Workflow Templates

How it works This workflow creates a multi-talented AI assistant named Simran that interacts with users via Telegram. It can handle text and voice messages, understand the user's intent, and perform various tasks. Step 1: Receive & Transcribe Input The workflow triggers on any new Telegram message. If it's a voice message, it uses AssemblyAI to transcribe it into text; otherwise, it processes the incoming text directly. Step 2: Understand User Intent Using a Large Language Model (LLM), the workflow analyzes the user's message to determine their goal, categorizing it as a general chat, a request to generate an image, a command to set a reminder, or a request to remember a specific piece of information. Step 3: Fetch Context & Route The assistant retrieves past conversation summaries from a MongoDB database to maintain context. Based on the user's intent, the workflow routes the task to the appropriate path. Step 4: Execute the Task Chat: Generates a response using an AI agent whose personality can be toggled between a standard assistant and a "Girlfriend Mode." It also analyzes the user's mood to tailor the response. Generate Image: Creates a detailed prompt and uses an image generation API to create and send a picture. Set Reminder: Parses the natural language request, creates an event in Google Calendar and a task in Google Tasks, and sends a confirmation. Remember Info: Saves specific user-provided information to a dedicated memory collection in MongoDB. Step 5: Respond and Save Memory The final output (text, voice message, or image) is sent back to the user on Telegram. The workflow then summarizes the interaction and saves it to the database to ensure continuity in future conversations. Set up steps Estimated Set up time: 20 - 30 minutes. Configure Credentials: You will need to add credentials for several services in your n8n instance: Telegram (Bot API Token) AssemblyAI (API Key) MongoDB Google (for Calendar, Tasks, Sheets, and Natural Language API) A Large Language Model (the workflow uses Google Gemini but can be adapted) An image generation service (the workflow uses the Together.xyz API) Set up External Services: Ensure your MongoDB instance has two collections: user_memory and memory_auto. Create a Google Sheet to manage the "Girlfriend Mode" status for different users. Ensure edge-tts is installed on the machine running your n8n instance for the text-to-speech functionality. Customize Nodes: Review the nodes with hardcoded IDs, such as Google Tasks and Google Sheets, and update them with your specific Task List ID and Sheet ID. The sticky notes inside the workflow provide more detailed instructions for specific nodes and segments.

This workflow sends a OpenAI GPT reply when an email is received from specific email recipients. It then saves the initial email and the GPT response to an automatically generated Google spreadsheet. Subsequent GPT responses will be added to the same spreadsheet. Additionally, when feedback is given for any of the GPT responses, it will be recorded to the spreasheet, which can then be used later to fine-tune the GPT model. Prerequisites OpenAI credentials Google credentials How it works This workflow is essentially a two-in-one workflow. It triggers off from two different nodes and have very different functionality from each trigger. The flow triggered from On email received node is as follows: Triggers off on the On email received node. Extract the email body from the email. Generate a response from the email body using the OpenAI node. Reply to the email sender using the Send reply to recipient node. A feedback link is also included in the email body which will trigger the On feedback given node. This is used to fine-tune the GPT model. Save the email body and OpenAI response to a Google Sheet. If a sheet does not exist, it will be created. The flow triggered from On feedback given node is as follows: Triggers off when a feedback link is clicked in the emailed GPT response. The feedback, either positive or negative, for that specific GPT response is then recorded to the Google Sheet.

