Workflow Templates

This automated n8n workflow checks daily class schedules, syncs upcoming classes to Google Calendar, and sends reminder notifications to students via email or SMS. Perfect for educational institutions to keep students informed about their daily classes and schedule changes. What This Workflow Does: Automatically checks class schedules every day Identifies today's classes and upcoming sessions Syncs class information to Google Calendar Sends personalized reminders to enrolled students Tracks reminder delivery status and logs activities Handles both email and SMS notification preferences Main Components Daily Schedule Check** - Triggers daily to check class schedules Read Class Schedule** - Retrieves today's class schedule from database/Excel Filter Today's Classes** - Identifies classes happening today Has Classes Today?** - Checks if there are any classes scheduled Read Student Contacts** - Gets student contact information for enrolled classes Sync to Google Calendar** - Creates/updates events in Google Calendar Create Student Reminders** - Generates personalized reminder messages Split Into Batches** - Processes reminders in manageable batches Email or SMS?** - Routes based on student communication preferences Prepare Email Reminders** - Creates email reminder content Prepare SMS Reminders** - Creates SMS reminder content Read Reminder Log** - Checks previous reminder history Update Reminder Log** - Records sent reminders Save Reminder Log** - Saves updated log data Essential Prerequisites Class schedule database/Excel file with student enrollments Student contact database with email and phone numbers Google Calendar API access and credentials SMTP server for email notifications SMS service provider (Twilio, etc.) for text reminders Reminder log file for tracking sent notifications Required Data Files: class_schedule.xlsx: Class ID | Class Name | Date | Time | Duration Instructor | Room | Students Enrolled | Status student_contacts.xlsx: Student ID | Name | Email | Phone | Preferred Contact Program | Class IDs | Active Status reminder_log.xlsx: Log ID | Date | Student ID | Class ID | Contact Method Status | Sent Time | Response Key Features ⏰ Daily Automation:** Runs automatically every day 📅 Calendar Sync:** Syncs classes to Google Calendar 📧 Smart Reminders:** Sends email or SMS based on preference 👥 Batch Processing:** Handles multiple students efficiently 📊 Activity Logging:** Tracks all reminder activities 🔄 Duplicate Prevention:** Avoids sending multiple reminders 📱 Multi-Channel:** Supports both email and SMS notifications Quick Setup Import workflow JSON into n8n Configure daily trigger schedule Set up class schedule and student contact files Connect Google Calendar API credentials Configure SMTP server for emails Set up SMS service provider (Twilio) Test with sample class data Activate workflow Parameters to Configure schedule_file_path: Path to class schedule file contacts_file_path: Path to student contacts file google_calendar_id: Google Calendar ID for syncing google_api_credentials: Google Calendar API credentials smtp_host: Email server settings smtp_user: Email username smtp_password: Email password sms_api_key: SMS service API key sms_phone_number: SMS sender phone number Sample Reminder Messages Email:** "Hi [Name], reminder: [Class Name] starts at [Time] in [Room]. See you there!" SMS:** "[Name], your [Class Name] class starts at [Time] in [Room]. Don't miss it!" Use Cases Daily class reminders for students Schedule change notifications Exam and assignment deadline alerts Teacher absence notifications Room change announcements

A short-term technical analysis agent for 15-minute candles on Binance Spot Market pairs. Calculates and interprets key trading indicators (RSI, MACD, BBANDS, ADX, SMA/EMA) and returns structured summaries, optimized for Telegram or downstream AI trading agents. This tool is designed to be triggered by another workflow (such as the Binance SM Financial Analyst Tool or Binance Quant AI Agent) and is not intended for standalone use. 🔧 Key Features ⏱️ Uses 15-minute kline data (last 100 candles) 📈 Calculates: RSI, MACD, Bollinger Bands, SMA/EMA, ADX 🧠 Interprets numeric data using GPT-4.1-mini 📤 Outputs concise, formatted analysis like: • RSI: 72 → Overbought • MACD: Cross Up • BB: Expanding • ADX: 34 → Strong Trend 🧠 AI Agent Purpose > You are a short-term analysis tool for spotting volatility, early breakouts, and scalping setups. Used by higher agents to determine: Entry/exit precision Momentum shifts Scalping opportunities ⚙️ How it Works Triggered externally by another workflow Accepts input: { "message": "BTCUSDT", "sessionId": "123456789" } Sends POST request to backend endpoint: https://treasurium.app.n8n.cloud/webhook/15m-indicators Fetches last 100 candles and calculates indicators Passes data to GPT for interpretation Returns summary with indicator tags for human readability 🔗 Dependencies This tool is triggered by: ✅ Binance SM Financial Analyst Tool ✅ Binance Spot Market Quant AI Agent 🚀 Setup Instructions Import into your n8n instance Make sure /15m-indicators webhook is active and calculates indicators correctly Connect your OpenAI GPT-4.1-mini credentials Trigger from upstream agent with Binance symbol and session ID Ensure all external calls (to Binance + webhook) are working 🧪 Example Use Cases | Use Case | Result | | ------------------------------------- | --------------------------------------- | | Short-term trade decision for ETHUSDT | Receives 15m signal indicators summary | | Input from Financial Analyst Tool | Returns real-time volatility snapshot | | Telegram bot asks for “DOGE update” | Returns momentum indicators in 15m view | 🎥 Watch Tutorial: 🧾 Licensing & Attribution © 2025 Treasurium Capital Limited Company Architecture, prompts, and trade report structure are IP-protected. No unauthorized rebranding or resale permitted. 🔗 For support: Don Jayamaha – LinkedIn

