✨🤖Automate Multi-Platform Social Media Content Creation with AI
Automate Multi-Platform Social Media Content Creation with AI Who is this for? Social Media Managers and Digital Marketers seeking to streamline content production across 7+ platforms (X/Twitter, Instagram, LinkedIn, Facebook, TikTok, Threads, YouTube Shorts) using AI-powered automation. What problem does this solve? Creating platform-optimized content at scale while maintaining brand consistency across multiple channels, reducing manual work by 80% through AI generation and automated publishing. What this workflow does AI Content Generation: Uses GPT-4/Gemini to create platform-specific posts Automatically generates hashtags, CTAs, and emoji placement Supports image/video suggestions and image creation using OpenAI or Pollinations.ai Uses SERP api to search for relavent content Approval Workflow: Sends formatted HTML emails for human review Implements double-approval system with Gmail integration Cross-Platform Publishing: One-click deployment to: Instagram/Facebook (via Graph API) X/Twitter (Official API) LinkedIn (Sales Navigator integration) Setup Credentials: OpenAI API key Google Gemini API Social media platform tokens (X, LinkedIn, Facebook) ImgBB for image hosting Gmail SERP API Telegram Configuration: Update all "your-unique-id" placeholders in API nodes Set email recipients in Gmail nodes Customize AI prompts Customization: Adjust character limits per platform Modify approval thresholds Add/remove social platforms as needed How to customize Content Style: Edit prompt templates in the "Social Media Content Factory" agent node Approval Process: Modify email templates Analytics: Connect to Google Sheets for performance tracking Image Generation: Switch between Pollinations.ai/DALL-E/Midjourney
By Joseph LePageGenerate Youtube video metadata (timestamps, tags, description, ...)
For Who? Content Creators Youtube Automation Marketing Team --- How it works? 1 - Enter the ID of the YTB channel to trigger the workflow when a new video is posted 2 - Apify scrape the last YTB video of the channel 3 - Wait until the dataset is completed in Apify and get it 4 - Verify if Metadata are not already generated and generate them with LLM 5 - Format all the data created and update YTB Video 📺 YouTube Video Tutorial: [](https://www.youtube.com/watch?v=HaQPAa6l5bU) --- SETUP Setup Input YTB Chanel : Go to the channel's page on YouTube, and look at the URL of the page. The channel ID is the value that comes after channel/ in the URL. Add it after "?channel_id=" You can also use free tools available to retrieve channel ID. Setup Output YTB Video Update : Connect your YTB account to your n8n instance thanks to the Google Cloud Console. You can find tutorials by typing "youtube api Oauth" on Google. APIs : For the following third-party integrations, replace ==[YOURAPITOKEN]== with your API Token or connect your account via Client ID / Secret to your n8n instance : Apify : https://docs.apify.com/api/v2/getting-started Youtube : https://docs.n8n.io/integrations/builtin/app-nodes/n8n-nodes-base.youtube/?utmsource=n8napp&utmmedium=nodesettingsmodal-credentiallink&utm_campaign=n8n-nodes-base.youTubetemplates-and-examples --- 👨💻 More Workflows : https://n8n.io/creators/nasser/
By NasserNathan: Your n8n personal assistant
Nathan is a proof of concept framework for creating a personal assistant who can handle various day to day functions for you.
By jasonStripe payment order sync – auto retrieve customer & product purchased
Overview This automation template is designed to streamline your payment processing by automatically triggering upon a successful Stripe payment. The workflow retrieves the complete payment session and filters the information to display only the customer name, customer email, and the purchased product details. This template is perfect for quickly integrating Stripe transactions into your inventory management, CRM, or notification systems. Step-by-Step Setup Instructions Stripe Account Configuration: Ensure you have an active Stripe account. Connect your Stripe Credentials. Retrieve Product and Customer Data: Utilize Stripe’s API within the automation to fetch the purchased product details. Retrieve customer information such as: email and full name. Integration and Response: Map the retrieved data to your desired format. Trigger subsequent nodes or actions such as sending a confirmation email, updating a CRM system, or logging the transaction. Pre-Conditions and Requirements Stripe Account: A valid Stripe account with access to API keys and webhook configurations. API Keys: Ensure you have your Stripe secret and publishable keys ready. Customization Guidance Data Mapping: Customize the filtering node to match your specific data schema or to include additional data fields if needed. Additional Actions: Integrate further nodes to handle post-payment actions like sending SMS notifications, updating order statuses, or generating invoices. Enjoy seamless integration and enhanced order management with this automation template!
