by Yaron Been
Scrape Indeed Job Listings for Hiring Signals Using Bright Data and LLMs How the flow runs Fill the form with job position you're hunting for. Bright data's scraper will scrape Indeed based on your requirments. Workflow waits for the snapshot. Data returns as JSON. Jobs append to Google Sheets. Each row goes to an LLM to analyze if you're a good fit for the job (based on your prompts). The LLMswrites YES or NO next to each job opportunity, helping you find job posts that are relevant to you. What you need Google Sheets with our template. Bright Data dataset and API key. OpenAI key for GPT‑4o mini (or any other LLM). n8n with required nodes. Form fields To Fill Job Location** – city or region. Keyword** – role or skills. Country** – two‑letter code. Setup steps Copy the sheet template link. Import the JSON workflow. Add your credentials in nodes. Test the form manually. Add a schedule if desired. Bright Data filter example [ { "country": "US", "domain": "indeed.com", "keyword_search": "Growth Marketer", "location": "Miami", "date_posted": "Last 24 hours" } ] Tips -Choose Last 24 hours often. -Increase wait time for big snapshots. -Narrow keywords to save credits. **Need help? **Email me anytime: Yaron@nofluff.online YouTube: @YaronBeen LinkedIn: https://www.linkedin.com/in/yaronbeen/ Bright Data Docs: https://docs.brightdata.com/introduction
by RealSimple Solutions
🧠 Analyze and Diagnose n8n Workflow Errors Automatically via OpenAI and Email > ⚠️ This template is available on ☁️ Cloud & 🖥️ self-hosted n8n instances with the OpenAI node enabled. 👤 Who is this for? This workflow is designed for n8n developers, automation engineers, and DevOps teams who want to automatically capture and analyze workflow errors, and receive professional HTML-styled diagnostics directly in their inbox. 💥 What problem does this solve? Manually troubleshooting failed workflows in n8n can be time-consuming. This template streamlines error detection by: Capturing workflow failures using the Error Trigger node Diagnosing root causes with the help of OpenAI Sending a fully-formatted, human-readable HTML error report via email Including practical resolutions and next-step suggestions This helps you or your team resolve issues faster and avoid repeated manual debugging. ⚙️ What this workflow does ⚡ Triggers on any n8n workflow error 📦 Extracts relevant error metadata including node, execution ID, and timestamps 🧠 Sends error content to OpenAI for analysis and recommendations 💌 Generates an HTML email report with inline styles and clear formatting 📥 Emails the result to a system administrator or support email 🛠️ Setup Install the OpenAI node in your self-hosted n8n instance. Add your OpenAI API Key securely in credentials. Configure the SMTP Email node with your email credentials. Adjust the Error Trigger to monitor specific workflows or all workflows. Set your preferred admin or dev email address in the final node. 🔧 How to customize this workflow to your needs 🧩 Use a [Set node] to define your variables, such as: Default admin email Workflow filter (optional) ✍️ Customize the prompt sent to OpenAI if you want deeper or more specific analysis 🎨 Modify the email HTML styles to match your brand or internal format 💾 Add additional logging (e.g., to Airtable, Google Sheets, or Notion) for long-term error tracking 📌 Sticky Note Title: Automated Error Reporter with AI-Powered Diagnosis Description: Captures any n8n error, sends it to OpenAI, and emails a beautiful HTML report to the administrator with steps to resolve the issue. Requires OpenAI credentials and SMTP configured.
