Workflow Templates

Use case LLMs have provided a lot of value for several use cases. Especially some OpenAI models are proving to be quite valuable. However, it's sometimes not super accessible to chat with these models. This workflow enables you to chate directly with OpenAI's GPT-3.5 via Telegram. How it works A simple telegram bot that connects to your botfather bot to give AI responses, using OpenAI's GPT 3.5 model, to a user's messages with emojis. What to do Add your telegram API key and your OpenAI api key and have fun!

Automated solution to extract and organize contact information from Upwork job postings, enabling direct outreach to potential clients who post jobs matching your expertise. 🚀 What It Does Scrapes job postings for contact information Extracts email addresses and social profiles Organizes leads in a structured format Enables direct outreach campaigns Tracks response rates 🎯 Perfect For Freelancers looking to expand their client base Agencies targeting specific industries Sales professionals in the gig economy Recruiters sourcing clients Digital marketing agencies ⚙️ Key Benefits ✅ Access to hidden contact information ✅ Expand your client base ✅ Beat the competition to opportunities ✅ Targeted outreach campaigns ✅ Higher response rates 🔧 What You Need Upwork account n8n instance Email service (for outreach) CRM (optional) 📊 Features Email pattern detection Social media profile extraction Company website discovery Lead scoring system Outreach tracking 🛠️ Setup & Support Quick Setup Start collecting leads in 20 minutes with our step-by-step guide 📺 Watch Tutorial 💼 Get Expert Support 📧 Direct Help Take control of your freelance career with direct access to potential clients. Transform how you find and secure projects on Upwork.

This template monitors a Google Drive folder, converts PDF documents into clean text chunks with Unstructured, generates OpenAI embeddings, and upserts vectors into Pinecone. It’s a practical, production-ready starting point for Retrieval-Augmented Generation (RAG) that you can plug into a chatbot, semantic search, or internal knowledge tools. How it works 1) Google Drive Trigger detects new files in a selected folder and downloads them. 2) The files are sent to Unstructured where they are split into smaller pieces (chunks). 3) The chunks are prepared to be sent to OpenAI where they are converted into vectors (embeddings). 4) The embeddings are recombined with their original data and the payload is prepared for upsert into the Pinecone index. Set up steps 1) In Pinecone, create an index with 1536 dimensions and configure it for text-embedding-3-small. 2) Copy the host url and paste it on the 'Pinecone Upsert' node. It should look something like this: https://{your-index-name}.pinecone.io/vectors/upsert. 3) Add Google Drive, OpenAI and Pinecone credentials in n8n. 4) Point the trigger to your ingest folder (you can use this article for demo). 5) Click the 'Open chat' button and enter the following: Which Git provider do the authors use?

Automating LinkedIn Connection Requests Use Case Automatically sending LinkedIn connection requests to prospects can significantly streamline your outreach process. This automation ensures you only send requests to users you're not already connected with, and can optionally include a personalized message. What This Automation Does This automation sends a LinkedIn connection request using the following input parameters: linked_url**: The LinkedIn profile URL of the person you want to connect with. airtop_profile**: The name of your Airtop Profile authenticated on LinkedIn. message* *(optional): The note you want to include with your connection request. How It Works Starts an Airtop browser session using your authenticated profile. Opens the target LinkedIn profile in a new browser window. Detects if you're already connected or if a connection request is pending. If the "Connect" button is available: If no message is provided, clicks "Connect" and sends the request without a note. If a message is provided, clicks "Add a note", types the message, and sends the request. Terminates the browser session. Setup Requirements Airtop API Key — free to generate. An Airtop Profile logged in to LinkedIn (requires one-time authentication). Next Steps Pair with People Enrichment**: Use with the LinkedIn Profile Finder to generate URLs before sending requests. CRM Integration**: Log connection attempts and responses in your CRM. Campaign Sequencing**: Combine with message follow-up automations for a complete outreach flow. Read more about automating Linkedin Connection Requests

