Workflow Templates

Revolut Extracts Analyzer This n8n template processes Revolut statements, normalizes transactions, and uses AI to categorize expenses automatically. Use cases include detecting subscriptions, separating internal transfers, and building dashboards to track spending. How it works Get Categories from Supabase** Download & Transform** Loop Over Items** LLM Categorizer** Insert into Supabase** How to use Start with the manual trigger node or replace it with a schedule/webhook. Connect Google Drive to provide Revolut CSV files. Ensure Supabase has tables for transactions and categories. Extend with notifications, reports, or BI tools. Requirements Google Drive for CSV files Supabase tables for categories & transactions LLM provider (OpenAI/Gemini)

Instantly access real-time Binance Spot Market data in Telegram! This workflow connects the Binance REST API with Telegram and optional GPT-4.1-mini formatting, delivering structured insights such as latest prices, 24h stats, order book depth, trades, and candlesticks directly into chat. 🔎 How It Works A Telegram Trigger listens for incoming user requests. User Authentication validates the Telegram ID to restrict access. A Session ID is generated from chat.id to manage session memory. The Binance AI Agent executes HTTP calls to the Binance public API: Latest Price (Ticker) → /api/v3/ticker/price?symbol=BTCUSDT 24h Statistics → /api/v3/ticker/24hr?symbol=BTCUSDT Order Book Depth → /api/v3/depth?symbol=BTCUSDT&limit=50 Best Bid/Ask Snapshot → /api/v3/ticker/bookTicker?symbol=BTCUSDT Candlestick Data (Klines) → /api/v3/klines?symbol=BTCUSDT&interval=15m&limit=200 Recent Trades → /api/v3/trades?symbol=BTCUSDT&limit=100 Utility Tools refine outputs: Calculator → computes spreads, midpoints, averages, % changes. Think → extracts and reformats JSON into human-readable fields. Simple Memory → saves symbol, sessionId, and user context. Message Splitter ensures outputs >4000 characters are chunked for Telegram. Final structured reports are sent back to Telegram. ✅ What You Can Do with This Agent Get real-time Binance Spot prices with 24h stats. Fetch order book depth and liquidity snapshots. View best bid/ask quotes. Retrieve candlestick OHLCV data across timeframes. Check recent trades (up to 100). Calculate spreads, mid-prices, % changes automatically. Receive clean, structured messages instead of raw JSON. 🛠️ Setup Steps Create a Telegram Bot Use @BotFather and save the bot token. Configure in n8n Import Binance AI Agent v1.02.json. Update the User Authentication node with your Telegram ID. Add Telegram credentials (bot token). Add OpenAI API key (Optional) Add Binance API key Deploy & Test Activate the workflow in n8n. Send BTCUSDT to your bot. Instantly receive Binance Spot Market insights inside Telegram. 📤 Output Rules Group outputs by Price, 24h Stats, Order Book, Candles, Trades. Respect Telegram’s 4000-char message limit (auto-split enabled). Only structured summaries — no raw JSON. 📺 Setup Video Tutorial Watch the full setup guide on YouTube: ⚡ Unlock Binance Spot Market insights instantly in Telegram — clean, fast, and API-key free. 🧾 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

