Ai fitness coach Strava data analysis and personalized training insights
Detailed Title "Triathlon Coach AI Workflow: Strava Data Analysis and Personalized Training Insights using n8n" --- Description This n8n workflow enables you to build an AI-driven virtual triathlon coach that seamlessly integrates with Strava to analyze activity data and provide athletes with actionable training insights. The workflow processes data from activities like swimming, cycling, and running, delivers personalized feedback, and sends motivational and performance improvement advice via email or WhatsApp. --- Workflow Details Trigger: Strava Activity Updates Node: Strava Trigger Purpose: Captures updates from Strava whenever an activity is recorded or modified. The data includes metrics like distance, pace, elevation, heart rate, and more. Integration: Uses Strava API for real-time synchronization. Step 1: Data Preprocessing Node: Code Purpose: Combines and flattens the raw Strava activity data into a structured format for easier processing in subsequent nodes. Logic: A recursive function flattens JSON input to create a clean and readable structure. Step 2: AI Analysis with Google Gemini Node: Google Gemini Chat Model Purpose: Leverages Google Gemini's advanced language model to analyze the activity data. Functionality: Identifies key performance metrics. Provides feedback and insights specific to the type of activity (e.g., running, swimming, or cycling). Offers tailored recommendations and motivational advice. Step 3: Generate Structured Output Node: Structure Output Purpose: Processes the AI-generated response to create a structured format, such as headings, paragraphs, and bullet lists. Output: Formats the response for clear communication. Step 4: Convert to HTML Node: Convert to HTML Purpose: Converts the structured output into an HTML format suitable for email or other presentation methods. Output: Ensures the response is visually appealing and easy to understand. Step 5: Send Email with Training Insights Node: Send Email Purpose: Sends a detailed email to the athlete with performance insights, training recommendations, and motivational messages. Integration: Utilizes Gmail or SMTP for secure and efficient email delivery. Optional Step: WhatsApp Notifications Node: WhatsApp Business Cloud Purpose: Sends a summary of the activity analysis and key recommendations via WhatsApp for instant access. Integration: Connects to WhatsApp Business Cloud for automated messaging. --- Additional Notes Customization: You can modify the AI prompt to adapt the recommendations to the athlete's specific goals or fitness levels. The workflow is flexible and can accommodate additional nodes for more advanced analysis or output formats. Scalability: Ideal for individual athletes or coaches managing multiple athletes. Can be expanded to include additional metrics or insights based on user preferences. Performance Metrics Handled: Swimming: SWOLF, stroke count, pace. Cycling: Cadence, power zones, elevation. Running: Pacing, stride length, heart rate zones. --- Implementation Steps Set Up Strava API Key: Log in to Strava Developers to generate your API key. Integrate the API key into the Strava Trigger node. Configure Google Gemini Integration: Use your Google Gemini (PaLM) API credentials in the Google Gemini Chat Model node. Customize Email and WhatsApp Messaging: Update the Send Email and WhatsApp Business Cloud nodes with the recipient’s details. Automate Execution: Deploy the workflow and use n8n's scheduling features or cron jobs for periodic execution. --- GET n8n Now N8N COURSE n8n Book Developer Notes Author: Amjid Ali improvements. Resources: See in Action: Syncbricks Youtube PayPal: Support the Developer Courses : SyncBricks LMS By using this workflow, triathletes and coaches can elevate training to the next level with AI-powered insights and actionable recommendations.