Complete AI Graphic Design Suite with OpenAI, Replicate & Google Drive Categories: AI Agents, Design Automation, Business Tools This workflow creates a complete AI-powered graphic design system that replaces expensive designers with intelligent automation. Featuring a conversational AI agent that orchestrates 5 specialized design tools, this suite can generate logos, style guides, gradients, and revisions on demand. Built by someone who's scaled automation agencies to $72K/month, this system demonstrates how AI agents can deliver real business value beyond simple chatbots. Benefits Complete Design Automation** - Generate logos, style guides, gradients, and revisions through natural conversation Conversational AI Interface** - Chat-based interaction makes design accessible to non-designers Professional Quality Output** - Uses advanced AI models and proven templates for consistent results Instant Delivery** - Generate designs in seconds vs. days of traditional design processes Scalable Business Tool** - Deploy for clients, embed on websites, or use internally Cost-Effective Solution** - Replace $82K/year designers with $30/month automation How It Works AI Agent Orchestration: Central conversational AI that understands design requests in natural language Automatically selects the right tool based on user needs and context Maintains conversation memory for iterative design improvements Provides professional, helpful responses with design expertise Logo Generation System: Creates professional logos using OpenAI's advanced image generation Supports various styles: minimalistic, corporate, creative, and industry-specific Automatically uploads to Google Drive with shareable links Perfect for startups, rebranding projects, and client work Style Guide Creation: Generates comprehensive brand guidelines using template-based approach Includes color palettes, typography, logo usage, and brand elements Uses AI to customize templates with client-specific information Delivers presentation-ready style guides for professional use Gradient Background Generator: Creates beautiful background gradients for websites and marketing materials Uses proven design templates with AI-powered customization Generates multiple variations and color combinations Perfect for landing pages, social media, and brand materials Design Editor & Revision System: Intelligently revises existing designs based on feedback Handles both Google Drive files and external image URLs Maintains design consistency while implementing requested changes Supports iterative improvements and client feedback cycles Advanced Upscaling Integration: Uses Replicate API to enhance image quality up to 4x resolution Professional print-quality output for all generated designs Seamlessly integrates with all design generation tools Perfect for high-resolution marketing materials and presentations Required Setup Configuration OpenAI API Setup: Connect your OpenAI API for: GPT-4 conversation handling and design guidance DALL-E (4o) image generation for all design tools Intelligent prompt processing and tool selection Google Drive Integration: Create template files for style guides and examples Set up OAuth credentials for file management Configure sharing permissions for client access Organize folders for different design categories Replicate API Configuration: Set up account for image upscaling capabilities Replace <your-replicate-api-key-here> with actual API key Configure upscaling factors (2x or 4x options) AI Agent System Message: Configure the agent with business context: You are a helpful, intelligent design assistant. You generate high-quality designs using the provided tools (generate logo, generate style guide, and generate gradient background). Then you can also upscale them, and finally, you can revise them. When you receive an image from a tool, wrap it in nice looking Markdown (atx) format and present it to the user. The only things you can generate are logos, style guides, and gradient backgrounds. Make sure to clarify which (as well as any additional information needed) so the prompt you send to the image model is optimal. If you are asked to adjust or revise an image, ask the user to define their changes as explicitly as possible. Chat Integration Options: Embedded website chat widget for client-facing design services Direct chat interface for internal team use Hosted chat endpoint for external integrations Business Use Cases Design Agencies** - Offer automated design services with instant delivery and unlimited revisions Marketing Teams** - Generate brand assets, social media graphics, and campaign materials on demand Startups** - Create professional branding without expensive design budgets Consultants** - Provide design services as value-added offerings to clients Web Developers** - Offer integrated design services alongside development projects E-commerce Businesses** - Generate product graphics, banners, and promotional materials Revenue Potential This system transforms design service economics: Replace $82K/year designers** with $30/month automation costs Instant delivery advantage** - complete designs in minutes vs. days Unlimited revisions** without additional designer time costs Premium service offering** - charge $1,500-5,000 per client implementation Scalable white-label solution** for agencies and consultants 24/7 availability** for time-sensitive client requests Difficulty Level: Intermediate Estimated Build Time: 2-3 hours Monthly Operating Cost: ~$30 (OpenAI + Replicate APIs) Watch My Complete Build Process Want to see exactly how I built this entire AI design system from scratch? I walk through the complete development process, including AI agent setup, tool integration, and the business strategy behind replacing expensive designers with intelligent automation. 🎥 Watch My Live Build: "This AI Agent Replaces an $82k/yr Graphic Designer (N8N)" This comprehensive tutorial shows the real development approach - including agent design patterns, tool orchestration, and the exact prompting strategies that deliver professional-quality results. Set Up Steps Core AI Agent Configuration: Set up chat trigger with embedded and hosted options Configure OpenAI chat model with design-focused system prompts Add memory buffer for conversation context and design iterations Design Tool Integration: Configure all 5 specialized design workflows as callable tools Set up proper data flow between agent and design generators Test tool selection logic with various design requests Template and Asset Management: Upload design templates to Google Drive for style guide generation Configure file sharing permissions for client access Set up organized folder structure for different design types Quality Control Setup: Test complete design workflows from request to delivery Validate AI output quality across all design categories Optimize prompts and templates based on actual usage patterns Client Integration Options: Embed chat widget on client websites for design services Set up hosted endpoints for external system integration Configure branding and messaging for client-facing interactions Advanced Extensions Scale the system with additional capabilities: Industry-Specific Templates** - Customize design styles for different verticals Brand Consistency Engine** - Maintain design standards across all generated assets Client Portal Integration** - Automated design delivery with approval workflows Multi-Language Support** - Generate designs with international text and cultural considerations Advanced Analytics** - Track design performance and client satisfaction metrics Print Production Tools** - Generate print-ready files with proper color profiles and dimensions Why This System Works The competitive advantage lies in intelligent automation combined with professional quality: Natural conversation interface** eliminates design tool complexity Template-based generation** ensures consistent, professional results Instant iteration capability** allows real-time design improvements Cost advantage** enables competitive pricing while maintaining margins 24/7 availability** provides service levels impossible with human designers Scalable delivery** handles multiple clients simultaneously without quality degradation Check Out My Channel For more advanced AI automation systems that generate real business results, explore my YouTube channel where I share the exact strategies used to build successful automation agencies and scale to $72K+ monthly revenue.

