Workflow Templates

Product Video Creator with Nano Banana & Veo 3.1 via Telegram Who's it for This workflow is perfect for: E-commerce sellers needing quick product videos Social media marketers creating content at scale Small business owners without video editing skills Product photographers enhancing their offerings Anyone selling on Instagram, TikTok, or mobile-first platforms What it does Transform basic product photos into professional marketing videos in under 2 minutes: Send a product photo to your Telegram bot Nano Banana analyzes and enhances your image with studio-quality lighting Veo 3.1 generates an 8-second vertical video with motion and audio Receive your scroll-stopping marketing video automatically Perfect for creating engaging vertical content without expensive tools or editing expertise. How it works Input → User sends product photo via Telegram with optional caption AI Analysis → Nano Banana analyzes product and generates detailed enhancement prompt Image Enhancement → Nano Banana creates commercial-grade photo (9:16, studio lighting) Video Generation → Veo 3.1 creates 8-second 1080p video with motion and audio Delivery → Auto-polls status every 30s, delivers final video to Telegram Requirements Google Cloud Platform Vertex AI API** enabled for Veo 3.1 Generative Language API** enabled for Nano Banana OAuth2 credentials Get credentials from Google Cloud Console Telegram Bot token from @BotFather n8n Self-hosted or cloud instance Setup Import workflow JSON into n8n Add credentials: Telegram API (bot token) Google OAuth2 API (client id and secret) Google PaLM API (API key) Update your Project ID in both Veo 3.1 nodes Activate workflow and test with a product photo How to customize Aspect Ratio: Choose 9:16 (vertical), 16:9 (horizontal) in "Generate Enhanced Image" and "Initiate veo 3.1" nodes Duration: Set 2 to 8 seconds by adjusting durationSeconds in "Initiate veo 3.1 Video Generation" Quality: Select 720p or 1080p by changing resolution in "Initiate veo 3.1 Video Generation" Audio: Enable or disable background music by toggling generateAudio in "Initiate veo 3.1 Video Generation" Enhancement Style: Match your brand aesthetic by editing the prompt in "AI Design Analysis" node Polling Time: Adjust retry interval by changing wait time in "Processing Delay (30s)" node Key Features 🔐 Direct Google APIs – No third-party services. Uses Nano Banana and Veo 3.1 directly via Google Cloud for maximum reliability and privacy ⚡ Fully Automated – Send photo, receive video. Zero manual work required 🎨 Studio Quality – Nano Banana delivers professional lighting, composition, and AI-powered color grading 📱 Mobile-First – Default 9:16 vertical format optimized for Instagram Reels, TikTok, and Stories 🔄 Smart Retry Logic – Automatically polls Veo 3.1 status every 30 seconds until video generation completes 🎵 Audio Included – Veo 3.1 generates background music automatically (can be disabled)

Cohere's new multimodal model releases make building your own Vision RAG agents a breeze. If you're new to Multimodal RAG and for the intent of this template, it means to embed and retrieve only document scans relevant to a query and then have a vision model read those scans to answer. The benefits being (1) the vision model doesn't need to keep all document scans in context (expensive) and (2) ability to query on graphical content such as charts, graphs and tables. How it works Page extracts from a technology report containing graphs and charts are downloaded, converted to base64 and embedded using Cohere's Embed v4 model. This produces embedding vectors which we will associate with the original page url and store them in our Qdrant vector store collection using the Qdrant community node. Our Vision RAG agent is split into 2 parts; one regular AI agent for chat and a second Q&A agent powered by Cohere's Command-A-vision model which is required to read contents of images. When a query requires access to the technology report, the Q&A agent branch is activated. This branch performs a vector search on our image embeddings and returns a list of matching image urls. These urls are then used as input for our vision model along with the user's original query. The Q&A vision agent can then reply to the user using the "respond to chat" node. Because both agents share the same memory space, it would be the same conversation to the user. How to use Ensure you have a Cohere account and sufficient credit to avoid rate limit or token usage restrictions. For embeddings, swap out the page extracts for your own. You may need to split and convert document pages to images if you want to use image embeddings. For chat, you may want to structure the agent(s) in another way which makes sense for your environment eg. using MCP servers. Requirements Cohere account for Embeddings and LLM Qdrant for vector store

