An open-source Python framework for real-time, graph-based knowledge in Neo4j

We’re thrilled to announce new Text2Cypher models and Google’s MCP Toolbox for Databases from the collaboration between Google Cloud and Neo4j.

The Model Context Protocol gives users a wide range of services they can connect to from the comfort of their AI client.

Graph Data Modeling Without Graph Databases: PostgreSQL and Hybrid Approaches for Agentic Systems 🖇️ Organizations implementing AI systems today face a practical challenge: maintaining multiple specialized databases (vector stores, graph databases, relational systems) creates significant operational complexity, increases costs, and introduces synchronization headaches. Companies like Writer (insight from a recent Waseem Alshikh interview with Harrison Chase) have tackled this problem by implementing graph-like structures directly within PostgreSQL, eliminating the need for separate graph databases while maintaining the necessary functionality. This approach dramatically simplifies infrastructure management, reduces the number of systems to monitor, and eliminates error-prone synchronization processes that can cost thousands of dollars in wasted resources. For enterprises focused on delivering business value rather than managing technical complexity, these PostgreSQL-based implementations offer a pragmatic path forward, though with important trade-offs when considering more sophisticated agentic systems. Writer implemented a subject-predicate-object triple structure directly in PostgreSQL tables rather than using dedicated graph databases. This approach maintains the semantic richness of knowledge graphs while leveraging PostgreSQL's maturity and scalability. Writer kept the conceptual structure of triples that underpin knowledge graphs implemented through a relational schema design. Instead of relying on native graph traversals, Writer developed a fusion decoder that reconstructs graph-like relationships at query time. This component serves as the bridge between the storage layer (PostgreSQL with its triple-inspired structure) and the language model, enabling sophisticated information retrieval without requiring a dedicated graph database's traversal capabilities. The approach focuses on query translation and result combination rather than storage structure optimization. Complementing the triple-based approach, PostgreSQL with extensions (PG Vector and PG Vector Scale) can function effectively as a vector database. This challenges the notion that specialized vector databases are necessary, Treating embeddings as derived data leads to a more natural and maintainable architecture. This reframes the database's role from storing independent vector embeddings to managing derived data that automatically synchronizes with its source. But a critical distinction between retrieval systems and agentic systems need to be made. While PostgreSQL-based approaches excel at knowledge retrieval tasks where the focus is on precision and relevance, agentic systems operate in dynamic environments where context evolves over time, previous actions influence future decisions, and contradictions need to be resolved. This distinction drives different architectural requirements and suggests potential complementary roles for different database approaches. | 15 comments on LinkedIn

Learn how to use Spanner Graph with LangChain workflows to build advanced, relationship-aware RAG systems, a.k.a. GraphRAG.

Learn about different types of graphs and their applications in data management and AI, as well as common misconceptions, in this article by Lulit Tesfaye.

Read about how our partners from Semantic Partners explore the latest iteration of the GraphDB's Talk To Your Graph feature

When implementing graph databases in modern software development, we often face a significant challenge: bridging the conceptual gap between ontology-focused knowledge representation and…

Build a graph for RAG application for a price of a chocolate bar! What is GraphRAG for you? What is GraphRAG? What does GraphRAG mean from your perspective? What if you could have a standard RAG and a GraphRAG as a combi-package, with just a query switch? The fact is, there is no concrete, universal

🚀 Introducing 𝗖𝘆𝗩𝗲𝗿: Schema-Aware Cypher Query Validation for Neo4j! We’re excited to share 𝗖𝘆𝗩𝗲𝗿, the Python library we developed to validate… | 12 comments on LinkedIn

Biel/Bienne, Switzerland – February 24, 2025 – Knowledge graphs are becoming critical infrastructure for enterprises handling large-scale, interconnected data. Yet, many existing solutions struggle with scalability, performance, and cost—forcing organizations into proprietary ecosystems with high op

Can work on ontologies with Neo4j, including those based on OWL (Web Ontology Language). However, Neo4j alone is not an ontology reasoner.

Bridge the gap and unlock hidden potential within your unstructured data

New features include community summaries, local and global retrievers, running retrievers in parallel, and custom prompt instructions.

When (not) to use GraphRAG

Imagine you’re solving a complex puzzle with scattered pieces of information.

Speaker: Gábor Szárnyas (LDBC)Event: Data Analytics developer room at FOSDEM 2025Talk page: https://fosdem.org/2025/schedule/track/analytics/Slides: https://...

Spanner Graph, now GA, includes a notebook experience, query feature and performance improvements and integrations with first- and third-party tools.

We're very happy to announce our latest release of Kùzu, version 0.8.0, is now available and ready to use! This release brings an exciting new feature that…

Explore its functionality and how it leverages the power of LLMs and Neo4j graph databases to improve data analysis and knowledge discovery.

Knowledge graphs are rapidly gaining traction as a way to store and query complex, interlinked data. Two major approaches dominate this…

Enabling an LLM to chat directly with structured graph data

Today, I conducted a comparison between ChatGPT and DeepSeek chat capabilities by providing them with a schema and a natural language question. I tasked them…

David Hughes presented “Unleashing the Power of Multimodal GraphRAG: Integrating Image Features for Deeper Insights” at Data Day Texas 2025.

G.V() 3.14.38 Release Notes: Now with Support for Neo4j, Memgraph, Neptune Analytics, Query Editor Improvements, and more! For the first time ever, G.V() can be used on non Apache TinkerPop graph databases. It is now compatible with Neo4j, Neo4j AuraDB, Memgraph and Amazon Neptune Analytics using the Cypher querying language.

Keith W. Hare, Convenor, ISO/IEC JTC1 SC32 WG3 Database Languages The GQL standard (ISO/IEC 39075:2024 Information technology — Database languages — GQL) was published in April, 2024.

Discover Neo4j's 2024 product innovations in cloud-native graph technology, enhancing performance, scalability, AI innovation, and developer productivity.

As technology advances, large language models (LLMs) and information retrieval/management methods evolve at breakneck speed. Knowledge…

A RAG System Built for Both Semantic Search and Structured Data Queries

Create Cypher queries with an easy-to-use drag-and-drop interface