Overview Automates your complete social media content pipeline: sources articles from Wallabag RSS, generates platform-specific posts with AI, creates contextual images, and publishes via GetLate API. Built with 63 nodes across two workflows to handle LinkedIn, Instagram, and Bluesky—with easy expansion to more platforms. Ideal for: Content marketers, solo creators, agencies, and community managers maintaining a consistent multi-platform presence with minimal manual effort. How It Works Two-Workflow Architecture: Content Aggregation Workflow Monitors Wallabag RSS feeds for tagged articles (#to-share-linkedin, #to-share-instagram, etc.) Extracts and converts content from HTML to Markdown Stores structured data in Airtable with platform assignment AI Generation & Publishing Workflow Scheduled trigger queries Airtable for unpublished content Routes to platform-specific sub-workflows (LinkedIn, Instagram, Bluesky) LLM generates optimized post text and image prompts based on custom brand parameters Optionally generates AI images and hosts them on Imgbb CDN Publishes via GetLate API (immediate or draft mode) Updates Airtable with publication status and metadata Key Features: Tag-based content routing using Wallabag's native system Swappable AI providers (Groq, OpenAI, Anthropic) Platform-specific optimization (tone, length, hashtags, CTAs) Modular design—duplicate sub-workflows to add new platforms in \~30 minutes Centralized Airtable tracking with 17 data points per post Set Up Steps Setup time: \~45-60 minutes for initial configuration Create accounts and get API keys (\~15 min) Wallabag (with RSS feeds enabled) GetLate (social media publishing) Airtable (create base with provided schema—see sticky notes) LLM provider (Groq, OpenAI, or Anthropic) Image service (Hugging Face, Fal.ai, or Stability AI) Imgbb (image hosting) Configure n8n credentials (\~10 min) Add all API keys in n8n's credential manager Detailed credential setup instructions in workflow sticky notes Set up Airtable database (\~10 min) Create "RSS Feed - Content Store" base Add 19 required fields (schema provided in workflow sticky notes) Get Airtable base ID and API key Customize brand prompts (\~15 min) Edit "Set Custom SMCG Prompt" node for each platform Define brand voice, tone, goals, audience, and image preferences Platform-specific examples provided in sticky notes Configure platform settings (\~10 min) Set GetLate account IDs for each platform Enable/disable image generation per platform Choose immediate publish vs. draft mode Adjust schedule trigger frequency Test and deploy Tag test articles in Wallabag Monitor the first few executions in draft mode Activate workflows when satisfied with the output Important: This is a proof-of-concept template. Test thoroughly with draft mode before production use. Detailed setup instructions, troubleshooting tips, and customization guidance are in the workflow's sticky notes. Technical Details 63 nodes**: 9 Airtable operations, 8 HTTP requests, 7 code nodes, 3 LangChain LLM chains, 3 RSS triggers, 3 GetLate publishers Supports**: Multiple LLM providers, multiple image generation services, unlimited platforms via modular architecture Tracking**: 17 metadata fields per post, including publish status, applied parameters, character counts, hashtags, image URLs Prerequisites n8n instance (self-hosted or cloud) Accounts: Wallabag, GetLate, Airtable, LLM provider, image generation service, Imgbb Basic understanding of n8n workflows and credential configuration Time to customize prompts for your brand voice Detailed documentation, Airtable schema, prompt examples, and troubleshooting guides are in the workflow's sticky notes. Category Tags #social-media-automation, #ai-content-generation, #rss-to-social, #multi-platform-posting, #getlate-api, #airtable-database, #langchain, #workflow-automation, #content-marketing

