Tool Calling in LangGraph | How LLM Detects & Calls Tools ToolNode

Description

Tool calling is where LangGraph becomes truly powerful. 🚀 In this video, we explain Tool Calling in LangGraph and show how an LLM detects, selects, and calls tools using ToolNode. You’ll see a complete, step-by-step breakdown of the internal flow when an LLM decides to invoke a tool. We build a simple custom tool in Python that adds two integers, attach it to the LLM, and then trace exactly what happens under the hood when the LLM triggers that tool. You’ll learn: What ToolNode is in LangGraph How to create and register a custom tool How an LLM detects a tool call How tool inputs are generated from the prompt How LangGraph executes the tool and returns results How tool output flows back into the LangGraph state Why tool calling is the foundation of AI agents This class builds on the previous lesson about state, reducers, and add_messages, and is a core prerequisite for advanced agentic workflows. 📌 Use cases: AI agents, tool-enabled chatbots, autonomous workflows 📌 Tech stack: Python, LangGraph, LLMs 📌 Course: LangGraph from Beginner to Advanced If you want to truly understand how LLMs use tools in LangGraph, this video is a must-watch. #LangGraph #ToolCalling #ToolNode #LLMTools #AIagents #LangGraphPython #PythonAI #AgenticAI #LLMDevelopment #GenerativeAI #LangChain #OpenAI #AIWorkflow #PythonProgramming #MachineLearning