Workflow Templates

Auto Source LinkedIn Candidates with GPT-4 Boolean Search & Google X-ray How It Works: User Input: The user pastes a job description or ideal candidate specifications into the workflow. Boolean Search String Generation: OpenAI processes the input and generates a precise LinkedIn Boolean search string formatted as: site:linkedin.com/in ("Job Title" AND "Skill1" AND "Skill2") This search string is optimized to find relevant LinkedIn profiles matching the provided criteria. Google Sheet Creation: A new Google Sheet is automatically created within a specified document to store extracted LinkedIn profile URLs. Google Search Execution: The workflow sends a search request to Google using an HTTP node with the generated Boolean string. Iterative Search & Data Extraction: The workflow retrieves the first 10 results from Google. If the desired number of LinkedIn profiles has not been reached, the workflow loops, fetching the next set of 10 results until the if condition is met. Data Storage: The workflow extracts LinkedIn profile URLs from the search results and saves them to the newly created Google Sheet for further review. Setup Steps: 1. API Key Configuration Under "Credentials", add your OpenAI API key from your OpenAI account settings. This key is used to generate the LinkedIn Boolean search string. 2. Adjust Search Parameters Navigate to the "If" node and update the condition to define the desired number of LinkedIn profiles to extract. The default is 50, but you can set it to any number based on your needs. 3. Establish Google Sheets Connection Connect your Google Sheets account** to the workflow. Create a document** to store the sourced LinkedIn profiles. The workflow automatically creates a new sheet for each new search, so no manual setup is needed. 4. Authenticate Google Search Google search requires authentication** for better results. Use the Cookie-Editor browser extension to export your header string and enable authenticated Google searches within the workflow. 5. Run the Workflow Execute* the workflow and monitor the *Google Sheet** for newly added LinkedIn profiles. Benefits: ✅ Automates profile sourcing, reducing manual search time. ✅ Generates precise LinkedIn Boolean search strings tailored to job descriptions. ✅ Extracts and saves LinkedIn profiles efficiently for recruitment efforts. This solution leverages OpenAI and advanced search techniques to enhance your talent sourcing process, making it faster and more accurate! 🚀

