Workflow Templates

This workflow contains community nodes that are only compatible with the self-hosted version of n8n. 🤖 AI Image Generator Telegram Bot Transform simple text descriptions into stunning AI-generated images through a Telegram bot powered by Google's Gemini 2.0 Flash image. This workflow automatically enhances user prompts with professional prompt engineering techniques and delivers high-quality images directly to your Telegram chat. 🎯 What This Workflow Does This automation creates an intelligent Telegram bot that: Receives text messages** from users describing what image they want Enhances prompts** using AI-powered prompt engineering to add artistic details, lighting, composition, and style specifications Generates images** using Google's Gemini 2.0 Flash image generation model Delivers results** instantly back to the user's Telegram chat ⚡️ Key Features Smart Prompt Enhancement**: Automatically transforms basic requests like "a cat on a windowsill" into detailed, professional prompts with lighting, composition, and style details Professional Image Quality**: Leverages Google's latest Gemini 2.0 Flash model for high-quality image generation Instant Delivery**: Images are generated and sent back to users within seconds User-Friendly**: Simple text-to-image conversion through familiar Telegram interface Structured Output**: Uses JSON schema to ensure consistent prompt formatting 🛠️ How It Works Telegram Trigger: Listens for incoming messages from users AI Prompt Enhancement: Uses Gemini 2.5 Flash Lite to analyze and expand user requests into detailed prompts Structured Processing: Formats the enhanced prompt using JSON schema for consistency Image Generation: Sends the enhanced prompt to Gemini 2.0 Flash image generation API File Conversion: Converts the generated image data to a file format Delivery: Sends the generated image back to the user via Telegram 🎭 Use Cases Creative Content Creation**: Generate artwork, illustrations, and visual concepts Social Media Content**: Create unique images for posts and stories Concept Visualization**: Turn ideas into visual representations Educational Materials**: Generate images for presentations and learning materials Personal Projects**: Create custom artwork for personal use 📋 Prerequisites Before setting up this workflow, you'll need: Required Accounts & APIs: Telegram Bot Token**: Create a bot via @BotFather Google AI Studio Account**: Access to Gemini API services Required Credentials: Telegram Bot API credentials Google Gemini API key with access to: Gemini 2.5 Pro (for prompt enhancement) Gemini 2.0 Flash Preview (for image generation) 🚀 Setup Instructions Import the Workflow: Copy the workflow JSON into your n8n instance Configure Telegram Bot: Create a new bot using @BotFather Add your bot token to the Telegram credentials in n8n Set Up Google Gemini API: Create API key from Google AI Studio and add to n8n credentials Activate the Workflow: Enable the workflow to start receiving messages 💡 Customization Options Prompt Templates**: Modify the prompt enhancement instructions for different artistic styles Image Parameters**: Adjust generation settings for different image formats and qualities Response Formats**: Customize how images are delivered (with captions, multiple formats, etc.) 📊 Example Interaction User Input: "Generate a beautiful image of a dragon from game of thrones" Enhanced Prompt: "A majestic, formidable dragon, reminiscent of those from Game of Thrones, perched atop a jagged, snow-capped mountain peak. The dragon is depicted with immense, leathery wings unfurled, scales glinting with iridescent blues and deep charcoals under the harsh, cold light. Its eyes glow with an inner, fiery amber. The scene is captured with a dramatic, low-angle wide shot, emphasizing the dragon's sheer scale against a turbulent, stormy sky filled with dark, bruised clouds and streaks of lightning. The atmosphere is oppressive and foreboding, with biting wind and swirling snow creating a sense of raw power and danger. The color palette is dominated by icy blues, stark greys, deep blacks, and the contrasting fiery glow of the dragon's eyes and perhaps a hint of internal fire. Rendered in a hyperrealistic, cinematic digital art style, with exceptional attention to detail on the scales, musculature, and the texture of the rocky environment. Lighting is dramatic and high-contrast, with sharp highlights on the dragon's form and deep, impenetrable shadows. Quality specifications include ultra-high detail, 8K resolution, photorealistic rendering, and an epic, awe-inspiring mood, evoking the grandeur and terror of powerful fantasy creatures." Result: 🔧 Technical Details AI Models**: Google Gemini 2.5 Pro (prompt enhancement) + Gemini 2.0 Flash Preview (image generation) Messaging**: Telegram Bot API Output Format**: High-quality images in standard formats Processing Time**: Typically 10-15 seconds per image for Gemini 2.5 Flash and 25-30 seconds Gemini for 2.5 Pro

