Automated 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 ChatPayLabs