Workflow Templates

Who is this for? 🧑🏻🫱🏻‍🫲🏻🤖 Humans and Robots alike. This workflow can be used as a Chat Trigger, as well as a Workflow Trigger. It will take a natural language request, and then generate a SQL query. The resulting query parameter will contain the query, and a sqloutput parameter will contain the results of executing such query. What's the use case? This template is most useful paired with other workflows that extract e-mail information and store it in a structured Postgres table, and use LLMs to understand inquiries about information contained in an e-mail inbox and formulate questions that needs answering. Plus, the prompt can be easily adapted to formulate SQL queries over any kind of structured database. Privacy and Economics As LLM provider I'm using Ollama locally, as I consider my e-mail extremely sensitive information. As model, phi4-mini does an excellent job balancing quality and efficiency. Setup Upon running for the first time, this workflow will automatically trigger a sub-section to read all tables and extract their schema into a local file. Then, either by chatting with the workflow in n8n's interface or by using it as a sub-workflow, you will get a query and a sqloutput response. Customizations If you want to work with just one particular table yet keep edits at bay, append a condition to the List all tables in a database step, like so: WHERE table_schema='public' AND table_name='my_emails_table_name' To repurpose this workflow to work with any other data corpus in a structured database, inspect the AI Agent user and system prompts and edit them accordingly.

Who is this for? This workflow is perfect for: Businesses and teams who need an automated solution to organize, analyze, and retrieve insights from their internal documents. Researchers who want to quickly analyze and query large collections of research papers, reports, or datasets. Customer support teams looking to streamline access to product documentation and support resources. Legal and compliance professionals needing to reference and query legal documents with confidence. AI enthusiasts and developers wanting to implement Retrieval-Augmented Generation (RAG) systems without starting from scratch. What problem is this workflow solving? Manually organizing, processing, and searching through documents can be time-consuming, error-prone, and inefficient. This workflow solves that by: Automating document processing** from Google Drive, supporting multiple formats like PDFs, CSVs, and Google Docs. Extracting, chunking, and enhancing document text**, preserving context and improving AI comprehension. Storing vector embeddings** in a secure, scalable Supabase vector database, enabling semantic search and retrieval. Providing an interactive AI chat interface** that allows users to ask natural language questions and get precise, document-based answers. This means teams can quickly access relevant insights from their document repositories—boosting productivity and ensuring accurate information retrieval. Key Features 🚀 End-to-End Document Processing: From Google Drive upload detection to vector embedding and storage. 🔍 Semantic Search & Retrieval: Users can ask complex, natural-language questions and receive contextually relevant answers. 🤖 AI-Powered Summaries & Metadata: Automatically generates document titles and summaries using Google Gemini AI. 📝 Smart Chunking & Contextual Enhancement: Breaks documents into smart chunks with overlap, preserving context and table integrity. 🔐 Secure & Scalable Vector Database: Stores and retrieves embeddings in a Supabase vector store for fast, reliable searches. 💬 Conversational AI Interface: Uses OpenAI to power natural, accurate, and cost-effective AI chat interactions. How does this workflow work? Monitors Google Drive for new files Extracts text from PDFs and CSVs (or Google Docs auto-converted) Splits text into context-preserving chunks Enhances chunk quality and stores embeddings in Supabase Enables natural language search and AI-powered chat interactions with the stored documents Typical Use Cases 📚 Corporate Knowledge Base 🔬 Research Paper Analysis 📞 Customer Support Document Query ⚖️ Legal Document Review and Analysis 🔍 Internal Team Documentation Search Why You’ll Love It This workflow lets you build a scalable, searchable, and AI-powered document system—without needing to write complex code or manage multiple systems. With this, you can: Stay organized with automated document processing. Deliver faster, more accurate answers to user queries. Reduce manual work and improve productivity. Gain a competitive edge with cutting-edge AI search capabilities. Setup Requirements An n8n instance with Google Drive, Supabase, OpenAI, and Gemini credentials configured. Access to a Supabase vector store for storing document embeddings. Configurable chunk size, overlap, and processing limits (default: 1000 characters per chunk, 20 chunks max).