By Matt F.Create authentic UGC video ads with GPT-4o, ElevenLabs & WaveSpeed lip-sync
This n8n template demonstrates how to create authentic-looking User Generated Content (UGC) advertisements using AI image generation, voice synthesis, and lip-sync technology. The workflow transforms product images into realistic customer testimonial videos that mimic genuine user reviews and social media content. Use cases are many: Generate authentic UGC-style ads for social media campaigns, create customer testimonial videos without hiring influencers, produce localized UGC content for different markets, automate TikTok/Instagram-style product reviews, or scale UGC ad production for e-commerce brands! Good to know The workflow creates UGC-style content that appears genuine and authentic Uses multiple AI services: OpenAI GPT-4o for analysis, ElevenLabs for voice synthesis, and WaveSpeed AI for image generation and lip-sync Voice synthesis costs vary by ElevenLabs plan (typically $0.18-$0.30 per 1K characters) WaveSpeed AI pricing: ~$0.039 per image generation, additional costs for lip-sync processing Processing time: ~3-5 minutes per complete UGC video Optimized for Malaysian-English content but easily adaptable for global markets How it works Product Input: The Telegram bot receives product images to create UGC ads for AI Analysis: ChatGPT-4o analyzes the product to understand brand, colors, and target demographics UGC Content Creation: AI generates authentic-sounding testimonial scripts and detailed prompts for realistic customer scenarios Character Generation: WaveSpeed AI creates believable customer avatars that look like real users reviewing products Voice Synthesis: ElevenLabs generates natural, conversational audio using gender-appropriate voice models UGC Video Production: WaveSpeed AI combines generated characters with audio to create TikTok/Instagram-style review videos Content Delivery: Final UGC videos are delivered via Telegram, ready for social media posting The workflow produces UGC-style content that maintains authenticity while showcasing products in realistic, relatable scenarios that resonate with target audiences. How to use Setup Credentials: Configure OpenAI API, ElevenLabs API, WaveSpeed AI API, Cloudinary, and Telegram Bot credentials Deploy Workflow: Import the template and activate the workflow Send Product Images: Use the Telegram bot to send product images you want to create UGC ads for Automatic UGC Generation: The workflow will automatically create authentic-looking customer testimonial videos Receive UGC Content: Get both testimonial images and final UGC videos ready for social media campaigns Pro tip: The workflow automatically detects product demographics and creates appropriate customer personas. For best UGC results, use clear product images that show the item in use. Requirements OpenAI API account for GPT-4o product analysis and UGC script generation ElevenLabs API account for authentic voice synthesis (requires voice cloning credits) WaveSpeed AI API account for realistic character generation and lip-sync processing Cloudinary account for UGC content storage and hosting Telegram Bot setup for content input and delivery n8n instance (cloud or self-hosted) Customizing this workflow Platform-Specific UGC: Modify prompts to create UGC content optimized for TikTok, Instagram Reels, YouTube Shorts, or Facebook Stories. Brand Voice: Adjust testimonial scripts and character personas to match your brand's target audience and tone. Regional Adaptation: Customize language, cultural references, and character demographics for different markets and demographics. UGC Style Variations: Create different UGC formats - unboxing videos, before/after comparisons, day-in-the-life content, or product demonstrations. Influencer Personas: Develop specific customer personas (age groups, lifestyles, interests) to create targeted UGC content for different audience segments. Content Scaling: Set up batch processing to generate multiple UGC variations for A/B testing different approaches and styles.
