AI Workflows
Artificial Intelligence tools and workflows
Automate Driver License Data Extraction with Google Drive, VLM Run, and Google Sheets
Streamline the process of extracting data from driver license images uploaded to Google Drive using VLM Run's AI OCR, and store the structured data in Google Sheets.
n8n$9.99Convert Reddit Threads into Short Vertical Videos with AI
# Convert Reddit threads into short vertical videos with AI ## Who is this for? This workflow is ideal for: - **Content creators** and **video editors** automating short-form content production - **Reddit storytellers** converting text posts into engaging TikTok, YouTube Shorts, or Reels - **Social media managers** repurposing community discussions into visual narratives ## What problem is this solving? Manually converting Reddit posts into vertical video content is time-consuming: - You have to read, summarize, write a script - Generate S - Find stock footage - Edit everything in a timeline This workflow automates the full pipeline. It converts any Reddit thread into a polished video with: - S narration - Subtitle overlays - B-roll from Pexels - Automatic rendering via Shotstack ## What this workflow does This workflow: 1. **Extracts Reddit post and comments** via Reddit API 2. **Summarizes the thread into structured clips** using OpenAI 3. **Generates search queries** for each clip for stock footage 4. **Queries Pexels API** for relevant vertical videos 5. **Generates S audio** for each clip using OpenAI Whisper 6. **Creates subtitles** matching the audio 7. **Uploads footage/audio to Shotstack** 8. **Renders a full vertical video (720x1280)** with synced S, subtitles, and b-roll 9. **Returns a final video URL** ## Setup - Create accounts and API keys for: - [Reddit Developer App](https://www.reddit.com/prefs/apps) - [OpenAI](https://platform.openai.com/) - [Pexels](https://www.pexels.com/api/) - [Shotstack](https://shotstack.io/) - Add credentials in n8n: - Reddit (HTTP Basic Auth) - OpenAI (API Key) - Shotstack (HTTP Header Auth) - Pexels (HTTP Header Auth) - Trigger via webhook or manual node. The input must include: ```json { "voice": "nova", "ttsSpeed": 1, "videoLength": 60, "redditLink": "https://www.reddit.com/r/example/comments/example_id/example_title" } ``` ## How to customize this workflow - **Tweak OpenAI prompts** to change tone or clip granularity - **Change stock source** by swapping Pexels for another API - **Adjust S voices** or languages by modifying the `voice` field - **Modify video styling** (fonts, colors, fit modes) in the timeline construction code node - **Control duration** by editing the character length formula in the `Limit comments length` node ## Additional Notes - All stock videos are selected to match clip themes using generalized keywords to avoid API misses - Includes `wait` nodes to ensure Shotstack's async upload/render processes complete before proceeding - Annotated with **sticky notes** explaining major sections like S, Reddit input, and media timeline - Avoids community nodes to ensure cloud compatibility ## Template Category **AI**, **Marketing**, **Building Blocks**, **Other (Content Creation)**
n8n$24.99Automate AI Image Generation and Storage in AWS S3 with n8n
This workflow automates the generation of AI-powered images using OpenAI and securely stores them in AWS S3, streamlining content creation and management.
n8n$14.99Convert YouTube Videos to Multilingual SEO Blogs Automatically
Transform YouTube videos into SEO-friendly, multilingual blog posts using this automated n8n workflow. Ideal for content creators and marketers seeking to repurpose video content efficiently.
n8n$4.99Analyze Tesla 15-Minute Market Trends with AI-Driven Indicators
Utilize AI to assess Tesla's short-term market momentum and structure using six technical indicators on a 15-minute timeframe. This workflow is part of a larger AI trading system.
n8n$9.99Create a Humorous Telegram Bot with AI and Postgres
Build a witty Telegram bot using n8n, OpenRouter, and Postgres to deliver jokes, motivational quotes, and playful roasts. This workflow tracks user interactions and provides statistics and leaderboards.