by SamirLiu
📝 What this workflow does Every morning at 8 a.m., this workflow fetches the latest AI-related articles from both GNews and NewsAPI. It merges up to 40 new articles daily, selects the 15 most relevant ones on AI technology and applications, and uses GPT-4.1 to generate concise summaries in accurate Traditional Chinese (while preserving essential English technical terms). Each summary also includes the article link for easy referral. The compiled digest is then posted to your designated Telegram account or group. 👥 Who is this for? AI enthusiasts, professionals, and anyone interested in artificial intelligence news Individuals and teams wanting a concise daily digest of AI developments in Traditional Chinese Telegram users who prefer automated information delivery 🎯 What problem does this workflow solve? With the rapid evolution of AI technology, it can be overwhelming to keep up with new developments. This workflow addresses information overload by automatically collecting, summarizing, and translating the most important AI news each morning — all delivered conveniently to your chosen Telegram channel or group. ⚙️ Setup 🔑 Add NewsAPI and GNews API Keys Register for accounts on NewsAPI.org and GNews to obtain your API keys. Input your NewsAPI key directly into the Fetch NewsAPI articles node. Input your GNews API key into the Fetch GNews articles node. 🤖 Set up your Telegram Bot Create a Telegram Bot via BotFather and copy the generated Bot Token. In n8n, create Telegram Bot credentials using this token. In the Send summary to Telegram node, enter the chat ID of your target user, group, or channel to receive the messages. 🧠 Configure OpenAI Credentials In n8n, create a new credential using your OpenAI API key. Assign this credential to the GPT-4.1 Model node (or equivalent OpenAI/AI nodes). After completing these steps, your workflow is fully configured to fetch, summarize, and deliver daily AI news to your selected Telegram chat automatically. 🛠️ How to customize this workflow 🔍 Change the topic:** Update the keywords in the NewsAPI and GNews nodes for other subjects (e.g., "blockchain", "quantum computing"). ⏰ Adjust delivery time:** Modify the scheduled trigger to your preferred hour. ✍️ Tweak summary style or language:** Refine the prompt in the AI summarizer node for different tones or translate into other languages as needed. 📦 Dependencies NewsAPI account GNews account Telegram Bot OpenAI API access (for GPT-4.1) or compatible AI model for Langchain agent
by Samir Saci
Tags: Accessibility, SEO, Blogging, Marketing, Automation, AI, Web Auditing Context Hey! I’m Samir, a Supply Chain Engineer and Data Scientist from Paris, and the founder of LogiGreen Consulting. In my personal blog, I share insights on how to use AI, automation, and data analytics to improve logistics, operations, and digital sustainability practices. > Have you heard about accessibility? In this workflow, I use n8n to improve the quality of alternative texts for images on my personal website. 📬 For business inquiries, you can connect with me on LinkedIn Who is this template for? This workflow is for: Bloggers* and *website owners* who want to *improve accessibility** SEO professionals** looking to boost page performance Web developers* and *product teams** automating web audits What does it do? This n8n workflow: 🔍 Downloads the HTML of a blog or web page 🖼️ Extracts all ` tags and their alt` attributes 📉 Detects missing or too-short alt texts 🤖 Sends those images to GPT-4o (with vision) to generate new alt descriptions 📄 Saves the results into a Google Sheet, updating the alt text when needed How it works Set a page URL using the Set node Download HTML content Extract image src and alt using a Code node Store results in a Google Sheet Filter images with altLength < 50 Send image URL to GPT-4o Update the Google Sheet with the newly generated newAlt text The AI alt texts are concise, descriptive, and accessibility-compliant. What do I need to get started? You’ll need: A Google Sheet to store the audit results An OpenAI account with GPT-4o access Follow the Guide! Follow the sticky notes in the workflow or check my tutorial to configure each node and start using AI to improve the accessibility of your website. 🎥 Watch My Tutorial Notes GPT-generated alt texts are limited to ~125–150 characters for best results Use this to comply with WCAG and improve Google indexing Easily adapt it to audit multiple domains or e-commerce catalogues This workflow was built using n8n version 1.85.4 Submitted: April 21, 2025
by Alex Kim