By Muhammad Farooq IqbalGmail assistant with full Gmail history RAG using OpenAI
🧠 RAG with Full Gmail history + Real time email updates in RAG using OpenAI & Qdrant > Summary: > This workflow listens for new Gmail messages, extracts and cleans email content, generates embeddings via OpenAI, stores them in a Qdrant vector database, and then enables a Retrieval‑Augmented‑Generation (RAG) agent to answer user queries against those stored emails. It’s designed for teams or bots that need conversational access to past emails. --- 🧑🤝🧑 Who’s it for Support teams who want to surface past customer emails in chatbots or help‑desk portals Sales ops that need AI‑powered summaries and quick lookup of email histories Developers building RAG agents over email archives --- ⚙️ How it works / What it does Trigger Gmail Trigger polls every minute for new messages. Fetch & Clean Get Mail Data pulls full message metadata and body. Code node normalizes the body (removes line breaks, collapses spaces). Embed & Store Embeddings OpenAI node computes vector embeddings. Qdrant Vector Store inserts embeddings + metadata into the emails_history collection. Batch Processing SplitInBatches handles large inbox loads in chunks of 50. RAG Interaction When chat message received → RAG Agent → uses Qdrant Email Vector Store as a tool to retrieve relevant email snippets before responding. Memory Simple Memory buffer ensures the agent retains recent context. --- 🛠️ How to set up n8n Instance Deploy n8n (self‑hosted or via Coolify/Docker). Credentials Create an OAuth2 credential in n8n for Gmail (with Gmail API scopes). Add your OpenAI API key in n8n credentials. Qdrant Stand up a Qdrant instance (self‑hosted or Qdrant Cloud). Note your host, port, and API key (if any). Import Workflow In n8n, go to Workflows → Import → paste the JSON you provided. Ensure each credential reference (Gmail & OpenAI) matches your n8n credential IDs. Test Click Execute Workflow or send a test email to your Gmail. Monitor n8n logs: you should see new points in Qdrant and RAG responses. --- 📋 Requirements n8n (Self-hosted or Cloud) Gmail API enabled on a Google Cloud project OpenAI API access (with Embedding & Chat endpoints) Qdrant (hosted or cloud) with a collection named emails_history --- 🎨 How to customize the workflow Change Collection Name Update the qdrantCollection.value in all Qdrant nodes if you prefer a different collection. Adjust Polling Frequency In the Gmail Trigger node, switch from everyMinute to everyFiveMinutes or a webhook‑style trigger. Metadata Tags In Enhanced Default Data Loader, tweak the metadataValues to tag by folder, label, or sender domain. Batch Size In SplitInBatches, change batchSize to suit your inbox volume. RAG Agent Prompt Customize the systemMessage in the RAG Agent node to set the assistant’s tone, instruct on date handling, or add additional tools. Additional Tools Chain other n8n nodes (e.g., Slack, Discord) after the RAG Agent to broadcast AI answers to team channels.
By Zain AliReduce LLM Costs with Semantic Caching using Redis Vector Store and HuggingFace
Stop Paying for the Same Answer Twice Your LLM is answering the same questions over and over. "What's the weather?" "How's the weather today?" "Tell me about the weather." Same answer, three API calls, triple the cost. This workflow fixes that. What Does It Do? Semantic caching with superpowers. When someone asks a question, it checks if you've answered something similar before. Not exact matches—semantic similarity. If it finds a match, boom, instant cached response. No LLM call, no cost, no waiting. First time: "What's your refund policy?" → Calls LLM, caches answer Next time: "How do refunds work?" → Instant cached response (it knows these are the same!) Result: Faster responses + way lower API bills The Flow Question comes in through the chat interface Vector search checks Redis for semantically similar past questions Smart decision: Cache hit? Return instantly. Cache miss? Ask the LLM. New answers get cached automatically for next time Conversation memory keeps context across the whole chat It's like having a really smart memo pad that understands meaning, not just exact words. Quick Start You'll need: OpenAI API key (for the chat model) huggingface API key (for embeddings) Redis 8.x (for vector magic) Get it running: Drop in your credentials Hit the chat interface Watch your API costs drop as the cache fills up That's it. No complex setup, no configuration hell. Tune It Your Way The distanceThreshold in the "Analyze results from store" node is your control knob: Lower (0.2): Strict matching, fewer false positives, more LLM calls Higher (0.5): Loose matching, more cache hits, occasional weird matches Default (0.3): Sweet spot for most use cases Play with it. Find what works for your questions. Hack It Up Some ideas to get you started: Add TTL: Make cached answers expire after a day/week/month Category filters: Different caches for different topics Confidence scores: Show users when they got a cached vs fresh answer Analytics dashboard: Track cache hit rates and cost savings Multi-language: Cache works across languages (embeddings are multilingual!) Custom embeddings: Swap OpenAI for local models or other providers Real Talk 💡 When it shines: Customer support (same questions, different words) Documentation chatbots (limited knowledge base) FAQ systems (obvious use case) Internal tools (repetitive queries) When to skip it: Real-time data queries (stock prices, weather, etc.) Highly personalized responses Questions that need fresh context every time Pro tip: Start with a higher threshold (0.4-0.5) and tighten it as you see what gets cached. Better to cache too much at first than miss obvious matches. Built with n8n, Redis, Huggingface and OpenAI. Open source, self-hosted, completely under your control.
