Workflow Templates

AI-Powered Product Research & SEO Content Automation Skip the guesswork and manual effort — this n8n flow automates the entire process of researching your product's online competition and generating high-quality SEO content. Whether you're launching a new product or optimizing existing listings, this workflow leverages real-time web data and AI-driven copywriting to deliver: 📈 Search-optimized metadata (Title, Description, Keywords) 🛍️ Engaging product descriptions tailored for marketing 📊 Auto-organized output ready for use in your content or e-commerce platform All of this happens with just one product title input! 🧠 How It Works • User submits a product title via a form. • The workflow uses Google Custom Search to gather real-time competitor content based on that title. • Titles, snippets, and keywords are extracted from the search results. • This information is sent to a language model (Google Gemini via LangChain) to generate: SEO-optimized metadata (Title, Description, Keywords) A compelling product description tailored for marketing • The AI-generated content is then parsed and organized into two categories: SEO data and product content. • The structured output is saved automatically into a connected Google Sheet for easy access or further automation. 🛠️ What Problems Does This Solve? Manual competitor research and writing SEO content from scratch can be: Time-consuming** Inconsistent in quality** Not optimized for search engines** Hard to scale for multiple products** This workflow automates the entire research + writing + structuring process. ✅ Key Benefits Instant Content Creation**: Generate polished SEO content in seconds. Competitor-Aware**: Pulls in real-time data from the web for relevant, market-aligned content. Scalable**: Easily repeat the process for multiple product titles with minimal effort. Data Centralization**: Stores everything in Google Sheets—great for collaboration or syncing with other tools. Customizable**: Easily extend or modify the workflow to include translations, publishing, or social media automation. ⚙️ Set-Up Steps • Connect Google Custom Search API with a valid API key and search engine ID (CX). • Connect and configure Google Gemini or LangChain with access credentials. • Provide access to a Google Sheet with columns for storing SEO and product data. • Estimated setup time: ~15–25 minutes depending on API access and sheet setup. 🚀 Let’s Get You Started with Automating Your LinkedIn Posts! Create your free n8n account and set up the workflow in just a few minutes using the link below: 👉 Start Automating with n8n Save time, stay consistent, and grow your LinkedIn presence effortlessly!

How It Works: • Scrapes company review data from Glassdoor using ScrapingBee. • Extracts demographic-based ratings using AI-powered text analysis. • Calculates workplace disparities with statistical measures like z-scores, effect sizes, and p-values. • Generates visualizations (scatter plots, bar charts) to highlight patterns of discrimination or bias. Example Visualizations: Set Up Steps: Estimated time: ~20 minutes. • Replace ScrapingBee and OpenAI credentials with your own. • Input the company name you want to analyze (best results with large U.S.-based organizations). • Run the workflow and review the AI-generated insights and visual reports. This workflow empowers users to identify potential workplace discrimination trends, helping advocate for greater equity and accountability. Additional Credit: Wes Medford For algorithms and inspiration

