Langflow Tutorial | Build RAG Apps & Agentic AI Without Coding

Langflow is one of the most powerful low-code/no-code platforms for building AI agents and AI applications—without the need for heavy coding. In this crash course tutorial, I walk you through everything you need to know to get started with Langflow and take your AI projects to the next level. 📦 Resources & Links: Code: https://github.com/Sandesh-hase/Stock-market-analysis-multiagent-framework.git Agentic AI Developemnt with Azure & Semantic Kernel Course: https://www.udemy.com/course/agentic-ai-development-with-azure-semantic-kernel/?referralCode=9528210DF4484E6A4810 Azure AI Foundry:Prompt Flow, Finetuning GPT4-o, RAG, LLMOps : https://www.udemy.com/course/develop-generative-ai-apps-in-azure-ai-foundry/?referralCode=33D87106042F2F66083D We begin with the basics of Langflow, including how to build a simple Langflow workflow, understanding its components, and exploring its basic operations. Then, we move into more advanced topics such as Retrieval-Augmented Generation (RAG), where I demonstrate how to integrate OpenAI models with AstraDB as a vector store. For the demo, I uploaded Mahindra’s latest XEV9E maintenance policy document and built a pod capable of answering real-world questions like: What is the warranty period? How do I charge the battery? What are the basic maintenance guidelines? Finally, we cover building a simple agent framework, showcasing how quickly you can design and deploy agentic AI workflows using Langflow. Whether you’re a beginner exploring AI development or an advanced user looking to build AI-powered applications and agents faster, this tutorial will give you practical insights, hands-on demonstrations, and step-by-step guidance. 👉 If you’re interested in AI agents, Langflow, RAG pipelines, and no-code AI apps, this is the video for you! #Langflow #AIagents #RAG