# FalkorDB

> Ultra-fast, Multi-tenant Graph Database


## Posts

- [Why the KPMG AI Report Demands GraphRAG for Enterprise AI](https://www.falkordb.com/blog/kpmg-ai-report-graphrag-ai-agents/): The latest KPMG AI Report highlights data quality and privacy as major roadblocks. Discover why GraphRAG is essential for enterprise AI agents.
- [ACID Transactions: Isolation Levels and Data Consistency](https://www.falkordb.com/blog/glossary/acid-transactions-isolation-data-consistency/): Understand ACID transactions. Learn how isolation levels impact data consistency in your applications, plus choosing the right model.
- [VectorRAG vs GraphRAG: Technical Challenges in Enterprise Generative AI](https://www.falkordb.com/blog/vectorrag-vs-graphrag-technical-challenges-enterprise-ai-march25/): 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.
- [A Guide to Implementing a GraphRAG Workflow Using FalkorDB, LangChain and LangGraph](https://www.falkordb.com/blog/graphrag-workflow-falkordb-langchain/): 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.
- [How to Deploy and Use a Graph Database](https://www.falkordb.com/blog/graph-database-deployment-guide/): 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: Modern Architecture for Scalable Apps](https://www.falkordb.com/blog/nosql-database-modern-architecture-scalability/): 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.
- [FalkorDB at NVIDIA GTC 2025: Driving Real-Time Knowledge Graphs for GenAI Applications](https://www.falkordb.com/news-updates/falkordb-nvidia-gtc-2025-real-time-knowledge-graphs/): 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.
- [Graph Clustering Algorithms: Usage and Comparison](https://www.falkordb.com/blog/graph-clustering-algorithms-comparison/): Learn about graph clustering algorithms: hierarchical, modularity-based, label propagation, spectral, and edge betweenness. Analyze their strengths, weaknesses, and optimal use cases.
- [Build AI Agents with Memory: LangChain + FalkorDB](https://www.falkordb.com/blog/building-ai-agents-with-memory-langchain/): 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 V4.8: Competing vendors require 7x the memory to hold the same dataset](https://www.falkordb.com/news-updates/v4-8-7x-more-efficient/): 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.
- [How Virtuous AI Created a High-performance, Multi-modal Data Store for Ethical AI Development​](https://www.falkordb.com/case-studies/how-virtuous-ai-created-a-high-performance-multi-modal-data-store-for-ethical-ai-development/): 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.
- [How XR.Voyage Overcame Scalability and Data management Challenges with FalkorDB](https://www.falkordb.com/case-studies/how-xr-voyage-overcame-scalability-and-data-management-challenges-with-falkordb/): 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.
- [How AdaptX Uncovers Hidden Potential in Their Clinical Data](https://www.falkordb.com/case-studies/how-adaptx-uncovers-hidden-potential-in-their-clinical-data/): 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.
- [Prepare for RedisGraph EOL: A Practical Guide to Transition to FalkorDB](https://www.falkordb.com/blog/redisgraph-eol-migration-guide/): Explore RedisGraph EOL in this dev guide with practical migration steps and technical validation tips for switching to FalkorDB.
- [Streamline Document Processing Pipelines with FalkorDB's String Loader](https://www.falkordb.com/news-updates/string-loader-knowledge-graphs/): Process documents directly using our string loader feature. Integrate LangChain and LlamaIndex to chunk and load data, building tailored knowledge graphs.
- [Skip the Setup: Deploy GraphRAG Apps with FalkorDB on Lightning.ai](https://www.falkordb.com/news-updates/falkordb-lightning-ai-integration-graphrag/): FalkorDB joins forces with Lightning.ai to simplify GraphRAG deployment. No local setup, cloud-ready, and optimized for scalable AI workflows.
- [New Release: GraphRAG-SDK 0.5 – Simplifying Knowledge Graph Integration for LLMs](https://www.falkordb.com/news-updates/graphrag-sdk-0-5-knowledge-graph-integration/): 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 V4.6 Introduces Load CSV](https://www.falkordb.com/news-updates/falkordbs-v4-6-introduces-load-csv/): 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.
- [Reducing High GraphRAG Indexing Costs: Strategies for Efficient Graph Database Management](https://www.falkordb.com/blog/reduce-graphrag-indexing-costs/): Explore practical methods to reduce GraphRAG Indexing Costs, including query optimization, efficient indexing techniques, and scalable LLM integration for graph databases.
- [Understanding Ontologies and Knowledge Graphs](https://www.falkordb.com/blog/understanding-ontologies-knowledge-graph-schemas/): Dive into ontologies, the semantic blueprints of knowledge graphs. Discover how they structure entities, relationships, and axioms to power intelligent data representation and reasoning.
- [Fed Up with Broken RAG? TrustGraph's agentic system now integrates with FalkorDB for knowledge extraction](https://www.falkordb.com/news-updates/trustgraph-autonomous-knowledge-extraction/): TrustGraph revolutionizes knowledge extraction through autonomous agents and graph-based RAG, now integrated with FalkorDB for enhanced document analysis and insights.
- [Overcome Gartner’s Top RAG Challenges with FalkorDB (5 Tips)](https://www.falkordb.com/blog/graph-rag-vs-vector-rag-solving-gartner-challenges/): 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-AG2.ai Integration: Build Multi-Agent Systems](https://www.falkordb.com/news-updates/ag2-integration-multi-agent-systems/): 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 Integrates with Cognee for Smarter AI Systems](https://www.falkordb.com/news-updates/falkordb-integrates-with-cognee-for-enhanced-ai-precision/): FalkorDB's integration with Cognee empowers developers to build smarter AI systems with improved query precision and reduced hallucinations.
- [GraphRAG SDK 0.4.0: Simplify RAG with Graph Databases](https://www.falkordb.com/news-updates/graphrag-sdk-release-simplifies-rag-with-graph-databases/): 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.
- [FalkorDB vs Neo4j: Graph Database Performance Benchmarks](https://www.falkordb.com/blog/graph-database-performance-benchmarks-falkordb-vs-neo4j/): 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.