What Is This? This workflow is a comprehensive solution for automating website audits and optimizations, leveraging advanced technologies to boost SEO effectiveness and overall site performance. Who Is It For? Designed for SEO specialists, digital marketers, webmasters, and content teams, this workflow empowers anyone responsible for website performance to automate and scale their audit processes. Agencies managing multiple client sites, in-house SEO teams aiming to save time on routine checks, and developers seeking to integrate data-driven insights into their deployment pipelines will all find this solution invaluable. By combining your site’s sitemap with Google Search Console and Google Analytics data, then applying AI-powered analysis, the workflow continuously uncovers actionable recommendations to boost search visibility, improve user engagement, and accelerate page performance. Whether you manage a single blog or oversee a sprawling e-commerce platform, this automated pipeline delivers precise, prioritized SEO improvements without manual data wrangling. How Does It Work? This end-to-end site analysis automation consists of five main stages: 1. URL Discovery Processes the sitemap.xml using HTTP Request and XML nodes to extract all site URLs. 2. Search Console Performance Analysis Uses the Google Search Console API to fetch detailed metrics for each page, including search position, clicks, impressions, and CTR. 3. Analytics Data Collection Connects to the Google Analytics API to automatically retrieve traffic metrics such as pageviews, average session duration, bounce rate, and conversions. 4. AI Data Processing Employs OpenAI models to perform in-depth analysis of the collected data. The artificial intelligence engine merges insights from all sources, identifies patterns, and produces detailed optimization recommendations. AI analyses website itsefl aswell. Consider testing different models. I do recommend at least trying out o4-mini. 5. Recommendation Generation Creates tailored suggestions for each page, in form of HTML table, that is being sent to your email. How To Set It Up? Accounts: An active n8n account or instance, API keys for Google Search Console and Google Analytics, an OpenAI access token. Enabled Google APIs: You will neeed at least following scopes: Google Search Console API Google Analytics Aadmin API Google Analytics Data API Scheduling: The workflow can run manually for ad hoc audits or be scheduled (daily, weekly) for continuous site monitoring. Testing: There are two nodes that are optional: "Sort for testing purposes" and "Limit for testing purposes" Together they randomly select items from sitemap and limit them to few so you don't need to run hundreds of sitemap.xml items at once, but you can run just a random batch first. Globals: There is node called "Globals- CHANGE ME!". You need to set up proper variables in there, which are: sitemap_url - self exlpainatory search_console_selector - for example "sc-domain:sailingbyte.com" but can be URL aswell- depends on how did you set up your search console analysis_start_date and analysis_end_date - date range for analytics, by default last 30 days analytics_selector_id - ID of Google Analytics setup, it is a large integer, you can find it in analytics url preceeded with letter "p", ex (your number is where there are X's): https://analytics.google.com/analytics/web/#/pXXXXXXXXX/reports/intelligenthome report_receiver - email which will receive report What's More? That's actually it. I hope that this automation will help your website improvement will be much easier! Thank you, perfect! Glad I could help. Visit my profile for other automations for businesses. And if you are looking for dedicated software development, do not hesitate to reach out!

Stay ahead of the market with this powerful, automated workflow that performs real-time sentiment analysis on stock market news. By leveraging the advanced capabilities of Google Gemini, this solution provides you with actionable insights to make informed investment decisions. This workflow is designed for investors, traders, and financial analysts who want to automate the process of monitoring news and gauging market sentiment for specific stocks. It seamlessly integrates with Google Sheets for input and output, making it easy to track a portfolio of stocks. Key Features & Benefits Automated Daily Analysis: The workflow is triggered daily, providing you with fresh sentiment analysis just in time for the market open. Dynamic Stock Tracking: Easily manage your list of tracked stocks from a simple Google Sheet. AI-Powered Insights: Utilizes Google Gemini's sophisticated language model to analyze news content for its potential impact on stock prices, including a sentiment score and a detailed rationale. Comprehensive News Aggregation: Fetches the latest news articles from EODHD for each of your specified stock tickers. Error Handling & Validation: Includes built-in checks for invalid stock tickers and formats the AI output for reliable data logging. Centralized Reporting: Automatically logs the sentiment score, rationale, and date into a Google Sheet for easy tracking and historical analysis. How It Works This workflow follows a systematic process to deliver automated sentiment analysis: Scheduled Trigger: The workflow begins each day at a specified time. Fetch Stock Tickers: It reads a list of stock tickers from your designated Google Sheet. Loop and Fetch News: For each ticker, it retrieves the latest news articles using the EODHD API. AI Sentiment Analysis: The collected news articles are then passed to a Google Gemini-powered AI agent. The agent is prompted to act as a stock sentiment analyzer, evaluating the news and generating: A sentiment score from -1 (strong negative) to 1 (strong positive). A detailed rationale explaining the basis for the score. Data Formatting & Validation: The AI's output is parsed and validated to ensure it is in the correct JSON format. Log to Google Sheets: The final sentiment score and rationale are appended to your Google Sheet, alongside the corresponding stock ticker and the current date. Nodes Used Schedule Trigger Google Sheets SplitInBatches HttpRequest (EODHD) If Code (JavaScript) AI Agent (LangChain) Google Gemini Chat Model This workflow is a valuable tool for anyone looking to harness the power of AI for financial market analysis. Deploy this automated solution to save time, gain a competitive edge, and make more data-driven trading decisions.