Summary Engage multiple, uniquely configured AI agents (using different models via OpenRouter) in a single conversation. Trigger specific agents with @mentions or let them all respond. Easily scalable by editing simple JSON settings. Overview This workflow is for users who want to experiment with or utilize multiple AI agents with distinct personalities, instructions, and underlying models within a single chat interface, without complex setup. It solves the problem of managing and interacting with diverse AI assistants simultaneously for tasks like brainstorming, comparative analysis, or role-playing scenarios. It enables dynamic conversations with multiple AI assistants simultaneously within a single chat interface. You can: Define multiple unique AI agents. Configure each agent with its own name, system instructions, and LLM model (via OpenRouter). Interact with specific agents using @AgentName mentions. Have all agents respond (in random order) if no specific agents are mentioned. Maintain conversation history across multiple turns. It's designed for flexibility and scalability, allowing you to easily add or modify agents without complex workflow restructuring. Key Features Multi-Agent Interaction:** Chat with several distinct AI personalities at once. Individual Agent Configuration:** Customize name, system prompt, and LLM for each agent. OpenRouter Integration:** Access a wide variety of LLMs compatible with OpenRouter. Mention-Based Triggering:** Direct messages to specific agents using @AgentName. All-Agent Fallback:** Engages all defined agents randomly if no mentions are used. Scalable Setup:** Agent configuration is centralized in a single Code node (as JSON). Conversation Memory:** Remembers previous interactions within the session. How to Set Up Configure Settings (Code Nodes): Open the Define Global Settings Code node: Edit the JSON to set user details (name, location, notes) and add any system message instructions that all agents should follow. Open the Define Agent Settings Code node: Edit the JSON to define your agents. Add or remove agent objects as needed. For each agent, specify: "name": The unique name for the agent (used for @mentions). "model": The OpenRouter model identifier (e.g., "openai/gpt-4o", "anthropic/claude-3.7-sonnet"). "systemMessage": Specific instructions or persona for this agent. Add OpenRouter Credentials: Locate the AI Agent node. Click the OpenRouter Chat Model node connected below it via the Language Model input. In the 'Credential for OpenRouter API' field, select or create your OpenRouter API credentials. How to Use Start a conversation using the Chat Trigger input. To address specific agents, include @AgentName in your message. Agents will respond sequentially in the order they are mentioned. Example: "@Gemma @Claude, please continue the count: 1" will trigger Gemma first, followed by Claude. If your message contains no @mentions, all agents defined in Define Agent Settings will respond in a randomized order. Example: "What are your thoughts on the future of AI?" will trigger Chad, Claude, and Gemma (based on your default settings) in a random sequence. The workflow will collect responses from all triggered agents and return them as a single, formatted message. How It Works (Technical Details) Settings Nodes: Define Global Settings and Define Agent Settings load your configurations. Mention Extraction: The Extract mentions Code node parses the user's input (chatInput) from the When chat message received trigger. It looks for @AgentName patterns matching the names defined in Define Agent Settings. Agent Selection: If mentions are found, it creates a list of the corresponding agent configurations in the order they were mentioned. If no mentions are found, it creates a list of all defined agent configurations and shuffles them randomly. Looping: The Loop Over Items node iterates through the selected agent list. Dynamic Agent Execution: Inside the loop: An If node (First loop?) checks if it's the first agent responding. If yes (true path -> Set user message as input), it passes the original user message to the Agent. If no (false path -> Set last Assistant message as input), it passes the previous agent's formatted output (lastAssistantMessage) to the next agent, creating a sequential chain. The AI Agent node receives the input message. Its System Message and the Model in the connected OpenRouter Chat Model node are dynamically populated using expressions referencing the current agent's data from the loop ({{ $('Loop Over Items').item.json.* }}). The Simple Memory node provides conversation history to the AI Agent. The agent's response is formatted (e.g., AgentName:\n\nResponse) in the Set lastAssistantMessage node. Response Aggregation: After the loop finishes, the Combine and format responses Code node gathers all the lastAssistantMessage outputs and joins them into a single text block, separated by horizontal rules (---), ready to be sent back to the user. Benefits Scalability & Flexibility:** Instead of complex branching logic, adding, removing, or modifying agents only requires editing simple JSON in the Define Agent Settings node, making setup and maintenance significantly easier, especially for those managing multiple assistants. Model Choice:** Use the best model for each agent's specific task or persona via OpenRouter. Centralized Configuration:** Keeps agent setup tidy and manageable. Limitations Sequential Responses:** Agents respond one after another based on mention order (or randomly), not in parallel. No Direct Agent-to-Agent Interaction (within a turn):* Agents cannot directly call or reply to each other *during the processing of a single user message. Agent B sees Agent A's response only because the workflow passes it as input in the next loop iteration. Delayed Output:* The user receives the combined response only *after all triggered agents have completed their generation.

This workflow creates a documentation system for n8n instances using Docsify.js. It serves a dynamic documentation website that allows users to: View an overview of all workflows in a tabular format Filter workflows by tags Access automatically generated documentation for each workflow Edit documentation with a live Markdown preview Visualize workflow structures using Mermaid.js diagrams > 📺 Check out the short 2-min demonstration on LinkedIn. Don't forget to connect! 🔧 Key Components Main Documentation Portal Serves a Docsify-powered website Provides a navigation sidebar with workflow tags Displays workflow status, creation date, and documentation links Documentation Generator Uses GPT model to auto-generate workflow descriptions Creates Mermaid.js diagrams of workflow structures Maintains consistent documentation format Live Editor Split-screen Markdown editor with preview Real-time Mermaid diagram rendering Save/Cancel functionality ⚙️ Technical Details Environment Setup Requires write access to the specified project directory Uses environment variables for n8n instance URL configuration Implements webhook endpoints for serving documentation ⚠️ Security Considerations > Note: The current implementation doesn't include authentication for editing. Consider adding authentication for production use. Dependencies Docsify.js for documentation rendering Mermaid.js for workflow visualization OpenAI GPT for documentation generation 🔍 Part of the n8n Observability Series This workflow is part of a broader series focused on n8n instance observability. Check out these related workflows: Workflow Dashboard - Get comprehensive analytics of your n8n instance Visualize Your n8n Workflows with Mermaid.js - Create beautiful workflow visualizations Each workflow in this series helps you better understand and manage your n8n automation ecosystem!

