LangGraph Day 1 Explained | Nodes Edges State Conditional Edges + Agentic RAG Basics

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LangGraph Day 1 Explained | Nodes Edges State Conditional Edges + Agentic RAG Basics 📄 Description In this video, we start learning LangGraph from scratch and understand how graph-based AI workflows are built for Agentic AI systems. We cover: LangGraph Basics Nodes & Edges START and END Nodes State Management Conditional Edges TypedDict State Streaming Graph Execution Sentiment Analysis Workflow Dynamic Routing Agentic RAG Introduction This session is extremely useful for: LangGraph Beginners AI Agent Developers Agentic AI Interviews Workflow Automation Multi-Step LLM Systems Production AI Architectures Reference Notes GitHub Repo https://github.com/switch2ai 🧠 What is LangGraph? LangGraph is a framework used to build graph-based AI workflows using LLMs. It is mainly used for: AI Agents Agentic RAG Workflow Automation Multi-step reasoning systems State-based AI execution 🧠 Graph Basics A graph mainly contains: Nodes Edges 🔹 Nodes Nodes are Python functions that perform operations. Example operations: LLM calls Tool execution Data processing Routing Memory updates In LangGraph: graph.add_node("Node-Name", function_name) 🔹 Edges Edges define connections between nodes. They decide workflow execution path. Example: Input → Node1 → Node2 → Output ⚙️ Steps to Build LangGraph Define Graph Add Nodes Add Edges Set Entry Point Set Finish Point Compile Graph Equivalent to: graph.set_entry_point("Node") or graph.add_edge(START, "Node") 🔹 END Node Defines final point of graph execution. Equivalent to: graph.set_finish_point("Node") or graph.add_edge("Node", END) 🌊 Stream Execution LangGraph supports streaming execution. Using app.stream(), we can see step-by-step node execution. Useful for: Debugging Observability Tracing workflows 🧠 State in LangGraph State is shared memory accessible to all nodes. All nodes can read/update state. State can store: Review Sentiment Messages Memory Intermediate outputs 🔹 State Data Types Supported state types: str int loat bool list dict TypedDict 😊 Sentiment Analysis Workflow Workflow: Review → Sentiment Analysis → Display Result LLM analyzes review sentiment and stores output inside state. Reference Notes ⚙️ Example State State contains: review sentiment note Example: { "review": "...", "sentiment": "...", "note": "..." } 🔀 Conditional Edges Conditional edges enable dynamic routing inside graph. Instead of fixed execution path, graph decides next node based on condition. 🧠 Example Conditional Workflow Review → Sentiment Analysis → Positive → Thank You Note → Negative → Apology Note This creates dynamic intelligent workflows. 🔹 Router Function Router decides next execution path. If sentiment is positive: Go to thank_you_note Else: Go to apology_note 🛠️ add_conditional_edges() Used for dynamic graph routing. Helps build intelligent agent workflows. Useful for: Decision systems Agentic AI Multi-step planning Autonomous workflows Reference Notes 🧠 TypedDict State TypedDict used for structured state management. Benefits: Better validation Clear schema Improved readability Production-ready architecture 🤖 Agentic RAG Introduction LangGraph is heavily used in Agentic RAG systems. Unlike traditional RAG, Agentic RAG can Reason Decide retrieval steps Use tools Perform multi-step planning Dynamically route workflows 🚀 Why LangGraph is Powerful? Supports cyclic workflows Handles stateful execution Better for agents compared to simple chains Supports conditional routing Ideal for production AI systems 🧠 Real-World Use Cases Customer support agents Review analysis systems Agentic RAG pipelines Workflow automation Multi-agent orchestration AI decision systems Managing shared state Conditional routing complexity Debugging graph execution Handling infinite loops State consistency Large workflow orchestration Why LangGraph over LangChain chains? Why graph architecture used? How state management works? Why conditional edges are important? How Agentic RAG differs from traditional RAG? How workflows scale in production? LangGraph enables graph-based AI workflows Nodes are Python functions Edges define execution flow State acts as shared memory Conditional edges enable intelligent routing TypedDict improves state management LangGraph is highly useful for Agentic AI systems 🔥 Hashtags #LangGraph #AgenticAI #AIAgents #RAG #LangChain #LLM #GenAI #ArtificialIntelligence #MachineLearning #Switch2AI 🔍 SEO Tags langgraph tutorial langgraph explained langgraph nodes edges conditional edges langgraph langgraph state management agentic rag tutorial langgraph ai agents langgraph workflow tutorial langgraph beginner guide langgraph interview questions