Functionality This workflow automates the handling of incoming emails by summarizing their content, generating appropriate responses, and validating the responses through a "Human-in-the-Loop" system. It integrates with IMAP email services (e.g., Gmail, Outlook) and uses AI models to streamline the email response process. The workflow ensures that all AI-generated responses are reviewed by a human before being sent, maintaining a high level of professionalism and accuracy. This approach is particularly useful for businesses that receive a high volume of emails and need to respond quickly while ensuring quality control. How It Works Email Trigger: The workflow starts with the Email Trigger (IMAP) node, which monitors an email inbox for new messages. When a new email arrives, it triggers the workflow. Email Preprocessing: The Markdown node converts the email's HTML content into plain text for easier processing by the AI models. Email Summarization: The Email Summarization Chain node uses an AI model (OpenAI) to generate a concise summary of the email. The summary is limited to 100 words and is written in a professional tone. Email Response Generation: The Write email node uses an AI model (OpenAI) to draft a professional response to the email. The response is based on the email content and is limited to 100 words. Human-in-the-Loop Approval: The Set Email text node prepares the drafted response for approval. The Approve Email node sends the drafted response to a human approver (e.g., an internal email address) for review. The email includes: The original message. The AI-generated response. The Approved? node checks if the response has been approved by the human reviewer. If approved, the workflow proceeds to send the response; otherwise, it stops. Sending the Response: The Send Email node sends the approved response back to the original sender. Key Features Automated Email Summarization**: Summarizes incoming emails to provide a quick overview of the content. AI-Powered Response Generation**: Drafts professional responses to emails using AI. Human-in-the-Loop Approval**: Ensures all AI-generated responses are reviewed and approved by a human before being sent. IMAP Integration**: Works with IMAP email services like Gmail and Outlook. Efficient Email Management**: Reduces the time and effort required to handle incoming emails while maintaining high-quality responses. This workflow is ideal for businesses looking to automate their email response process while maintaining control over the quality of outgoing communications. It leverages AI to handle repetitive tasks and ensures that all responses are reviewed by a human, providing a balance between automation and human oversight. Need help customizing? Contact me for consulting and support or add me on Linkedin.

Fact-Checking Workflow Documentation Overview This workflow is designed for automated fact-checking of texts. It uses AI models to compare a given text with a list of facts and identify potential discrepancies or hallucinations. Components 1. Input The workflow can be initiated in two ways: a) Manually via the "When clicking 'Test workflow'" trigger b) By calling from another workflow via the "When Executed by Another Workflow" trigger Required inputs: facts: A list of verified facts text: The text to be checked 2. Text Preparation The "Code" node splits the input text into individual sentences Takes into account date specifications and list elements 3. Fact Checking Each sentence is individually compared with the given facts Uses the "bespoke-minicheck" Ollama model for verification The model responds with "Yes" or "No" for each sentence 4. Filtering and Aggregation Sentences marked as "No" (not fact-based) are filtered The filtered results are aggregated 5. Summary A larger language model (Qwen2.5) creates a summary of the results The summary contains: Number of incorrect factual statements List of incorrect statements Final assessment of the article's accuracy Usage Ensure the "bespoke-minicheck" model is installed in Ollama (ollama pull bespoke-minicheck) Prepare a list of verified facts Enter the text to be checked Start the workflow The results are output as a structured summary Notes The workflow ignores small talk and focuses on verifiable factual statements Accuracy depends on the quality of the provided facts and the performance of the AI models Customization Options The summarization function can be adjusted or removed to return only the raw data of the issues found The AI models used can be exchanged if needed This workflow provides an efficient method for automated fact-checking and can be easily integrated into larger systems or editorial workflows.

