Zero-config Python graph database with process isolation and Unix socket communication. Build graph apps instantly without Docker or port management.

I'm excited to announce that DeepGraph AI is open-sourcing GraphLite—the first fully open-source embedded graph database implementing the ISO GQL standard (ISO/IEC 39075:2024) in Rust.

Learn how TypeDB combines graph semantics with strong schema and reasoning to model complex domains.

DuckDB community extension that adds support for SQL/PGQ and graph algorithms

Graph database implementing the RDF and SPARQL standards. Very fast and scales to hundreds of billions of triples on a single commodity machine. - ad-freiburg/qlever

When we present QLever, people often ask "how is this possible" as our speed and scale is on another dimension. We now have a page in the wiki that goes into a bit more detail on why and how this is possible. In short: • Purpose built for large scale graph data, not retrofitted • Indexing optimized for fast queries without full in-memory loading • Designed in C++ for efficiency and low overhead • Integrated full text and spatial search in the same engine • Fast interactive queries even on hundreds of billions of triples Link to the wiki page in the comments.

Qlever: graph database implementing the RDF and SPARQL standards. Very fast and scales to hundreds of billions of triples on a single commodity machine. Sounds to good to be true, anyone tested this out? https://lnkd.in/esXKt79J #GraphDatbase #ontology #RDF | 14 comments on LinkedIn

A sophisticated knowledge graph memory system that stores interconnected information with rich semantic structure using Neo4j. - shuruheel/mcp-neo4j-shan

I’ve tested countless Retrieval-Augmented Generation (RAG) systems, and they all failed to meet my expectations. I found a simple two-step…

𝗙𝗹𝗲𝘅𝗶𝗯𝗹𝗲 𝗚𝗿𝗮𝗽𝗵𝗥𝗔𝗚 𝗼𝗿 𝗥𝗔𝗚 is now flexing to the max using LlamaIndex, supports 𝟳 𝗴𝗿𝗮𝗽𝗵 𝗱𝗮𝘁𝗮𝗯𝗮𝘀𝗲𝘀, 𝟭𝟬 𝘃𝗲𝗰𝘁𝗼𝗿 𝗱𝗮𝘁𝗮𝗯𝗮𝘀𝗲𝘀, 𝟭𝟯 𝗱𝗮𝘁𝗮 𝘀𝗼𝘂𝗿𝗰𝗲𝘀, 𝗟𝗟𝗠𝘀, Docling 𝗱𝗼𝗰 𝗽𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴, 𝗮𝘂𝘁𝗼 𝗰𝗿𝗲𝗮𝘁𝗲 𝗞𝗚𝘀, 𝗚𝗿𝗮𝗽𝗵𝗥𝗔𝗚, 𝗛𝘆𝗯𝗿𝗶𝗱 𝗦𝗲𝗮𝗿𝗰𝗵, 𝗔𝗜 𝗖𝗵𝗮𝘁 (shown Hyland products web page data src) 𝗔𝗽𝗮𝗰𝗵𝗲 𝟮.𝟬 𝗢𝗽𝗲𝗻 𝗦𝗼𝘂𝗿𝗰𝗲 𝗚𝗿𝗮𝗽𝗵: Neo4j ArcadeDB FalkorDB Kuzu NebulaGraph, powered by Vesoft (coming Memgraph and 𝗔𝗺𝗮𝘇𝗼𝗻 𝗡𝗲𝗽𝘁𝘂𝗻𝗲) 𝗩𝗲𝗰𝘁𝗼𝗿: Qdrant, Elastic, OpenSearch Project, Neo4j 𝘃𝗲𝗰𝘁𝗼𝗿, Milvus, created by Zilliz (coming Weaviate, Chroma, Pinecone, 𝗣𝗼𝘀𝘁𝗴𝗿𝗲𝗦𝗤𝗟 + 𝗽𝗴𝘃𝗲𝗰𝘁𝗼𝗿, LanceDB) Docling 𝗱𝗼𝗰𝘂𝗺𝗲𝗻𝘁 𝗽𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴 𝗗𝗮𝘁𝗮 𝗦𝗼𝘂𝗿𝗰𝗲𝘀: using LlamaIndex readers: working: Web Pages, Wikipedia, Youtube, untested: Google Drive, Msft OneDrive, S3, Azure Blob, GCS, Box, SharePoint, previous: filesystem, Alfresco, CMIS. 𝗟𝗟𝗠𝘀: 𝗟𝗹𝗮𝗺𝗮𝗜𝗻𝗱𝗲𝘅 𝗟𝗟𝗠𝘀 (OpenAI, Ollama, Claude, Gemini, etc.) 𝗥𝗲𝗮𝗰𝘁, 𝗩𝘂𝗲, 𝗔𝗻𝗴𝘂𝗹𝗮𝗿 𝗨𝗜𝘀, 𝗠𝗖𝗣 𝘀𝗲𝗿𝘃𝗲𝗿, 𝗙𝗮𝘀𝘁𝗔𝗣𝗜 𝘀𝗲𝗿𝘃𝗲𝗿 𝗚𝗶𝘁𝗛𝘂𝗯 𝘀𝘁𝗲𝘃𝗲𝗿𝗲𝗶𝗻𝗲𝗿/𝗳𝗹𝗲𝘅𝗶𝗯𝗹𝗲-𝗴𝗿𝗮𝗽𝗵𝗿𝗮𝗴: https://lnkd.in/eUEeF2cN 𝗫.𝗰𝗼𝗺 𝗣𝗼𝘀𝘁 𝗼𝗻 𝗙𝗹𝗲𝘅𝗶𝗯𝗹𝗲 𝗚𝗿𝗮𝗽𝗵𝗥𝗔𝗚 𝗼𝗿 𝗥𝗔𝗚 𝗺𝗮𝘅 𝗳𝗹𝗲𝘅𝗶𝗻𝗴 https://lnkd.in/gHpTupAr 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗲𝗱 𝗦𝗲𝗺𝗮𝗻𝘁𝗶𝗰𝘀 𝗕𝗹𝗼𝗴: https://lnkd.in/ehpjTV7d