- [LlamaIndex RAG: Build Efficient GraphRAG Systems](https://www.falkordb.com/blog/llamaindex-rag-implementation-graphrag/): Discover how to leverage LlamaIndex RAG with FalkorDB to create efficient GraphRAG systems. Enhance LLM performance with knowledge graph-powered retrieval and generation.
- [Welcome to FalkorDB - The Future of Graph Databases](https://www.falkordb.com/blog/the-future-of-graph-databases/): At FalkorDB, we are redefining the boundaries of what's possible with graph databases.
- [2024 Tech Trailblazers Awards](https://www.falkordb.com/news-updates/2024-tech-trailblazers-awards/): Our work on knowledge graphs and GraphRAG has earned us a spot among the 2024 Tech Trailblazers Awards finalists. Cast your vote!
- [AI Agents: Memory Systems and Graph Database Integration](https://www.falkordb.com/blog/ai-agents-memory-systems/): 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.
- [FalkorDB vs Neo4j: Choosing the Right Graph Database for AI](https://www.falkordb.com/blog/falkordb-vs-neo4j-for-ai-applications/): When building AI-driven systems, FalkorDB vs Neo4j graph databases offer different advantages. Find the best fit for your AI needs.
- [Knowledge Graph Visualization: Uses, Challenges and Benefits](https://www.falkordb.com/blog/knowledge-graph-visualization-insights/): Knowledge graph visualization offers deep insights, enhancing decision-making for AI applications with FalkorDB.
- [FalkorDB GraphRAG-SDK Adds Ollama and Azure OpenAI Support](https://www.falkordb.com/news-updates/graphrag-sdk-ollama-azure-openai-support/): 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.
- [Vector Database vs. Graph Database for AI: When to Use Each and When to Combine Them](https://www.falkordb.com/blog/vector-database-vs-graph-database/): Vector databases retrieve semantically similar content from embeddings. Graph databases retrieve explicit entities and relationships through traversals.
- [Enhancing AI Agents Memory with Memary](https://www.falkordb.com/blog/enhance-ai-agents-memory-memary/): Memary is an open-source memory layer designed for AI agents, focusing on emulating human memory processes to enhance agent capabilities. This technical approach has resonated with developers
- [Knowledge Graph Tools: What They Are and Their Benefits](https://www.falkordb.com/blog/how-to-use-knowledge-graph-tools-for-ai/): Knowledge graphs have become a game-changer in building Retrieval-Augmented Generation (RAG) applications, often referred to as GraphRAG.
- [FalkorDB-Browser Version 0.7.0](https://www.falkordb.com/blog/falkordb-browser-v070-update/): 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](https://www.falkordb.com/blog/edges-in-falkordb/): 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.
- [How to Build a Knowledge Graph: A Step-by-Step Guide](https://www.falkordb.com/blog/how-to-build-a-knowledge-graph/): Driving meaningful insights from vast amounts of unstructured data has often been a daunting task.
- [Introducing FalkorDB-Cloud Cluster Support on GCP: Multi-Graph, Multi-Tenant, and Scale-Out Power!](https://www.falkordb.com/blog/falkordb-cloud-cluster-support-on-gcp/): We’re thrilled to announce that FalkorDB-Cloud has added full support for clusters on Google Cloud Platform (GCP)!
- [GraphRAG-SDK v0.2](https://www.falkordb.com/blog/introducing-graphrag-sdk-v0-2-expanding-ai-and-knowledge-graph-horizons/): 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.
- [Code Visualization: Benefits, Best Practices & Popular Tools](https://www.falkordb.com/blog/code-visualization/): Modern software architectures are complex systems of interconnected components.
- [Advanced RAG Techniques: What They Are & How to Use Them](https://www.falkordb.com/blog/advanced-rag/): Retrieval-Augmented Generation (RAG) has become a mainstream approach for working with large language models (LLMs) since its introduction in early research.
- [Migrate from Relational Database to Graph Database](https://www.falkordb.com/blog/relational-database-to-graph-database/): Discover the process of migrating from a relational database to a graph database. This guide covers schema analysis, data transformation, and optimization techniques for AI/ML workflows.
- [What is GraphRAG? Different Types, Limitations, and When to Use](https://www.falkordb.com/blog/what-is-graphrag/): Retrieval-augmented generation (RAG) has emerged as a powerful technique to address key limitations of large language models (LLMs).
- [CodeGraph: Build Queryable Knowledge Graphs from Code](https://www.falkordb.com/blog/code-graph/): CodeGraph maps any Git repo into a queryable Knowledge Graph using AST parsing, FalkorDB, and LLMs—so you can trace calls, find dead code, and query in plain English.
- [Knowledge Graph and LLM Integration: Benefits & Challenges](https://www.falkordb.com/blog/knowledge-graph-llm/): In today’s AI landscape, there are two key technologies that are transforming machine understanding, reasoning, and natural language processing: Large Language Models (LLMs) and Knowledge Graphs (KGs).
- [Knowledge graph vs vector database: Which one to choose?](https://www.falkordb.com/blog/knowledge-graph-vs-vector-database/): Large Language Models (LLMs) are powerful Generative AI models that can learn statistical relationships between words, which enables them to generate human-like text, translate languages, write different kinds of creative content, and answer questions in an informative way.
- [Beyond Rows and Columns: Exploring the Missing Third Dimension](https://www.falkordb.com/blog/beyond-rows-and-columns-exploring-the-missing-third-dimension/): Row-oriented databases are optimized for retrieving and updating complete records.
- [Efficient State Machine Modeling Using FalkorDB](https://www.falkordb.com/blog/state-machine-simulator/): The latest release of FalkorDB V4.0.5 includes a new ability to easily clone graphs.
- [Survey: GraphRAG and Knowledge Graphs for Large Language Models](https://www.falkordb.com/blog/surevy-graphrag-and-knowledge-graphs-in-large-language-models/): The seminal paper “Unifying Large Language Models and Knowledge Graphs: A Roadmap” published on June 14, 2023, presents a comprehensive framework for integrating the emergent capabilities of Large Language Models (LLMs) with the structured knowledge representation of Knowledge Graphs (KGs) Authored by Shirui Pan, Linhao Luo, Yufei Wang, Chen Chen, Jiapu Wang, and Xindong Wu, the paper outlines three general frameworks for this unification: KG-enhanced LLMs, LLM-augmented KGs, and Synergized LLMs + KGs.
