n8n Masterclass: Build AI Agents & Automate Workflows (Beginner to Pro) n8n Masterclass: From Beginner to AI Agent Builder This masterclass provides a comprehensive guide to building AI automations using n8n, a low -code/no-code automation tool. It covers setting up n8n, understanding its core concepts, building example workflows, creating RAG AI agents, working with APIs and HTTP requests, and implementing best practices for workflow optimization. Chapter 1: Introduction to n8n This chapter introduces n8n , a low-code/no-code automation tool that allows users to automate processes and build workflows with minimal coding knowledge. It highlights the ease of use and accessibility for beginners while retaining flexibility for advanced users. Key features: Low-code/no-code interface : Drag-and-drop functionality for building workflows. Accessibility : No prior programming experience needed. Flexibility : Allows for custom code and advanced integrations. Chapter 2: Setting up n8n This chapter details the setup process for n8n, comparing self-hosted and cloud-hosted options. Self-Hosted: Pros : Full control, customization, data ownership, cost-effective (depending on server maintenance). Cons : Requires technical knowledge for setup, maintenance, and updates. Cloud-Hosted: Pros : Easier setup, managed updates and scaling, reliability, suitable for beginners . Cons : Subscription-based cost, data stored and processed in the cloud (potential limitations for sensitive data). Chapter 3: n8n Interface and Core Concepts This chapter explores the n8n user interface and introduces the core components of workflow building: workflows , nodes , and executions . Workflows : Represent the overall automation instructions. Nodes : Building blocks of workflows, each representing a single step or action. Executions : Instances of a workflow running, triggered manually or automatically. Chapter 4: Node Types and Workflow Building This chapter categorizes the different types of nodes in n8n and demonstrates workflow construction through a practical example. Node Types: Trigger nodes : Start workflows (e.g ., manual trigger, webhook, email trigger). Action nodes : Perform specific tasks (e.g., send email, create record, API request). Data transformation nodes : Modify data flowing through the workflow (e.g., add fields, change values). ** Logic nodes**: Control workflow flow based on conditions (e.g., if node, switch node). Chapter 5: Building a Customer Order Processing Workflow This chapter builds a complete workflow that automatically processes customer orders, summarizes them using a large language model (like ChatGPT), and sends a report via email. This example showcases the practical application of various node types and their interaction. Chapter 6: RAG (Retrieval Augmented Generation) and Vector Databases This chapter introduces RAG , a technique combining retrieval and generation to improve AI model accuracy and relevance. It explains the use of vector databases (like Pinecone) to store and retrieve information efficiently. The process of embedding data into a vector database is also detailed. Chapter 7: Building a RAG AI Agent This chapter demonstrates building a RAG AI agent using n8n, Pinecone, and OpenAI. The agent can answer questions based on information retrieved from a PDF document stored in Google Drive. This involves using nodes for data loading, text splitting, embedding, and querying the vector database. Chapter 8: APIs and HTTP Requests This chapter explains how to work with APIs and HTTP requests within n8n. It covers concepts like API endpoints , HTTP methods (GET, POST), and how to use n8n's built- in nodes to interact with various APIs. Examples include accessing weather data from OpenWeatherMap and searching LinkedIn profiles via Google. Chapter 9: Best Practices and Next Steps This chapter concludes with best practices for workflow optimization, including error handling, scalability, and utilizing n8n's features effectively. It also encourages further exploration of n8n's capabilities, participation in the community, and continued learning.