by higashiyama
Personalized Learning Content Aggregator with AI Filtering Who’s it for This workflow is for learners, educators, and professionals who want to automatically collect and filter the most relevant educational articles, tutorials, and resources based on specific keywords. How it works Fetches content from RSS feeds and Reddit based on user-defined keywords. AI analyzes and filters the articles to keep only relevant, educational, and non-promotional posts. Saves curated results into a Google Sheet for easy review. How to set up Connect your Google Sheets and AI (OpenAI or LangChain) credentials. Add your RSS feed URLs and keywords to Google Sheets. Adjust schedule timing in the trigger node (default: 8 AM & 6 PM daily). Run the workflow and check the results in your Google Sheet. Requirements Google Sheets account for storage. RSS feed URLs and keyword list. AI node (OpenAI / Gemini / Claude) for filtering logic. How to customize Change or add new content sources (e.g., YouTube, Medium, Dev.to). Adjust AI prompt criteria to match your learning goals. Save results to other platforms (e.g., Notion, Slack, or Airtable). Note: This workflow uses no personal identifiers or API keys directly in nodes. All credentials are safely stored in n8n’s credential manager.
by Robert Breen
This workflow pulls marketing data from Google Sheets, aggregates spend by channel, generates an AI-written summary, and outputs a formatted PDF report using a custom HTML template on PDF.co. ⚙️ Setup Instructions 1️⃣ Prepare Your Google Sheet Copy this template into your Google Drive: Sample Marketing Data Add or update your marketing spend data in rows 2–100. Connect Google Sheets in n8n Go to n8n → Credentials → New → Google Sheets (OAuth2) Log in with your Google account and grant access Select the Spreadsheet ID and Worksheet in the workflow 2️⃣ Set Up PDF.co for PDF Reports Create a free account at PDF.co In PDF.co Dashboard → HTML to PDF Templates, create a new Mustache template Paste the HTML provided at the bottom of this description Save, and note your Template ID In n8n → Credentials → New → PDF.co API, paste your API Key and save In the workflow, select your PDF.co credential in the Create PDF node Replace the templateId with your Template ID 🧠 How It Works Google Sheets Node**: Pulls marketing spend data Summarize Nodes**: Aggregate total spend and spend per channel OpenAI Node**: Writes a daily summary of marketing performance Code Node**: Converts aggregated data into the correct shape for the PDF template PDF.co Node: Generates a final, formatted **PDF report 📬 Contact Need help customizing this (e.g., filtering by campaign, sending reports by email, or formatting your PDF)? 📧 robert@ynteractive.com 🔗 Robert Breen 🌐 ynteractive.com 📄 HTML Template (for PDF.co) > Paste this into a new HTML Template on PDF.co and reference its Template ID in your workflow. <!DOCTYPE html> <html> <head> <meta charset="utf-8" /> <title>Invoice {{invoiceNumber}}</title> <style> body { font-family: Arial, Helvetica, sans-serif; margin: 36px; color: #222; } .header { display: flex; justify-content: space-between; align-items: center; } .brand { max-height: 56px; } h1 { margin: 12px 0 4px; font-size: 22px; } .meta { font-size: 12px; color: #555; } .two-col { display: flex; gap: 24px; margin-top: 16px; } .box { flex: 1; border: 1px solid #ddd; padding: 12px; border-radius: 6px; } .label { font-size: 11px; color: #666; text-transform: uppercase; letter-spacing: .02em; } table { width: 100%; border-collapse: collapse; margin-top: 16px; } th, td { border-bottom: 1px solid #eee; padding: 10px 8px; font-size: 13px; } th { background: #fafafa; text-align: left; } tfoot