This workflow automates the entire process of creating SEO-optimized meta titles and descriptions. It analyzes your webpage, spies on top-ranking competitors for the same keywords, and then uses a multi-step AI process to generate compelling, length-constrained meta tags. 🤖 How It Works This workflow operates in a three-phase process for each URL you provide: Phase 1: Self-Analysis When you add a URL to a Google Sheet with the status "New", the workflow scrapes your page's content. The first AI then performs a deep analysis to identify the page's primary keyword, semantic keyword cluster, search intent, and target audience. Phase 2: Competitor Intelligence The workflow takes your primary keyword and performs a live Google search. A custom code block intelligently filters the search results to identify true competitors. A second AI analyzes their meta titles and descriptions to find common patterns and successful strategies. Phase 3: Master Generation & Update The final AI synthesizes all gathered intelligence—your page's data and the competitor's winning patterns—to generate a new, optimized meta title and description. It then writes this new data back to your Google Sheet and updates the status to "Generated". ⚙️ Setup Instructions You should be able to set up this workflow in about 10-15 minutes ⏱️. 🔑 Prerequisites You will need the following accounts and API keys: A Google Account with access to Google Sheets. A Google AI / Gemini API key. A SerpApi key for Google search data. A ScrapingDog API key for reliable website scraping. 🛠️ Configuration Google Sheet Setup: Create a new Google Sheet. The workflow requires the following columns: URL, Status, Current Meta Title, Current Meta Description, Generated Meta Title, Generated Meta Description, and Ranking Factor. Add Credentials: Google Sheets Nodes: Connect your Google account credentials to the Google Sheets Trigger & Google Sheets nodes. Google Gemini Nodes: Add your Google Gemini API key to the credentials for all three Google Gemini Chat Model nodes. Scrape Website Node: In this HTTP Request node, go to Query Parameters and replace <your-api-key> with your ScrapingDog API key. Googl SERP Node: In this HTTP Request node, go to Query Parameters and replace <your-api-key> with your SerpApi API key. Configure Google Sheets Nodes: Copy the Document ID from your Google Sheet's URL. Paste this ID into the "Document ID" field in the following nodes: Google Sheets Trigger, Get row(s) in sheet1, and Update row in sheet. In each of those nodes, select the correct sheet name from the "Sheet Name" dropdown. ✅ Activate Workflow Save and activate the workflow. To run it, simply add a new row to your Google Sheet containing the URL you want to process and set the "Status" column to New.

AI powered Automated Crypto Insights with Chart-img and BrowserAI Tired of paying for costly crypto updates? Or reading long analyses? This n8n workflow automates the delivery of personalized crypto insights, using Chart-img for capturing coin graphs of BTC, ETH, SOL, and XRP as base64 images, and BrowserAI for web scraping and information gathering of news and articles. This setup ensures thorough market coverage and timely updates, without breaking the bank. Overview Designed for crypto enthusiasts, traders, and analysts, this workflow automates the process of collecting and distributing valuable crypto information. It’s perfect for anyone wanting consistent and accurate updates conveniently. Setup Instructions Pre-conditions Chart-img Account: Register for a Chart-img account and obtain an API key here. BrowserAI Account: Sign up for BrowserAI and get your API key from your BrowserAI dashboard. Step-by-Step Setup 🗓️ Schedule and Date Calculation Triggers twice daily at 8AM and 8PM to ensure up-to-date insights, and can be changed to your like. Calculates yesterday’s date dynamically for accurate data retrieval. 📊 Coin Graph Capture with Chart-img Uses Chart-img API to capture 24-hour graphs for BTC, ETH, SOL, and XRP. Converts images to base64 strings for easy integration into analysis. 🌐 Web Scraping with BrowserAI Creates tasks in BrowserAI to gather the latest crypto news and insights. Automates data extraction for comprehensive market analysis. ⌛ Monitor and Complete Tasks Incorporates status checks to ensure BrowserAI tasks complete successfully before proceeding. ✏️ Analyze and Synthesize Information Combines graph data with web-scraped insights for an enriched summary. Uses AI to generate simple, informative descriptions under 60 words to not overload you. 📩 Deliver Insights Efficiently Sends the compiled analysis to your Telegram, with easy options to switch to WhatsApp, email, or any other communication channel. Customization Guidance Content Personalization:** Customize the datasets and keywords for tailored updates. Modify Schedule:** Adjust triggering times according to your needs using n8n’s scheduling options. This workflow delivers a seamless and cost-effective approach to staying informed about crypto market trends, combining the latest technology for superior insights. ++WARNING:++ This template is intended for personal use only and does not constitute financial advice. Any actions taken using this tool are solely the user's responsibility.