n8n$14.99Automate PDF Analysis and Reporting with Mistral OCR and AI Agents
Leverage Mistral's advanced OCR capabilities to transform complex PDFs into detailed research reports or concise newsletters using AI-driven workflows.
n8n$14.99Text-to-Image Generation with Flux AI, Google Drive Storage & Sheets Logging
# AI Image Generator with Flux AI Generate realistic, high-quality images from text prompts using the **Flux AI Text-to-Image Generator API** via RapidAPI, and seamlessly store the results in Google Drive and log them in Google Sheets - all automated using **n8n**. --- ## What This Workflow Does This no-code automation enables you to: - Enter a **custom text prompt** using a web form. - Generate a **photorealistic image** using **[Flux AI's Text-to-Image Generator](https://rapidapi.com/skdeveloper/api/text-to-image-generator-flux)** via [RapidAPI](https://rapidapi.com/skdeveloper/api/text-to-image-generator-flux). - Upload the image to **Google Drive**. - Log the prompt and result in a **Google Sheet**. - Capture and log errors in a fallback sheet. --- ## Use Case Ideal for: - Digital artists and marketers - Social media managers - Brand mockup creators - Rapid concept prototyping All without writing a single line of code. --- ## Benefits - **No-code automation** for AI-generated images - **Cloud storage** and structured logging - **Error handling** built-in - **Fast content creation** for design, branding, or concept testing - **Powered by** the [Flux AI Text-to-Image Generator API](https://rapidapi.com/skdeveloper/api/text-to-image-generator-flux) via **RapidAPI** --- ## Node-by-Node Breakdown ### 1. On Form Submission - Accepts user input for a creative text prompt. - Example: “A silver can with vapor and blue lightning background.” - Benefit: No technical knowledge needed. --- ### 2. HTTP Request - Flux AI API - Sends the prompt to the **[Flux AI Text-to-Image Generator API](https://rapidapi.com/skdeveloper/api/text-to-image-generator-flux)** via **RapidAPI**. - Returns an image encoded in base64. - Benefit: Seamless integration with cutting-edge image generation. --- ### 3. Code Node - Base64 Decoder - Converts the base64 image to a binary `.jpg` file. - Benefit: Readies the image for upload/download/sharing. --- ### 4. Google Drive - Uploads the generated image to your Google Drive folder. - Benefit: Secure, shareable cloud storage. --- ### 5. Google Sheets - Success Log - Appends a row with the original prompt, filename, and generation date. - Benefit: Tracks history of all generated images. --- ### 6. IF Node - Error Detection - Checks if the image generation failed. - Benefit: Prevents workflow from halting and routes to error logging. --- ### 7. Google Sheets - Error Log - Logs failed prompts and error messages. - Benefit: Helps identify what went wrong (e.g., malformed prompt). --- ## Challenges Solved | Problem | How This Workflow Fixes It | |---------|----------------------------| | Manual prompt-based image generation is slow | Fully automated with Flux AI | | No storage pipeline for generated images | Integrated with Google Drive | | No audit trail for prompts/images | Logged into Google Sheets | | Errors go unnoticed in image generation | Built-in error check and logging | | Users lack API access or dev experience | Friendly web form UI | --- ## API Spotlight This workflow is powered by the **[Flux AI Text-to-Image Generator API](https://rapidapi.com/skdeveloper/api/text-to-image-generator-flux)** - available exclusively on **RapidAPI**. ### Why use this API? - Ultra-fast text-to-image rendering - High-resolution results - Developer-friendly and cost-effective - Great for branding, mockups, and visuals We've integrated this API to make advanced image generation accessible with just a prompt - no AI or dev experience required.
n8n$9.99Automate Image Data Extraction with Gemini AI via API
This n8n workflow creates an API endpoint for extracting structured data from images using Gemini AI. It processes an image URL and returns extracted details in JSON format, ideal for automating document processing tasks.