- [How to Build a Movie Recommendation System Powered by Knowledge Graph FalkorDB](https://www.falkordb.com/blog/how-to-build-a-movie-recommendation-system-powered-by-knowledge-graph-falkordb/): When you surf through Amazon Prime, you are met with a screen that lists the top movies for you. Netflix and Hulu do the same.
- [Building Advanced RAG Applications Using FalkorDB, LangChain, Diffbot API, and OpenAI](https://www.falkordb.com/blog/rag-applications-using-falkordb-langchain-diffbot-api-and-openai/): The introduction of the Knowledge Graph Database in the realm of evolving Large Language Models has changed the way RAG applications are getting built.
- [FalkorDB 4.0 Beta released - Major improvements and Critical bug fixes!](https://www.falkordb.com/blog/falkordb-4-0-beta-released-major-improvements-and-critical-bug-fixes/): We’re excited to announce that FalkorDB 4.0 Beta is now available for download and testing.
- [Released! FalkorDB 4.0-a1 - Vector Search Index & Bolt Protocol](https://www.falkordb.com/blog/released-falkordb-4-0-a1-vector-search-index-bolt-protocol/): We are thrilled to announce the release of FalkorDB version 4.0.0-a1, a major update that brings two exciting features to our graph database platform.
- [RAG battle: vector database vs knowledge graph](https://www.falkordb.com/blog/rag-battle-vector-database-vs-knowledge-graph/): The potential of using LLMs for knowledge extraction is nothing less than amazing, in this last couple of months we've seen a rush towards integrating large language models to perform a variety of tasks, e.g.
- [Scale Out](https://www.falkordb.com/blog/scale-out/): The ability to scale out a database is crucial, in this short post I would like to walk through how FalkorDB scales out.
- [What is RAG (Retrieval Augmented Generation)?](https://www.falkordb.com/blog/what-is-a-rag-retrieval-augmented-generation/): In this blog post, I will explain what is RAG, why it is useful, and how to build it using Vector Database and Knowledge Graph as a leading option for RAG.
- [Building a Q&A System](https://www.falkordb.com/blog/building-a-qa-system/): If you are looking for a simple way to build a Q&A system based on your knowledge graph, you should check out LangChain.
- [Building and Querying a Knowledge Graph](https://www.falkordb.com/blog/building-and-querying-a-knowledge-graph/): If you are looking for a simple way to create and query a knowledge graph based on your internal documents, you should check out LlamaIndex.
- [Building & Querying a Knowledge Graph from Unstructured Data](https://www.falkordb.com/blog/building-querying-a-knowledge-graph-from-unstructured-data/): Diffbot API, FalkorDB, and LangChain are a great combination for building intelligent applications that can understand and answer questions from unstructured data.
- [Your Agent Gets Dumber the Longer It Runs. Here's How a Graph Fixes It.](https://www.falkordb.com/blog/agent-memory-for-long-running-agents/): TL;DR. Agent memory gets less accurate, slower, more expensive and less private the longer an agent runs.
- [GraphRAG vs. Vector RAG: Where the Accuracy Gap Actually Comes From](https://www.falkordb.com/reference/graphrag-vs-vector-rag-accuracy-gap/): FalkorDB scores 71.48 on GraphRAG-Bench against 55.39 for reranked vector RAG. Where the 16-point gap comes from, and when it matters.
- [The Hidden Reason Most RAG Projects Never Reach Production](https://www.falkordb.com/reference/why-rag-projects-never-reach-production/): Stale indexes, weak entity resolution, no traceability, poor retrieval on relationship-heavy questions. The four reasons RAG stalls before production.
- [Why Enterprise AI Agents Need a Context Layer, Not a Bigger Context Window](https://www.falkordb.com/reference/enterprise-ai-agents-context-layer/): Bigger context windows do not fix stale, ungoverned, untraceable retrieval. Why enterprise AI agents need a knowledge graph as a persistent context layer.
- [Shortest path at any distance: Dijkstra, A*, and CCH in FalkorDB](https://www.falkordb.com/blog/shortest-path-dijkstra-a-star-cch/): Most shortest path algorithms get slower as two points get farther apart.
- [Finding Shortest Paths at Scale: CCH, Dijkstra, and A* in FalkorDB](https://www.falkordb.com/news-updates/shortest-paths-at-scale-cch-dijkstra-a-star/): Roi Lipman, CTO & Co-founder, and Gabriel Gomez, Core Software Developer, break down how FalkorDB finds shortest paths at scale.
- [How Builders at Hack for Humanity Used FalkorDB to Solve Real Community Problems](https://www.falkordb.com/blog/how-builders-at-hack-for-humanity-used-falkordb-to-solve-real-community-problems/): Hack for Humanity is a global hackathon.
- [Announcing FalkorDB 6.0](https://www.falkordb.com/blog/announcing-falkordb-6-0/): FalkorDB 6.0 is the first release built on the new Rust engine. 39% fewer CPU instructions, 226 bugs closed, and existing Cypher queries work without changes.
- [How Graph Architecture Grounds RAG and Prevents Hallucinations](https://www.falkordb.com/blog/how-graph-architecture-grounds-rag-and-prevents-hallucinations/): Preventing LLM hallucinations in a RAG system is usually treated as a model problem. It is not. An LLM that hallucinates isn't broken.
- [Memory Meets Motion: The FalkorDB Standouts](https://www.falkordb.com/blog/memory-meets-motion-the-falkordb-standouts/): On August 3, 2026, builders gathered at Frontier Tower in San Francisco for Memory Meets Motion, an eight-hour hackathon built around one mandate: every project had to make real, load-bearing use of four sponsor technologies: FalkorDB as the memory layer, RocketRide.ai as the execution and orchestration engine, Guild.ai for multi-agent coordination, and LaserData as the real-time event stream.
- [Your Agent's Memory Problem Is a Retrieval Architecture Problem](https://www.falkordb.com/blog/ai-agent-memory-retrieval-architecture/): Your AI agent ships. Testing looks fine.
