Agents & Automations

This n8n template shows how to extract selected pages from a generated PDF with the PDF Toolkit by www.customjs.space. Notice: Community nodes can only be installed on self-hosted instances of n8n. What this workflow does: - Downloads each PDF using an HTTP Request. - Extracts pages from the PDF file as needed. Requirements: - Self-hosted n8n instance - CustomJS API key for extracting PDF files. - PDF files to be merged to be converted into a PDF. Workflow Steps: - Manual Trigger: Runs with user interaction. - Download PDF File: Pass URLs for PDF files to merge. - Extract Pages from PDF: Extract selected pages from a generated PDF. Usage: 1. Get API key from CustomJS. 2. Sign up to CustomJS platform. 3. Navigate to your profile page. 4. Press "Show" button to get API key. 5. Set Credentials for CustomJS API on n8n. 6. Copy and paste your API key generated from CustomJS here. Design workflow: - A Manual Trigger for starting workflow. - HTTP Request Nodes for downloading PDF files. - Extract Pages from PDF. You can replace logic for triggering and returning results. For example, you can trigger this workflow by calling a webhook and get a result as a response from the webhook. Simply replace Manual Trigger and Write to Disk nodes. Perfect for: - Taking a note of specific pages from PDF files. - Splitting PDF file into multiple parts.

Every time a new article is created in an RSS feed, Make will automatically analyze the text content. Then, the results are iterated and saved as a CSV file in your Google Drive folder.

Unleashing Creativity: Transforming Children's English Storytelling with Automation and AI In the realm of children's storytelling, automation is revolutionizing the way captivating tales are created and shared. This article highlights the transformative power of setting up a workflow for AI-powered children's English storytelling on Telegram. By delving into the use cases and steps involved, we uncover how this innovative approach is inspiring young minds and fostering a love for storytelling in children. Use Case The workflow for children's stories is a game-changer for content creators, educators, and parents seeking to engage children through imaginative and educational storytelling. Here's how this workflow is making a difference: Streamlined Content Creation: By providing a structured framework and automation for story generation, audio creation, and image production, the workflow simplifies the process of crafting captivating children's stories. Enhanced Educational Resources: Teachers can leverage this workflow to develop interactive educational materials that incorporate storytelling, making learning more engaging for students. Personalized Parental Engagement: Parents can share personalized stories with their children, nurturing a passion for reading and creativity while strengthening family bonds through shared storytelling experiences. Community Connection: Organizations and community groups can use the workflow to connect with their audience and promote literacy and creativity by creating and sharing children's stories. Inspiring Imagination: Through automated creation and sharing of enchanting stories, the workflow aims to spark imagination, inspire young minds, and instill a love for storytelling in children. By embracing automation in children's storytelling, we unleash creativity, inspire young minds, and create magical experiences that resonate with both storytellers and listeners alike.