n8n$9.99PostgreSQL Conversational Agent with Claude & DeepSeek (Multi-KPI, Secure)
# Conversational PostgreSQL Agent Enable AI-driven conversations with your PostgreSQL database using a secure and visual-free agent powered by n8n's Model Context Protocol (MCP). This template allows users to ask multiple KPIs in a single message, returning consolidated insights - more efficient than the original Conversing with Data template. --- ## Why This Template Unlike the Conversing with Data workflow, which handles one KPI per message, this version: - Supports multi-KPI questions - Returns structured, human-readable reports - Uses fewer AI calls, making it faster and cheaper - Avoids raw SQL execution for enhanced security **Estimated cost per full multi-request run: ~$0.01** This template is optimized for efficiency. Each message can return 2-4 KPIs (You can change the MaxIteration of the Agent to make it more, it is currently set up at 30 iterations) using a single Claude 3.5 Haiku session and DeepSeek-based SQL generation - balancing speed, reasoning, and affordability. --- ## Sample Use Case **User:** "Can you show product performance, revenue trends, and top 5 customers?" **Agent:** - Uses `Listables` and `GetableSchema` - Generates three SQL queries using `get_query_and_data` - Returns: **Product Performance** 1. High-Waist Jeans - 10 units, $1,027 revenue 2. Denim Jacket - 10 units, $783 revenue **Sales Trends** - Peak Month: January 2024 - 32 units, $2,378 - Average Monthly Units: 10-16 **Customer Insights** 1. Bob Brown - $1,520 spent 2. Diana Wilson - $925 spent All from one natural prompt. --- ## Real-World Interaction Screenshot  --- ## What's Inside | Node | Purpose | |----------------------------|-----------------------------------------------------------| | MCP Server Trigger | Receives user queries via `/mcp/...` | | AI Agent + Memory | Understands and plans multi-step queries | | Think Tool | Breaks down the user's question into structured goals | | get_query_and_data | Generates SQL securely from natural language | | Listables, GetSchema | AI tools to explore DB safely | | Read/Insert/Update Tools | Execute structured operations (never raw SQL) | | checkdatabase Subflow | Validates SQL, formats response as clean text | --- ## Model Selection Recommendations This template uses two types of models, selected for cost-performance balance and role alignment: **1. Claude 3.5 Haiku (Anthropic) - for the MCP Agent** The main conversational agent uses Claude 3.5 Haiku, ideal for MCP because it was built by Anthropic - the creators of the MCP standard. It's fast, affordable, and performs excellently in tool-calling and reasoning tasks. **2. DeepSeek - for the SQL subworkflow** The subworkflow that turns natural language into SQL uses DeepSeek. It's one of the most affordable and performant models available today for structured outputs like SQL, making it a perfect fit for utility logic. This setup provides top-tier reasoning + low-cost execution. --- ## Security Benefits - No raw SQL accepted from the user or LLM - All queries are parameterized - Schema is dynamically retrieved - Final output is clean, safe, and human-readable --- ## Try a Prompt > "Show me the top 5 products by units sold and revenue, total monthly sales trend, and top 5 customers by spending." In one message, the agent will: - Generate and run multiple queries - Use the schema to validate logic - Return a single, comprehensive answer --- ## How to Use 1. Upload both workflow files into your n8n instance: - `Build_your_own_PostgreSQL_MCP_server_No_visuals_.json` - `checkdatabase.json` 2. Set up PostgreSQL credentials (e.g., "Postgres account 3") 3. Confirm model setup: - Claude 3.5 Haiku for the main agent - DeepSeek for the subflow 4. Use the `/mcp/...` URL from the MCP Server Trigger to connect your frontend or chatbot 5. Ask questions naturally - the agent takes care of planning, querying, and formatting --- ## Customization Ideas - Swap Claude or DeepSeek for OpenAI, Mistral, Gemini, etc. - Export insights to Slack, Notion, or Google Sheets - Add Switch nodes to control access to specific tables - Integrate with any front-end app, internal dashboard, or bot --- ## What's Included - `Build_your_own_PostgreSQL_MCP_server_No_visuals_.json` - MCP agent logic - `checkdatabase.json` - SQL generation and formatting utility workflow These must be uploaded into your n8n workspace for the template to function. --- ## Comparison: Conversing with Data vs This Workflow | Feature | Conversing with Data | This Workflow |
n8n$14.99Automate File Summarization with OpenAI Assistants
Upload various document types to automatically generate concise summaries using OpenAI's Assistant API.