- [10 Network Analysis Applications Shaping Industries in 2026](https://www.falkordb.com/blog/network-analysis-applications/): You're probably sitting on connected data right now. The real question is whether your team can turn it into decisions.
- [Your n8n Agent Has Amnesia. Give It a Knowledge Graph](https://www.falkordb.com/blog/your-n8n-agent-has-amnesia-give-it-a-knowledge-graph/): The whole machine, documented: point n8n at hosted GraphRAG, hand an AI Agent five graph tools, publish a chat page, and keep a FalkorDB knowledge graph in lock-step with every merged PR.
- [Rewriting FalkorDB in Rust: Make It Work, Make It Stable, Then Make It Fast](https://www.falkordb.com/blog/rewriting-falkordb-in-rust/): 80,000 lines of Rust, 357 merged pull requests, 1,585 TCK scenarios and 1,322 flow tests green.
- [Master Data Management: A Practical Guide for AI](https://www.falkordb.com/blog/master-data-management/): Your support team sees one version of a customer in Salesforce, another in HubSpot, and a third in the ERP.
- [Maximal Independent Set: Graph Database Use Cases](https://www.falkordb.com/blog/maximal-independent-set/): Some problems announce themselves as graph problems. Most don't.
- [Powering Agentic Workflows with a Knowledge Graph for n8n and LangGraph](https://www.falkordb.com/news-updates/powering-agentic-workflows-with-a-knowledge-graph-for-n8n-and-langgraph/): Agentic workflows are only as smart as the data behind them.
- [Supply Chain Database Guide for AI and Analytics Workloads](https://www.falkordb.com/blog/supply-chain-database/): A late shipment lands on your desk and three systems tell three different stories. ERP says it left on time. The warehouse system says the pallet never moved.
- [How to Build Chatbots That Actually Work in Production](https://www.falkordb.com/blog/how-to-build-chatbots/): You're probably staring at a bot that looks fine in a demo and then falls apart the moment real users ask messy questions, switch topics midstream, or expect it to remember what happened two turns ago.
- [Encryption in Transit: A Practical Guide for Modern Systems](https://www.falkordb.com/blog/encryption-in-transit/): You have probably already done the easy part. The browser shows a lock, the API gateway has a certificate, and the compliance checklist says TLS is enabled.
- [10 Top Python Graph Libraries: A 2026 Guide](https://www.falkordb.com/blog/python-graph-libraries/): You're staring at a graph-shaped problem, and the first choice is already awkward.
- [Pattern Matching in SQL](https://www.falkordb.com/blog/pattern-matching-in-sql/): You're trying to answer a simple question in SQL, then the query starts growing teeth.
- [What Is Agentic Workflows](https://www.falkordb.com/blog/what-is-agentic-workflows/): You're probably dealing with the same kind of mess most AI teams hit early on.
- [Shipping Enterprise AI as a Claude Skill on FalkorDB](https://www.falkordb.com/case-studies/legal-graphrag-claude-skill-falkordb/): How SERGAS Group cut legal escalations ~50% by shipping a FalkorDB knowledge graph as a Claude Skill, with cited answers in under a minute.
- [Vehicle Routing Problems: A Guide to Models & Solutions](https://www.falkordb.com/blog/vehicle-routing-problems/): Orders are piling up, dispatch is juggling driver calls, and the route plan that looked fine at 6 a.m. is already wrong by 8.
- [Payment Fraud Prevention: A Graph-Based Architectural Guide](https://www.falkordb.com/blog/payment-fraud-prevention/): Fraud is no longer a marginal checkout problem.
- [Topological Sort Algorithm: A Practical Guide for 2026](https://www.falkordb.com/blog/topological-sort-algorithm-2/): You usually notice dependency ordering only when it breaks.
- [GraphRAG by FalkorDB: A Knowledge Graph App You Can Actually Question](https://www.falkordb.com/blog/knowledge-graph-rag-app/): Upload your documents, watch a knowledge graph build itself in real time, and get answers you can actually verify. No code, no Cypher, no pipeline to assemble.
- [Talk to Your Tables: Agentic Graph Analytics Inside Snowflake](https://www.falkordb.com/blog/talk-to-your-tables-agentic-graph-analytics-inside-snowflake/): Graph questions, who's connected to whom, through what, how many hops away, are miserable to answer in SQL.
- [Build GraphRAG with FalkorDB, LangChain & LangGraph](https://www.falkordb.com/blog/graphrag-langchain-langgrap/): The FalkorDB LangChain integration lets Python developers turn a low-latency graph database into the retrieval backbone of an LLM application.
- [Using FalkorDB to Give Engineers X-Ray Vision Into Their Data Pipelines](https://www.falkordb.com/blog/falkordb-x-ray-vision-data-pipelines/): At a large retail enterprise, data engineers make changes to pipelines every day. A new transformation, a refactored join, a column renamed or dropped.
- [Beyond In-Memory Graphs: Query, Share, and Scale Your Agent Graph Projection with FalkorDB and ActiveGraph](https://www.falkordb.com/blog/beyond-in-memory-graphs/): ActiveGraph keeps two distinct storage layers, an event store that is the durable source of truth, and a graph store that is a live projection of current state.
- [Give Your Agents a Brain: Mastering Knowledge Graphs and Agentic Memory](https://www.falkordb.com/news-updates/give-your-agents-a-brain/)
- [Curing AI Amnesia: A Memory Your Agent Builds by Chatting](https://www.falkordb.com/blog/curing-ai-amnesia/): Talk to most AI agents and you are talking to a goldfish. Three seconds of memory, then the conversation ends and everything you said is gone.
- [Cypher Query Cheatsheet for Developers: Essential Patterns and Syntax](https://www.falkordb.com/blog/cypher-query-cheatsheet-for-developers/): Most graph database tutorials start with toy examples, three nodes, two edges, a single MATCH clause.
- [Beyond Vector Search: Why a Code Graph Is the Secret to Chatting With Complex Codebases](https://www.falkordb.com/blog/code-graph-is-the-secret/): TL;DR: Vector search retrieves code that looks similar, not code that is actually connected.
