AI Workflows
Artificial Intelligence tools and workflows
Automate Professional COE Blog Creation and Publishing with AI and Google Drive
This n8n workflow automates the creation of professional Center of Excellence (COE) blog posts from chat topics using AI, and publishes them directly to Google Drive.
n8n$9.99Automate AI News Curation from TechCrunch to Slack Using OpenAI
This n8n workflow automatically scrapes TechCrunch articles, filters for AI-related content using OpenAI, and delivers curated summaries to your Slack channels. Ideal for teams needing up-to-date AI industry insights without manual browsing.
n8n$9.99Automate Podcast Script Creation from Telegram Voice Notes
Effortlessly convert Telegram voice messages into structured podcast scripts in Notion using AI transcription and analysis.
n8n$4.99Legal Case Research Extractor and Data Miner with Bright Data MCP & Google Gemini
### Notice Community nodes can only be installed on self-hosted instances of n8n. ### Who this is for The Legal Case Research Extractor is a powerful automated workflow designed for legal tech teams, researchers, law firms, and data scientists focused on transforming unstructured legal case data into actionable, structured insights. This workflow is tailored for: - Legal Researchers automating case law data mining - Litigation Support Teams handling large volumes of case records - Legal Tech Startups building AI-powered legal research assistants - Compliance Analysts extracting case-specific insights - AI Developers working on legal NLP, summarization, and search engines ### What problem is this workflow solving? Legal case data is often locked in semi-structured or raw HTML formats, scattered across jurisdiction-specific websites. Manually extracting and processing this data is tedious and inefficient. This workflow automates: - Extraction of legal case data via Bright Data's powerful MCP infrastructure - Parsing of HTML into clean, readable text using Google Gemini LLM - Structuring and delivering the output through webhook and file storage ### What this workflow does **Input** - Set the Legal Case Research URL node is responsible for setting the legal case URL for the data extraction. **Bright Data MCP Data Extractor** - Bright Data MCP Client For Legal Case Research node is responsible for the legal case extraction via the Bright Data MCP tool - scrape_as_html **Case Extractor** - Google Gemini based Case Extractor is responsible for producing a paginated list of cases **Loop through Legal Case URLs** - Receives a collection of legal case links to process - Each URL represents a different case from a target legal website **Bright Data MCP Scraping** - Utilizes Bright Data's scrape_as_html MCP mode - Retrieves raw HTML content of each legal case **Google Gemini LLM Extraction** - Transforms raw HTML into clean, structured text - Performs additional information extraction if required (e.g., case summary, court, jurisdiction etc.) **Webhook Notification** - Sends extracted legal case content to a configurable webhook URL - Enables downstream processing or storage in legal databases **Binary Conversion & File Persistence** - Converts the structured text to binary format - Saves the final response to disk for archival or further processing ### Pre-conditions 1. Knowledge of Model Context Protocol (MCP) is highly essential. Please read this blog post - [model-context-protocol](https://www.anthropic.com/news/model-context-protocol) 2. You need to have the [Bright Data](https://brightdata.com/) account and do the necessary setup as mentioned in the **Setup** section below. 3. You need to have the Google Gemini API Key. Visit [Google AI Studio](https://aistudio.google.com/) 4. You need to install the Bright Data MCP Server [@brightdata/mcp](https://www.npmjs.com/package/@brightdata/mcp) 5. You need to install the [n8n-nodes-mcp](https://github.com/nerding-io/n8n-nodes-mcp) ### Setup 1. Please make sure to set up n8n locally with MCP Servers by navigating to [n8n-nodes-mcp](https://www.youtube.com/watch?v=NUb73ErUCsA) 2. Please make sure to install the Bright Data MCP Server [@brightdata/mcp](https://www.npmjs.com/package/@brightdata/mcp) on your local machine. 3. Sign up at [Bright Data](https://brightdata.com/). 4. Create a Web Unlocker proxy zone called mcp_unlocker on Bright Data control panel. 5. Navigate to Proxies & Scraping and create a new Web Unlocker zone by selecting Web Unlocker API under Scraping Solutions. 6. In n8n, configure the Google Gemini (PaLM) API account with the Google Gemini API key (or access through Vertex AI or proxy). 7. In n8n, configure the credentials to connect with MCP Client (SDIO) account with the Bright Data MCP Server as shown below.  Make sure to copy the Bright Data API_TOKEN within the Environments textbox above as API_TOKEN=<your-token> ### How to customize this workflow to your needs **Target New Legal Portals** - Modify the legal case input URLs to scrape from different state or federal case databases **Customize LLM Extraction** - Modify the prompt to extract specific fields: case number, plaintiff, case summary, outcome, legal precedents etc. - Add a summarization step if needed **Enhance Loop Handling** - Integrate with a Google Sheet or API to dynamically fetch case URLs - Add error handling logic to skip failed cases and log them **Improve Security & Compliance** - Redact sensitive information before sending via webhook - Store processed case data in encrypted cloud storage **Output Formats** - Save as PDF, JSON, or Markdown - Enable output to cloud storage (S3, Google Drive) or legal document management systems
n8n$14.99Automate Supabase Management with AI-Powered Natural Language Commands
This workflow allows you to manage your Supabase database using natural language commands, facilitated by an AI agent. It translates chat messages into database actions, enabling you to create, update, delete, or search records without writing SQL.