n8n$4.99Automate Hairstyle Edits and LINE Group Sharing with OpenAI
This workflow automates the process of editing hairstyles in images using OpenAI, uploading the results to Cloudinary, and sharing them with a LINE group. Ideal for salons and creative teams seeking efficient and consistent hairstyle modifications.
n8n$4.99Create an AI Chatbot with Google Drive, Llama 3, and Qdrant RAG
Set up an AI chatbot that uses Google Drive documents for knowledge retrieval, powered by Llama 3 and Qdrant for context-aware responses.
n8n$9.99Build Comprehensive Entity Profiles with GPT-4, Wikipedia & Vector DB for Content
This n8n template demonstrates how to build an intelligent entity research system that automatically discovers, researches, and creates comprehensive profiles for business entities, concepts, and terms. Use cases are many: try automating glossary creation for technical documentation, building standardized definition databases for compliance teams, researching industry terminology for content creation, or developing training materials with consistent entity explanations! ## Good to know Each entity research typically costs $0.08-$0.34, depending on the complexity and sources required. The workflow includes smart duplicate detection to minimize unnecessary API calls. The workflow requires multiple AI services and a vector database, so setup time may be longer than simpler templates. Entity definitions are stored locally in your Qdrant database and can be reused across multiple projects. ## How it works The workflow checks your existing knowledge base first to avoid duplicate research on entities you've already processed. If the entity is new, an AI research agent intelligently combines your vector database, Wikipedia, and live web research to gather comprehensive information. The system creates structured entity profiles with definitions, categories, examples, common misconceptions, and related entities - perfect for business documentation. AI-powered validation ensures all entity profiles are complete, accurate, and suitable for business use before storage. Each researched entity gets stored in your Qdrant vector database, creating a growing knowledge base that improves research efficiency over time. The workflow includes multiple stages of duplicate prevention to avoid unnecessary processing and API costs. ## How to use The manual trigger node is used as an example, but feel free to replace this with other triggers such as form submissions, content management systems, or automated content pipelines. You can research multiple related entities in sequence, and the system will automatically identify connections and relationships between them. Provide topic and audience context to get tailored explanations suitable for your specific business needs. ## Requirements - OpenAI API account for GPT-4-mini (entity research and validation) - Qdrant vector database instance (local or cloud) - Ollama with nomic-embed-text model for embeddings - **Automate Web Research with GPT-4, Claude & Apify for Content Analysis and Insights** workflow (for live web research capabilities) - Anthropic API account for Claude Sonnet 4 (used by the web research workflow) - Apify account for web scraping (used by the web research workflow) ## Customizing this workflow Entity research automation can be adapted for many specialized domains. Try focusing on specific industries like legal terminology (targeting official legal sources), medical concepts (emphasizing clinical accuracy), or financial terms (prioritizing regulatory definitions). You can also customize the validation criteria to match your organization's specific quality standards.
n8n$24.99Integrate Multi-Source AI Agent with OpenAI, Perplexity, and Google Sheets
Leverage a powerful AI agent that combines OpenAI's intelligence with real-time web searches, vector databases, and Google Sheets for comprehensive data retrieval and decision-making.
n8n$9.99Create a Multi-Language Telegram RAG Chatbot with AI Supervision and Google Drive Automation
Develop a sophisticated multi-agent RAG chatbot that processes diverse user queries using AI agents and automates data ingestion from Google Drive and websites.