How it works: Accesses a target website, searches for new PDFs, and downloads them automatically. Extracts content from each PDF and sends it to an AI for summarization. Delivers the AI-generated summary directly to a Discord channel. Marks processed URLs in Google Sheets to avoid duplicates. Set up steps: Configure the website URL in the HTTP Request node. Connect to Google Cloud API (enable Drive & Sheets) and link your spreadsheet. Set up an OpenRouter API key and choose your preferred AI model. Create a Discord webhook for notifications.

Automate tweet engagement on X (formerly Twitter) Description Automate professional engagement on X (formerly Twitter) by searching for, filtering, liking, and replying to tweets that match your key topics. This workflow enables you to engage consistently and efficiently with relevant conversations, using your defined professional role and the power of GPT for filtering and replies. Save time and maintain high-quality interactions, while staying focused on your business or personal brand interests. How it Works Rotating Topic Selection The workflow selects one search term from your list on each run, using a rotating index based on the date. Search Tweets & Extract Essentials Searches X (formerly Twitter) for tweets matching the chosen topic, then extracts only the tweet id and text for further processing. GPT‑Based Filtering with Role Context Filters tweets based on your role and strict criteria, removing non-English tweets, memes, spam, Grok-generated content, political posts, internships, and more. Engagement Loop For every filtered tweet, the workflow likes the post, generates a professional, concise reply with GPT (matching language and context), and posts the reply. Wait nodes ensure compliance with Twitter’s API rate limits (can be adjusted for paid API tiers). Requirements X (Twitter) API credentials (for searching, liking, and replying to tweets) OpenAI API key (for GPT-based steps) Setup Steps Obtain your X (Twitter) API credentials. Obtain your OpenAI API key. Configure the schedule in the trigger node to your desired frequency (e.g., every 3 days or daily). Set your list of topics and professional role in the variables node. How to Customize the Workflow (Optional) Adjust prompts** in the GPT nodes to fine-tune filtering and reply style. Upgrade your Twitter API plan** to increase request limits and search for more tweets per run. Change tweet processing logic:** For high-volume engagement (e.g., analyzing 100+ tweets per run), consider switching to a per-tweet loop for advanced filtering and response handling. This workflow enables scalable, professional, and targeted engagement on X (formerly Twitter), fully customizable to your audience and objectives.

