by Trung Tran
📚 Telegram RAG Chatbot with PDF Document & Google Drive Backup An upgraded Retrieval-Augmented Generation (RAG) chatbot built in n8n that lets users ask questions via Telegram and receive accurate answers from uploaded PDFs. It embeds documents using OpenAI and backs them up to Google Drive. 👤 Who’s it for Perfect for: Knowledge workers who want instant access to private documents Support teams needing searchable SOPs and guides Educators enabling course material Q&A for students Individuals automating personal document search + cloud backup ⚙️ How it works / What it does 💬 Telegram Chat Handling User sends a message Triggered by the Telegram bot, the workflow checks if the message is text. Text message → OpenAI RAG Agent If the message is text, it's passed to a GPT-powered document agent. This agent: Retrieves relevant info from embedded documents using semantic search Returns a context-aware answer to the user Send answer back The bot sends the generated response back to the Telegram user. Non-text input fallback If the message is not text, the bot replies with a polite unsupported message. 📄 PDF Upload and Embedding User uploads PDFs manually A manual trigger starts the embedding flow. Default Data Loader Reads and chunks the PDF(s) into text segments. Insert to Vector Store (Embedding) Text chunks are embedded using OpenAI and saved for retrieval. Backup to Google Drive The original PDF is uploaded to Google Drive for safekeeping. 🛠️ How to set up Telegram Bot Create via BotFather Connect it to the Telegram Trigger node OpenAI Use your OpenAI API key Connect the Embeddings and Chat Model nodes (GPT-3.5/4) Ensure both embedding and querying use the same Embedding node Google Drive Set up credentials in n8n for your Google account Connect the “Backup to Google Drive” node PDF Ingestion Use the “Upload your PDF here” trigger Connect it to the loader, embedder, and backup flow ✅ Requirements Telegram bot token OpenAI API key (GPT + Embeddings) n8n instance (self-hosted or cloud) Google Drive integration PDF files to upload 🧩 How to customize the workflow | Feature | How to Customize | |-------------------------------|-------------------------------------------------------------------| | Auto-ingest from folders | Add Google Drive/Dropbox watchers for new PDFs | | Add file upload via Telegram | Extend Telegram bot to receive PDFs and run the embedding flow | | Track user questions | Log Telegram usernames and questions to a database | | Summarize documents | Add summarization step on upload | | Add Markdown or HTML support | Format replies for better Telegram rendering | Built with 💬 Telegram + 📄 PDF + 🧠 OpenAI Embeddings + ☁️ Google Drive + ⚡ n8n
by Jay Emp0
🤖 Reddit Auto-Comment Assistant (AI-Driven Marketing Workflow) Automate how you reply to Reddit posts using AI-generated, first-person comments that sound human, follow subreddit rules, and (optionally) promote your own links or products. 🧩 Overview This workflow monitors Reddit mentions (via F5Bot Gmail alerts) and automatically: Fetches the relevant Reddit post. Checks the subreddit’s rules for self-promotion. Generates a comment using GPT-5 style prompting (human-like tone, <255 chars). Optionally promotes your chosen product from Google Sheets. Posts the comment automatically It’s ideal for creators, marketers, or founders who want to grow awareness organically and authentically on Reddit — without sounding like a bot. 