The n8n Nostr Community Node is a tool that integrates Nostr functionality into n8n workflows, allowing users to interact with the Nostr protocol seamlessly. It provides both read and write capabilities and can be used for various automation tasks. Disclaimer This node is ideal for self-hosted n8n setups, as ++community nodes are not supported on n8n cloud++. It opens up exciting possibilities for integrating workflows with the decentralized Nostr protocol. n8n Community Node for Nostr n8n-nodes-nostrobots Features Write Operations**: Send notes and events (kind1) to the Nostr network. Read Operations**: Fetch events based on criteria such as event ID, public key, hashtags, mentions, or search terms. Utility Functions**: Convert events into different formats like naddr or nevent and handle key transformations between bech32 and hex formats. Trigger Events**: Monitor the Nostr network for specific mentions or events and trigger workflows automatically. Use Cases Automating note posting without exposing private keys. Setting up notifications for mentions or specific events. Creating bots or AI assistants that respond to mentions on Nostr. Installation Install n8n on your system. Add the Nostr Community Node to your instance. Configure your credentials using a Nostr secret key (supports bech32 or hex formats).

iMessage AI-Powered Smart Calorie Tracker > 📌 What it looks like in use: > This image shows a visual of the workflow in action. Use it for reference when replicating or customizing the template. This n8n template transforms a user-submitted food photo into a detailed, friendly, AI-generated nutritional report — sent back seamlessly as a chat message. It combines OpenAI's visual reasoning, Postgres-based memory, and real-time messaging with Blooio to create a hands-free calorie and nutrition tracker. 🧠 Use Cases Auto-analyze meals based on user-uploaded images. Daily/weekly/monthly diet summaries with no manual input. Virtual food journaling integrated into messaging apps. Nutrition companion for healthcare, fitness, and wellness apps. 📌 Good to Know ⚠️ This uses GPT-4 with image capabilities, which may incur higher usage costs depending on your OpenAI pricing tier. Review OpenAI’s pricing. The model uses visual reasoning and estimation to determine nutritional info — results are estimates and should not replace medical advice. Blooio is used for sending/receiving messages. You will need a valid API key and project set up with webhook delivery. A Postgres database is required for long-term memory (optional but recommended). You can use any memory node with it. ⚙️ How It Works Webhook Trigger The workflow begins when a message is received via Blooio. This webhook listens for user-submitted content, including any image attachments. Image Validation and Extraction A conditional check verifies the presence of attachments. If images are found, their URLs are extracted using a Code node and prepared for processing. Image Analysis via AI Agent Images are passed to an OpenAI-based agent using a custom system prompt that: Identifies the meal, Estimates portion sizes, Calculates calories, macros, fiber, sugar, and sodium, Scores the meal with a health and confidence rating, Responds in a chatty, human-like summary format. Memory Integration A Postgres memory node stores user interactions for recall and contextual continuity, allowing day/week/month reports to be generated based on cumulative messages. Response Aggregation & Summary Messages are aggregated and summarized by a second AI agent into a single concise message to be sent back to the user via Blooio. Message Dispatch The final message is posted back to the originating conversation using the Blooio Send Message API. 🚀 How to Use The included webhook can be triggered manually or programmatically by linking Blooio to a frontend chat UI. You can test the flow using a manual POST request containing mock Blooio payloads. Want to use a different messages app? Replace the Blooio nodes with your preferred messaging API (e.g., Twilio, Slack, Telegram). ✅ Requirements OpenAI API access with GPT-4 Vision or equivalent multimodal support. Blooio account with access to incoming and outgoing message APIs. Optional: Postgres DB (e.g., via Neon) for tracking message context over time. 🛠️ Customising This Workflow Prompt Tuning** Tailor the system prompt in the AI Agent node to fit specific diets (e.g., keto, diabetic), age groups, or regionally-specific foods. Analytics Dashboards** Hook up your Postgres memory to a data visualization tool for nutritional trends over time. Multilingual Support** Adjust the response prompt to translate messages into other languages or regional dialects. Image Preprocessing** Insert a preprocessing node before sending images to the model to resize, crop, or enhance clarity for better results.