📈 Get daily and on-demand Tesla (TSLA) trading signals via Telegram—powered by GPT-4.1 and real-time market data. This is the central AI supervisor that orchestrates seven sub-agents for technical analysis, price pattern recognition, and news sentiment. Reports are delivered in structured Telegram-ready HTML, optimized for traders seeking fast, intelligent decision-making signals. ⚠️ This master agent requires 7 connected sub-workflows to function. One of them, the News & Sentiment Agent, also requires a DeepSeek Chat API key for language processing. 🔌 Required Sub-Workflows You must download and publish the following workflows: Tesla Financial Market Data Analyst Tool Tesla News and Sentiment Analyst Tool (Requires DeepSeek Chat API Key) Tesla 15min Indicators Tool Tesla 1hour Indicators Tool Tesla 1day Indicators Tool Tesla 1hour & 1day Klines Tool Tesla Quant Technical Indicators Webhooks Tool (Requires Alpha Vantage Premium API Key) 📍 See all tools at: 🔗 https://n8n.io/creators/don-the-gem-dealer/ 🔍 What This Agent Does Listens to your trading query via Telegram Calls the Financial Analyst and News & Sentiment Analyst These agents aggregate: RSI, MACD, BBANDS, SMA, EMA, ADX Candlestick pattern + volume divergence analysis News summaries and sentiment scoring via DeepSeek Chat GPT-4.1 composes the final structured TSLA trade report with: Spot and leverage setups Signal rationale Confidence score Sentiment tag News summary 🧠 Output Example TSLA Trading Report (Daily Summary) Spot Trade • Action: Buy • Entry: 172.45 • TP: 182.00 • SL: 169.80 • Signal: RSI bounce + Bullish Engulfing • Sentiment: Neutral Leveraged Position • Position: Long • Leverage: 3x • TP: 186 • SL: 170 • Confidence: High (83/100) 📰 Top News • Tesla Model Y delivery surge – Electrek • Options market pricing in upside – Bloomberg • FSD delayed in Canada – TeslaNorth 🛠️ Setup Instructions 1. Import All 8 Workflows Ensure all sub-agents above are published in your n8n instance. 2. Create Your Telegram Bot Use @BotFather to generate the token and connect to the trigger/send nodes. 3. Connect OpenAI GPT-4.1 Add your OpenAI credentials for GPT-4.1 in the designated node. 4. Add DeepSeek Chat API Key Sign up at https://deepseek.com and insert your DeepSeek Chat credentials in the News Agent. 5. Add Alpha Vantage Premium API Key Sign up at https://www.alphavantage.co/premium/ Use HTTP Header Auth for webhook-based indicator fetchers. 6. Replace Telegram ID Update the placeholder <<replace your ID here>> with your actual Telegram numeric ID in the auth node. 📌 Included Sticky Notes ✅ Telegram Bot Setup ✅ Agent Routing & Memory ✅ Financial vs. Sentiment Trigger Flow ✅ Report Formatting (HTML) ✅ API Requirements (GPT-4.1, DeepSeek, Alpha Vantage) ✅ Troubleshooting & Licensing 🧾 Licensing & Attribution © 2025 Treasurium Capital Limited Company Architecture, prompts, and trade report structure are IP-protected. No unauthorized rebranding permitted. 🔗 For support: LinkedIn – Don Jayamaha 🚀 Deploy the Tesla Quant Trading AI system with GPT-4.1, DeepSeek Chat, and Alpha Vantage Premium—right into Telegram. All 8 workflows are required. 🎥 Tesla Quant AI Agent – Live Demo Experience the power of the Tesla Quant Trading AI Agent in action.