Template Guide for Employee Shortlisting AI Agent AutomationOverview This template automates the process of shortlisting job applicants using ERPNext, n8n, and AI-powered decision-making tools like Google Gemini and OpenAI. It reduces manual effort, ensures fast evaluations, and provides justifiable decisions about applicants. This is ideal for businesses aiming to streamline their recruitment process while maintaining accuracy and professionalism. What Does This Template Do? - Webhook Integration with ERPNext: Automatically triggers the workflow when a job application is created in ERPNext. - Resume Validation: Ensures resumes are attached and correctly processes various file formats like PDF and DOC. - AI-Powered Evaluation: Uses AI to compare resumes against job descriptions and provides a: - Fit Level (Strong, Moderate, or Weak) - Score (0–100) - Justification for the decision. - Automated Decision Making: Based on AI-generated scores: - Candidates with a score of 80 or higher are Accepted. - Candidates below 80 are Rejected. - Applications missing required fields or attachments are put On Hold. - ERPNext Integration: Updates applicant records in ERPNext, including custom fields such as justification, fit level, and scores. - Notifications: Notifies candidates via email, WhatsApp, or SMS about their application status.Step-by-Step GuideStep 1: Set Up ERPNext Webhook - Go to Webhooks in ERPNext. - Create a webhook for the Job Applicant DocType. - Set the trigger to Insert. - Pin and test the webhook to ensure proper data flow.Step 2: Import the Template into n8n - Open your n8n instance. - Import the provided workflow template. - Check all nodes for proper configuration.Step 3: Configure Credentials - Add your ERPNext API credentials to the ERPNext nodes. - Add credentials for AI services like OpenAI or Google Gemini. - Configure additional services like WhatsApp or email if you plan to use them for notifications.Step 4: Test Resume Validation - Test how the workflow handles different file types (e.g., PDF, DOC, JPG). - Ensure resumes without the proper format or attachment are flagged and rejected.Step 5: AI Evaluation - The AI model (Google Gemini or OpenAI) will evaluate resumes against job descriptions. - Customize the AI prompt to suit your job evaluation needs. - The output will include a Fit Level, Score, Rating, and Justification.Step 6: Decision Automation - The workflow automatically categorizes applicants: - Accepted for scores ≥ 80. - Rejected for scores < 80. - On Hold if essential fields or attachments are missing.Step 7: Update ERPNext Records - The workflow updates the Job Applicant record in ERPNext with: - Status (Accepted, Rejected, On Hold) - AI-generated Fit Level, Score, Rating, and Justification.Step 8: Notify Candidates - Configure notification nodes (email, WhatsApp, or SMS). - Inform candidates about their application status and include feedback if required.How It Works - Trigger: The workflow starts when a job application is submitted in ERPNext. - Validation: Checks if the resume is attached and in the correct format. - AI Evaluation: Compares the resume with the job description and generates a decision. - ERPNext Update: Updates the applicant's record with the decision and justification. - Notification: Sends a personalized notification to the candidate.Dos and Don’tsDos: - Customize Prompts: Tailor the AI prompt to match your specific job evaluation requirements. - Test the Workflow: Run sample data to ensure the process works as intended. - Secure Your Credentials: Keep your API credentials safe and do not share them publicly. - Optimize for Different Formats: Ensure the workflow can handle all types of resumes you expect.Don’ts: - Avoid Manual Intervention: Let the workflow handle most of the tasks to ensure efficiency. - Do Not Skip Testing: Always test the workflow with various scenarios to avoid errors. - Do Not Overlook Notifications: Ensure candidates are notified promptly to maintain professionalism.Customization Options - Add logic for more file types (e.g., scanned images using OCR). - Enhance the AI prompts to analyze more complex resume data. - Integrate additional tools like Slack or Trello for recruitment tracking.Support If you encounter issues or want to explore more possibilities with AI-driven automation, feel free to reach out: - Email: [email protected] - Website: ERPNext and Other Courses - LinkedIn: Amjid Ali

This AI agent can access data provided by another n8n workflow. Since that workflow can be used to retrieve any data from any service, this template can be used to give an agent access to any data. Note: To use this template, you need to be on n8n version 1.19.4 or later.