Automatically convert documents from Google Drive into vector embeddings using OpenAI, LangChain, and PGVector — fully automated through n8n. ⚙️ What It Does This workflow monitors a Google Drive folder for new files, supports multiple file types (PDF, TXT, JSON), and processes them into vector embeddings using OpenAI’s text-embedding-3-small model. These embeddings are stored in a Postgres database using the PGVector extension, making them query-ready for semantic search or RAG-based AI agents. After successful processing, files are moved to a separate “vectorized” folder to avoid duplication. 💡 Use Cases Powering Retrieval-Augmented Generation (RAG) AI agents Semantic search across private documents AI assistant knowledge ingestion Automated document pipelines for indexing or classification 🧠 Workflow Highlights Trigger Options:** Manual or Scheduled (3 AM daily by default) Supported File Types:** PDF, TXT, JSON Embedding Stack:** LangChain Text Splitter, OpenAI Embeddings, PGVector Deduplication:** Files are moved after processing License:** CC BY-SA 4.0 Author:** AlexK1919 🛠 What You’ll Need Google Drive OAuth2** credentials (connected to Search Folder, Download File, and Move File nodes) OpenAI API Key** (used in the Embeddings OpenAI node) Postgres + PGVector** database (connected in the Postgres PGVector Store node) 🔧 Step-by-Step Setup Instructions Create Google OAuth2 credentials in n8n and connect them to all Google Drive nodes. Set your source folder ID in the Search Folder node — this is where incoming files are placed. Set your processed folder ID in the Move File node — files will be moved here after vectorization. Ensure you have a PGVector-enabled Postgres instance and input the table name and collection in the Postgres PGVector Store node. Add your OpenAI credentials to the Embeddings OpenAI node and select text-embedding-3-small. Optional: Activate the Schedule Trigger node to run daily or configure your own schedule. Run manually by triggering When clicking ‘Test workflow’ for on-demand ingestion. 🧩 Customization Tips Want to support more file types or enhance the pipeline? Add new extractors**: Use Extract from File with other formats like DOCX, Markdown, or HTML. Refine logic by file type**: The Switch node routes files to the correct extraction method based on MIME type (application/pdf, text/plain, application/json). Pre-process with OCR**: Add an OCR step before extraction to handle scanned PDFs or images. Add filters**: Enhance the Search Folder or Switch node logic to skip specific files or folders. 📄 License This workflow is available under Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license. You are free to use, adapt, and share this workflow for non-commercial purposes under the terms of this license. Full license details: https://creativecommons.org/licenses/by-nc-sa/4.0/
by Ranjan Dailata
Who this is for? Extract & Summarize Yelp Business Review is an automated workflow that extracts the Yelp business reviews using Bright Data Web Unlocker, process and formats the raw data, summarizes using the Google Gemini's LLM, and forward the concise summary with the review respose to a specified webhook endpoint. This workflow is tailored for: Local SEO Specialists who need structured insights from Yelp reviews to optimize listings. Business Owners wanting quick summaries of what customers love or complain about. Reputation Managers who monitor brand sentiment and identify customer pain points. Data Analysts & Researchers extracting Yelp review patterns at scale. AI Product Builders needing clean Yelp review data as input for their LLMs or recommender systems. What problem is this workflow solving? Yelp reviews are rich in customer sentiment but messy to work with manually. This workflow solves: The pain of scraping Yelp review content manually. The challenge of building the structured data with the summary. The need for structured outputs suitable for analysis, reports, or AI input. What this workflow does This automated pipeline does the following: Bright Data Integration**: Queries Yelp and scrapes business listing data using Bright Data's Web Unlocker. Structured Data Formatting**: Formats the Yelp review data to a structured response in JSON format. Google Gemini Summarization**: Sends the cleaned reviews to Google Gemini to: Output Delivery**: Returns the structured response with the concise summary over the webhook endpoint. Setup Sign up at Bright Data. Navigate to Proxies & Scraping and create a new Web Unlocker zone by selecting Web Unlocker API under Scraping Solutions. In n8n, configure the Header Auth account under Credentials (Generic Auth Type: Header Authentication). The Value field should be set with the Bearer XXXXXXXXXXXXXX. The XXXXXXXXXXXXXX should be replaced by the Web Unlocker Token. In n8n, configure the Google Gemini(PaLM) Api account with the Google Gemini API key (or access through Vertex AI or proxy). Update the Yelp Business Review URL with the Bright Data zone by navigating to the Set Yelp URL with the Bright Data Zone node. Update the Webhook Notifier for the merged response node with the Webhook endpoint of your choice. How to customize this workflow to your needs This workflow is built to be flexible - whether you’re a market researcher, entrepreneur, or data analyst. Here's how you can adapt it to fit your specific use case: Target Specific Business Categories** Update the Yelp Business Review input to scrape different businesses like gyms, salons etc. Limit Reviews** Add filters by description, location, page range to get the top reviews. Tweak the Data Extraction Node** Update the Structured Data Extractor node Output Parser for building the JSON response with the appropriate fields or attributes. Tweak the Summarization Prompt** Modify the Gemini prompt to generate a comprehensive summary. Send Output to Other Destinations** Replace the Webhook URL to forward output to: Google Sheets Airtable Slack or Discord Custom API endpoints
