LangGraph + Neo4j Crash Course for Beginners: AI Agent Development using Knowldege Graph | Hands-on! - Temporal RAG: Real-Time Knowledge Graphs for AI Agents using Graphiti, Neo4j and LangGraph

Resources: - GitHub Repo: https://github.com/homayounsrp/temporal_rag - Graphiti: https://github.com/getzep/graphiti - GraphRAG VS TemporalRAG: GraphRAG vs TemporalRAG: Which One Should You Actually Use? (Full Breakdown) https://youtu.be/L9NkyJjvxnY In this video I show you how to build an agent that queries and updates a temporal knowledge graph. It ingests episodes into Neo4j using Graphiti, then uses LangGraph and OpenAI to answer questions or update the knowledege graph using new information. Stack: Python, Graphiti, Neo4j, LangGraph, LangChain, OpenAI Features: - Query the knowledge graph in natural language - Automatically update the graph with new episodes - Temporal understanding with episode-based data ingestion - The agent uses LangGraph orchestration with tools for graph search and updates, making it interactive for exploring and maintaining temporal data. ⏱️ TIMESTAMPS 0:00 - Intro 0:46 - Recap 2:48 - The Power of Graphiti 4:50 - Agent System Design 5:26 - Implementation 7:59 - Demo TemporalRAG, Graphiti, Knowledge Graph, Neo4j, LangGraph, LangChain, OpenAI, LLM Agent, Python Tutorial, Retrieval Augmented Generation, RAG, Graph Database, AI Agent, Natural Language Processing, GPT-4, LangGraph Tutorial, Neo4j Python, Knowledge Graph Tutorial, Temporal AI, Agentic AI, LLM Tools, Graph Database Tutorial, Python AI, OpenAI API, LangChain Tutorial, Graphiti Framework, AI Development, Machine Learning, Data Science