SurrealDB v3 solves the graph RAG ugliness of a vector DB here, a graph DB there, a document store somewhere else. With native vectors, documents, and graph relations all in one engine, SurrealDB v3 finally gives a unified substrate for #GraphRAG. One query language. One data model. One place to store chunks, embeddings, entities, and edges.
But as an all‑in‑one platform for building #KnowledgeGraphs, it’s one of the most compelling options available today.

https://surrealdb.com/3.0

Zvec by Alibaba is a lightweight, lightning-fast, in-process vector database. Beats any other vector db out there. https://zvec.org/en/ #VectorDatabase

OpenAI acquired the claw but there are various alternatives and even some with semantic graph brains. MemU has semantic linking, multi-hop reasoning, a unified multi-modal memory. Not using a graph database though. Am sure vendors are figuring out how to catch this train, won't take long.
#GraphAI
https://memu.pro/memory-graph-agent

The Knwler hairball graph visualization (Cytoscape) has been replaced by a custom layout algorithm and SVG rendering.The layout now depicts much more clearly the relationships. The concentric circles and fading edges (proportional to distance) are organized according to degree and minimize crossings.
Hope you like it.
https://github.com/Orbifold/knwler
#GraphViz #KnowledgeGraphs

Memgraph 3.8 is here with real‑time graph intelligence.

Three things stand out:
- Atomic GraphRAG: graph augmented questions with one query.
- Vector Search in the Same Store: new hybrid graph‑vector patterns
- Parallel Runtime Improvements: analytical and traversal-heavy queries now run faster thanks to deeper parallelization across the runtime.

https://memgraph.com/blog/memgraph-3-8-release-atomic-graphrag-vector-single-store-parallel-runtime
#GraphDatabase #GraphRAG #KnowledgeGraphs

Anthropic knowledge-work plugin for in-house legal teams, primarily designed for Cowork. It automates contract review, NDA triage, compliance workflows, legal briefings, and templated responses -- all configurable to your organization's specific playbook and risk tolerances.
#LegalAI #LegalTech
https://github.com/anthropics/knowledge-work-plugins?tab=readme-ov-file

GraIL is a graph neural network for inductive knowledge graph reasoning. Unlike embedding-based approaches, GraIL model is able to predict relations between nodes that were unseen during training and achieves state-of-the-art results in this inductive setting. https://github.com/kkteru/grail #KnowledgeGraphs #GraphMachineLearning

KGTK is a Python library for easy manipulation with #KnowledgeGraphs. It provides a flexible framework that allows chaining of common graph operations, such as: extraction of subgraphs, filtering, computation of graph metrics, validation, cleaning, generating embeddings, and more. https://kgtk.readthedocs.io/en/latest/

Makes my day🙂 Graph ML identifies promising additional drug indications for diseases such as acute lymphoblastic leukemia and Alzheimer’s disease.
https://www.nature.com/articles/s41551-025-01598-z
#KnowledgeGraphs #GraphML #DrugRepurposing

Reconciliation of triple stores and property graphs is an old topic and many have tried to tackle it (from every angle and tech). It's polarizing and the latest RDF-* and SPARQL-* announcements turn an already complex field into more syntax and complexity. When flexibility equals complexity it seldom leads to more adoption.
#GraphDatabases #RDF #KnowledgeGraphs
https://olafhartig.blog.liu.se/
https://www.ontotext.com/knowledgehub/fundamentals/what-is-rdf-star/

Hamilton is yet another Apache workflow platform competing with Airflow, Dagster, Kestra, Prefect, Windmill and whatnot. It's a crowded space, not speaking of self-service platforms like n8n, dify and MS Foundry, PowerApps. Becoming more and more difficult to advice customers what to use. https://hamilton.apache.org/

SurrealDB is now even easier to install than before thanks to the new Docker Extension. Literally just a click.
#GraphDatabase
https://surrealdb.com/

Graphblas algorithms is a collection of GraphBLAS algorithms written using python-graphblas. It can be used directly or as a backend for NetworkX. It's fast, very fast.
#GraphAnalytics
https://github.com/python-graphblas/graphblas-algorithms

Mermaid diagrams shine as an easy solution to integrate flowcharts and all sorts of graphs into markdown. It allows #LLMs to generate diagrams without any JS or specific framework. So far so good, but did you know there's way more than diagrams? You can have whole apps defined through markdown and let AI define UI in a framework-independent fashion. https://markdown-ui.blueprintlab.io/

Mindgraph helps generating and querying your ever-expanding knowledge graphs with #LLMs with support for #NebulaGraph, #FalkorDB and #NexusDB. Nothing enterprisey but fun to use. https://github.com/yoheinakajima/mindgraph #KnowledgeGraph

Yet another ontology and #GraphRAG project, specifically targeting local LLMs:
- Extracts entities and relations from engineering PDFs
- Builds a materialized knowledge graph (NetworkX)
- Prevents semantic drift using community-aware retrieval
- Supports hybrid retrieval (Graph + Vector / FAISS)
- Runs targeted LLM improve cycles for factual gaps
- Evaluates multiple RAG strategies
- Generates DOCX and PDF technical documentation automatically
https://github.com/tvlnsiva/graphrag-engineering-pdfs

Semantica is a semantic layer for knowledge engineering which promises to transform chaotic data into intelligent knowledge. A comprehensive OSS framework powering Knowledge Graph-Powered RAG (GraphRAG), AI Agents, Multi-Agent Systems, and AI applications with structured semantic knowledge.

100% Open Source • MIT Licensed • Production Ready • Community Driven https://github.com/Hawksight-AI/semantica
#GraphRAG #KnowledgeGraphs

Traditional tools like Protégé are great—but they’re built for specialists. Unless you’re fluent in RDF/OWL, even drawing a single box can feel like a steep climb. Termboard takes a different path. It’s designed for everyone—Business Analysts, Product Owners, Researchers, and Developers. Instead of forcing domain experts through technical hoops, it lets them capture business knowledge directly, in a way that feels natural.
https://termboard.com/
#Graphs #Ontology #KnowledgeGraphs

Graph-constrained Reasoning (GCR) is a novel framework that bridges structured knowledge in knowledge graphs (KG) with unstructured reasoning in LLMs. GCR ensures faithful KG-grounded reasoning by integrating KG structure into the LLM decoding process through KG-Trie. This allows LLMs to directly reason on graphs and generate faithful reasoning paths grounded in KGs to achieve accurate reasoning with zero reasoning hallucination. https://github.com/RManLuo/graph-constrained-reasoning #KnowledgeGraphs #LLMs

An overview of the graph database jungle with lots of easy to understand graphics and tables. Highly advised, very well done 🫶.. https://arxiv.org/pdf/1910.09017
#GraphDatabase #KnowledgeGraphs