitive data. Organizations need AI capabilities while ensuring: ✅ Sensitive data never leaves secure environments ✅ Compliance with regulations (GDPR, HIPAA, PCI, SOX) ✅ Real-time decision making about data sensitivity ✅ Comprehensive audit trails for regulatory review The Concept: Intelligent Data Classification + Smart Routing The goal of this concept is to build the foundations of the safe and compliant use of LLMs in Agentic workflows by automatically detecting sensitive data, applying sanitization rules, and intelligently routing requests through secure processing channels. This workflow will analyze the user's chat or webhook input and attempt to detect PII using the Enhanced PII Pattern Detector. If detected, the workflow will process that input via a series of Compliance, Auditing, and Security steps which log and sanitizes the request prior to any LLM being pinged. Why Multi-Tier Routing? Traditional systems use binary decisions (sensitive/not sensitive). Our 3-tier approach provides: ✅ Granular Security: Critical PII gets maximum protection ✅ Performance Optimization: Clean data gets full cloud capabilities ✅ Cost Efficiency: Expensive local processing only when needed ✅ User Experience: Maintains conversational flow across security levels Why Context-Aware Detection? Regex patterns alone miss contextual sensitivity. Our approach: ✅ Catches Intent: "Bank account" discussion is sensitive even without account numbers ✅ Reduces False Negatives: Medical discussions stay secure even without explicit medical IDs ✅ Proactive Protection: Identifies sensitive contexts before PII is shared ✅ Compliance Alignment: Matches how regulations actually define sensitive data Why Risk Scoring vs Binary Classification? Binary PII detection creates artificial boundaries. Risk scoring provides: ✅ Nuanced Decisions: Multiple low-risk patterns might aggregate to high risk ✅ Adaptive Thresholds: Organizations can adjust sensitivity based on their needs ✅ Better UX: Users aren't unnecessarily restricted for low-risk scenarios ✅ Audit Transparency: Clear reasoning for every routing decision Why Comprehensive Monitoring? Privacy systems require trust and verification: ✅ Compliance Proof: Audit trails demonstrate regulatory compliance ✅ Performance Optimization: Identify bottlenecks and improve efficiency ✅ Security Validation: Ensure no sensitive data leakage occurs ✅ Operational Insights: Understand usage patterns and system health How to Install: All that you will need for this workflow are credentials for your LLM providers such as Ollama, OpenRouter, OpenAI, Anthropic, etc. This workflow is customizable and allows the user to define the best LLM and storage/memory solutions for their specific use case.

This workflow connects Telegram bots with LangChain nodes in n8n. The main AI Agent Node is configured as a Conversation Agent. It has a custom System Prompt which explains the reply formatting and provides some additional instructions. The AI Agent has several connections: OpenAI GPT-4 model is called to generate the replies Window Buffer Memory stores the history of conversation with each user separately There is an additional Custom n8n Workflow tool (Dall-E 3 Tool). AI Agent uses this tool when the user requests an image generation. In the lower part of the workflow, there is a series of nodes that call Dall-E 3 model with the user Telegram ID and a prompt for a new image. Once image is ready, it is sent back to the user. Finally, there is an extra Telegram node that masks HTML syntax for improved stability in case the AI Agent replies using the unsupported format.

Description This n8n automation template delivers a full-stack AI content pipeline designed for marketing teams, content creators, SaaS founders, and growth hackers. It combines prompt chaining, GPT-4o agents, and Google Sheets to generate engaging, SEO-friendly blogs—end to end. What This Template Does: 📝 Generates blog topic ideas using a domain-specific AI agent (e.g., for Sparrow API testing) 📄 Creates a blog outline with key sections and headings ✅ Evaluates & refines the outline to ensure clarity, flow, and engagement 🧾 Writes the full blog content in structured, long-form paragraphs 📥 Appends the blog to Google Sheets with the current date Built With: GPT-4o (via Azure OpenAI) LangChain Agents for task-specialized prompt chaining Google Sheets integration for automatic publishing Schedule Trigger for periodic content generation Ideal Use Cases: SaaS teams looking to scale inbound content API platforms (like Sparrow) publishing technical how-tos SEO agencies automating client blog content Solo founders growing product visibility via thought leadership

This is a workflow where a support channel on Telegram is being used to gather customer feedback. Depending on certain keywords in the customer's message, this workflow creates a ticket with a tag in your Freshdesk instance. The customer is then sent a message on Telegram and an item is created on Monday.com for tracking.

This workflow takes a text file as input. It pulls the information from the text file and used it as a parameter to execute a command for each text line. This workflow references a file /home/n8n/filelist.txt in the Read Binary File node which will need to be changed to work properly. You can also edit the Execute Command node to modify what happens for each of these lines of text. Note: This workflow requires the Execute Command node which is only available on the on-premise version of n8n.