By Amjid AliIT support chatbot with Google Drive, Pinecone & Gemini | AI doc processing
This n8n template empowers IT support teams by automating document ingestion and instant query resolution through a conversational AI. It integrates Google Drive, Pinecone, and a Chat AI agent (using Google Gemini/OpenRouter) to transform static support documents into an interactive, searchable knowledge base. With two interlinked workflows—one for processing support documents and one for handling chat queries—employees receive fast, context-aware answers directly from your support documentation. Overview Document Ingestion Workflow Google Drive Trigger: Monitors a specified folder for new file uploads (e.g., updated support documents). File Download & Extraction: Automatically downloads new files and extracts text content. Data Cleaning & Text Splitting: Utilizes a Code node to remove line breaks, trim extra spaces, and strip special characters, while a text splitter segments the content into manageable chunks. Embedding & Storage: Generates text embeddings using Google Gemini and stores them in a Pinecone vector store for rapid similarity search. Chat Query Workflow Chat Trigger: Initiates when an employee sends a support query. Vector Search & Context Retrieval: Retrieves the top relevant document segments from Pinecone based on similarity scores. Prompt Construction: A Code node combines the retrieved document snippets with the user’s query into a detailed prompt. AI Agent Response: The constructed prompt is sent to an AI agent (using OpenRouter Chat Model) to generate a clear, step-by-step solution. Key Benefits & Use Case Imagine a large organization where every IT support document—from troubleshooting guides to system configurations—is stored in a single Google Drive folder. When an employee encounters an issue (e.g., “How do I reset my VPN credentials?”), they simply type the query into a chat interface. Instantly, the workflow retrieves the most relevant context from the ingested documents and provides a detailed, actionable answer. This process reduces resolution times, enhances support consistency, and significantly lightens the load on IT staff. Prerequisites A valid Google Drive account with access to the designated folder. A Pinecone account for storing and retrieving text embeddings. Google Gemini (or OpenRouter) credentials to power the Chat AI agent. An operational n8n instance configured with the necessary nodes and credentials. Workflow Details 1 Document Ingestion Workflow Google Drive Trigger Node: Listens for file creation events in the specified folder. Google Drive Download Node: Downloads the newly added file. Extract from File Node: Extracts text content from the downloaded file. Code Node (Data Cleaning): Cleans the extracted text by removing line breaks, trimming spaces, and eliminating special characters. Recursive Text Splitter Node: Segments the cleaned text into manageable chunks. Pinecone Vector Store Node: Generates embeddings (via Google Gemini) and uploads the chunks to Pinecone. 2 Chat Query Workflow Chat Trigger Node: Receives incoming user queries. Pinecone Vector Store Node (Query): Searches for relevant document chunks based on the query. Code Node (Context Builder): Sorts the retrieved documents by relevance and constructs a prompt merging the context with the query. AI Agent Node: Sends the prompt to the Chat AI agent, which returns a detailed answer. How to Use Import the Template: Import the template into your n8n instance. Configure the Google Drive Trigger: Set the folder ID (e.g., 1RQvAHIw8cQbtwI9ZvdVV0k0x6TM6H12P) and connect your Google Drive credentials. Set Up Pinecone Nodes: Enter your Pinecone index details and credentials. Configure the Chat AI Agent: Provide your Google Gemini (or OpenRouter) API credentials. Test the Workflows: Validate the document ingestion workflow by uploading a sample support document. Validate the chat query workflow by sending a test query and verifying the returned support information. Additional Notes Ensure all credentials (Google Drive, Pinecone, and Chat AI) are correctly set up and tested before deploying the workflows in production. The template is fully customizable. Adjust the text cleaning, splitting parameters, or the number of document chunks retrieved based on your support documentation's size and structure. This template not only enhances IT support efficiency but also offers a scalable solution for managing and leveraging growing volumes of support content.
By AI IncarnationMulti-channel workflow error alerts with Telegram, Gmail & messaging apps
The Error Notification workflow is designed to instantly notify you whenever any other n8n workflow encounters an error, using popular communication channels like Telegram and Gmail—with optional support for Discord, Slack, and WhatsApp. 💡 Why Use Error Notification workflow? Immediate Awareness: Get instant alerts when workflows fail, preventing unnoticed errors and downtime. Multi-Channel Flexibility: Notify your team via Telegram, Gmail, and optionally Slack, Discord, or WhatsApp. Detailed Context: Receive rich error information including the error message, node name, time, and execution link for quicker fixes. Easy Integration: Built with native n8n nodes and customizable code, simple to adopt without complex setup. Open Source & Free: Use and adapt this workflow at no cost, making professional error monitoring accessible. ⚡ Who Is This For? n8n Workflow Developers: Quickly spot and respond to automation issues in development or production. Operations Teams: Maintain uptime and swiftly troubleshoot errors across multiple workflows. Small to Medium Businesses: Gain professional error alerting without expensive monitoring tools. Automation Enthusiasts: Enhance your automation reliability with real-time failure notifications. ❓ What Problem Does It Solve? This workflow embedd error detection and notification directly within your n8n instance. It automates the process of catching errors as they occur, compiling meaningful context, and delivering it instantly via your preferred messaging platforms. This drastically reduces your response time to issues and streamlines error management, improving your automation reliability and operational confidence. 