By Tihomir MateevAuto-enrich new CRM companies with ChatGPT web research via Tavily
Overview This template benefits anyone who wants to: automate web research on a prospect company compile that research into an easily readable note and save the note into CentralStationCRM Tools in this workflow CentralStationCRM, the easy and intuitive CRM Software for small teams. Here is our API Documentation if you want to customize the workflow. ChatGPT, the well-known ai chatbot Tavily, a web search service for large language models Disclaimer Tavily Web Search is (as of yet) a community node. You have to activate the use of community nodes inside your n8n account to use this workflow. Workflow Screenshot Workflow Description The workflow consists of: a webhook trigger an ai agent node an http request node The Webhook Trigger The Webhook is set up in CentralStationCRM to trigger when a new company is created inside the CRM. The Webhook Trigger Node in n8n then fetches the company data from the CRM. The AI Agent Node The node uses ChatGPT as ai chat model and two Tavily Web Search operations ('search for information' and 'extract URLs') as tools. Additionally, it uses a simple prompt as tool, telling the ai model to re-iterate on the research data if applicable. The AI Agent Node takes the Company Name and prompts ChatGPT to "do a deep research" on this company on the web. "The research shall help sales people get a good overview about the company and allow to identify potential opportunities." The AI Agent then formats the results into markdown format and passes them to the next node. The CentralStationCRM protocol node This is an HTTP Request to the CentralStationCRM API. It creates a 'protocol' (the API's name for notes in the CRM) with the markdown data it received from the previous node. This protocol is saved in CentralStationCRM, where it can easily be accessed as a note when clicking on the new company entry. Customization ideas Even though this workflow is pretty simple, it poses interesting possibilities for customization. For example, you can alter the Webhook trigger (in CentralstationCRM and n8n) to fire when a person is created. You have to alter the AI prompt as well and make sure the third node adds the research note to the person, not a company, via the CentralStationCRM API. You could also swap the AI model used here for another one, comparing the resulting research data and get a deeper understanding of ai chat models. Then of course there is the prompt itself. You can definitely double down on the information you are most interested in and refine your prompt to make the ai bot focus on these areas of search. Start experimenting a bit! Preconditions For this workflow to work, you need a CentralStationCRM account with API Access an n8n account with API Access an Open AI account with API Access Have fun with our workflow!