Effortlessly generate, review, and publish SEO-optimized blog posts to WordPress using AI and automation. How It Works AI Topic Generation: Gemini suggests trending blog topics matching your agency's services. Content Research: Tavily fetches recent relevant articles for each generated topic. Human Review: Choose the preferred article for publishing through a Telegram notification. AI Rewriting: Gemini rewrites the selected article into a polished, SEO-friendly post. Image Generation & Publishing: The workflow creates a featured image with Gemini or OpenAI, then publishes the post (with dynamic categories and images) to WordPress. Audit Trail: Every published post is logged to Google Sheets, and final details are sent to Telegram. Set Up Steps Estimated setup time: 15–30 minutes (excluding API approval/wait times). Connect your WordPress, Gemini (Google), Tavily, Google Sheets, and Telegram accounts. Configure your preferred posting schedule in the “Schedule Trigger.” Adjust prompts or messages to fit your agency’s niche or editorial voice if needed. Note: Detailed customizations and advanced configuration tips are included in the sticky notes within the workflow.

Automatically generate and send course certificates when new participants are added to Google Sheets This workflow creates PDF certificates using Stencil, stores them in Google Drive, and emails them to participants. How it works A new row is added to the Google Sheets document (via form, webhook, or manual entry) The workflow generates a PDF certificate using the Stencil API The PDF is uploaded to a Google Drive folder for archiving The certificate is sent to the participant via Outlook The Google Sheet is updated with the file link and send timestamp Setup steps Create a free account at stencilpdf.com and set up a certificate template Connect your Google account and select the target Sheet and Drive folder Connect your Outlook account for sending emails Configure the Stencil API credentials (Bearer Auth) Adjust the email template text as needed Prerequisites Free Stencil account with certificate template Google account (Sheets + Drive) Outlook/Microsoft 365 account `

📁 Analyze uploaded images, videos, audio, and documents with specialized tools — powered by a lightweight language-only agent. 🧭 What It Does This workflow enables multimodal file analysis using Google Gemini tools connected to a text-only LLM agent. Users can upload images, videos, audio files, or documents via a chat interface. The workflow will: Upload each file to Google Gemini and obtain an accessible URL. Dynamically generate contextual prompts based on the file(s) and user message. Allow the agent to invoke Gemini tools for specific media types as needed. Return a concise, helpful response based on the analysis. 🚀 Use Cases Customer support**: Let users upload screenshots, documents, or recordings and get helpful insights or summaries. Multimedia QA**: Review visual, audio, or video content for correctness or compliance. Educational agents**: Interpret content from PDFs, diagrams, or audio recordings on the fly. Low-cost multimodal assistants: Achieve multimodal functionality **without relying on large vision-language models. 🎯 Why This Architecture Matters Unlike end-to-end multimodal LLMs (like Gemini 1.5 or GPT-4o), this template: Uses a text-only LLM (Qwen 32B via Groq) for reasoning. Delegates media analysis to specialized Gemini tools. ✅ Advantages | Feature | Benefit | | ----------------------- | --------------------------------------------------------------------- | | 🧩 Modular | LLM + Tools are decoupled; can update them independently | | 💸 Cost-Efficient | No need to pay for full multimodal models; only use tools when needed | | 🔧 Tool-based Reasoning | Agent invokes tools on demand, just like OpenAI’s Toolformer setup | | ⚡ Fast | Groq LLMs offer ultra-fast responses with low latency | | 📚 Memory | Includes context buffer for multi-turn chats (15 messages) | 🧪 How It Works 🔹 Input via Chat Users submit a message and (optionally) files via the chatTrigger. 🔹 File Handling If no files: prompt is passed directly to the agent. If files are included: Files are split, uploaded to Gemini (to get public URLs). Metadata (name, type, URL) is collected and embedded into the prompt. 🔹 Prompt Construction A new chatInput is dynamically generated: User message Media: [array of file data] 🔹 Agent Reasoning The Langchain Agent receives: The enriched prompt File URLs Memory context (15 turns) Access to 4 Gemini tools: IMG: analyze image VIDEO: analyze video AUDIO: analyze audio DOCUMENT: analyze document The agent autonomously decides whether and how to use tools, then responds with concise output. 🧱 Nodes & Services | Category | Node / Tool | Purpose | | --------------- | ---------------------------- | ------------------------------------- | | Chat Input | chatTrigger | User interface with file support | | File Processing | splitOut, splitInBatches | Process each uploaded file | | Upload | googleGemini | Uploads each file to Gemini, gets URL | | Metadata | set, aggregate | Builds structured file info | | AI Agent | Langchain Agent | Receives context + file data | | Tools | googleGeminiTool | Analyze media with Gemini | | LLM | lmChatGroq (Qwen 32B) | Text reasoning, high-speed | | Memory | memoryBufferWindow | Maintains session context | ⚙️ Setup Instructions 1. 🔑 Required Credentials Groq API key** (for Qwen 32B model) Google Gemini API key** (Palm / Gemini 1.5 tools) 2. 🧩 Nodes That Need Setup Replace existing credentials on: Upload a file Each GeminiTool (IMG, VIDEO, AUDIO, DOCUMENT) lmChatGroq 3. ⚠️ File Size & Format Considerations Some Gemini tools have file size or format restrictions. You may add validation nodes before uploading if needed. 🛠️ Optional Improvements Add logging and error handling (e.g., for upload failures). Add MIME-type filtering to choose the right tool explicitly. Extend to include OCR or transcription services pre-analysis. Integrate with Slack, Telegram, or WhatsApp for chat delivery. 🧪 Example Use Case > "Hola, ¿qué dice este PDF?" Uploads a document → Agent routes it to Gemini DOCUMENT tool → Receives extracted content → LLM summarizes it in Spanish. 🧰 Tags multimodal, agent, langchain, groq, gemini, image analysis, audio analysis, document parsing, video analysis, file uploader, chat assistant, LLM tools, memory, AI tools 📂 Files This template is ready to use as-is in n8n. No external webhooks or integrations required.

