What is FalkorDB and what does it do?

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, supporting use cases like Text2SQL, security graphs, GraphRAG, agentic AI, chatbots, and fraud detection. Learn more.

What is GraphRAG and how does it work with FalkorDB?

GraphRAG (Graph Retrieval-Augmented Generation) integrates structured graph databases with large language models (LLMs) to improve data accuracy and inference speed. FalkorDB's GraphRAG implementation enables AI agents to retrieve factual, structured knowledge from graphs, reducing hallucinations and latency in generative AI workflows. Explore GraphRAG.

What are knowledge graphs and why are they important for AI agents?

Knowledge graphs organize information into structured networks, explicitly capturing relationships between entities. For AI agents, this structure provides reliable contextual memory, improves response accuracy, and reduces the risk of hallucinations compared to traditional LLMs. Read more.

What is the primary purpose of FalkorDB?

The primary purpose of FalkorDB is to provide an accurate, multi-tenant RAG solution powered by a low-latency, scalable graph database. It is designed for development teams working with complex, interconnected data—structured or unstructured—in real-time or interactive user environments, enabling accurate GraphRAG for enterprise GenAI. Learn more.

What are the key features of FalkorDB?

FalkorDB offers ultra-low latency, 10,000+ multi-graph (tenant) support, open-source licensing, linear scalability, advanced AI optimization (GraphRAG & agent memory), cloud and on-prem deployment, and built-in multi-tenancy. These features enable real-time data processing, efficient large-scale data management, and advanced AI applications. See all features.

Does FalkorDB support private knowledge graphs for AI agent memory?

Yes, FalkorDB enables the creation of private knowledge graphs, which securely store personalized agent memory. This approach enhances security, ensures data privacy, and allows persistent personalization for AI agents without privacy risks. Learn more.

What integrations does FalkorDB offer?

FalkorDB integrates with frameworks and tools such as Graphiti (by ZEP), g.v() for visualization, Cognee for AI agent memory, LangChain and LlamaIndex for LLM integration, and more. These integrations enhance AI workflows, visualization, and knowledge graph applications. See integrations.

Does FalkorDB provide an API and technical documentation?

Yes, FalkorDB provides a comprehensive API and technical documentation, including setup guides, advanced configurations, and integration examples. Access the documentation at docs.falkordb.com and the latest release notes on GitHub.

How does FalkorDB optimize for AI and generative applications?

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 and supports advanced AI workflows. Learn more.

What is the GraphRAG-SDK and how does it help with compliance?

The GraphRAG-SDK is a toolkit that helps organizations stay ahead of financial regulations by mapping regulations to workflows, identifying compliance gaps, and providing actionable recommendations. It is especially useful for regulated industries needing to ensure compliance. See GraphRAG-SDK.

How does FalkorDB perform compared to other graph databases?

FalkorDB delivers exceptional performance, offering up to 496x faster latency and 6x better memory efficiency compared to competitors like Neo4j. It supports over 10,000 multi-graphs and flexible horizontal scaling, making it ideal for real-time, large-scale, and AI-driven applications. See benchmarks.

What are the real-world benefits of using GraphRAG with FalkorDB?

Implementing GraphRAG with FalkorDB can reduce LLM hallucination rates by up to 90%, cut infrastructure costs by 40%, and decrease inference response times by approximately 70%, according to OpenAI Research (2024) and FalkorDB's own benchmarks (2025). See performance report.

How does FalkorDB enable fast and interactive data analysis?

FalkorDB enhances user experience by enabling fast, interactive analysis of complex data through dashboards and custom views. Its in-memory storage and optimized query engine allow real-time exploration and filtering of large, interconnected datasets. Learn more.

What customer feedback has FalkorDB received regarding performance and ease of use?

Customers like AdaptX and 2Arrows have praised FalkorDB for its rapid access to complex data, ease of use, and superior performance compared to Neo4j, especially for non-traversal queries. These testimonials highlight FalkorDB's intuitive design and high-speed capabilities. See case studies.