Phishing Email Detection and Reporting with n8nWho is this for? This workflow is designed for IT teams, security professionals, and managed service providers (MSPs) looking to automate the process of detecting, analyzing, and reporting phishing emails. What problem is this workflow solving? Phishing emails are a significant cybersecurity threat, and manually detecting and reporting them is time-consuming and prone to errors. This workflow streamlines the process by automating email analysis, generating detailed reports, and logging incidents in a centralized system like Jira. What this workflow does This workflow automates phishing email detection and reporting by integrating Gmail and Microsoft Outlook email triggers, analyzing the content and headers of incoming emails, and generating Jira tickets for flagged phishing emails. Here’s what happens: - Email Triggers: Captures incoming emails from Gmail or Microsoft Outlook. - Email Analysis: Extracts email content, headers, and metadata for analysis. - HTML Screenshot: Converts the email’s HTML body into a visual screenshot. - AI Phishing Detection: Leverages ChatGPT to analyze the email and detect potential phishing indicators. - Jira Integration: Automatically creates a Jira ticket with detailed analysis and attaches the email screenshot for review by the security team. - Customizable Reports: Includes options to customize ticket descriptions and adapt the workflow to organizational needs. Setup - Authentication: Set up Gmail and Microsoft Outlook OAuth credentials in n8n to access your email accounts securely. - API Keys: Add API credentials for the HTML screenshot service (hcti.io) and ChatGPT. - Jira Integration: Configure your Jira project and issue types in the workflow. - Workflow Configuration: Update sticky notes and nodes to include any additional setup or configuration details unique to your system. How to customize this workflow to your needs - Email Filters: Modify email triggers to filter specific subjects or sender addresses. - Analysis Scope: Adjust the ChatGPT prompt to refine phishing detection logic. - Integration: Replace Jira with your preferred ticketing system or modify the ticket fields to include additional information. This workflow provides an end-to-end automated solution for phishing email management, enhancing efficiency and reducing security risks. It’s perfect for teams looking to minimize manual effort and improve incident response times.

This workflow combines OpenAI, Retrieval-Augmented Generation (RAG), and WooCommerce to create an intelligent personal shopping assistant. It handles two scenarios: Product Search: Extracts user intent (keywords, price ranges, SKUs) and fetches matching products from WooCommerce. General Inquiries: Answers store-related questions (e.g., opening hours, policies) using RAG and documents stored in Google Drive. How It Works 1. Chat Interaction & Intent Detection - Chat Trigger: Starts when a user sends a message ("When chat message received"). - Information Extractor: Uses OpenAI to analyze the message and determine if the user is searching for a product or asking a general question. - Extracts: search (true/false), keyword, priceRange, SKU, category (if product-related). Example: { "search": true, "keyword": "red handbags", "priceRange": { "min": 50, "max": 100 }, "SKU": "BAG123", "category": "women's accessories" } 2. Product Search (WooCommerce Integration) - AI Agent: If search: true, routes the request to the personal_shopper tool. - WooCommerce Node: Queries the WooCommerce store using extracted parameters (keyword, priceRange, SKU). - Filters products in stock (stockStatus: "instock"). - Returns matching products (e.g., "red handbags under €100"). 3. General Inquiries (RAG System) - RAG Tool: If search: false, uses the Qdrant Vector Store to retrieve store information from documents. - Google Drive Integration: Documents (e.g., store policies, FAQs) are stored in Google Drive. Downloaded, split into chunks, and embedded into Qdrant for semantic search. - OpenAI Chat Model: Generates answers based on retrieved documents (e.g., "Our store opens at 9 AM"). Set Up Steps 1. Configure the RAG System - Google Drive Setup: Upload store documents. Update the Google Drive2 node with your folder ID. - Qdrant Vector Database: Clean the collection (update Qdrant Vector Store node with your URL). Use Embeddings OpenAI to convert documents into vectors. 2. Configure OpenAI & WooCommerce - OpenAI Credentials: Add your API key to all OpenAI nodes (OpenAI Chat Model, Embeddings OpenAI, etc.). - WooCommerce Integration: Connect your WooCommerce store (credentials in the personal_shopper node). Ensure product data is synced and accessible. 3. Customize the AI Agent - Intent Detection: Modify the Information Extractor’s system prompt to align with your store’s terminology. - RAG Responses: Update the tool description to reflect your store’s documents. This template is ideal for e-commerce businesses needing a hybrid assistant for product discovery and customer support. Need help customizing? Contact me for consulting and support or add me on LinkedIn.