Manage Calendar with Voice & Text Commands using GPT-4, Telegram & Google Calendar This n8n workflow transforms your Telegram bot into a personal AI calendar assistant, capable of understanding both voice and text commands in Romanian, and managing your Google Calendar using the GPT-4 model via LangChain. Whether you want to create, update, fetch, or delete events, you can simply speak or write your request to your Telegram bot — and the assistant takes care of the rest. 🚀 Features Voice command support using Telegram voice messages (.ogg) Transcription using OpenAI Whisper Natural language understanding with GPT-4 via LangChain Google Calendar integration: ✅ Create Events 🔁 Update Events ❌ Delete Events 📅 Fetch Events Responses sent back via Telegram 🛠️ Step-by-Step Setup Instructions 1. Create a Telegram Bot Go to @BotFather on Telegram. Send /newbot and follow the instructions. Save the Bot Token. 2. Configure Telegram Trigger Node Paste the Telegram token into the Telegram Trigger and Telegram nodes. Set updates to ["message"]. 3. Set up OpenAI Credentials Get an OpenAI API key from https://platform.openai.com Create a credential in n8n for OpenAI. This is used for both transcription and AI reasoning. 4. Set up Google Calendar In Google Cloud Console: Enable Google Calendar API Set up OAuth2 credentials Add your n8n redirect URI (usually https://yourdomain/rest/oauth2-credential/callback) Create a credential in n8n using Google Calendar OAuth2 Grant access to your calendar (e.g., "Family" calendar). ⚙️ Customization Options 🗣️ Change Language or Locale The transcription node uses "en" for English. Change to another locale if needed. ✏️ Edit Prompt You can modify the prompt in the AI Agent node to include your name, work schedule, or specific behavior expectations. 📆 Change Calendar Logic Adjust time ranges or filters in the Get Events node Add custom logic before Create Event (e.g., validation, conflict checks) 📚 Helpful Tips Make sure n8n has HTTPS enabled to receive Telegram updates. You can test the flow first using only text, then voice. Use AI memory or vector stores (like Supabase) if you want context-aware planning in the future.

This n8n template demonstrates one approach to achieve a more natural and less frustration conversations with AI agents by reducing interrupts by predicting the end of user utterances. When we text or chat casually, it's not uncommon to break our sentences over multiple messages or when it comes to voice, break our speech with the odd pause or umms and ahhs. If an agent replies to every message, it's likely to interrupt us before we finish our thoughts and it can get very annoying! Previously, I demonstrated a simple technique for buffering each incoming message by 5 seconds but that approach still suffers in some scenarios when more time is needed. This technique has no arbitrary time limit and instead uses AI to figure out when its the agent's turn based on the user's message, allowing for the user to take all the time they need. How it works Telegram messages are received but no reply is generated for them by default. Instead they are sent to the prediction subworkflow to determine if a reply should be generated. The prediction subworkflow begins by checking Redis for the current user's prediction session state. If this is a new "utterance", it kicks off the "predict end of utterance" loop - the purpose of which is to buffer messages in a smart way! New users message can continue to be accepted by the workflow until enough is collected to allow our prediction classifier to determine the end of the utterance has been reached. The loop is then broken and the buffered chat messages are combined and sent to the AI agent to generate a response and sent to the user via the telegram node. The prediction session state is then deleted to signal the workflow is ready to start again with a new message. How to use This system sits between your preferred chat platform and the AI agent so all you need to do is replace the telegram nodes as required. Where LLM-only prediction isn't working well enough, consider more traditional code-based checking of heuristics to improve the detection. Ideally you'll want a fast but accurate LLM so your user isn't waiting longer than they have to - at time of writing Gemini-2.5-flash-lite was the fastest in testing but keep a look out for smaller and more powerful LLMs in the future. Requirements Gemini for LLM Redis for session management Telegram for chat platform