An intelligent IT support agent that uses Azure AI Search for knowledge retrieval, Microsoft Entra ID integration for user management, and Jira for ticket creation. The agent can answer questions using internal documentation and perform administrative tasks like password resets. How It Works The workflow operates in three main sections: Agent Chat Interface: A chat trigger receives user messages and routes them to an AI agent powered by Google Gemini. The agent maintains conversation context using buffer memory and has access to multiple tools for different tasks. Knowledge Management: Users can upload documentation files (.txt, .md) through a form trigger. These documents are processed, converted to embeddings using OpenAI's API, and stored in an Azure AI Search index with vector search capabilities. Administrative Tools: The agent can query Microsoft Entra ID to find users, reset passwords, and create Jira tickets when issues need escalation. It uses semantic search to find relevant internal documentation before responding to user queries. The workflow includes a separate setup section that creates the Azure AI Search service and index with proper vector search configuration, semantic search capabilities, and the required field schema. Prerequisites To use this template, you'll need: n8n cloud or self-hosted instance Azure subscription with permissions to create AI Search services Microsoft Entra ID (Azure AD) access with user management permissions OpenAI API account for embeddings Google Gemini API access Jira Software Cloud instance Basic understanding of Azure resource management Setup Instructions Import the template into n8n. Configure credentials: Add Google Gemini API credentials Add OpenAI API credentials for embeddings Add Microsoft Azure OAuth2 credentials with appropriate permissions Add Microsoft Entra ID OAuth2 credentials Add Jira Software Cloud API credentials Update workflow parameters: Open the "Set Common Fields" nodes Replace <azure subscription id> with your Azure subscription ID Replace <azure resource group> with your target resource group name Replace <azure region> with your preferred Azure region Replace <azure ai search service name> with your desired service name Replace <azure ai search index name> with your desired index name Update the Jira project ID in the "Create Jira Ticket" node Set up Azure infrastructure: Run the manual trigger "When clicking 'Test workflow'" to create the Azure AI Search service and index This creates the vector search index with semantic search configuration Configure the vector store webhook: Update the "Invoke Query Vector Store Webhook" node URL with your actual webhook endpoint The webhook URL should point to the "Semantic Search" webhook in the same workflow Upload knowledge base: Use the "On Knowledge Upload" form to upload your internal documentation Supported formats: .txt and .md files Documents will be automatically embedded and indexed Test the setup: Use the chat interface to verify the agent responds appropriately Test knowledge retrieval with questions about uploaded documentation Verify Entra ID integration and Jira ticket creation Security Considerations Use least-privilege access for all API credentials Microsoft Entra ID credentials should have limited user management permissions Azure credentials need Search Service Contributor and Search Index Data Contributor roles OpenAI API key should have usage limits configured Jira credentials should be restricted to specific projects Consider implementing rate limiting on the chat interface Review password reset policies and ensure force password change is enabled Validate all user inputs before processing administrative requests Extending the Template You could enhance this template by: Adding support for additional file formats (PDF, DOCX) in the knowledge upload Implementing role-based access control for different administrative functions Adding integration with other ITSM tools beyond Jira Creating automated escalation rules based on query complexity Adding analytics and reporting for support interactions Implementing multi-language support for international organizations Adding approval workflows for sensitive administrative actions Integrating with Microsoft Teams or Slack for notifications

