Conversational Telegram bot with GPT-5/GPT-4o for text and voice messages
This n8n workflow leverages a Telegram Message Trigger to activate an intelligent AI Agent capable of processing both text and voice messages. When a user sends a message in text or in voice format, the workflow captures and transcribes it (if necessary), then passes it to the AI Agent for understanding and response generation. To enhance user experience, the bot also displays a typing indicator while processing requests, simulating a natural, human-like interaction. Key Features Multi-Modal Input: Supports both text messages and voice notes from users. Real-Time Interaction: Shows a “typing…” action in Telegram while the AI processes the input. AI Agent Integration: Provides intelligent, context-aware, and conversational responses. Seamless Feedback Loop: Replies are sent directly back to the user within Telegram for smooth interaction. How It Works The workflow triggers whenever a message or voice note is received on Telegram. If the input is a voice note, the workflow transcribes it into text. The text input is sent to the AI Agent for processing. While processing, the bot sends a typing indicator to the user. Once the AI generates a response, the workflow sends it back to the user in Telegram. Setup Instructions Create a Telegram Bot: Use @BotFather to create a bot and obtain your bot token. Configure n8n Credentials: Add Telegram API credentials in n8n with your bot token. Add credentials for any speech-to-text service used for voice transcription (e.g., Open AI Transcribe A Recording). Import the Workflow: Import this workflow into your n8n instance. Update all credential nodes to use your Telegram and transcription service credentials. Set Webhook URLs: Ensure Telegram webhook is set properly for your bot to receive messages. Make sure your n8n instance is publicly accessible for Telegram callbacks. Test the Workflow: Send text messages and voice notes to your Telegram bot and observe the AI responses. Customization Guidance Add new message handlers: Extend the workflow to handle additional message types (images, documents, etc.). Improve transcription: Swap or add speech-to-text services for better accuracy or language support. Enhance AI Agent: Customize prompts and context management to tailor the AI’s personality and responses. AI Model Flexibility: Swap between different AI models (e.g., GPT-5, GPT-4, Claude, or custom LLMs) based on task type, cost, or performance preferences. By default, I use GPT-4o in this template. However, you can use the latest GPT-5 model by changing them in OpenAI Chat Model Node. It will show you a list of all the available models to choose. Tool-Based Control: Add custom tools to the AI Agent such as calendar access, Notion, Google Sheets, web search, database queries, or custom APIs—allowing for dynamic, multi-functional agents Security and Implementation Notes The Telegram node manages message reception and sending but does not directly handle AI processing. Voice transcription requires integration with external APIs; secure those credentials in n8n and monitor usage. To simulate typing, the workflow uses Telegram’s “sendChatAction” API method, providing users with feedback that the bot is processing. Ensure your AI API keys and Telegram tokens are securely stored in n8n credentials and not exposed in workflows or logs. Benefits Handles natural conversational inputs with text or voice. Provides a smooth, engaging user experience via typing indicators. Easy integration of advanced AI conversational agents with Telegram. Flexible for personal assistants, helpdesks, or interactive chatbots.
By RoninimousWorking with Excel spreadsheet files (xls & xlsx)
This workflow will help guide you through obtaining a spreadsheet file, reading it, making a change then saving it to local or cloud storage.