📊 WEEX Spot Market Quant AI Agent (All-in-One Multi-Agent Trading System) ⚡ Overview This multi-agent n8n workflow delivers an automated, intelligent trading analysis system for the WEEX Spot Market. It uses GPT-4o to interpret user prompts, route them to the correct sub-agent tools, analyze technical indicators, price data, sentiment insights, and return concise trading signals via Telegram or downstream automations. No need to download additional workflows—everything is embedded in this single orchestrated agent. 🧠 Core Features 🔹 Single-entry architecture → Built-in orchestration logic with no external subworkflow dependencies 🔹 Multi-timeframe indicator analysis → 15m, 1h, 4h, and 1d 🔹 Sentiment + news insights from crypto sources 🔹 Live price, volume, kline, and order book analysis 🔹 LLM-powered signal evaluation using GPT-4o 🔹 Telegram integration for fast human queries or autonomous alerts 🤖 Built-In Agent Modules | Module | Description | | ----------------------------------- | ---------------------------------------------------------- | | ✅ Financial Analyst Tool | Routes prompts, interprets tokens, and triggers sub-agents | | ✅ News & Sentiment Analyst Tool | Gathers real-time sentiment from crypto news sources | | ✅ Technical Indicator Tools | 15m, 1h, 4h, 1d indicators using WEEX spot market data | | ✅ Price & Order Book Agent | Fetches real-time stats, price, and structure | | ✅ Trading Signal Evaluator | GPT-4o merges all data and generates trading decision | 🖥️ Prompt Flow Example User Input: “Should I long or short ETH on WEEX today?” → Financial Analyst Agent interprets the query → Fetches multi-timeframe indicators, live price, sentiment → GPT-4o evaluates conditions and creates recommendation → Output delivered via Telegram: 📈 ETH/USDT Overview • Price: \$3,710 • 4h RSI: 64.5 – Slightly Overbought • MACD: Bullish Crossover • Market Sentiment: 🔼 Positive Recommendation: Consider long entry with stop at \$3,640. 🔧 Setup Instructions Follow these steps to fully deploy and operate the WEEX Quant AI Agent in your n8n environment: 🟢 Get Telegram Bot API Key Create your bot via @BotFather on Telegram Save the token it gives you (format: 123456789:ABCdefGHIjkLMNopQRStuvWXyz) 🔑 Add OpenAI / DeepSeek Chat API Key Compatible with GPT-4o (OpenAI) or DeepSeek Chat 📈 (Optional) WEEX API Keys If expanding to live trading or authenticated data, get a WEEX Spot API key from your account dashboard Not required for the analysis agent to function 🔗 Connect Telegram to n8n Use Telegram Trigger and Telegram node with your API key Ensure webhook is set correctly (or use polling mode) ✅ Example Use Cases | Scenario | Outcome | | ---------------------------------- | ----------------------------------------------------- | | “Is BTC bullish or bearish?” | Merged indicator + sentiment + price analysis summary | | “Get 15m and 4h trends for SOL” | Multi-timeframe volatility vs macro trend report | | “Latest crypto news on XRP” | Real-time filtered news + DeepSeek sentiment summary | | “What’s the order book structure?” | Level-by-level spread analysis with buy/sell volumes | 🎥 Watch the Live Demo 👨‍💼 Licensing & Support 🧾 © 2025 Treasurium Capital Limited Company Architecture, prompts, and trade signal framework are IP-protected. No unauthorized rebranding or replication permitted. 📩 Connect with the Creator Don Jayamaha – LinkedIn Profile

How it Works This workflow lets you build a Messenger AI Agent capable of understanding text, images, and voice notes, and replying intelligently in real time. It starts by receiving messages from a Facebook Page via a Webhook, detects the message type (text, image, or audio), and routes it through the right branch. Each input is then prepared as a prompt and sent to an AI Agent that can respond using text generation, perform quick calculations, or fetch information from Wikipedia. Finally, the answer is formatted and sent back to Messenger via the Graph API, creating a smooth, fully automated chat experience. Set Up Steps Connect credentials Add your OpenAI API key and Facebook Page Access Token in n8n credentials. Plug the webhook Copy the Messenger webhook URL from your workflow and paste it into your Facebook Page Developer settings (Webhook → Messages → Subscribe). Customize the agent Edit the System Message of the AI Agent to define tone, temperature, and purpose (e.g. “customer support”, “math assistant”). Enable memory & tools Turn on Simple Memory to keep conversation context and activate tools like Calculator or Wikipedia. Test & deploy Switch to production mode, test text, image, and voice messages directly from Messenger. Benefits 💬 Multi-modal Understanding — Handles text, images, and audio messages seamlessly. ⚙️ Full Automation — End-to-end workflow from Messenger to AI and back. 🧠 Smart Replies — Uses OpenAI + Wikipedia + Calculator for context-aware answers. 🚀 No-Code Setup — Build your first Messenger AI in less than 30 minutes. 🔗 Extensible — Easily connect more tools or APIs like Airtable, Google Sheets, or Notion.

AI Recommender: From Food Photo to Restaurant and Book (Google Books Integrated) What it does Analyzes a food photo with an AI vision model to extract dish name + category Searches nearby restaurants with Google Places and selects the single best (rating → reviews tie-break) Finds a matching book via Google Books and posts a tidy summary to Slack Who it’s for Foodies, bloggers, and teams who want a plug-and-play flow that turns a single food photo into a dining pick + themed reading. How it works Google Drive Trigger detects a new photo Dish Classifier (Vision LLM) → JSON (dish_name, category, basic macros) Search Google Places near your origin; Select Best Place (AI) Recommend Book (AI) → Search Google Books → format details Post to Slack (JP/EN both possible) Requirements Google Drive / Google Places / Google Books credentials, LLM access (OpenRouter/OpenAI), Slack OAuth. Customize Edit origin/radius in Set Origin & Radius, tweak category→keyword mapping in Normalize Classification, adjust Slack channel & message in Post to Slack.

