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Transform T-Shirt Mockups into Print-Ready Designs with AI
This n8n workflow converts rough or outdated T-shirt mockup designs into refined, print-ready artwork using AI. It leverages OpenAI's vision model to analyze and enhance the design, ensuring a clean output optimized for printing.
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Automate Web Data Extraction Using Vision-Based AI with Google Sheets and ScrapingBee
Streamline your web scraping tasks with a vision-based AI agent that integrates with Google Sheets and ScrapingBee, enabling efficient data extraction from webpages.
n8nFree
Automate Image Generation and Video Creation with Flux, Kraken, and Runway
This workflow automates the process of generating images using Flux, hosting them on Kraken, and converting them into videos with Runway ML. Ideal for creators and automation enthusiasts looking to streamline content creation.

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Automate Restaurant Bookings and Orders with Voice AI and PostgreSQL

This workflow enables restaurants to automate table reservations, food orders, and information requests using voice AI technology integrated with VAPI and PostgreSQL.

Automate Viral Clip Generation from YouTube Videos with Vizard AI

This workflow automates the extraction of high-potential viral clips from YouTube videos using Vizard AI and shares them on Slack for team review.

Build a Secure Document Q&A System with Llama3, Postgres, Qdrant, and Google Drive

Create a private, on-premises AI assistant that leverages your documents for intelligent Q&A. This workflow integrates Google Drive, Llama3, Postgres, and Qdrant to ensure data privacy while providing robust AI capabilities.

Create a Local MCP Server for Question Answering with RAG

Set up a local MCP Server that utilizes a semantic database for Retrieval Augmented Generation to answer questions. This workflow integrates with Qdrant and Ollama to provide efficient information retrieval and response generation.

Transform Telegram Bot into a Multimodal AI Assistant with OpenAI and SERP

This workflow enhances your Telegram bot into a powerful AI assistant capable of processing text, voice, images, and documents. It leverages OpenAI, SERP, and vector store technologies to deliver comprehensive and context-aware responses.

Automate Social Media Content Creation from Video Transcripts with Gemini AI and Airtable

This workflow automates the creation of social media content from video transcripts using Gemini AI and Airtable. It streamlines the process of generating platform-specific content, saving time and ensuring consistency across social media channels.

Automate Transcription of Telegram Voice Messages with OpenAI Whisper and Google Workspace

Effortlessly transcribe Telegram voice messages and store them in Google Sheets, while backing up the audio in Google Drive using OpenAI Whisper.

Generate Videos from Images with Wan 2.2 I2V A14B AI Model

# Wan Video Wan 2.2 I2v A14b Video Generator ## Description Image-to-video at 720p and 480p with Wan 2.2 A14B ## Overview This n8n workflow integrates with the Replicate API to use the wan-video/wan-2.2-i2v-a14b model. This powerful AI model can generate high-quality video content based on your inputs. ## Features - Easy integration with Replicate API - Automated status checking and result retrieval - Support for all model parameters - Error handling and retry logic - Clean output formatting ## Parameters ### Required Parameters - **prompt** (string): Prompt for video generation - **image** (string): Input image to generate video from ### Optional Parameters - **seed** (integer, default: None): Random seed. Leave blank for random - **num_frames** (integer, default: 81): Number of video frames. 81 frames give the best results - **resolution** (string, default: 480p): Resolution of video. 832x480px corresponds to 16:9 aspect ratio, and 480x832px is 9:16 - **sample_shift** (number, default: 5): Sample shift factor - **sample_steps** (integer, default: 30): Number of generation steps. Fewer steps means faster generation, at the expense of output quality. 30 steps is sufficient for most prompts - **frames_per_second** (integer, default: 16): Frames per second. Note that the pricing of this model is based on the video duration at 16 fps ## How to Use 1. Set up your Replicate API key in the workflow. 2. Configure the required parameters for your use case. 3. Run the workflow to generate video content. 4. Access the generated output from the final node. ## API Reference - Model: [wan-video/wan-2.2-i2v-a14b](https://replicate.com/wan-video/wan-2.2-i2v-a14b) - API Endpoint: https://api.replicate.com/v1/predictions ## Requirements - Replicate API key - n8n instance - Basic understanding of video generation parameters