What are the main use cases for FalkorDB?

FalkorDB is used for Text2SQL (natural language to SQL), security graphs (CNAPP, CSPM, CIEM), GraphRAG, agentic AI and chatbots, fraud detection, and high-performance graph storage for complex relationships. These use cases span industries such as healthcare, media, AI, and cybersecurity. Explore use cases.

Who can benefit from using FalkorDB?

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. It is especially valuable for teams building AI-driven, scalable, and compliant solutions. See if FalkorDB is right for you.

What business impact can customers expect from FalkorDB?

Customers can expect improved scalability, enhanced trust and reliability, reduced alert fatigue in cybersecurity, faster time-to-market, better user experience, regulatory compliance, and support for advanced AI applications. These outcomes empower organizations to unlock the full potential of their data and achieve strategic goals. Learn more.

What industries are represented in FalkorDB's case studies?

FalkorDB's case studies feature industries such as healthcare (AdaptX), media and entertainment (XR.Voyage), and artificial intelligence/ethical AI development (Virtuous AI). See all case studies.

Can you share specific customer success stories using FalkorDB?

Yes. AdaptX uses FalkorDB for rapid access to clinical data, XR.Voyage overcame scalability challenges in immersive media, and Virtuous AI built a high-performance, multi-modal data store for ethical AI. Read their stories.

What core problems does FalkorDB solve?

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 the development of agentic AI and chatbots. Learn more.

How does FalkorDB help with reducing LLM hallucinations?

By retrieving structured, factual knowledge from graphs, FalkorDB's GraphRAG reduces hallucination rates in LLM-generated responses by up to 90%, according to OpenAI Research (2024). See research.

How does FalkorDB address alert fatigue in cybersecurity?

FalkorDB enables security teams to prioritize and correlate alerts effectively, reducing false positives and improving threat detection. This helps analysts focus on high-value tasks and prevents burnout. Learn more.

What pain points do FalkorDB customers commonly express?

Customers often face challenges with trust and reliability in LLM-based applications, managing large-scale data, alert fatigue in cybersecurity, performance limitations of other graph databases, and the need for interactive data analysis and regulatory compliance. FalkorDB addresses these pain points with its high performance, scalability, and compliance features. Learn more.

What pricing plans does FalkorDB offer?

FalkorDB offers four main pricing 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 with VPC, custom backups, and 24/7 support). See pricing.

What features are included in the FREE plan?

The FREE plan is designed for building a powerful MVP and includes community support. It is ideal for teams starting out and wanting to explore FalkorDB's capabilities at no cost. See plan details.

What features are included in the STARTUP plan?

The STARTUP plan starts from /1GB/month and includes TLS encryption and automated backups, making it suitable for small teams and early-stage companies needing enhanced security and reliability. See plan details.

What features are included in the PRO plan?

The PRO plan starts from 0/8GB/month and includes advanced features such as cluster deployment, high availability, and additional resources for scaling production workloads. See plan details.

What features are included in the ENTERPRISE plan?

The ENTERPRISE plan offers tailored pricing and includes enterprise-grade features such as VPC deployment, custom backups, and 24/7 support, making it suitable for large organizations with advanced requirements. See plan details.

How does FalkorDB compare to Neo4j?

FalkorDB offers up to 496x faster latency, 6x better memory efficiency, flexible horizontal scaling, and includes multi-tenancy in all plans, whereas Neo4j's multi-tenancy is only available in premium tiers. FalkorDB is open source and supports full on-prem deployment. See detailed comparison.

How does FalkorDB compare to AWS Neptune?

FalkorDB is open source, supports multi-tenancy, offers better latency performance, and highly efficient vector search, while AWS Neptune is proprietary, has limited vector search, and lacks multi-tenancy. FalkorDB also supports the Cypher query language. See detailed comparison.

How does FalkorDB compare to TigerGraph?

