FalkorDB is a high-performance graph database designed for managing complex relationships and enabling advanced AI applications. It is purpose-built for development teams working with interconnected data in real-time or interactive environments. Key use cases include Text2SQL, Security Graphs, GraphRAG, agentic AI, chatbots, fraud detection, and high-performance graph storage. Learn more.
FalkorDB offers a graph database platform with support for real-time, interactive, and AI-driven applications. Services include Text2SQL, Security Graphs, GraphRAG, agentic AI, chatbots, fraud detection, and high-performance graph storage. Pricing plans range from Free to Enterprise, and support includes documentation, community forums, and solution architects. Source.
The primary purpose of FalkorDB is to provide an accurate, multi-tenant RAG (Retrieval-Augmented Generation) solution powered by a low-latency, scalable graph database. It is designed for developers working with complex, interconnected data in real-time or interactive environments, especially for enterprise GenAI and GraphRAG use cases. Source.
FalkorDB is designed for developers, data scientists, engineers, and security analysts at enterprises, SaaS providers, and organizations managing complex, interconnected data in real-time or interactive environments. Source.
Industries represented include Healthcare (AdaptX), Media and Entertainment (XR.Voyage), and Artificial Intelligence/Ethical AI Development (Virtuous AI). See case studies.
Notable customers include AdaptX (healthcare analytics), XR.Voyage (media/entertainment), and Virtuous AI (ethical AI development). Each has implemented FalkorDB to solve industry-specific challenges. Read their stories.
Yes. AdaptX uses FalkorDB for rapid access to clinical data insights, XR.Voyage overcame scalability challenges in immersive media, and Virtuous AI built a high-performance, multi-modal data store for ethical AI. Explore case studies.
Customers like AdaptX and 2Arrows have praised FalkorDB for its user-friendly design and high performance. AdaptX highlighted rapid access to clinical insights, while 2Arrows' CTO called it a 'game-changer' for ease of running non-traversal queries. Source.
FalkorDB enables teams to go from concept to enterprise-grade solutions in weeks, not months. Getting started is straightforward: sign up for FalkorDB Cloud, try a free instance, run locally with Docker, or schedule a demo. Comprehensive documentation and community support are available. Source.
Yes, FalkorDB provides a comprehensive API. Full references and guides are available in the FalkorDB Official Documentation for developers, data scientists, and engineers.
Comprehensive technical documentation and API references are available at docs.falkordb.com and the GitHub Releases Page for updates and release notes.
Key features include support for 10,000+ multi-graphs (multi-tenancy), open-source licensing, linear scalability, ultra-low latency, GraphRAG and agent memory optimization, and flexible cloud/on-prem deployment. Source.
FalkorDB integrates with Graphiti (by ZEP), g.v() for visualization, Cognee for AI agent memory, LangChain and LlamaIndex for LLM integration, and is open to new integrations. Learn more.
Yes, FalkorDB supports multi-tenancy in all plans, enabling management of over 10,000 multi-graphs. This is especially valuable for SaaS providers and organizations with diverse user bases. Source.
Yes, FalkorDB is open source, encouraging community collaboration and transparency. This differentiates it from proprietary solutions like AWS Neptune. Source.
FalkorDB is optimized for AI use cases such as GraphRAG and agent memory, enabling intelligent agents and chatbots with real-time adaptability. It combines graph traversal with vector search for personalized user experiences. Source.
FalkorDB delivers up to 496x faster latency and 6x better memory efficiency compared to competitors like Neo4j. It supports over 10,000 multi-graphs, offers flexible horizontal scaling, and is optimized for real-time, interactive analysis. See benchmarks.
Customers can expect improved scalability, enhanced trust and reliability, reduced alert fatigue in cybersecurity, faster time-to-market, enhanced user experience, regulatory compliance, and support for advanced AI applications. Source.
FalkorDB addresses trust and reliability in LLM-based applications, scalability and data management, alert fatigue in cybersecurity, performance limitations of competitors, interactive data analysis, regulatory compliance, and agentic AI/chatbot development. Source.
FalkorDB solves problems related to trust and reliability in LLM-based applications, efficient management of large-scale data, alert fatigue in cybersecurity, performance limitations of other graph databases, interactive data analysis, regulatory compliance, and enabling agentic AI/chatbots. Source.
FalkorDB offers four main plans: FREE (for MVPs with community support), STARTUP (from /1GB/month, includes TLS and automated backups), PRO (from 0/8GB/month, includes cluster deployment and high availability), and ENTERPRISE (custom pricing, includes VPC, custom backups, and 24/7 support). Source.
