✨🤖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 LePageStripe 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.Extract and analyze Truth Social posts for stock market impact with Airtop & Slack
Trump-o-meter: Extract and Evaluate Truth Social Posts Use Case Automatically extracting posts from Donald Trump's Truth Social account and estimating their potential impact on the U.S. stock market enables teams to monitor high-profile communications that may influence financial markets. This automation streamlines intelligence gathering for analysts, traders, and policy observers. What This Automation Does This automation retrieves up to 3 posts from Donald Trump's Truth Social profile and outputs structured information including: Author name Image URL Post text Post URL Estimated stock market impact: Direction: positive, negative, or neutral Magnitude: None, Small, Medium, Large How It Works Creates a browser session on Truth Social using an Airtop profile. Navigates to https://truthsocial.com/@realDonaldTrump. Uses a natural language prompt with a defined JSON schema to extract structured data for up to 3 posts. Splits the results into individual post items. Filters posts that contain actual content and have a non-zero estimated market impact. Sends selected posts and impact summaries to a Slack channel. Terminates the browser session to clean up. Setup Requirements Airtop API Key — free to generate. An Airtop Profile that is connected and logged into Truth Social. A Slack workspace and authorized app with write permissions to a target channel. Next Steps Integrate with Trading Signals: Link output to financial alert systems or dashboards for timely insights. Expand Monitoring: Extend to other high-impact accounts (e.g., politicians, CEOs). Enhance Analysis: Add sentiment scoring or topic classification for deeper context. Legal Disclaimer This tool is intended solely for informational and analytical purposes. The market impact estimations provided are speculative and should not be construed as financial advice. Do not make investment decisions based on this automation. Always consult with a licensed financial advisor before making any trades. Read more about Trump-o-meter automation
By AirtopGmail 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 MateevCreate, update, and get a person from Copper
This workflow allows you to create, update, and get a person from Copper. Copper node: This node will create a new person in Copper. Copper1 node: This node will update the information of the person that we created using the previous node. Copper2 node: This node will retrieve the information of the person that we created earlier.
By Harshil AgrawalSend 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