Workflow Templates

Goodreads Quote Extraction with Bright Data and Gemini This workflow demonstrates how to fetch data specifically from Goodreads web pages using Bright Data and then extract specific information (quotes) from that data using a Google Gemini AI model. How it works The workflow is triggered manually. It sends a request to a Bright Data collector to scrape data from a predefined list of Goodreads URLs. The collected text data from Goodreads is then passed to a Google Gemini AI node. The AI node processes the text and extracts quotes based on a specified JSON schema output format. Set up steps Setting up this workflow should take only a few minutes. You will need a Bright Data API key to configure the 'Header Auth' credential. You will need a Google Gemini API key to configure the 'Google Gemini(PaLM) Api account' credential. Ensure the correct Bright Data collector ID is set in the 'Perform Bright Data Web Request' node URL. Make sure the full list of target Goodreads URLs is correctly added to the 'Perform Bright Data Web Request' node's body. Link your created credentials to the respective nodes ('Perform Bright Data Web Request' and 'Quotes Extractor'). Keep detailed descriptions for specific node configurations in sticky notes inside your workflow canvas.

Who is this template for? This workflow template is designed for everyone with a Gmail address, who wants to forward all Netflix emails, including temporary login codes, to friends and family effortlessly. How it works Scans your Gmail inbox every minute for new e-mails from Netflix Forwards all Netflix e-mails to all desired e-mail addresses via the e-mail provider Mailjet Setup Steps Connect your Google Mail Account to n8n following the official n8n instructions Add all recipients you want to the recipients array at the "Set all recipients" node. Create and connect your Mailjet Account to n8n following the official n8n instructions. Note: You cannot use an Gmail e-mail address as the sender address, as mailjet does not support this. I recommend using your own email address from a custom domain. This works perfectly.

LinkedIn Profile Enrichment Workflow Who is this for? This workflow is ideal for recruiters, sales professionals, and marketing teams who need to enrich LinkedIn profiles with additional data for lead generation, talent sourcing, or market research. What problem is this workflow solving? Manually gathering detailed LinkedIn profile information can be time-consuming and prone to errors. This workflow automates the process of enriching profile data from LinkedIn, saving time and ensuring accuracy. What this workflow does Input: Reads LinkedIn profile URLs from a Google Sheet. Validation: Filters out already enriched profiles to avoid redundant processing. Data Enrichment: Uses RapidAPI's Fresh LinkedIn Profile Data API to retrieve detailed profile information. Output: Updates the Google Sheet with enriched profile data, appending new information efficiently. Setup Google Sheet: Create a sheet with a column named linkedin_url and populate it with the profile URLs to enrich. RapidAPI Account: Sign up at RapidAPI and subscribe to the Fresh LinkedIn Profile Data API. API Integration: Replace the x-rapidapi-key and x-rapidapi-host values with your credentials from RapidAPI. Run the Workflow: Trigger the workflow and monitor the updates to your Google Sheet. How to customize this workflow Filter Criteria**: Modify the filter step to include additional conditions for processing profiles. API Configuration**: Adjust API parameters to retrieve specific fields or extend usage. Output Format**: Customize how the enriched data is appended to the Google Sheet (e.g., format, column mappings). Error Handling**: Add steps to handle API rate limits or missing data for smoother automation. This workflow streamlines LinkedIn profile enrichment, making it faster and more effective for data-driven decision-making.

What this workflow does Monitors Google Drive: The workflow triggers whenever a new CSV file is uploaded. Uses AI to Identify PII Columns: The OpenAI node analyzes the data and identifies PII-containing columns (e.g., name, email, phone). Removes PII: The workflow filters out these columns from the dataset. Uploads Cleaned File: The sanitized file is renamed and re-uploaded to Google Drive, ensuring the original data remains intact. How to customize this workflow to your needs Adjust PII Identification: Modify the prompt in the OpenAI node to align with your specific data compliance requirements. Include/Exclude File Types: Adjust the Google Drive Trigger settings to monitor specific file types (e.g., CSV only). Output Destination: Change the folder in Google Drive where the sanitized file is uploaded. Setup Prerequisites: A Google Drive account. An OpenAI API key. Workflow Configuration: Configure the Google Drive Trigger to monitor a folder for new files. Configure the OpenAI Node to connect with your API Set the Google Drive Upload folder to a different location than the Trigger folder to prevent workflow loops.