How It Works This template is a complete, hands-on tutorial for building a RAG (Retrieval-Augmented Generation) pipeline. In simple terms, you'll teach an AI to become an expert on a specific topic—in this case, the official n8n documentation—and then build a chatbot to ask it questions. Think of it like this: instead of a general-knowledge AI, you're building an expert librarian. 🔧 Workflow Overview The workflow is split into two main parts: Part 1: Indexing the Knowledge (📚 Building the Library) This is a one-time process you run manually. The workflow will: Automatically scrape all pages of the n8n documentation. Break them down into small, digestible chunks. Use an AI model to create a numerical representation (an embedding) for each chunk. Store these embeddings in n8n's built-in Simple Vector Store. > This is like a librarian reading every book and creating a hyper-detailed index card for every paragraph. > ⚠️ Important: This in-memory knowledge base is temporary. It will be erased if you restart your n8n instance. You'll need to run the indexing process again in that case. Part 2: The AI Agent (🧠 The Expert Librarian) This is the chat interface. When you ask a question: The AI agent doesn't guess the answer. It searches the knowledge base to find the most relevant “index cards” (chunks). It feeds those chunks to a language model (Gemini) with strict instructions: > “Answer the user's question using ONLY this information.” This ensures answers are accurate, factual, and grounded in your documents. 🚀 Setup Steps > Total setup time: ~2 minutes > Indexing time: ~15–20 minutes This template uses n8n’s built-in tools, so no external database is needed. 1. Configure OpenAI Credentials You’ll need an OpenAI API key (for GPT models). In your n8n workflow: Go to any of the three OpenAI nodes (e.g., OpenAI Chat Model). Click the Credential dropdown → + Create New Credential. Enter your OpenAI API key and save. 2. Apply Credentials to All Nodes Your new credential is now saved. Go to the other two OpenAI nodes (e.g., OpenAI Embeddings) and select the newly created credential from the dropdown. 3. Build the Knowledge Base Find the Start Indexing manual trigger node (top-left of the workflow). Click the Execute Workflow button to start indexing. > ⚠️ Be patient: This takes 15–20 minutes to scrape and process the full documentation. > You only need to do this once per n8n session. 4. Chat With Your Expert Agent After indexing completes, activate the entire workflow (toggle at the top). Open the RAG Chatbot chat trigger node (bottom-left). Copy its Public URL. Open it in a new tab and ask questions about n8n! Example questions: "How does the IF node work?" "What is a sub-workflow?" 👤 Credits All credits go to Lucas Peyrin 🔗 lucaspeyrin on n8n.io

This workflow contains community nodes that are only compatible with the self-hosted version of n8n. This comprehensive RAG workflow enables your AI agents to answer user questions with contextual knowledge pulled from your own documents — using metadata-rich embeddings stored in Supabase. 🔧 Key Features: RAG Agents powered by GPT-4.5 or GPT-3.5 via OpenRouter or OpenAI. Supabase Vector Store to store and retrieve document embeddings. Cohere Reranker to improve response relevance and quality. Metadata Agent to enrich vectorized data before ingestion. PDF Extraction Flow to automatically parse and upload documents with metadata. ✅ Setup Steps: Connect your Supabase Vector Store. Use OpenAI Embeddings (e.g. text-embedding-3-small). Add API keys for OpenAI and/or OpenRouter. Connect a reranker like Cohere. Process documents with metadata before embedding. Start chatting — your AI agent now returns context-rich answers from your own knowledge base! Perfect for building AI assistants that can reason, search and answer based on internal company data, academic papers, support docs, or personal notes.

👔 Who is this for? Entrepreneurs and startup founders preparing for investors Business consultants drafting complete client plans Strategy teams building long-term business models Accelerators, incubators, or pitch trainers ❓ What problem does this workflow solve? Writing a full business plan takes days of work, multiple tools, and often gets stuck in messy docs or slides. This template automates every major section, generating a clean, detailed, and professional business plan with AI in just minutes. ⚙️ What this workflow does Starts with a chat message asking for your business idea or startup concept Passes the idea through 83 intelligent agents, each handling a full business plan chapter: Executive Summary Problem & Solution Product Description Market Research Competitor Analysis Business Model Marketing Strategy (includes guerrilla ideas) Operational Plan Financial Plan Team & Advisors Roadmap Conclusion & Next Steps Each section uses tailored prompts and business logic Combines all outputs into a structured, professional Markdown file Final result: a ready-to-export business plan in seconds 🛠️ Setup Import this template into your n8n instance Replace the “LLM Chat Model” node with your preferred model (Ollama, GPT-4, etc.) Start from the chat input node — describe your startup or idea Wait for all agents to finish Download the final Business plan file 🤖 LLM Flexibility (Choose Your Model) Supports: OpenAI (GPT-4 / GPT-3.5) Ollama (LLaMA 3.1, Mistral, DeepSeek, etc.) Any compatible N8N chat model To change the model, just replace the “Language Model” node — no other updates required 📌 Notes All nodes are clearly named by function (e.g., “Market Research Generator”) Sticky notes included for clarity Generates high-quality plans suitable for VCs or accelerators Modular: you can turn off or reorder any chapter 📩 Need help? Email: sinamirshafiee@gmail.com Happy to support setup, LLM switching, or custom section development.

