LangChain Agents Tutorial #2 - Tools, ReAct Loop & Live Demo

About this lesson

🤖 Welcome to Episode #2 of the LangChain v1 Complete Series! In this tutorial, we dive deep into AGENTS - the powerful feature that lets LLMs take ACTIONS in the real world using TOOLS! 📌 What You'll Learn in This Video: ✅ Why we need Agents (LLMs can talk but can't act!) ✅ The difference between LLM and Agent ✅ What are Tools and why they matter ✅ Real-world tool examples (Calculator, Weather, Database, APIs) ✅ How Agents think - The ReAct Loop (Reasoning + Acting) ✅ Agent Architecture explained visually ✅ Live coding demo - Building your first agent with tools ✅ Creating custom tools with @tool decorator ✅ Running agents with create_agent() 🔧 Tools Covered: - Calculator Tool for accurate math - Web Search Tool for live data - Database Tool for SQL queries - File Reader Tool for documents - Custom business logic tools 💻 Code Examples in This Video: ```python from langchain.tools import tool from langchain_openai import ChatOpenAI from langchain.agents import create_agent # Define a calculator tool @tool def calc(expr: str): """Evaluates a math expression.""" return eval(expr) # Create agent model = ChatOpenAI(model="gpt-4o-mini") agent = create_agent(model=model, tools=[calc]) # Run agent agent.invoke("What is 1234 * 9876?") ``` 🎯 Who Is This For? - Beginners learning LangChain - Developers building AI applications - Anyone interested in AI Agents - Students and professionals in AI/ML ⏱️ Timestamps: 00:00 - Introduction 01:30 - Why Do We Need Agents? 03:45 - Limitations of Normal LLMs 05:20 - What is an Agent? 07:15 - LLM vs Agent Comparison 09:30 - What Are Tools? 11:45 - Real-World Tool Examples 14:20 - Agent Architecture Diagram 16:30 - How Agents Think (ReAct Loop) 19:15 - Example Workflow Walkthrough 21:30 - Live Demo: Define a Tool 23:45 - Live Demo: Create Agent 25:30 - Live Demo: Run Agent 27:15 - Summary & Next Steps 📦 Requirements: - Python 3.10+ - langchain - langchain-openai - langchain-groq (optional) - python-dotenv 🔗 Useful

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