A medium-term trend analyzer for the Binance Spot Market that leverages core technical indicators across 4-hour candle data to provide human-readable swing-trade signals via AI. 🎥 Watch Tutorial: 🎯 What It Does Accepts a Binance trading pair (e.g., AVAXUSDT) Sends the symbol to an internal webhook for technical indicator calculation Computes 4h RSI, MACD, Bollinger Bands, SMA, EMA, ADX Returns structured, GPT-analyzed signals ready for Telegram delivery 🧠 AI Agent Details Model:** GPT-4.1-mini (OpenAI Chat) Agent Role:** Translates raw indicator values into sentiment-labeled signals Memory:** Tracks session + symbol context for cleaner multi-turn logic 🔗 Required Backend Workflow To calculate indicators, this tool depends on: POST https://treasurium.app.n8n.cloud/webhook/4h-indicators { "symbol": "AVAXUSDT" } Returns a JSON object with the latest 40×4h candle-based calculations. 📥 Input Format { "message": "AVAXUSDT", "sessionId": "telegram_chat_id" } 📊 Sample Output 🕓 4h Technical Signals – AVAXUSDT • RSI: 64 → Slightly Bullish • MACD: Bullish Cross above baseline • BB: Upper band touch – volatility expanding • EMA > SMA → Confirmed Upside Momentum • ADX: 31 → Strengthening Trend 📚 Use Case Scenarios | Use Case | Result | | ----------------------------- | ---------------------------------------------------- | | Swing trend confirmation | Uses 4h indicators to validate or reject setups | | Breakout signal confluence | Helps assess if momentum is real or noise | | Inputs to Quant AI or Analyst | Supports higher-frame trade recommendation synthesis | 🛠️ Setup Instructions Import the JSON template into your n8n workspace. Set your OpenAI API credentials for the GPT node. Ensure the /webhook/4h-indicators backend tool is live and accessible. Connect this to your Binance Financial Analyst Tool or master Quant AI orchestrator. 🤖 Parent Workflows That Use This Tool Binance SM Financial Analyst Tool Binance Spot Market Quant AI Agent 📎 Sticky Notes & Annotations This workflow includes internal sticky notes describing: Node roles (GPT, webhook, memory) System behavior (reasoning agent logic) Telegram formatting guidance 🔐 Licensing & Attribution © 2025 Treasurium Capital Limited Company All architecture, prompt logic, and signal formatting are proprietary. Redistribution or rebranding is prohibited. 🔗 Connect with the creator: Don Jayamaha – LinkedIn

