Run an AI-powered degree audit for each senior student. This template reads student rows from Google Sheets, evaluates completed courses against hard-coded program requirements, and writes back an AI Degree Summary of what's still missing…
Download this n8n workflow template and start using it instantly.
Run an AI-powered degree audit for each senior student. This template reads student rows from Google Sheets, evaluates completed courses against hard-coded program requirements, and writes back an AI Degree Summary of what's still missing (major core, Gen Eds, major electives, and upper-division credits). It's designed for quick advisor/registrar review and SIS prototypes.
Trigger: Manual — When clicking "Execute workflow"
Core nodes: Google Sheets, OpenAI Chat Model, (optional) Structured Output Parser
Programs included: Computer Science BS, Business Administration BBA, Psychology BA, Mechanical Engineering BS, Biology BS (Pre-Med), English Literature BA, Data Science BS, Nursing BSN, Economics BA, Graphic Design BFA
Senior_data) with: StudentID, Name, Program, Year, CompletedCourses.CompletedCourses to built-in requirements (per program) and computes Missing items + a short Summary.StudentID (updates AI Degree Summary; you can also map the raw Missing array to a column if desired).Example JSON (for one student): { "StudentID": "S001", "Program": "Computer Science BS", "Missing": [ "GEN-REMAIN | General Education credits remaining | 6", "CS-EL-REM | CS Major Electives (200+ level) | 6", "UPPER-DIV | Additional Upper-Division (200+ level) credits needed | 18", "FREE-EL | Free Electives to reach 120 total credits | 54" ], "Summary": "All core CS courses are complete. Still need 6 Gen Ed credits, 6 CS electives, and 66 total credits overall, including 18 upper-division credits — prioritize 200/300-level CS electives." }
In n8n → Credentials → New → Google Sheets (OAuth2) and sign in.
In the Google Sheets nodes, select your spreadsheet and the Senior_data tab.
Ensure your input sheet has at least: StudentID, Name, Program, Year, CompletedCourses.
In n8n → Credentials → New → OpenAI API, paste your key.
In the OpenAI Chat Model node, select that credential and a model (e.g., gpt-4o or gpt-5).
Category: Education / Student Information Systems
Tags: degree-audit, registrar, google-sheets, openai, electives, upper-division, graduation-readiness
v1.0.0 — Initial release: Senior_data in/out, 10 programs, AI Degree Summary output, append/update by StudentID.
When clicking ‘Execute workflow’ (Manual Trigger): Starts the workflow manually when you click 'Execute workflow' — used for testing and manual runs.
OpenAI Chat Model1 (LLM Chat Open AI): Provides the OpenAI chat model (GPT) that powers the AI reasoning in this workflow.
Structured Output Parser1 (Output Parser Structured): Forces the AI output into a structured JSON schema for reliable downstream use.
Degree Audit Agent (Agent): The AI agent that orchestrates the workflow — it reasons over the input and decides which tools to call.
Add Student Degree Summary (Google Sheets): Reads from or writes rows to a Google Sheets spreadsheet.
Get Student Data1 (Google Sheets): Reads from or writes rows to a Google Sheets spreadsheet.
How it works This template launches your very first AI Agent —an AI-powered chatbot that can do more than just talk— it can take action using tools. Think of an AI Agent as a smart assistant, and the tools are the apps on its phone. By connecting it to other nodes, you give your agent the ability to interact with real-world data and services, like checking the weather, fetching news, or even sending emails on your behalf. This workflow is designed to be the perfect starting point: The Chat Interface: A Chat Trigger node provides a simple, clean interface for you to talk to your agent. The Brains: The AI Agent node receives your messages, intelligently decides which tool to use (if any), and formulates a helpful response. Its personality and instructions are fully customizable in the "System Message". The Language Model: It uses Google Gemini to power its reasoning and conversation skills. The Tools: It comes pre-equipped with two tools to demonstrate its capabilities: 1. Get Weather: Fetches real-time weather forecasts. 2. Get News: Reads any RSS feed to get the latest headlines. The Memory: A Conversation Memory node allows the agent to remember the last few messages, enabling natural, follow-up conversations. Set up steps Setup time: ~2 minutes You only need one thing to get started: a free Google AI API key. 1. Get Your Google AI API Key: Visit Google AI Studio at aistudio.google.com/app/apikey. Click "Create API key in new project" and copy the key that appears. 2. Add Your Credential in n8n: On the workflow canvas, go to the Connect your model (Google Gemini) node. Click the Credential dropdown and select + Create New Credential. Paste your API key into the API Key field and click Save. 3. Start Chatting! Go to the Example Chat node. Click the "Open Chat" button in its parameter panel. Try asking it one of the example questions, like: "What's the weather in Paris?" or "Get me the latest tech news."* That's it! You now have a fully functional AI Agent. Try adding more tools (like Gmail or Google Calendar) to make it even more powerful.