By n8n TeamAuto-label incoming Gmail messages with AI nodes
This workflow uses AI to analyze the content of every new message in Gmail and then assigns specific labels, according to the context of the email. Default configuration of the workflow includes 3 labels: „Partnership” - email about sponsored content or cooperation, „Inquiry” - email about products, services, „Notification” - email that doesn't require response. You can add or edit labels and descriptions according to your use case. 🎬 See this workflow in action in my YouTube video about automating Gmail. How it works? Gmail trigger performs polling every minute for new messages (you can change the trigger interval according to your needs). The email content is then downloaded and forwarded to an AI chain. 💡 The prompt in the AI chain node includes instructions for applying labels according to the email content - change label names and instructions to fit your use case. Next, the workflow retrieves all labels from the Gmail account and compares them with the label names returned from the AI chain. Label IDs are aggregated and applied to processed email messages. ⚠️ Label names in the Gmail account and workflow (prompt, JSON schema) must be the same. Set up steps Set credentials for Gmail and OpenAI. Add labels to your Gmail account (e.g. „Partnership”, „Inquiry” and „Notification”). Change prompt in AI chain node (update list of label names and instructions). Change list of available labels in JSON schema in parser node. Optionally: change polling interval in Gmail trigger (by default interval is 1 minute). If you like this workflow, please subscribe to my YouTube channel and/or my newsletter.
By OskarDeep Research - Sales Lead Magnet Agent
Want to check out all my flows, follow me on: https://maxmitcham.substack.com/ https://www.linkedin.com/in/max-mitcham/ This automation flow is designed to generate comprehensive, research-backed lead magnet articles based on a user-submitted topic, conduct deep research across multiple sources, and automatically create a professional Google Doc ready for LinkedIn sharing. ⚙️ How It Works (Step-by-Step): 📝 Chat Input (Entry Point) A user submits a topic through the chat interface: Topic for lead magnet content Target audience (automatically detected) Company context (when relevant) 🔍 Query Builder Agent An AI agent refines the input by: Converting the topic into 5 targeted research queries Determining if topic relates to *company for specialized research Using structured output parsing for consistent results 📚 Research Leader Agent Conducts comprehensive research that: Uses Perplexity API for real-time web research Integrates *company knowledge base when relevant Creates detailed table of contents with research insights Identifies key trends, expert opinions, and case studies 📋 Project Planner Agent Structures the content by: Generating professional title and subtitle Creating 8-10 logical chapter outlines Developing detailed writing prompts for each section Ensuring step-by-step actionable guidance ✍️ Research Assistant Team Multiple AI agents write simultaneously: Each agent writes one chapter with proper citations Maintains consistent voice across all sections Includes real-world examples and implementation steps Uses both web research and *company knowledge 📝 Editor Agent Professional content polishing: Refines tone for authenticity and engagement Adds image placeholders where appropriate Ensures proper flow between chapters Optimizes for LinkedIn lead magnet format 📄 Google Docs Creation Automated document generation: Creates new Google Doc with formatted content Sets proper sharing permissions (public link) Organizes in designated company folder Returns shareable URL for immediate use 🛠️ Tools Used: n8n: Workflow orchestration platform Anthropic Claude: Primary AI model for content generation OpenRouter: Backup AI model options Perplexity API: Real-time research capabilities *Company Knowledge Hub: Internal documentation access Google Docs API: Document creation and formatting Google Drive API: File management and sharing 📦 Key Features: End-to-end automation from topic to published document Multi-agent approach ensures comprehensive coverage Real-time research with proper citations Company-specific knowledge integration Professional editing and formatting Automatic Google Docs creation with sharing Scalable content generation (3-5 minutes per article) 🚀 Ideal Use Cases: B2B companies building thought leadership content Sales teams creating industry-specific lead magnets Marketing departments scaling content production Consultants developing expertise-demonstrating resources SaaS companies creating feature-focused educational content Startups establishing market presence without content teams
By Max MitchamGoogle Maps business scraper with contact extraction via Apify and Firecrawl
Who is this for? Marketing agencies, sales teams, lead generation specialists, and business development professionals who need to build comprehensive business databases with contact information for outreach campaigns across any industry. What problem is this workflow solving? Finding businesses and their contact details manually is time-consuming and inefficient. This workflow automates the entire process of discovering businesses through Google Maps and extracting their digital contact information from websites, saving hours of manual research. What this workflow does This automated workflow runs every 30 minutes to: Scrape business data from Google Maps using Apify's Google Places crawler Save basic business information (name, address, phone, website) to Google Sheets Filter businesses that have websites Scrape each business's website content using Firecrawl Extract