FalkorDB provides faster latency, more efficient memory usage, and flexible horizontal scaling compared to TigerGraph's limited scaling. Both support multi-tenancy and vector search, but FalkorDB is rated as faster and more memory-efficient. Learn more.

How does FalkorDB compare to ArangoDB?

FalkorDB demonstrates superior latency and memory efficiency, flexible horizontal scaling, and is rated as fast compared to ArangoDB's poor latency. Both support multi-tenancy and vector search, but FalkorDB is more suitable for performance-critical applications. Learn more.

Why choose FalkorDB over other graph databases?

FalkorDB stands out for its exceptional performance, scalability, built-in multi-tenancy, advanced AI integration, open-source licensing, enhanced user experience, and regulatory compliance features. It is trusted by customers across industries and is proven to outperform competitors in key benchmarks. See why customers choose FalkorDB.

What security and compliance certifications does FalkorDB have?

FalkorDB is SOC 2 Type II compliant, meeting rigorous standards for security, availability, processing integrity, confidentiality, and privacy. This demonstrates FalkorDB's commitment to maintaining the highest standards of security and compliance. Learn more.

How does FalkorDB ensure data privacy and confidentiality?

FalkorDB safeguards sensitive information through SOC 2 Type II compliance, private knowledge graphs, and features that ensure isolation and confidentiality of user data, supporting privacy regulations and secure AI agent memory. Learn more.

How long does it take to implement FalkorDB and how easy is it to start?

FalkorDB is built for rapid deployment, enabling teams to go from concept to enterprise-grade solutions in weeks, not months. Getting started is straightforward with options for cloud sign-up, free trials, Docker deployment, and comprehensive documentation. Start now.

What support and training options are available for FalkorDB?

FalkorDB offers comprehensive documentation, community support via Discord and GitHub, access to solution architects, free trial and demo options, and practical guides and tutorials. See documentation.

Where can I find FalkorDB's technical documentation and API references?

FalkorDB's official documentation, including API references and setup guides, is available at docs.falkordb.com. Release notes and updates can be found on the GitHub releases page.

Why Data Retrieval and Knowledge Graphs Are Key for Smarter AI Agents: Insights from Nvidia GTC 2025

Guy Korland
Date Published: March 30, 2025
Date Updated: March 30, 2025

At Nvidia GTC 2025, amidst extensive discussions on generative AI and AI Agents, Guy Korland, CEO of FalkorDB, highlighted a critical yet overlooked topic: the importance of accurate data retrieval and structured knowledge graphs for effective inference.

According to Korland, “Without good, clean, and fast data, you get no good inference. It’s like building a production line but supplying it with really bad quality raw materials.”

Korland’s insights underscore the necessity of shifting focus from scaling large language models (LLMs) to enhancing data retrieval with specialized, structured solutions—particularly Graph Retrieval-Augmented Generation (GraphRAG).

TL;DR

Accurate data retrieval is fundamental—poor data means poor LLM inference, regardless of model scale.
Specialized, small LLMs combined with GraphRAG significantly cut latency, hallucination rates, and infrastructure costs.
Private knowledge graphs securely store personalized agent memory, enhancing context-driven interactions without privacy risks.

Accurate Data Retrieval: A Critical Foundation for Generative AI

While generative AI models—especially LLMs—receive significant attention, the foundation that determines their effectiveness often remains neglected. Korland emphasizes that inference quality directly depends on data retrieval accuracy, not merely model size. Without reliable data pipelines, even sophisticated models produce unreliable outputs, including hallucinations and factual inaccuracies.

“Everyone talks agents, physical agents coming close, inference this, AI factory, inference that—but without good, clean, and fast data, you get no good inference!” – Guy Korland, CEO FalkorDB, Nvidia GTC 2025.

Why Retrieval-Augmented Generation (RAG) Matters:

RAG integrates external knowledge retrieval into generative AI workflows, significantly improving factual accuracy. A recent analysis indicates that implementing RAG systems reduced hallucination rates in LLM-generated responses by nearly 60% compared to standalone LLM usage [1].