Who is this for? This workflow is ideal for: - HR teams and recruiters seeking to streamline resume screening. - Hiring managers who want quick, summarized candidate insights. - Recruitment agencies handling large volumes of applicant data. - Startups and small businesses looking to automate hiring without complex systems. - AI and automation professionals who want to build smart HR workflows using n8n and OpenAI. What problem is this workflow solving? / Use Case Manually reviewing resumes is time-consuming, inconsistent, and prone to human bias. This workflow automates the resume intake and evaluation process—ensuring that each applicant is screened, summarized, and scored using a consistent, data-driven method. It enhances efficiency and supports better hiring decisions. What this workflow does - Accepts resume submissions via form and saves files to Google Drive. - Extracts key information from resumes using AI (e.g., name, contact, education, experience). - Summarizes candidate qualifications into a short, readable profile. - Allows HR to rate applicants and leave comments. - Logs all extracted data and evaluations into a centralized Google Sheet for tracking. Setup - Resume is submitted through an n8n form. - The uploaded file is automatically stored in Google Drive. - n8n uses OpenAI and document parsing tools to extract candidate data. - Extracted information is structured and summarized using GPT. - A review form is triggered for internal HR rating and notes. - All data is appended to a Google Sheet for records and filtering. How to customize this workflow to your needs - Change the form tool (e.g., Typeform, Tally, or custom HTML) based on your stack. - Adapt the summary prompt to align with your specific role requirements. - Add filters to auto-flag top-tier candidates based on score or skills. - Integrate Slack or email to notify hiring managers when top resumes are processed. - Connect to your ATS if you want to push processed resumes into your recruitment system.

Tags: Automation, AI, Marketing, Content Creation I’m a Supply Chain Data Scientist and content creator who writes regularly about data-driven optimization, logistics, and sustainability. Promoting blog articles on LinkedIn used to be a manual task — until I decided to automate it with N8N and GPT-4o. This workflow lets you automatically extract blog posts, clean the content, and generate a professional LinkedIn post using an AI Agent powered by GPT-4o — all in one seamless automation. Save hours of repetitive work and boost your reach with AI. 📬 For business inquiries, you can add me on LinkedIn. Who is this template for? This template is perfect for: - Bloggers and writers who want to promote their content on LinkedIn - Marketing teams looking to automate professional post-generation - Content creators using Ghost platforms It generates polished LinkedIn posts with: - A hook - A quick summary - A call-to-action - A signature to drive readers to your contact page How does it work? This workflow runs in N8N and performs the following steps: 🚀 Triggers manually or you can add a scheduler 📰 Pulls recent blog posts from your Ghost site (via API) 🧼 Cleans the HTML content for AI input 🤖 Sends content to GPT-4o with a tailored prompt to create a LinkedIn post 📄 Records all data (post content + LinkedIn output) in a Google Sheet What do I need to start? You don’t need to write a single line of code. Prerequisites: - A Ghost CMS account with blog content - A Google Sheet to store generated posts - An OpenAI API Key - Google Sheets API connected via OAuth2 Next Steps Use the sticky notes in the workflow to understand how to: - Add your Ghost API credentials - Link your Google Sheet - Customize the AI prompt (e.g., change the author name or tone) - Optionally add auto-posting to LinkedIn using tools like Buffer or Make 🎥 Watch My Tutorial 🚀 Want to explore how automation can scale your brand or business? 📬 Let’s connect on LinkedIn. Notes You can adapt this template for Twitter, Facebook, or even email newsletters by adjusting the prompt and output channel. This workflow was built using n8n 1.85.4 Submitted: April 9th, 2025