Who’s it for Influencers and social-media teams who want to comment automatically on posts from selected Instagram profiles—scaling engagement while staying within platform limits. How it works / What it does Schedule Trigger runs every 2 h. Profile Post Extractor fetches up to 20 posts per profile from your CSV. Select Cookie rotates Instagram session-cookies. Get Random Post selects one. Create Comment (GPT-4o) writes ≤150-character reply in your chosen language. Builds instagram_post_to_comment.csv, uploads to SharePoint. Phantombuster Autocomment Agent posts it. Logged in instagram_posts_already_commented.csv to prevent repeats. How to set up Same as the auto-liker, but also add instagram_posts_already_commented.csv (header postUrl). Define tracked profiles in profiles_instagram.csv. Configure comment prompt & language in Set ENV Variables. Profile CSV format Your profiles_instagram.csv must contain a header profileUrl and valid Instagram profile URLs. Example: profileUrl https://www.instagram.com/brand_account/ https://www.instagram.com/influencer123/

On my never-ending quest to find the best embeddings model, I was intrigued to come across Voyage-Context-3 by MongoDB and was excited to give it a try. This template implements the embedding model on a Arxiv research paper and stores the results in a Vector store. It was only fitting to use Mongo Atlas from the same parent company. This template also includes a RAG-based Q&A agent which taps into the vector store as a test to helps qualify if the embeddings are any good and if this is even noticeable. How it works This template is split into 2 parts. The first part being the import of a research document which is then chunked and embedded into our vector store. The second part builds a RAG-based Q&A agent to test the vector store retrieval on the research paper. Read the steps for more details. How to use First ensure you create a Voyage account voyageai.com and a MongoDB database ready. Start with Step 1 and fill in the "Set Variables" node and Click on the Manual Execute Trigger. This will take care of populating the vector store with the research paper. To use the Q&A agent, it is required to publish the workflow to access the public chat interface. This is because "Respond to Chat" works best in this mode and not in editor mode. To use for your own document, edit the "Set Variables" node to define the URL to your own document. This embeddings approach should work best on larger documents. Requirements Voyageai.com account for embeddings. You may need to add credit to get a reasonable RPM for this workflow. MongoDB database either self-hosted or online at https://www.mongodb.com. OpenAI account for RAG Q&A agent. Customising this workflow The Voyage embeddings work with any vector store so feel free to swap out to other such as Qdrant or Pinecone if you're not a fan of MongoDB Atlas. If you're feeling brave, instead of the 3 sequential pages setup I have, why not try the whole document! Fair warning that you may hit memory problems if your instance isn't sufficiently sized - but if it is, go head and share the results!

How it works User Signup & Verification: The workflow starts when a user signs up. It generates a verification code and sends it via SMS using Twilio. Code Validation: The user replies with the code. The workflow checks the code and, if valid, creates a session for the user. Conversational AI: Incoming SMS messages are analyzed by Chat GPT AI for sentiment, intent, and urgency. The workflow stores the conversation context and generates smart, AI-powered replies. Escalation Handling: If the AI detects urgency or frustration, the workflow escalates the session—alerting your team and sending a supportive SMS to the user. Set up steps Estimated setup time:** 10–20 minutes for most users. What you’ll need:** A free n8n account (self-hosted or cloud) Free Twilio account (for SMS) OpenAI API key (for AI) A PostgreSQL database (Supabase, Neon, or local) Setup process:** Import this workflow into n8n. Add your Twilio and OpenAI credentials as environment variables or n8n credentials. Update webhook URLs in your Twilio console (for incoming SMS). (Optional) Adjust sticky notes in the workflow for detailed, step-by-step guidance.

Coinbase AI Agent instantly fetches real-time market data directly in Telegram! This workflow integrates the Coinbase REST API with Telegram (plus optional AI-powered formatting) to deliver the latest crypto price, order book, candles, and trade stats in seconds. Perfect for crypto traders, analysts, and investors who want actionable market data at their fingertips—without API keys. How It Works A Telegram bot listens for user requests (e.g., BTC-USD). The workflow calls Coinbase public endpoints (no key required) to fetch real-time data: Latest price (ticker) 24h stats (open, high, low, close, volume) Order book snapshots (best bid/ask + depth) Candlestick data (OHLCV for multiple intervals) Recent trades (executed orders) A Calculator node derives useful values like mid-price and spread. An AI or “Think” node reshapes JSON into clear, human-readable messages. A splitter ensures long messages are broken into safe Telegram chunks. The final market insights are sent instantly back to Telegram. What You Can Do with This Agent This Telegram bot gives you: ✅ Get instant price & 24h stats for any Coinbase spot pair. ✅ Monitor live order books with top bids/asks. ✅ Analyze candle data (e.g., 15m, 1h, 4h, 1d). ✅ Track recent trades to see market activity. ✅ Receive clean, structured reports—optionally AI-enhanced. Set Up Steps Create a Telegram Bot Use @BotFather on Telegram to create your bot and get an API token. Configure in n8n Import the provided workflow JSON. Add your Telegram credentials (bot token + your Telegram ID for authentication). (Optional) Add an OpenAI key if you want AI-enhanced formatting. Deploy and Test Send a query like BTC-USD to your bot. Instantly receive Coinbase spot data in Telegram! 🚀 Unlock powerful, real-time Coinbase market insights directly in Telegram—no Coinbase API key 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 or resale permitted. 🔗 For support: LinkedIn – Don Jayamaha