Automates the process of generating, storing, and publishing engaging LinkedIn posts derived from books (PDFs) using AI and vector search. 🧠 Overview This workflow: Watches a Google Drive folder for new or updated book PDFs. Extracts and embeds the content using OpenAI. Stores the data in a Pinecone vector database. Uses a LangChain agent to generate post ideas. Creates concise LinkedIn posts with hook, insight, CTA. Updates a Google Sheet and posts to LinkedIn. 🛠 Workflow Breakdown 📥 1. Google Drive Trigger Trigger:** Watches a folder for new or updated PDF files. Action:** Downloads the updated PDF. 📄 2. Extract and Embed Content Extract from File:** Parses PDF to extract text. Text Splitter:** Breaks text into chunks. Embeddings (OpenAI):** Converts chunks into vector embeddings. Pinecone Vector Store:** Saves the embeddings with the book name as namespace. 🧠 3. Post Idea Generation (LangChain Agent) Uses a prompt to: Search Pinecone DB Extract insights Format into 5 LinkedIn post ideas with: Hook Insight CTA Memory buffer** and structured output parser are used for clean AI interaction. ✍️ 4. Post Creation Each idea is: Split Rewritten with a GPT model prompt to match LinkedIn tone Styled for under 600 characters Includes emojis, hashtags, and tone guidelines 📊 5. Google Sheet Integration Saves all generated posts to a Google Sheet. Marks status: "published" or "no". 🔁 6. Scheduled Publishing Every day: Pulls an unpublished post Publishes it to LinkedIn Updates the post's status and timestamp in the Google Sheet ⚙️ Setup Guide 📂 Google Drive Create a folder for book PDFs Connect your Google Drive account to n8n Provide access token with file read permission 📊 Google Sheets Create a Google Sheet with columns: bookname, hook, insight, cta, postContent, published, date Add credentials in n8n with read/write permission 🧠 Pinecone Set up a Pinecone project and index (linkdenpost) Namespace will be auto-named using the book filename 🔑 API Credentials Required OpenAI API** (for embeddings and post generation) Pinecone API** (for vector storage and retrieval) LinkedIn OAuth2** (to publish posts) Google Drive & Sheets** credentials 🔁 Flow Summary graph TD A[Google Drive Trigger] --> B[Download PDF] B --> C[Extract Text] C --> D[Text Splitter] D --> E[Create Embeddings] E --> F[Pinecone Vector Store] F --> G[LangChain Agent] G --> H[Structured Output (5 Post Ideas)] H --> I[Split Ideas] I --> J[Format as LinkedIn Post (GPT)] J --> K[Store in Google Sheet] L[Schedule Trigger] --> M[Get Unpublished Post] M --> N[Post to LinkedIn] N --> O[Mark as Published] 🧪 Prompt Example (Used in LangChain Agent) You are a content strategist. Search the Pinecone vector DB containing a book. Generate 5 unique LinkedIn post ideas with: A Hook (curiosity driven) Insight (summary < 100 words) CTA ("Agree or disagree?", etc.) Respond in structured JSON: [ { "Hook": "...", "Insight": "...", "CTA": "..." }, ... ] ✅ Output Sample { "Hook": "Why your lab's results might be invalid 😱", "Insight": "ISO/IEC 17025 stresses that labs must plan and address risks to impartiality and validity.", "CTA": "Does your lab audit for these risks?" } 📆 Schedule Control Uses Schedule Trigger to post daily at a set time. Ensures automation with LinkedIn and accurate Google Sheet syncing. 📝 Notes Posts remain professional and concise for a LinkedIn audience Works with any PDF book Supports multi-book pipelines You can filter and tag books by filename or folder for segmenting post styles

Stay ahead of trends by automating your content research. This workflow fetches trending keywords from Google Trends RSS, extracts key insights from top articles, and saves structured summaries in Google Sheets—helping you build a data-driven editorial plan effortlessly. How it works Fetch Google Trends RSS – The workflow retrieves trending keywords along with three related article links. Extract & Process Content – It fetches the content of these articles, cleans the HTML, and generates a concise summary using Jina AI. Store in Google Sheets – The processed insights, including the trending keyword and summary, are saved in a pre-configured Google Sheet. Setup Steps Prepare a Google Sheet – Ensure you have a Google Sheet ready to store the extracted data. Configure API Access – Set up Google Sheets API and any required authentication. Get Jina.ai API key Adjust Workflow Settings – A dedicated configuration node allows you to fine-tune how data is processed and stored. Customization Modify the RSS source to focus on specific Google Trends regions or categories. Adjust the content processing logic to refine how article summaries are created. Expand the workflow to integrate with CMS (e.g., WordPress) for automated content planning. This workflow is ideal for content strategists, SEO professionals, and news publishers who want to quickly identify and act on trending topics without manual research. 