This n8n workflow operates as a two-agent system where each agent has a specialized task. The process flows from initial user input to a final analysis, with a seamless handoff between the agents. How it works The Chat Trigger The entire process begins when you send a message using n8n's chat interface. This message serves as the initial prompt or query for the system. The Research Agent Takes Over The user's message is first sent to the Research Agent. This agent's job is to understand the query and gather relevant information. To do this, it has access to: LLM: Google Gemini, which acts as the agent's "brain" to process language and make decisions. Tools: web_search: It uses this tool (powered by your self-hosted SearXNG instance) to perform live searches on the internet. get_current_date: It can access the current date, which is useful for context-aware or time-sensitive research. The Research Agent uses these tools to find the most relevant information related to your query and then compiles it into a concise summary. Handoff to the Sentiment Analysis Agent Once the Research Agent has completed its task, it passes its findings directly to the Sentiment Analysis Agent. The Final Analysis The Sentiment Analysis Agent receives the text from the Research Agent. Its sole purpose, as defined by its system prompt, is to analyze the sentiment of the provided information. It determines if the content is positive, negative, or neutral and formulates a final response. This final analysis is then sent back to you in the chat, completing the workflow. Set up steps Select the Language Model (LLM): This workflow is pre-configured with Google Gemini. You can select a different model for the agents as needed. Configure LLM Credentials: Ensure that valid credentials for your chosen LLM are correctly set up within your n8n instance. Set Up the SearXNG Connection: Configure the node to connect to your self-hosted SearXNG instance. This enables the agent's web search capabilities. Define the Research Agent's Task: Customize the system prompt for the "Research Agent" to define its role, instructions, and how it should conduct its research. Define the Sentiment Analysis Agent's Task: Adjust the system prompt for the "Sentiment Analysis Agent" to specify how it should analyze the information provided by the Research Agent. Test the Workflow: Use the built-in chat interface in the n8n canvas to send a message and verify that the agents are functioning correctly.

. IOT device control with MQTT and webhook This workflow is for users wanting a practical example of how to control IOT systems using the MQTT protocol in an an n8n environment. The template provides typical n8n MQTT and Webhook node implementation and configuration settings necessary to set IOT device inputs and outputs. How it works A webpage with IOT control 'on and 'off' buttons is presented to the user. When a button is selected on the webpage the value is sent via a webhook to trigger the active workflow. The workflow set node then prepares the received value into a message payload. It then passes the message to the MQTT node for publishing the topic with the payload to a cloud based MQTT broker. A remote ESP32 micro-controller subscribes to the broker and reads the payload contained in the topic. The ESP32 will then toggle the GPIO pin depending on the topic payload value. The IOT control webpage The webpage is a simple HTML page containing the clickable 'on' and 'off' buttons. It also has the get webhook URL that sends the selected value to the n8n workflow in this case running locally. The URL webhook format is http://localhost:5678/webhook/pin-control?value=action The webpage code IOT-control.html IOT device The IOT device is an ESP32 micro-controller running on a remote network. To keep it simple GPIO2 is selected as the control output. In this case when the received value is "on" GPIO2 goes high a led will turn on in the ESP32. It will go off when the received value is "off". The program for the ESP32 IOT control is 'main.py' . You will require a micropython interpreter to be uploaded to the ESP32 for the program to run automatically. The code can be easily edited and modified to accommodate any further attached IOT devices. The ESP32 main.py code main.py How to customise this workflow to your needs ESP32 You will need a working ESP32 installed with a micro-python interpreter. The code main.py is provided. The main.py program can loaded and edited with a python IDE. I used Thonny for this example. Use a free MQTT broker to get started. I used "broker.emqx.io" in the code. IOT Control Webpage The webpage contains HTML and can be easily edit to enhance functionality. The embedded webhook is configured for n8n production mode. http://localhost:5678/webhook/pin-control?value=action If you want to run the page in test mode you will use the following URL. http://localhost:5678/webhook-test/pin-control?value=action n8n workflow. The workflow is a good demonstration of how to control IOT devices using n8n. Following these steps will give a good insight for microcontroller automation.

