Apr 2
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Learn how LangGraph transforms simple LangChain chatbots into powerful AI agents with StateGraph, loops, and conditional workflows. In this comprehensive video, we'll show you exactly how to build a production-ready AI Research Assistant that searches the web, evaluates trustworthiness, extracts facts, and generates intelligent reports using nodes, edges, and shared state management. What You'll Learn: • LangChain vs LangGraph: When to use each framework • How StateGraph works with nodes, edges, and persistent memory • State management patterns for production AI agents • LangGraph Workflow Demo Timestamps: 00:00 - Introduction to LangGraph 00:20 - LangChain vs LangGraph: What’s the real difference? 01:19 - Deep Research Assistant Use Case Example 01:53 - Traditional approach pain points 02:21 - Orchestration in LangGraph 03:08 - What is StateGraph? 03:39 - LangGraph Workflow 04:42 - LangGraph Adoption in Business Requirements 05:16 - Demo - Installing LangGraph Ecosystem 05:50 - Demo - Sequential Workflow vs Stateful Workflow 06:29 - Demo - Chunking Strategy and Embedding 07:37 - Demo - StateGraph 08:29 - Demo - Nodes, Edges and Routing 09:38 - Demo - Loops and Iterations 10:15 - Demo - Tool Integration 10:45 - Demo - Memory and State 11:27 - Demo - Build Your Own Research Assistant 12:52 - Conclusion #A.I. | #Agentics | #Artificial | #Intelligence | #Google #Colab | #ML | #Data | #Analytics | #Animation | #AI | #ArtificialIntelligence | #Code | #Coding | #Software | #Program | #Programming | #Kotlin | #Koog | #Warpfusion | #Stablediffusion | #Ktor | #Python | #LLM | #Machine | #Learning | #Agentics | #Warp | #Fusion | #Stable | #Diffusion | #Hugging | #Face | #Android | #Studio | #Sxela | #Models | Agents | #LangChain | #LangGraph | #VectorDB | #Prompt | #Engineering | #Vector | #Databases | #Tutorial