Workflow Templates

This automated n8n workflow monitors ingredient price changes from external APIs or manual sources, analyzes historical trends, and provides smart buying recommendations. The system tracks price fluctuations in a PostgreSQL database, generates actionable insights, and sends alerts via email and Slack to help restaurants optimize their purchasing decisions. What is Price Trend Analysis? Price trend analysis uses historical price data to identify patterns and predict optimal buying opportunities. The system analyzes price movements over time and generates recommendations on when to buy ingredients based on current trends and historical patterns. Good to Know Price data accuracy depends on the reliability of external API sources Historical data improves recommendation accuracy over time (recommended minimum 30 days) PostgreSQL database provides robust data storage and complex trend analysis capabilities Real-time alerts help capture optimal buying opportunities Dashboard provides visual insights into price trends and recommendations How It Works Daily Price Check - Triggers the workflow daily to monitor price changes Fetch API Prices - Retrieves the latest prices from an external ingredient pricing API Setup Database - Ensures database tables are ready before inserting new data Store Price Data - Saves current prices to the PostgreSQL database for tracking Calculate Trends - Analyzes historical prices to detect patterns and price movements Generate Recommendations - Suggests actions based on price trends (buy/wait/stock up) Store Recommendations - Saves recommendations for future reporting Get Dashboard Data - Gathers necessary data for dashboard generation Generate Dashboard HTML - Builds an HTML dashboard to visualize insights Send Email Report - Emails the dashboard report to stakeholders Send Slack Alert - Sends key alerts or recommendations to Slack channels Database Structure The workflow uses PostgreSQL with two main tables: price_history - Historical price tracking with columns: id (Primary Key) ingredient (VARCHAR 100) - Name of the ingredient price (DECIMAL 10,2) - Current price value unit (VARCHAR 50) - Unit of measurement (kg, lbs, etc.) supplier (VARCHAR 100) - Source supplier name timestamp (TIMESTAMP) - When the price was recorded created_at (TIMESTAMP) - Record creation time buying_recommendations - AI-generated buying suggestions with columns: id (Primary Key) ingredient (VARCHAR 100) - Ingredient name current_price (DECIMAL 10,2) - Latest price price_change_percent (DECIMAL 5,2) - Percentage change from previous price trend (VARCHAR 20) - Price trend direction (INCREASING/DECREASING/STABLE) recommendation (VARCHAR 50) - Buying action (BUY_NOW/WAIT/STOCK_UP) urgency (VARCHAR 20) - Urgency level (HIGH/MEDIUM/LOW) reason (TEXT) - Explanation for the recommendation generated_at (TIMESTAMP) - When recommendation was created Price Trend Analysis The system analyzes historical price data over the last 30 days to calculate percentage changes, identify trends (INCREASING/DECREASING/STABLE), and generate actionable buying recommendations based on price patterns and movement history. How to Use Import the workflow into n8n Configure PostgreSQL database connection credentials Set up external ingredient pricing API access Configure email credentials for dashboard reports Set up Slack webhook or bot credentials for alerts Run the Setup Database node to create required tables and indexes Test with sample ingredient data to verify price tracking and recommendations Adjust trend analysis parameters based on your purchasing patterns Monitor recommendations and refine thresholds based on actual buying decisions Requirements PostgreSQL database access External ingredient pricing API credentials Email service credentials (Gmail, SMTP, etc.) Slack webhook URL or bot credentials Historical price data for initial trend analysis Customizing This Workflow Modify the Calculate Trends node to adjust the analysis period (currently 30 days) or add seasonal adjustments. Customize the recommendation logic to match your restaurant's buying patterns, budget constraints, or supplier agreements. Add additional data sources like weather forecasts or market reports for more sophisticated predictions.

