Latest GraphRAG, RAG and Knowledge Graphs news, case studies and insights.
VirtuousAI, an ethical AI platform, leverages FalkorDB to create a centralized data store for public and private data, enabling high-performance
XR.Voyage is a cloud-hosted immersive experience platform that aims to gamify exploration, interaction and transformation of your data sets. They
Matthew Goos, the CTO of AdaptX, explains how his company uses FalkorDB as a core component of its solution. AdaptX
VirtuousAI, an ethical AI platform, leverages FalkorDB to create a centralized data store for public and private data, enabling high-performance
XR.Voyage is a cloud-hosted immersive experience platform that aims to gamify exploration, interaction and transformation of your data sets. They
At Nvidia GTC 2025, FalkorDB presents on real-time knowledge graphs for GenAI. Learn how graph databases enable LLM-enhanced reasoning and
FalkorDB unveils v4.8: up to 42% more memory efficient, outpacing Neo4j by 7x. The latest FalkorDB release focuses on optimizing
Process documents directly using our string loader feature. Integrate LangChain and LlamaIndex to chunk and load data, building tailored knowledge
FalkorDB joins forces with Lightning.ai to simplify GraphRAG deployment. No local setup, cloud-ready, and optimized for scalable AI workflows.
At Nvidia GTC 2025, FalkorDB presents on real-time knowledge graphs for GenAI. Learn how graph databases enable LLM-enhanced reasoning and
Learn about graph clustering algorithms: hierarchical, modularity-based, label propagation, spectral, and edge betweenness. Analyze their strengths, weaknesses, and optimal use
Discover how to implement GraphRAG using FalkorDB’s hybrid query capabilities combined with LangChain and LangGraph to build AI systems that
Discover how to deploy FalkorDB graph databases on AWS/GCP, run Cypher queries, and unlock the power of interconnected data.
NoSQL database architectures overcome relational limitations with schema flexibility and distributed processing. Learn how graph, document, key-value, and columnar NoSQL
Learn about graph clustering algorithms: hierarchical, modularity-based, label propagation, spectral, and edge betweenness. Analyze their strengths, weaknesses, and optimal use
Learn to build AI agents with memory using LangChain and FalkorDB. This integration enables context-aware AI applications, leveraging graph databases
Explore RedisGraph EOL in this dev guide with practical migration steps and technical validation tips for switching to FalkorDB.
Explore practical methods to reduce GraphRAG Indexing Costs, including query optimization, efficient indexing techniques, and scalable LLM integration for graph
Dive into ontologies, the semantic blueprints of knowledge graphs. Discover how they structure entities, relationships, and axioms to power intelligent
Discover the process of migrating from a relational database to a graph database. This guide covers schema analysis, data transformation,
Graph RAG emerges as a powerful solution to Gartner’s RAG system challenges, offering improved data representation, retrieval mechanisms, and context
Technical analysis of graph database benchmarks comparing FalkorDB and Neo4j performance metrics. FalkorDB achieves 500x faster p99 and 10x faster
Discover how to leverage LlamaIndex RAG with FalkorDB to create efficient GraphRAG systems. Enhance LLM performance with knowledge graph-powered retrieval
At FalkorDB, we are redefining the boundaries of what’s possible with graph databases. Our advanced, ultra-low latency solution is designed
Explore how AI agents leverage memory systems and graph databases to maintain context, process relationships, and make autonomous decisions. Analysis
When building AI-driven systems, FalkorDB vs Neo4j graph databases offer different advantages. Find the best fit for your AI needs.
Knowledge graph visualization offers deep insights, enhancing decision-making for AI applications with FalkorDB.
Unstructured data is all the data that isn’t organized in a predefined format but is stored in its native form.
Memary is an open-source memory layer designed for AI agents, focusing on emulating human memory processes to enhance agent capabilities.
Knowledge graphs have become a game-changer in building Retrieval-Augmented Generation (RAG) applications, often referred to as GraphRAG. These applications enhance
FalkorDB-Browser 0.7.0 packs new features and improvements to improve your graph database workflow. Try FalkorDB-Browser and take advantage of faster
Edges in FalkorDB enable efficient graph representation and traversal using GraphBLAS tensors. Learn how FalkorDB uses GraphBLAS to support advanced
Driving meaningful insights from vast amounts of unstructured data has often been a daunting task. As data volume and variety
We’re thrilled to announce that FalkorDB-Cloud has added full support for clusters on Google Cloud Platform (GCP)! This update brings scale-out capabilities, multi-tenant architecture, and multi-graph
We’re excited to announce the release of GraphRAG-SDK v0.2, packed with powerful new features that take knowledge graph-based AI applications
Modern software architectures are complex systems of interconnected components. As projects grow, keeping track of all their moving parts becomes
Retrieval-Augmented Generation (RAG) has become a mainstream approach for working with large language models (LLMs) since its introduction in early
Discover the process of migrating from a relational database to a graph database. This guide covers schema analysis, data transformation,
Retrieval-augmented generation (RAG) has emerged as a powerful technique to address key limitations of large language models (LLMs). By augmenting
Code is the foundation of modern software, but as codebases grow in complexity, understanding and navigating them becomes increasingly challenging.
Ultra-fast, multi-tenant graph database using sparse matrix representations and linear algebra, ideal for highly technical teams that handle complex data in real-time, resulting in fewer hallucinations and more accurate responses from LLMs.
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SOLUTIONS
Simply ontology creation, knowledge graph creation, and agent orchestrator
Ultra-fast, multi-tenant graph database using sparse matrix representations and linear algebra, ideal for highly technical teams that handle complex data in real-time, resulting in fewer hallucinations and more accurate responses from LLMs.
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CTO at Intel Ignite Tel-Aviv
I enjoy using FalkorDB in the GraphRAG solution I'm working on.
As a developer, using graphs also gives me better visibility into what the algorithm does, when it fails, and how it could be improved. Doing that with similarity scoring is much less intuitive.
Dec 2, 2024
Ultra-fast, multi-tenant graph database using sparse matrix representations and linear algebra, ideal for highly technical teams that handle complex data in real-time, resulting in fewer hallucinations and more accurate responses from LLMs.
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