This cutting-edge n8n automation is a sophisticated video intelligence tool designed to transform raw video content into actionable insights. By intelligently connecting Google Drive, AI analysis, and automated processing, this workflow: Discovers Video Content: Automatically retrieves videos from Google Drive Supports scheduled or on-demand analysis Eliminates manual content searching Advanced AI Analysis: Leverages Google Gemini AI Provides comprehensive video insights Extracts meaningful content summaries Intelligent Processing: Validates file status Prepares content for AI analysis Ensures high-quality insight generation Seamless Workflow Integration: Automated scheduling Cross-platform content processing Reduces manual intervention Key Benefits 🤖 Full Automation: Zero-touch video intelligence 💡 AI-Powered Insights: Advanced content analysis 📊 Comprehensive Processing: Detailed video understanding 🌐 Multi-Platform Synchronization: Seamless content flow Workflow Architecture 🔹 Stage 1: Content Discovery Scheduled Trigger**: Automated workflow initiation Google Drive Integration**: Video file retrieval Intelligent File Selection**: Identifies target videos Prepares for AI analysis 🔹 Stage 2: Content Preparation File Download** LLM Chain Processing** AI-Ready Content Formatting** 🔹 Stage 3: AI Analysis Gemini API Integration** Comprehensive Content Examination** Intelligent Insight Generation** 🔹 Stage 4: Result Structuring Analysis Result Formatting** Structured Insight Preparation** Ready-to-Use Intelligence** Potential Use Cases Content Creators**: Video content analysis Marketing Teams**: Content insight generation Educational Institutions**: Lecture and presentation review Research Organizations**: Automated video intelligence Media Companies**: Rapid content assessment Setup Requirements Google Drive Connected Google account Configured video folder Appropriate sharing settings Google Gemini API API credentials Configured analysis parameters Access to Gemini Pro model n8n Installation Cloud or self-hosted instance Workflow configuration API credential management Future Enhancement Suggestions 🤖 Multi-model AI analysis 📊 Detailed insight scoring 🔔 Automated reporting 🌐 Cross-platform insight sharing 🧠 Advanced content categorization Technical Considerations Implement robust error handling Use secure API authentication Maintain flexible content processing Ensure compliance with AI usage guidelines Ethical Guidelines Respect content privacy Maintain transparent analysis practices Ensure appropriate content usage Protect intellectual property rights Hashtag Performance Boost 🚀 #AIVideoAnalysis #ContentIntelligence #GeminiAI #VideoInsights #AutomatedLearning #AIWorkflow #MachineLearning #ContentAnalytics #TechInnovation #AIAutomation Workflow Visualization [Schedule Trigger] ⬇️ [Download from Drive] ⬇️ [LLM Chain Processing] ⬇️ [Check File Status] ⬇️ [Analyze Video] ⬇️ [Format Analysis Result] Connect With Me Ready to revolutionize your video intelligence? 📧 Email: Yaron@nofluff.online 🎥 YouTube: @YaronBeen 💼 LinkedIn: Yaron Been Transform your video content analysis with intelligent, automated solutions!