🎧 Daily RSS Digest & Podcast Generation This workflow automates the creation of a daily sports podcast from your favorite news sources. It fetches articles, uses AI to write a digest and a two-person dialogue, and produces a single, merged audio file with KOKORO TTS ready for listening. ✨ How it works: 📰 Fetch & Filter Daily News: The workflow triggers daily, fetches articles from your chosen RSS feeds, and filters them to keep only the most recent content. ✍️ Generate AI Digest & Script: Using Google Gemini, it first creates a written summary of the day's news. A second AI agent then transforms this news into an engaging, conversational podcast script between two distinct AI speakers. 🗣️ Generate Voices in Chunks: The script is split into individual lines of dialogue. The workflow then loops through each line, calling a Text-to-Speech (TTS) API to generate a separate audio file (an MP3 chunk) for each part of the conversation. 🎛️ Merge Audio with FFmpeg: After all the audio chunks are created and saved locally, a command-line script generates a list of all the files and uses FFmpeg to losslessly merge them into a single, seamless MP3 file. All temporary files are then deleted. 📤 Send the Final Podcast: The final, merged MP3 is read from the server and delivered directly to your Telegram chat with a dynamic, dated filename. You can modify: 📰 The RSS Feeds to any news source you want. 🤖 The AI Prompts to change the tone, language, or style of the digest and podcast. 🎙️ The TTS Voices used for the two speakers. 📫 The Final Delivery Method (e.g., send to Discord, save to Google Drive, etc.). Perfect for creating a personalized, hands-free news briefing to listen to on your commute. Inspired by: https://n8n.io/workflows/6523-convert-newsletters-into-ai-podcasts-with-gpt-4o-mini-and-elevenlabs/

Automated Cryptocurrency Analysis & Reporting with Google Gemini and CoinGecko This powerful template is an n8n workflow that automates cryptocurrency market data analysis and delivers reports directly to your inbox. It fetches real-time data from CoinGecko API, generates AI-powered analysis, and sends the report via email. Features Scheduled Execution**: Runs automatically at a set time daily (default: 10:00 AM). Customizable Analysis**: Personalize analysis content/language via "AI Prompt" nodes. Easy Scalability**: Duplicate node groups to add more cryptocurrencies. Flexible AI Integration**: Defaults to Google Gemini, but supports ChatGPT/Ollama. Setup Instructions n8n Installation: Install n8n (self-hosted or Cloud version). Email Account Setup: Add email service credentials in n8n (e.g., Microsoft Outlook OAuth2). AI Model Credentials (Google Gemini): Obtain API key from Google AI Studio and add to n8n "Credentials". Import Template: Copy the JSON code into n8n as a new workflow. Customization Change Cryptocurrencies**: Update ids= parameter in HTTP Request nodes (e.g., ids=bitcoin). Edit AI Prompt**: Modify text in "AI Prompt" nodes. Use Different AI Model**: Replace Google Gemini with supported alternatives. Update Email Address**: Change recipient in "Send Mail" nodes. `

Generate AI video clips to promote products, services or events on social media. Use gotoHuman as an interface to control and supervise each step of the workflow to create content that's actually worth posting. How it works gotoHuman will show the workflow steps that need approval or input in its' inbox and notify you via email or Slack. We choose from different topics for our post suggested by AI We select the image style, a product to show, and review an AI-generated tag line We use Fal.ai to generate an image that serves as a reference image for our video clip. And we use Cloudinary to add an overlay for the tag line. We review the image in gotoHuman and can iterate on it by retrying or even changing the prompt. We review the video clip that's generated with Fal.ai based on the approved image and can, again, retry or reprompt. How to set up Most importantly, install the gotoHuman node before importing this template! (Just add the node to a blank canvas before importing) Follow the instructions shown along the workflow and in the incl. video guide. You mainly need to set