Feb 12
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In this video, we dive deep into the fundamental building blocks of LangGraph, the powerful framework for building complex, agentic LLM workflows. 🚀 If you want to move beyond simple linear chains and build advanced AI applications that can think, loop, and remember, understanding these four core concepts is essential. What you will learn in this lesson: The 4 Pillars of LangGraph: 1. Graphs: Pictorial representations of your end-to-end workflow. 2. Nodes: Individual steps like Python functions, agent invocations, or tool calls. 3. Edges: The rules and logic that connect nodes and define the flow. 4. State: The shared data object (using TypedDict) that persists across your graph. The Development Workflow: How to define state, write node functions, wire edges, and compile your graph. Hands-on Demo: Follow along as we build a "Hello World" LangGraph application that simulates a chat interaction from scratch. Prerequisites: Basic knowledge of Python and familiarity with LangChain. This is part of our series on the Foundations of LangGraph. Don't forget to like and subscribe to stay updated on future modules where we add LLMs and real-world tools to these workflows! #LangGraph #AIAgents #LangChain #Python #GenerativeAI #LLMWorkflows #MachineLearning