td { border-top: 2px solid #ddd; font-size: 13px; } .right { text-align: right; } .totals td { padding: 6px 8px; } .grand { font-weight: 700; font-size: 14px; } .notes { margin-top: 18px; font-size: 12px; color: #444; } </style> </head> <body> Invoice {{invoiceNumber}} Date: {{invoiceDate}} | Due: {{dueDate}} {{#company.logoUrl}} {{/company.logoUrl}} From {{company.name}} {{company.address}} {{company.phone}} {{company.email}} Bill To {{billTo.name}} {{billTo.address}} {{billTo.email}} Description Qty Unit Price Line Total {{#items}} {{line}} {{description}} {{qty}} {{unitPriceFmt}} {{lineTotalFmt}} {{/items}} Subtotal {{subTotalFmt}} Tax ({{taxRatePct}}) {{taxAmountFmt}} Discount -{{discountFmt}} Total {{totalFmt}} Notes: {{notes}} Terms: {{terms}} </body> </html>
by Don Jayamaha Jr
Create your own Bitcoin Liquidity Exchange Channel with an AI Agent—fully integrated with 10 major centralized exchanges. This workflow acts as a liquidity intelligence agent, connecting multiple exchange order books into a unified dataset, then applying AI analysis to generate actionable trading insights. It’s the ultimate tool for Bitcoin traders, analysts, community managers, and researchers who need cross-exchange liquidity monitoring—delivered instantly through Telegram. 🔌 Supported Exchanges (Integrated) Binance Coinbase Bybit MEXC Gate.io Bitget OKX Kraken HTX (Huobi) Crypto.com 🌟 What Makes This Workflow Special? This isn’t just raw order book data—it’s an AI-powered aggregator that: Fetches BTC/USDT order books (up to 5000 levels deep) from 10 exchanges Normalizes & merges** liquidity data into a single view Uses GPT-4.1 or GPT-4.1-mini to detect liquidity clusters, imbalances, and support/resistance Generates two structured outputs: Liquidity Report (raw snapshots from all exchanges) AI Trading Brief (intraday + weekly signals) Publishes insights directly into a Telegram channel 🔍 What You Can Do 📊 Cross-Exchange Liquidity View Monitor total liquidity depth across top 10 exchanges Spot hidden bid/ask clusters and weak order book levels ⚡ Real-Time Signals Detect when liquidity evaporates at key price points Receive intraday + weekly trading briefs 📢 Community Ready Run your own public or private Telegram channel with automated liquidity updates ✅ Example Alerts “BTC liquidity depth update: $30M bid wall forming at $62,000 across Binance & OKX.” “Ask-side liquidity dropped 20% in the last hour on Bybit + Coinbase.” “Daily summary: Cross-exchange liquidity balanced, net inflow +3.2%.” “Liquidity cluster detected: strong support between $61,800 – $62,150.” 🛠️ Setup Instructions Create a Telegram Bot Use @BotFather to generate a bot token Add the bot to your channel and get the channel ID Configure API Keys OpenAI API Key (GPT-4.1 or GPT-4.1-mini) Telegram Bot Token + Channel ID Import Workflow into n8n Add credentials in the Set node (no hardcoding in HTTP nodes) Configure schedule trigger (15m, hourly, daily, etc.) Deploy & Test Run the workflow and confirm liquidity + AI insights appear in Telegram ⚙️ Workflow Architecture AI Brain** → GPT-4.1 or GPT-4.1-mini Data Sources** → 10 centralized exchanges (BTC/USDT order books) Data Normalization** → Unified liquidity dataset Outputs** → Liquidity Report (raw exchange stats) AI Trading Brief (signals + summaries) Delivery** → Telegram Channel 📝 Included Sticky Notes System Overview** (workflow purpose & design) Exchange Data Integration** (order book depth per CEX) Setup Guide** (Telegram + API keys) Customization Notes** (change frequency, extend signals) Legal Disclaimer** (AI analysis, not financial advice) Your Bitcoin liquidity insights—unified, AI-analyzed, and delivered in real time to Telegram.