- [What Is Hybrid Search in AI? Combining Vectors, Graphs, and Keywords](https://www.falkordb.com/blog/what-is-hybrid-search-in-ai/): Most retrieval systems in production today rely on a single search modality, and each one fails in predictable ways. Keyword search misses semantic intent.
- [Memgraph vs Neo4j: Performance and Architecture for Production Workloads](https://www.falkordb.com/comparison/memgraph-vs-neo4j-performance-and-architecture-for-production-workloads/): Choosing a graph database for production workloads requires looking past marketing buzzwords and evaluating core architectural designs.
- [Cypher Query Language: The Complete Guide for Graph Database Developers](https://www.falkordb.com/blog/cypher-query-language/): You have a graph with fifty million nodes representing users, products, transactions, and the invisible threads connecting them all.
- [AWS Neptune vs Neo4j: Architectural Differences for Production Workloads](https://www.falkordb.com/comparison/aws-neptune-vs-neo4j-architectural-differences-for-production-workloads/): Choosing a graph database for production workloads requires looking past marketing buzzwords and evaluating core architectural designs.
- [TigerGraph vs Neo4j: Architectural Trade-Offs for Production Workloads](https://www.falkordb.com/comparison/neo4j-vs-tigergraph/): Choosing a graph database for production workloads requires looking past marketing buzzwords and evaluating core architectural designs.
- [GraphRAG, AI Agents, and Memory: How Graph Databases Power the Next Wave of AI in 2026](https://www.falkordb.com/blog/graph-database-ai-agents/): AI agents that forget everything between invocations are operationally useless for complex workflows.
- [Graph Database Anti-Patterns That Kill AI Application Performance](https://www.falkordb.com/blog/graph-database-anti-patterns-ai-performance/): A single bloated super node can turn a sub-millisecond graph traversal into a multi-second catastrophe.
- [FalkorDB vs TigerGraph: Which Graph Database Is Best for AI Workloads?](https://www.falkordb.com/blog/falkordb-vs-tigergraph/): A single graph traversal that takes 200 milliseconds instead of 2 milliseconds can destroy the responsiveness of an entire AI pipeline.
- [Graph Database Explained: A Beginner's Guide for Developers](https://www.falkordb.com/blog/graph-database-explained/): You have a social network with ten million users, and someone asks: "Show me all friends-of-friends who also attended the same university and work in the same city."
- [Three New Graph Database Algorithms in FalkorDB: MaxFlow, Harmonic Centrality, and Programmable UDF Traversal](https://www.falkordb.com/news-updates/falkordb-graph-database-algorithms-maxflow-udf/): FalkorDB adds MaxFlow, Harmonic Centrality, and programmable UDF traversal. These three graph database algorithms expand what you can compute, optimize, and automate directly inside the graph layer.
- [FalkorDB on Snowflake: Cypher vs SQL](https://www.falkordb.com/blog/graph-queries-cypher-vs-sql-snowflake/): Evaluating FalkorDB as a Snowflake Native App for relationship-heavy workloads. Our tests show Cypher's [:ROUTE*N] pattern simplifies complex pathfinding, maintaining query conciseness and performance where SQL requires escalating self-joins, often leading to timeouts at deeper traversals.
- [Podcast Recap: Building Real-Time Context for AI Agents](https://www.falkordb.com/news-updates/incremental-knowledge-graph-indexing/): Stop re-indexing entire corpora. This summary explains how CocoIndex and FalkorDB enable real-time, incremental knowledge graph updates for production-grade AI agents.
- [GraphRAG SDK 1.0: The Road to Building a Production-Grade Knowledge Graph Pipeline](https://www.falkordb.com/blog/graphrag-sdk-knowledge-graph/): FalkorDB GraphRAG SDK 1.0 is a production-ready, LLM-agnostic framework for building knowledge graph pipelines that ranked #1 on GraphRAG-Bench Novel and Medical datasets.
- [FalkorDB Browser Update – April 2026 Release Notes](https://www.falkordb.com/news-updates/falkordb-browser-updates-april-2026/): New FalkorDB browser features let developers save frequent queries, copy connection strings for any architecture, see node and edge stats instantly, and customize result tables—cutting friction in GraphRAG development.
- [Graph Algorithms for Cybersecurity: FalkorDB Webinar Insights](https://www.falkordb.com/blog/graph-algorithms-cybersecurity-webinar/): The FalkorDB webinar demonstrates graph algorithms for cybersecurity: ransomware spread simulation, Log4Shell dependency tracing, and max-flow data leakage analysis, showing practical UDF and flow network applications.
- [How Securin Runs 7-Hop Threat Intelligence Queries in Under 350ms with FalkorDB](https://www.falkordb.com/case-studies/securin-falkordb-graph-case-study/): Securin optimized their AI security agents by migrating from leading graph database provider to FalkorDB, achieving 0.3s latencies on complex 7-hop queries and eliminating a 25% query failure rate.
- [FalkorDB on Snowflake: Native Graph Database for Snowflake's AI Data Cloud](https://www.falkordb.com/blog/falkordb-on-snowflake-graph-database/): FalkorDB on Snowflake lets you query warehouse tables as native graphs with Cypher, SPCS-backed compute pools, reference binding security, and explicit lifecycle control for production workloads.
- [What are Context Graphs? And How to Build One with FalkorDB](https://www.falkordb.com/blog/context-graphs-ai-agents-long-term-memory/): Context graphs transform AI agents from stateless responders into context-aware systems with institutional memory. Learn how to capture the "why" behind decisions using FalkorDB's graph technology.
- [Beyond Flat Memory: Persistent Graph-Structured LLM Memory with mem0-falkordb](https://www.falkordb.com/blog/graph-memory-llm-agents-mem0-falkordb/): mem0-falkordb gives AI agents persistent graph memory via FalkorDB — replacing flat vector retrieval with entity-relationship traversal, per-user graph isolation, and sub-140ms query performance.
- [FalkorDB canvas web component for embeddable UI](https://www.falkordb.com/blog/falkordb-canvas-web-component/): The FalkorDB canvas web component lets you take canvas data and embed the default browser canvas as a standalone component in your own UI.
