Free AI image generator - n8n automation workflow with Gemini/ChatGPT
This n8n template demonstrates how to use AI to generate custom images from scratch - fully automated, prompt-driven, and ready to deploy at scale. Use cases are many: You can use it for marketing visuals, character art, digital posters, storyboards, or even daily image generation for your personal purposes. How It Works The flow is triggered by a chat message in N8N or via Telegram. The default image size is 1080 x 1920 pixels. To use a different size, update the values in the “Fields - Set Values” node before triggering the workflow. The input is parsed into a clean, structured prompt using a multi-step transformation process. Our AI Agent sends the final prompt to Google Gemini’s image model for generation (you can also integrate with OpenAI or other chat models). The raw image data created by the AI Agent will be run through a number of codes to make sure it's feasible for your preview if needed and downloading. Then, we use an HTTP node to fetch the result so you can preview the image. You can send it back to the chat message in N8N or Telegram, or save it locally to your disk. How To Use Download the workflow package. Import the package into your N8N interface. Set up the credentials in the following nodes for tool access and usability: "Telegram Trigger"; "AI Agent - Create Image From Prompt"; "Telegram Response" or "Save Image To Disk" (based on your wish). Activate the "Telegram Response" OR "Save Image To Disk" node to specify where you want to save your image later. Open the chat interface (via N8N or Telegram). Type your image prompt or detailed descriptions and send. Wait for the process to run and finish in a few seconds. Check the result in your desired saving location. Requirements Google Gemini account with image generation access. Telegram bot access and chat setup (optional). Connection to local storage (optional). How To Customize We’re setting the default image size to 1080 x 1920 pixels and the default image model to "flux". You can customize both of these values in the “Fields – Set Values” node. Supported image model options include: "flux", "kontext", "turbo", and "gptimage". In the “AI Agent – Create Image From Prompt” node, you can also change the AI chat model. By default, it uses Google Gemini, but you can easily replace it with OpenAI ChatGPT, Microsoft AI Copilot, or any other compatible provider. Need Help? Join our community on different platforms for support, inspiration and tips from others. Website: https://www.agentcircle.ai/ Etsy: https://www.etsy.com/shop/AgentCircle Gumroad: http://agentcircle.gumroad.com/ Discord Global: https://discord.gg/d8SkCzKwnP FB Page Global: https://www.facebook.com/agentcircle/ FB Group Global: https://www.facebook.com/groups/aiagentcircle/ X: https://x.com/agent_circle YouTube: https://www.youtube.com/@agentcircle LinkedIn: https://www.linkedin.com/company/agentcircle
By Agent CircleGet a Slack alert when a workflow went wrong
This workflow allows you to have a Slack alert when one of your n8n workflows gets an issue. Error trigger: This node launched the workflow when one of your active workflows gets an issue Slack node: This node sends you a customized message to alert you and to check the error ⚠️ You don't have to activate this workflow for it to be effective
By PaulinePeriodically send data from HTTP Request node to Telegram
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By Harshil AgrawalDocument Q&A with RAG: Query PDF content using Weaviate and OpenAI
RAG over a PDF with Weaviate This workflow allows you to upload a PDF file and ask questions about it using the Question and Answer Chain and the Weaviate Vector Store nodes. Who it's for This workflow is the simplest possible implementation of RAG with Weaviate in n8n. It's intended to act as an extendable template for RAG over your own documents. Prerequisites An existing Weaviate cluster. You can view instructions for setting up a local cluster with Docker here or a Weaviate Cloud cluster here. API keys to generate embeddings and power chat models. We use OpenAI, but feel free to switch out the models as you like. Self-hosted n8n instance. See this video for how to get set up in just three minutes. How it works Part 1: Manually upload data In this example, we manually upload a 100+ page article from arXiv called "A Survey of Large Language Models". But you can replace this with your own more advanced data pipeline, if you wish. Part 2: Embed and load data into Weaviate collection Here, we generate embeddings for the full-text of the article and store them in Weaviate. Part 3: Perform RAG over PDF file with Weaviate In this part of the workflow, you can enter your query by running the Chat Node and get a RAG response grounded in context via the Question and Answer Chain node. How to run the workflow Go through the prerequisites, creating a Weaviate cluster (can be local or cloud), downloading self-hosted n8n, and adding your API keys and other credentials. Select the embedding and chat models you'd like to use. Upload a PDF file you want to ask questions about. Execute the rest of the workflow.
