LangGraph Components #10 – ReAct Agents with Memory Saver | Smarter AI Agents in LangGraph

Description

Welcome to LangGraph Components Part 10 – ReAct Agents with Memory Saver! 🚀 In this tutorial, we enhance the ReAct (Reason + Act) architecture with Memory Saving & Context Management — making your agents more efficient, lightweight, and context-aware 🧠💾 You’ll learn how to build a ReAct Agent that can remember key steps, drop unnecessary data, and keep context short for optimal reasoning and faster performance. 💡 What You’ll Learn ✅ What is a ReAct Agent with Memory ✅ How to enable context persistence between reasoning steps ✅ How to save memory dynamically inside LangGraph ✅ How to use LangGraph state pruning and context summarization ✅ Optimize ReAct loops for long-running workflows ⚙️ Key Concepts Covered ReAct Framework (Reason + Act) Memory Compression / Summarization State Graph Optimization Efficient Context Management Pruning long memory chains in LangGraph 🧰 Tech Stack LangGraph LangChain Core Groq / OpenAI LLMs RunnableLambda Python 3.10+ 📌 GitHub Repository (Code + Notes) 👉 https://github.com/dearnidhi/Agentic-AI-HandsOn-Bootcamp 📩 Connect with Me: ✉️ Email: nidhiyachouhan12@gmail.com 📸 Instagram: @dear_nidhi | @codenidhi 💼 LinkedIn: https://www.linkedin.com/in/nidhi-chouhan-544650b4/ ✨ Don’t forget to LIKE 👍, SHARE 📢 & SUBSCRIBE 🔔 for more LangGraph & Agentic AI tutorials! LangGraph ReAct Memory Saver, LangGraph Components, LangGraph Tutorial, LangGraph Memory Optimization, ReAct Agent with Memory, LangChain ReAct Agent, LangGraph Context Saver, LangGraph StateGraph, LangGraph Reasoning Agent, LangGraph LangChain Integration, Agentic AI Bootcamp, Nidhi Chouhan LangGraph, CodeNidhi LangGraph, LangGraph Context Compression, LangGraph Pruning #LangGraph #LangChain #ReActAgent #LangGraphMemory #AgenticAI #LangGraphTutorial #AI #MachineLearning #LangGraphComponents #CodeNidhi #NidhiChouhan #GenAI #Python #MemorySaver #ReasonAndAct