Build a 100% local RAG with n8n, Ollama and Qdrant. This agent uses a semantic database (Qdrant) to answer questions about PDF files. Tutorial Click here to view the YouTube Tutorial How it works Build a chatbot that answers based on documents you provide it (Retrieval Augmented Generation). You can upload as many PDF files as you want to the Qdrant database. The chatbot will use its retrieval tool to fetch the chunks and use them to answer questions. Installation 1. Install n8n + Ollama + Qdrant using the Self-hosted AI starter kit 2. Make sure to install Llama 3.2 and mxbai-embed-large as embeddings model. How to use it 1. First run the "Data Ingestion" part and upload as many PDF files as you want 2. Run the Chatbot and start asking questions about the documents you uploaded
This template quickly shows how to use RAG in n8n. Who is this for? This template is for everyone who wants to start giving knowledge to their Agents through RAG. Requirements Have a PDF with custom knowledge that you want to provide to your agent. Setup No setup required. Just hit Execute Workflow, upload your knowledge document and then start chatting. How to customize this to your needs 1. Add custom instructions to your Agent by changing the prompts in it. 2. Add a different way to load in knowledge to your vector store, e.g. by looking at some Google Drive files or loading knowledge from a table. 2. Exchange the Simple Vector Store nodes with your own vector store tools ready for production. 3. Add a more sophisticated way to rank files found in the vector store. For more information read our docs on RAG in n8n.
How it works This template is a complete, hands-on tutorial that lets you build and interact with your very first AI Agent. Think of an AI Agent as a standard AI chatbot with superpowers. The agent doesn't just talk; it can use tools to perform actions and find information in real-time. This workflow is designed to show you exactly how that works. 1. The Chat Interface (Chat Trigger): This is your window to the agent. It's a fully styled, public-facing chat window where you can have a conversation. 2. The Brain (AI Agent Node): This is the core of the operation. It takes your message, understands your intent, and intelligently decides which "superpower" (or tool) it needs to use to answer your request. The agent's personality and instructions are defined in its extensive system prompt. 3. The Tools (Tool Nodes): These are the agent's superpowers. We've included a variety of useful and fun tools to showcase its capabilities: Get a random joke. Search Wikipedia for a summary of any topic. Calculate a future date. Generate a secure password. Calculate a monthly loan payment. Fetch the latest articles from the n8n blog. 4. The Memory (Memory Node): This gives the agent a short-term memory, allowing it to remember the last few messages in your conversation for better context. When you send a message, the agent's brain analyzes it, picks the right tool for the job, executes it, and then formulates a helpful response based on the tool's output. Set up steps Setup time: ~3 minutes This template is nearly ready to go out of the box. You just need to provide the AI's "brain." 1. Configure Credentials: This workflow requires an API key for an AI model. Make sure you have credentials set up in your n8n instance for either Google AI (Gemini) or OpenAI. 2. Choose Your AI Brain (LLM): By default, the workflow uses the Google Gemini node. If you have Google AI credentials, you're all set! If you prefer to use OpenAI, simply disable the Gemini node and enable the OpenAI node. You only need one active LLM node. Make sure it is connected to the Agent parent node. 3. Explore the Tools: Take a moment to look at the different tool nodes connected to the Your First AI Agent node. This is where the agent gets its abilities! You can add, remove, or modify these to create your own custom agent. 4. Activate and Test! Activate the workflow. Open the public URL for the Example Chat Window node (you can copy it from the node's panel). Start chatting! Try asking it things like: "Tell me a joke." "What is n8n?" "Generate a 16-character password for me." "What are the latest posts on the n8n blog?" "What is the monthly payment for a $300,000 loan at 5% interest over 30 years?"