This workflow contains community nodes that are only compatible with the self-hosted version of n8n. **This n8n template automates candidate outreach, call transcription, and structured feedback capture for HR teams and recruiters. It triggers on a new candidate row added in a Google Sheet, initiates a call using Vapi.ai, processes the transcript using Google Gemini, extracts key information like CTC, experience, and notice period, and then updates the same Google Sheet with parsed insights. This is ideal for recruiters or HR teams conducting high-volume candidate outreach and wanting to scale initial data collection using automated voice bots and AI transcription analysis.** How it works Trigger: Listens for new rows added to a Google Sheet (e.g., a new candidate lead). Call Initiation: Uses Vapi.ai to make a phone call to the candidate using an assistant bot. Transcript Retrieval: After the call, fetches the conversation transcript from the Vapi API. AI Transcript Analysis: Google Gemini parses the transcript and extracts structured fields like: Work experience Current & expected CTC Notice period & negotiability Work preferences and location Data Mapping: Extracted insights are mapped to structured JSON fields. Google Sheet Update: The same row in the source Sheet is updated with the collected information. Use Cases Pre-screening calls for job applicants Collecting missing candidate information asynchronously Replacing manual HR data entry with AI-powered automation Smart CRM updates from voice interactions Requirements Before you run this workflow, ensure the following: ✅ Google account with access to Google Sheets API ✅ Vapi.ai account with: Assistant ID Phone number ID Active API key ✅ Google Gemini API (via PaLM) enabled ✅ n8n version 1.40.0 or later with relevant credentials configured How to use Import the workflow into n8n. Set up your credentials for: Google Sheets Trigger Google Sheets Vapi.ai (add Bearer token) Google Gemini Replace the placeholder values in: Assistant ID Phone number ID Google Sheet ID and tab Start the workflow and add a row to the Google Sheet. Wait for the automated call and let the AI extract and populate the data. Customising this workflow Replace Google Gemini with OpenAI or Claude if preferred. Add sentiment analysis on the transcript using an LLM. Modify the Sheet column structure to add additional fields. Add a filter node to skip candidates with incomplete phone numbers. Use a Webhook trigger instead of Google Sheets to integrate with job portals or ATS.

This workflow demonstrates a simple way to run evals on a set of test cases stored in a Google Sheet. The example we are using comes from an info extraction task dataset, where we tested 6 different LLMs on 18 different test cases. You can see our sample data in this spreadsheet here to get started. Once you have this working for our dataset, you can plug in your own test cases matching different LLMs to see how it works with your own data. How it works: It loads test cases from Google Sheets. For each row in our Google Sheet, it grabs the source document, converting it to text. Our "LLM judge" passes the input/output of each LLM to GPT-4.1 to evaluate each test case (Pass/Fail + Reason). It logs the outcome to a Google Sheet. A 0.5s pause between each request gets around OpenAI's API rate limits. Set up steps: Add your credentials for Google Sheets, Google Drive, and OpenRouter. Make a copy of the original data spreadsheet so that you can edit it yourself. You will need to plug your version in the Update Results node to see the spreadsheet update on each run of the loop.

Automate LinkedIn engagement without sounding like a bot. This workflow: 🌍 Detects language & tone (German / English) 👍 Chooses the right reaction (like / celebrate / support …) 🗣 Generates a personalised comment in your voice and mentions the author 📲 Optional Telegram review – approve ✅ or regenerate ❌ before posting 💸 Runs on cost-efficient GPT-4o mini or Claude 3.5 Haiku ☁️ Publishes comment + reaction via the Unipile API Setup (≈ 15-30 min) Unipile – connect LinkedIn → copy account_id, dsn, then create an Access-Token (X-API-KEY). Telegram (optional) – create a bot, add a credential named YOUR TELEGRAM ACCOUNT. OpenAI / Anthropic – add your API key and keep one LLM node (delete the other). Open the “Defining guardrails” node and replace the credential placeholders. (Optional) Tweak role, comment_length and openers_example_1-3 for your brand voice. Security: no live keys included – all secrets are placeholders. Best for: solopreneurs, marketing teams, personal-branding consultants.