By Mary NewhauserSum or aggregate a column of spreadsheet or table data
This workflow shows how to sum multiple items of data, like you would in Excel or Airtable when summing up the total of a column. It uses a Function node with some javascript to perform the aggregation of numeric data. The first node is simply mock data to avoid needing a credential to run the workflow. The second node actually performs the summation - the javascript has various comments in case you need to edit the JS. For example, to sum multiple items of data. Below is an example of the type of data this workflow can sum - so anything that is in a tabular form (Airtable, GSHeets, Postgres etc).
By Max TkaczWeb scraping & screenshot automation with GPT 4.1 mini and Firecrawl
🔍 Overview This template uses Firecrawl’s /search API to perform AI-powered web scraping and screenshots — no code required. Just type natural language prompts, and an AI Agent will convert them into precise Firecrawl queries. ⚙️ Setup Get your Firecrawl API Key from https://firecrawl.dev Add it to n8n using HTTP Header Auth: Key: Authorization Value: Bearer YOURAPIKEY 🚀 What It Does Turns natural language into smart search queries Scrapes web data and captures full-page screenshots Returns titles, links, content, and images 💡 Example Input: > Find AI automation pages on YouTube (exclude Shorts) Result: json { "query": "intitle:AI automation site:youtube.com -shorts", "limit": 5 }
By Luan CorreiaAutomated blog publishing with Google Trends, GPT-4, Pexels & WordPress
This n8n template demonstrates how to automatically generate and publish blog posts using trending keywords, AI-generated content, and watermarked stock images. Use cases include maintaining an active blog with fresh SEO content, scaling content marketing without manual writing, and automating the full publishing pipeline from keyword research to WordPress posting. Good to know At time of writing, each AI content generation step will incur costs depending on your OpenAI pricing plan. Image search is powered by Pexels, which provides free-to-use stock images. The workflow also applies a watermark for branding. Google Trends data may vary by region, and results depend on availability in your selected location. How it works The workflow begins with a scheduled trigger that fetches trending keywords from Google Trends. The XML feed is converted to JSON and filtered for relevant terms, which are logged into a Google Sheet for tracking. One random keyword is selected, and OpenAI is used to generate blog content around it. A structured output parser ensures the text is clean and well-formatted. The system then searches Pexels for a matching image, uploads it, adds metadata for SEO, and applies a watermark. Finally, the complete article (text and image) is published directly to WordPress. How to use The schedule trigger is provided as an example, but you can replace it with other triggers such as webhooks or manual inputs. You can also customize the AI prompt to match your niche, tone, or industry focus. For higher volumes, consider adjusting the keyword filtering and batching logic. Requirements OpenAI account for content generation Pexels API key for stock image search Google account with Sheets for keyword tracking WordPress site with API access for publishing Customising this workflow This automation can be adapted for different use cases. Try adjusting the prompts for technical blogs, fashion, finance, or product reviews. You can also replace the image source with other providers or integrate your own media library. The watermark feature ensures branding, but it can be modified or removed depending on your needs.