1. How it Works This n8n workflow automates fine-tuning OpenAI models through these key steps: Manual Trigger: Starts with the "When clicking ‘Test workflow’" event to initiate the process. Downloads a .jsonl file from Google Drive.Upload to OpenAI: Uploads the .jsonl file to OpenAI via the "Upload File" node (with purpose "fine-tune"). Create Fine-tuning Job: Sends a POST request to the endpoint https://api.openai.com/v1/fine_tuning/jobs with: { "training_file": "{{ $json.id }}", "model": "gpt-4o-mini-2024-07-18" } OpenAI automatically starts training the model based on the provided file. Interaction with the Trained Model: An "AI Agent" uses the custom model (e.g., ft:gpt-4o-mini-2024-07-18:n3w-italia::XXXX7B) to respond to chat messages. 2. Set up Steps To configure the workflow: Prepare the Training File: Create a .jsonl file following the specified syntax (e.g., travel assistant Q/A examples). Upload it to Google Drive and update the ID in the "Google Drive" node. Configure Credentials: - Google Drive: Connect an account via OAuth2 (googleDriveOAuth2Api). - OpenAI: Add your API key in the "OpenAI Chat Model" and "Upload File" nodes. Customize the Model: In the "OpenAI Chat Model" node, specify the name of your fine-tuned model (e.g., ft:gpt-4o-mini-...). Update the HTTP request body (Create Fine-tuning Job) if needed (e.g., a different base model). Start the Workflow: Use the manual trigger ("Test workflow") to begin the upload and training process. Test the model via the "Chat Trigger" (chat messages). Integrated Documentation: Follow the instructions in the Sticky Notes to: - Properly format the .jsonl (Step 1). - Monitor progress on OpenAI (Step 2, link: https://platform.openai.com/finetune/). Note: Ensure the .jsonl file adheres to OpenAI’s required structure and that credentials are valid.

Video Guide I prepared a detailed guide that showed the whole process of building a call analyzer. Who is this for? This workflow is ideal for sales teams, customer support managers, and online education services that conduct follow-up calls with clients. It’s designed for those who want to leverage AI to gain deeper insights into client needs and upsell opportunities from recorded calls. What problem does this workflow solve? Many follow-up sales calls lack structured analysis, making it challenging to identify client needs, gauge interest levels, or uncover upsell opportunities. This workflow enables automated call transcription and AI-driven analysis to generate actionable insights, helping teams improve sales performance, refine client communication, and streamline upselling strategies. What this workflow does This workflow transcribes and analyzes sales calls using AssemblyAI, OpenAI, and Supabase to store structured data. The workflow processes recorded calls as follows: Transcribe Call with AssemblyAI: Converts audio into text with speaker labels for clarity. Analyze Transcription with OpenAI: Using a predefined JSON schema, OpenAI analyzes the transcription to extract metrics like client intent, interest score, upsell opportunities, and more. Store and Access Results in Supabase: Stores both transcription and analysis data in a Supabase database for further use and display in interfaces. SetupPreparation - Create Accounts: Set up accounts for N8N, Supabase, AssemblyAI, and OpenAI. - Get Call Link: Upload audio files to public Supabase storage or Dropbox to generate a direct link for transcription. - Prepare Artifacts for OpenAI: - Define Metrics: Identify business metrics you want to track from call analysis, such as client needs, interest score, and upsell potential. - Generate JSON Schema: Use GPT to design a JSON schema for structuring OpenAI’s responses, enabling efficient storage, analysis, and display. - Create Analysis Prompt: Write a detailed prompt for GPT to analyze calls based on your metrics and JSON schema. Scenario 1: Transcribe Call with AssemblyAI - Set Up Request: - Header Authentication: Set Authorization with AssemblyAI API key. - URL: POST to https://api.assemblyai.com/v2/transcript/. - Parameters: - audio_url: Direct URL of the audio file. - webhook_url: URL for an N8N webhook to receive the transcription result. - Additional Settings: - speaker_labels (true/false): Enables speaker diarization. - speakers_expected: Specify expected number of speakers. - language_code: Set language (default: en_us). Scenario 2: Process Transcription with OpenAI - Webhook Configuration: Set up a POST webhook to receive AssemblyAI’s transcription data. - Get Transcription: - Header Authentication: Set Authorization with AssemblyAI API key. - URL: GET https://api.assemblyai.com/v2/transcript/<transcript_id>. - Send to OpenAI: - URL: POST to https://api.openai.com/v1/chat/completions. - Header Authentication: Set Authorization with OpenAI API key. - Body Parameters: - Model: Use gpt-4o-2024-08-06 for JSON Schema support, or gpt-4o-mini for a less costly option. - Messages: - system: Contains the main analysis prompt. - user: Combined speakers’ utterances to analyze in text format. - Response Format: - type: json_schema. - json_schema: JSON schema for structured responses. - Save Results in Supabase: - Operation: Create a new record. - Table Name: demo_calls. - Fields: - Input: Transcription text, audio URL, and transcription ID. - Output: Parsed JSON response from OpenAI’s analysis.