This workflow automates the process of creating short videos from multiple image references (up to 7 images). It uses "Vidu Reference to Video" model, a video generation API to transform a user-provided prompt and image set into a consistent, AI-generated video. This workflow automates the process of generating AI-powered videos from a set of reference images and then uploading them to TikTok and Youtube. The process is initiated via a user-friendly web form. Advantages ✅ Consistent Video Creation: Uses multiple reference images to maintain subject consistency across frames. ✅ Easy Input: Just a simple form with prompt + image URLs. ✅ Automation: No manual waiting—workflow checks status until video is ready. ✅ SEO Optimization: Automatically generates a catchy, optimized YouTube title using AI. ✅ Multi-Platform Publishing: Uploads directly to Google Drive, YouTube, and TikTok in one flow. ✅ Time Saving: Removes repetitive tasks of video generation, download, and manual uploading. ✅ Scalable: Can run periodically or on-demand, perfect for content creators and marketing teams. ✅ UGC & Social Media Ready: Designed for creating viral short videos optimized for platforms like TikTok and YouTube Shorts. How It Works Form Trigger: A user submits a web form with two key pieces of information: a text Prompt describing the desired video and a list of Reference images (URLs separated by commas or new lines). Data Processing: The workflow processes the submitted image URLs, converting them from a text string into a proper array format for the AI API. AI Video Generation: The processed data (prompt and image array) is sent to the Fal.ai VIDU API endpoint (reference-to-video) to start the video generation job. This node returns a request_id. Status Polling: The workflow enters a loop where it periodically checks the status of the generation job using the request_id. It waits for 60 seconds and then checks if the status is "COMPLETED". If not, it waits and checks again. Result Retrieval: Once the video is ready, the workflow fetches the URL of the generated video file. Title Generation: Simultaneously, the original user prompt is sent to an AI model (GPT-4o-mini via OpenRouter) to generate an optimized, engaging title for the social media post. Upload & Distribution: The video file is downloaded from the generated URL. A copy is saved to a specified Google Drive folder for storage. The video, along with the AI-generated title, is automatically uploaded to YouTube and TikTok via the Upload-Post.com API service. Set Up Steps This workflow requires configuration and API keys from three external services to function correctly. Step 1: Configure Fal.ai for Video Generation Create an account and obtain your API key. In the "Create Video" HTTP node, edit the "Header Auth" credentials. Set the following values: Name: Authorization Value: Key YOUR_FAL_API_KEY (replace YOUR_FAL_API_KEY with your actual key) Step 2: Configure Upload-Post.com for Social Media Uploads Get an API key from your Upload-Post Manage Api Keys dashboard (10 free uploads per month). In both the "HTTP Request" (YouTube) and "Upload on TikTok" nodes, edit their "Header Auth" credentials. Set the following values: Name: Authorization Value: Apikey YOUR_UPLOAD_POST_API_KEY (replace YOUR_UPLOAD_POST_API_KEY with your actual key) Crucial: In the body parameters of both upload nodes, find the user field and replace YOUR_USERNAME with the exact name of the social media profile you configured on Upload-Post.com (e.g., my_youtube_channel). Step 3: Configure Google Drive (Optional Storage) The "Upload Video" node is pre-configured to save the video to a Google Drive folder named "Fal.run". Ensure your Google Drive credentials in n8n are valid and that you have access to this folder, or change the folderId parameter to your desired destination. Step 4: Configure AI for Title Generation The "Generate title" node uses OpenAI to access the gpt-5-mini model.. Need help customizing? Contact me for consulting and support or add me on Linkedin.