By CentralStationCRMPersonalized cold email generator with Supabase, Smartlead & Google Gemini AI
n8n Workflow: AI-Personalized Email Outreach (Smartlead) 🔄 Purpose This workflow automates cold email campaigns by: Fetching leads Generating hyper-personalized email content using AI Sending emails via Smartlead API Logging campaign activity into Google Sheets --- 🧩 Workflow Structure Schedule Trigger Starts the workflow automatically at scheduled intervals. Ensures continuous campaign execution. Get Leads Fetches lead data (name, email, company, role, industry). Serves as the input for personalization. Loop Over Leads Processes each lead one by one. Maintains individualized email generation. Aggregate Lead Data Collects and formats lead attributes. Prepares structured input for the AI model. Basic LLM Chain 1 Generates personalized snippets/openers using AI. Tailored based on company, role, and industry. Update Row (Google Sheets) Saves AI outputs (snippets) for tracking and QA. Basic LLM Chain 2 Expands snippet into a full personalized email draft. Includes subject line + email body. Information Extractor Extracts structured fields from AI output: Subject Greeting Call-to-Action (CTA) Closing Update Row (Google Sheets) Stores finalized draft in Google Sheets. Provides visibility and audit trail. Code Formats email into Smartlead-compatible payload. Maps fields like subject, body, and recipient details. Smartlead API Request Sends the personalized email through Smartlead. Returns message ID and delivery status. Basic LLM Chain 3 (Optional) Generates follow-up versions for multi-step campaigns. Ensures varied engagement over time. Information Extractor (Follow-ups) Structures follow-up emails into ready-to-send format. Update Row (Google Sheets) Updates campaign logs with: Smartlead send status Message IDs AI personalization notes --- ⚙️ Data Flow Summary Trigger → Runs workflow Get Leads → Fetch lead records LLM Personalization → Create openers + full emails Google Sheets → Save drafts & logs Smartlead API → Send personalized email Follow-ups → Generate and log structured follow-up messages --- 📊 Use Case Automates hyper-personalized cold email outreach at scale. Uses AI to improve response rates with contextual personalization. Provides full visibility by saving drafts and send logs in Google Sheets. Integrates seamlessly with Smartlead for sending and tracking.
By Rahi UppalCreate high-converting sales copy with Hormozi Framework, LangChain & Google Docs
Note: This workflow assumes you already have your product’s Amazon reviews saved in a Google Sheet. If you still need those reviews, run my Amazon Reviews Scraper workflow first, then plug the resulting spreadsheet into this template. What it does Transforms any draft Google Doc into multiple high-converting sales pages. It blends Alex Hormozi’s value-stacking tactics with persona targeting based on Maslow’s Hierarchy of Needs, using your own customer reviews for proof and voice of customer (VOC). Perfect for • Growth and creative strategists • Freelance copywriters and agencies • Founders sharpening offers and funnels Apps used Google Sheets, Google Docs, LangChain OpenRouter LLM How it works Form Trigger collects Drive folder IDs, base copy URL and options. Workflow fetches the draft copy and product feature doc. It samples reviews, extracts VOC insights and maps them to Maslow needs. LLM drafts headlines and hooks following Hormozi’s \$100M Offers principles. Personas drive tone, objections and urgency in each copy variant. Loop writes one Google Doc per variant in your chosen folder. Customer analysis docs are saved to a second folder for reuse. Setup Share two Drive folders, copy the IDs (text after folders/). Paste each ID into Customer Analysis Folder ID and Advertorial Copy Folder ID. Provide File Name, Base copy (Google Docs URL) and Product Feature/USPs Doc. Optional: Reviews Sheet URL, Number of reviews to use, Target City. Set Number of Copies you need (1–20). Add Google Docs OAuth2 and Google Sheets OAuth2 credentials in n8n. If you have any questions in running the workflow, feel free to reach out to me at my youtube channel: https://www.youtube.com/@lifeofhunyao
By HunyaoGenerate structured company descriptions with Bedrijfsdata Web RAG & OpenAI