by PollupAI
Who is this for? This workflow is designed for Customer Satisfaction Managers (CSM), sales professionals, and operations managers who need to automate the analysis of client transcripts, save summarized notes to HubSpot, and route relevant feedback to the appropriate departments via email. What problem is this workflow solving? / Use Case Manually processing client conversations, extracting key insights, and distributing them to the right teams is time-consuming and error-prone. This workflow automates: Transcript analysis** using AI (OpenAI) to identify relevant content. HubSpot integration** to log meeting notes against client records. Email routing** to ensure feedback reaches the correct departments (e.g., support, sales, product, admin). What this workflow does Input Transcript: Accepts a client conversation transcript (e.g., from emails, calls, or chats). HubSpot Sync: Searches for the client’s HubSpot ID using their email. Uploads a summarized version of the conversation as meeting notes. AI-Powered Routing: Uses an OpenAI model to analyze the transcript and categorize content by department. Triggers emails (via Gmail) to route feedback to the relevant teams. Form Completion: Ends the workflow with optional user confirmation. Setup Prerequisites: n8n instance (cloud or self-hosted). HubSpot API credentials (for contact lookup and notes upload). OpenAI API key (for transcript analysis). Gmail account (for sending emails). Configuration: Replace placeholder nodes (e.g., HubSpot, OpenAI, Gmail) with your authenticated accounts. Define email templates and recipient addresses for routing. Adjust the OpenAI prompt to match your categorization criteria (e.g., "support," "billing"). How to customize this workflow to your needs Transcript Sources**: Extend the workflow to pull transcripts from other sources (e.g., Zoom, Slack). Departments**: Modify the routing logic to include additional teams or conditions. Notifications**: Add Slack/MS Teams alerts for urgent feedback. Error Handling**: Introduce retries or fallback actions for failed HubSpot/Gmail steps.
by Ranjan Dailata
Who this is for? Extract & Summarize Indeed Company Info is an automated workflow that extracts the Indeed company profile information using Bright Data Web Unlocker, transform it using Google Gemini’s LLM, and forward the transformed response with the summary to a specified webhook for downstream use. This workflow is tailored for: Recruiters and HR teams looking to assess companies quickly during talent sourcing. Job seekers researching potential employers and needing summarized company insights. Market researchers and analysts monitoring competitor or industry players. What problem is this workflow solving? Searching and evaluating company profiles on Indeed manually can be time-consuming and inefficient, especially when dealing with large volumes of companies. Manually browsing, copying, and summarizing company descriptions, reviews, and ratings from Indeed hinders productivity and limits real-time insights. This workflow solves this by: Automating the extraction of company details from Indeed using Bright Data Web Unlocker. Summarizing the raw data using Google Gemini's language model for a quick, human-readable overview. Sending the transformed response with the summary to a chosen endpoint, like Slack, Notion, Airtable, or a custom webhook. What this workflow does This automated pipeline does the following: Scrape Indeed company profile pages (e.g., ratings, description, reviews) using Bright Data’s Web Unlocker. Transform the scraped content into structured JSON using n8n’s built-in tools. Summarize and extract meaningful insights using Google Gemini's large language model. Forward the summarized data to a specified webhook or app for real-time access, storage, or analysis. Forward the formatted response to a specified webhook or app for real-time access, storage, or analysis. Setup Sign up at Bright Data. Navigate to Proxies & Scraping and create a new Web Unlocker zone by selecting Web Unlocker API under Scraping Solutions. In n8n, configure the Header Auth account under Credentials (Generic Auth Type: Header Authentication). In n8n, configure the Google Gemini(PaLM) Api account with the Google Gemini API key (or access through Vertex AI or proxy). Update the search query, Bright Data zone by navigating to the Set Indeed Search Query node. Update the Webhook Notifier with the Webhook endpoint of your choice. How to customize this workflow to your needs This workflow is built to be flexible - whether you're a company or a market researcher, entrepreneur, or data analyst. Here’s how you can adapt it to fit your specific use case: Changing the data source**: Replace the Indeed search input with other job or business listing platforms if needed (e.g., Glassdoor, Crunchbase) Refining the LLM prompt**: Tailor the Gemini prompt to transform or summarize the Indeed company information in a specific format. Routing the output to different destinations**: Send summaries or transformed response to Google Sheets, Airtable, or CRMs like HubSpot or Salesforce etc.