🪄 Prompt To Video (MagicHour API) with Music & YouTube Automate AI video creation, background music, YouTube uploads, and result logging — all from a single text prompt. ⚡ Overview This n8n template turns a text prompt into a complete AI-generated video using the MagicHour API, adds background music, generates YouTube metadata, uploads to YouTube, and logs results in Google Sheets — all in one flow. Perfect for creators, marketers, and startups producing YouTube content at scale — from daily AI Shorts to explainers or marketing clips. 🧩 Use Cases 🎥 Daily AI-generated Shorts 🧠 Product explainers 🚀 Marketing & brand automation 🔁 Repurpose blog posts into videos 💡 AI storytelling or creative projects ⚙️ How It Works Trigger when a new row is added to Google Sheets or via Chat input. Gemini parses and normalizes the text prompt. MagicHour API generates the AI video. Poll until the render completes. (Optional) Mix background audio using MediaFX. Gemini generates YouTube title, description, and tags. Upload the video to YouTube with metadata. Log YouTube URL, metadata, and download link back to Google Sheets. 🧰 Requirements Service Purpose MagicHour API Key Text-to-video generation Gemini API Key Prompt parsing & metadata YouTube OAuth2 Video uploads Google Sheets OAuth2 Trigger & logging (Optional) MediaFX Node Audio mixing 🗂️ Google Sheets Setup Column Description Prompt Text prompt for video Background Music URL (Optional) Royalty-free track Status Tracks flow progress YouTube URL Auto-filled after upload Metadata Title, tags, and description JSON Date Generated (Optional) Auto-filled with video creation date 📅 100 Daily Prompts Automation You can scale this workflow to generate one video per day from a batch of 100 prompts in Google Sheets. Setup Steps Add 100 prompts to your Google Sheet — one per row. Set the Status column for each to Pending. Use a Cron Trigger in n8n to run the workflow once daily (e.g., at 9 AM). Each run picks one Pending prompt, generates a video, uploads to YouTube, then marks it as Done. Continues daily until all 100 prompts are processed. Example Cron Expression 0 9 * * * → Runs the automation every day at 9:00 AM. Node Sequence [Schedule Trigger (Daily)] → [Get Pending Prompt from Sheets] → [Gemini Prompt Parser] → [MagicHour Video Generation] → [Optional: MediaFX Audio Mix] → [Gemini Metadata Generator] → [YouTube Upload] → [Update Row in Sheets] 💡 Optional Enhancements: Add a notification node (Slack, Discord, or Email) after each upload. Add a counter check to stop after 100 videos. Add a “Paused” column to skip specific rows. 🧠 Gemini Integration Gemini handles: JSON parsing for MagicHour requests Metadata generation (title, description, tags) Optional creative rewriting of prompts 🎧 Audio Mixing (Optional) Install MediaFX Community Node → Settings → Community Nodes → n8n-nodes-mediafx Use it to blend background music automatically into videos. 🪶 Error Handling Avoid “Continue on Fail” in key nodes Use IF branches for MagicHour API errors Add retry/timeout logic for polling steps 🧱 Node Naming Tips Rename generic nodes for clarity: Merge → Merge Video & Audio If → Check Video Completion HTTP Request → MagicHour API Request 🚀 How to Use Add MagicHour, Gemini, YouTube, and Sheets credentials Replace background music with your own track Use Google Sheets trigger or daily cron for automation Videos are created, uploaded, and logged — hands-free ⚠️ Disclaimer This template uses community nodes (MediaFX). Install and enable them manually. MagicHour API usage may incur costs based on video duration and quality. 🌐 SEO Keywords MagicHour API, n8n workflow, AI video generator, automated YouTube upload, Gemini metadata, AI Shorts, MediaFX, Google Sheets automation, AI marketing, content automation.