up your credentials for gotoHuman, OpenAI, Fal.ai and Cloudinary import the review templates with these IDs in gotoHuman: Z7V1jyImY1pho9eY039R,0GBaOCWd27tqV562kkCL,E2wlCVPWmk2UnLHVt4uu,DitPdbIapS4rBxBTIYGt,Z2T7nFwkXVFQlD6z50eV select these templates in the gotoHuman nodes do a quick setup for Cloudinary Requirements You need accounts for gotoHuman (human supervision) OpenAI (ideation) Fal.ai (image/video generation) Cloudinary (text overlay) How to customize Adjust/Replace the workflow triggers as needed Change the prompt in the topics generation node Replace the product image URLs used in the "gotoHuman - Content" node Adjust the available styles for image generation in the gotoHuman review template and the prompts they link to in the "Set Initial Image Prompt" node Adjust the prompt used for video generation in the "Set Initial Video Prompt" node If you want to use a different service/model for image and video generation, replace the nodes related to Fal.ai. Also, if you do not need a text overlay, remove the Cloudinary nodes.

🤖 Universal E-Commerce AI Assistant (Shopify, WooCommerce & RAG) This powerful n8n workflow deploys a sophisticated, multi-talented AI chatbot designed to streamline your e-commerce and customer support operations. The AI assistant can intelligently understand user queries and route them to the correct specialized agent, whether it's for Shopify, WooCommerce, or general knowledge questions answered by a Retrieval-Augmented Generation (RAG) system. This template automates responses to a wide range of inquiries, from checking Shopify order statuses with GraphQL to fetching product lists from WooCommerce, and even answering general questions by looking up information in a Pinecone vector database. How It Works ⚙️ The workflow operates in a series of logical steps, starting from the moment a user sends a message. 💬 Chat Trigger: The workflow activates when a user sends a message in the n8n chat interface. It captures the user's input and a unique session ID to track the conversation. 🧠 Intelligent Routing: The user's query is first sent to a Router Agent powered by GPT-4o-mini. This agent's sole purpose is to classify the intent of the message and output one of three keywords: SHOPIFY, WOOCOMMERCE, or None of them. 🔀 Conditional Branching: Based on the Router's output, a series of IF nodes direct the conversation down one of three paths: General Queries Path Shopify Path WooCommerce Path 📚 General Queries (RAG): If the query is not about e-commerce, it's handled by a RAG agent. Embedding: The user's question is converted into a vector embedding using AWS Bedrock. Retrieval: The workflow searches a Pinecone Vector Store to find the most relevant information from your knowledge base. Generation: A GPT-4o-mini agent receives the context from Pinecone and generates a comprehensive, helpful answer. 🛍️ E-Commerce Specialists: If the query is about Shopify or WooCommerce, it's passed to a dedicated agent. Shopify Agent: This agent uses Google Gemini and has a suite of tools to manage Shopify tasks. It can Get Order info, Fetch All Products, or run complex queries using the powerful GraphQL tool. WooCommerce Agent: This agent also uses Google Gemini and is equipped with tools to Fetch Order Details and Fetch All Products from a WooCommerce store. 🗣️ Conversation Memory: Each agent (Router, General, Shopify, WooCommerce) is connected to its own Memory node. This allows the chatbot to remember previous parts of the conversation for a more natural and context-aware interaction. 🏁 Merge & Respond: All three paths converge at a final Merge node. This ensures that no matter which agent handled the request, the final answer is streamlined into a single output and sent back to the user in the chat. Nodes Used 🔗 Triggers: Chat Trigger: Starts the workflow when a chat message is received. AI & Agents: AI Agent: Four separate agents for Routing, Shopify, WooCommerce, and General Queries. OpenAI Chat Model: Uses GPT-4o-mini for the Router and General Queries agent. Google Gemini Chat Model: Uses Google Gemini for the Shopify and WooCommerce agents. Tools & Data: Shopify Tool: To get products and order information from Shopify. WooCommerce Tool: To get products and order information from WooCommerce. GraphQL Tool: For advanced, custom queries to the Shopify API. Pinecone Vector Store: To retrieve context for the RAG agent. AWS Bedrock Embeddings: To create vector embeddings for Pinecone. Logic & Memory: IF Node: To conditionally route the workflow. Merge Node: To consolidate the different branches before ending. Window Buffer Memory: Four nodes to provide conversational memory to each agent. Setup Guide 🛠️ To use this workflow, you'll need to configure several nodes with your own credentials and settings. 