n8n$19.99WordPress Content Automation Machine with HUMAN-IN-THE-LOOP & DEEP RESEARCH
## **HUMAN-IN-THE-LOOP Content Automation Pro with Deep Research & Airtable for Power Users - Collaborative AI for WordPress Blog Automation** **The BEST n8n Content Automation workflow** in the market is getting even better, now enabling you to have full control over the entire process. It's time to unlock the power of true **collaborative creation with GenAI!** **Want to leverage AI automation but don't want to give up full control over the whole content creation process?** Get the best of both worlds - get **“Human-in-the-Loop”** with this supreme n8n workflow template! This template blends the **power of AI** with the crucial oversight of **human expertise and intervention**, all managed through a central, streamlined Airtable interface. Say goodbye to generic content and hello to high-quality, deeply researched articles published directly to your WordPress site with full control. Check out [my Youtube channel](https://www.youtube.com/@nminhduc/) for a full walkthrough.      ### **What problem is this workflow solving? / Use cases** Fully automated content generation tools often lack the nuance, accuracy, and strategic control necessary for truly impactful content. This workflow solves that critical gap by integrating powerful AI capabilities into a structured, human-supervised content creation process managed via a simple interface. It automates the most repetitive and time-consuming tasks - like research gathering, chapter ideations, domain copywriting - while strategically embedding essential human review points at critical junctures. Instead of simply providing a topic and receiving a finished article of uncertain quality, you actively guide the process step-by-step within an interface. You review, refine, and approve AI suggestions for chapters and content before finalizing and publishing. This approach makes it perfect for creating high-quality, deeply researched, and brand-aligned blog posts for WordPress more efficiently, without ever sacrificing essential editorial control. ### **Who is this for?** This workflow template is meticulously designed for individuals and teams deeply involved in content creation who seek a powerful blend of AI-driven efficiency and essential human-controlled quality assurance: * **Content Creators & Bloggers:** Who want to leverage AI for drafting and research but maintain full editorial control. * **Marketing Teams:** Looking to scale content production efficiently while ensuring brand voice and quality consistency. * **SEO Specialists:** Needing to produce well-researched, structured, and SEO-optimized content regularly. * **Agencies:** Managing content creation for multiple clients with a structured, repeatable process. * **Anyone who prefers a human-in-the-loop approach** over fully automated black box content generators. ### **How this workflow works** This template automates significant portions of the WordPress content creation process, utilizing Airtable as the central hub for management and user interaction, while leveraging AI for research and copywriting. The system is composed of four interconnected n8n flows, each handling a distinct stage of the content lifecycle. In addition, there is a sub-workflow dedicated to the research tool (Perplexity).  #### **Content Creation Process in Airtable** The entire journey from idea to published post is managed within your Airtable base, offering transparency and control. Progress through each tab sequentially to complete the content creation process and produce a high-quality article that fulfills your requirements. 1. **Configure Settings (“Settings” table):** Define global parameters (website details, target audience, writing style, category IDs, about us, CTA, etc.) in the “Settings” table. These serve as default inputs for the AI. 2. **Create Topics (“Create Topics” table):** Add a record to the “Create Topics” table for each new blog post idea. Mark the topic as ready by setting its Status field to “To Do” **and** ticking the Execute Flow checkbox to initiate the first flow. 3. **Generate Chapters (“Generate Chapters” table):** The first flow calls to the sub-workflow to do online research and generates potential chapter outlines based on your topic and settings, saving them to the “Generate Chapters” table. 4. **Select Chapters to Write (“Select Chapters” table):** Review the suggested chapters in the “Select Chapters” table. Select
n8n$24.99Automate Bing Copilot Searches and Summarize Results with AI
Streamline your research process by automating Bing Copilot searches, extracting structured data, and generating concise summaries with AI. This workflow integrates with Bright Data and Google Gemini to enhance efficiency and accuracy.
n8n$14.99Automate Personalized Job Application Icebreakers with GPT-4 and Google Sheets
This workflow automates the creation of personalized email icebreakers for job applications using data from Google Sheets and GPT-4. It streamlines the process by fetching job listing data, generating customized icebreakers, and updating the sheet with the new content.