This workflow contains community nodes that are only compatible with the self-hosted version of n8n. This template generates structured synthetic company content using live web data from the Bedrijfsdata.nl API combined with an LLM. Provide a company domain (directly or via a Bedrijfsdata.nl ID) and the workflow retrieves relevant website and search engine content, then produces ready-to-use descriptions of the company, its offerings, and its target audience. ✨ Features Create high-quality Dutch-language company descriptions on demand Automatically pull live web content via Bedrijfsdata.nl RAG Domain & RAG Search Structured JSON output for consistent downstream use (e.g., CRM updates, lead qualification) Flexible trigger: run from ProspectPro ID, domain input, or another workflow Secure, modular, and extendable structure (error handling included) 🏢 Example Output The workflow produces structured content fields you can directly use in your sales, marketing, or enrichment flows: company_description – 1-2 paragraph summary of the company productsandservices – detailed overview of offerings target_audience – specific characteristics of ideal customers (e.g., industry, location, company size, software usage) Example: { "company_description": "Bedrijfsdata.nl B.V. is een Nederlands bedrijf dat uitgebreide data levert over meer dan 3,7 miljoen bedrijven in Nederland...", "productsandservices": "Het bedrijf biedt API-toegang tot bedrijfsprofielen, sectoranalyses, en SEO-gegevens...", "target_audience": "Nederlandse MKB's die behoefte hebben aan actuele bedrijfsinformatie voor marketing- of salesdoeleinden..." } ⚙ Requirements n8n instance or cloud workspace Install the Bedrijfsdata.nl n8n Verified Community Node OpenAI API credentials (tested with gpt-4.1-mini and gpt-3.5-turbo) Bedrijfsdata.nl developer account (14-day free trial, 500 credits) 🔧 Setup Instructions Trigger configuration Use Bedrijfsdata.nl ID (default) or provide a domain directly Can be called from another workflow using “Execute Workflow” Configure API credentials Bedrijfsdata.nl API key OpenAI API key Customize Output (Optional) Adjust prompt in the LLM node to create other types of synthetic content Extend structured output schema for your use case Integrate with Your Stack Example node included to update HubSpot descriptions Replace or extend to match your CRM, database, or messaging tools 🔐 Security Notes Input validation for required domain Dedicated error branches for invalid input, API errors, LLM errors, and downstream integration errors RAG content checks before running the LLM 🧪 Testing Run workflow with a Bedrijfsdata.nl ID linked to a company with a known website Review generated JSON output Verify content accuracy before production use 📌 About Bedrijfsdata.nl Bedrijfsdata.nl operates the most comprehensive company database in the Netherlands. With real-time data on 3.7M+ businesses and AI-ready APIs, we help Dutch SMEs enrich their CRM, workflows, and marketing automation. Built on 25+ years of experience in data collection and enrichment, our technology brings corporate-grade data quality to every organisation. Website: https://www.bedrijfsdata.nl Developers: https://developers.bedrijfsdata.nl API docs: https://docs.bedrijfsdata.nl 📞 Support Email: klantenservice@bedrijfsdata.nl Phone: +31 20 789 50 50 Support hours: Monday–Friday, 09:00–17:00 CET
By OlivierSend daily mortgage rate updates from Mortgage News Daily to messaging platforms
AI-Powered Mortgage Rate Updates with Client Messaging Keep your clients informed without the repetitive work. This workflow automatically pulls the latest mortgage rates, cleans the data, and uses AI to craft polished messages you can send directly to clients. Whether you want professional emails, quick SMS-style updates, or even CRM-ready messages, this setup saves time while making you look on top of the market. How it Works Daily Trigger – Runs on a schedule you choose (default: multiple times per day). Fetch Rates – Pulls the latest mortgage rates from Mortgage News Daily (you can swap to another source). Clean Data – Prepares and formats the raw rate data for messaging. AI Messaging – Uses Google AI Studio (Gemini) to generate text/email content that’s clear, professional, and client-ready. You can customize the prompt to adjust tone or style. Include variables (like client names or CRM fields) for personalized outreach. Send Updates – Delivers the AI-crafted message to Discord by default for you to copy and send to your clients or upload yto your bulk iMessage or email tool, but can be adapted for: Slack, Telegram, WhatsApp, or Gmail Why Use This Save hours - No more copy-pasting rates into client messages. Look prepared - Clients see you as proactive, not reactive. Customizable - Use AI prompts to match your personal voice, include client-specific details, or change the delivery channel. Scalable – Works for one agent or an entire brokerage team. With this workflow, by the time your client asks “what are rates today?”, they’ll already have a polished update waiting in their inbox or chat. 🚀
By Kaden Reese