🧠 Workflow Diagram 🚀 Key Features | Feature | Description | |----------|--------------| | AI-Generated Reddit Replies | Uses GPT-powered reasoning and prompt structure that mimics a senior marketing pro typing casually. | | Rule-Aware Posting | Reads subreddit rules and adapts tone — no promo where it’s not allowed. | | Product Integration | Pulls product name + URL from your Google Sheet automatically. | | Full Automation Loop | From Gmail → Gsheet → Reddit | | Evaluation Metrics | Logs tool usage, link presence, and formatting to ensure output quality. | 🧰 Setup Guide 1️⃣ Prerequisites | Tool | Purpose | |------|----------| | n8n Cloud or Self-Host | Workflow automation environment | | OpenAI API key | For comment generation | | Reddit OAuth2 credentials | To post comments | | Google Sheets API | To fetch and evaluate products | | Gmail API | To read F5Bot alerts | 2️⃣ Import the Workflow Download Reddit Assistant.json In n8n, click Import Workflow → From File Paste your credentials in the corresponding nodes: Reddit account Gmail account Gsheet account OpenAI API 3️⃣ Connect Your Google Sheets You’ll need two Google Sheets: | Sheet | Purpose | Example Tab | |--------|----------|-------------| | Product List | Contains all your product names, URLs, goals, and CTAs | promo | | Reddit Evaluations | Logs AI performance metrics and tool usage | reddit evaluations | 4️⃣ Set Up Gmail Trigger (F5Bot) Subscribe to F5Bot alerts for keywords like "blog automation" or your brand name. Configure Gmail Trigger to only pull from sender: admin@f5bot.com. 5️⃣ Configure AI Agent Prompt The built-in prompt follows a GPT-5-style structured reasoning chain: Reads the Reddit post + rules. Determines if promotion is allowed. Fetches product data from Google Sheets. Writes a short, human comment (<255 chars). Avoids buzzwords and fake enthusiasm. 📊 Workflow Evaluations The workflow includes automatic evaluation nodes to track: | Metric | Description | |--------|--------------| | contains link | Checks if comment includes a URL | | contains dash | Detects format breaks | | Tools Used | Logs which AI tools were used in reasoning | | executionTime | Monitors average latency | 💡 Why This Workflow Has Value | Value | Explanation | |--------|--------------| | Saves time | Automates Reddit marketing without manual engagement. | | Feels human | AI comments use a fast-typing, casual tone (e.g., “u,” “ur,” “idk”). | | Follows rules | Respects subreddits where promo is banned. | | Data-driven | Logs performance across 10 test cases for validation. | | Monetizable | Can promote Gumroad, YouTube, or SaaS products safely. | ⚙️ Example Use Case > “I used this automation to pull $1.4k by replying to Reddit posts about blog automation. > Each comment felt natural and directed users to my n8n workflow.”
by Rahul Joshi
Description Transform Figma design files into detailed QA test cases with AI-driven analysis and structured export to Google Sheets. This workflow helps QA and product teams streamline design validation, test coverage, and documentation — all without manual effort. 🎨🤖📋 What This Template Does Step 1: Trigger manually and input your Figma file ID. 🎯 Step 2: Fetches the full Figma design data (layers, frames, components) via API. 🧩 Step 3: Sends structured design JSON to GPT-4o-mini for intelligent test case generation. 🧠 Step 4: AI analyzes UI components, user flows, and accessibility aspects to generate 5–10 test cases. ✅ Step 5: Parses and formats results into a clean structure. Step 6: Exports test cases directly to Google Sheets for QA tracking and reporting. 📊 Key Benefits ✅ Saves 2–3 hours per design by automating test case creation ✅ Ensures consistent, comprehensive QA documentation ✅ Uses AI to detect UX, accessibility, and functional coverage gaps ✅ Centralizes output in Google Sheets for easy collaboration Features Figma API integration for design parsing GPT-4o-mini model for structured test generation Automated Google Sheets export Dynamic file ID and output schema mapping Built-in error handling for large design files Requirements Figma Personal Access Token OpenAI API key (GPT-4o-mini) Google Sheets OAuth2 credentials Target Audience QA and Test Automation Engineers Product & Design Teams Startups and Agencies validating Figma prototypes Setup Instructions Connect your Figma token as HTTP Header Auth (X-Figma-Token). Add your OpenAI API key in n8n credentials (model: gpt-4o-mini). Configure Google Sheets OAuth2 and select your sheet. Input Figma file ID from the design URL. Run once manually, verify output, then enable for regular use.