Set up a chat with your documents without the complex vector store setup. This templates helps you ingest** your PDF / text / MD documents into a knowledge graph use the graph as the knowledge base for your AI chatbots (and other workflows) visualize the main topics* and *gaps** in your documents (good for observability and research) The knowledge base is provided using the InfraNodus GraphRAG with the knowledge graphs offering high-quality responses without the need to set up complex RAG vector store workflows. The advantages of using GraphRAG instead of the standard vector stores for knowledge are: Easy and quick to set up and update** — no complex data import workflows needed A knowledge graph offers a holistic and interactive view of your knowledge base (accessible via our API or a web interface — also shareable) Better retrieval of relations** between the document chunks = higher quality responses How it works This template uses the InfraNodus knowledge graph as a knowledge base for your n8n AI agent node. The knowledge graph contains the documents you can upload using this template from your Google Drive. When the user asks a question via the chat interface, the agent forwards this question to the InfraNodus knowledge graph, retrieves a response, a summary, and a list of matching statements (based advanced Graph RAG), then delivers the final response back the user. Here's a description step by step: Step 1: Upload your documents Put the PDF / text / MD files you want to chat with into a folder on your Google drive Authorize access to that folder using the Google drive node in the template. Add the InfraNodus API key to the InfraNodus Save to Graph HTTP node Optional: change the name of the graph you want to save the data to in the InfraNodus HTTP node (in the name field of the HTTP post request). Run the workflow to ingest all the files and save them into the graph Optional: check the link provided in the Step 1 workflow description to see the visualization of your knowledge base. It will look something like that: Note:* you can replace the PDF to Text convertor node with a better quality *PDF convertor* from ConvertAPI which respects the original file layout and doesn't split text into small chunks Step 2: Chat with your documents Deactive the trigger in the Step 1 Activate the chat trigger in the Step 2 Add your InfraNodus API credentials to Knowledge Base GraphRAG InfraNodus node Optional: change the graph name in the Knowledge Base node to match the name you provided in the step 1 above Run the chat and ask the question Watch the magic How to use You need an InfraNodus GraphRAG API account and key to use this workflow. Create an InfraNodus account Get the API key at https://infranodus.com/api-access and create a Bearer authorization key for the InfraNodus HTTP nodes. Requirements An InfraNodus account and API key An OpenAI (or any other LLM) API key A Google Drive OAuth access (follow the n8n instructions) Optional: ConvertAPI API key for better quality PDF conversion Customizing this workflow You can customize this workflow by adding several experts to your AI agent. Check out the complete guide at https://support.noduslabs.com/hc/en-us/articles/20174217658396-Using-InfraNodus-Knowledge-Graphs-as-Experts-for-AI-Chatbot-Agents-in-n8n Also check out the video tutorial with a demo: For support and feedback, please, contact us at https://support.noduslabs.com To learn more about InfraNodus: https://infranodus.com

🚀 How it works (Fonctionnement résumé) : Ce template permet de transformer un document (PDF, TXT, DocX...) en post LinkedIn engageant, prêt à être publié ou validé par email, le tout avec l’aide d’une IA spécialisée en copywriting LinkedIn. Voici les étapes clés : Formulaire de dépôt : L'utilisateur charge un fichier ou colle un texte. Détection du type de contenu : Un Switch analyse le type de fichier (PDF, DOCX, TXT, ou texte brut). Attention pour DocX nécessite un compte Make pour transformer le doc (mais cela fonctionne aussi sans docX) Extraction du contenu : Selon le format, le bon module d'extraction est utilisé. Génération d’un post LinkedIn : L'IA transforme le contenu en post LinkedIn selon une méthodologie de copywriting optimisée. Validation par email : Un email est envoyé à l’utilisateur pour approbation avec possibilité d’ajouter une image. Publication automatique : Si l'utilisateur valide, le post est publié sur LinkedIn. ⚙️ Setup Steps : Connecte tes comptes : Google Docs OAuth LinkedIn OAuth OpenAI (via gpt-4.1-mini ou un autre modèle) SMTP + IMAP pour l'envoi et la lecture d'emails Configure les champs du formulaire dans le nœud Form Trigger selon ton usage. Personnalise le prompt IA dans le nœud AI Agent si tu veux adapter le ton ou la méthodologie. Vérifie les emails dans le nœud d'envoi (Send Email) et de lecture (Email Trigger (IMAP)), pour que la validation fonctionne. Teste le workflow avec différents fichiers pour t'assurer que tous les types sont bien traités (PDF, DOCX, TXT, etc.). 🧩 Cas d’usage typiques : Créer des posts à partir de notes de réunion ou de rapports. Valoriser un article ou une publication professionnelle sous forme de contenu LinkedIn. Déléguer à l'IA le premier jet de ton contenu réseau. Bonus surveille une newsletter de ta messagerie pour proposer un post pertinent sur LinkedIn (vous pouvez supprimer il fonctionne en parallèle)

GBP Exchange Rate Email Workflow Sends email with table that have GBP exchange rate for few money value every day, if user clicks the button to run it. How it works: Data come from API Changed into HTML table Before is send inside email