by Mario
Dynamically switch between LLMs for AI Agents using LangChain Code Purpose This example workflow demonstrates a way to connect multiple LLMs to a single AI Agent/LangChain Node and programmatically use one – or in this case loop through them. What it does This AI workflow takes in customer complaints and generates a response that is being validated before returned. If the answer was not satisfactory, the response will be generated again with a more capable model. How it works A LangChain Code Node allows multiple LLMs to be connected to a single Basic LLM Chain. On every call only one LLM is actually being connected to the Basic LLM Chain, which is determined by the index defined in a previous Node. The AI output is later validated by a Sentiment Analysis Node If the result was not satisfactory, it loops back to the beginning and executes the same query with the next available LLM The loop ends either when the result passed the requirements or when all LLMs have been used before. Setup Clone the workflow and select the belonging credentials. You'll need an OpenAI Account, alternatively you can swap the LLM nodes with ones from a different provider like Anthropic after the import. How to use Beware that the order of the used LLMs is determined by the order they have been added to the workflow, not by the position on the canvas. After cloning this workflow into your environment, open the chat and send this example message: > I really love waiting two weeks just to get a keyboard that doesn’t even work. Great job. Any chance I could actually use the thing I paid for sometime this month? Most likely you will see that the first validation fails, causing it to loop back to the generation node and try again with the next available LLM. Since AI responses are unpredictable, the results and number of tries will differ for each run. Disclaimer Please note, that this workflow can only run on self-hosted n8n instances, since it requires the LangChain Code Node.
by Sean Lon
Target Audience You will find this workflow or template perfect if you are in the internal talent acquisition teams, recruitment agencies, HR professionals, and hiring managers seeking to bulk automate the initial screening of CVs and resumes. Eg. Automatically get result of candidate who has been shortlisted/rejected with its rationale and score automatically. By eliminating manual evaluation and screening, you get smart AI-Agent helping you to have standardized efficient, and scalable solution for handling large volumes of applications. With bulk automation, you can focus strategic decision-making rather than tedious screening tasks, ensuring a faster, more accurate, and fair hiring process. Key focus This workflow focusses on having a more organized file-folder management, trackable candidate cv, maintainable job description, autonomous ai-agent. Organized Folder-File Structure – CVs are automatically categorized based on their status, ensuring a structured workflow and easy retrieval Candidate Tracker – A real-time tracking system records the state of each CV, allowing recruiters to monitor the shortlisted, rejected, or KIV (Keep in View) candidates. AI Agent for Decision Automation – The AI autonomously orchestrates screening decisions, replacing manual LLM configurations with dynamic AI-driven evaluations for scalability and accuracy. Maintainable Job Description Management – A structured job description file ensures continuous updates, keeping hiring criteria flexible and aligned with recruitment needs. Email Notifications – The system automatically sends receipt confirmations upon processing completion, providing timely updates to recruiters. Features - Workflow Automated Resume Screening Workflow This workflow leverages Groq Llama4 for intelligent resume analysis, speeding the screening process by generating a matching score, result (shortlisted/rejected/kiv), and key insights/rationale into their suitability for provided job description. Step-by-Step Process: Monitors Google Drive:** Listens and checks for new resume cv in google drive . Retrieve Resume:** Downloads the CV resumes from google drive . Extract Resume Data:* Extract *text content** from CV resume PDF files Extract Job Description Data:* Extract *text content** from job description Analyze with Groq:** Generate a matching score based on job requirements. [SCORE: 1-10] Provide decision into their job suitability. [SHORTLISTED/REJECTED/KIV] Provide actionable insights into their job suitability. [REASON] This ensures a fast, efficient, and accurate screening process, eliminating manual evaluation. Setup Guide Step-by-Step Instructions Ensure all credentials are ready and setup (groq, gdrive ,gmail, gsheet, gdoc) View official n8n documentation on node setup accordingly. See also the notes of setup . Folder & File Setup 1. Create a google-drive folder like this View directory example 2. Create a job description like this View file example 3. Configure a tracker like this ( Candidate Name, AI Score,AI Verdict, AI Reason) View file example email conversations report as you like. You are ready to go!