by Yasser Sami
Human-in-the-Loop LinkedIn Post Generator (Telegram + AI) This n8n template demonstrates how to build a human-in-the-loop AI workflow that helps you create professional LinkedIn posts via Telegram. The agent searches the web, drafts content, asks for your approval, and refines it based on your feedback — ensuring every post sounds polished and on-brand. Who’s it for Content creators and marketers who want to save time drafting LinkedIn posts. SaaS founders or solopreneurs who regularly share updates or insights. Anyone who wants an AI writing assistant with human control in the loop. How it works / What it does Trigger: The workflow starts when you send a message to the Telegram bot asking it to write a LinkedIn post (e.g., “Write a LinkedIn post about AI in marketing”). Research: The AI agent uses the Tavily tool to search the web and gather context for your topic. Drafting: An AI model (OpenAI or Gemini) creates a professional LinkedIn post based on the findings. Human-in-the-loop: The bot sends the draft to you in Telegram and asks: “Good to go?” If you approve → The post is saved to a Google Sheet, ready to publish. If you disapprove and give feedback → The feedback is sent to a second AI agent that revises and improves the post. The improved draft is sent back to you again for final approval. Finalization: Once approved, the post is appended to a Google Sheet — your ready-to-post content library. This workflow combines AI creativity with human oversight to produce polished, authentic LinkedIn content every time. How to set up Import this template into your n8n account. Connect your Telegram bot (via Telegram Trigger and Send Message nodes). Connect your Google Sheets account to store approved posts. Set up your AI model provider (OpenAI or Gemini) and Tavily API key for web search. Activate the workflow and start chatting with your AI writing assistant on Telegram! Requirements n8n account. Telegram bot token. OpenAI or Google Gemini account (for text generation). Tavily API key (for web search). Google Sheets account (for saving approved posts). How to customize the workflow Post Tone**: Adjust AI prompts to match your personal voice (professional, storytelling, inspirational, etc.). Approval Logic**: Modify the approval step to allow multiple revision loops or add a “draft-only” mode. Storage Options**: Instead of Google Sheets, save approved posts to Notion, Airtable, or your CMS. Multi-platform**: Extend the same logic for X (Twitter) or Threads by changing the final output destination. Branding**: Add your brand guidelines or preferred hashtags to the AI prompts for consistent style. This template helps you write better LinkedIn posts faster — keeping you in full control while AI does the heavy lifting.
by Stephan Koning
VEXA: AI-Powered Meeting Intelligence I'll be honest, I built this because I was getting lazy in meetings and missing key details. I started with a simple VEXA integration for transcripts, then added AI to pull out summaries and tasks. But that just solved part of the problem. The real breakthrough came when we integrated Mem0, creating a persistent memory of every conversation. Now, you can stop taking notes and actually focus on the person you're talking to, knowing a system is tracking everything that matters. This is the playbook for how we built it. How It Works This isn't just one workflow; it's a two-part system designed to manage the entire meeting lifecycle from start to finish. Bot Management: It starts when you flick a switch in your CRM (Baserow). A command deploys or removes an AI bot from Google Meet. No fluff—it's there when you need it, gone when you don't. The workflow uses a quick "digital sticky note" in Redis to remember who the meeting is with and instantly updates the status in your Baserow table. AI Analysis & Memory: Once the meeting ends, VEXA sends the transcript over. Using the client ID (thank god for redis) , we feed the conversation to an AI model (OpenAI). It doesn't just summarize; it extracts actionable next steps and potential risks. All this structured data is then logged into a memory layer (Mem0), creating a permanent, searchable record of every client conversation. Setup Steps: Your Action Plan This is designed for rapid deployment. Here's what you do: Register Webhook: Run the manual trigger in the workflow once. This sends your n8n webhook URL to VEXA, telling it where to dump transcripts after a call. Connect Your CRM: Copy the vexa-start webhook URL from n8n. Paste it into your Baserow automation so it triggers when you set the "Send Bot" field to Start_Bot. Integrate Your Tools: Plug your VEXA, Mem0, Redis, and OpenAI API credentials into n8n. Use the Baserow Template: I've created a free Baserow template to act as your control panel. Grab it here: https://baserow.io/public/grid/t5kYjovKEHjNix2-6Rijk99y4SDeyQY4rmQISciC14w. It has all the fields you need to command the bot. Requirements An active n8n instance or cloud account. Accounts for VEXA.ai, Mem0.ai, Baserow, and OpenAI. A