🚀 Google Sheets Fields Copy and paste these column headers into your Google Sheet: | Column Name | Description | |------------------------|-------------| | status | Initial status of the keyword (e.g., "idea") | | trending_keyword | Trending keyword extracted from Google Trends | | approx_traffic | Estimated traffic for the trending keyword | | pubDate | Date when the keyword was fetched | | news_item_url1 | URL of the first related news article | | news_item_title1 | Title of the first news article | | news_item_url2 | URL of the second related news article | | news_item_title2 | Title of the second news article | | news_item_url3 | URL of the third related news article | | news_item_title3 | Title of the third news article | | news_item_picture1 | Image URL from the first news article | | news_item_source1 | Source of the first news article | | news_item_picture2 | Image URL from the second news article | | news_item_source2 | Source of the second news article | | news_item_picture3 | Image URL from the third news article | | news_item_source3 | Source of the third news article | | abstract | AI-generated summary of the articles (limited to 49,999 characters) | Instructions Open Google Sheets and create a new spreadsheet. Copy the column names from the table above. Paste them into the first row of your Google Sheet.

🔧 Workflow Summary This system automates LinkedIn lead generation and enrichment in six clear stages: 1. Lead Collection (via Apollo.io) Automatically pulls leads based on keywords, roles, or industries using Apollo’s API. Captures name, job title, company, and LinkedIn profile URL. You can kick off the workflow via form, webhook, WhatsApp, Telegram, or any other custom trigger that passes search parameters. 2. LinkedIn Username Extraction Extracts usernames from LinkedIn profile URLs using a script step. These usernames are required for further enrichment using RapidAPI. 3. Email Retrieval (via Apollo.io User ID) Fetches verified work email using the Apollo User ID. Email validity is double-checked using www.mails.so filtering out undeliverable or inactive emails by checking MX records and deliverability. 4. Profile Summary (via LinkedIn API on RapidAPI) Enriches lead data by pulling bio/summary details to understand their background and expertise. 5. Activity Insights (Posts & Reposts) Collects recent posts or reposts to help craft personalised messages based on what they’re currently engaging with. 6. Leads Sheet Update All data is written into a Google Sheet. New columns are populated dynamically without erasing existing data. ⸻ ✅ Smart Retry Logic Each workflow is equipped with a fail-safe system: Tracks status per row: ✅ done, ❌ failed, ⏳ pending Failed rows are automatically retried after a custom delay (e.g., 2 weeks). Ensures minimal drop-offs and complete data coverage. 📊 Google Sheets Setup Make a copy of the following: Template 1: Apollo Leads Scraper & Enrichment Template 2: Final Enriched Leads The system appends data (like emails, bios, activity) step by step. 🔐 API Credentials Needed 1. Apollo API Sign up and generate API key at Apollo Developer Portal Be sure to enable the “Master API Key” toggle so the same key works for all endpoints. 2. LinkedIn Data API (via RapidAPI) Subscribe at RapidAPI - LinkedIn Data Use your key in the x-rapidapi-key header. 3. Mails.so API Get your API Key from mails.so dashboard 🛠️ Troubleshooting – LinkedIn Lead Machine ✅ Common Mistakes & Fixes 1. API Keys Not Working Make sure API keys for Apollo, RapidAPI, and mails.so are correct. Apollo “Master API Key” must be enabled. Keys should be saved as Generic Credentials in n8n. 2. Leads Not Found Check if the search query (keyword/job title) is too narrow. Apollo might return empty results if the filters are incorrect. 