1\. AI Model Credentials OpenAI: Create an API key in your OpenAI Platform dashboard. Add this credential to the Router Model and GPT-4o-mini nodes. Google Gemini: Create an API key in your Google AI Studio dashboard. Add this credential to the Shopify Chat Model and WooCommerce Chat Model nodes. 2\. E-Commerce Platform Credentials Shopify: You will need a Shopify Access Token. Follow the n8n documentation to generate one. Add the credential to the Fetch All Products and Get Order info nodes. WooCommerce: Create API credentials from your WordPress dashboard. Add the credential to the Fetch All Products2 and Fetch Order Details nodes. 3\. RAG System Credentials (Pinecone & AWS) Pinecone: Sign up for a Pinecone account and create an API key. Add your Pinecone credentials in n8n. In the Pinecone Vector Store node, set the pineconeIndex to the name of your index. You must have a pre-existing index with data for the RAG to work. AWS: Create an AWS account and an IAM user with programmatic access to Amazon Bedrock. Add your AWS credentials in n8n. Select your AWS credentials in the AWS Bedrock Embeddings node. 4\. GraphQL Node Configuration In the GraphQL node, you must update the endpoint URL. Replace the placeholder https://{subdomain}.myshopify.com/admin/api/2025-04/graphql.json with your own Shopify store's GraphQL API endpoint.

Disclaimer: Community nodes are used, and template can only be used on self-hosted n8n instances. This n8n template from Intuz provides a complete solution to automate your entire B2B lead generation pipeline, from discovering recently funded companies to drafting hyper-personalized outreach emails with AI. Who's this workflow for? Sales Development Representatives (SDRs) Business Development Teams Growth Hackers Startup Founders Marketing Agencies How it works 1. Scrape Funded Companies: The workflow begins by using Apify to scrape a target list of recently funded companies directly from a Crunchbase search. 2. Enrich with Apollo.io: It takes each company and uses the Apollo.io API to find key decision-makers (like VPs, Directors) and enrich their contact information, including finding their email addresses. 3. Populate Google Sheets: All the gathered lead data—company name, contact name, title, email, LinkedIn URL, etc.—is neatly organized and added to a Google Sheet. 4. AI-Personalized Email Crafting: The workflow sends the lead's information to OpenAI (GPT-4) with a highly specialized prompt, instructing it to write a concise, impactful, and hyper-personalized "first touch" cold email. 5. Update Lead List with Email Content: Finally, the unique, AI-generated email is saved back into the Google Sheet alongside the corresponding lead's information, making it ready for you to send. Pre-conditions and Requirements Before you can successfully execute this workflow, you must have the following accounts, credentials, and assets in place. 1. n8n Instance: You need an active n8n instance (self-hosted). 2. Apify Account & Crunchbase Access: Apify Account: A registered account on Apify. Crunchbase Account: An active, logged-in Crunchbase account (a paid subscription is recommended for accessing detailed search filters). 3. Apollo.io API: You need an Apollo.io plan that includes API access. You can generate the API from settings. 4. Google Sheet: Create a new Google Sheet to store your leads. The workflow is configured for two tabs: one for raw data ("HealthCare" in the template) and one for email generation ("Company sheet"). 