Redis database . Your Baserow table must have these fields: Meeting Link, Bot Name, Send Bot, and Status. Next Steps: Getting More ROI This workflow is the foundation. The real value comes from what you build on top of it. Automate Follow-ups:** Use the AI-identified next steps to automatically trigger follow-up emails or create tasks in your project management tool. Create a Unified Client Memory:** Connect your email and other communication platforms. Use Mem0 to parse and store every engagement, building a complete, holistic view of every client relationship. Build a Headless CRM:** Combine these workflows to build a fully AI-powered system that handles everything from lead capture to client management without any manual data entry. Copy the workflow and stop taking notes
by Shelly-Ann Davy
Automate Bug Reports: GitHub Issues → AI Analysis → Jira Tickets with Slack & Discord Alerts Automatically convert GitHub issues into analyzed Jira tickets with AI-powered severity detection, developer assignment, and instant team alerts. Overview This workflow captures GitHub issues in real-time, analyzes them with GPT-4o for severity and categorization, creates enriched Jira tickets, assigns the right developers, and notifies your team across Slack and Discord—all automatically. Features AI-Powered Triage**: GPT-4o analyzes bug severity, category, root cause, and generates reproduction steps Smart Assignment**: Automatically assigns developers based on mentioned files and issue context Two-Way Sync**: Posts Jira ticket links back to GitHub issues Multi-Channel Alerts**: Rich notifications in Slack and Discord with action buttons Time Savings**: Eliminates 15-30 minutes of manual triage per bug Customizable Routing**: Easy developer mapping and priority rules What Gets Created Jira Ticket: Original GitHub issue details with reporter info AI severity assessment and categorization Reproduction steps and root cause analysis Estimated completion time Automatic labeling and priority assignment GitHub Comment: Jira ticket link AI analysis summary Assigned developer and estimated time Team Notifications: Severity badges and quick-access buttons Developer assignment and root cause summary Color-coded priority indicators Use Cases Development teams managing 10+ bugs per week Open source projects handling community reports DevOps teams tracking infrastructure issues QA teams coordinating with developers Product teams monitoring user-reported bugs Setup Requirements Required: GitHub repository with admin access Jira Software workspace OpenAI API key (GPT-4o access) Slack workspace OR Discord server Customization Needed: Update developer email mappings in "Parse GPT Response & Map Data" node Replace YOUR_JIRA_PROJECT_KEY with your project key Update Slack channel name (default: dev-alerts) Replace YOUR_DISCORD_WEBHOOK_URL with your webhook Change your-company.atlassian.net to your Jira URL Setup Time: 15-20 minutes Configuration Steps Import workflow JSON into n8n Add credentials: GitHub OAuth2, Jira API, OpenAI API, Slack, Discord Configure GitHub webhook in repository settings Customize developer mappings and project settings Test with sample GitHub issue Activate workflow Expected Results 90% faster bug triage (20 min → 2 min per issue) 100% consistency in bug analysis Zero missed notifications Better developer allocation Improved bug documentation Tags GitHub, Jira, AI, GPT-4, Bug Tracking, DevOps, Automation, Slack, Discord, Issue Management, Development, Project Management, OpenAI, Webhook, Team Collaboration
by Mantaka Mahir
How it works This workflow automates the process of converting Google Drive documents into searchable vector embeddings for AI-powered applications: • Takes a Google Drive folder URL as input • Initializes a Supabase vector database with pgvector extension • Fetches all files from the specified Drive folder • Downloads and converts each file to plain text • Generates 768-dimensional embeddings using Google Gemini • Stores documents with embeddings in Supabase for semantic search Built for the Study Agent workflow to power document-based Q&A, but also works perfectly for any RAG system, AI chatbot, knowledge base, or semantic search application that needs to query document collections. Set up steps Prerequisites: • Google Drive OAuth2 credentials • Supabase account with Postgres connection details • Google Gemini API key (free tier available) Setup time: ~10 minutes Steps: Add your Google Drive OAuth2 credentials to the Google Drive nodes Configure Supabase Postgres credentials in the SQL node Add Supabase API credentials to the Vector Store node Add Google Gemini API key to the Embeddings node Update the input with your Drive folder URL Execute the workflow Note: The SQL query will drop any existing "documents" table, so backup data if needed. Detailed node-by-node instructions are in the sticky notes within the workflow. Works with: Study Agent (main use case), custom AI agents, chatbots, documentation search, customer support bots, or any RAG application.