3. LinkedIn URLs Missing or Invalid Ensure Apollo is returning valid LinkedIn URLs. Improper URLs will cause username extraction and enrichment steps to fail. 4. Emails Not Coming Through Apollo may not have verified emails for all leads. mails.so might reject invalid or expired email addresses. 5. Google Sheet Not Updating Make sure the Google Sheet is shared with the right Google account (linked to n8n). Check if the column names match and data isn’t blocked due to formatting. 6. Status Columns Not Changing Each row must have done, failed, or pending in the status column. If the status doesn’t update, the retry logic won’t trigger. 7. RapidAPI Not Returning Data Double-check if username is present and valid. Make sure the RapidAPI plan is active and within limits. 8. Workflow Not Running Check if the trigger node (form, webhook, etc.) is connected and active. Make sure you’re passing the required inputs (keyword, role, etc.). Need Help? Contact www.KrupalPatel.com for support and custom workflow development

📈 How it works This n8n workflow allows you to interact with Binance Spot Trading API directly to: Place Limit Buy and Limit Sell orders Place Market Buy and Market Sell orders Query account info* and *open orders** Cancel all open orders** for a specific symbol All requests are signed using Binance's HMAC SHA256 signature method for secure trading. ⚙️ Setup Steps Create Binance API Credentials in n8n: Go to Credentials > New Choose Binance API Add api_key and api_secret Save as Binance API Import this workflow into your n8n instance. Update default values: In Set Parameter nodes like LimitBuy Parameter, change: symbol (e.g. BTCUSDT) quantity, price as needed Run the workflow manually via the Execute workflow trigger. ✅ Notes Credential node is marked with instructions. HMAC signatures are automatically calculated before making each request. HTTP nodes are preconfigured for Binance API v3. 🔒 No API key or secret is included.

Description: The closest definition to this workflow is a cheaper Modular Version of Perplexity online API empowered by LLM models that outperform the Perplexity Lama Model. This flow provides a seamless way to conduct detailed web searches, extract data, and generate insightful reports based on real-time information. It provides a webhook-based flow that gets any search question and reports back the results via a multi-level web search analysis and domain-specific emulation of an agent to deliver an unbiased expert report. This Flow is Ideal for market research, competitive analysis, or any scenario where actionable, structured insights are needed. A more complete, step-by-step guide is provided within the workflow, ensuring you have all the details to set up and customize each component. This tool is designed to function similarly to Perplexity by performing semantic search, reranking, and follow-up queries. However, it offers a unique advantage—complete customization at every stage. Modify any part of the process, from query refinement to data extraction, allowing you to tailor the workflow to your specific needs. Key Features: AI-Powered Query Generation and Expert Emulation**: Uses Google Gemini to transform user queries into expert-level searches, providing accurate and context-aware results. Dual-Stage Semantic Search with Intelligent Reranking**: Performs an initial search, reranks results, and refines the query based on findings to conduct a second, more targeted search. Top-Result Data Extraction**: Extracts content from the top three results of each search, capturing relevant insights from six total sources. Customizable API Options**: Pre-configured with free APIs (Google Gemini, DuckDuckGo, and Article Extraction APIs) but easily adaptable to other APIs if preferred. Automated, Insightful Reporting**: Synthesizes data into a cohesive report, providing expert-level insights tailored to the user’s query. Instructions for API Setup: This workflow is designed to work with free-tier APIs, offering a cost-effective way to retrieve high-quality data. Here’s how to set up each API, with detailed instructions included in the workflow: Google Gemini API (for Query Generation and Analysis): Visit Google AI Studio and log in. Create a free API key under "Get API Key" → "Create API Key in New Project." The free tier includes up to 15 requests per minute, 1 million tokens