- [What's the Best Database for Knowledge Graphs? Pros & Cons](https://www.falkordb.com/blog/best-database-for-knowledge-graphs-falkordb-neo4j/): Choosing the best database for knowledge graphs determines your AI stack's performance ceiling. We compare FalkorDB, Neo4j, ArangoDB, Neptune, and JanusGraph—analyzing latency, GraphRAG readiness, and operational overhead.
- [How to Improve Query Performance in Graph Databases Using Array & Range Indexing](https://www.falkordb.com/blog/array-range-indexing-graph-database-performance/): FalkorDB introduces native array and range indexing for graph databases, enabling O(log n) queries on multivalued properties without schema normalization, delivering 10-100x speedups.
- [Text-to-SQL with Knowledge Graphs: Solving Multi-Hop Query Problems](https://www.falkordb.com/blog/text-to-sql-knowledge-graphs/): Vector databases fail at complex Text-to-SQL when queries span multiple tables. We built QueryWeaver using FalkorDB to map schema relationships as a knowledge graph, solving multi-hop queries that vectors miss.
- [Extending FalkorDB with User-Defined Functions and FLEX](https://www.falkordb.com/blog/falkordb-udfs-javascript-graph-extension/): FalkorDB introduces user-defined functions (UDFs) written in JavaScript, allowing developers to extend graph database functionality with custom logic. FLEX library provides pre-built functions for text processing, similarity metrics, and date manipulation.
- [How Semantic Layers Eliminate SQL Hallucinations in LLM Systems](https://www.falkordb.com/blog/sql-hallucinations-semantic-layers/): Text-to-SQL systems hallucinate tables and columns because LLMs get no relational context. Graph-based semantic layers represent the schema as a knowledge graph, so retrieval can traverse real foreign keys, prune irrelevant tables, and ground every generated query in the actual database.
- [Graph Databases: A Technical Guide to Modern Data Relationships](https://www.falkordb.com/blog/graph-database-guide/): A technical guide to graph databases covering core concepts, query languages, use cases, and implementation practices for AI architects and developers.
- [FalkorDB v4.14.10: Memory Optimization Through Compact Storage Architecture](https://www.falkordb.com/news-updates/falkordb-v4-14-10-memory-optimization-compact-storage/): FalkorDB v4.14.10 reduces memory footprint by 30% through compact storage architecture, accelerates write operations via batch processing, and implements auto-shrink for deletion cycles.
- [Code Graph Analysis: Visualize Source Code Structure](https://www.falkordb.com/blog/code-graph-analysis-visualize-source-code/): Code graph analysis converts source code into visual relationship maps. FalkorDB's tool parses repositories to reveal function calls, class hierarchies, and module dependencies developers miss.
- [FalkorDB Partners with Datax Solution to Expand Distribution in Middle East](https://www.falkordb.com/news-updates/falkordb-datax-middle-east-distribution-partnership/): FalkorDB partners with Datax Solution FZC as Middle East distributor, extending access to low-latency graph database built for GraphRAG, AI workloads & real-time analytics across the region.
- [MCP for agent memory: Graphiti + FalkorDB for persistent, multi-tenant knowledge graphs](https://www.falkordb.com/blog/mcp-knowledge-graph-graphiti-falkordb/): Learn how to implement Model Context Protocol agent memory using Graphiti MCP Server with FalkorDB for isolated, persistent context and multi-tenant knowledge graphs in production AI systems.
- [How to Build an AI RAG Workflow using FalkorDB + N8N + Graphiti Agent Module](https://www.falkordb.com/blog/ai-rag-workflow-falkordb-n8n-graphiti/): Connect your FalkorDB knowledge graph to N8N to take full advantage of your graph database. In a vector database, you're searching for similarity. Sometimes the "most similar" document isn't actually what you need. In a graph database, you're traversing explicit relationships. Either a parameter belongs to an endpoint, or it doesn't. There's no guessing.
- [Personal Access Tokens: Secure API Authentication for FalkorDB Browser](https://www.falkordb.com/blog/falkordb-personal-access-tokens-api-auth/): FalkorDB Browser now supports Personal Access Tokens for secure API authentication. Generate granular, long-lived JWT credentials for automated scripts and CI/CD pipelines using our documented API.
- [Graph Power, Zero Friction: The "SQLite moment" for Graph Databases with FalkorDBLite](https://www.falkordb.com/blog/falkordblite-embedded-python-graph-database/): FalkorDBLite brings SQLite-style simplicity to graph databases. Spin up a lightweight sub-process with zero network overhead, process isolation, and no configuration required for prototyping and testing.
- [Finding Connections With Graph Algorithms in Complex Networks](https://www.falkordb.com/blog/finding-connections-with-graph-algorithms-in-complex-networks/): Graph algorithms go beyond basic queries to surface hidden attack paths, insider threats, and structural vulnerabilities. Learn how Betweenness Centrality, WCC, and CDLP detect risk in real systems.
- [Beyond Rows and Columns: The Hybrid Future of Databases](https://www.falkordb.com/blog/hybrid-databases-architecture-oltp-olap-graphs/): FalkorDB unites OLTP precision and OLAP speed through sparse adjacency matrices, enabling vectorized graph traversal and direct node access for AI-driven connected data workloads.
- [FalkorDB Named Finalist in Swiss Armed Forces Cyber Startup Challenge 2025](https://www.falkordb.com/blog/falkordb-cyber-startup-challenge-2025-finalist/): FalkorDB has been selected as one of three finalists for the Cyber Startup Challenge 2025, competing for a CHF 100,000 contract to integrate graph database technology into Swiss cyber defense.
- [FalkorDB LangChain JS/TS Integration Release](https://www.falkordb.com/blog/falkordb-langchain-js-ts-integration/): FalkorDB's new LangChain JS/TS package lets users query graph data with natural language. The integration auto-generates Cypher queries and returns plain language responses.
- [KuzuDB to FalkorDB Migration](https://www.falkordb.com/blog/kuzudb-to-falkordb-migration/): KuzuDB has gone through EOL. FalkorDB has put together a simple, 2-step migration guide for developers looking for an alternative graph database.