Build Website Q&A Chatbot with RAG, OpenAI GPT-4 Mini, and Supabase Vector DB

## What problem does this workflow solve? Many websites lack a smart, searchable interface. Visitors often leave due to unanswered questions. This workflow transforms any website into a **Retrieval-Augmented Generation (RAG)** chatbot—automatically extracting content, creating embeddings, and enabling real-time, context-aware chat on your own site. --- ## What does this workflow do? 1. Accepts a website URL through a form trigger. 2. Fetches and cleans website content. 3. Parses content into smaller sections. 4. Generates vector embeddings using OpenAI (or your embedding model). 5. Stores embeddings and metadata in **Supabase's vector database**. 6. When a user asks a question: - Searches Supabase for relevant chunks via similarity search. - Retrieves matching content as context. - Sends context + question to OpenAI to generate an accurate answer. 7. Returns the AI-generated response to the user in the chat interface. --- ## Setup Instructions ### Website Form Trigger - Use a **Form / HTTP Trigger** to submit website URLs for indexing. ### Content Extraction & Chunking - Use HTTP nodes to fetch HTML. - Clean and parse it (e.g., remove scripts, ads). - Use a **Function node** to split into manageable text chunks. ### Embedding Generation - Call OpenAI (or Cohere) to generate embeddings for each chunk. - Insert vectors and metadata into Supabase via its **API or n8n Supabase node**. ### User Query Handling - Use a **Chat Trigger** (webhook/UI) to receive user questions. - Convert the question into an embedding. - Query Supabase with similarity search (e.g., `match_documents` RPC). - Retrieve top-matching chunks and feed them into OpenAI with the user question. - Return the reply to the user. ### AI & Database Setup - **OpenAI API key** for embedding and chat. - A **Supabase project** with: - `vector` extension enabled - Tables for document chunks and embeddings - A similarity search function like `match_documents` ## How to Embed the Chat Widget on Your Website You can add the chatbot interface to your website with a simple JavaScript snippet. ### Steps: 1. Open the When chat message received node 2. Copy Chat URL 3. Make sure, Make Chat Publicly Available toggle is enabled 4. Make sure the mode is Embedded Chat 5. Follow the instructions given on this package [here](https://www.npmjs.com/package/@n8n/chat#a-cdn-embed). --- ## How it Works 1. **Submit URL** — Form Trigger 2. **Fetch Website Content** — HTTP Request 3. **Clean & Chunk Content** — Function Node 4. **Make Embeddings** (OpenAI/Cohere) 5. **Store in Supabase** — embeddings + metadata 6. **User Chat** — Chat Trigger 7. **Search for Similar Content** — Supabase similarity match 8. **Generate Answer** — OpenAI completion w/ context 9. **Send Reply** — Chat interface returns answer --- ## Why Supabase? Supabase offers a scalable Postgres-based vector database with extensions like `pgvector`, making it easy to: - Store vector data alongside metadata - Run ANN (Approximate Nearest Neighbor) similarity searches - Integrate seamlessly with n8n and your chatbot UI --- ## Who can use this? - **Documentation websites** - **Support portals** - **Product/Landing pages** - **Internal knowledge bases** Perfect for anyone who wants a **smart, website-specific chatbot** without building an entire AI stack from scratch. --- ## Ready to Deploy? Plug in your: - OpenAI API Key - Supabase project credentials - Chat UI or webhook endpoint and launch your **AI-powered, website-specific RAG chatbot** in minutes!

Automate YouTube Shorts Script Creation with AI and Telegram

Streamline your YouTube Shorts content creation by automating script generation from video links using AI and Telegram.

Automate Web Browsing with AI-Powered Airtop and Slack Integration

This workflow automates web interactions by simulating human browsing using AI tools and Airtop, with optional Slack notifications for updates.