n8n$14.99Automate Translation of WordPress Posts with AI and Google Docs
This workflow automates the translation of WordPress blog posts into any language using AI Translate Pro, and stores the translated content in Google Docs for easy access and editing.
n8n$9.99Stock Fundamental Analysis & AI-Powered Reports with Mistral and AlphaVantage
# Fundamental Analysis, Stock Analysis, and AI Integration in the Fundamental Analysis Tool --- ## Overview of the Tool The **Fundamental Analysis Tool** is an automated workflow designed to evaluate a stock's fundamentals using financial data and AI-driven insights. Built on the n8n automation platform, it: 1. **Collects** financial data for a user-specified stock from AlphaVantage. 2. **Processes** and structures this data for analysis. 3. **Analyzes** the data using the Mistral AI model to provide expert-level insights. 4. **Generates** a visually appealing HTML report with charts and delivers it via email. The tool is triggered by a form where users input a **stock symbol** (e.g., NVDA for NVIDIA) and their **email address**. From there, it follows a three-stage process: **data retrieval**, **data processing**, and **AI analysis with report generation**. --- ## 1. Fundamental Analysis: The Foundation Fundamental analysis involves evaluating a company's intrinsic value by examining its financial health, competitive position, and market environment. This tool performs fundamental analysis by: ### Data Retrieval - **Data Types**: Six types of data are retrieved via HTTP requests: - **Overview**: General company details (e.g., sector, industry, market cap). - **Income Statement**: Revenue, net income, and profitability metrics. - **Balance Sheet**: Assets, liabilities, and equity. - **Cash Flow**: Operating, investing, and financing cash flows. - **Earnings Calendar**: Upcoming earnings events. - **Earnings**: Historical earnings data (annual and quarterly). ### Key Metrics Analyzed The tool structures this data into **8 categories** critical to fundamental analysis, as defined in the Code1 node: 1. **Economic Moats & Competitive Advantage**: Assesses sustainable advantages (e.g., R&D spending, gross profit). 2. **Financial Health & Profitability**: Examines ROE, debt levels, and dividend yield. 3. **Valuation & Market Sentiment**: Evaluates P/E ratio, PEG ratio, and book value. 4. **Management & Capital Allocation**: Reviews market cap justification and cash allocation (e.g., R&D, buybacks). 5. **Industry & Risk Exposure**: Analyzes revenue cyclicality and geopolitical risks. 6. **Key Metrics to Probe**: Investigates net income trends and gross margins. 7. **Red Flags**: Identifies risks like inventory issues or stock dilution. 8. **Final Checklist**: Summarizes pricing power and risk/reward potential. These categories cover the core pillars of fundamental analysis, ensuring a holistic evaluation of the stock's intrinsic value and risks. --- ## 2. Stock Analysis: Tailored Insights The tool performs stock-specific analysis by focusing on the user-provided **stock symbol**. Here's how it tailors the process: ### Input and Customization - **Form Submission**: Users enter a stock symbol (e.g., NVDA) and email via the On Form Submission node. - **Dynamic Data Fetching**: The Set Variables node passes the stock symbol to the API calls, ensuring the analysis is specific to the chosen stock. ### Processing for Relevance - **Data Filtering**: The workflow limits historical data to the **last 5 years** (via the Limit node), focusing on recent trends. - **Merging and Cleaning**: The Merge and Code2 nodes combine and refine the data, removing irrelevant fields (e.g., quarterly reports) and aggregating annual reports for consistency. ### Output - The final report is titled with the stock's name (e.g., Fundamental Analysis - NVIDIA), ensuring the analysis is clearly tied to the user's chosen stock. This stock-specific approach makes the