Overview Healthcare organizations face significant challenges in digitizing and processing medical records while maintaining strict HIPAA compliance. This workflow provides a secure, automated solution for extracting clinical data from various medical documents including discharge summaries, lab reports, clinical notes, prescription records, and scanned medical images (JPG, PNG). What You Can Do Extract clinical data from medical documents while maintaining HIPAA compliance Process handwritten notes and scanned medical images with OCR Automatically identify and protect PHI (Protected Health Information) Generate structured data from various medical document formats Maintain audit trails for regulatory compliance Who It's For Healthcare providers, medical billing companies, clinical research organizations, health information exchanges, and medical practice administrators who need to digitize and extract data from medical records while maintaining HIPAA compliance. The Problem It Solves Manual medical record processing is time-consuming, error-prone, and creates compliance risks. Healthcare organizations struggle to extract structured data from handwritten notes, scanned documents, and various medical forms while protecting PHI. This template automates the extraction process while maintaining the highest security standards for Protected Health Information. Setup Instructions: Configure Google Drive credentials with proper medical record access controls Install the PDF Vector community node from the n8n marketplace Configure PDF Vector API credentials with HIPAA-compliant settings Set up secure database storage with encryption at rest Define PHI handling rules and extraction parameters Configure audit logging for regulatory compliance Set up integration with your Electronic Health Record (EHR) system Key Features: Secure retrieval of medical documents from Google Drive HIPAA-compliant processing with automatic PHI masking OCR support for handwritten notes and scanned medical images Automatic extraction of diagnoses with ICD-10 code validation Medication list processing with dosage and frequency information Lab results extraction with reference ranges and flagging Vital signs capture and normalization Complete audit trail for regulatory compliance Integration-ready format for EHR systems Customization Options: Define institution-specific medical terminology and abbreviations Configure automated alerts for critical lab values or abnormal results Set up custom extraction fields for specialized medical forms Implement medication interaction warnings and contraindication checks Add support for multiple languages and international medical coding systems Configure integration with specific EHR platforms (Epic, Cerner, etc.) Set up automated quality assurance checks and validation rules Implementation Details: The workflow uses advanced AI with medical domain knowledge to understand clinical terminology and extract relevant information while automatically identifying and protecting PHI. It processes various document formats including handwritten prescriptions, lab reports, discharge summaries, and clinical notes. The system maintains strict security protocols with encryption at rest and in transit, ensuring full HIPAA compliance throughout the processing pipeline. Note: This workflow uses the PDF Vector community node. Make sure to install it from the n8n community nodes collection before using this template.

Enterprise Knowledge Search with GPT-4 Turbo, Google Drive & Academic APIs This workflow provides an enterprise-grade RAG (Retrieval-Augmented Generation) system that intelligently searches multiple sources and generates AI-powered responses using GPT-4 Turbo. How it works This workflow provides an enterprise-grade RAG (Retrieval-Augmented Generation) system that intelligently searches multiple sources and generates AI-powered responses using GPT-4 Turbo. Key Steps Form Input - Collects user queries with customizable search scope, response style, and language preferences Intelligent Search - Routes queries to appropriate sources (web, academic papers, news, internal documents) Data Aggregation - Unifies and processes information from multiple sources with quality scoring AI Processing - Uses GPT-4 Turbo to generate context-aware, source-grounded responses Response Enhancement - Formats outputs in various styles (comprehensive, concise, technical, etc.) Multi-Channel Delivery - Delivers results via webhook, email, Slack, and optional PDF generation Data Sources & AI Models Search Sources Web Search**: Google, Bing, DuckDuckGo integration Academic Papers**: arXiv, PubMed, Google Scholar via Crossref API News Articles**: News API, RSS feeds, real-time news Technical Documentation**: GitHub, Stack Overflow, documentation sites Internal Knowledge**: Google Drive, Confluence, Notion integration AI Models GPT-4 Turbo**: Primary language model for response generation Embedding Models**: For semantic search and similarity matching Custom Prompts**: Specialized prompts for different response styles Set up steps Setup time: 15-20 minutes Configure API credentials - Set up OpenAI API, News API, Google Drive, and other service credentials Set up search sources - Configure academic databases, news APIs, and internal knowledge sources Connect analytics - Link Google Sheets for usage tracking and performance monitoring Configure notifications - Set up Slack channels and email templates for automated alerts Test the workflow - Run sample queries to verify all components are working correctly Keep detailed configuration notes in sticky notes inside your workflow