Automated AI Research Assistant: From Query to Polished Report with Jina & Gemini Turn a single research question into a comprehensive, multi-source report with proper citations. This workflow automates the entire research process by leveraging the web-crawling power of Jina AI and the advanced reasoning capabilities of Google's Gemini models. Simply input your query, and this AI-powered assembly line will search the web, scrape relevant sources, summarize the content, draft a structured research paper, and finally, evaluate and polish the report for accuracy and formatting. ✨ Key Features 🔎 Dynamic Web Search**: Kicks off by searching the web with Jina AI based on your initial query. 📚 Multi-Source Content Scraping**: Automatically reads and extracts content from the top 10 search results. 🧠 AI-Powered Summarization**: Uses a Gemini agent to intelligently summarize each webpage, retaining the core information. ✍️ Automated Report Generation**: A specialized "Generator Agent" synthesizes the summarized data into a structured research paper, complete with an executive summary, introduction, discussion, and conclusion. ✅ Citation & Quality Verification**: A final "Evaluator Agent" meticulously checks the generated report for citation accuracy, logical flow, and markdown formatting, delivering a polished final document. 📈Rate-Limit Ready**: Includes a configurable Wait node to ensure stable execution when dealing with multiple API calls. 📝 What This Workflow Does This workflow is designed to be your personal research assistant. It addresses the time-consuming process of gathering, reading, and synthesizing information from multiple online sources. Instead of spending hours manually searching, reading, and citing, you can delegate the entire task to this workflow and receive a well-structured and cited report as the final output. It's perfect for students, researchers, content creators, and analysts who need to quickly compile information on any given topic. ⚙️ How It Works (Step-by-Step) Initiate with a Query: The workflow starts when you send your research question or topic to the Chat Trigger node. Search the Web: The user's query is passed to the Jina AI node, which performs a web search and returns the top 10 most relevant URLs. Scrape, Summarize, Repeat: The workflow then loops through each URL: Read Content: The Jina AI node scrapes the full text content from the URL. Summarize: A Summarizer Agent powered by Google Gemini reads the scraped content and the original user query, then generates a concise summary. Wait: A one-second pause helps to avoid hitting API rate limits before processing the next URL. Aggregate the Knowledge: Once the loop is complete, a Code node gathers all 10 individual summaries into a single, neatly structured list. Draft the Research Report: This aggregated data is fed to the Generator Agent. Following a detailed prompt, this Gemini-powered agent writes a full research report, structuring it with headings and adding inline citations for every piece of information it uses. Evaluate and Finalize: The generated draft is passed to the final Evaluator Chain. This agent acts as a quality control supervisor. It verifies that all claims are correctly cited, refines the content for clarity and academic tone, and polishes the markdown formatting to produce the final, ready-to-use report. 🚀 How to Use This Workflow Credentials: Click on Use template, then configure your credentials for the following nodes: Jina AI: You will need a Jina AI API key for the Search web and Read URL content nodes. Get your key from here: JinaAI API Key Google Gemini: You will need a Google Gemini API key for the Summarizer Model, Generator Model, and Evaluator Model nodes. Get your key from here: Gemini API Key Activate Workflow: Make sure the workflow is active in your n8n instance. Start Research: Send a chat message with your research topic to the webhook URL provided in the When chat message received node. Get Your Report: Check the output of the final node, Evaluator Chain, to find your completed and polished research report. Nodes Used Chat Trigger Jina AI Code (Python) Split in Batches (Looping) Wait AI Agent Basic LLM Chain Google Gemini Chat Model