tool practical for investors analyzing individual companies rather than broad market trends. --- ## 3. AI Integration: Expert-Level Insights The integration of **AI** (via the Mistral model or others) is what sets this tool apart, automating complex analysis and report generation. Here's how AI is woven into the workflow: ### Data Preparation for AI - **Structuring**: The Code1 node organizes the raw data into a JSON schema aligned with the eight fundamental analysis categories. - This structured data is fed into the AI for analysis. ### AI Analysis - **Node**: Basic LLM Chain uses the **Mistral AI model**. - **Prompt**: The AI is instructed to act as an expert financial advisor with 50 years of experience and answer specific questions for each category, such as: - *Economic Moats*: What sustainable competitive advantages protect the company's margins? - *Financial Health*: Is ROE driven by leverage or true profitability? - *Red Flags*: Are supply chain issues a concern? - **Output**: The AI generates a JSON response with detailed insights, e.g.: ```json { "Economic Moats & Competitive Advantage": "NVIDIA's leadership in GPU technology and strong R&D investment...", "Financial Health & Profitability": "ROE of 25% is exceptional, driven by profitability rather than leverage...", ... } ```
n8n$24.99Create AI Videos with Scripts, Images & HeyGen Avatars (**LIMITED-TIME OFFER**)
### Short Content Automation (AI Video System with Bulk Gen, Avatar & Music Customization) **LIMITED-TIME OFFER: AI Video Automation (Previously $59)** [Previously template](https://n8n.io/workflows/4107-create-ai-videos-with-openai-scripts-leonardo-images-and-heygen-avatars/)  **Transform Your Content Game in Minutes - Save Hours of Work!** Introducing a powerful **AI Video System** to automate short video creation - from **scriptwriting** to **avatar + image generation**, **music**, and **assembly** - all with n8n. This is **not** a basic template. It's a **complete, customizable system** with step-by-step videos and my personal enhancements.  --- ### Why $10 Now? (Regular Price: $59) Over **4000 people** viewed my original automation - [see it here](https://n8n.io/workflows/4107-create-ai-videos-with-openai-scripts-leonardo-images-and-heygen-avatars) - and I receive many messages from people who want it. I learned n8n from **free templates** and I want to give back. People asking for a more accessible price - this is my answer. --- ### Limited-Time Deal: 7 Days Only! After that, this price **goes away** and the blueprint will be **unlisted**. --- ### What You Get * Full n8n Workflow: From text prompt to ready-to-publish short video * AI Tools Integration: OpenAI, Leonardo.AI, HeyGen, etc. * Customization Options: Avatar, Voice, Music, Style * Video Setup Guide: Step-by-step walkthrough * Clean UI, easy-to-use, even if you're not a developer --- ### Made With Love by an n8n Creator This system is based on **months of refinement and feedback** from users like you. Now it's your turn to scale your content, automate your work, and stand out. --- ### Only $10 - Let's Build Together. > Get it now. Use it forever. No subscriptions. Just value.
n8n$24.99Automate EU Sustainability Legislation Monitoring with n8n, OpenAI, and Google
This workflow automates the monitoring of EU legislative procedures related to sustainability, using n8n to scrape data, OpenAI for classification, and Google Sheets and Tasks for organization.
n8n$14.99AI-Driven Document Interaction Using Nextcloud and OpenAI
This workflow enables AI-powered interactions with documents stored in Nextcloud, utilizing LangChain and OpenAI to answer user queries based on document content.
n8n$14.99Automate Cloudflare Certificate Management with AI Integration
Deploy a fully functional MCP server to manage Cloudflare certificates with AI-driven automation. This workflow requires no configuration and supports all certificate operations.