About: This workflow automates the transcription of YouTube videos by processing a video URL provided via a chat message. Designed for users who need quick access to video content in text form, this workflow ensures a seamless experience for transcribing videos on demand, regardless of the topic. Who is this for? This workflow is designed for individuals who need quick and accurate transcriptions of YouTube videos without watching them in full. It is particularly useful for: Students who need text-based notes from educational videos. Researchers looking to extract information from lectures or discussions. Professionals who prefer reading over watching videos. Casual users who want an efficient way to summarize video content. What problem is this workflow solving? Manually transcribing YouTube videos is time-consuming and prone to errors. Watching long videos just to extract key information is inefficient. This workflow automates transcription, allowing users to quickly convert video content into text. Use cases include: Summarizing lectures or webinars. Extracting insights from interviews and discussions. Creating searchable text from video content. Generating reference material without watching entire videos. What This Workflow Does? This workflow automates the transcription of YouTube videos by: Accepting Input: User provide a YouTube video URL through a chat message. Processing the Video: It utilizes an external transcription service to retrieve the full transcript of the YouTube video from the provided URL. Enhancing Output: An AI model (OpenAI) refines the transcription for accuracy and readability. Delivering Results: The final text transcript is returned to the user via the chat interface. Setup: Install n8n: Ensure you have n8n installed and running. Import the Workflow: Copy the JSON workflow file into your n8n instance. Configure API Keys: Set up your Supadata (Supadata) API key for transcription. Configure the OpenAI (OpenAI) API key for additional processing. Run the Workflow: Provide a YouTube video URL and receive a transcription in response. How to customize this workflow to your needs: The workflow is flexible and can be tailored to suit specific requirements. Here are some customization ideas: Language Support:** Adjust the transcription language in both the HTTP Request and OpenAI nodes to support transcriptions in different languages (e.g., French, German). Integrate with Other Services:** Store transcriptions in a database, send them via email, or connect with a document management system. Notification:** Add a notification node (e.g., email or Slack) to alert you when the transcription is complete, especially for long videos. Quality Check:** Integrate an additional AI step to summarize or highlight key points in the transcript for quicker insights. This workflow is designed to be scalable, efficient, and adaptable to various transcription needs. Limitations Video Length Limitation:** Very long videos may not have a complete transcription due to constraints in processing capacity or service limitations. Transcription Dependency:** The accuracy of the transcription relies entirely on the presence of video captions or subtitles. If a video lacks these, no transcription will be generated. Access Restrictions:** Private or restricted YouTube videos may not be accessible for transcription due to permission limitations. Processing Time:** The time required to process a video can vary significantly, especially for longer videos, depending on the transcription service and server resources. Regional Restrictions:** Some YouTube videos may have geographic or regional access limitations, which could prevent the workflow from retrieving the content for transcription.

In this guide, we’ll walk you through setting up an AI-driven workflow that automatically processes highly-rated food photos from a Google Sheet, generates AI-powered captions, shares them to Pinterest, and updates the sheet to reflect the posts. Ready to automate your food photo sharing? Let’s dive in! What’s the Goal? Automatically detect and process highly-rated food photos (4 stars or above) from a Google Sheet. Use AI to generate engaging and relevant captions. Share the photos with captions to Pinterest via the Pinterest API. Update the Google Sheet to mark photos as posted. Enable scheduled automation for consistent posting. By the end, you’ll have a self-running system that shares your best food photos effortlessly. Why Does It Matter? Manual photo sharing is time-consuming and inconsistent. Here’s why this workflow is a game changer: Zero Human Error**: AI ensures consistent captions and posting accuracy. Time-Saving Automation**: Automatically handle photo sharing, boosting efficiency. Scheduled Posting**: Maintain a regular presence on Pinterest without manual effort. Focus on Creativity**: Free your team from repetitive posting tasks. Think of it as your tireless social media assistant that keeps your Pinterest feed vibrant. How It Works Here’s the step-by-step magic behind the automation: Step 1: Trigger the Workflow Detect new photos to post using the Daily Post Scheduler node (e.g., once daily). Initiate the workflow at a scheduled time to check for new food photos. Step 2: Fetch Food Photos from Sheet Retrieve rows from the Google Sheet that contain food photo metadata like image URLs, ratings, and status. Step 3: Filter 4+ Star Dishes Filter only those food entries with high ratings (4 stars or above) and unposted status. Step 4: AI Caption Generator Use AI (e.g., GPT/OpenAI) to create engaging and relevant captions for selected food photos. Step 5: Upload to Pinterest Automatically post the food photo with the generated caption to Pinterest via the Pinterest API. Step 6: Mark as Posted in Sheet Update the Google Sheet to reflect that the photo has been successfully shared. How to Use the Workflow? Importing a workflow in n8n is a straightforward process that allows you to use pre-built workflows to save time. Below is a step-by-step guide to importing the Automated Food Photo Sharing workflow in n8n. Steps to Import a Workflow in n8n Obtain the Workflow JSON Source the Workflow: Workflows are shared as JSON files or code snippets, e.g., from the n8n community, a colleague, or exported from another n8n instance. Format: Ensure you have the workflow in JSON format, either as a file (e.g., workflow.json) or copied text. Access the n8n Workflow Editor Log in to n8n (via n8n Cloud or self-hosted instance). Navigate to the Workflows tab in the n8n dashboard. Click Add Workflow to create a blank workflow. Import the Workflow Option 1: Import via JSON Code (Clipboard): Click the three dots (⋯) in the top-right corner to open the menu. Select Import from Clipboard. Paste the JSON code into the text box. Click Import to load the workflow. Option 2: Import via JSON File: Click the three dots (⋯) in the top-right corner. Select Import from File. Choose the .json file from your computer. Click Open to import. Setup Notes Google Sheet Columns**: Ensure your Google Sheet includes the following columns: Image URL, Rating (numeric, e.g., 1-5), Feedback (text), Pin Title, Pin Description, Destination URL, Board ID, and Status (e.g., "Pending" or "Posted"). Google Sheets Credentials**: Configure OAuth2 settings in the Fetch Food Photos node with your Google Sheet ID and credentials. AI Model**: Set up the AI Caption Generator node with OpenAI credentials (e.g., API key). Pinterest API**: Authorize the Upload to Pinterest node with Pinterest API credentials (e.g., Bearer Token) and obtain the Board ID. Scheduling**: Adjust the Daily Post Scheduler node to your preferred posting time (e.g., daily at 9 AM).