contact information including emails, LinkedIn, Facebook, Instagram, and Twitter profiles Store all extracted data in organized Google Sheets for easy access and follow-up Setup Required Services: Google Sheets account with OAuth2 setup Apify account with API access for Google Places scraping Firecrawl account with API access for website scraping Pre-setup: Copy this Google Sheet Configure your Apify and Firecrawl API credentials in n8n Set up Google Sheets OAuth2 connection Update the Google Sheet ID in all Google Sheets nodes Quick Start: The workflow includes detailed sticky notes explaining each phase. Simply configure your API credentials and Google Sheet, then activate the workflow. How to customize this workflow to your needs Change search criteria: Modify the Apify scraping parameters to target different business types (restaurants, gyms, salons, etc.) or locations Adjust schedule: Change the trigger interval from 30 minutes to your preferred frequency Add more contact fields: Extend the extraction code to find additional contact information like WhatsApp or Telegram Filter criteria: Modify the filter conditions to target businesses with specific characteristics Batch size: Adjust the batch processing to handle more or fewer websites simultaneously Perfect for lead generation, competitor research, and building targeted marketing lists across any industry or business type.
By Naveen Choudhary💥🛠️Build a web search chatbot with GPT-4o and MCP Brave Search
MCP AI Chatbot using Brave Search Disclaimer: This workflow only works with local installations of n8n because it uses a community MCP node Who is this for? This workflow is ideal for developers, automation enthusiasts, and businesses looking to integrate AI-powered chat capabilities into their workflows. It's particularly useful for those leveraging Brave Search and MCP tools to enhance user interactions and streamline data retrieval. What problem is this workflow solving? This workflow addresses the challenge of creating an intelligent chatbot that can process user queries, execute searches using Brave Search, and provide responses enriched by AI. It simplifies the integration of multiple tools into a cohesive system, saving time and effort for users who need a robust conversational AI solution. What this workflow does Listens for incoming chat messages using the Chat Trigger node. Processes user input with an AI Agent powered by GPT-4o. Retrieves relevant tools using the MCP Get Brave Tools node. Executes specific search queries via the MCP Execute Brave Search node. Maintains short-term memory of conversations with the Simple Memory node. Setup Prerequisites: Access to an n8n instance (self-hosted). API credentials for OpenAI and MCP Client Tools. Brave Search API key. Steps: Import the workflow JSON into your n8n instance. Configure the API credentials for OpenAI and MCP Client Tools in their respective nodes. Set up your Brave Search API key in the MCP nodes. https://brave.com/search/api/ Testing: Use the built-in chat interface to send test messages. Verify that the chatbot processes queries and returns results as expected. How to customize this workflow to your needs Modify the AI Agent's prompt settings to tailor responses to your specific use case. Adjust the memory buffer in the Simple Memory node to retain more or less conversational context. Replace or add additional tools in the MCP nodes to expand functionality.
By Joseph LePageExecute an SQL query in Microsoft SQL
No description available.
By tanaypantDaily newsletter service using Excel, Outlook and AI
This n8n template builds a newsletter ("daily digest") delivery service which pulls and summarises the latest n8n.io template in select categories defined by subscribers. It's scheduled to run once a day and sends the newsletter directly to subscriber via a nicely formatted email. If you've had trouble keeping up with the latest and greatest templates beign published daily, this workflow can save you a lot of time! How it works A scheduled trigger pulls a list of subscribers (email and category preferences) from an Excel workbook. We work out unique categories amongst all subscribers and only fetch the latest n8n website templates from these categories to save on resources and optimise the number of API calls we make. The fetched templates are summarised via AI to produce a short description which is more suitable for our email format. For each subscriber, we filter and collect only the templates relevant to their category preferences (as defined in the Excel) and ensure that duplicate templates or those which have been "seen before" are omitted. A HTML node is then used to generate the email newsletter. HTML emails are the perfect format since we can add links back to the template. Finally, we use the Outlook node to send the email digest to the subscriber. How to use Populate your Excel sheet with 3 columns: name, email and categories. Categories is a comma-delimited list of categories which match the n8n template website. The available categories are AI, SecOps, Sales, IT Ops, Marketing, Engineering, DevOps, Building Blocks, Design, Finance, HR, Other, Product and Support. To subscribe a new user, simply add their email to the Excel sheet with at least one category. To unsubscribe a user, remove them from the sheet. If you're not interested in paid templates, you may want to filter them out after fetching. Requirements Microsoft Excel for subscriber list Microsoft Outlook for delivering emails OpenAI for AI-generated descriptions Customising the workflow Use AI to summarise the week's trend of templates types and use-cases This template can be the basis for other similar newsletters - just pull in a list of things from anywhere!
