GraphRAG is a variation of retrieval-augmented generation (RAG) architecture that integrates a knowledge graph with large language models (LLMs), addressing limitations of traditional vector-based RAG in providing reasoning capabilities and understanding relationships between diverse concepts. By leveraging a knowledge graph, GraphRAG can improve response accuracy, offer more explainability and transparency into retrieved information, and help answer complex questions. However, it introduces an extra step for creating the knowledge graph using LLMs to extract entities and relationships, maintaining and updating the graph as new data arrives is an ongoing operational burden, and it may lead to response latency and scalability challenges as the knowledge base grows. GraphRAG can be implemented with MongoDB Atlas and LangChain, offering a unified database for documents, vectors, and graphs, simplifying the architecture and reducing operational overhead, and greatly simplifying the development experience.