Multi-Source RAG System with GPT-4 Turbo, News & Academic Papers Integration This workflow provides an enterprise-grade RAG (Retrieval-Augmented Generation) system that intelligently searches multiple sources and generates AI-powered responses using GPT-4 Turbo. How it works This workflow provides an enterprise-grade RAG (Retrieval-Augmented Generation) system that intelligently searches multiple sources and generates AI-powered responses using GPT-4 Turbo. Key Steps Form Input - Collects user queries with customizable search scope, response style, and language preferences Intelligent Search - Routes queries to appropriate sources (web, academic papers, news, internal documents) Data Aggregation - Unifies and processes information from multiple sources with quality scoring AI Processing - Uses GPT-4 Turbo to generate context-aware, source-grounded responses Response Enhancement - Formats outputs in various styles (comprehensive, concise, technical, etc.) Multi-Channel Delivery - Delivers results via webhook, email, Slack, and optional PDF generation Data Sources & AI Models Search Sources Web Search**: Google, Bing, DuckDuckGo integration Academic Papers**: arXiv, PubMed, Google Scholar News Articles**: News API, RSS feeds, real-time news Technical Documentation**: GitHub, Stack Overflow, documentation sites Internal Knowledge**: Google Drive, Confluence, Notion integration AI Models GPT-4 Turbo**: Primary language model for response generation Embedding Models**: For semantic search and similarity matching Custom Prompts**: Specialized prompts for different response styles Set up steps Setup time: 15-20 minutes Configure API credentials - Set up OpenAI API, ScrapeGraphAI, Google Drive, and other service credentials Set up search sources - Configure academic databases, news APIs, and internal knowledge sources Connect analytics - Link Google Sheets for usage tracking and performance monitoring Configure notifications - Set up Slack channels and email templates for automated alerts Test the workflow - Run sample queries to verify all components are working correctly Keep detailed configuration notes in sticky notes inside your workflow

Description Automate the discovery and analysis of PDF files across a deeply nested OneDrive folder structure. This workflow recursively searches folders, filters for new or updated PDFs, extracts text, and uses a Mistral AI agent to generate a concise Executive Summary, Key Findings, and Structured Metadata (Date, Location, etc.), storing all insights into a n8n Data Table for easy access and further automation. Key Features & How It Works Scheduled Trigger & Recursive Folder Search: The workflow runs automatically (scheduled for 8 PM in this template) to monitor a specified main folder on OneDrive. It performs a deep, multi-level search (up to 8 layers) across subfolders to ensure no documents are missed. Smart Deduplication & Filtering: It checks new files against an internal n8n Data Table using the Compare Datasets node, ensuring only new or unique PDF files are processed, saving AI credits and processing time. A size check is also included, preventing attempts to process excessively large files. AI-Powered Document Intelligence (Mistral LLM): For each new PDF, the workflow extracts the text and passes it to a Mistral AI model for dual-stream analysis: Overview Agent: Generates an impartial, professional Executive Summary, a list of Key Findings & Data Points, and the document's Scope/Context. Document Information Agent: Extracts crucial metadata, including the single most relevant date, location (City/State/Country), and professional information (Name, Title, Organization). Structured Output and Archiving: AI outputs are meticulously validated and reformatted into a clean JSON object using Structured Output Parsers. The complete analysis, along with the original file name and path, is then logged as a new row in an n8n Data Table. Setup Notes OneDrive Folder: You must specify the exact name of your main folder in the 'Search for Main Folder' node. Data Table: Ensure your n8n Data Table exists with the required columns: Summary, Key_Findings, Scope, Date, Location, File_Name, and Path. Deep Folder Structure: The current configuration supports up to 8 levels of subfolders. If your files go deeper, you may need to add more "Get items in a folder" and "If" nodes. AI Customization: Review the AI agent prompts and the structured output schemas to customize the fields you want to extract or the summary style you require. Extend This Workflow The final output is organized data. You can easily extend this workflow to: Send daily/weekly digest emails with new summaries. Sync the extracted data to a Google Sheet, Airtable, or other database. Add a secondary AI agent to perform follow-up actions based on the "Key Findings."