- [A Practical Guide to Security Graphs and Cloud Entitlements](https://www.falkordb.com/blog/security-graphs-cloud-entitlements-guide/): Read about security graphs to model complex cloud relationships between users, roles, policies, and resources. Using FalkorDB, query indirect access chains, enforce least privilege, and detect lateral movement risks in real time for CIEM and threat analysis.
- [Implementing Agentic Memory with Graphiti](https://www.falkordb.com/blog/implementing-agentic-memory-graphiti/): Rewatch FalkorDB's 'Implementing Agentic Memory' workshop with Graphiti and QueryWeaver, and read the questions and answers from the session.
- [FalkorDB Now Fully Supported in G.V() 3.38.90](https://www.falkordb.com/blog/falkordb-gv-integration-graph-viz-ai/): G.V() 3.38.90 brings full FalkorDB support, letting AI developers connect their LLM-optimized graph database for instant visualization. Perfect for agentic AI applications and advanced chatbots.
- [Graph Database Architecture for Cloud Security Applications](https://www.falkordb.com/blog/graph-database-multi-tenant-cloud-security/): Security teams need real-time attack path discovery across multi-cloud environments. Graph databases process entity relationships for sub-100ms threat analysis.
- [Talk with Your Graph Database: Natural Language Queries with FalkorDB and Text-to-Cypher](https://www.falkordb.com/blog/text-to-cypher-natural-language-graph-queries/): We built Text-to-Cypher to let you chat with FalkorDB graphs using plain English. Get instant Cypher query generation via REST API or MCP server. Open source, Docker-ready, with real-time SSE updates.
- [String Interning in Graph Databases: Save Memory, Boost Performance](https://www.falkordb.com/blog/string-interning-graph-database/): One of the lesser-known challenges is the memory consumed by storing identical string values over and over again. Think of common attributes like “city,” “category,” or “status” that might appear on thousands, or even millions, of nodes and edges. A powerful solution to this problem is string interning.
- [Building Temporal Knowledge Graphs with Graphiti](https://www.falkordb.com/blog/building-temporal-knowledge-graphs-graphiti/): Zep founder Daniel Chalef joins FalkorDB, details Graphiti, a graph framework for real-time LLM memory and RAG. It handles dynamic data better than vector search.
- [Neo4j to FalkorDB: Migrate for Speed & Scale](https://www.falkordb.com/blog/neo4j-to-falkordb-migration-guide/): Migrate from Neo4j to FalkorDB for ultra-low latency, multi-tenancy, and GenAI-ready graph solutions. Follow our step-by-step guide.
- [6 Reasons you need a graph to store your security data](https://www.falkordb.com/blog/store-security-data-in-a-graph/): As a Cloud Security vendor, you're building protection systems for environments that constantly change. Your customers deploy across containers, serverless, ephemeral compute, and IaC pipelines. You need to correlate misconfigurations, permissions, and runtime signals across identity, network, and workload layers, at scale, in real time, without losing performance or exceeding the scoped operational costs. That’s where graphs come in.
- [Consuming FalkorDB on major clouds just got easier](https://www.falkordb.com/news-updates/byoc-major-cloud-providers/): FalkorDB BYOC deployment now spans AWS and Google Cloud Platform. Run fully managed graph database instances directly within your cloud subscription while maintaining data sovereignty across all major cloud providers.
- [Getting Started with Graphiti and FalkorDB: A Practical Guide](https://www.falkordb.com/blog/graphiti-get-started/): This post is a hands-on walkthrough for developers who want to get up and running with Graphiti and FalkorDB, fast.
- [FalkorDB v4.10.2 Released](https://www.falkordb.com/news-updates/v4-10-2-released/): This maintenance and enhancement release delivers significant performance improvements for parameter-heavy operations and strengthens operational reliability across cloud deployments.
- [Agents Without Memory Are Just Expensive Chatbots: Why Zep’s Graphiti Chose FalkorDB](https://www.falkordb.com/blog/graphiti-falkordb-multi-agent-performance/): Graphiti now supports FalkorDB backend for multi-agent environments, addressing performance and isolation requirements in production AI agent deployments with sub-10ms queries.
- [FalkorDB Achieves SOC 2 Type II Certification](https://www.falkordb.com/news-updates/soc2-type-2-certification/): FalkorDB achieves SOC 2 Type II certification, demonstrating enterprise-grade security controls for multi-tenant graph database operations and mission-critical applications.
- [Building Knowledge Graphs: Lessons from VCPedia and Fractal KG](https://www.falkordb.com/blog/building-knowledge-graphs-lessons-from-vcpedia-and-fractal-kg/): Knowledge graphs represent complex relationships as nodes and edges. This workshop demonstrated practical implementations through two systems: VCPedia for venture capital data extraction and Fractal KG for self-organizing graph structures.
- [Bank of America’s Erica: A Case Study in Small LLMs and GraphRAG Efficiency at Scale](https://www.falkordb.com/blog/graphrag-small-llms-bank-of-america/): Bank of America proves small LLMs and GraphRAG deliver scalable, compliant AI with low latency and precise context retrieval in production environments.
- [FalkorDB Named HackerNoon “Startup of the Year 2024”](https://www.falkordb.com/news-updates/falkordb-hackernoon-startup-of-the-year-2024/): Developers voted FalkorDB HackerNoon Startup of the Year 2024 for Tel Aviv after shipping sparse-matrix storage, AVX-accelerated queries, and multi-tenant clustering with sub-10 ms tail latency.
- [Can a Redis-Based Graph Database Scale for Enterprise AI? Multigraph Topology Explained](https://www.falkordb.com/news-updates/multigraph-topology-isolation-linear-scale/): Hands-on look at Multigraph Topology: how key-space slots isolate customer graphs, why replicas sustain 120 K QPS, and when to scale masters vs replicas in graph ACL systems.
- [FalkorDB's Implementation of the Model Context Protocol (MCP)](https://www.falkordb.com/blog/mcp-integration-falkordb-graphrag/): MCP simplifies how LLMs access tools and graph databases. Learn how FalkorDB’s MCP server enables structured, scalable AI integrations.