This workflow template is designed for dental assistants and anyone looking to automate appointment scheduling. It integrates Google Calendar for booking appointments and Google Sheets as a database to store patient information. How It Works The user interacts with the chatbot to schedule an appointment. The chatbot collects necessary details and checks availability via Google Calendar. If the requested time is available, the AI books the appointment. If unavailable, the AI suggests alternative time slots. Once booked, the AI logs the appointment details into Google Sheets for record-keeping. Setup Instructions 📌 Watch this 🎥 Setup Video for detailed instructions on running and customizing this workflow. Step 1: Set Up Credentials OpenAI API Key (for chatbot functionality). Google Account (for Google Sheets & Google Calendar integration). Step 2: Choose the Right Tools Select the correct Google Calendar in the Google Calendar tool. Choose the appropriate Google Sheets file in the Google Sheets tool. Step 3: Test Run a test to ensure everything works correctly. Once tested. Example Templates Below are sample Google Sheets template to help you get started.

This workflow contains community nodes that are only compatible with the self-hosted version of n8n. 🎯 How it works This workflow captures new lead information from a web form, enriches it with Apollo.io data, qualifies the lead using AI, and—if the lead is strong—automatically sends a personalized outreach email via Gmail and logs the result in Google Sheets. 🛠️ Key Features 📩 Lead form capture with validation 🔍 Enrichment via Apollo API 🤖 Lead scoring using AI (LangChain + Groq) 📧 Dynamic email generation & sending via Gmail 📊 Logging leads with job title & org into Google Sheets ✅ Conditional email sending (score ≥ 6 only) 🧪 Set up steps Estimated time: 15–20 minutes Add your Apollo API Key to the HTTP Header credential (never hardcode!) Connect your Gmail account for sending emails Connect your Google Sheets account and set up the correct spreadsheet & sheet name Enable LangChain/Groq credentials for lead scoring and AI-generated emails Update the form endpoint to your live webhook if needed 📌 Sticky Notes Add the following mandatory sticky notes inside your workflow: FormTrigger Node: "Collects lead info via form. Ensure your form is connected to this endpoint." HTTP Request Node: "Enrich lead using Apollo.io API. Add your API key via header-based authentication." AI Agent (Lead Score): "Scores lead from 1-10 based on job title and industry match. Only leads with score ≥ 6 proceed." AI Agent (Email Composer): "Generates a concise, polite email using lead’s job title & company. Modify tone if needed." Google Sheets Append: "Logs enriched lead with job title, org, and LinkedIn URL. Customize sheet structure if needed." Gmail Node: "Sends personalized outreach email if lead passes score threshold. Uses AI-generated content." 💸 Free or Paid? Free – No paid API services are required (Apollo has a free tier).

This template is for Self-Hosted N8N Instances only. This n8n demonstrates how to build a simple SQLite MCP server to perform local database operations as well as use it for Business Intelligence. This MCP example is based off an official MCP reference implementation which can be found here -https://github.com/modelcontextprotocol/servers/tree/main/src/sqlite How it works A MCP server trigger is used and connected to 5 tools: 2 Code Node and 3 Custom Workflow. The 2 Code Node tools use the SQLLite3 library and are simple read-only queries and as such, the Code Node tool can be simply used. The 3 custom workflow tools are used for select, insert and update queries as these are operations which require a bit more discretion. Whilst it may be easier to allow the agent to use raw SQL queries, we may find it a little safer to just allow for the parameters instead. The custom workflow tool allows us to define this restricted schema for tool input which we'll use to construct the SQL statement ourselves. All 3 custom workflow tools trigger the same "Execute workflow" trigger in this very template which has a switch to route the operation to the correct handler. Finally, we use our Code nodes to handle select, insert and update operations. The responses are then sent back to the the MCP client. How to use This SQLite MCP server allows any compatible MCP client to manage a SQLite database by supporting select, create and update operations. You will need to have a SQLite database available before you can use this server. Connect your MCP client by following the n8n guidelines here - https://docs.n8n.io/integrations/builtin/core-nodes/n8n-nodes-langchain.mcptrigger/#integrating-with-claude-desktop Try the following queries in your MCP client: "Please create a table to store business insights and add the following..." "what business insights do we have on current retail trends?" "Who has contributed the most business insights in the past week?" Requirements SQLite for database. MCP Client or Agent for usage such as Claude Desktop - https://claude.ai/download Customising this workflow If the scope of schemas or tables is too open, try restrict it so the MCP serves a specific purpose for business operations. eg. Confine the querying and editing to HR only tables before providing access to people in that department. Remember to set the MCP server to require credentials before going to production and sharing this MCP server with others!