This workflow introduces beginners to one of the most fundamental concepts in n8n: looping over items. Using a simple use case—generating LinkedIn captions for content ideas—it demonstrates how to split a dataset into individual items, process them with AI, and collect the output for review or export. ✅ Key Features 🧪 Create Dummy Data**: Simulate a small dataset of content ideas. 🔁 Loop Over Items**: Process each row independently using the SplitInBatches node. 🧠 AI Caption Creation**: Automatically generate LinkedIn captions using OpenAI. 🧰 Tool Integration**: Enhance AI output with creativity-injection tools. 🧾 Final Output Set**: Collect the original idea and generated caption. 🧰 What You’ll Need ✅ An OpenAI API key ✅ The LangChain nodes enabled in your n8n instance ✅ Basic knowledge of how to trigger and run workflows in n8n 🔧 Step-by-Step Setup 1️⃣ Run Workflow Node**: Manual Trigger (Run Workflow) Purpose**: Manually start the workflow for testing or learning. 2️⃣ Create Random Data Node**: Create Random Data (Code) What it does**: Simulates incoming data with multiple content ideas. Code**: return [ { json: { row_number: 2, id: 1, Date: '2025-07-30', idea: 'n8n rises to the top', caption: '', complete: '' } }, { json: { row_number: 3, id: 2, Date: '2025-07-31', idea: 'n8n nodes', caption: '', complete: '' } }, { json: { row_number: 4, id: 3, Date: '2025-08-01', idea: 'n8n use cases for marketing', caption: '', complete: '' } } ]; 3️⃣ Loop Over Items Node**: Loop Over Items (SplitInBatches) Purpose**: Sends one record at a time to the next node. Why It Matters**: Loops in n8n are created using this node when you want to iterate over multiple items. 4️⃣ Create Captions with AI Node**: Create Captions (LangChain Agent) Prompt**: idea: {{ $json.idea }} System Message**: You are a helpful assistant creating captions for a LinkedIn post. Please create a LinkedIn caption for the idea. Model**: GPT-4o Mini or GPT-3.5 Credentials Required**: OpenAI Credential Go to: OpenAI API Keys Create a key and add it in n8n under credentials as “OpenAi account” 5️⃣ Inject Creativity (Optional) Node**: Tool: Inject Creativity (LangChain Tool) Purpose**: Demonstrates optional LangChain tools that can enhance or manipulate input/output. Why It’s Cool**: A great way to show chaining tools to AI agents. 6️⃣ Output Table Node**: Output Table (Set) Purpose**: Combines original ideas and generated captions into final structure. Fields**: idea: ={{ $('Create Random Data').item.json.idea }} output: ={{ $json.output }} 💡 Educational Value This workflow demonstrates: Creating dynamic inputs with the Code node Using SplitInBatches to simulate looping Sending dynamic prompts to an AI model Using Set to structure the output data Beginners will understand how item-level processing works in n8n and how powerful looping combined with AI can be. 📬 Need Help or Want to Customize This? Robert Breen Automation Consultant | AI Workflow Designer | n8n Expert 📧 robert@ynteractive.com 🌐 ynteractive.com 🔗 LinkedIn 🏷️ Tags n8n loops OpenAI LangChain workflow training beginner LinkedIn automation caption generator

What Problem Does It Solve? Customers often ask product questions or prices in comments. Businesses waste time replying manually, leading to delays. Some comments only need a short thank-you reply, while others need a detailed private response. This workflow solves these by: Replying with a friendly public comment. Sending a private message with details when needed. Handling compliments, complaints, and unclear comments in a consistent way. How to Configure It Facebook Setup Connect your Facebook Page credentials in n8n. Add the webhook URL from this workflow to your Facebook App/Webhook settings. AI Setup Add your Google Gemini API key (or swap for OpenAI/Claude). The included prompt is generic — you can edit it to match your brand tone. Optional Logging If you want to track processed messages, connect a Notion database or another CRM. How It Works Webhook catches new Facebook comments. AI Agent analyzes the comment and categorizes it (question, compliment, complaint, unclear, spam). Replying: For questions/requests → public reply + private message with full details. For compliments → short thank-you reply. For complaints → apology reply + private message for clarification. For unclear comments → ask politely if they need help. For spam/offensive → ignored (no reply). Replies and messages are sent instantly via the Facebook Graph API. Customization Ideas Change the AI prompt to match your brand voice. Add forwarding to Slack/Email if a human should review certain replies. Log conversations in Notion, Google Sheets, or a CRM for reporting. Expand to Instagram or WhatsApp with small adjustments. If you need any help Get In Touch