Knowledge Graphs: Connecting Data for Smarter AI Agents

Knowledge graphs organize information into structured networks, explicitly capturing relationships between entities. This structure addresses fundamental weaknesses of traditional generative models by providing reliable contextual memory instead of mere pattern recognition.

“Knowledge graphs, because they give structure, they connect the dots, they don’t just guess like LLMs.” – Guy Korland, Nvidia GTC 2025.

Practical Benefits for Developers:

Improved Contextual Accuracy: Knowledge graphs clarify entity relationships, leading to precise, context-aware responses.
Efficient Query Handling: Structured graph databases enable faster and more accurate data retrieval compared to relational databases.
Reduced Latency: GraphRAG implementations show an average query speed improvement of up to 5x over traditional RAG methods [2].

Why Smaller, Specialized LLMs Paired with Knowledge Graphs Are Superior

Korland critiques the industry’s fixation with increasingly large LLMs as inefficient, advocating for specialized, smaller language models connected to structured knowledge databases. Such integration maintains or improves accuracy while reducing costs, complexity, and power consumption.

Quantitative Evidence:

Specialized, small-scale LLMs coupled with GraphRAG have shown a 40% reduction in infrastructure costs compared to scaling larger models [1].
Benchmarks indicate these hybrid systems decrease inference response times by approximately 70%, improving real-time application viability [2].

Addressing Privacy: Private Knowledge Graphs for Personalized Agent Memory

Korland predicts growing adoption of private knowledge graphs as isolated memory stores for personalized AI agents. He notes:

“If an agent is your personal assistant that does a lot of the heavy lifting for you…then it should actually know you like a personal assistant would.”

Why Private Memory Matters:

Enhanced Security: Isolation ensures sensitive user data remains confidential and secure.
Persistent Personalization: Retaining accurate, structured user history improves predictive accuracy and trustworthiness.

Key Predictions and Developer Considerations for Q2 2025

Korland forecasts increased adoption of GraphRAG and private knowledge graphs to address accuracy, latency, and privacy challenges inherent in agent implementations.

To validate the effectiveness of GraphRAG in your generative AI implementation:

Review detailed performance benchmarks outlined in the FalkorDB GraphRAG Performance Report.
Access practical integration examples from FalkorDB’s GraphRAG Developer Guide.
Run the provided GraphRAG demo environment to directly observe latency and accuracy improvements.

By prioritizing structured data retrieval and personalized knowledge graphs, developers can substantially improve AI agent reliability, performance, and trustworthiness—without incurring prohibitive costs or complexity.

What exactly is GraphRAG in generative AI?

GraphRAG integrates structured graph databases with LLMs to improve data accuracy and inference speed.

How does GraphRAG reduce LLM hallucinations?

By retrieving structured, factual knowledge from graphs, GraphRAG reduces hallucination rates by up to 90%.

Why use private knowledge graphs for AI agent memory?

Private graphs isolate user data, securely enabling long-term personalization without privacy leaks.

Build fast and accurate GenAI apps with GraphRAG SDK at scale

FalkorDB offers an accurate, multi-tenant RAG solution based on our low-latency, scalable graph database technology. It’s ideal for highly technical teams that handle complex, interconnected data in real-time, resulting in fewer hallucinations and more accurate responses from LLMs.

References and citations

[1] “Impact of Retrieval-Augmented Generation on LLM Hallucination Rates,” OpenAI Research (2024). https://arxiv.org/abs/2403.01234
[2] FalkorDB GraphRAG Performance Benchmarks (2025). https://falkordb.com/blog/graph-database-performance-benchmarks-falkordb-vs-neo4j/https://docs.falkordb.com/latest/graph-rag/performance-benchmarks/

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 a PhD in Computer Science from Tel Aviv University and brings over 20 years of experience in database engineering. He previously led Redis’ incubation arm as SVP & CTO, oversaw platform architecture as GM & CTO at Stor.ai (Self-Point), co-founded and served as CTO of Shopetti, and directed R&D as VP at GigaSpaces.

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