By Cojocaru DavidGenerate educational social media carousels with GPT-4.1, Templated.io & Google Drive
🎯 Description Automatically generates, designs, stores, and logs complete Instagram carousel posts. It transforms a simple text prompt into a full post with copy, visuals, rendered images, Google Drive storage, and a record in Google Sheets. ⚙️ Use case / What it does This workflow enables creators, educators, or community managers to instantly produce polished, on-brand carousel assets for social media. It integrates OpenAI GPT-4.1, Pixabay, Templated.io, Google Drive, and Google Sheets into one continuous content-production chain. 💡 How it works 1️⃣ Form Trigger – Collects the user prompt via a simple web form. 2️⃣ OpenAI GPT-4.1 – Generates structured carousel JSON: titles, subtitles, topic, description, and visual keywords. 3️⃣ Code (Format content) – Parses the JSON output for downstream use. 4️⃣ Google Drive (Create Folder) – Creates a subfolder for the new carousel inside “RRSS”. 5️⃣ HTTP Request (Pixabay) – Searches for a relevant image using GPT’s visual suggestion. 6️⃣ Code (Get first result) – Extracts the top Pixabay result and image URL. 7️⃣ Templated.io – Fills the design template layers (titles/subtitles/topic/image). 8️⃣ HTTP Request (Download renders) – Downloads the rendered PNGs from Templated.io. 9️⃣ Google Drive (Upload) – Uploads the rendered images into the created folder. 10️⃣ Google Sheets (Save in DB) – Logs metadata (title, topic, folder link, description, timestamp, status). 🔗 Connectors used OpenAI GPT-4.1 (via n8n LangChain node) Templated.io API (design rendering) Pixabay API (stock image search) Google Drive (storage + folder management) Google Sheets (database / logging) Form Trigger (input collection) 🧱 Input / Output Input: User-submitted “Prompt” (text) via form Output: Generated carousel images stored in Google Drive Spreadsheet row in Google Sheets containing title, topic, description, Drive URL, status ⚠️ Requirements / Setup Valid credentials for: OpenAI API (GPT-4.1 access) Templated.io API key Pixabay API key Google Drive + Google Sheets OAuth connections Existing Google Drive folder ID for RRSS storage Spreadsheet with matching column headers (Created At, Title, Topic, Folder URL, Description, Status) Published form URL for user prompts 🌍 Example applications / extensions Educational themes (mental health, fitness, sustainability). Extend to auto-publish to Instagram Business via Meta API. Add Notion logging or automated email notifications. Integrate scheduling (Cron node) to batch-generate weekly carousels.
By Bastian DiazAI chatbot call center: Taxi service (Production-ready, part 3)
Workflow Name: 🛎️ Taxi Service Template was created in n8n v1.90.2 Skill Level: High Categories: n8n, Chatbot Stacks Execute Sub-workflow Trigger node Chat Trigger node Redis node Postgres node AI Agent node If node, Switch node, Code node, Edit Fields (Set) Prerequisite Execute Sub-workflow Trigger: Taxi Service Workflow (or your own node) Sub-workflow: Taxi Service Provider (or your own node) Sub-workflow: Demo Call Back (or your own node) Production Features Scaling Design for n8n Queue mode in production environment Service Data from external Database with Caching Mechanism Optional Long Terms Memory design Find Route Distance using Google Map API Optional Multi-Language Wait Output example Error Management What this workflow does? This is a n8n Taxi Service Workflow demo. It is the core node for Taxi Service. It will receive message from the Call Center Workflow, handling the QA from the caller, and pass to each of the Taxi Service Provider Workflow to process the estimation. How it works The Flow Trigger node will wait for the message from Call Center or other Sub-workflow. When message is received, it will first check for the matching Service from the PostgreSQL database. If no service or service is inactive, output Error. Next, always reset the Session Data in Cache, with channel_no set to taxi Next, delete the previous Route Data in Cache Trigger a AI Agent to process the fare estimation question to create the Route Data Use the Google Map Route API to calculate the distance. Repeat until created the route data, then pass to all the Taxi Service Provider for an estimation. Set up instructions Pull and Set up the required SQL from our Github repository. Create you Redis credentials, refer to n8n integration documentation for more information. Select your Credentials in Service Cache, Save Service Cache, Reset Session, Delete Route Data, Route Data, Update User Session and Create Route Data. Create you Postgres credentials, refer to n8n integration documentation for more information. Select your Credentials in Load Service Data, Postgres Chat Memory, Load User Memory and Save User Memory. Modify the AI Agent prompt to fit your need Set you Google Map API key in Find Route Distance How to adjust it to your needs By default, this template will use the sys_service table provider information, you could change it for your own design. You can use any AI Model for the AI Agent node Learn we use the prompt for the Load/Save User Memory on demand. Include is our prompt for the taxi service. It is a flexible design which use the data from the Service node to customize the prompt, so you could duplicate this workflow as another service. Create difference Taxi Providers to process the and feedback the estimate.