by Cong Nguyen
📄 What this workflow does This workflow automatically turns a topic and a reference image URL into a finished, branded article image. It uses GPT-4o to generate a short, detailed image prompt, sends it to FAL Flux image-to-image for rendering, polls until the job is completed, downloads and resizes the image, overlays your company logo, and finally saves the branded result into a specified Google Drive folder. 👤 Who is this for Content teams who need consistent, on-brand article images. Marketing teams looking to scale blog and landing page visuals. Designers who want to automate repetitive resizing and branding tasks. Anyone who needs a pipeline from topic → AI illustration → Google Drive asset. ✅ Requirements OpenAI (GPT-4o) API credentials (for image prompt generation). FAL API key for Flux image-to-image generation. Google Drive OAuth2 connection + target folder ID for saving images. A company logo file/URL (direct download link from Google Drive or any public URL). ⚙️ How to set up Connect OpenAI GPT-4o in the “Create prompt” node. Add your FAL API key to all HTTP Request nodes (generate image, check image finish, Get image link). Replace the logo link in “Get company’s logo” with your own logo URL. Configure the Google Drive node with your OAuth2 credentials and set the correct Folder ID. Update the image_url in “Link image” (or pass from upstream data). Test the workflow end-to-end with a sample subject and image. 🔁 How it works Form/Manual Trigger → Input subject + reference image URL. GPT-4o → Generates a <70-word sharp/detailed prompt (no text/logos). FAL Flux (HTTP Request) → Submits job for image-to-image generation. Polling Loop → Wait + check status until COMPLETED. Download Image → Retrieves generated image link. Resize Image → Standardize to 800×500 pixels. Get & Resize Logo → Fetch company logo, resize for branding. Composite → Overlay logo onto article image. Save to Google Drive → Final branded image saved in target folder. 💡 About Margin AI Margin AI is your AI Service Companion. We help organizations design intelligent, human-centric automation — from content pipelines and branding workflows to customer insights and sales enablement. Our tailored AI solutions scale marketing, operations, and creative processes with ease.
by Robert Breen
Automatically research new leads in your target area, structure the results with AI, and append them into Google Sheets — all orchestrated in n8n. ✅ What this template does Uses Perplexity to research businesses (coffee shops in this example) with company name + email Cleans and structures the output into proper JSON using OpenAI Appends the new leads directly into Google Sheets, skipping duplicates > Trigger: Manual — “Start Workflow” 👤 Who’s it for Sales & marketing teams** who need to prospect local businesses Agencies** running outreach campaigns Freelancers** and consultants looking to automate lead research ⚙️ How it works Set Location → define your target area (e.g., Hershey PA) Get Current Leads → pull existing data from your Google Sheet to avoid duplicates Research Leads → query Perplexity for 20 businesses, excluding already-scraped ones Write JSON → OpenAI converts Perplexity output into structured Company/Email arrays Split & Merge → align Companies with Emails row-by-row Send Leads to Google Sheets → append or update leads in your sheet 🛠️ Setup instructions Follow these sticky-note setup steps (already included in the workflow): 1) Connect Google Sheets (OAuth2) In n8n → Credentials → New → Google Sheets (OAuth2) Sign in with your Google account and grant access In the Google Sheets node, select your Spreadsheet and Worksheet Example sheet: https://docs.google.com/spreadsheets/d/1MnaU8hSi8PleDNVcNnyJ5CgmDYJSUTsr7X5HIwa-MLk/edit#gid=0 2) Connect Perplexity (API Key) Sign in at https://www.perplexity.ai/account Generate an API key: https://docs.perplexity.ai/guides/getting-started In n8n → Credentials → New → Perplexity API, paste your key 3) Connect OpenAI (API Key) In n8n → Credentials → New → OpenAI API Paste your OpenAI API key In the OpenAI Chat Model node, select your credential and a vision-capable model (e.g., gpt-4o-mini, gpt-4o) 🔧 Requirements A free Google account An OpenAI API key (https://platform.openai.com) A Perplexity API key (https://docs.perplexity.ai) n8n self-hosted or cloud instance 🎨 How to customize Change the Search Area in the Set Location node Modify the Perplexity system prompt to target different business types (e.g., gyms, salons, restaurants) Expand the Google Sheet schema to include more fields (phone, website, etc.) 📬 Contact Need help customizing this (e.g., filtering by campaign, sending reports by email, or formatting your Google Sheet)? 📧 robert@ynteractive.com 🔗 Robert Breen 🌐 ynteractive.com
by Cheng Siong Chin
