by AlQaisi
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!
by Vitali
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.
by Rajeet Nair
Overview This workflow automates customer support ticket processing using AI-powered analysis. Incoming tickets from email (IMAP) or a webhook endpoint are automatically cleaned, translated to English if necessary, analyzed with AI, and routed based on urgency and category. The workflow can automatically generate draft replies for simple tickets or escalate critical issues to your support team. It also updates your CRM or helpdesk system with structured ticket insights and logs observability metrics for monitoring support performance. This automation helps support teams reduce manual triage work, respond faster to customers, and ensure urgent issues receive immediate attention. How It Works 1. Ticket Intake The workflow begins when a support request is received from one of two sources: IMAP Email Trigger** – Reads incoming support emails from a mailbox. Webhook Trigger** – Accepts tickets from external systems such as websites, chatbots, or applications. Both triggers feed the message into a unified processing pipeline. 2. Content Cleaning The workflow extracts readable text from incoming messages using an HTML extraction node. This ensures that emails or formatted messages can be analyzed reliably. 3. Ticket Data Normalization Incoming data is standardized to ensure consistent processing across all ticket sources. The workflow generates fields such as: ticket_id user_email original_message timestamp source_channel 4. Language Detection & Translation An AI agent detects the original language of the ticket. If the message is not written in English, it is automatically translated while preserving the original meaning and tone. 5. AI Support Intelligence A second AI agent analyzes the ticket and produces structured insights including: Sentiment (positive, neutral, negative) Urgency level (low, medium, high, critical) Ticket category Issue summary Customer churn risk score Recommended action path 6. Intelligent Routing A Switch node routes the ticket based on the AI analysis: Auto Reply Path** – Generates a draft response. Escalation Path** – Sends the ticket to a support escalation webhook. 7. Draft Reply Generation If the ticket qualifies for automatic handling, an AI agent generates a professional support response based on the ticket content, sentiment, and category. 8. CRM / Helpdesk Update The workflow sends structured ticket information to a CRM or helpdesk system, including: Ticket ID Category Sentiment Urgency Churn risk score AI-generated summary Draft reply 9. Observability Metrics The workflow logs operational metrics such as response time, ticket category, urgency, sentiment, and escalation status. These metrics can be sent to an observability or monitoring system. Setup Instructions Configure Email Credentials (Optional) Add IMAP credentials if you want to process support emails. Configure the Webhook Trigger Use the webhook URL generated by the workflow to receive support tickets from external systems. Add AI Model Credentials Connect your Anthropic API credentials to power the AI agents used for translation, analysis, and response generation. Configure Workflow Variables In the Workflow Configuration node, provide: CRM or Helpdesk API URL Escalation webhook URL Observability logging endpoint (optional) Connect Your CRM or Helpdesk System Ensure the API endpoint accepts JSON payloads containing ticket data and AI insights. Use Cases AI-powered customer support ticket triage Handling multilingual support requests Automatically generating draft responses Escalating critical support tickets Monitoring support performance metrics Requirements Anthropic API credentials IMAP email credentials (optional) CRM or Helpdesk API endpoint Escalation webhook endpoint Optional observability or monitoring endpoint
by Friedemann Schuetz
Welcome to my Airbnb Telegram Agent Workflow! This workflow creates an intelligent Telegram bot that helps users search and find Airbnb accommodations using natural language queries and voice messages. DISCLAIMER: This workflow only works with self-hosted n8n instances! You have to install the n8n-nodes-mcp-client Community Node! What this workflow does This workflow processes incoming Telegram messages (text or voice) and provides personalized Airbnb accommodation recommendations. The AI agent understands natural language queries, searches through Airbnb data using MCP tools, and returns mobile-optimized results with clickable links, prices, and key details. Key Features: Voice message support (speech-to-text and text-to-speech) Conversation memory for context-aware responses Mobile-optimized formatting for Telegram Real-time Airbnb data access via MCP integration This workflow has the following sequence: Telegram Trigger - Receives incoming messages from users Text or Voice Switch - Routes based on message type Voice Processing (if applicable) - Downloads and transcribes voice messages Text Preparation - Formats text input for the AI agent Airbnb AI Agent - Core logic that: Lists available MCP tools for Airbnb data Executes searches with parsed parameters Formats results for mobile display Response Generation - Sends formatted text response Voice Response (optional) - Creates and sends audio summary Requirements: Telegram Bot API**: Documentation Create a bot via @BotFather on Telegram Get bot token and configure webhook OpenAI API**: Documentation Used for speech transcription (Whisper) Used for chat completion (GPT-4) Used for text-to-speech generation MCP Community Client Node**: Documentation Custom integration for Airbnb data Requires MCP server setup with Airbnb/Airtable connection Provides tools for accommodation search and details Important: You need to set up an MCP server with Airbnb data access. The workflow uses MCP tools to retrieve real accommodation data, so ensure your MCP server is properly configured with the Airtable/Airbnb integration. Configuration Notes: Update the Telegram chat ID in the trigger for your specific bot Modify the system prompt in the Airbnb Agent for different use cases The workflow supports both individual users and can be extended for group chats Feel free to contact me via LinkedIn, if you have any questions!