Transform your meetings into actionable insights automatically! This workflow captures meeting audio, transcribes conversations, generates AI summaries, and emails the results to participants—all without manual intervention. What's the Goal? Auto-record meetings** when they start and stop when they end Transcribe audio** to text using Vexa Bot integration Generate intelligent summaries** with AI-powered analysis Email summaries** to meeting participants automatically Eliminate manual note-taking** and post-meeting admin work Never miss important discussions** or action items again Why Does It Matter? Save 90% of Post-Meeting Time**: No more manual transcription or summary writing Never Lose Key Information**: Automatic capture ensures nothing falls through cracks Improve Team Productivity**: Focus on discussions, not note-taking Perfect Meeting Records**: Searchable transcripts and summaries for future reference Instant Distribution**: Summaries reach all participants immediately after meetings How It Works Step 1: Meeting Detection & Recording Start Meeting Trigger**: Detects when meeting begins via Google Meet webhook Launch Vexa Bot**: Automatically joins meeting and starts recording End Meeting Trigger**: Detects meeting end and stops recording Step 2: Audio Processing & Transcription Stop Vexa Bot**: Ends recording and retrieves audio file Fetch Meeting Audio**: Downloads recorded audio from Vexa Bot Transcribe Audio**: Converts speech to text using AI transcription Step 3: AI Summary Generation Prepare Transcript**: Formats transcribed text for AI processing Generate Summary**: AI model creates concise meeting summary with: Key discussion points Decisions made Action items assigned Next steps identified Step 4: Distribution Send Email**: Automatically emails summary to all meeting participants Setup Requirements Google Meet Integration: Configure Google Meet webhook and API credentials Set up meeting detection triggers Test with sample meeting Vexa Bot Configuration: Add Vexa Bot API credentials for recording Configure audio file retrieval settings Set recording quality and format preferences AI Model Setup: Configure AI transcription service (e.g., OpenAI Whisper, Google Speech-to-Text) Set up AI summary generation with custom prompts Define summary format and length preferences Email Configuration: Set up SMTP credentials for email distribution Create email templates for meeting summaries Configure participant list extraction from meeting metadata Import Instructions Get Workflow JSON: Copy the workflow JSON code Open n8n Editor: Navigate to your n8n dashboard Import Workflow: Click menu (⋯) → "Import from Clipboard" → Paste JSON → Import Configure Credentials: Add API keys for Google Meet, Vexa Bot, AI services, and SMTP Test Workflow: Run a test meeting to verify end-to-end functionality Your meetings will now automatically transform into actionable summaries delivered to your inbox!

How it works This workflow adds a priority to each Todoist item in your inbox, based on a list of projects that you add in the workflow. Setup Add your Todoist credentials Add your OpenAI credentials Set your project names and add priority

How it works • Transcribes a WhatsApp voice or text message from a prospect using Whisper or GPT • Extracts key information (name, need, context, urgency) via AI • Matches the most relevant service pack by comparing the prospect’s need with Airtable data • Dynamically fills a branded template via APITEMPLATE (HTML or PDF) • Generates a clean, personalized business proposal — including dynamic links (payment, calendar, etc.) • Sends the final PDF back instantly via WhatsApp or email Set up steps • ⏱ Estimated setup time: 45–60 minutes • ✅ You’ll need:  ◦ WhatsApp Business Cloud API access (with webhook configured)  ◦ OpenAI API key (Whisper + GPT)  ◦ Airtable (to store service packs and client input)  ◦ APITEMPLATE account (template with placeholders like {{nom}}, {{prix}}, {{lien_reservation}}, etc.)  ◦ n8n instance (cloud or self-hosted) • 📦 Create your service packs in Airtable with associated links (Stripe, Calendly…) • 🔗 The proposal auto-includes these links dynamically inside the PDF • 🚀 Workflow orchestrates the end-to-end process: from WhatsApp input to PDF delivery

How it works This template is a complete, hands-on tutorial that lets you build and interact with your very first AI Agent. Think of an AI Agent as a standard AI chatbot with superpowers. The agent doesn't just talk; it can use tools to perform actions and find information in real-time. This workflow is designed to show you exactly how that works. The Chat Interface (Chat Trigger): This is your window to the agent. It's a fully styled, public-facing chat window where you can have a conversation. The Brain (AI Agent Node): This is the core of the operation. It takes your message, understands your intent, and intelligently decides which "superpower" (or tool) it needs to use to answer your request. The agent's personality and instructions are defined in its extensive system prompt. The Tools (Tool Nodes): These are the agent's superpowers. We've included a variety of useful and fun tools to showcase its capabilities: Get a random joke. Search Wikipedia for a summary of any topic. Calculate a future date. Generate a secure password. Calculate a monthly loan payment. Fetch the latest articles from the n8n blog. The Memory (Memory Node): This gives the agent a short-term memory, allowing it to remember the last few messages in your conversation for better context. When you send a message, the agent's brain analyzes it, picks the right tool for the job, executes it, and then formulates a helpful response based on the tool's output. Set up steps Setup time: ~3 minutes This template is nearly ready to go out of the box. You just need to provide the AI's "brain." Configure Credentials: This workflow requires an API key for an AI model. Make sure you have credentials set up in your n8n instance for either Google AI (Gemini) or OpenAI. Choose Your AI Brain (LLM): By default, the workflow uses the Google Gemini node. If you have Google AI credentials, you're all set! If you prefer to use OpenAI, simply disable the Gemini node and enable the OpenAI node. You only need one active LLM node. Make sure it is connected to the Agent parent node. Explore the Tools: Take a moment to look at the different tool nodes connected to the Your First AI Agent node. This is where the agent gets its abilities! You can add, remove, or modify these to create your own custom agent. Activate and Test! Activate the workflow. Open the public URL for the Example Chat Window node (you can copy it from the node's panel). Start chatting! Try asking it things like: "Tell me a joke." "What is n8n?" "Generate a 16-character password for me." "What are the latest posts on the n8n blog?" "What is the monthly payment for a $300,000 loan at 5% interest over 30 years?"