How it works Automates the retrieval of Calvin and Hobbes daily comics. Extracts the comic image URL from the website. Translates comic dialogues to English and Korean. Posts the comic and translations to Discord daily. Set up steps Estimated setup time: ~10-15 minutes. Use a Schedule Trigger to automate the workflow at 9 AM daily. Add nodes for parameter setup, HTTP request, data extraction, and integration with Discord. Add detailed notes to each node in the workflow for easy understanding.

This workflow automates the process of retrieving Hugging Face paper summaries, analyzing them with OpenAI, and storing the results in Notion. Here’s a breakdown of how it works: ⏰ Scheduled Trigger: The flow is set to run automatically at 8 AM on weekdays. 📄 Fetching Paper Data: It fetches Hugging Face paper summaries using their API. 🔍 Data Check: Before processing, the workflow checks if the paper already exists in Notion to avoid duplicates. 🤖 Content Analysis with OpenAI: If the paper is new, it extracts the summary and uses OpenAI to analyze the content. 📥 Store Results in Notion: After analysis, the summarized data is saved in Notion for easy reference. ⚙️ Set Up Steps for Automation: Follow these steps to set up this automated workflow with Hugging Face, OpenAI, and Notion integration: 🔑 Obtain API Tokens: You’ll need the Notion and OpenAI API tokens to authenticate and connect these services with n8n. 🔗 Integration in n8n: Link Hugging Face, OpenAI, and Notion by configuring the appropriate nodes in n8n. 🔧 Configure Workflow Logic: Set up a cron trigger for automatic execution at 8 AM on weekdays. Use an HTTP request node to fetch Hugging Face paper data. Add logic to check if the data already exists in Notion. Set up the OpenAI integration to analyze the paper’s summary. Store the results in Notion for easy access and reference.

This n8n template demonstrates an approach to perform bot-to-human handoff using Human-in-the-loop functionality as a switch. In this experiment, we play with the idea of states we want our agent to be in which controls its interaction with the user. First state - the agent is onboarding the user by collecting their details for a sales inquiry. After which, they are handed-off / transferred to a human to continue the call. Second state - the agent is essentially "deactivated" as further messages to the bot will not reach it. Instead, a canned response is given to the user. The human agent must "reactivate" the bot by completing the human-in-the-loop form and give a summary of their conversation with the user. Third state - the agent is "reactivated" with context of the human-to-user conversation and is set to provide after-sales assistance. A tool is made available to the agent to again delegate back to the human agent when requested. ### How it works This template uses Telegram to handle the interaction between the user and the agent. Each user message is checked for a session state to ensure it is guided to the right stage of the conversation. For this, we can use Redis as a simple key-value store. When no state is set, the user is directed through an onboarding step to attain their details. Once complete, the agent will "transfer" the user to a human agent - technically, all this involves is an update to the session state and a message to another chat forwarding the user's details. During this "human" state, the agent cannot reply to the user and must wait until the human "transfers" the conversation back. The human can do this by replying to the "human-in-the-loop" message with a summary of their conversation with the user. This session state now changes to "bot" and the context is implanted in the agent's memory so that the agent can respond to future questions. At this stage of the conversation, the agent is now expected to handle and help the user with after-sales questions. The user can at any time request transfer back to the human agent, repeating the previous steps as necessary. ### How to use Plan your user journey! Here is a very basic example of a sales inquiry with at most 3 states. More thought should be developed when many more states are involved. You may want to better log and manage session states so no user is left in limbo. Try connecting the user and sessions to your CRM. Note, the Onboarding agent and After-Sales agent have separate chat memories. When adding more agents, it is recommended to continue having separate chat memories to help focus between states. ### Requirements - Telegram for chatbot & interface - Redis for session store and chat memory - OpenAI for AI agent Not using Telegram? This template works with WhatsApp and other services with equivalent functionality.