n8n$4.99Generate Contextual Recommendations from Slack Using Pinecone
This advanced Retrieval-Augmented Generation (RAG) automation template for n8n enables contextual, real-time recommendations using Slack messages as input. The workflow extracts referenced documents from Google Drive, performs semantic retrieval from Pinecone, and generates next-step advice using GPT-4 - tailored specifically for executives and knowledge workers. Perfect for AI copilots, Slack-based assistants, or CEO coaching tools, this no-code RAG implementation gives you the building blocks to combine unstructured inputs with memory-augmented intelligence. **What This Template Does** - Triggers from a Slack Message or Mention Monitors a Slack channel using a bot, capturing user input in real-time. - Extracts Key Info from Message GPT-4 parses the message to identify the subject person and Google Drive link (if present). - Downloads File from Google Drive Automatically fetches and extracts PDF content using the built-in extractor. - Retrieves Metadata from Google Sheets & Pinecone Looks up user ID from Google Sheets and retrieves context from Pinecone based on embeddings and reranking. Contextual Response via GPT-4 (RAG) Combines user data and document context to generate a single, actionable next step using a tightly scoped GPT-4 prompt. - Auto-Fixes & Structures Output Ensures formatted response with recommended_action, rationale, and optional risk_note. - Sends Final Output Back to Slack Posts the recommendation directly to the channel as a reply. **Required Integrations** - Slack Bot with channels:history & app_mentions:read - Google Drive OAuth for file fetching - Google Sheets for ID mapping - Pinecone for vector document retrieval - Azure OpenAI or OpenAI GPT-4 for language processing - (Optional) Cohere for reranking results **Ideal Use Cases** - Executive coaching bots (e.g., for CEOs or founders) - Slack-based internal AI assistants - AI-powered document summarization with memory - Actionable recommendations based on real Slack conversations - Enterprise knowledge augmentation from vector DBs **Why This Template Stands Out** 1. Combines live Slack interaction, file ingestion, and Pinecone retrieval into a fully RAG-powered response system. 2. AI prompts are carefully scoped for actionable, context-aware, and time-bound responses. 3. No-code setup with modular components for scaling or adapting to new use cases (e.g., different roles or goals).
n8n$14.99Detect text within new Google Drive images using Google Cloud Vision (OCR) and save it to Google Drive as a new file
Every time a new image containing text is uploaded to your Google Drive folder, Make will detect the text using Google Cloud Vision (OCR) and save the text as a new file to a specified Google Drive folder.
Make$4.99Automate Video Creation with Bytedance Omni-Human AI via Replicate API
Streamline your video production process by integrating the Bytedance Omni-Human AI model with the Replicate API in n8n. This workflow automates video generation, handling API authentication, parameter setup, and error management for seamless video creation.
n8n$9.99Automate Podcast Transcript Summaries and Keyword Extraction with OpenAI and Gmail
Transform your podcast episodes into concise summaries and extract key SEO-friendly keywords using OpenAI, then receive the results directly in your Gmail inbox.
n8n$4.99Interactive AI Chatbot with Custom JavaScript Color Selector
This workflow creates an AI-powered conversational agent that returns a random color, excluding specified colors, using custom JavaScript code. Ideal for dynamic user interactions and personalized responses.
n8n$4.99Automate LinkedIn Candidate Sourcing with Google X-Ray and OpenAI
Streamline your recruitment process by automatically generating and executing LinkedIn Boolean searches using OpenAI and Google X-Ray, storing results in Google Sheets.
n8n$14.99Generate AI-Ready XML Sitemaps from Screaming Frog Website Crawls
This workflow helps you generate an **llms.txt** file (if you're unfamiliar with it, check out [this article](https://towardsdatascience.com/llms-txt-414d5121bcb3/)) using a **Screaming Frog export**. [Screaming Frog](https://www.screamingfrog.co.uk/seo-spider/) is a well-known website crawler. You can easily crawl a website. Then, export the **internal_html** section in CSV format. ## How It Works: A **form** allows you to enter: - The **name of the website** - A **short description** - The **internal_html.csv** file from your Screaming Frog export Once the form is submitted, the **workflow is triggered automatically**, and you can **download the llms.txt file directly from n8n**. ## Downloading the File Since the last node in this workflow is **Convert to File**, you will need to **download the file directly from the n8n UI**. However, you can easily **add a node** (e.g., Google Drive, OneDrive) to automatically upload the file **wherever you want**. ## AI-Powered Filtering (Optional): This workflow includes a **text classifier node**, which is **deactivated by default**. - You can **activate it** to apply a more **intelligent filter** to select URLs for the `llms.txt` file. - Consider modifying the **description** in the classifier node to specify the type of URLs you want to include. ## How to Use This Workflow 1. **Crawl the website** you want to generate an `llms.txt` file for using **Screaming Frog**. 2. **Export the internal_html** section in CSV format.  3. In **n8n**, click **Test Workflow**, fill in the form, and **upload** the `internal_html.csv` file. 4. Once the workflow is complete, go to the **Export to File** node and **download the output**. **That's it! You now have your llms.txt file!** **Recommended Usage:** Use this workflow **directly in the n8n UI by clicking** Test Workflow and uploading the file in the form.
n8n$14.99Automate Airline Web Check-in Data Extraction and Storage
This workflow extracts airline web check-in information from Google Sheets, processes it using AI to generate structured JSON data, and stores it in a Postgres vector database for advanced search capabilities.
n8n$9.99Advanced WordPress Auto-Blogging with Deep Research and AI Content Generation
Automate the creation of in-depth WordPress blog content using multi-level research and AI-driven content generation. This workflow enhances content depth and authority by integrating PerplexityAI for research and OpenAI for content creation.
