Building LLM Agents with Langgraph. Part - 2

Welcome to my multi-part series on building LLM Agents! In Part 1, we took a deep dive into LangChain, exploring everything from core concepts to hands-on coding. We integrated a vector database like Pinecone and walked through how Retrieval-Augmented Generation (RAG) works step by step. In Part 2, we’ll delve into LangGraph, a powerful tool for orchestrating complex language workflows and visualizing data flows. By the end of this series, you’ll have a practical understanding of how to build advanced AI-driven Agents that combine LangChain, LangGraph, and Pinecone. Whether you’re a beginner or looking to expand your AI toolkit, this series has you covered. What You’ll Learn in Part - 1 Simplifying LLM Apps with LangChain Learn how LangChain streamlines the process of building language model applications. Connecting to Pinecone Discover how to integrate your app with Pinecone for seamless vector storage. Building a Simple RAG-Based Application Create a foundational Retrieval-Augmented Generation application from scratch. Part - 2 Constructing Graphs with LLM See how to design and manage workflows using LangGraph’s graph-based approach. Persisting Graphs Understand best practices for saving and retrieving your graph data. Human-in-the-Loop Interrupt Scenarios Implement a client solution that allows human intervention and real-time decision-making. Debugging in LangGraph Learn how to track and visualize messages flowing between nodes for easier debugging. Introducing LangGraph LangGraph helps you manage and visualize complex language workflows, giving you full control over how data flows through your applications. Let’s get started on our journey to mastering LangChain and LangGraph together! LangGraph Code: GitHub: rsarosh/LangGraph Client Code: GitHub: rsarosh/langgraph_client