How It Works This workflow automates emissions data validation and compliance reporting for environmental managers, sustainability officers, and compliance teams across manufacturing, energy, and transportation sectors. Manual verification of emissions data against multiple regulatory frameworks such as GHG Protocol, EPA standards, is time-consuming and error-prone, risking missed deadlines and penalties. On a set schedule, the system ingests synthetic emissions data and deploys specialist AI agents in parallel: one verifies data accuracy, another reviews accounting methodology, and a third assesses regulatory compliance. An orchestrator consolidates all findings and routes outcomes intelligently, while non-compliant results trigger exception handling and corrective action workflows. Teams gain audit-ready records, consistent framework alignment, and timely reporting without manual bottlenecks. Setup Steps Configure API credentials with Llama-3.1-70B-Instruct model access Set up schedule trigger for monthly/quarterly reporting cycles Connect Google Sheets for compliant report storage with appropriate folder permissions Configure compliance routing logic based on validation outcomes Customize AI agent prompts for specific regulatory frameworks and industry requirements Prerequisites NVIDIA NIM API key and Google Sheets access with write permissions. Use Cases Automates monthly GHG reporting and EPA compliance submissions Customization Extend with region-specific regulations and integrate live emissions monitoring systems Benefits Cuts report preparation time by 80% and eliminates manual calculation errors
by Joe Swink
This template has a two part setup: Ingest PDF files from S3, extract text, chunk, embed with OpenAI embeddings, and index into a Qdrant collection with metadata. Provide a chat entry point that uses an Agent with OpenAI to retrieve from the same Qdrant collection as a tool and answer proposal knowledge questions. What it does Lists objects in an S3 bucket, loops through keys, downloads each file, and extracts text from PDFs. Chunks text and loads it into Qdrant with metadata for retrieval. Exposes a chat trigger wired to an Agent using an OpenAI chat model. Adds a retrieve as tool Qdrant node so the Agent can ground answers in the indexed corpus. Why it is useful Simple pattern for building a proposal or knowledge base from PDFs stored in S3. End to end path from ingestion to retrieval augmented answers. Easy to swap models or collections, and to extend with more tools. Setup notes Attach your own AWS credentials to the two S3 nodes and set your bucket name. Attach your Qdrant credentials to both Qdrant nodes and set your collection. Attach your OpenAI credentials to the embedding and chat nodes. The sanitized template uses placeholders for bucket and collection names.
by Kareem
Transform meeting notes into organized tasks automatically This workflow uses AI to extract action items, decisions, and key details from any meeting notes format—then creates tasks in Asana and sends a formatted summary to Slack. Perfect for sales teams, project managers, and anyone who wants to stop manually tracking action items from meetings. What gets extracted Action items with assignees and due dates Key decisions made Pain points or challenges mentioned Budget discussions Next meeting dates How it works The workflow uses a simple form where you paste meeting notes (from AI notetakers like Otter.ai, manual notes, or any text). GPT-4o analyzes the content and extracts structured data. Each action item becomes an Asana task with the assignee name, due date, and full meeting context in the notes. All tasks are then aggregated into a formatted Slack message with clickable links, key decisions, pain points, and budget info. Your team gets a complete meeting summary without reading through pages of notes. Setup requirements OpenAI API key for GPT-4o Asana workspace with OAuth2 connection Slack workspace with OAuth2 connection Customization ideas Replace the form trigger with an email trigger to auto-process notes sent to a specific inbox Modify the AI prompt to extract additional fields like risks, dependencies, or next steps Add conditional logic to route different meeting types to different Asana projects or Slack channels Connect to other project management tools like ClickUp, Monday.com, or Jira instead of Asana Add Google Calendar integration to automatically schedule next meetings Good to know GPT-4o costs approximately $0.01-0.03 per meeting analysis The form can be shared with your team for easy submission All meeting context is preserved in Asana task notes for reference Slack messages include clickable task links for quick access
by PDF Vector