This n8n demonstrates how to build your own Github MCP server to personalise it to your organisation's repositories, issues and pull requests. This n8n implementation, though not as fully featured as the official MCP server offered by Github, allows you to control precisely what access and/or functionality is granted to users which can make MCP use simpler and in some cases, more secure. The use-case in this template is to simply view and comment on issues within a specific repository but can be extended to meet the needs of your team. This MCP example is based off an official MCP reference implementation which can be found here https://github.com/modelcontextprotocol/servers/tree/main/src/github How it works A MCP server trigger is used and connected to 3 custom workflow tools. We're using custom workflow tools as there is quite a few nodes required for each task. Behind these tools are regular Github nodes although preconfigured with credentials and targeted repository. The "Get Issue Comments" and "Create Issue Comment" tools depend on obtaining an Issue Number first. The agent should call the "Get Latest Issues" tool for this. How to use This Github MCP server allows any compatible MCP client to view and comment on Github Issues. You will need to have a Github account and repository access available before you can use this server. Connect your MCP client by following the n8n guidelines here - https://docs.n8n.io/integrations/builtin/core-nodes/n8n-nodes-langchain.mcptrigger/#integrating-with-claude-desktop Try the following queries in your MCP client: "Can you get me the latest issues about MCP?" "What is the current progress on Issue 12345?" "Please can you add a comment to Issue 12345 that they should try installing the latest version and see if that works?" Requirements Github for account and repository access. The repository need not be your own but you'll still need to ensure you have the correct permissions. MCP Client or Agent for usage such as Claude Desktop - https://claude.ai/download Customising this workflow Extend this template to interactive with pull requests or workflows within your own company's Github repositories. Alternatively, pull in metrics and generate reports for programme managers. Remember to set the MCP server to require credentials before going to production and sharing this MCP server with others!

How the sequence works: A "Schedule Trigger" node activates the automation at a defined schedule. An "Airtable" node will search for previously posted questions in your question database. Airtable Base Template: here An "Aggregate" node will take all the questions from Airtable and compress them to a single output. ChatGPT, or a model of your choice, will generate a discussion question based on the options in the system prompt. The discussion question will be posted to the subreddit of your choice by the "Reddit" node. You can choose between a text, image, or link post! The recently-posted discussion question will then be uploaded to your Airtable base using the "Airtable" node. This will be used to prevent ChatGPT from creating duplicate questions. Setup Steps The setup process will take about 5 minutes. An Airtable base template is also pre-made for you here: https://airtable.com/app6wzQqegKIJOiOg/shrzy7L9yv8BFRQdY Set the recurrence in the "Schedule" node Create an Airtable account, you can use the link here. Get an Airtable personal access token here. Configure the "Get Previous Discussions" Airtable node. Configure the options in brackets in the "Generate New Discussion" node. Set the desired subreddit to post to and the post type(text, image, or link) in the "Post Discussion" node. Configure the "Create Archived Discussion" node to map to the Airtable base(required) and specific subreddit(optional).