by Dataki
This template is a simple AI Agent that acts as a Google Calendar Assistant. It is designed for beginners to have their "first AI Agent" performing common tasks and to help them understand how it works. For new users of n8n, AI Agents, and OpenAI: This template involves using an OpenAI API Key. If you are new to AI Agents, make sure to research and understand key concepts such as: "Tokens"** (used for API requests), "Tool calling"** (how the AI interacts with external tools), OpenAI's usage costs** (how you will be billed for API usage). Functionality It has two main functionalities: Create events** in a calendar Retrieve events** from a calendar How you can use it Everything is explained with sticky notes in the workflow. It is ready-to-use: all you need to do is connect your OpenAI credentials, and you can start using the workflow.
by Friedemann Schuetz
Welcome to my Simple OpenAI Image Generator Workflow! This workflow creates an image with the new OpenAI image model "GPT-Image-1" based on a form input. This workflow has the following sequence: Form trigger (image prompt and image size input) Generate the Image via OpenAI API. Return the image to the input form for download. The following accesses are required for the workflow: OpenAI API access: Documentation Instructions Link your OpenAI Platform account in the “OpenAI Image Generation” node ("Credential Type") You can contact me via LinkedIn, if you have any questions: https://www.linkedin.com/in/friedemann-schuetz
by Yaron Been
This workflow provides automated access to the Ibm Granite Granite 3.3 8B Instruct AI model through the Replicate API. It saves you time by eliminating the need to manually interact with AI models and provides a seamless integration for text generation tasks within your n8n automation workflows. Overview This workflow automatically handles the complete text generation process using the Ibm Granite Granite 3.3 8B Instruct model. It manages API authentication, parameter configuration, request processing, and result retrieval with built-in error handling and retry logic for reliable automation. Model Description: Granite-3.3-8B-Instruct is a 8-billion parameter 128K context length language model fine-tuned for improved reasoning and instruction-following capabilities. Key Capabilities Advanced text generation and processing** Natural language understanding and generation** Intelligent text manipulation and analysis** Tools Used n8n**: The automation platform that orchestrates the workflow Replicate API**: Access to the Ibm Granite/granite-3.3-8b-instruct AI model Ibm Granite Granite 3.3 8B Instruct**: The core AI model for text generation Built-in Error Handling**: Automatic retry logic and comprehensive error management How to Install Import the Workflow: Download the .json file and import it into your n8n instance Configure Replicate API: Add your Replicate API token to the 'Set API Token' node Customize Parameters: Adjust the model parameters in the 'Set Text Parameters' node Test the Workflow: Run the workflow with your desired inputs Integrate: Connect this workflow to your existing automation pipelines Use Cases Content Writing**: Generate articles, blogs, and marketing copy Code Generation**: Assist with programming and code documentation Text Analysis**: Process and analyze large volumes of text data Automated Communication**: Generate responses and communication templates Connect with Me Website**: https://www.nofluff.online YouTube**: https://www.youtube.com/@YaronBeen/videos LinkedIn**: https://www.linkedin.com/in/yaronbeen/ Get Replicate API**: https://replicate.com (Sign up to access powerful AI models) #n8n #automation #ai #replicate #aiautomation #workflow #nocode #textgeneration #nlp #aiwriting #textai #contentgeneration #aitext #machinelearning #artificialintelligence #aitools #automation #digitalart #contentcreation #productivity #innovation
by n8n Team
The workflow is an automated process designed for incident management and tracking, specifically by integrating Splunk alerts with a Jira ticketing system using n8n. The initial step in the workflow is a Webhook Trigger, which is set up to receive POST requests with data from Splunk to initiate the workflow. Once the workflow is triggered, the "Set Host Name" node cleans up the hostname received from Splunk, ensuring that it is alphanumeric for consistency and security purposes. Subsequently, the "Search Ticket" node interacts with Jira through a Jira Query Language (JQL) request to locate any existing issues that match the sanitized hostname. The workflow splits at the "IF Ticket Not Exists" node, which checks for the presence of a key indicating a matching issue. If an issue exists, the workflow proceeds to add a comment to the identified issue, and if not, it creates a new Jira issue. At the false path, the "Add Ticket Comment" node appends a new comment to the existing Jira issue, encapsulating details from the Splunk alert, such as the timestamp and the alert description.