By JimleukCreate a Google Analytics data report with AI and sent it to e-mail and Telegram
What this workflow does This workflow retrieves Google Analytics data from the last 7 days and the same period in the previous year. The data is then prepared by AI as a table, analyzed and provided with a small summary. The summary is then sent by email to a desired address and, shortened and summarized again, sent to a Telegram account. This workflow has the following sequence: time trigger (e.g. every Monday at 7 a.m.) retrieval of Google Analytics data from the last 7 days assignment and summary of the data retrieval of Google Analytics data from the last 7 days of the previous year allocation and summary of the data preparation in tabular form and brief analysis by AI. sending the report as an email preparation in short form by AI for Telegram (optional) sending as Telegram message. Requirements The following accesses are required for the workflow: Google Analytics (via Google Analytics API): Documentation AI API access (e.g. via OpenAI, Anthropic, Google or Ollama) SMTP access data (for sending the mail) Telegram access data (optional for sending as Telegram message): Documentation Feel free to contact me via LinkedIn, if you have any questions!
By Friedemann SchuetzSend Daily Meetings in Google Calendar to Telegram
This workflow automatically sends you a list of your daily meetings every morning via a Telegram bot. Use Cases: This workflow is useful for anyone who wants to be automatically informed of their daily meetings, especially for busy professionals, students, and anyone with a hectic schedule. Setup: Google Calendar connected to n8n A Telegram bot created and connected to n8n Your Telegram user ID specified Notes: You need to replace the placeholder in the Telegram node with your actual Telegram user ID. You can customize the formatting of the Telegram message in the JavaScript Code node.
By EmadExtract business leads from Google Maps with Dumpling AI to Google Sheets
Who is this for? This workflow is built for marketers, sales teams, agencies, virtual assistants, and anyone who regularly researches or contacts local businesses. It's ideal for building lead lists, tracking competitors, or creating location-specific outreach campaigns. --- What problem is this workflow solving? Instead of manually searching Google Maps and copying business info into spreadsheets, this automation pulls structured business data (e.g. restaurants, gyms, service providers) and logs it directly into Google Sheets. It saves hours of work and ensures cleaner, more usable data. --- What this workflow does The workflow takes a Google Maps search query (like "best restaurants in New York") and sends it to Dumpling AI. It returns a list of places including their name, address, website, phone number, rating, and more. Each result is split into a row and automatically added to a Google Sheet. --- Setup Dumpling AI Sign up at Dumpling AI Generate your API key In the HTTP Request node, select Header Auth and paste your key in the Authorization field Google Sheets Create a sheet with tab name Leads Add the following column headers to row 1: Name, Address, Phone number, Website, Rating, Price Level, Type, Booking Link, Position Connect your Google Sheets account and link this sheet in the node Customize the Query In the HTTP node, replace the query string (e.g., "best+restaurants+in+New+York") with your own search term Run It Use the manual trigger to test Optionally swap in a Schedule or Webhook node to run it automatically --- How to customize this workflow to your needs Change the search query to target different cities or business types Use filters to only save leads with a minimum rating or price level Add GPT to summarize listings or qualify leads Swap Google Sheets for Airtable or a CRM system for deeper integration
By YangEnrich company data from Google Sheet with OpenAI Agent and ScrapingBee