per minute, and up to 1,500 requests per day. Brave Search API (for Web Search): To attain the free web search API tier from Brave, follow these steps: Visit api.search.brave.com Create an account Subscribe to the free plan (no charge) Navigate to the API Keys section Generate an API key. For the subscription type, choose "Free". Article Extraction API (for Content Extraction): Register on RapidAPI.com and subscribe to the Article Extraction API. The free plan allows up to 300 extractions per month. Enter your API key in each of the 6 extraction nodes for content retrieval. Alternative: In the workflow, we have provided the full instructions on how to replace the current flow with alternative API Keys and provided suggestions such as Scraper Tech API. Additional Tip: To use other APIs, you can generate a cURL request in RapidAPI’s playground, and then paste it into the HTTP Request node in n8n. This approach streamlines integration by automatically filling in headers and request details. Why Choose This Workflow? The Intelligent Online Web Researcher offers an all-in-one solution for complex, customizable online research. Unlike other tools that provide automated semantic search, this workflow is fully modifiable, allowing you to tailor each step, from the initial query and reranking to data extraction and reporting. With built-in instructions and a structure that’s easy to adapt, it’s ideal for commercial applications that require real-time, high-quality insights. Tags: Online Research, Web Search, Market Analysis, Web Search Automation, Data Extraction, Semantic Search, API Integration, Competitive Intelligence, Business Intelligence, Real-Time Reporting, Web Scrape, Data Crawler, Perplexity

This workflow contains community nodes that are only compatible with the self-hosted version of n8n. How it works This advanced automation builds a fully autonomous SEO blog writer using n8n, Scrapeless, LLMs, and Pinecone vector database. It’s powered by a Retrieval-Augmented Generation (RAG) system that collects high-performing blog content, stores it in a vector store, and then generates new blog posts based on that knowledge—endlessly. Part 1: Build a Knowledge Base from Popular Blogs Scrape existing articles** from a well-established writer (in this case, Mark Manson) using the Scrapeless node. Extract content from blog pages* and store it in *Pinecone**, a powerful vector database that supports similarity search. Use Gemini Embedding 001** or any other supported embedding model to encode blog content into vectors. Result**: You’ll have a searchable vector store of expert-level content, ready to be used for content generation and intelligent search. Part 2: SERP Analysis & AI Blog Generation Use Scrapeless' SERP node to fetch search results based on your keyword and search intent. Send the results to an LLM (like Gemini, OpenRouter, or OpenAI) to generate a keyword analysis report in Markdown → then converted to HTML. Extract long-tail keywords, search intent insights, and content angles from this report. Feed everything into another LLM with access to your Pinecone-stored knowledge base, and generate a fully SEO-optimized blog post. Set up steps Prerequisites Scrapeless API key Pinecone account and index setup An embedding model (Gemini, OpenAI, etc.) n8n instance with Community Node: n8n-nodes-scrapeless installed Credential Configuration Add your Scrapeless and Pinecone credentials in n8n under the "Credentials" tab Choose embedding dimensions according to the model you use (e.g., 768 for Gemini Embedding 001) Key Highlights Clones a real content creator**: Replicates knowledge and writing style from top-performing blog authors. Auto-scrapes hundreds of blog posts** without being blocked. Stores expert content** in a vector DB to build a reusable knowledge base. Performs real-time SERP analysis** using Scrapeless to fetch and analyze search data. Generates SEO blog drafts** using RAG with detailed keyword intelligence. Output includes**: blog title, HTML summary report, long-tail keywords, and AI-written article body. RAG + SEO: The Future of Content Creation This template combines: AI reasoning** from large language models Reliable data scraping** from Scrapeless Scalable storage** via Pinecone vector DB Flexible orchestration** using n8n nodes This is not just an automation—it’s a full-stack SEO content machine that enables you to: Build a domain-specific knowledge base Run intelligent keyword research Generate traffic-ready content on autopilot 💡 Use Cases SaaS content teams cloning competitor success Affiliate marketers scaling high-traffic blog production Agencies offering automated SEO content services AI researchers building personal knowledge bots Writers automating first-draft generation with real-world tone

