Neo4j launched Virtual Graph, a zero-copy, zero-ETL solution that brings native graph intelligence to Databricks and Snowflake, enabling Cypher queries and graph algorithms directly on existing cloud …
Neo4j relaunched its GraphAcademy learning platform with an AI assistant called E.L.A.I.N.E. and a new MCP server for IDE integration. The platform offers free hands-on courses and labs for learning C…
Neo4j partnered with RocketRide, Butterbase, Daytona, Nebius, and Opsera at HackwithBay 3.0, where 200 builders used graph databases to build thoughtful agents for productivity. First-place team VeriG…
Neo4j's Aura Agent Hackathon drew 40 AI agent submissions from developers worldwide, with winners including a misinformation-mapping ConspiracyGraph Agent and a regulatory-risk graph agent, demonstrat…
Neo4j launched a redesigned GraphAcademy with a hands-on Neo4j Fundamentals course, editable data models, team learning features, an onboarding assistant, and public learner profiles. The update aims …
Neo4j argues that most enterprise AI projects fail due to data that lacks context, flexibility, and standardization, and introduces a knowledge layer to make data AI-ready. Gartner reports 63% of orga…
Neo4j Chief Scientist proposes that AI agents require three types of structured memory—long-term, short-term, and reasoning memory—organized as context graphs to ensure reliability and accuracy in aut…
Neo4j has been named a 'One to Watch' in Snowflake's 2026 Modern Marketing Data Stack Report for its graph database and analytics integration with Snowflake's AI Data Cloud, enabling advanced graph-dr…
Neo4j launched the Agent Memory Service (NAMS), a managed cloud service providing persistent, structured memory for LLM agents using a graph database with native vector indexes. The service offers sho…
Neo4j announced Virtual Graph, a zero-copy graph reasoning solution that allows users to run Cypher queries, graph algorithms, and AI agents directly on Snowflake data without ETL or data movement. Th…
Neo4j announced dedicated field engineers for startups and released a new edition of its weekly newsletter covering knowledge graph maintenance, a GraphRAG semantic layer built from BigQuery and GCP K…
Neo4j launched Virtual Graph for Databricks, enabling graph-based querying of lakehouse data without moving it. The tool translates Cypher queries to SQL, making relationships explicit for humans and …
Neo4j's graph database product specialist explains the differences between taxonomy, ontology, and knowledge graph, describing how taxonomies organize hierarchical categories, ontologies define semant…
Knowledge graphs are emerging as the accountability layer for high-stakes AI systems in pharma, law, insurance, and defense, enabling traceability and reducing failures. Examples include China's Syste…
Neo4j announced support for post-quantum hybrid key exchange in its 2025.01 release to defend against future quantum computer attacks and current Harvest Now, Decrypt Later threats. The feature uses X…
Neo4j released Neocarta, a Python library and MCP server, to build graph-based semantic layers on GCP BigQuery and Knowledge Catalog for agentic AI applications. The tool maps database metadata and bu…
Neo4j acquires GraphAware, integrating its Hume platform to offer governments an open-standards alternative to proprietary tools like Palantir Gotham. The company also launches Neo4j Virtual Graph for…
Neo4j's director of partner marketing explains the differences between graphs, knowledge graphs, and context graphs in a three-part series, highlighting how each builds on the previous to provide conn…
Neo4j's director of partner marketing explains in a blog post how graphs, knowledge graphs, and context graphs help customers build AI-powered applications by connecting data, creating shared understa…
Neo4j's director of partner marketing argues that enterprise AI success depends on an ecosystem of cloud, data, and AI partners, with graph technology providing the connected context layer to ground A…