Who's this for? If you own a website and need to analyze your Matomo analytics data so you can increse the number of frequent visitors If you need to create an SEO report on what are the common trends amongst your most frequent visitors If you want to grow your site based on suggestions from data Matomo is an analytics tool that can give you details of each individual visitor. Much more powerful than Google analytics. Watch youtube tutorial here Get my SEO A.I. agent system here Read more -> How to create an A.I. Agent to analyze Matomo analytics using n8n for free Here's the A.I. output: Keywords showing the most improvement: Openrouter N8N. Keywords needing attention: Ai Generated Reference Letter Obsidian Second Brain Suggested actions for improvement: Optimize for "best Docker Synology" despite stable ranking, an improvement to top 10 is an achievable goal. Since "2nd brain app for developer" is of interest to a developer. Consider writing a blog post on how the app addresses the specific pain points of developers. Use case Instead of hiring an SEO expert, I run this report weekly. It looks at the data for the past week and looks for visitors with more than 3 visits and recommends ideas to convert more visitors into frequent visitors. How it works The workflow gathers matomo analytics for the past 7 days. We then parse the data The data is sent to Openrouter and using a FREE LLM, it analyses the data. It stores the results in baserow How to use this Input your Matomo analytics credentials Input your Matomo site ID Input your Openrouter.ai credentials Input your baserow credentials You will need to create a baserow database with columns: Dates, Notes, Blog. Created by Rumjahn

🚀 Automated Investor Intelligence: CrunchBase to Google Sheets Data Harvester! Workflow Overview This cutting-edge n8n automation is a sophisticated investor intelligence tool designed to transform market research into actionable insights. By intelligently connecting CrunchBase, data processing, and Google Sheets, this workflow: Discovers Investor Insights: Automatically retrieves latest investor data Tracks key investment organizations Eliminates manual market research efforts Intelligent Data Processing: Filters investor-specific organizations Extracts critical investment metrics Ensures comprehensive market intelligence Seamless Data Logging: Automatically updates Google Sheets Creates real-time investor database Enables rapid market trend analysis Scheduled Intelligence Gathering: Daily automated tracking Consistent investor insight updates Zero manual intervention required Key Benefits 🤖 Full Automation: Zero-touch investor research 💡 Smart Filtering: Targeted investment insights 📊 Comprehensive Tracking: Detailed investor intelligence 🌐 Multi-Source Synchronization: Seamless data flow Workflow Architecture 🔹 Stage 1: Investor Discovery Scheduled Trigger**: Daily market scanning CrunchBase API Integration** Intelligent Filtering**: Investor-specific organizations Key investment metrics Most recent data 🔹 Stage 2: Data Extraction Comprehensive Metadata Parsing** Key Information Retrieval** Structured Data Preparation** 🔹 Stage 3: Data Logging Google Sheets Integration** Automatic Row Appending** Real-Time Database Updates** Potential Use Cases Venture Capitalists**: Investment ecosystem mapping Startup Scouts**: Investor trend analysis Market Researchers**: Comprehensive investment insights Business Development**: Strategic partnership identification Investment Analysts**: Market intelligence gathering Setup Requirements CrunchBase API API credentials Configured access permissions Investor organization tracking setup Google Sheets Connected Google account Prepared tracking spreadsheet Appropriate sharing settings n8n Installation Cloud or self-hosted instance Workflow configuration API credential management Future Enhancement Suggestions 🤖 Advanced investment trend analysis 📊 Multi-source investor aggregation 🔔 Customizable alert mechanisms 🌐 Expanded investment stage tracking 🧠 Machine learning insights generation Technical Considerations Implement robust error handling Use secure API authentication Maintain flexible data processing Ensure compliance with API usage guidelines Ethical Guidelines Respect business privacy Use data for legitimate research Maintain transparent information gathering Provide proper attribution Hashtag Performance Boost 🚀 #InvestorIntelligence #VentureCapital #MarketResearch #AIWorkflow #DataAutomation #StartupEcosystem #InvestmentTracking #BusinessIntelligence #TechInnovation #StartupFunding Workflow Visualization [Daily Trigger] ⬇️ [Fetch Investor Data] ⬇️ [Extract Investor Fields] ⬇️ [Log to Google Sheets] Connect With Me Ready to revolutionize your investor research? 📧 Email: Yaron@nofluff.online 🎥 YouTube: @YaronBeen 💼 LinkedIn: Yaron Been Transform your market intelligence with intelligent, automated workflows!