By ChatPayLabsAirtable base backups to S3
This workflow exports every table in a base as its own CSV, saves the files in a time-stamped folder in Amazon S3, pings you on Slack, and optionally prunes older copies. You get an automated weekly backup that is easy to inspect or re-import as needed. You can easily swap the S3 node for the storage provider of your choice. ++How it works++ Weekly Backup Schedule trigger fires weekly Sets and formats the week ex. [2025-W12] Create a folder in S3 bucket with the week Loops through all tables in Airtable base creating CSVs and uploading to the new path Slack message is sent on completion Monthly Prune Schedule trigger fires weekly Sets a cut-off date 4 weeks in the past Lists folders in S3 Deletes all folders > 4 weeks old ++Setup Steps++ Clone workflow Swap credentials for Airtable, AWS, and Slack Ensure AWS credential has appropriate IAM policy to manage bucket & objects Set workflow to "Active"
By Autonomous WorkGoogle Play review intelligence with Bright Data & Telegram alerts
Google Play Review Intelligence with Bright Data & Telegram Alerts Overview This n8n workflow automates the process of scraping Google Play Store reviews, analyzing app performance, and sending alerts for low-rated applications. It integrates with Bright Data for web scraping, Google Sheets for data storage, and Telegram for notifications. Workflow Components ✅ Trigger Input Form Type: Form Trigger Purpose: Initiates the workflow with user input Input Fields: URL (Google Play Store app URL) Number of reviews to fetch Function: Captures user requirements to start the scraping process 🚀 Start Scraping Request Type: HTTP Request (POST) Purpose: Sends scraping request to Bright Data API Endpoint: https://api.brightdata.com/datasets/v3/trigger Parameters: Dataset ID: gd_m6zagkt024uwvvwuyu Include errors: true Limit multiple results: 5 Custom Output Fields: url, reviewid, reviewername, review_date reviewrating, review, appurl, app_title appdeveloper, appimages, app_rating appnumberofreviews, appwhat_new appcontentrating, appcountry, numof_reviews 🔄 Check Scrape Status Type: HTTP Request (GET) Purpose: Monitors the progress of the scraping job Endpoint: https://api.brightdata.com/datasets/v3/progress/{snapshot_id} Function: Checks if the dataset scraping is complete ⏱️ Wait for Response 45 sec Type: Wait Node Purpose: Implements polling mechanism Duration: 45 seconds Function: Pauses workflow before checking status again 🧩 Verify Completion Type: IF Condition Purpose: Evaluates scraping completion status Condition: status === "ready" Logic: True: Proceeds to fetch data False: Loops back to status check 📥 Fetch Scraped Data Type: HTTP Request (GET) Purpose: Retrieves the final scraped data Endpoint: https://api.brightdata.com/datasets/v3/snapshot/{snapshot_id} Format: JSON Function: Downloads completed review and app data 📊 Save to Google Sheet Type: Google Sheets Node Purpose: Stores scraped data for analysis Operation: Append rows Target: Specified Google Sheet document Data Mapping: URL, Review ID, Reviewer Name, Review Date Review Rating, Review Text, App Rating App Number of Reviews, App What's New, App Country ⚠️ Check Low Ratings Type: IF Condition Purpose: Identifies poor-performing apps Condition: review_rating < 4 Logic: True: Triggers alert notification False: No action taken 📣 Send Alert to Telegram Type: Telegram Node Purpose: Sends performance alerts Message Format: ⚠️ Low App Performance Alert 📱 App: {app_title} 🧑💻 Developer: {app_developer} ⭐ Rating: {app_rating} 📝 Reviews: {appnumberof_reviews} 🔗 View on Play Store Workflow Flow Input Form → Start Scraping → Check Status → Wait 45s → Verify Completion ↑ ↓ └──── Loop ────┘ ↓ Fetch Data → Save to Sheet & Check Ratings ↓ Send