I am excited to announce the formation of a Data Façades W3C Community Group. Façade-X, initially introduced at SEMANTICS 2021 and successfully implemented by the SPARQL Anything project, provides a simple yet powerful, homogeneous view over diverse and heterogeneous data sources (e.g., CSV, JSON, XML, and many others). With the recent v1.0.0 release of SPARQL Anything, the time was right to work on the long-term stability and widespread adoption of this approach by developing an open, vendor-neutral technology. The Façade-X concept was born to allow SPARQL users to query data in any structured format in plain SPARQL. Therefore, the choice of a W3C community group to lead efforts on specifications is just natural. Specifications will enhance its reliability, foster innovation, and encourage various vendors and projects—including graph database developers — to provide their own compatible implementations. The primary goals of the Data Façades Community Group is to: Define the core specification of the Façade-X method. Define Standard Mappings: Formalize the required mappings and profiles for connecting Façade-X to common data formats. Define the specification of the query dialect: Provide a reference for the SPARQL dialect, configuration conventions (like SERVICE IRIs), and the functions/magic properties used. Establish Governance: Create a monitored, robust process for adding support for new data formats. Foster Collaboration: Build connections with relevant W3C groups (e.g., RDF & SPARQL, Data Shapes) and encourage involvement from developers, businesses, and adopters. Join us! With Luigi Asprino Ivo Velitchkov Justin Dowdy Paul Mulholland Andy Seaborne Ryan Shaw ... CG: https://lnkd.in/eSxuqsvn Github: https://lnkd.in/dkHGT8N3 SPARQL Anything #RDF #SPARQL #W3C #FX

Introducing Brahmand: a Graph Database built on top of ClickHouse. Extending ClickHouse with native graph modeling and OpenCypher, merging OLAP speed with graph analysis. While it’s still in early development, it’s been fun writing my own Cypher parser, query planner with logical plan, analyzer, and optimizer in Rust. On the roadmap: native JSON support, bolt protocol, missing Cypher features like WITH, EXISTS, and variable-length relationship matches, along with bitmap-based optimizations and distributed cluster support. Feel free to check out the repo: https://lnkd.in/d-Bhh-qD I’d really appreciate a ⭐ if you find it useful!

Nodestream is a developer friendly Python framework for materializing and working with graph databases.

In case you were wondering what I have been up to lately, Hydra is a large part of it. This is the open source graph programming language I alluded to last year at the Knowledge Graph Conference. Hydra is almost ready for its 1.0 release, and I am planning on making it into a community project, possibly through the Apache Incubator. In this initial demo video, we take an arbitrary tabular dataset and use Hydra + Claude to map it into a property graph. More specifically, we use the LLM once to construct a pair of schemas and a mapping. From there, we apply the mapping deterministically and efficiently to each row of data, without additional calls to the LLM. The recording was a little too long for LinkedIn, so I broke it into two parts. I will post part 2 momentarily (edit: part 2 is here: https://lnkd.in/gZmHicXu). More videos will follow as we get closer to the release. GitHub: https://lnkd.in/g8v2hvd5 Discord: https://bit.ly/lg-discord

Our SPARQL Notebook extension for Visual Studio Code makes it super easy to document SPARQL queries and run them, either against live endpoints or directly on local RDF files. I just (finally!) published a 15-minute walkthrough on our YouTube channel Giant Global Graph. It gives you a quick overview of how it works and how you can get started. Link in the comments. Fun fact: I recorded this two years ago and apparently forgot to hit publish. Since then, we've added new features like improved table renderers with pivoting support, so it's even more useful now. Check it out! | 11 comments on LinkedIn

A full working Cypher script that simulates a Tendering System with multiple workflows, AI agent interactions, conversations, approvals, and more — all modeled and executed natively in a Graph.

Release announcement for Kuzu 0.11.0

In this post, we describe how to improve the quality of the Cypher queries generated by Text2Cypher via graph schema pruning, viewed through the lens of context engineering.

Every year, businesses and consumers lose billions of dollars to fraud, with consumers reporting $12.5 billion lost to fraud in 2024, a 25% increase year over year. People who commit fraud often work together in organized fraud networks, running many different schemes that companies struggle to detect and stop. In this post, we discuss how to use Amazon Neptune Analytics, a memory-optimized graph database engine for analytics, and GraphStorm, a scalable open source graph machine learning (ML) library, to build a fraud analysis pipeline with AWS services.

Transform a relational database into a knowledge graph using cognee and Kuzu, enabling advanced querying and semantic search on your data.

Want to explore the Anthropic Transformer-Circuit's as a queryable graph? Wrote a script to import the graph json into Neo4j - code in Gist. https://lnkd.in/eT4NjQgY https://lnkd.in/e38TfQpF Next step - write directly from the circuit-tracer library to the graph db. https://lnkd.in/eVU_t6mS

Integrating Neo4j, DuckDB, and Kestra for Metadata-Driven Graph Design

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

🚀 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

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…

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

Authors: Makbule Gulcin Ozsoy, Leila Messallem, Jon Besga

A deep dive into LangChain’s implementation of graph construction with LLMs

Many people have asked about loading Microsoft Research #GraphRAG data into Neo4j. I wrote a quick notebook last night to import Documents, Chunks (TextUnit)… | 27 comments on LinkedIn