n8n$4.99Manage Appian Tasks with Ollama Gwen LLM and Postgres Memory
This workflow is a simple example of using n8n as an AI chat interface into Appian. It connects a local LLM, persistent memory, and API tools to demonstrate how an agent can interact with Appian tasks. What this workflow does: - **Chat interface**: Accepts user input through a webhook or chat trigger. - **Local LLM (Ollama)**: Runs on qwen2.5:7b with an 8k context window. - **Conversation memory**: Stores chat history in Postgres, keyed by sessionId. - **AI Agent node**: Handles reasoning, follows system rules (helpful assistant persona, date formatting, iteration limits), and decides when to call tools. - **Appian integration tools**: - **List tasks**: Fetches a user's tasks from Appian. - **Create task**: Submits data for a new task in Appian (title, description, hours, cost). How it works: 1. A user sends a chat message. 2. The workflow normalizes fields such as text, username, and sessionId. 3. The AI Agent processes the message using Ollama and Postgres memory. 4. If the user asks about tasks, the agent calls the Appian APIs. 5. The result, either a task list or confirmation of a new task, is returned through the webhook. Why this is useful: - Demonstrates how to build a basic Appian connector in n8n with an AI chat front end. - Shows how an LLM can decide when to call Appian APIs to list or create tasks. - Provides a pattern that can be extended with more Appian endpoints, different models, or custom system prompts.
n8n$9.99Automate YouTube Video Summaries to Discord with AI and Slack Approval
This n8n workflow automates the process of summarizing YouTube videos using AI, sending them for approval via Slack, and distributing approved summaries to Discord, while logging all data in Google Sheets.
n8n$9.99Automate YouTube Video Analysis with AI Summaries and Email Alerts
This workflow automates the analysis of YouTube videos by extracting transcripts, generating AI-powered summaries, and sending results via email. Ideal for content creators, marketers, and researchers.
n8n$14.99Create an AI Chatbot with InfraNodus for Enhanced Knowledge-Based Responses
Develop an AI chatbot that leverages InfraNodus to access a comprehensive knowledge base, enhancing response accuracy and relevance.
n8n$4.99YouTube RAG Search with Frontend Using Apify, Qdrant, and AI
### Ever wanted to build your own RAG search over Youtube videos? Well, now you can! This n8n template shows how you can build a very capable Youtube search engine powered by Apify, Qdrant, and your LLM of choice to quickly and efficiently browse over many videos for research. I originally started this template to ask questions on the n8n @ scale office-hours livestream videos but then extended it to include the latest videos on the official channel. **Check out a demo here**: [https://jimleuk.app.n8n.cloud/webhook/n8n_videos](https://jimleuk.app.n8n.cloud/webhook/n8n_videos) ## How it works * Stage 1 is to collect the Youtube video transcripts and push them into a vector database. For this, I've used Apify to scrape Youtube and Qdrant to store the embeddings. * Transcripts are broken down into smaller chunks and carefully tagged with metadata to assist in later search and filtering. * Stage 2 is to build a web frontend for the user to query the vectorized transcripts. I'm using a webhook to serve a simple web app and API to dynamically fetch the results. * When searching for a video, I've opted to use Qdrant's search groups API which, in this use-case, performs better as it returns a wider range of video results. * In the web frontend, when the user clicks on the results, the matching Youtube video plays in an embedded video player. ## How to use * Once credentials are all set, first run steps 1 - 3 to populate your vector store. * Next, set the workflow to active to expose the web frontend. Visit the webhook URL in your browser to use it. * If only for personal use, you may want to remove the rate limiting mechanism in step 4. ## Requirements * Apify for Youtube Channel and Video Scraping * Qdrant for Vector store * OpenAI for LLM and Embeddings ## Customizing the template * Not interested in official n8n videos? Swap to a different channel - this template will work on many as long as videos are not private or set to prevent embeds. * Technically any vector store should work but may not have the same grouping API. Use the simple vector store node and revert back to basic searching instead.
n8n$24.99
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