n8n$24.99Generate & Test SQL Code with GP/OpenRouter AI and PostgreSQL Sandbox
This is the core AI agent used for [isra36.com](https://isra36.com). Don't trust complex AI-generated SQL queries without double-checking them in a safe environment. That's where isra36 comes in. It automatically creates a test environment with the necessary data, generates code for your task, runs it to double-check for correctness, and handles errors if necessary. If you enable auto-fixing, isra36 will detect and fix issues on its own. If not, it will ask for your permission before making changes during debugging. In the end, you get thoroughly verified code along with full details about the environment it ran in. ## Setup It is an embedded chat for the website, but you can pin input data and run it on your own n8n instance. ### Input data 1. `sessionId`: uuid_v4. **Required** to handle ongoing conversations and to create table names (used as a prefix). 2. `threadId`: string | nullable. If `aiProvider` is **openai**, conversation history is managed on OpenAI's side. This is not needed in the first request—it will start a new conversation. For ongoing conversations, you must provide this value. You can get it from the `OpenAIMainBrain` node output after the first run. If you want to start a new conversation, just leave it as `null`. 3. `apiKey`: string. Your API key for the selected `aiProvider`. 4. `aiProvider`: string. Currently supported values: **openai**, **openrouter**. 5. `model`: string. The AI model key (e.g., `gpt-4.1`, `o3-mini`, or any supported model key from OpenRouter). 6. `autoErrorFixing`: boolean. If `true`, it will automatically fix errors encountered when running code in the environment. If `false`, it will ask for your permission before attempting a fix. 7. `chatInput`: string. The user's prompt or message. 8. `currentDbSchemaWithData`: string. A JSON representation of the database schema with sample data. Used to inform the AI about the current database structure during an ongoing conversation. Please use the `[]` value in the first request. Example string for filled db structure: `{users:[{id:1,name:John Doe,email:john.d@example.com},{id:2,name:Jane Smith,email:jane.s@example.com}],products:[{product_id:101,product_name:Laptop,price:999.99}]}` Make sure to fill in your credentials: 1. Your OpenAI or OpenRouter API key. 2. Access to a local PostgreSQL database for test execution. You can view your generated tables using your preferred PostgreSQL GUI. We recommend **DBeaver**. Alternatively, you can activate the **Deactivated DB Visualization** nodes below. To use them, connect each to the most recent successful **Set** node and manually adjust the output. However, the easiest and most efficient method is to use a GUI. ## Workflow Explanation 1. We store all input values in the `localVariables` node. Please use this node to *get* the necessary data. 2. `OpenAI` has a built-in assistant that manages chat history on their side. For OpenRouter, we handle chat history locally. That's why we use separate nodes like `ifOpenAi` and `isOpenAi`. Note that `if` logic can also be used *inside* nodes. 3. The `AutoErrorFixing` loop will run only a limited number of times, as defined by the `isMaxAutoErrorReached` node. This prevents infinite loops. 4. The `Execute_AI_result` node connects to the PostgreSQL test database used to execute queries. ## Guidance on customization This setup is built for **PostgreSQL**, but it can be adapted to any **programming language**, and the logic can be extended to any **programming framework**. To customize the logic for other programming languages: 1. Change the `instruction` parameter in the `localVariables` node. 2. Replace the `Execute_AI_result` PostgreSQL node with another executable node. For example, you can use the HTTP Request node. 3. Update the `GenerateErrorPrompt` node's `prompt` parameter to generate code specific to your target language or framework. *Any workflows built on top of this must credit the original author and be released under an open-source license.*
n8n$24.99Automate TikTok Video Transcription and Translation with OpenAI and Google Docs
This workflow automates the extraction, translation, and storage of TikTok video transcripts using OpenAI GPT-4 and Google Docs. It's ideal for content creators and marketers who need efficient, AI-powered text processing.
n8n$9.99Automate Comprehensive Stock Analysis with AI and SEC 10K Reports
Leverage AI to automate the analysis of SEC 10K reports, streamlining the stock analysis process from planning to publication. This workflow involves a Senior Research Analyst, a team of Research Analysts, and a Senior Editor to produce detailed stock reports efficiently.
n8n$14.99Automate Intelligent Web and Local Searches Using Brave Search API and Google Gemini
This n8n workflow leverages AI to intelligently perform web and local searches using the Brave Search API, powered by Google Gemini. It selects the appropriate search tool based on natural language queries and exposes this functionality as a single, callable MCP tool.
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