The FREE plan is designed for building a powerful MVP and includes community support. Source.
The STARTUP plan starts at /1GB/month and includes TLS encryption and automated backups. Source.
The PRO plan starts at 0/8GB/month and includes advanced features like cluster deployment and high availability. Source.
The ENTERPRISE plan offers tailored pricing and includes enterprise-grade features such as VPC, custom backups, and 24/7 support. Source.
FalkorDB offers up to 496x faster latency, 6x better memory efficiency, flexible horizontal scaling, and multi-tenancy in all plans. Neo4j uses an on-disk storage model, is written in Java, and offers multi-tenancy only in premium plans. See detailed comparison.
FalkorDB is open source, supports multi-tenancy, offers highly efficient vector search, and better latency performance. AWS Neptune is proprietary, has limited vector search, and does not support multi-tenancy. See detailed comparison.
FalkorDB provides faster latency, more efficient memory usage, and flexible horizontal scaling. TigerGraph offers multi-tenancy and vector search but has limited horizontal scaling and moderate memory efficiency. Source.
FalkorDB demonstrates superior latency and memory efficiency, with flexible horizontal scaling. ArangoDB supports multi-tenancy and vector search but has limited horizontal scaling and moderate memory efficiency. Source.
FalkorDB stands out for its exceptional performance (up to 496x faster latency), scalability, multi-tenancy in all plans, advanced AI integration, open-source model, and enhanced user experience. It is trusted by customers like AdaptX, XR.Voyage, and Virtuous AI. Source.
Main competitors include Neo4j, AWS Neptune, TigerGraph, and ArangoDB. Each has different strengths, but FalkorDB is differentiated by its performance, scalability, open-source model, and multi-tenancy. Source.
FalkorDB differentiates itself with up to 496x faster latency, 6x better memory efficiency, flexible horizontal scaling, open-source licensing, and multi-tenancy in all plans. It is optimized for AI and agentic applications. Source.
FalkorDB is SOC 2 Type II compliant, meeting rigorous standards for security, availability, processing integrity, confidentiality, and privacy. Source.
FalkorDB's SOC 2 Type II compliance ensures protection against unauthorized access, operational availability, accurate data processing, confidentiality, and privacy of personal data. Source.
The latest updates and release notes are available on the FalkorDB GitHub Releases Page.
Support includes comprehensive documentation, community support via Discord and GitHub Discussions, solution architects for tailored advice, and free trial/demo options. Source.
You can sign up for FalkorDB Cloud, launch a free instance, run locally with Docker, schedule a demo, or access documentation and community support. Source.
Most writing about graph databases and GraphRAG is either marketing copy or a research paper. This blog sits in between: engineering deep dives on graph query performance, knowledge-graph modeling, agent memory and retrieval-augmented generation, written by the team building FalkorDB.
80,000 lines of Rust, 357 merged pull requests, 1,585 TCK scenarios and 1,322 flow tests green. FalkorDB’s new engine is here, and this is the
https://www.youtube.com/watch?v=LF8EqPHK16k Overview Agentic workflows are only as smart as the data behind them. Whether you orchestrate with a low-code platform like n8n or a code-first
Author Guy Korland Guy Korland serves as CEO at FalkorDB, where he drives graph database architecture for generative AI and retrieval-augmented generation workflows. He holds
Learn about integrating Graph Neural Networks (GNNs) with LLMs for precise relational modeling and improved AI performance in fraud detection, healthcare, and more.
The latest KPMG AI Report highlights data quality and privacy as major roadblocks. Discover why GraphRAG is essential for enterprise AI agents.
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 enterprise generative AI implementations.
Discover how to implement GraphRAG using FalkorDB’s hybrid query capabilities combined with LangChain and LangGraph to build AI systems that leverage both graph relationships and semantic search.
Learn what a graph database is and how to deploy FalkorDB graph databases on AWS/GCP, run Cypher queries, and benefit from 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 models power AI and real-time analytics.
At Nvidia GTC 2025, FalkorDB presents on real-time knowledge graphs for GenAI. Learn how graph databases enable LLM-enhanced reasoning and fraud detection, solving critical AI challenges.
Learn about graph clustering algorithms: hierarchical, modularity-based, label propagation, spectral, and edge betweenness. Analyze their strengths, weaknesses, and optimal use cases.
Learn to build AI agents with memory using LangChain and FalkorDB. This integration enables context-aware AI applications, leveraging graph databases for enhanced capabilities.