> 🛠️ Note: This workflow uses a custom Mastodon API request. Ensure your server supports bookmark access, and that your access token has the right permissions. OAuth or token-based credentials must be configured. 🧑‍💼 Who is this for? This workflow is ideal for digital researchers, social media users, and knowledge workers who want to automatically archive Mastodon bookmarks into their Raindrop.io collection for future reference and tagging. 🔧 What problem is this solving? Mastodon users often bookmark posts they want to read or save for later, but there's no native integration to archive them outside the app. This workflow solves that by syncing bookmarked posts from Mastodon to Raindrop, making them more accessible, organized, and searchable long-term. ⚙️ What this workflow does Triggers on schedule (or manually). Tracks the latest fetched min_id using workflow static data to avoid duplicates. Sends an HTTP GET request to the Mastodon bookmarks API, using bearer token authentication. Validates and processes the bookmarks if new entries exist. Parses pagination metadata (e.g. min_id) from response headers. Splits response array to handle individual bookmarks. Filters out entries with missing data. Saves each post to Raindrop.io, using its title and URL. Use the card URL if exist. Updates the min_id to remember where it left off. 🚀 Setup Create a Mastodon access token with access to bookmarks. Add a credential in n8n of type HTTP Bearer Auth with your token. Create and connect a Raindrop OAuth2 credential. Replace {VOTRE SERVEUR MASTODON} with your Mastodon server's base URL. (Optional) Adjust the scheduling interval under the "Schedule Trigger" node. Make sure the Raindrop collection ID is correct or leave it as default (-1) as this is the index for the `Unsorted` collection. 🧪 How to customize this workflow To save to a specific Raindrop collection, change the collectionId in both Raindrop nodes. You can extend the Code node to pull additional metadata like author, hashtags, or content excerpts. Add an Email or Slack node after Raindrop to notify you of saved bookmarks.

Notice Community nodes can only be installed on self-hosted instances of n8n. Who this is for? The Search Engine Intelligence Extractor is a powerful n8n automation that leverages Bright Data’s MCP based AI Agents to simulate human-like searches across Google, Bing, and Yandex, and then distills clean, structured insights using Google Gemini. This workflow is tailored for: SEO analysts researching competitors or market trends Market researchers needing real-time search visibility Journalists & content writers gathering contextual insights AI developers creating intelligent assistants Digital marketers tracking brand mentions or news What problem is this workflow solving? Traditional scraping of search engines is often blocked, cluttered, or filled with irrelevant information. Manually analyzing and cleaning this data for insight is time-consuming. This workflow solves the problem by: Simulating real user search behavior via Bright Data MCP based AI Agent Performing multi-platform search (Google, Bing, Yandex) in one unified flow Extracting clean, human-readable results (stripping ads, navigation, etc.) Structuring the content using Google Gemini LLM Automating delivery via Webhook or saving to disk What this workflow does Input Fields Node: Accepts the search query Accepts action for example - Perform a google search. Replace the action with bing, yandex etc. for other search providers Accepts Webhook notification URL Bright Data MCP Agent Execution: Triggers Bright Data’s intelligent search agent Handles search navigation, result loading, pagination Human Readable Data Extractor: Cleanses HTML, removes ads, footers, irrelevant links Produces a readable narrative of results Final Output Handling: Saves the processed response to disk Sends the structured data to a Webhook for real-time use Pre-conditions Knowledge of Model Context Protocol (MCP) is highly essential. Please read this blog post - model-context-protocol You need to have the Bright Data account and do the necessary setup as mentioned in the Setup section below. You need to have the Google Gemini API Key. Visit Google AI Studio You need to install the Bright Data MCP Server @brightdata/mcp You need to install the n8n-nodes-mcp Setup Please make sure to setup n8n locally with MCP Servers by navigating to n8n-nodes-mcp Please make sure to install the Bright Data MCP Server @brightdata/mcp on your local machine. Sign up at Bright Data. Create a Web Unlocker proxy zone called mcp_unlocker on Bright Data control panel. Navigate to Proxies & Scraping and create a new Web Unlocker zone by selecting Web Unlocker API under Scraping Solutions. In n8n, configure the Google Gemini(PaLM) Api account with the Google Gemini API key (or access through Vertex AI or proxy). In n8n, configure the credentials to connect with MCP Client (STDIO) account with the Bright Data MCP Server as shown below. Make sure to copy the Bright Data API_TOKEN within the Environments textbox above as API_TOKEN=<your-token> How to customize this workflow to your needs Add Scheduled Execution Add a Cron trigger to run this workflow on a set schedule (e.g., daily/weekly keyword tracking). Push Results to Custom Destinations Connect output to: Google Sheets (for analytics or dashboards) PostgreSQL or MySQL DBs (for structured storage) Notion or Airtable (for content pipelines) Slack or Email (for alerting teams) Customize Webhook Notifications Update the Webhook URL in the notification node to push processed results to external APIs, CRMs, or real-time dashboards.

