Understanding ReAct LLM Agents using LangGraph

Описание: 🔍 What is ReAct?
ReAct (Reason + Act) is a powerful prompting technique where an LLM not only reasons through the problem but also takes actions (like making API calls or database searches) before continuing. This is a step beyond Chain of Thought (CoT) prompting, which focuses on internal reasoning but doesn't interact with external tools or sources.

💡 Why ReAct over Chain of Thought?

CoT is like thinking out loud 🤔

ReAct is like thinking, checking facts, and then answering ✅
This hybrid approach makes ReAct perfect for use cases that require reasoning + tool use, like Retrieval-Augmented Generation (RAG).

🧠 Use Case:
We built a Medical Assistant that queries both the PubMed Central Repository and Wikipedia to deliver accurate and evidence-based responses. Ideal for research support, clinical decision-making, or educational purposes.

📂 GitHub Template Code:
👉 https://github.com/BillDhawal/react_l...

📹 Watch the video to see how we wired everything using LangGraph, and how ReAct empowers the agent to make smarter decisions by combining reasoning + real-time retrieval.

Let's build more intelligent, explainable, and helpful AI agents together! 💬

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