Generate AI Videos from Scripts with DeepSeek, Synthesia, and Together.ai

# Automated Video Generator (n8n Workflow) ## Features - End-to-End Video Creation from user idea or transcript - AI-Powered Scriptwriting using LLMs (e.g., DeepSeek via OpenRouter) - Voiceover Generation with customizable voices - Image Scene Generation using generative models like together.ai - Clip Creation & Concatenation into a full video - Dynamic Caption Generation with styling options - Google Drive & Sheets Integration for asset storage and progress tracking ## How It Works User Submits Form with: - Main topic or transcript - Desired duration - Voice - Visual style (e.g., Pixar, Lego, Cyberpunk) - Image generation provider AI generates a script: A catchy title, description, hook, full script, and call to action using a language model. Text-to-Speech (TTS): The script is turned into audio using the selected voice, with timestamped captions generated. Scene Segmentation: The script is split into 5-6 second segments for visual storyboarding. Image Prompt Creation: Each scene is converted into an image prompt in the selected style (e.g., anime close-up of a racing car). Image Generation: Prompts are sent to together.ai or fal.ai to generate scenes. Clip Creation: Each image is turned into a short video clip (Ken Burns-style zoom) based on script timing. Video Assembly: - All clips are concatenated into a single video. - Captions are overlaid using the earlier timestamps. - Final Output is uploaded to Google Drive, Telegram, and links are saved in Google Sheets. ## Initial Setup ### 1. Set Up TTS Voice (Text-to-Speech) Run your TTS server locally using Docker. ### 2. Set Up NCA-Toolkit The nca-toolkit appears to be a custom video/image processing backend used via HTTP APIs: - http://host.docker.internal:9090/v1/image/transform/video - http://host.docker.internal:9090/v1/video/concatenate - http://host.docker.internal:9090/v1/ffmpeg/compose #### Steps: - Clone or build the nca-toolkit container (if it's a private tool): - Ensure it exposes port 9090. - It should support endpoints for: - Image to video (zoom effect) - Video concatenation - Audio + video merging - Caption overlay via FFmpeg - Run it locally with Docker: - `docker run -d -p 9090:80 your-nca-toolkit-image` ### 3. Set Up together.ai (Image Generation) (Optional. You can use ChatGPT API Instead) This handles image generation using models like FLUX.1-schnell. #### Steps: - Create an account at: https://www.together.ai - Generate your API key

Build an Interactive Slack AI Chatbot with OpenRouter and Postgres

Create a Slack AI chatbot that supports multiple chat threads, a thinking UI, and chat history storage using OpenRouter and Postgres.

Automate Scrum Master Tasks with AI, Slack, and Asana Integration

Streamline your Scrum process by automating data collection and analysis using AI, Slack, and Asana. This workflow assists Scrum Masters by identifying potential impediments and providing actionable insights for sprint delivery.

"Multi-AI Agent Chatbot for PostgreSQL/Supabase DB and QuickCharts + Cool Router"

# Multi-AI Agent Chatbot for Postgres/Supabase Databases and QuickChart Generation ## Who is this for? This workflow is ideal for **data analysts**, **developers**, and **business intelligence teams** who need an AI-powered chatbot to query Postgres/Supabase databases and generate dynamic charts for data visualization. ## What problem does this solve? It simplifies data exploration by combining conversational AI with database querying and chart generation. Users can interact with their database using natural language, retrieve insights, and visualize data without manual SQL queries or chart configuration. ## What this workflow does 1. **AI-Powered Chat Interface**: - Accepts natural language prompts to query databases or generate charts. - Routes user requests through a tool agent system to determine the appropriate action (query or chart). 2. **Database Querying**: - Executes SQL queries on Postgres/Supabase databases based on user input. - Retrieves schema information, table definitions, and specific data records. 3. **Dynamic Chart Generation**: - Uses QuickChart to create bar charts, line charts, or other visualizations from database records. - Outputs a shareable chart URL or JSON configuration for further customization. 4. **Memory Integration**: - Maintains chat history using Postgres memory nodes, enabling context-aware interactions. Workflow diagram showcasing AI agents, database querying, and chart generation paths. ## Setup 1. **Prerequisites**: - A Postgres-compatible database (e.g., Supabase). - API credentials for OpenAI. 2. **Configuration Steps**: - Add your database connection credentials in the Postgres nodes. - Set up OpenAI credentials for GPT-4-mini in the language model nodes. - Adjust the QuickChart schema in the QuickChart Object Schema node to fit your use case. 3. **Testing**: - Trigger the chat workflow via the "When chat message received" node. - Test with prompts like "Generate a bar chart of sales data" or "Show me all users in the database." ## How to customize this workflow - **Modify AI Prompts** - **Add Chart Types** - **Integrate Other Tools**