Who is this for? This subworkflow is ideal for developers and automation builders working with UniPile and n8n to automate message enrichment and LinkedIn lead routing. What problem is this workflow solving? UniPile separates personal and organization accounts into two different API endpoints. This flow handles both intelligently so you're not missing sender context due to API quirks or bad assumptions. What this workflow does This subworkflow is used by: LinkedIn Auto Message Router with Request Detection** LinkedIn AI Response Generator with Slack Approval** It receives a message sender ID and tries to enrich it using UniPile's /people and /organizations endpoints. It returns a clean, consistent profile object regardless of which source was used. Setup Generate a UniPile API token and save it in your n8n credentials Make sure this subworkflow is triggered correctly by your parent flows Test both people and organization lookups to verify responses are normalized How to customize this workflow to your needs Add a secondary enrichment layer using tools like Clearbit or FullContact Customize the fallback logic or error handling Expand the returned data for more AI context or user routing (e.g., job title, region)

Use Case When tracking your contacts and leads in Hubspot CRM, every new contact might be a potential customer. To guarantee that you're keeping the overview you'd normally need to look at every new lead that is coming in manually to identify high-quality leads to prioritize their engagement and optimize the sales process. This workflow saves the work and does it for you. What this workflow does The workflow runs every 5 minutes. On every run, it checks the Hubspot CRM for contacts that were added since the last check. It then checks if they meet certain criteria (in this case if they are making +5m annual revenue) and alerts you in Slack for every match. Setup Add Hubspot, and Slack credentials. Click on Test workflow. How to adjust this workflow to your needs Change the schedule interval Adjust the criteria to send alerts

Build an MCP Server which has access to a semantic database to perform Retrieval Augmented Generation (RAG) Tutorial Click here to watch the full tutorial on YouTube How it works This MCP Server has access to a local semantic database (Qdrant) and answers questions being asked to the MCP Client. AI Agent Template Click here to navigate to the AI Agent n8n workflow which uses this MCP server Warning This flow only runs local and cannot be executed on the n8n cloud platform because of the MCP Client Community Node. Installation Install n8n + Ollama + Qdrant using the Self-hosted AI starter kit Make sure to install Llama 3.2 and mxbai-embed-large as embeddings model. Activate the n8n flow Run the "RAG Ingestion Pipeline" and upload some PDF documents How to use it Run the MCP Client workflow and ask a question. It will be either answered by using the semantic database or the search engine API. More detailed instructions Missed a step? Find more detailed instructions here: https://brightdata.com/blog/ai/news-feed-n8n-openai-bright-data

Who is this template for? This workflow template is designed for content creators, researchers, educators, and professionals who need quick, accurate summaries of YouTube videos. It’s ideal for those looking to save time, extract key insights, or repurpose video content into concise formats for reports, studies, or social media. What does it do? The workflow automates the process of summarizing YouTube videos by extracting the transcript, analyzing the content, and generating a concise summary. It leverages AI tools to ensure accuracy and relevance, making it easier to digest lengthy videos in seconds. Why is it useful? This template saves hours of manual effort by automating video summarization, enabling users to focus on analyzing or sharing insights rather than watching entire videos. It’s particularly useful for staying updated with trends, conducting research, or creating content efficiently. How does it work? The workflow integrates with YouTube’s Transcript API powered by Apify Actor to fetch video transcripts, process the text using AI-powered summarization tools, and deliver a clear, concise summary. Setup Instructions You need an Apify account and an API key to connect with the Actor. Follow the steps below: Create a Free Account. Choose the appropriate Actor from the Apify search. Under the Integration tab, click on “Use API endpoints.” Select the API that best suits your needs.