- [Graph Query Language Complexity: A Misunderstood Element for LLM and GraphRAG Integration](https://www.falkordb.com/blog/glossary/graph-query-language-falkordb-llm/): Graph Query Language demystified: learn how FalkorDB simplifies querying for LLM and GraphRAG workflows using Cypher-like syntax.
- [Knowledge Graphs in AI: A Developer’s Guide to Structure, Scale, and Use](https://www.falkordb.com/blog/glossary/knowledge-graph-llms-graphrag/): A developer-focused guide to understanding and using knowledge graphs for GraphRAG, LLM integration, schema design, and high-precision retrieval in 2025.
- [FalkorDB for .NET Just Landed — NFalkorDB v1.0.0 Now Live](https://www.falkordb.com/news-updates/nfalkordb-v1-dotnet-release/): NFalkorDB v1.0.0 lets you run FalkorDB graph queries directly from .NET using C# extension methods—no wrappers, no cache layers, no guesswork.
- [How GraphRAG Outperforms Vector Search in Enterprise LLM Accuracy](https://www.falkordb.com/blog/graphrag-accuracy-diffbot-falkordb/): 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 queries.
- [Why Data Retrieval and Knowledge Graphs Are Key for Smarter AI Agents: Insights from Nvidia GTC 2025](https://www.falkordb.com/news-updates/data-retrieval-graphrag-ai-agents/): Accurate data retrieval via GraphRAG boosts inference, reduces hallucinations, and secures personalized memory storage for AI agents.
- [Why Your GenAI Project Needs AI-Ready Data: How to Get It Right](https://www.falkordb.com/blog/ai-ready-data-generative-ai/): Without AI-ready data, most generative AI projects fail. Learn to standardize enterprise data with graph databases like FalkorDB for scalable AI success.
- [Can Graph Neural Networks Actually Help With LLM Hallucinations?](https://www.falkordb.com/blog/graph-neural-networks-llm-integration/): Learn about integrating Graph Neural Networks (GNNs) with LLMs for precise relational modeling and improved AI performance in fraud detection, healthcare, and more.

## Pages

- [GraphRAG](https://www.falkordb.com/graphrag/): A specialized toolkit for building GraphRAG systems: knowledge graphs, ontology management and LLMs in one pipeline.
- [QueryWeaver: Graph-Powered Text-to-SQL](https://www.falkordb.com/queryweaver-text-to-sql/): Securely connect your databases and query in plain English. Get dramatically more accurate results for complex enterprise schemas.
- [FalkorDB | Graph Database](https://www.falkordb.com/falkordb-graph-database/): Already have an account? Sign in Already have an account?
- [FalkorDB Startup Program](https://www.falkordb.com/startup-program/): Join FalkorDB's Startup Program, designed for entrepreneurs ready to integrate graphs into their product to build smarter, scale faster, and sell more.
- [FalkorDB vs AWS Neptune](https://www.falkordb.com/falkordb-vs-aws-neptune/): Ready to leave behind ballooning costs, poor latency and inadequate performance?
- [Graph-Powered Cyber Security](https://www.falkordb.com/cyber-security/): The only open-source, multi-tenant and ultra-fast security graph for CNAPP, CSPM & CIEM that's ready to scale with you.
- [Use Cases](https://www.falkordb.com/use-cases/): Stop choosing between scale, latency, and accuracy.
- [FalkorDB vs Neo4j](https://www.falkordb.com/falkordb-vs-neo4j/): Ready to leave behind ballooning costs, poor latency and inadequate performance?
- [GraphRAG-SDK](https://www.falkordb.com/graphrag-sdk/): A specialized toolkit for building Graph Retrieval-Augmented Generation (GraphRAG) systems, integrating knowledge graphs, ontology management, and large language models (LLMs) to deliver accurate, efficient, and customizable RAG workflows.
- [Media](https://www.falkordb.com/media-center/): Download FalkorDB's sales and marketing collateral and assets for promotional use.
- [Plans](https://www.falkordb.com/plans/): Start free with multi-graph and multi-tenancy from day one. Paid plans add TLS, clustering and high availability. For Enterprise, talk to sales.
- [Homepage](https://www.falkordb.com/): Combine LLMs with domain-specific knowledge graphs to reduce hallucinations and enrich AI responses.
- [Book a Demo](https://www.falkordb.com/get-a-demo/): FalkorDB's graph database is designed for development teams managing structured and unstructured interconnected data in real-time or interactive environments.
- [Graph Database Graph Size Calculator](https://www.falkordb.com/graph-database-graph-size-calculator/): Estimate the memory requirements for your graph database based on nodes, edges, and properties.
- [Community Projects](https://www.falkordb.com/community-projects/): Wondering what people actually build with a graph database once GraphRAG moves past the demo stage?
- [Try Free](https://www.falkordb.com/try-free/): Get instant access to cloud resources and advanced capabilities to handle complex graph-based AI applications with ease.
- [CR Lobby](https://www.falkordb.com/cr-lobby/)
- [Privacy Policy](https://www.falkordb.com/privacy-policy/): This Privacy Policy describes Our policies and procedures on the collection, use and disclosure of Your information when You use the Service and tells You about Your privacy rights and how the law protects You.
- [Blog](https://www.falkordb.com/blog/): Most writing about graph databases and GraphRAG is either marketing copy or a research paper.
- [Contact Us](https://www.falkordb.com/contact-us/): Setting up your own integration is the fastest and most efficient way to start using FalkorDB.
- [Jobs](https://www.falkordb.com/jobs/): Design, implement, test, and optimize core components of the FalkorDB graph database engine. Collaborate to integrate with LLMs and AI applications.
- [Company](https://www.falkordb.com/company/): FalkorDB provides a unified solution that seamlessly integrates the capabilities of Knowledge Graphs and Vector Databases. Its low-latency, Redis-powered architecture is designed to efficiently handle both graph traversal and vector similarity searches, thus eliminating the need for separate systems and reducing integration complexity.

## Optional

- [Agent (MCP protocol)](websites-agents.hostinger.com/www.falkordb.com/mcp)

[comment]: # (Generated by Hostinger Tools Plugin)