ReAct Prompting in Langchain: Python Tutorial for Advanced AI Prompts

🧠 Don’t miss out! Get FREE access to my Skool community — packed with resources, tools, and support to help you with Data, Machine Learning, and AI Automations! 📈 https://www.skool.com/data-and-ai-aut... Take your AI agents to the next level with ReAct Prompting! In this advanced Langchain + Python tutorial, you’ll learn how to combine reasoning and action to create more intelligent, tool-using LLMs. Perfect for developers building autonomous agents, chatbots, and smart workflows. 🚀 Hire me for Data Work: https://ryanandmattdatascience.com/da... 👨‍💻 Mentorships: https://ryanandmattdatascience.com/me... 📧 Email: [email protected] 🌐 Website & Blog: https://ryanandmattdatascience.com/ 🖥️ Discord:   / discord   📚 *Practice SQL & Python Interview Questions: https://stratascratch.com/?via=ryan 📖 *SQL and Python Courses: https://datacamp.pxf.io/XYD7Qg 🍿 WATCH NEXT OpenAI/Langchain Playlist:    • How to Build Your First AI LLM Prompts wit...   Chain of Thought Prompting:    • Unlock the Power of Chain of Thought Promp...   Vector Embeddings:    • LangChain (OpenAI) Vector Embeddings For B...   Langchain with Streamlit:    • Learn to Build Exciting LLM applications w...   ReAct prompting (Reasoning and Action) is a powerful AI technique for building complex systems that combine reasoning with tool use. In this video, I break down ReAct prompting through three hands-on examples using LangChain, OpenAI, and various tools including DuckDuckGo search, Wikipedia, and calculator functions. We start by understanding how ReAct works with its thought-action-observation loop, then build two working examples using LangChain agents and tools. I show you how to set up your environment, configure agents, and create custom tool chains for solving multi-step problems. The first example uses search and math tools to solve complex time calculations, while the second example leverages Wikipedia for fact-checking. Finally, we explore a manual ReAct prompt based on the original research paper to see how the technique works under the hood. Throughout the video, you'll see real results, including both successes and failures, giving you an honest look at ReAct prompting's capabilities and limitations. I explain why token costs can be high with this approach, when to use ReAct versus simpler solutions, and share tips for improving prompt reliability. By the end, you'll understand how to implement ReAct prompting in your own projects and know which tools work best for different use cases. TIMESTAMPS 00:00 Introduction to ReAct Prompting 01:40 Installing Dependencies 02:20 Setting Up OpenAI API Key 02:50 Importing Required Libraries 04:40 Creating Language Model and Tools 06:02 Building the Math Tool 06:40 Setting Up Duck Duck Go Search 07:32 Creating the ReAct Agent 09:53 First Example: 50 Mile Race Question 13:00 Troubleshooting Prompt Issues 15:38 Second Example: Wikipedia Integration 17:23 Setting Up Wikipedia Tools 18:30 Running Wikipedia Query 19:53 Third Example: Manual ReAct from Paper 22:40 Final Thoughts and Best Practices OTHER SOCIALS: Ryan’s LinkedIn:   / ryan-p-nolan   Matt’s LinkedIn:   / matt-payne-ceo   Twitter/X: https://x.com/RyanMattDS Who is Ryan Ryan is a Data Scientist at a fintech company, where he focuses on fraud prevention in underwriting and risk. Before that, he worked as a Data Analyst at a tax software company. He holds a degree in Electrical Engineering from UCF. Who is Matt Matt is the founder of Width.ai, an AI and Machine Learning agency. Before starting his own company, he was a Machine Learning Engineer at Capital One. *This is an affiliate program. We receive a small portion of the final sale at no extra cost to you.