Automatically generate new WordPress posts from RSS feed articles using OpenAI GPT-3 completions. Streamline content creation with AI-driven insights.

This n8n workflow automates the import of your Google Keep notes into a structured Google Sheet, using Google Drive and OpenAI for AI-powered processing. It's perfect for users who want to turn exported Keep notes into a searchable, filterable spreadsheet – optionally enhanced by AI summarization or transformation. Who is this for? - Researchers, knowledge workers, and digital minimalists who rely on Google Keep and want to better organize or analyze their notes. - Anyone who regularly exports Google Keep notes and wants a clean, automated workflow to store them in Google Sheets. - Users looking to apply AI to process, summarize, or extract insights from raw notes. What problem is this workflow solving? Exporting Google Keep notes via Google Takeout gives you unstructured .json files that are hard to read and manage. This workflow solves that by: - Filtering relevant .json files - Extracting note content - (Optionally) applying AI to analyze or summarize each note - Writing the result into a structured Google Sheet What this workflow does - Google Drive Search: Looks for .json files inside a specified "Keep" folder. - Loop: Processes files in batches of 10. - File Filtering: Filters by .json extension. - Download + Extract: Downloads each file and extracts note content from JSON. - Optional Filtering: Only keeps non-archived notes or those meeting content criteria. - AI Processing (optional): Uses OpenAI to summarize or transform the note content. - Prepare for Export: Maps note fields to be written. - Google Sheets: Appends or updates the target sheet with the note data. How to customize this workflow to your needs - Skip AI processing: If you don't need summaries or transformations, remove or disable the OpenAI Chat Model node. - Filter criteria: Customize the Filter node to extract only recent notes, or those containing specific keywords. - AI prompts: Edit the Tools Agent or Chat Model node to instruct the AI to summarize, extract tasks, categorize notes, etc. - Field mapping: Adjust the “Set fields for export” node to control what gets written to the spreadsheet. Use this template to build a powerful knowledge extraction tool from your Google Keep archive – ideal for backups, audits, or data-driven insights.

Reach out to me for any setup help/consulting. Automate the curation and sharing of trending GitHub discussions from Hacker News to Twitter and LinkedIn. This workflow leverages AI to generate engaging posts, streamlining your social media content creation and distribution. How it WorksCrawl Hacker News for GitHub Posts: The workflow fetches trending GitHub-related discussions from Hacker News. Extract Key Information: Relevant data such as post titles, URLs, and metadata are extracted and filtered to focus only on unposted content. Fetch Additional Details: For each GitHub post, the workflow retrieves extra information from the GitHub repository page to enrich the post content. Generate Social Media Posts: Using AI, the workflow automatically generates tailored posts for Twitter and LinkedIn based on the collected data. Post to Twitter & LinkedIn: The generated content is posted to your Twitter and LinkedIn accounts. Track and Log Posts: Each post is logged in Airtable for tracking, and its status is updated to ensure no duplicate posts are made. Telegram Notification: After posting, a summary of the posts is sent to your Telegram chat for real-time updates. Requirements - n8n Account: Set up and configured. Sign up here. - API Credentials: Valid keys for LinkedIn, Twitter, Airtable, OpenAI, and Telegram. - Airtable Base: Configured with fields such as Title, URL, Post Content, Status, and Timestamp. Get started with Airtable. - Telegram Chat ID: For receiving real-time notifications. Set Up Steps 1. Clone the Workflow: Import the workflow into your n8n environment using the provided JSON. 2. Configure API Credentials: Enter your API keys for LinkedIn, Twitter, Airtable, OpenAI, and Telegram into the respective nodes. 3. Set Up Airtable Base: Create an Airtable base with fields such as Title, URL, Post Content, Status, and Timestamp. 4. Customize Telegram Chat ID: Modify the 'Ping me' node with your Telegram chat ID to receive notifications. 5. Run the Workflow: Activate the workflow to start the automated content curation and posting process. Additional Resources - n8n AI Agentic Workflows Guide - n8n AI Workflow Tutorial - n8n Community Tutorial on Building an AI-Powered Telegram Bot Note: Chat GPT prompt should/can be tweaked in the step to give the desired behaviour. Sample Posts

