Neo4j's Going Meta Season 3 recap highlights the growing industry consensus that AI agents require ontologies, as argued by Frank Coyle of UC Berkeley at the AI Engineer World's Fair. The season's epi…
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…
Neo4j's Sr. Manager of Technical Product Marketing explains GraphSAGE, an inductive graph embedding model that generates embeddings for new nodes without retraining, using a three-step process of samp…
Neo4j's Aura Graph Analytics now supports GraphSAGE, an inductive node embedding method that generates embeddings for unseen nodes without retraining, addressing the cold-start problem in growing grap…
Neo4j announced that every Aura instance now includes a hosted Model Context Protocol (MCP) server, enabling AI clients like Claude Desktop and Cursor to query graph data with zero setup and OAuth-bas…
Neo4j published a technical guide for building a Neo4j-backed agent on IBM watsonx Orchestrate, combining a native Orchestrate agent that queries Neo4j via the Model Context Protocol (MCP) with a Lang…
Microsoft Copilot Studio can now be wired to Neo4j with per-user Okta SSO, enabling identity-scoped GraphRAG where the database enforces access based on the signed-in user's Okta groups mapped to Neo4…
Neo4j, a graph database company, reports that graph technology can cut AI hallucination rates by 44% and is essential for enterprise AI agents to reason over connected data. The technology stores data…
Neo4j's Graph ML and GenAI Research team introduced the Meta Knowledge Graph (MKG), a graph-structured context layer that supplies AI agents with operational metadata—semantic descriptions of enterpri…
Neo4j released AgentMemory for .NET, bringing graph-native short-term, long-term, and reasoning memory to Microsoft Agent Framework and Semantic Kernel developers, and published recordings from its Gr…
Neo4j explains that a context graph acts as a persistent memory system for AI agents, linking long-term enterprise knowledge, short-term conversation history, and reasoning memory to ensure consistent…
Semvec, a semantic memory system from Versino PsiOmega, achieves near-mem0 quality on the LOCOMO benchmark with zero generative-LLM calls at ingest, reducing context tokens by 87% and ingest time by ~…
Neo4j's starter repo powered the winning project at Hack the Video Agent Context Graph, a one-day hackathon hosted by HackerSquad at the AWS Builder Loft on Market Street, where 151 builders created 3…
AgentMemory for .NET, a native .NET implementation of Neo4j's graph-native agent memory model, has been released by independent developer José L. Latorre and verified 178 out of 178 against Neo4j's Te…
Neo4j's Enterprise Knowledge Layer proposes a shared, governed graph substrate to fix enterprise AI failures caused by scattered business context across agents, according to Jesús Barrasa. A benchmark…
An independent study by the UK's National Innovation Centre for Data (NICD) found that GraphRAG, a combination of vector and graph retrieval-augmented generation, makes AI agents 80% more truthful tha…
Public sector AI systems fail on high-consequence workforce questions because they lack a knowledge layer that connects fragmented data, according to Neo4j's Global Head of Public Sector & Workforce I…
Neo4j announced MCP for Aura, a hosted Model Context Protocol server included at no additional cost on Free, Professional, and Business Critical tiers of its Aura graph database service, with Virtual …
AI agents make decisions through a five-stage loop — understanding the goal, gathering context, deciding, acting, and learning — and failures typically begin when agents retrieve incomplete or disconn…
Enterprise AI fails not because of models or scaffolding but because agents lack the implicit business semantics that applications and humans supply themselves, according to Neo4j. The company propose…