A developer benchmarked four retrieval architectures over the same graph database against 150 questions, finding that an agentic RAG pipeline hit 100% accuracy at 552 LLM tokens per question and 10.2 …
A developer built three question-answering pipelines — RAG, GraphRAG, and Agentic GraphRAG — on TigerGraph Savanna to test when agentic loops actually beat simpler retrieval, using ~2,900 Wikipedia Ol…
Graph-based contextual retrieval, which uses GraphRAG to traverse a context graph rather than relying on vector similarity alone, improves AI agent accuracy by following explicit relationships between…
Researchers at KAIST and Microsoft published "Follow the Entities: A Corpus Map for Agentic Search" (arXiv:2609.37226), quantifying how much compute LLM search agents waste navigating raw document cor…
TRACE, a tree-relational enhancement framework for oncology large language models, improved label-free evaluation and supervised fine-tuning across ten oncology classification tasks and the MedQuAD Ca…
A hackathon team built FraudGraph AI, an agentic fraud investigation platform that combines TigerGraph's graph database with GraphRAG to trace relationships between transactions, cards, customers, and…
A developer built Agentic Fraud Sentinel, a real-time fraud detection system that combines TigerGraph's GraphRAG engine with a FastAPI backend and a Vercel-hosted dashboard to traverse multi-hop entit…
A team built CaseGuard, an autonomous AI fraud investigator that combines TigerGraph, GSQL, GraphRAG and agentic reasoning to investigate suspicious transactions, gather extra evidence when confidence…
Neo4j published a comparison of vector RAG and GraphRAG, stating that GraphRAG has been shown to more than double factual accuracy while offering better token efficiency and a traceable path back to e…
Neo4j published the NODES 2026 agenda for its free 24-hour virtual conference on November 12, featuring 100+ speakers across AI Engineering, Data Intelligence and Modern Applications tracks, alongside…
An open-source Graph RAG library called post-graph-rag, released under Apache 2.0 on GitHub and PyPI, indexes documents in PostgreSQL using pgvector and closes superseded relationships from document o…
Neo4j Inc. will host its GraphSummit event on Sept. 24, with theCUBE, SiliconANGLE Media's livestreaming studio, providing an exclusive broadcast, according to theCUBE principal analyst Paul Nashawaty…
Cassie Shum, speaking at QCon, presented lessons learned from building production-ready agentic AI systems on knowledge graphs, drawing on her work at RelationalAI and more than 20 years as an enginee…
Researchers introduced Mosaic, a training-free framework that formulates GraphRAG retrieval as a per-query control problem, with an LLM analyzer converting query-specific evidence requirements into a …
A developer's analysis of GraphRAG benchmark comparisons reveals that results vary dramatically depending on whether an LLM judge or ground-truth metrics are used. The same paper reports GraphRAG winn…
Neo4j published a startup guide for building graph applications on its Aura cloud database, recommending installation of the Neo4j CLI and agent skills first, then using the neo4j-getting-started-skil…
John Abah released ABAH, an open-source platform that uses GraphRAG, PlatformIO, and Ollama to enable local LLM-assisted embedded hardware programming, including offline firmware generation and flashi…
LinkedIn's principal AI researcher Praveen Bodigutla said the company built a cognitive memory agent to provide agentic personalization for its hiring assistant, moving off GraphRAG in favor of a tree…
A production AI tutor's context engineering experiments, detailed in the newsletter LAI #139, found that reducing tokens via summarization increased costs by roughly 2x despite sending 41% fewer token…
Informatiqs argues that Graph Neural Networks (GNNs) are the strategic choice for enterprise generative AI on Google Cloud Platform (GCP), emphasizing the shift from flat vector embeddings to graph-ba…