This workflow uses AI to analyze customer sentiment from product feedback. If the sentiment is negative, AI will determine whether offering a coupon could improve the customer experience. Upon completing the sentiment analysis, the workflow creates personalized email templates. This solution streamlines the process of engaging with customers post-purchase, particularly when addressing dissatisfaction, and ensures that outreach is both personalized and automated. This workflow won 1st place in our last AI contest. Note: To use this template, you need to be on n8n version 1.19.4 or later.

This workflow illustrates how to use Perplexity AI in your n8n workflow. Perplexity is a free AI-powered answer engine that provides accurate, trusted, and real-time answers to any question. Credentials Setup 1. Go to the Perplexity dashboard, purchase some credits, and create an API Key. 2. In the Perplexity Request node, use Generic Credentials, Header Auth. - For the name, use the value "Authorization". - For the value "Bearer pplx-e4...59ea" (Your Perplexity API Key). AI ModelSonar Pro is the current top model used by Perplexity. If you want to use a different one, check the model cards on their documentation.

Who is this for? This workflow is ideal for individuals focused on nutrition tracking, meal planning, or diet optimization—whether you’re a health-conscious individual, fitness coach, or developer working on a healthtech app. It also fits well for anyone who wants to capture their meal data via voice or text, without manually entering everything into a spreadsheet. What problem is this workflow solving? Manually logging meals and breaking down their nutritional content is time-consuming and often skipped. This workflow automates that process using Telegram for input, OpenAI for natural language understanding, and Google Sheets for structured tracking. It enables users to record meals by typing or sending voice messages, which are transcribed, analyzed for nutrients, and automatically stored for tracking and review. What this workflow does This n8n automation lets users send either a text or voice message to a Telegram bot describing their meal. The workflow then: - Receives the Telegram message - Checks if it’s a voice message - If yes: Downloads the audio file and transcribes it using OpenAI - If no: Uses the text input directly Sends the meal description to OpenAI to extract a structured list of ingredients and nutritional details. Parses and stores the results in Google Sheets. Responds via Telegram with a personalized confirmation message. A testing interface also allows you to simulate prompts and view structured outputs for development or debugging. Setup - Create a Telegram bot via BotFather and note the API token. - Create an empty Google Sheet and store the sheet ID in the environment. - Set up your OpenAI credentials in the n8n credential manager. - Customize the “List of Ingredients and Nutrients” node with your prompt if needed. - (Optional) Use the “Testing” section to simulate messages and refine outputs before going live. How to customize this workflow to your needs - Enhance prompts in the OpenAI node to improve the structure and accuracy of responses. - Add new fields in the Google Sheet and corresponding logic in the parser if you want more detail. - Adjust the Telegram response to provide motivational feedback, dietary tips, or summaries. - Upgrade to the “Pro” version mentioned in the contact section for USDA database integration and complete nutrient breakdowns. This is a lightweight, AI-powered meal logging automation that transforms voice or text into actionable nutrition data—perfect for making healthy eating easier and more data-driven.

This n8n workflow shows an easy way to automate the creation of social media assets using AI and a service like BannerBear. Designed for the busy marketer, leveraging AI image generation capabilities can help cut down production times and allow reinvesting into higher quality content. How it works This workflow generates social media banners for online events. Using a form trigger, a user can define the banner text and suggest an image to be generated. This request is passed to OpenAI's Dalle-3 image generation service to produce a relevant graphic for the event banner. This generated image is uploaded and sent to BannerBear where a template will use it and the rest of the form data to produce the banner. BannerBear returns the final banner which can now be used in an assortment of posts and publications. Requirements - A BannerBear.com account and template is required. - An OpenAI account to use the Dalle-3 service. Customising the workflow We've only shown a small section of what BannerBear has to offer. With experimentation and other asset generating services such as AI audio and video, you should be able to generate more than just static banners!