by Cheng Siong Chin
How It Works Scheduled triggers run automated price checks across multiple travel data sources. The collected data is aggregated, validated, and processed through an AI analysis layer that compares trends, detects anomalies, and evaluates multi-criteria factors such as price movement, seasonality, and route demand. The system then routes results into booking preparation, report generation, and notification modules. When target price conditions are met, alerts are sent and records are updated accordingly. Setup Steps Connect Google Flights and Skyscanner APIs using authenticated tokens. Configure the OpenAI API for enhanced analysis and multi-factor evaluation. Link Google Sheets for storing historical price data. Add WordPress site credentials to enable automated report publishing. Enable email notifications for price alerts and updates. Adjust the scheduler frequency within the Schedule Price Check node to control how often the workflow runs. Prerequisites Google Flights API, Skyscanner API, flight booking service credentials, OpenAI API key, Google Sheets access, WordPress admin account, email service configured. Use Cases Travel agencies automating client alerts for price drops. Corporate travel managers monitoring bulk bookings. Customization Modify price thresholds in Multi-Criteria Decision node. Add airline or destination filters in search parameters. Benefits Eliminates manual price monitoring. Reduces booking delays through automation.
by Cheng Siong Chin
How It Works The workflow runs on a monthly trigger to collect both current-year and multi-year historical HDB data. Once fetched, all datasets are merged with aligned fields to produce a unified table. The system then applies cleaning and normalization rules to ensure consistent scales and comparable values. After preprocessing, it performs pattern mining, anomaly checks, and time-series analysis to extract trends and forecast signals. An AI agent, integrating OpenAI GPT-4, statistical tools, and calculator nodes, synthesizes these results into coherent insights. The final predictions are formatted and automatically written to Google Sheets for reporting and downstream use. Setup Steps 1) Configure fetch nodes to pull current-year HDB data and three years of historical records. 2) Align and map column names across all datasets. 3) Set normalization and standardization parameters in the cleaning node. 4) Add your OpenAI API key (GPT-4) and link the model, forecasting tool, and calculator nodes. 5) Authorize Google Sheets and configure sheet and cell mappings for automated export. Prerequisites Historical data source with API access (3+ years of records) OpenAI API key for GPT-4 model Google Sheets account with API credentials Basic understanding of time series data Use Cases Real Estate: Forecast property prices using multi-year historical HDB/market data with confidence intervals Finance: Predict market trends by aggregating years of transaction or pricing records Customization Data Source: Replace HDB/fetch nodes with stock prices, sensor data, sales records, or any historical dataset Analysis Window: Adjust years fetched (2-5 years) based on data availability and prediction horizon Benefits Automation: Monthly scheduling eliminates manual data gathering and analysis Consolidation: Merges fragmented year-by-year data into unified historical view
by Calvin Cunningham
Use Cases -Personal or family budget tracking. -Small business expense logging via Telegram -Hands-free logging (using voice messages) How it works: -Trigger receives text or voice. -Optional branch transcribes audio to text. -AI parses into a structured array (SOP enforces schema). -Split Out produces 1 item per expense. -Loop Over Items appends rows sequentially with a Wait, preventing missed writes. -In parallel, Item Lists (Aggregate) builds a single summary string; Merge (Wait for Both) releases one final Telegram confirmation. Setup Instructions Connect credentials: Telegram, Google, OpenAI. Sheets: Create a sheet with headers Date, Category, Merchant, Amount, Note. Copy Spreadsheet ID + sheet name. Map columns in Append to Google Sheet. Pick models: set Chat model (e.g., gpt-4o-mini) and Whisper for transcription if using audio. Wait time: keep 500–1000 ms to avoid API race conditions. Run: Send a Telegram message like: Gas 34.67, Groceries 82.45, Coffee 6.25, Lunch 14.90. Customization ideas: -Add categories map (Memory/Set) for consistent labeling. -Add currency detection/formatting. -Add error-to-Telegram path for invalid schema.