Create a recommendation tool without hallucinations based on RAG with the Qdrant Vector database. This example is based on movie recommendations on the IMDB-top1000 dataset. You can provide your wishes and your "big no's" to the chatbot, for example: "A movie about wizards but not Harry Potter", and get top-3 recommendations. How it works a video with the full design process Upload IMDB-1000 dataset to Qdrant Vector Store, embedding movie descriptions with OpenAI; Set up an AI agent with a chat. This agent will call a workflow tool to get movie recommendations based on a request written in the chat; Create a workflow which calls Qdrant's Recommendation API to retrieve top-3 recommendations of movies based on your positive and negative examples. Set Up Steps You'll need to create a free tier Qdrant Cluster (Qdrant can also be used locally; it's open-sourced) and set up API credentials You'll OpenAI credentials You'll need GitHub credentials & to upload the IMDB Kaggle dataset to your GitHub.

Who’s it for 👥 This template is perfect for content creators, marketers, and researchers managing WeChat public account articles! 🚀 It’s ideal for n8n newcomers or anyone wanting to save time on manual content analysis, especially if you use Google Sheets for tracking. 📊 Whether you’re into AI, 欧阳良宜, or automation, this is for you! 😄 How it works / What it does 🔧 This workflow automates the retrieval, filtering, classification, and summarization of WeChat articles. 🌐 It reads RSS feed links from a Google Sheet, filters articles from the last 10 days ⏳, cleans HTML content 🧹, classifies them as relevant or not 🎯, generates insightful Chinese summaries with AI 🤖, and saves results to Google Sheets and Notion. 📝 Outputs are Slack-formatted for team collaboration! 💬 How to set up 🛠️ Prepare Google Sheets: Use your own documentId (replace the example) and set up sheets "Save Initial Links" (gid=198451233) and "Save Processed Data" (gid=1936091950). 📋 Configure Credentials: Add Google Sheets and OpenAI API credentials—avoid hardcoding keys! 🔐 Set RSS Feed: Update the rss_feed_url in the "RSS Read" node with your WeChat RSS feed. 🌐 Customize AI: Tweak "Relevance Classification" and "Basic LLM Chain" prompts for your topics (e.g., 欧阳良宜, AI). 🎨 Notion (Optional): Swap the databaseId (e.g., 22e79d55-2675-8055-a143-d55302c3c1b1) with your own. 📚 Run Workflow: Trigger manually via the "When clicking ‘Execute workflow’" node. 🚀 Requirements ✅ n8n account with Google Sheets and OpenAI integrations. Access to a WeChat public account RSS feed. Basic JSON and node config knowledge. How to customize the workflow 🎛️ Topic Adjustment: Update categories in "Relevance Classification" for new topics (e.g., "technology", "education"). 🌱 Summary Length: Modify the LLM prompt in "Basic LLM Chain" to adjust length or style. ✂️ Output Destination: Add Slack or Email nodes for more outputs. 📩 Date Filter: Change the "IF (Filter by Date)" condition (e.g., 7 days instead of 10). ⏰ Scalability: Use a "Schedule Trigger" node for automation. ⏳