This n8n workflow demonstrates how to create a really simple yet effective customer support channel and pipeline by combining Slack, Linear and AI tools. Built on n8n's ability to integrate anything, this workflow is intended for small support teams who want to maximise re-use of the tools they already have with an interface which is doesn't require any onboarding. Read the blog post here: https://blog.n8n.io/automated-customer-support-tickets-with-n8n-slack-linear-and-ai/ How it works The workflow is connected to a slack channel setup with the customer to capture support issues. Only messages which are tagged with a "✅" reaction are captured by the workflow. Messages are tagged by the support team in the channel. Each captured support issue is sent to the AI model to classify, prioritise and rewrite into a support ticket. The generated support ticket is uploaded to Linear for the support team to investigate and track. Support team is able to report back to the user via the channel when issue is fixed. Requirements Slack channel to be monitored Linear account and project Customising this workflow Don't have Linear? This workflow can work just as well with traditional ticketing systems like JIRA.

This template provides a fully automated system for monitoring news on any topic you choose. It leverages Linkup's AI-powered web search to find recent, relevant articles, extracts key information like the title, date, and summary, and then neatly organizes everything in an Airtable base. Stop manually searching for updates and let this workflow deliver a curated news digest directly to your own database, complete with a Slack notification to let you know when it's done. This is the perfect solution for staying informed without the repetitive work. Who is this for? Marketing & PR professionals:** Keep a close eye on industry trends, competitor mentions, and brand sentiment. Analysts & researchers:** Effortlessly gather source material and data points on specific research topics. Business owners & entrepreneurs:** Stay updated on market shifts, new technologies, and potential opportunities without dedicating hours to reading. Anyone with a passion project:** Easily follow developments in your favorite hobby, field of study, or area of interest. What problem does this solve? Eliminates manual searching:** Frees you from the daily or weekly grind of searching multiple news sites for relevant articles. Centralizes information:** Consolidates all relevant news into a single, organized, and easily accessible Airtable database. Provides structured data:** Instead of just a list of links, it extracts and formats key information (title, summary, URL, date) for each article, ready for review or analysis. Keeps you proactively informed:** The automated Slack notification ensures you know exactly when new information is ready, closing the loop on your monitoring process. How it works Schedule: The workflow runs automatically based on a schedule you set (the default is weekly). Define topics: In the Set news parameters node, you specify the topics you want to monitor and the time frame (e.g., news from the last 7 days). AI web search: The Query Linkup for news node sends your topics to Linkup's API. Linkup's AI searches the web for relevant news articles and returns a structured list containing each article's title, URL, summary, and publication date. Store in Airtable: The workflow loops through each article found and creates a new record for it in your Airtable base. Notify on Slack: Once all the news has been stored, a final notification is sent to a Slack channel of your choice, letting you know the process is complete and how many articles were found. Setup Configure the trigger: Adjust the Schedule Trigger node to set the frequency and time you want the workflow to run. Set your topics: In the Set news parameters node, replace the example topics with your own keywords and define the news freshness that you'd like to set. Connect your accounts: Linkup: Add your Linkup API key in the Query Linkup for news node. Linkup's free plan includes €5 of credits monthly, enough for about 1,000 runs of this workflow. Airtable: In the Store one news node, select your Airtable account, then choose the Base and Table where you want to save the news. Slack: In the Notify in Slack node, select your Slack account and the channel where you want to receive notifications. Activate the workflow: Toggle the workflow to "Active", and your automated news monitoring system is live! Taking it further Change your database:* Don't use Airtable? Easily swap the *Airtable* node for a *Notion, **Google Sheets, or any other database node to store your news. Customize notifications:* Replace the *Slack* node with a *Discord, **Telegram, or Email node to get alerts on your preferred platform. Add AI analysis:** Insert an AI node after the Linkup search to perform sentiment analysis on the news summaries, categorize articles, or generate a high-level overview before saving them.