Description Automates the forwarding of messages from WhatsApp (via Evolution API) to Chatwoot, enabling seamless integration between external WhatsApp users and internal Chatwoot agents. It supports both text and media messages, ensuring that customer conversations are centralized and accessible for support teams. What Problem Does This Solve? Managing conversations across multiple platforms can lead to fragmented support and lost context. This subworkflow bridges the gap between WhatsApp and Chatwoot, automatically forwarding messages received via the Evolution API to a Chatwoot inbox. It simplifies communication flow, centralizes conversations, and enhances the support team's productivity. Features Support for plain text messages Support for media messages: images, videos, documents, and audio Automatic media upload to Chatwoot with proper attachment rendering Automatic contact association using WhatsApp number and Chatwoot API Designed to work with Evolution API webhooks or any message source Prerequisites Before using this automate, make sure you have: Evolution API credentials with incoming message webhook configured A Chatwoot instance with access token and API endpoint An existing Chatwoot inbox (preferably API channel) A configured HTTP Request node in n8n for Chatwoot API calls Suggested Usage This subworkflow should be attached to a parent workflow that receives WhatsApp messages via the Evolution API webhook. Ideal for: Centralized customer service operations WhatsApp-to-CRM/chat routing Hybrid automation workflows where human agents need to reply from Chatwoot It ensures that all incoming WhatsApp messages are properly converted and forwarded to Chatwoot, preserving message content and structure.

The Prompt converter workflow tackles the challenge of turning your natural language video ideas into perfectly formatted JSON prompts tailored for Veo 3 video generation. By leveraging Langchain AI nodes and Google Gemini, this workflow automates and refines your input to help you create high-quality videos faster and with more precision—think of it as your personal video prompt translator that speaks fluent cinematic! 💡 Why Use Prompt Converter? Save time: Automate converting complex video prompts into structured JSON, cutting manual formatting headaches and boosting productivity. Avoid guesswork: Eliminate unclear video prompt details by generating detailed, cinematic descriptions that align perfectly with Veo 3 specs. Improve output quality: Optimize every parameter for Veo 3's video generation model to get realistic and stunning results every time. Gain a creative edge: Turn vague ideas into vivid video concepts with AI-powered enhancement—your video project's secret weapon. ⚡ Perfect For Video creators: Content developers wanting quick, precise video prompt formatting without coding hassles. AI enthusiasts: Developers and hobbyists exploring Langchain and Google Gemini for media generation. Marketing teams: Professionals creating video ads or visuals who need consistent prompt structuring that saves time. 🔧 How It Works ⏱ Trigger: User submits a free text prompt via message or webhook. 📎 Process: The text goes through an AI model that understands and reworks it into detailed JSON parameters tailored for Veo 3. 🤖 Smart Logic: Langchain nodes parse and optimize the prompt with cinematic details, set reasonable defaults, and structure the data precisely. 💌 Output: The refined JSON prompt is sent to Google Gemini for video generation with optimized settings. 🔐 Quick Setup Import the JSON file to your n8n instances Add credentials: Azure OpenAI, Gemini API, OpenRouter API Customize: Adjust prompt templates or default parameters in the Prompt converter node Test: Run your workflow with sample text prompts to see videos come to life 🧩 You'll Need Active n8n instances Azure OpenAI API Gemini API Key OpenRouter API (alternative AI option) 🛠️ Level Up Ideas Add integration with video hosting platforms to auto-upload generated videos 🧠 Nodes Used Prompt Input** (Chat Trigger) OpenAI** (Azure OpenAI GPT model) Alternative** (OpenRouter API) Prompt converter** (Langchain chain LLM for JSON conversion) JSON parser** (structured output extraction) Generate a video** (Google Gemini video generation) Made by: Khaisa Studio Tags: video generation, AI, Langchain, automation, Google Gemini Category: Video Production Need custom work? Contact me