by Ranjan Dailata
Who this is for? The Automate Etsy Data Mining with Bright Data Scrape & Google Gemini workflow is designed for eCommerce analysts, product researchers, and AI developers seeking to extract actionable insights from Etsy listings at scale. It is ideal for: eCommerce Entrepreneurs** - Researching product demand and competition. Market Analysts** - Tracking pricing, reviews, and trends across Etsy categories. Product Managers** - Identifying niche opportunities and design inspirations. Data Scientists & AI Engineers** - Automating product intelligence pipelines. Growth Hackers** - Leveraging Etsy insights to refine product-market fit. What problem is this workflow solving? Manually browsing Etsy to analyze product listings, pricing, reviews, and seller activity is slow, inconsistent, and unscalable. Scraping Etsy requires unlocking JavaScript-heavy content and structuring noisy data for analysis. This workflow solves: Automated and scalable scraping of Etsy product listings using Bright Data’s infrastructure. A fully paginated data structured Estry production data extraction via the Google Gemini LLM. Enables faster decision-making for product research and competitive analysis via the fully automated paginated data extraction. What this workflow does Receives input: Sets the Esty URL for the data extraction and analysis. Uses Bright Data's Web Unlocker to extract content from relevant sites. Cleans and preprocesses the scraped content for readability. Sends the content to Google Gemini for: Enriched results including: Data persistence over the disk. Sends the response to a target system via Webhook notification. Setup Sign up at Bright Data. Navigate to Proxies & Scraping and create a new Web Unlocker zone by selecting Web Unlocker API under Scraping Solutions. In n8n, configure the Header Auth account under Credentials (Generic Auth Type: Header Authentication). The Value field should be set with the Bearer XXXXXXXXXXXXXX. The XXXXXXXXXXXXXX should be replaced by the Web Unlocker Token. A Google Gemini API key (or access through Vertex AI or proxy). Update the Set Esty Search Query for setting the brand content URL and the Bright Data Zone name. Update the Webhook HTTP Request node with the Webhook endpoint of your choice. How to customize this workflow to your needs Input Sources** : Replace the static URL with dynamic input from Google Sheets, Webhook, or Airtable to research multiple niches. Prompt Customization** : Adjust Gemini prompts to extract specific insights for example: List key features of the product Summarization of the review themes Data Output Options** : Update the Webhook notification to save data to: Google Sheets Notion or Airtable SQL/NoSQL Slack/Email
by mariskarthick
QuantumDefender AI is a next-generation intelligent cybersecurity assistant designed to harness the symbolic strength of quantum computing’s promise alongside cutting-edge AI capabilities. This sophisticated agent empowers SOC analysts, red teamers, and security researchers with rapid threat investigation, operational automation, and intelligent command execution—all driven by GPT-4 and integrated tools, accessible through Telegram or on any medium. 🔑 Key Features: Expert-Level Cybersecurity Research & Analysis: Leverages powerful AI models to deliver clean, detailed, domain-specific insights across detection, remediation, and offensive security. Command & Control: Executes Linux shell commands, autonomous scripts, and system operations securely in isolated environments. Real-Time Web Intelligence: Utilizes integrated Langsearch API to provide timely internet research with contextual relevance. Calendar & Scheduling Automation: Manage Google Calendar events or any similar application(create, update, delete, retrieve) dynamically from chat. Multi-Tool Orchestration: Combines calculator functions, internet searches, command execution, and messaging for comprehensive operational support. Telegram-native Chatbot: Delivers an adaptive, memory-informed, and interactive conversational experience with immediate typing indicators and high responsiveness. Conversation & Session Management: Maintains context-aware, session-based memory to enable smooth, multi-turn dialogues with individual users. Sends “typing…” indicators during processing to ensure an interactive, user-friendly chat experience. Operates exclusively within Telegram, delivering rich, timely responses and leveraging all Telegram bot capabilities. Execution Intelligence & Safety: Fully autonomous in deciding which tools to invoke, how frequently, and in what sequence to fulfill user requests comprehensively and responsibly. Operates within a secure temporary folder environment to contain all command executions safely and avoid persistent or harmful side effects. Enforces strict safety protocols to avoid running malicious or destructive commands, maintaining ethical standards and compliance. Use Cases: Cybersecurity researchers and operators seeking an intelligent assistant to accelerate investigations and automate routine tasks. Red team professionals requiring on-the-fly command execution and information gathering integrated with tactical chat interactions. SOC teams aiming to augment their alert triage and incident handling workflows with AI-powered analysis and action. Anyone looking for a robust multi-tool AI chatbot integrated with real-world operational capabilities. Setup Requirements: OpenAI API key for GPT-4.1-nano language processing. Telegram Bot API credentials with proper webhook setup to receive and respond to messages. Google OAuth credentials for Calendar integration if calendar features are used. SSH access credentials for executing commands on remote hosts, if remote execution is enabled. Internet connectivity for the Langsearch web search API. Customization & Extensibility: The workflow is built modularly with n8n’s flexible node system. Users can extend it by adding more tools, integrating other services (ticketing, threat intel, scanning tools), or modifying interaction logic to suit specialized operational needs and environments. Created by Mariskarthick M Senior Security Analyst | Detection Engineer | Threat Hunter | Open-Source Enthusiast