5. OpenAI Account: An account with OpenAI with API access and billing set up. Setup Instructions 1. Apify Connection: Connect your Apify account in the Run an Actor node. You'll need an apify scrapper, here's the link In the Custom Body field, update the search.url with your target Crunchbase discovery URL and provide a valid cookie for authentication. 2. Apollo.io Connection: Connect your Apollo.io account using HTTP Header Authentication in the three Apollo nodes. You will need to provide your API key. 3. Google Sheets Connection: Connect your Google Sheets account. Create a spreadsheet and update the Document ID and Sheet Name in the three Google Sheets nodes to match yours. Ensure your sheet columns are set up to receive the data. 4. OpenAI Connection: Connect your OpenAI account in the Message a model node. The prompt is pre-engineered for high-quality output, but you can tailor it to better fit your specific value proposition. 5. Activate Workflow: Click "Execute workflow" to run the automation manually and watch your AI-powered lead list build itself. Customization Guide This workflow is a powerful template. To adapt it to your specific business needs, you should review and modify the following nodes. 1. Changing Your Target Companies (The Source) Node: Run an Actor What to change: The search.url parameter inside the customBody. How to do it: Go to Crunchbase and perform a search for your ideal companies (e.g., filter by different funding rounds, industry, location, keywords, etc.). Copy the URL from your browser's address bar after the search results have loaded. Paste this new URL as the value for "search.url" in the node. You can also adjust "count": 10 to pull more or fewer companies per run. Be mindful of Apify and Apollo credit usage. 2. Defining Your Ideal Contact Persona Node: Apollo - Get User What to change: The person_seniorities and person_titles arrays in the jsonBody. How to do it: 1. Seniority: Modify the person_seniorities list to match who you sell to. Examples: ["c_level", "founder"] or ["manager", "contributor"]. 2. Job Titles: This is crucial. Replace the existing list of titles ("engineering", "technology", etc.) with keywords relevant to your target buyer. For example, if you sell to marketing teams, you might use: ["marketing", "demand generation", "growth", "content", "brand"]. 3. Configuring Your Google Sheet Destination Nodes: Append or update row in sheet and Update row in sheet What to change: The documentId and sheetName. How to do it: Open your Google Sheet. The documentId is the long string of characters in the URL between /d/ and /edit. Copy and paste it into the "Document ID" field in both nodes. The sheetName (or Sheet ID/gid) needs to be set for your specific tabs. Make sure the sheet names/IDs in the nodes match the tabs in your document. Column Mapping: If you change the column names in your Google Sheet, you must update the column mapping inside these nodes to ensure the data is written to the correct place. 4. Tailoring the AI Email Generation Node: Message a model (OpenAI) What to change: The prompt, the model, and the input variables. How to do it: The Prompt: This is the heart of your outreach. Read the entire prompt carefully and edit it to reflect your company's value proposition, tone of voice, and specific call-to-action. Value Proposition: Change the line "We help them cut that specific infrastructure spend..." to match what your product does. Use a powerful, single data point if you have one. Call-to-Action (CTA): Modify the final question ("Curious if infra efficiency is on your roadmap...") to something that fits your sales process. Tone: Adjust the initial instructions (e.g., "Your tone is that of a peer...") if you want a different style. The Model: The workflow uses gpt-4.1. You can switch to a different model like gpt-4o (potentially better/faster) or gpt-3.5-turbo (much cheaper, but lower quality) depending on your budget and needs. Input Variables: The prompt uses {{ $json['Company Name'] }}, {{ $json['Person Designation'] }}, and {{ $json.Industry }}. If you want to add more personalization (e.g., based on a company's funding amount), you would first need to ensure that data is passed to this node, then add the new variable (e.g., {{ $json['Funding Amount'] }}) into the prompt. Connect with us Website: https://www.intuz.com/services Email: getstarted@intuz.com LinkedIn: https://www.linkedin.com/company/intuz Get Started: https://n8n.partnerlinks.io/intuz For Custom Workflow Automation Click here- Get Started