Turn any LinkedIn post into a personalized cold email opener that sounds like a human wrote it in seconds. Whether you're in sales, partnerships, or outreach, this tool reads LinkedIn posts like a human, distills the core message, and gives you a smart, conversational opener to kick off the relationship the right way. How It Works: 1.) Paste the post + author info into a short form. 2.) AI reads the post like a B2B sales expert would. 3.) Output = personalized opener, company name, prospect’s name, and next steps. 4.) Copy-paste into your cold email and hit send. The opener isn’t generic fluff — it references real details, sounds natural, and shows you actually paid attention. Perfect For: SDRs and BDRs Agency outreach Partnership prospecting Any cold outreach that starts with a real conversation Setup Steps Setup time: ~2-3 mins 1.) Add your OpenAI credentials (or use n8n’s built-in credits). 2.) Open the form and test it with the sample post. 3.) Tweak the AI prompt if you want to target a different niche or tone. (Optional) Connect to Google Sheets, a CRM, or your email tool. You're live.

This n8n template demonstrates an approach to image embeddings for purpose of building a quick image contextual search. Use-cases could for a personal photo library, product recommendations or searching through video footage. How it works A photo is imported into the workflow via Google Drive. The photo is processed by the edit image node to extract colour information. This information forms part of our semantic metadata used to identify the image. The photo is also processed by a vision-capable model which analyses the image and returns a short description with semantic keywords. Both pieces of information about the image are combined with the metadata of the image to form a document describing the image. This document is then inserted into our vector store as a text embedding which is associated with our image. From here, the user can query the vector store as they would any document and the relevant image references and/or links should be returned. Requirements Google account to download image files from Google Drive. OpenAI account for the Vision-capable AI and Embedding models. Customise this workflow Text summarisation is just one of many techniques to generate image embeddings. If the results are unsatisfactory, there are dedicated image embedding models such as Google's vertex AI multimodal embeddings.

Who is this for? Jira users who want to automate the generation of a Lessons Learned or Retrospective report after an Epic is Done. What problem is this workflow solving? / use case Lessons Learned / Retrospective reports are often omitted in Agile teams because they take time to write. With the use of n8n and AI this process can be automated. What is this workflow doing Triggers automatically upon an Epic reaching the "Done" status in Jira. Collects all related tasks and comments associated with the completed Epic. Intelligently filters the gathered data to provide the LLM with the most relevant information. Utilizes an LLM with a structured System Message to generate insightful reports. Delivers the finalized report directly to your specified Google Docs document. Setup Create a Jira API key and follow the Credentials Setup in the Jira trigger node. Create credentials for Google Docs and paste your document ID into the Node. How to customize this workflow to your needs Change the System Message in the AI Agent to fit your needs.