Telegram Alert Configuration Requirements API Keys & Credentials Bright Data API Key: Required for web scraping Google Sheets OAuth2: For data storage access Telegram Bot Token: For alert notifications Setup Parameters Google Sheet ID: Target spreadsheet identifier Telegram Chat ID: Destination for alerts N8N Instance ID: Workflow instance identifier Key Features Data Collection Comprehensive app metadata extraction Review content and rating analysis Developer and country information App store performance metrics Quality Monitoring Automated low-rating detection Real-time performance alerts Continuous data archiving Integration Capabilities Bright Data web scraping service Google Sheets data persistence Telegram instant notifications Polling-based status monitoring Use Cases App Performance Monitoring Track rating trends over time Identify user sentiment patterns Monitor competitor performance Quality Assurance Early warning for rating drops Customer feedback analysis Market reputation management Business Intelligence Review sentiment analysis Performance benchmarking Strategic decision support Technical Notes Polling Interval: 45-second status checks Rating Threshold: Alerts triggered for ratings < 4 Data Format: JSON with structured field mapping Error Handling: Includes error tracking in dataset requests Result Limiting: Maximum 5 multiple results per request For any questions or support, please contact: info@incrementors.com or fill out this form https://www.incrementors.com/contact-us/
By IncrementorsMonitor lead response time SLA breaches with Google Sheets & Telegram alerts
Description Never miss a lead again with this SLA Breach Alert automation powered by n8n! This workflow continuously monitors your Google Sheets for un-replied leads and automatically triggers instant Telegram alerts, ensuring your team takes immediate action. By running frequent SLA checks, enriching alerts with direct Google Sheet links, and sending real-time notifications, this automation helps prevent unattended leads, reduce response delays, and boost customer engagement. What This Template Does 📅 Runs every 5 minutes to monitor SLA breaches 📋 Fetches lead data (status, contact, timestamps) from Google Sheets 🕒 Identifies leads marked “Un-replied” beyond the 15-minute SLA 🔗 Enriches alerts with direct Google Sheet row links for quick action 📲 Sends Telegram alerts with lead details for immediate response Step-by-Step Setup Prepare Your Google Sheet Create a sheet with the following columns (minimum required): Lead Name Email Phone Status (values: Replied, Un-replied) Timestamp (time of last update/reply) Set Up Google Sheets in n8n Connect your Google account in n8n. Point the workflow to your sheet (remove any hardcoded document IDs before sharing). Configure SLA Check Use the IF node to filter leads where: Status = Un-replied Time since timestamp > 15 minutes Enrich Alerts with Links Add a Code node to generate direct row links to the sheet. Set Up Telegram Bot Create a Telegram bot via @BotFather. Add the bot to your team chat. Store the botToken securely (remove chatId before sharing templates). Send Alerts Configure the Telegram node in n8n to send lead details + direct Google Sheet link. Customization Guidance Adjust the SLA window (e.g., 30 minutes or 1 hour) by modifying the IF node condition. Add more fields from Google Sheets (e.g., Company, Owner) to enrich the alert. Replace Telegram with Slack or Email if your team prefers a different channel. Extend the workflow to auto-assign leads in your CRM once alerted. Perfect For Sales teams that need to respond to leads within strict SLAs Support teams ensuring no customer request is ignored Businesses aiming to keep lead response times sharp and consistent
By Rahul Joshi