Chat to write or reword a blog post. The workflow stores each result in Google Sheets and uses a sub-workflow “Google tool” to count rows per session (your running context). If a session exceeds a row threshold, the flow can branch (e.g., stop or notify). ⚙️ Setup Instructions 1️⃣ Set Up OpenAI Connection Go to OpenAI Platform Navigate to OpenAI Billing Add funds to your billing account Copy your API key into the OpenAI credentials in n8n 2️⃣ Prepare Your Google Sheet Connect your Data in Google Sheets Use this format: Sample Sheet Row 1 = column names (e.g., session, Rows, output) Data in rows 2–100 (or more if you prefer) In n8n, use Google Sheets OAuth2 → pick your Spreadsheet and Worksheet (Optional) You can adapt this to Airtable, Notion, or a Database 🧠 How It Works Chat Trigger**: Provide a topic (write) or paste existing text (reword). Code Node (“Choose to Write or Edit Blog”)**: Builds a system_prompt + user_prompt Instructs the agent to call the Google tool (sub-workflow) with only the sessionid to count existing rows. Tool Workflow (“google”)**: Fetches rows from the sheet → filters by session → summarizes row count. Agent (“Blog Writer & Editor”)**: Returns structured JSON (items/rows, session, blog body). Store (Google Sheets)**: Appends { session, Rows, output } to the sheet. If Node**: Example rule: Rows > 3 → branch/limit/notify as needed. 💬 Example Prompts “Write a 600-word blog about n8n agents with 3 bullet takeaways. Session: abc123.” “Reword this post into a concise LinkedIn article. Session: launchQ3:\n<your text here>” “Draft a blog intro and 5 SEO headlines on marketing automation. Session: mkt-01.” 📬 Contact Need help tailoring this to Airtable/Notion/DB, or adding auto-publishing? 📧 rbreen@ynteractive.com 🔗 Robert Breen 🌐 ynteractive.com

Instantly fetch real-time Bitget spot market data directly in Telegram! This workflow integrates the Bitget REST v2 API with Telegram (plus optional AI-powered formatting) to deliver the latest crypto price, order book, candles, and recent trades. Perfect for crypto traders, analysts, and investors who need reliable market data at their fingertips—no API key required.  Sign-up for Bitget for 6,200 USDT in rewards to trade: Collect Now How It Works A Telegram bot listens for user requests (e.g., BTCUSDT). The workflow connects to Bitget public endpoints to fetch: Ticker (latest price & 24h stats) Order book depth (top bids/asks) Recent trades (price, side, volume, timestamp) Candlestick data (1m, 15m, 1h, 4h, 1d) Historical candles (optional, for backfill before endTime) A Calculator node derives useful metrics like mid-price and spread. A Think node reshapes raw JSON into Telegram-ready text. A splitter ensures reports over 4000 characters are chunked safely. The final market insights are delivered instantly back to Telegram. What You Can Do with This Agent ✅ Track live prices & 24h stats for any Bitget spot pair. ✅ Monitor order book liquidity and spreads in real-time. ✅ Analyze candlesticks across multiple timeframes. ✅ Review recent trades to see execution flow. ✅ Fetch historical candles for extended market context. ✅ Receive clean, structured reports with optional AI-enhanced formatting. Set Up Steps Create a Telegram Bot Use @BotFather to generate a bot token. Configure in n8n Import Bitget AI Agent v1.02.json into your n8n instance. Add your Telegram credentials (bot token + your Telegram ID in the User Authentication node). Add an OpenAI key if you want AI-powered formatting. (Optional) Add an *Bitget api key** . Deploy and Test Send BTCUSDT to your bot. Get live Bitget spot data instantly in Telegram! 🚀 Unlock powerful, real-time Bitget insights in Telegram—zero setup, zero API keys required! 📺 Setup Video Tutorial Watch the full setup guide on YouTube: 🧾 Licensing & Attribution © 2025 Treasurium Capital Limited Company Architecture, prompts, and trade report structure are IP-protected. No unauthorized rebranding permitted. 🔗 For support: Don Jayamaha – LinkedIn