Overview Researchers and academic institutions need efficient ways to process and analyze large volumes of research papers and academic documents, including scanned PDFs and image-based materials (JPG, PNG). Manual review of academic literature is time-consuming and makes it difficult to identify trends, track citations, and synthesize findings across multiple papers. This workflow automates the extraction and analysis of research papers and scanned documents using OCR technology, creating a searchable knowledge base of academic insights from both digital and image-based sources. What You Can Do Extract key information from research papers automatically, including methodologies, findings, and citations Build a searchable database of academic insights from both digital and image-based sources Track citations and identify research trends across multiple papers Synthesize findings from large volumes of academic literature efficiently Who It's For Research institutions, university libraries, R&D departments, academic researchers, literature review teams, and organizations tracking scientific developments in their field. The Problem It Solves Literature reviews require reading hundreds of papers to identify relevant findings and methodologies. This template automates the extraction of key information from research papers, including methodologies, findings, and citations. It builds a searchable database that helps researchers quickly find relevant studies and identify research gaps. Setup Instructions: Install the PDF Vector community node with academic features Configure PDF Vector API with academic search enabled Configure Google Drive credentials for document access Set up database for storing extracted research data Configure citation tracking preferences Set up automated paper ingestion from sources Configure summary generation parameters Key Features: Google Drive integration for research paper retrieval (PDFs, JPGs, PNGs) OCR processing for scanned documents and images Automatic extraction of paper metadata and structure from any format Methodology and findings summarization from PDFs and images Citation network analysis and metrics Multi-paper trend identification Searchable research database creation Integration with academic search engines Customization Options: Add field-specific extraction templates Configure automated paper discovery from arXiv, PubMed, etc. Implement citation alert systems Create research trend visualizations Add collaboration features for research teams Build API endpoints for research queries Integrate with reference management tools Implementation Details: The workflow uses PDF Vector's academic features to understand research paper structure and extract meaningful insights. It processes papers from various sources, identifies key contributions, and creates structured summaries. The system tracks citations to measure impact and identifies emerging research trends by analyzing multiple papers in a field. Note: This workflow uses the PDF Vector community node. Make sure to install it from the n8n community nodes collection before using this template.
by Pixril
Overview This workflow deploys a fully autonomous "AI SEO Agency" inside your n8n instance. Unlike simple chatbots, this is a hierarchical agent swarm. A "Director" agent acts as the project manager, using live market data to orchestrate a team of 6 specialized AI workers. It takes a single user prompt and turns it into a comprehensive, professional-grade SEO strategy report. Key Features Hierarchical Architecture:** A "Manager" agent delegates tasks to "Worker" agents. Live Market Research:* Uses *SerpApi** to fetch real-time Google Search results and competitor data. Conversational Memory:** Remembers business details across the chat session. 6 Expert Specialists:** Includes dedicated agents for Keyword Research, Technical SEO, Link Building, Analytics, Local SEO, and Content Writing. How it works Analysis: The SEO Director Agent receives your request and consults its memory. Live Research: The Director uses the SerpApi tool to perform live Google searches on the target website and niche to gather context. Delegation: Based on the research, the Director dynamically assigns tasks to the relevant Specialist Agents (e.g., calling the "Technical Specialist" for site speed issues or the "Keyword Specialist" for content ideas). Synthesis: The Director compiles the outputs from all specialists into one cohesive, actionable strategy report. Set up steps Estimated time: 5 minutes OpenAI Keys: Add your OpenAI API Key to all 7 "OpenAI Chat Model" nodes (1 for the Director, 6 for the Specialists). SerpApi Key: Open the "SEO Director Agent" node. Under "Tools" > "SerpApi," add your SerpApi Key (free tier available). This enables the live Google Search capability. Run: Toggle the workflow to "Active" and start chatting! About the Creator Built by Pixril. We specialize in building advanced, production-ready AI agents for n8n. Find more professional workflows in our shop: https://pixril.etsy.com
by IranServer.com