by Rahul Joshi
📊 Description Generate high-quality, SEO-optimized content briefs automatically using AI, real-time keyword research, SERP intelligence, and historical content context. This workflow standardizes user inputs, fetches search metrics, analyzes competitors, and produces structured SEO briefs with quality scoring and version control. It also stores all versions in Google Sheets and generates HTML previews for easy review and publishing. 🤖📄📈 What This Template Does Normalizes user input from the chat trigger into structured fields (intent, topic, parameters). ✏️ Fetches real-time keyword metrics such as search volume, CPC, and difficulty from DataForSEO. 🔍 Retrieves SERP insights through SerpAPI for top competitors, headings, and content gaps. 🌐 Loads historical brief versions from Google Sheets for continuity and versioning. 📚 Uses an advanced GPT-4o-mini agent to generate a complete SEO brief with title, metadata, keywords, outline, entities, and internal links. 🤖 Calculates detailed SEO, differentiation, and completeness quality scores. 📊 Validates briefs against quality thresholds (outline length, keywords, word count, overall score). ⚡ Stores approved briefs in Google Sheets with version control and timestamping. 🗂️ Generates an HTML preview with styled formatting for team review or CMS use. 🖥️ Sends Slack alerts when a brief does not meet quality standards. 🚨 Key Benefits ✅ Fully automated SEO content brief generation ✅ Uses real-time keyword + SERP + competitor intelligence ✅ Ensures quality through automated scoring and validation ✅ Built-in version control for content operations teams ✅ Beautiful HTML preview ready for editors or clients ✅ Reduces research time from hours to minutes ✅ Ideal for content agencies, SEO teams, and AI-powered workflows Features Chat-triggered brief generation Real-time DataForSEO keyword metrics SERP analysis tool integration GPT-4o-mini structured AI agent Google Sheets integration for storing & retrieving versions Automated quality scoring (SEO, gaps, completeness) HTML preview builder with rich formatting Slack alerting for low-quality briefs Semantic entities, content gaps, competitor insights Requirements OpenAI API (GPT-4o-mini or compatible model) DataForSEO access credentials (Basic Auth) SerpAPI key for SERP extraction Google Sheets OAuth2 integration Optional: Slack webhook for quality alerts Target Audience SEO teams generating large amounts of content briefs Content agencies scaling production with automation Marketing teams building data-driven content strategies SaaS teams wanting automated keyword-based briefs Anyone needing structured, high-quality content briefs from chat Step-by-Step Setup Instructions Connect your OpenAI API credential and confirm GPT-4o-mini availability. 🔌 Add DataForSEO HTTP Basic Auth for keyword metrics. 📊 Connect SerpAPI for SERP analysis tools. 🌐 Add Google Sheets OAuth2 and link your content_versions sheet. 📄 Optional: Add a Slack webhook URL for quality alerts. 🔔 Test by sending a topic via the chat trigger. Review the generated SEO brief and HTML preview. Enable the workflow for continued use in your content pipeline. 🚀
by Jamot
How it works Your WhatsApp AI Assistant automatically handles customer inquiries by linking your Google Docs knowledge base to incoming WhatsApp messages. The system instantly processes customer questions, references your business documentation, and delivers AI-powered responses through OpenAI or Gemini - all without you lifting a finger. Works seamlessly in individual chats and WhatsApp groups where the assistant can respond on your behalf. Set up steps Time to complete: 15-30 minutes Step 1: Create your WhapAround account and connect your WhatsApp number (5 minutes) Step 2: Prepare your Google Doc with business information and add the document ID to the system (5 minutes) Step 3: Configure the WhatsApp webhook and map message fields (10 minutes) Step 4: Connect your OpenAI or Gemini API key (3 minutes) Step 5: Send a test message to verify everything works (2 minutes) Optional: Set up PostgreSQL database for conversation memory and configure custom branding/escalation rules (additional 15-20 minutes) Detailed technical configurations, webhook URLs, and API parameter settings are provided within each workflow step to guide you through the exact setup process.