Note: This template only works for self-hosted n8n. This n8n template demonstrates how to use the Langchain code node to track token usage and cost for every LLM call. This is useful if your templates handle multiple clients or customers and you need a cheap and easy way to capture how much of your AI credits they are using. How it works In our mock AI service, we're offering a data conversion API to convert Resume PDFs into JSON documents. A form trigger is used to allow for PDF upload and the file is parsed using the Extract from File node. An Edit Fields node is used to capture additional variables to send to our log. Next, we use the Information Extractor node to organise the Resume data into the given JSON schema. The LLM subnode attached to the Information Extractor is a custom one we've built using the Langchain Code node. With our custom LLM subnode, we're able to capture the usage metadata using lifecycle hooks. We've also attached a Google Sheet tool to our LLM subnode, allowing us to send our usage metadata to a google sheet. Finally, we demonstrate how you can aggregate from the google sheet to understand how much AI tokens/costs your clients are liable for. Check out the example Client Usage Log - https://docs.google.com/spreadsheets/d/1AR5mrxz2S6PjAKVM0edNG-YVEc6zKL7aUxHxVcffnlw/edit?usp=sharing How to use SELF-HOSTED N8N ONLY** - the Langchain Code node is only available in the self-hosted version of n8n. It is not available in n8n cloud. The LLM subnode can only be attached to non-"AI agent" nodes; Basic LLM node, Information Extractor, Question & Answer Chain, Sentiment Analysis, Summarization Chain and Text Classifier. Requirements Self-hosted version of n8n OpenAI for LLM Google Sheets to store usage metadata Customising this template Bring the custom LLM subnode into your own templates! In many cases, it can be a drop-in replacement for the regular OpenAI subnode. Not using Google Sheets? Try other databases or a HTTP call to pipe into your CRM.

Who is this for? This workflow is ideal for SEO specialists, web designers, and digital marketers who want to quickly draft effective landing page layouts by referencing established competitors. It suits users who need a fast, structured starting point for web design while ensuring competitive relevance. What problem is this workflow solving? / Use case Designing a high-converting landing page from scratch can be time-consuming. This workflow automates the process of analyzing a competitor’s website, identifying essential sections, and producing a tailored layout—helping users save time and improve their website’s effectiveness. What this workflow does The workflow fetches and analyzes your chosen competitor’s landing page, using web scraping and structure-detection nodes in n8n. It identifies primary sections like hero banners, service highlights, testimonials, and contact forms, and then generates a simplified, customizable layout suitable for wireframing or initial design. Setup Prepare your unique services and target audience profile for customization later. Gather the competitor’s landing page URL you wish to analyze. Run the workflow, inputting your competitor’s URL when prompted. How to customize this workflow to your needs After generating the initial layout, adapt section names and content blocks to highlight your services and brand messaging. Add or remove sections based on your objectives and audience insights. Integrate additional nodes for richer analysis, such as keyword extraction or design pattern detection, to tailor the output further.

Automatically Categorize Gmail Emails with GPT-4o-mini Multi-Label Analysis Description The "Automatically Categorize Gmail Emails with GPT-4o-mini Multi-Label Analysis" template is designed specifically for professionals, business owners, entrepreneurs, and anyone struggling to manage a high volume of daily emails. It solves common inbox problems such as email overload, missed important messages, manual sorting inefficiencies, and unorganized inbox clutter. By using intelligent content analysis powered by GPT-4o-mini, this workflow automatically categorizes incoming Gmail messages with relevant labels, ensuring efficient email management and significantly boosting productivity. Workflow Overview How It Works Email Detection**: Continuously monitors your Gmail inbox every minute to detect new incoming emails. Content Extraction**: Retrieves key email components including sender details, subject line, and body content for analysis. Intelligent Labeling**: Utilizes GPT-4o-mini AI to contextually analyze each email and assign 1-3 relevant labels based on your existing Gmail label structure. Automatic Application**: Applies the selected labels directly to your emails, equipped with robust error-handling mechanisms to ensure accuracy and reliability. Key Benefits Organized Inbox**: Automatically maintains inbox order and clarity. Time-Saving**: Reduces manual email management effort significantly. Customization**: Fully adaptable to specific labeling and organizational requirements. Pre-conditions Before using this template, ensure the following prerequisites are met: Active Gmail account with OAuth2 enabled. Active OpenAI account with GPT-4o-mini API key. Clearly defined labels set up in your Gmail account (e.g., "Work", "Personal", "Urgent"). Setup Instructions Follow these straightforward setup steps to activate the workflow: Connect Gmail Account Authorize your Gmail account using OAuth2 (takes approximately 2-3 minutes). Configure OpenAI GPT-4o-mini API Enter and validate your GPT-4o-mini API key to enable advanced email analysis. Establish Gmail Labels Ensure necessary labels are created within Gmail. Examples include "Work", "Personal", and "Urgent". Activate and Verify Click the "Activate" button in n8n. Send a test email to your Gmail inbox to confirm that labels are applied correctly. Customization Tips You can easily customize this workflow to fit your specific needs: Modify Gmail Labels**: Create and adapt labels to match your business or personal categorization strategy. Adjust GPT-4o-mini Criteria**: Fine-tune the AI prompts to improve accuracy and relevance based on your unique email management needs. Expand the Workflow**: Integrate additional conditions, actions, or external applications to further automate and optimize your email management processes. Improve your daily workflow efficiency and achieve a clutter-free Gmail inbox by leveraging the power of GPT-4o-mini today.