Automated Blog Content Generation from Google Trends to WordPress This n8n workflow automatically generates SEO-friendly blog content based on trending topics from Google Trends and publishes it to WordPress. Perfect for content creators, bloggers, and digital marketers who want to stay on top of trending topics with minimal manual effort. Who's it for Content creators** who need fresh, trending topic ideas Bloggers** looking to automate their content pipeline Digital marketers** wanting to capitalize on trending searches WordPress site owners** seeking automated content generation SEO professionals** who want to target trending keywords How it works The workflow operates on a scheduled basis (daily at 8:45 PM by default) and follows this process: Trend Discovery: Fetches the latest trending searches from Google Trends for a specific country (Iran by default) Content Research: Performs Google searches on the top 3 trending topics to gather detailed information AI Content Generation: Uses OpenAI's GPT-4o model to create SEO-friendly blog posts based on the trending topics and search results Structured Output: Ensures the generated content has proper title and content structure Auto-Publishing: Automatically creates draft posts in WordPress The AI is specifically prompted to create engaging, SEO-optimized content without revealing the automated sources, ensuring natural-sounding blog posts. How to set up Install required community node: n8n-nodes-serpapi for Google Trends and Search functionality Configure credentials: SerpApi: Sign up at serpapi.com and add your API key OpenAI: Add your OpenAI API key for GPT-4o access WordPress: Configure your WordPress site credentials Customize the country code: Change the "Country" field in the "Edit Fields" node (currently set to "IR" for Iran) Adjust the schedule: Modify the "Schedule Trigger" to run at your preferred time Test the workflow: Run it manually first to ensure all connections work properly Requirements SerpApi account** (for Google Trends and Search data) OpenAI API access** (for content generation using GPT-4o) WordPress site** with API access enabled How to customize the workflow Change target country**: Modify the country code in the "Edit Fields" node to target different regions Adjust content quantity**: Change the limit in the "Limit" node to process more or fewer trending topics Modify AI prompt**: Edit the prompt in the "Basic LLM Chain" node to change writing style or focus Schedule frequency**: Adjust the "Schedule Trigger" for different posting frequencies Content status**: Change from "draft" to "publish" in the WordPress node for immediate publishing Add content filtering**: Insert additional nodes to filter topics by category or keywords
by AI/ML API | D1m7asis
📲 AI Multi-Model Telegram Chatbot (n8n + AIMLAPI) This n8n workflow enables Telegram users to interact with multiple AI models dynamically using #model_id commands. It also supports a /models command to list all available models. Each user has a daily usage limit, tracked via Google Sheets. 🚀 Key Features Dynamic Model Selection:** Users choose models on-the-fly via #model_id (e.g., #openai/gpt-4o). /models Command:** Lists all available models grouped by provider. Daily Limit Per User:** Enforced using Google Sheets. Prompt Parsing:** Extracts model and message from user input. Logging:** Logs every request & result into Google Sheets for usage tracking. Seamless Telegram Delivery:** Responses are sent directly back to the chat. 🛠 Setup Guide 1. 📲 Create a Telegram Bot Go to @BotFather Use /newbot → Set name & username. Copy the generated API token. 2. 🔐 Add Telegram Credentials to n8n Go to n8n > Credentials > Telegram API. Create a new credential with the BotFather token. 3. 📗 Google Sheets Setup Create a Google Sheet named Sheet1. Add columns: user_id | date | query | result Share the sheet with your Service Account or OAuth Email (depending on auth method). 4. 🔌 Connect AIMLAPI Get your API key from AIMLAPI. In n8n > Credentials, add AI/ML API: API Key: your_key_here. 5. ⚙️ Customize Limits & Enhancements Adjust daily limits in the Set Daily Limit node. Optional: Add NSFW content filtering. Implement alias commands. Extend with /help, /usage, /history. Add inline button UX (advanced). 💡 How It Works ➡️ Command Examples: Start a chat with a specific model: #openai/gpt-4o Write a motivational quote. Request available models list: /models ➡️ Workflow Logic: Receives a Telegram message. Switch node checks if the message is /models or a prompt. For /models, it fetches and sends a grouped list of models. For prompts: Checks usage limits. Parses #model_id and prompt text. Dynamically routes the request to the chosen model. Sends the AI's response back to the user. Logs the query & result to Google Sheets. If daily limit exceeded → sends a limit exceeded message. 🧪 Testing & Debugging Tips Test via a separate Telegram chat. Use Console/Set nodes to debug payloads. Always test commands by messaging the bot (not via "Execute Node"). Validate cases: Missing #model_id. Invalid model_id. Limit exceeded handling.