Automate Google Sheets with AI-Powered Data Management and Calculations

Leverage AI to manage and calculate data in Google Sheets efficiently. Ideal for businesses and teams needing AI-driven insights and automation.

Answer Questions from Documents with RAG Using Supabase, OpenAI, & Cohere Reranker

This workflow contains community nodes that are only compatible with the self-hosted version of n8n. This comprehensive RAG workflow enables your AI agents to answer user questions with contextual knowledge pulled from your own documents, using metadata-rich embeddings stored in Supabase. **Key Features:** - RAG Agents powered by GP-4.5 or GP-3.5 via OpenRouter or OpenAI. - Supabase Vector Store to store and retrieve document embeddings. - Cohere Reranker to improve response relevance and quality. - Metadata Agent to enrich vectorized data before ingestion. - PDF Extraction Flow to automatically parse and upload documents with metadata. **Setup Steps:** 1. Connect your Supabase Vector Store. 2. Use OpenAI Embeddings (e.g., text-embedding-3-small). 3. Add API keys for OpenAI and/or OpenRouter. 4. Connect a reranker like Cohere. 5. Process documents with metadata before embedding. 6. Start chatting - your AI agent now returns context-rich answers from your own knowledge base! Perfect for building AI assistants that can reason, search, and answer based on internal company data, academic papers, support docs, or personal notes.

Comprehensive Research Report Generator with Gemini AI, Web Search, & PDF Delivery

## This workflow is an autonomous AI research assistant that transforms a single topic into a comprehensive, multi-chapter report. Designed for researchers, students, and content creators, it automates the entire process from planning and online research to writing, formatting, and delivering a final PDF report directly to your email. ## How it works **Trigger:** The workflow begins when a user submits a research topic and an email address through a form. - **AI Planning:** An AI agent breaks the main topic into five focused subtopics. It then generates an engaging report title, a detailed introduction, and chapter headings, saving this initial structure to a Google Sheet. - **Parallel Research and Writing:** The workflow then splits into five parallel paths, one for each subtopic. In each path, it uses the Avily Search API to gather real-time information from the web. A dedicated AI writer then synthesizes this research into a complete, well-formatted HTML chapter with inline citations. - **Content Aggregation:** As each chapter is completed, its content, sources, and section titles are saved to the central Google Sheet. - **Final Assembly and Delivery:** Once all chapters are written, the workflow compiles the title, introduction, a newly generated table of contents, all chapters, and a complete list of sources into a single HTML document. This document is sent to APIemplate.io to be converted into a professionally formatted PDF, which is then emailed as an attachment to the address provided in the form. ## Set up steps - **Google Sheets:** - Make a copy of the provided Google Sheet template. - Connect your Google account in the credentials menu. - Update all Google Sheets nodes to use your copied sheet by selecting it from the list. - **AI Language Model (Google Gemini):** - Sign up for an API key from the Google AI Platform. - Connect your account in the Google Gemini Chat Model nodes. This template is pre-configured for the affordable gemini-pro-flash-lite model. - **Avily Search API:** - Sign up for a free account at Avily and get an API key. - In the Avily HTTP Request nodes, create a new Header Auth credential. For the Name, enter X-Avily-API-Key and for the Value, paste your Avily API key. - **APIemplate.io:** - Sign up for a free account at APIemplate.io and get an API key. - Connect your account in the Generate PDF nodes credentials. - **Gmail:** - Connect the Gmail account you want to send the final report from in the Send Report nodes credentials.