Automate document translation and ensure translation accuracy using Straker Verify, Google Drive and Slack. **How it works?
** A workflow step is set up to "watch" a Google Drive folder. When your team members place new files in this folder, they are downloaded. Straker Verify then translates them and provides a quality score. Once Straker Verify has completed this, the job info is fetched, the translation is saved to an output folder and you are notified via Slack. What problem does this solve? When using AI to translate documents, you have no idea about the quality and accuracy of the output. This template answers the question “How good is my translation?” so you have a high level of confidence before you publish. Who is this for? This workflow template is designed for businesses needing translation and localization of documents such as text docs, presentations, web pages, transcripts, video subtitles and others. Use it to build workflows that localize your content at scale while maintaining translation quality, accuracy and compliance. Set up instructions Straker Verify Integration with n8n **Connect to Straker Verify: Obtain Your API Key Sign Up/Log In:** Visit https://verify.straker.ai/ to create an account or log in. Navigate to API Keys: Go to `Verify → Settings → API Keys. Copy Your Key: Find and copy your API key. Add Key to n8n: In n8n, go to Settings → Credentials → Straker Verify. Set Up Credentials for the Straker Verify Node Open n8n and go to "Credentials". From the left sidebar, click on "Credentials". Search for "Straker Verify" and select "Straker Verify API" from the dropdown. Paste the copied access token from the Verify app and save it. Assign Credentials to the Node 1.Go to your workflow and open the Straker Verify node. 2.Select the "Straker Verify API" credentials you just created from the dropdown (Credentials to connect with), and save. ✅ You are now ready to use the workflow. Workflow Steps Step 1: Initiate Workflow – Upload Files to Google Drive 1.Upload one or more files to the designated Google Drive folder. This triggers the "New File in Google Drive" node in n8n. Step 2: Verify Token Balance The workflow checks your token balance via: Get Current User Balance User Has Enough Tokens Not enough tokens? You will receive a Slack message: Not enough tokens Please top up and re-upload your files. Step 3: Select Workflow The system fetches available workflows via: Fetch All Users Workflows No matching workflow found? You'll be notified via Slack: No workflow found Step 4: Create Project in Straker Verify The following steps are handled automatically: Fetch language/project options Download files from Google Drive Create a new project using: Create Straker Project ✅ You'll receive a Slack confirmation: > "Project created – ID: xxxxxx" Step 5: Process Completion & File Return Once files are processed by Straker: The Incoming Translation Result webhook is triggered. The workflow: Downloads processed files: Get File Content from Strakerh - Uploads them to Google Drive: Upload File to Google Drivee Step 6: Confirmation - Workflow Complete You'll receive a final Slack message: > "Workflow complete – Files are now available in Google Drive"

⚠️ This template uses only official n8n nodes. No community nodes required. 🧑‍💼 Who is this for? This workflow is designed for: Legal tech founders Marketing freelancers or consultants Agencies supporting lawyers and small law firms Anyone doing outbound outreach in the legal niche ❓ What problem is this solving? LinkedIn is a goldmine for targeting legal professionals — but scraping and personalizing outreach is tedious and expensive. Most tools either: Require paid LinkedIn Sales Navigator Can’t personalize at scale Violate LinkedIn’s TOS This workflow solves that by using free Google Search, OpenRouter AI, and GPT-4o to find, enrich, and message up to 1,000 solo lawyers per day — without using browser automation or scrapers. ⚙️ What this workflow does Uses Google Programmable Search to find solo lawyers and small firm founders on LinkedIn Parses each profile’s name, title, profile URL, and snippet Saves raw lead data to Google Sheets Uses OpenRouter Sonar Pro to enrich each profile with external content Generates a personalized, 1-line message using GPT-4o Appends the final message into Google Sheets for outreach 🛠️ Setup Estimated time: 15–20 minutes ✅ Google Programmable Search Enable the Custom Search API on Google Cloud Create a programmable search engine set to search the full web Copy your API key and CX ID ✅ Google Sheets Create a sheet with columns: Name, Title, Profile URL, Outreach Message Share the sheet with your OAuth-connected Google account ✅ OpenRouter Sign up at openrouter.ai Fund with at least $5 and generate your API key Use the model perplexity/sonar-pro for real-time research ✅ GPT-4o (optional) You can use your OpenAI key or route GPT-4o via OpenRouter All setup-specific values are marked clearly in sticky notes and placeholders. 🛠️ How to customize this workflow to your needs Change the Google search query to match your industry (e.g., "founder" AND "therapist" site:linkedin.com/in) Modify the AI prompt to match your tone (formal, casual, humorous) Connect the final output to your CRM (like HubSpot, Airtable, etc.) Add a second outreach message variant to A/B test performance 📌 Sticky Notes & Annotations All nodes are clearly renamed for understandability (e.g., Find Lawyer Profiles, Parse LinkedIn Search Results) Color-coded sticky notes explain: Setup instructions Required credentials Use case 🗂 Category AI Sales Marketing