Who this is for? This workflow is designed for: Marketing analysts, **SEO specialists, and content strategists who want automated intelligence on their online competitors. Growth teams** that need quick insights from SERP (Search Engine Results Pages) without manual data scraping. Agencies** managing multiple clients’ SEO presence and tracking competitive positioning in real-time. What problem is this workflow solving? Manual competitor research is time-consuming, fragmented, and often lacks actionable insights. This workflow automates the entire process by: Fetching SERP results from multiple search engines (Google, Bing, Yandex, DuckDuckGo) using Thordata’s Scraper API. Using OpenAI GPT-4.1-mini to analyze, summarize, and extract keyword opportunities, topic clusters, and competitor weaknesses. Producing structured, JSON-based insights ready for dashboards or reports. Essentially, it transforms raw SERP data into strategic marketing intelligence — saving hours of research time. What this workflow does Here’s a step-by-step overview of how the workflow operates: Step 1: Manual Trigger Initiates the process on demand when you click “Execute Workflow.” Step 2: Set the Input Query The “Set Input Fields” node defines your search query, such as: > “Top SEO strategies for e-commerce in 2025” Step 3: Multi-Engine SERP Fetching Four HTTP request tools send the query to Thordata Scraper API to retrieve results from: Google Bing Yandex DuckDuckGo Each uses Bearer Authentication configured via “Thordata SERP Bearer Auth Account.” Step 4: AI Agent Processing The LangChain AI Agent orchestrates the data flow, combining inputs and preparing them for structured analysis. Step 5: SEO Analysis The SEO Analyst node (powered by GPT-4.1-mini) parses SERP results into a structured schema, extracting: Competitor domains Page titles & content types Ranking positions Keyword overlaps Traffic share estimations Strengths and weaknesses Step 6: Summarization The Summarize the content node distills complex data into a concise executive summary using GPT-4.1-mini. Step 7: Keyword & Topic Extraction The Keyword and Topic Analysis node extracts: Primary and secondary keywords Topic clusters and content gaps SEO strength scores Competitor insights Step 8: Output Formatting The Structured Output Parser ensures results are clean, validated JSON objects for further integration (e.g., Google Sheets, Notion, or dashboards). 4. Setup Prerequisites n8n Cloud or Self-Hosted instance** Thordata Scraper API Key** (for SERP data retrieval) OpenAI API Key** (for GPT-based reasoning) Setup Steps Add Credentials Go to Credentials → Add New → HTTP Bearer Auth → Paste your Thordata API token. Add OpenAI API Credentials for the GPT model. Import the Workflow Copy the provided JSON or upload it into your n8n instance. Set Input In the “Set the Input Fields” node, replace the example query with your desired topic, e.g.: “Google Search for Top SEO strategies for e-commerce in 2025” Execute Click “Execute Workflow” to run the analysis. How to customize this workflow to your needs Modify Search Query Change the search_query variable in the Set Node to any target keyword or topic. Change AI Model In the OpenAI Chat Model nodes, you can switch from gpt-4.1-mini to another model for better quality or lower cost. Extend Analysis Edit the JSON schema in the “Information Extractor” nodes to include: Sentiment analysis of top pages SERP volatility metrics Content freshness indicators Export Results Connect the output to: Google Sheets / Airtable** for analytics Notion / Slack** for team reporting Webhook / Database** for automated storage Summary This workflow creates an AI-powered Competitor Intelligence System inside n8n by blending: Real-time SERP scraping (Thordata) Automated AI reasoning (OpenAI GPT-4.1-mini) Structured data extraction (LangChain Information Extractors)