Overview This n8n workflow processes user feedback automatically, tags it with sentiment, and links it to relevant insights in Notion. It uses GPT-4 to analyze each feedback entry, determine whether it corresponds to an existing insight or a new one, and update the Notion databases accordingly. It helps teams centralize and structure qualitative user feedback at scale. Who It’s For Product teams looking to organize and prioritize user feedback. Founders or solo builders seeking actionable insights from qualitative data. Anyone managing a Notion workspace where feedback is collected and needs to be tagged or linked to features and improvements. Prerequisites A Notion account with: A Feedback database (must include fields for feedback content and status). An Insights database with multi-select fields for Solution, User Persona, and a relation to Feedback. The Notion template (linked below) helps you get started quickly — just remove the mock data. A configured Notion API integration in n8n. 👉 Don’t forget to connect the n8n integration to the correct Notion page. An OpenAI API key Notion Template This workflow is designed to work seamlessly with a pre-configured Notion template that includes the required feedback and insights structure. 👉 User Feedback Analysis – Notion Template How It Works The workflow is triggered when a feedback item is updated in Notion (e.g. new feedback is submitted). Sentiment analysis (Positive, Neutral, or Negative) is run using OpenAI and stored in a select field in Notion. The AI agent analyzes the feedback to: Identify whether it matches an existing insight. Or create a new insight in Notion with a concise name, solution, and user persona. The feedback is then linked to the appropriate insight and marked as "Processed." How to Use It Connect your Notion databases in all Notion nodes (including those used by the AI agent) for both Feedback and Insights — follow the node names provided. Ensure your OpenAI and Notion credentials are correctly set. Set up your product context: Define a “Product Overview” and list your “Core Features”. This helps the AI agent categorize insights more accurately. (The Basecamp product is used as an example in the template.) (Optional) Modify the prompt to better fit your specific product context. Once feedback is added or updated in Notion, the workflow triggers automatically. Notes Only feedback with the status Received is processed. New insights are only created if no relevant match is found. Feedback is linked to insights via Notion’s relation property. A fallback parser is included to fix potential formatting issues in the AI output. You can swap the default n8n memory for a more robust backend like Supabase. 🙏 Please share your feedback with us. It helps us tremendously!

This workflow contains community nodes that are only compatible with the self-hosted version of n8n. Telegram Voice AI Assistant This n8n template creates a multimodal Telegram bot that dynamically responds to users: Replies with voice** when receiving voice messages (using ElevenLabs TTS) Replies with text** for text-based queries Supports custom AI tools (e.g., crypto APIs, databases, or custom functions) Built with LangChain Agents, it can integrate any external API or data source into conversations. Key Features 🎙️ Smart Response Logic Voice Query? → Voice Reply** Transcribes audio via ElevenLabs STT Processes with AI (Groq/Gemini) Converts text response to natural speech (ElevenLabs TTS) Text Query? → Text Reply** Bypasses TTS/STT for faster responses 🛠️ Extensible AI Tools Add your own tools: Database lookups Weather/stock APIs Custom Python functions RAG (document retrieval) Supports multi-step tool chaining (e.g., "Get BTC price → analyze trends → summarize") 🌐 Language & Context Auto-detects user language (via Telegram’s language_code) Maintains session memory (remembers conversation history) Use Cases Voice-first customer support** Crypto/analytics assistants** (e.g., "What’s Ethereum’s current gas fee?") Multilingual FAQ bots** Educational tutors** (voice-interactive learning) Requirements Telegram Bot Token** ElevenLabs API Key** (For TTS/STT) Groq API Key* or *Google Gemini API Key** Customization Tips Change AI personality*: Modify the *systemMessage in the Voice Assistant node Add more models**: Swap Groq/Gemini for OpenAI, Anthropic, etc. Extend functionality**: Add RAG (Retrieval-Augmented Generation) for document queries Take this template to create a Siri-like AI assistant for Telegram in minutes! 🚀

Transform static digital assets into dynamic, self-updating powerhouses that stay relevant for years to come! This workflow solves a common problem: once you publish forms, emails, or templates, their content becomes frozen in time. Users discovering them months later see outdated information, missed opportunities, and stale offers. Stop losing opportunities to stale content – make your digital assets work harder and stay fresher, automatically! Here's how it works: 🔗 Stable embed links mean your original assets never need updating 🔄 Dynamic URL redirects that automatically point to the latest pages 🖼️ Auto-updating images that showcase fresh offers or content 📅 Scheduled updates keep everything current without manual intervention Perfect for: Workflow sticky notes that become evergreen marketing billboards Registration forms with current promotions Email signatures with latest offers Website banners that stay seasonally relevant Any digital asset you want to "future-proof" The magic: Set it up once, embed the stable URLs/images in your content, then forget about it. Years later, users will still see fresh, as current information automatically pulled from your workflow. Requirements: Free accounts with GitHub (image storage) and shorten.rest (URL redirects). Both can be swapped for your preferred services. Follow me on LinkedIn for more tips on AI automation and n8n workflows!