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What is GraphRAG? Different Types, Limitations, and When to Use
Retrieval-augmented generation (RAG) has emerged as a powerful technique to
Guy Korland
July 31, 2024
Latest GraphRAG, RAG and Knowledge Graphs news, case studies and insights.
Retrieval-augmented generation (RAG) has emerged as a powerful technique to
When building AI-driven systems, FalkorDB vs Neo4j graph databases offer different advantages. Find the best fit for your AI needs.
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
NFalkorDB v1.0.0 lets you run FalkorDB graph queries directly from .NET using C# extension methods—no wrappers, no cache layers, no
Accurate data retrieval via GraphRAG boosts inference, reduces hallucinations, and secures personalized memory storage for AI agents.
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
NFalkorDB v1.0.0 lets you run FalkorDB graph queries directly from .NET using C# extension methods—no wrappers, no cache layers, no
Accurate data retrieval via GraphRAG boosts inference, reduces hallucinations, and secures personalized memory storage for AI agents.
MCP simplifies how LLMs access tools and graph databases. Learn how FalkorDB’s MCP server enables structured, scalable AI integrations.
Graph Query Language demystified: learn how FalkorDB simplifies querying for LLM and GraphRAG workflows using Cypher-like syntax.
A developer-focused guide to understanding and using knowledge graphs for GraphRAG, LLM integration, schema design, and high-precision retrieval in 2025.
Diffbot’s KG-LM Benchmark showed GraphRAG outperforming vector RAG 3.4x. FalkorDB’s 2025 SDK pushes that to 90%+ accuracy for schema-heavy enterprise
Without AI-ready data, most generative AI projects fail. Learn to standardize enterprise data with graph databases like FalkorDB for scalable
Learn about integrating Graph Neural Networks (GNNs) with LLMs for precise relational modeling and improved AI performance in fraud detection,
The latest KPMG AI Report highlights data quality and privacy as major roadblocks. Discover why GraphRAG is essential for enterprise
Understand ACID transactions. Learn how isolation levels impact data consistency in your applications, plus choosing the right model.
A technical deep-dive comparing VectorRAG vs GraphRAG architectures across 10 critical engineering dimensions, helping AI architects make data-driven decisions for
Discover how to implement GraphRAG using FalkorDB’s hybrid query capabilities combined with LangChain and LangGraph to build AI systems that
Learn what a graph database is and how to deploy FalkorDB graph databases on AWS/GCP, run Cypher queries, and benefit
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
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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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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