This workflow demonstrates how to enrich data from a list of companies in a spreadsheet. While this workflow is production-ready if all steps are followed, adding error handling would enhance its robustness. Important notes Check legal regulations: This workflow involves scraping, so make sure to check the legal regulations around scraping in your country before getting started. Better safe than sorry! Mind those tokens: OpenAI tokens can add up fast, so keep an eye on usage unless you want a surprising bill that could knock your socks off! 💸 Main Workflow Node 1 - Webhook This node triggers the workflow via a webhook call. You can replace it with any other trigger of your choice, such as form submission, a new row added in Google Sheets, or a manual trigger. Node 2 - Get Rows from Google Sheet This node retrieves the list of companies from your spreadsheet. here is the Google Sheet Template you can use. The columns in this Google Sheet are: Company: The name of the company Website: The website URL of the company These two fields are required at this step. Business Area: The business area deduced by OpenAI from the scraped data Offer: The offer deduced by OpenAI from the scraped data Value Proposition: The value proposition deduced by OpenAI from the scraped data Business Model: The business model deduced by OpenAI from the scraped data ICP: The Ideal Customer Profile deduced by OpenAI from the scraped data Additional Information: Information related to the scraped data, including: Information Sufficiency: Description: Indicates if the information was sufficient to provide a full analysis. Options: "Sufficient" or "Insufficient" Insufficient Details: Description: If labeled "Insufficient," specifies what information was missing or needed to complete the analysis. Mismatched Content: Description: Indicates whether the page content aligns with that of a typical company page. Suggested Actions: Description: Provides recommendations if the page content is insufficient or mismatched, such as verifying the URL or searching for alternative sources. Node 3 - Loop Over Items This node ensures that, in subsequent steps, the website in "extra workflow input" corresponds to the row being processed. You can delete this node, but you'll need to ensure that the "query" sent to the scraping workflow corresponds to the website of the specific company being scraped (rather than just the first row). Node 4 - AI Agent This AI agent is configured with a prompt to extract data from the content it receives. The node has three sub-nodes: OpenAI Chat Model: The model used is currently gpt4-o-mini. Call n8n Workflow: This sub-node calls the workflow to use ScrapingBee and retrieves the scraped data. Structured Output Parser: This parser structures the output for clarity and ease of use, and then adds rows to the Google Sheet. Node 5 - Update Company Row in Google Sheet This node updates the specific company's row in Google Sheets with the enriched data. Scraper Agent Workflow Node 1 - Tool Called from Agent This is the trigger for when the AI Agent calls the Scraper. A query is sent with: Company name Website (the URL of the website) Node 2 - Set Company URL This node renames a field, which may seem trivial but is useful for performing transformations on data received from the AI Agent. Node 3 - ScrapingBee: Scrape Company's Website This node scrapes data from the URL provided using ScrapingBee. You can use any scraper of your choice, but ScrapingBee is recommended, as it allows you to configure scraper behavior directly. Once configured, copy the provided "curl" command and import it into n8n. Node 4 - HTML to Markdown This node converts the scraped HTML data to Markdown, which is then sent to OpenAI. The Markdown format generally uses fewer tokens than HTML. Improving the Workflow It's always a pleasure to share workflows, but creators sometimes want to keep some magic to themselves ✨. Here are some ways you can enhance this workflow: Handle potential errors Configure the scraper tool to scrape other pages on the website. Although this will cost more tokens, it can be useful (e.g., scraping "Pricing" or "About Us" pages in addition to the homepage). Instead of Google Sheets, connect directly to your CRM to enrich company data. Trigger the workflow from form submissions on your website and send the scraped data about the lead to a Slack or Teams channel.
By Dataki