How it works ++Download the google sheet here++ and replace this with the googles sheet node: Google sheet , upload to google sheets and replace in the google sheets node. Scheduled trigger: Runs once a day at 8 AM (server time). Fetch product list: Reads your “master” sheet (product_url + last known price) from Google Sheets. Loop with delay: Iterates over each row (product) one at a time, inserting a short pause (20 s) between HTTP requests to avoid blocking. Scrape current price: Loads each product_url, extracts the current price via a simple CSS selector. Compare & normalize: Compares the newly scraped price against the “last_price” from your sheet, calculates percentage change, and tags items where price_changed == true. On price change: Send alert: Formats a Telegram message (“Price Drop” or “Price Hike”) and pushes it to your configured chat. Log history: Appends a new row to a separate “price_tracking” tab with timestamp, old price, new price, and % change. Update master sheet: After a 1 min pause, writes the updated current_price back to your “master” sheet so future runs use it as the new baseline. Set up step Google Sheets credentials (~5 min) Create a Google Sheets OAuth credential in n8n. Copy your sheet’s ID and ensure you have two tabs: product_data (columns: product_url, price) price_tracking (columns: timestamp, product_url, last_price, current_price, price_diff_pct, price_changed) Paste the sheet ID into both Google Sheets nodes (“Read” and “Append/Update”). Telegram credentials (~5 min) Create a Telegram Bot token via BotFather. Copy your chat_id (for your target group or personal chat). Add those credentials to n8n and drop them into the “Telegram” node. Workflow parameters (~5 min) Verify the schedule in the Schedule Trigger node is set to 08:00 (or adjust to your preferred run time). In the Loop Over Items node, confirm “Batch Size” is 1 (to process one URL at a time). Adjust the Delay to avoid Request Blocking node if your site requires a longer pause (default is 20 s). In the Parse Data From The HTML Page node, double-check the CSS selector matches how prices appear on your target site. Once credentials are in place and your sheet tabs match the expected column names, the flow should be ready to activate. Total setup time is under 15 minutes—detailed notes are embedded as sticky comments throughout the workflow to help you tweak selectors, change timeouts, or adjust sheet names without digging into code.

🚀 Local Multi-LLM Testing & Performance Tracker This workflow is perfect for developers, researchers, and data scientists benchmarking multiple LLMs with LM Studio. It dynamically fetches active models, tests prompts, and tracks metrics like word count, readability, and response time, logging results into Google Sheets. Easily adjust temperature 🔥 and top P 🎯 for flexible model testing. Level of Effort: 🟢 Easy – Minimal setup with customizable options. Setup Steps: Install LM Studio and configure models. Update IP to connect to LM Studio. Create a Google Sheet for result tracking. Key Outcomes: Benchmark LLM performance. Automate results in Google Sheets for easy comparison. Version 1.0

What this template does This workflow will read your Zotero Library and extract Meta Data from the articles of one collection in your bibliography. You can personalize the output for optimized results. How it works Mainly, follow the instructions in the Post it notes: Go to https://www.zotero.org/settings/security and find your USER ID (It's right under the APPLICATIONS Section. On the same website, create a New Private Key. In the "Collections" Node, select Generic Credential Type > Header Auth > Create New Credential using: NAME: Zotero-API-Key VALUE: [Your Private Key] Run your Flow to check if it works and open the "Select Collection" node. See the Results of the previous node as TABLE and copy the "KEY" of the collection you want to use. After that you should have a working flow that reads your bibliography. You can edit or delete the last 2 nodes to personalize your results (Filter and Edit Fields)

This n8n template combines an AI agent with n8n's multi-page forms to create a novel interaction which allows automated question-and-answer sessions. One of the more obvious use-cases of this interaction is what I'm calling the AI interviewer. You can read the full post here: https://community.n8n.io/t/build-your-own-ai-interview-agents-with-n8n-forms/62312 Live demo here: https://jimleuk.app.n8n.cloud/form/driving-lessons-survey How it works A form trigger is used to start the interview and a new session is created in redis to capture the transcript. An AI agent is then tasked to ask questions to the user regarding the topic of the interview. This is setup as a loop so the questions never stop unless the user wishes to end the interview. Each answer is recorded in our session set up earlier between questions. When the user requests to end the interview we break the loop and show the interview completion screen. Finally, the session is then saved in a Google Sheet which can then be shared with team members and for the purpose of data analysis. How to use You'll need to be on a n8n instance that is accessible to your target audience. Not technical enough to setup your own server? Try out n8n cloud and instantly deploy template! Remember to activate the workflow so the form trigger is published and available for users to use. Requirements Groq LLM for AI agent. Feel free to swap this out for any other LLM. Redis(-compatible) storage for capturing sessions Customising this workflow The next step would be adding tools! AI interviews with knowledge retrieval could definitely open up other possibilities. Eg. An onboarding wizard generating questions by pulling facts from internal knowledgebase.