What this template does This workflow will collect order data as it is produced, then send a summary email of all orders at the end of every day, formatted in a table. It receives new orders via webhook and stores in Airtable. At 7PM every day, it sends a summary email with the day's orders in a HTML table Setup: Instructions Video Create a new table in Airtable and give it a field time with type date, orderID with type number, and orderPrice also with type number. Create a new access token if you haven't already at https://airtable.com/create/tokens/new. Make sure to give the token the scopes data.records:read, data.records:write, schema.bases:read and access to whichever table you choose to store the orders. A pop-up window appears with the token. Use this token to make Create New Credential > Access Token for Airtable in the Store Order and Airtable Get Today's Orders nodes. Create access credentials for your Gmail as described here: https://developers.google.com/workspace/guides/create-credentials. Use the credentials from your client_secret.json in the Send to Gmail node. In the Store Order node, change Base and Table to the database and table in your Airtable account you wish to use to store orders. Make sure to use these same values in the Airtable Get Today's Orders node. Every time an order is created in your system, send a POST request to Webhook from your order software. Each request must contain a single order containing fields 'orderID' and 'orderPrice' (or, edit Set Order Fields to select which incoming fields you wish to save) Change the schedule time for sending email from Everyday at 7PM to whichever time you choose. Test: Activate the workflow. From the node Webhook, copy Production URL Send the following CURL request to the URL given to you: curl -X POST -H "Content-Type: application/json" -d '{"orderID": 12345, "orderPrice": 99.99}' YOUR_URL_HERE It should say Node executed successfully. Now check your Airtable and confirm the order was stored in the right place.

Generate Summaries from Uploaded Files using OpenAI Assistants API 📑 Overview Upload a document (PDF, DOCX, PPTX, TXT, CSV, JSON, or Markdown) and receive an AI-generated summary containing: title** – 5-10 words summary** – 1-2 sentences bullets** – 3-5 key points tags** – 3-6 short keywords The workflow: Stores the file in OpenAI. Runs an Assistant with File Search and Code Interpreter enabled. Polls until the run finishes. Retrieves the summary JSON. ✅ Prerequisites OpenAI Assistant Create one at <https://platform.openai.com/assistants> Enable File Search and Code Interpreter Note: The assistant ID starts with asst_ OpenAI API credential setup in n8n Go to Credentials → New → HTTP Header Auth Header name: Authorization Value: Bearer YOUR-OPENAI-API-KEY (replace YOUR-OPENAI-API-KEY with your OpenAI API secret key for your assistant, starts with sk-) Name it: openAIApiHeader 🔧 Setup Import the workflow JSON. When n8n prompts for a credential, choose openAIApiHeader for every HTTP Request node. Open Run Assistant → Body and replace "assistant_id": "REPLACE_WITH_YOUR_ASSISTANT_ID" with your real ID (starts with asst_…). Save. 🚀 How it works | # | Node | Purpose | |---|------|---------| | 1 | On form submission | User uploads a file (File). | | 2 | Upload File | POST /v1/files (multipart) → returns file_id. | | 3 | Create Thread | Creates a thread and attaches the uploaded file. | | 4 | Run Assistant | Starts the run using your assistant_id. | | 5 | Poll Run Status → Wait 2 s → IF | Loops until status = completed. | | 6 | Fetch Summary | GET /v1/threads/{thread_id}/messages → summary JSON. | 🖌️ Customisation ideas Edit the user prompt in Create Thread to change summary length, tone, or language. Add an HTTP Response node after Fetch Summary to return plaintext to the uploader. Replace the polling loop with OpenAI’s forthcoming wait-for-run endpoint when available. No community nodes required. Works on any n8n Cloud plan (Starter, Pro, Enterprise) or self-hosted Community Edition.