This n8n template demonstrates how you can leverage existing support site search to power your Support Chatbots and agents. Building a support chatbot need not be complicated! If building and indexing vector stores or duplicating data isn't necessarily your thing, an alternative implementation of the RAG approach is to leverage existing knowledge-bases such as support portals. In this way, document management and maintenance of your support agent is significantly reduced. Disclaimer: This template example uses AcuityScheduling's help center website but is not associated, supported nor endorsed by the company. How it works A simple AI agent is connected with chat trigger to receive user queries. The AI agent is instructed to fetch information from the knowledge-base via the attached custom workflow tool (aka "knowledgebase tool"). There is no step to replicate the entire support articles database into a vector store. You may choose not too because of time, cost and maintainence involved. Instead, the tool leverages the existing support portal's search API to retrieve knowledge-base articles. Finally, the search results are formatted before sending an aggregated response back to the agent. How to use? Customise the subworkflow to work with your own support portal API and format accordingly. Try the following queries How do I connect my icloud to acuityScheduling? How do I download past invoices for my Acuity account? Requirements OpenAI for LLM. If your organisation's APIs require authorisation, you may need to add custom credentials as necessary. Customising this workflow Add additional tools to reach other parts of your internal knowledgebase. Not using OpenAI? Feel free to swap but ensure the LLM has tools/function calling support.

Who is this for? This workflow is designed for HR professionals, employer branding teams, talent acquisition strategists, market researchers, and business intelligence analysts who want to monitor, understand, and act upon employee sentiment and company perception on Glassdoor. It's ideal for organizations that value real-time feedback, are tracking employer brand perception, or need summarized insights for leadership reporting without sifting through thousands of raw reviews. What problem is this workflow solving? Manually reviewing and analyzing Glassdoor reviews is tedious, subjective, and not scalable especially for larger companies or those with many subsidiaries. This workflow: Automates review collection by making a Glassdoor company request via the Bright Data Web Scrapper API. Uses Google Gemini to summarize the content. Sends an actionable summary to HR dashboards, leadership teams, or alert systems via the Webhook notification. What this workflow does Makes an HTTP Request to Glassdoor via the Bright Data Web Scrapper API. Polls the BrightData Glassdoor for the completion of the request. Downloads the Glassdoor response when a new snapshot is ready. Sends the prompt to Google Gemini for summarization. Delivers the summarized insights (strengths, weaknesses, sentiment, patterns) to a configured webhook or dashboard endpoint. Setup Sign up at Bright Data. Navigate to Proxies & Scraping and create a new Web Unlocker zone by selecting Web Unlocker API under Scraping Solutions. In n8n, configure the Header Auth account under Credentials (Generic Auth Type: Header Authentication). The Value field should be set with the Bearer XXXXXXXXXXXXXX. The XXXXXXXXXXXXXX should be replaced by the Web Unlocker Token. A Google Gemini API key (or access through Vertex AI or proxy). A webhook or endpoint to receive the summary (e.g., Slack, Notion, or custom HR dashboard). How to customize this workflow to your needs Change Summary Focus by updating the Summarization of Glassdoor Response node Summarization methods and prompts to extract specific insights: Cultural feedback Leadership issues Compensation comments Exit motivation Update the HTTP Request to Glassdoor node with a specific Glassdoor Company information that you are looking for. Format the output to produce a customized summary to Markdown or HTML for rich delivery. Integrate with HR Systems BambooHR, Workday, SAP SuccessFactors via API. Google Sheets or Airtable

Reddit Poster to Discord This workflow checks Reddit every 15 minutes for new posts and sends selected posts to a Discord channel via webhook. Flow Overview: Schedule Trigger Runs every 15 minutes. Fetch Latest Posts Retrieves up to 3 new posts from any subreddit. Filter Posts Skips moderator or announcement posts based on author ID. Fetch Full Post Data Gets full details for the remaining post. Extract Image URL Parses the post to extract a direct image link. Send to Discord Sends the post title, image, and link to a Discord webhook. Setup Notes: Create a Reddit app and connect credentials in n8n. Add your subreddit name to both Reddit nodes. Connect a Discord webhook for posting.