🔧 What This Workflow Does ⏱ Trigger: Listens for any error generated in your n8n workflows using the n8n Error Trigger node. 📎 Step 2: Executes a Code node that formats a detailed error message capturing workflow name, error node, description, timestamp, and an execution URL. 🔍 Step 3: Sends the formatted error notification to multiple communication channels: Telegram and Gmail by default, plus optionally Discord, Slack, and WhatsApp (disabled by default). 💌 Step 4: Delivers rich, parsed HTML-formatted messages to ensure error readability and immediate actionability. 🔐 Setup Instructions Import the provided .json file into your n8n instance (Cloud or self-hosted). Set up credentials: Gmail OAuth credentials for sending emails via Gmail node Telegram API credentials for Telegram notifications (Optional) Discord Webhook URL credential for Discord notifications (Optional) Slack Webhook credential for Slack notifications (Optional) WhatsApp connection credentials (if enabled) Customize the Code node if needed to adjust the error message format or target chat IDs. Update the chat IDs and recipient details in each notification node according to your channels. Test the workflow by manually triggering an error in another workflow to verify proper notifications. 🧩 Pre-Requirements Active n8n instance (cloud or self-hosted) with version supporting Error Trigger node Telegram bot credentials and chat ID (Optional) Gmail, Discord, Slack, or WhatsApp accounts and webhook credentials if you want to use those channels 🛠️ Customize It Further Enable and configure additional notification nodes like Slack or WhatsApp to fit your team's communication style. Customize the error message template in the Code node to include extra metadata or format it differently (e.g., markdown). Integrate with incident management tools via webhook nodes or create tickets automatically on error. 🧠 Nodes Used Error Trigger Code Telegram Gmail Discord (disabled) Slack (disabled) WhatsApp (disabled) Sticky Note (for description) 📞 Support Made by: khaisa Studio Tag: notification,error,monitoring,workflow,automation,alerts Category: Monitoring & Alerts Need a custom? Need a custom? contact me on LinkedIn or Web
By Khairul MuhtadinAnalyze and chat with XML files using GPT and LangChain
This workflow allows interactive conversation with the content of an XML file using OpenAI and LangChain. It fetches an XML feed from a specified URL, parses the XML, and enables an AI agent to respond to user queries based on the XML's structure and data. What It Does: Triggered via webhook or manual execution. Sets and fetches an external XML feed URL. Parses the XML into a readable format. Connects OpenAI GPT via LangChain for intelligent chat. AI agent answers questions like extracting nodes, attributes, or structure from the XML.
By Sarfaraz Muhammad SajibAnswer questions with factual web search using Telegram, Tavily and GPT-5
🧠 Telegram Search Assistant — Tavily + AI/ML API This n8n workflow lets users ask questions in Telegram and receive concise, fact-based answers. It performs a web search with Tavily, then uses AIMLAPI (GPT-5) to summarize results into a clear 3–4 sentence reply. The flow ensures grounded, non-hallucinated answers. --- 🚀 Features 📩 Telegram-based input ⌨️ Typing indicator for better UX 🔎 Web search with Tavily (JSON results) 🧠 Summarization with AIMLAPI (openai/gpt-5-chat-latest) 📤 Replies in the same chat/thread ✅ Guardrails against hallucinations --- 🛠 Setup Guide 📲 Create Telegram Bot Talk to @BotFather Use /newbot → choose a name and username Save the bot token 🔐 Set Up Credentials in n8n Telegram API: use your bot token Tavily: add your Tavily API key AI/ML API: add your API key Base URL: https://api.aimlapi.com/v1 🔧 Configure the Workflow Open the n8n editor and import the JSON Update credentials for Telegram, Tavily, and AIMLAPI --- ⚙️ Flow Summary | Node | Function | |--------------------------|-----------------------------------------------| | 📩 Receive Telegram Msg | Triggered when user sends text | | ⌨️ Typing Indicator | Shows “typing…” to user | | 🔎 Web Search | Queries Tavily with user’s message | | 🧠 LLM Summarize | Summarizes search JSON into a factual answer | | 📤 Reply to Telegram | Sends concise answer back to same thread | --- 📁 Data Handling By default: no data stored Optional: log queries & answers to Google Sheets or a database --- 💡 Example Prompt Flow User sends: When is the next solar eclipse in Europe? Bot replies: The next solar eclipse in Europe will occur on August 12, 2026. It will be visible as a total eclipse across Spain, with partial views in much of Europe. The maximum eclipse will occur around 17:46 UTC. --- 🔄 Customization Add commands: /help, /sources, /news Apply rate-limits per user Extend logging to Google Sheets / DB Add NSFW / profanity filters before search --- 🧪 Testing Test end-to-end in Telegram (not just “Execute Node”) Add a fallback reply if Tavily returns empty results Use sticky notes for debugging & best practices --- 📎 Resources 🔗 AI/ML API Docs 🔗 Tavily Search API
By AI/ML API | D1m7asis