Automate Construction Progress Tracking with AI and Google Services

This n8n workflow automates the monitoring of construction site progress by analyzing daily photos using Google Gemini AI. It updates stakeholders with real-time progress summaries and logs data into Google Sheets for effective project management.

Create a Speech-to-Text API with OpenAI GPT-4 mini Transcribe

## Description This template provides a simple and powerful backend for adding speech-to-text capabilities to any application. It creates a dedicated webhook that receives an audio file, transcribes it using OpenAI's `gpt-4-mini` model, and returns the clean text. To help you get started immediately, you'll find a **complete, ready-to-use HTML code example** right inside the workflow in a sticky note. This code creates a functional recording interface you can use for testing or as a foundation for your own design. ## Who is this for? * **Developers:** Quickly add a transcription feature to your application by calling this webhook from your existing frontend or backend code. * **No-code/Low-code builders:** Embed a functional audio recorder and transcription service into your projects by using the example code found inside the workflow. * **API enthusiasts:** A lean, practical example of how to use n8n to wrap a service like OpenAI into your own secure and scalable API endpoint. ## **What problem does this solve?** * **Provides a ready-made API:** Instantly gives you a secure webhook to handle audio file uploads and transcription processing without any server setup. * **Decouples frontend from backend:** Your application only needs to know about one simple webhook URL, allowing you to change the backend logic in n8n without touching your app's code. * **Offers a clear implementation pattern:** The included example code provides a working demonstration of how to send an audio file from a browser and handle the response—a pattern you can replicate in any framework. ## How it works This solution works by defining a clear API contract between your application (the client) and the n8n workflow (the backend). 1. **The client-side technique:** * Your application's interface records or selects an audio file. * It then makes a `POST` request to the n8n webhook URL, sending the audio file as `multipart/form-data`. * It waits for the response from the webhook, parses the JSON body, and extracts the value of the `transcript` key. You can see this exact pattern in action in the example code provided in the workflow's sticky note. 2. **The n8n workflow (backend):** * The **Webhook** node catches the incoming `POST` request and grabs the audio file. * The **HTTP Request** node sends this file to the OpenAI API. * The **Set** node isolates the transcript text from the API's response. * The **Respond to Webhook** node sends a clean JSON object (`{transcript: your text here...}`) back to your application. ## **Setup** 1. **Configure the n8n workflow:** * In the **Transcribe with OpenAI** node, add your OpenAI API credentials. * Activate the workflow to enable the endpoint. * Click the Copy button on the **Webhook** node to get your unique **Production Webhook URL**. 2. **Integrate with the frontend:** * Inside the workflow, find the sticky note labeled Example Frontend Code Below. Copy the complete HTML from the note below it. * **Important:** In the code you just copied, find the line `const WEBHOOK_URL = YOUR WEBHOOK URL;` and replace the placeholder with the Production Webhook URL from n8n. * Save the code as an HTML file and open it in your browser to test. ## **Taking it further** * **Save transcripts:** Add an **Airtable** or **Google Sheets** node to log every transcript that comes through the workflow. * **Error handling:** Enhance the workflow to catch potential errors from the OpenAI API and respond with a clear error message. * **Analyze the transcript:** Add a **Language Model** node after the transcription step to summarize the text, classify its sentiment, or extract key entities before sending the response.

AI-Powered Twitter Posting Bot for Automated Engagement

The 'X / Twitter Posting Bot' is a sophisticated automation tool designed to streamline the process of posting on Twitter. By integrating advanced AI capabilities, the workflow generates engaging content based on user requests and timestamps, ensu...

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Communication (2,463)Business Operations & ERPs (1,540)Other (1,425)Productivity (1,202)Marketing (1,145)Data & Analytics (995)File & Document Management (802)CRM - Sales (604)Notifications (580)Social Media (562)

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