by Davide
This workflow is designed to intelligently route user queries to the most suitable large language model (LLM) based on the type of request received in a chat environment. It uses structured classification and model selection to optimize both performance and cost-efficiency in AI-driven conversations. It dynamically routes requests to specialized AI models based on content type, optimizing response quality and efficiency. Benefits Smart Model Routing**: Reduces costs by using lighter models for general tasks and reserving heavier models for complex needs. Scalability**: Easily expandable by adding more request types or LLMs. Maintainability**: Clear logic separation between classification, model routing, and execution. Personalization**: Can be integrated with session IDs for per-user memory, enabling personalized conversations. Speed Optimization**: Fast models like GPT-4.1 mini or Gemini Flash are chosen for tasks where speed is a priority. How It Works Input Handling: The workflow starts with the "When chat message received" node, which triggers the process when a chat message is received. The input includes the chat message (chatInput) and a session ID (sessionId). Request Classification: The "Request Type" node uses an OpenAI model (gpt-4.1-mini) to classify the incoming request into one of four categories: general: For general queries. reasoning: For reasoning-based questions. coding: For code-related requests. google: For queries requiring Google tools. The classification is structured using the "Structured Output Parser" node, which enforces a consistent output format. Model Selection: The "Model Selector" node routes the request to one of four AI models based on the classification: Opus 4 (Claude 4 Sonnet): Used for coding requests. Gemini Thinking Pro: Used for reasoning requests. GPT 4.1 mini: Used for general requests. Perplexity: Used for search (Google-related) requests. AI Processing: The selected model processes the request via the "AI Agent" node, which includes intermediate steps for complex tasks. The "Simple Memory" node retains session context using the provided sessionId, enabling multi-turn conversations. Output: The final response is generated by the chosen model and returned to the user. Set Up Steps Configure Trigger: Ensure the "When chat message received" node is set up with the correct webhook ID to receive chat inputs. Define Classification Logic: Adjust the prompt in the "Request Type" node to refine classification accuracy. Verify the output schema in the "Structured Output Parser" node matches expected categories (general, reasoning, coding, google). Connect AI Models: Link each model node (Opus 4, Gemini Thinking Pro, GPT 4.1 mini, Perplexity) to the "Model Selector" node. Ensure credentials (API keys) for each model are correctly configured in their respective nodes. Set Up Memory: Configure the "Simple Memory" node to use the sessionId from the input for context retention. Test Workflow: Send test inputs to verify classification and model routing. Check intermediate outputs (e.g., request_type) to ensure correct model selection. Activate Workflow: Toggle the workflow to "Active" in n8n after testing. Need help customizing? Contact me for consulting and support or add me on Linkedin.
by keisha kalra
Try It Out! This n8n template helps you create SEO-optimized Blog Posts for your businesses website or for personal use. Whether you're managing a business or helping local restaurants improve their digital presence, this workflow helps you build SEO-Optimized Blog Posts in seconds using Google Autocomplete and People Also Ask (SerpAPI). Who Is It For? This is helpful for people looking to SEO Optimize either another person's website or their own. How It Works? You start with a list of blog inspirations in Google Sheets (e.g., “Best Photo Session Spots”). The workflow only processes rows where the “Status” column is not marked as “done”, though you can remove this condition if you’d like to process all rows each time. The workflow pulls Google Autocomplete suggestions and PAA questions using: A custom-built SEO API I deployed via Render (for Google Autocomplete + PAA), SerpAPI (for additional PAA data). These search insights are merged. For example, if your blog idea is “Photo Session Spots,” the workflow gathers related Google search phrases and questions users are asking. Then, GPT-4 is used to draft a full blog post based on this data. The finished post is saved back into your Google Sheet. How To Use Fill out the “Blog Inspiration” column in your Google Sheet with the topics you want to write about. Update the OpenAI prompt in the ChatGPT node to match your tone or writing style. (Tip: Add a system prompt with context about your business or audience.) You can trigger this manually, or replace it with a cron schedule, webhook, or other event. Requirements A SerpAPI account to get PAA An OpenAI account for ChatGPT Access to Google Sheets and n8n How To Set-Up? Your Google Sheet should have three columns: "Blog Inspiration", "Status" → set this to “done” when a post has been generated, "Blog Draft" → this is automatically filled by the workflow. To use the SerpAPI HTTP Request node: 1. Drag in an HTTP Request node, 2. Set the Method and URL depending on how you're using SerpAPI: Use POST to run the actor live on each request. Use GET to fetch from a static dataset (cheaper if reusing the same data). 3. Add query parameters for your SerpAPI key and input values. 4. Test the node. Refer to this n8n documentation for more help! https://docs.n8n.io/integrations/builtin/cluster-nodes/sub-nodes/n8n-nodes-langchain.toolserpapi/. The “Autocomplete” node connects to a custom web service I built and deployed using Render. I wrote a Python script (hosted on GitHub) that pulls live Google Autocomplete suggestions and PAA questions based on any topic you send. This script was turned into an API and deployed as a public web service via Render. Anyone can use it by sending a POST request to: https://seo-api2.onrender.com/get-seo-data (the URL is already in the node). Since this is hosted on Render’s free tier, if the service hasn’t been used in the past ~15 minutes, it may “go to sleep.” When that happens, the first request can take 10–30 seconds to respond while it wakes up. Happy Automating!
by Jonathan
Task: Conditional filtering and branching items Why: Filtering and branching data based on conditions allows you to build complex workflows that work with more than one data flow scenario Main use cases: Filter out data that is not relevant for the rest of the workflow Split data to several branches of the workflow, where we want the data to be treated differently in the rest of the workflow
by n8n Team
This workflow implements a custom tool via JavaScript code which returns a random color to users and excludes the given colors. Note that to use this template, you need to be on n8n version 1.19.4 or later.