In 2026, contextual memory will no longer be a novel technique; it will become table stakes for many operational agentic AI ...

GraphRAG (Graphs + Retrieval Augmented Generation) is a technique for richly understanding text datasets by combining text extraction, network analysis, and LLM prompting and summarization into a ...

Memgraph, a leader in open-source in-memory graph databases purpose-built for dynamic, real-time enterprise applications, is releasing two new tools specifically architected to open up the power of ...

Abstract: This paper investigates a GraphRAG framework that integrates knowledge graphs into the Retrieval-Augmented Generation (RAG) architecture to enhance networking applications. While RAG has ...

Graph Neural Networks (GNNs) and GraphRAG don’t “reason”—they navigate complex, open-world financial graphs with traceable, multi-hop evidence. Here’s why BFSI leaders should embrace graph-native AI ...

Large Language Models (LLMs) have set new benchmarks in natural language processing, but their tendency for hallucination—generating inaccurate outputs—remains a critical issue for knowledge-intensive ...

What if your AI could not only retrieve information but also uncover the hidden relationships that make your data truly meaningful? Traditional vector-based retrieval methods, while effective for ...