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Resources: - Project Repo on GitHub: https://github.com/homayounsrp/React_Agent - ReACT Agent from scratch (Documentation to understand what is happening under the hood): https://langchain-ai.github.io/langgraph/how-tos/react-agent-from-scratch/ 🕒 Timestamps: 00:00:29 Agent Structure (Pipeline) 00:01:11 Files Structure 00:01:33 env Configuration (Neo4j & openAI) 00:03:32 Knowledge Graph Retrieval 00:04:03 ReACT Agent - Tools 00:04:48 ReACT Agent - Output Parser 00:05:34 ReACT Agent - System Prompt 00:06:00 ReACT Agent - Node 00:07:38 Running Agent ------ 🚀 Unlock the Power of Agentic AI with LangGraph and Neo4j – Build Smarter AI Agents with Graph RAG Hey everyone! I'm super excited to share this comprehensive tutorial where I guide you step-by-step through building a powerful AI agent using LangGraph and Neo4j. This project combines cutting-edge techniques in agentic AI, graph databases, and retrieval-augmented generation (RAG) to show you how to build AI that can truly reason over structured knowledge. In this project I dived into how to use LangGraph Studio and Neo4j together to create agentic RAG systems that are robust, scalable, and production-ready. Whether you're working on a personal RAG project, an enterprise RAG chatbot, or exploring how knowledge graph RAG pipelines can help enhance LLM RAG accuracy, this tutorial has you covered. 🧠 Why This Matters The intersection of LLMs, knowledge graphs, and graph databases is where the future of AI integration is headed. Most large language models are limited by the flatness of traditional document stores. By combining LangGraph’s agent framework with Neo4j’s graph database, you can build agents that understand relationships, navigate complex data, and query precisely using Cypher. This is how we move from traditional question-answering to truly agentic AI — models that reason, plan, and act. 🎯 What You’ll Learn Here’s a detailed breakdown of what I walk through in the tutorial: - Setting Up LangGraph I’ll show you how to initialize your LangGraph agent, define nodes, edges, and workflows in LangGraph Studio, and how to bring your first agent to life. - Connecting to Neo4j You'll learn how to spin up a Neo4j instance, set up credentials, and use LangGraph tools to connect directly to your Neo4j database. - Real-World Use Cases and Challenges I explained about the challenges you may face when pushing the agent to the production and making it public to the users 🛠️ Key Technologies We’ll Use - LangGraph – for building and orchestrating agent workflows - Neo4j – the graph database at the heart of our knowledge graph RAG system - LangChain – for added LLM integrations if needed - Python – to glue it all together 🧩 Who Is This For? This tutorial is for: - Developers wanting to build graph-aware agents - Data scientists exploring AI integration with graph databases - AI enthusiasts curious about agent frameworks and RAG - Teams looking to power their products with agentic AI or LLM RAG pipelines - Anyone exploring LangGraph tutorials, Neo4j tutorials, or LLM-based RAG chatbots --------- LangGraph,Neo4j,AI Agent,Agent Framework,Cypher Query,Knowledge Graph,AI Integration,Building agent,developing agent,agentic ai,agentic rag,llm rag,graph rag,graph database,langgraph studio,building rag,knowledge graph rag,langchain tutorial,langgraph tuturial,agent tuturial,llm rag tutorial,neo4j tutorial,rag chatbot,rag database,rag project
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