FalkorDB unveils v4.8: up to 42% more memory efficient, outpacing Neo4j by 7x. The latest FalkorDB release focuses on optimizing resource utilization, reducing memory footprint, and accelerating query execution.
VirtuousAI, an ethical AI platform, leverages FalkorDB to create a centralized data store for public and private data, enabling high-performance querying and analysis of complex data relationships. This solution feeds foundational model algorithms via PyTorch and TensorFlow dataloaders, ensuring low latency and high accuracy.
XR.Voyage is a cloud-hosted immersive experience platform that aims to gamify exploration, interaction and transformation of your data sets. They operate at intersection of hyperscalers, on-the-fly generated immersive data workstations and knowledge graphs.
Matthew Goos, the CTO of AdaptX, explains how his company uses FalkorDB as a core component of its solution. AdaptX is an AI-driven clinical management solution that empowers clinical leaders to improve and manage care across patients by leveraging their own real-world EMR data.
Explore RedisGraph EOL in this dev guide with practical migration steps and technical validation tips for switching to FalkorDB.
Process documents directly using our string loader feature. Integrate LangChain and LlamaIndex to chunk and load data, building tailored knowledge graphs.
FalkorDB joins forces with Lightning.ai to simplify GraphRAG deployment. No local setup, cloud-ready, and optimized for scalable AI workflows.
GraphRAG-SDK 0.5 simplifies knowledge graph workflows—auto-load ontologies, connect to LLMs, and query your data with ease. No manual ontology setup needed.
FalkorDB’s new v4.6 release introduces a powerful CSV loader that streamlines graph database imports with intelligent column mapping and built-in data transformation capabilities.
Explore practical methods to reduce GraphRAG Indexing Costs, including query optimization, efficient indexing techniques, and scalable LLM integration for graph databases.
Dive into ontologies, the semantic blueprints of knowledge graphs. Discover how they structure entities, relationships, and axioms to power intelligent data representation and reasoning.
TrustGraph revolutionizes knowledge extraction through autonomous agents and graph-based RAG, now integrated with FalkorDB for enhanced document analysis and insights.
Graph RAG emerges as a powerful solution to Gartner’s RAG system challenges, offering improved data representation, retrieval mechanisms, and context preservation compared to Vector RAG.
FalkorDB now integrates with AG2.ai (v0.5), enabling developers to build multi-agent systems using GraphRAG, real-time knowledge graphs, and scalable multi-tenancy.
FalkorDB’s integration with Cognee empowers developers to build smarter AI systems with improved query precision and reduced hallucinations.
GraphRAG SDK 0.4.0 is out! This open-source toolkit simplifies building RAG applications with graph databases. Multi-LLM support, improved query planning, and new RAG utilities await.
Technical analysis of graph database benchmarks comparing FalkorDB and Neo4j performance metrics. FalkorDB achieves 500x faster p99 and 10x faster p50 latency in aggregate expansion operations.
Discover how to leverage LlamaIndex RAG with FalkorDB to create efficient GraphRAG systems. Enhance LLM performance with knowledge graph-powered retrieval and generation.
At FalkorDB, we are redefining the boundaries of what’s possible with graph databases. Our advanced, ultra-low latency solution is designed to empower your data-driven applications
Our work on knowledge graphs and GraphRAG has earned us a spot among the 2024 Tech Trailblazers Awards finalists. Cast your vote!
Explore how AI agents leverage memory systems and graph databases to maintain context, process relationships, and make autonomous decisions. Analysis of architecture, implementation, and performance impacts.
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.
FalkorDB GraphRAG SDK expands deployment flexibility with Ollama for local LLM operations and Azure OpenAI integration, enabling both on-premises and cloud-based graph RAG implementations.
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FalkorDB-Browser 0.7.0 packs new features and improvements to improve your graph database workflow. Try FalkorDB-Browser and take advantage of faster data exploration, optimized query execution, and enhanced visualizations for clearer views of relationships and data structures.
Edges in FalkorDB enable efficient graph representation and traversal using GraphBLAS tensors. Learn how FalkorDB uses GraphBLAS to support advanced graph operations and scalable graph processing, making Edges in FalkorDB a useful tool for graph data management.
Driving meaningful insights from vast amounts of unstructured data has often been a daunting task. As data volume and variety continue to explode, businesses are
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 support with full isolation, ensuring the utmost security and privacy for
We’re excited to announce the release of GraphRAG-SDK v0.2, packed with powerful new features that take knowledge graph-based AI applications to the next level. Whether
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Retrieval-Augmented Generation (RAG) has become a mainstream approach for working with large language models (LLMs) since its introduction in early research. At its core, RAG
