# FalkorDB > Ultra-fast, Multi-tenant Graph Database ## Posts - [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. Our advanced, ultra-low latency solution is designed to empower your data-driven applications with unparalleled performance, scalability, and ease of use. Whether you’re managing complex relationships, conducting deep analytics, or building the next generation of AI-driven applications, FalkorDB is the database you’ve been waiting for. Why Choose FalkorDB? 1. Ultra Low Latency Experience performance like never before. FalkorDB is up to 200x faster than other graph databases, ensuring that your queries return results in the blink of an eye. Whether you’re dealing with millions of nodes or billions, FalkorDB’s […] - [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. Use vectors when similarity is enough; use graphs when an AI application must reason across connected facts, multi-hop relationships, provenance, or permissions. Many production AI systems combine both approaches: vector search finds the relevant content, and graph traversal supplies the relationship context around it. Vector database vs. graph database: the short answer The two systems answer different questions. A vector database answers “what content looks most like this query?” A graph database answers “what is this entity connected to, and how?” Neither is a […] - [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. These applications enhance the reasoning capabilities of large language models (LLMs) by providing structured context from a knowledge base.  By organizing information into a graph format, knowledge graphs allow for more interconnected and structured data, enabling LLMs to retrieve relevant context with greater accuracy.  Recent research shows that this approach leads to more informed and contextually appropriate responses from LLMs, especially when handling complex queries requiring deep understanding and reasoning across various domains. To build a knowledge graph, information is structured into nodes and […] - [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. As data volume and variety continue to explode, businesses are increasingly seeking technologies that can effectively capture and interpret the information contained within these datasets to inform strategic decisions. Recent advancements in large language models (LLMs) have opened new avenues for uncovering the meanings behind unstructured data. However, LLMs typically lack long-term memory, necessitating the use of external storage solutions to retain the insights derived from data. One of the most effective methods for achieving this is through Knowledge Graphs. Knowledge graphs help structure information by […] - [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)! This update brings scale-out capabilities, multi-tenant architecture, and multi-graph support with full isolation, ensuring the utmost security and privacy for each graph within the same database instance. With FalkorDB on GCP, you can manage multiple isolated graphs for various domains or clients without sacrificing performance or security, all within a single database. How to Create a 3-Shard Cluster with FalkorDB-Cloud Deploying a FalkorDB cluster with 3 shards and 3 replicas is incredibly easy using FalkorDB-Cloud. Follow these simple steps to get started: Step 1: Sign In to FalkorDB-Cloud Visit FalkorDB-Cloud and sign in or create a new account if you don’t […] - [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. Whether you’re working with multi-model AI systems, multiple knowledge graphs, or multi-agent environments, this update brings new capabilities to simplify your workflow and enhance your insights. Let’s dive into the major updates in v0.2, complete with code examples to help you get started. New Features in GraphRAG-SDK v0.2 Multi-Model Support: Integrating Google Gemini and OpenAI GPT Models GraphRAG-SDK v0.2 adds support for Google’s Gemini models in addition to OpenAI’s GPT models, giving you the flexibility to choose […] - [Code Visualization: Benefits, Best Practices & Popular Tools](https://www.falkordb.com/blog/code-visualization/): Modern software architectures are complex systems of interconnected components. As projects grow, keeping track of all their moving parts becomes increasingly challenging. Complex control flows, deeply nested structures, and inconsistent naming conventions can overwhelm developers, making it difficult to understand the overall architecture. To simplify these complexities, code visualization has been a cornerstone of software engineering since its early days. By revealing architectural patterns and relationships within the code, it streamlines development, improves collaboration, aids in refactoring, reduces bugs, and accelerates onboarding. In this article, we take a comprehensive deep-dive into code visualization and its approaches, the various tools that exist, […] - [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. At its core, RAG gathers knowledge from various sources and generates answers using a language model. However, with basic RAG, also known as Naive RAG, you may encounter challenges in obtaining accurate results for complex queries and face slow response times and higher costs when dealing with large datasets. To address these challenges, researchers have developed several advanced RAG techniques. This article provides an overview of these advanced methods to help you achieve better results when Naive RAG falls short. […] - [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/): Highlights Retrieval-augmented generation (RAG) has emerged as a powerful technique to address key limitations of large language models (LLMs). By augmenting LLM prompts with relevant data retrieved from various sources, RAG ensures that LLM responses are factual, accurate, and free from hallucinations. GraphRAG extends the capabilities of RAG by using knowledge graphs to represent information, allowing it to handle complex queries that require multi-hop reasoning rather than just relying on semantic similarity. Unlike vector-based retrieval-augmented generation (VectorRAG) which struggles with complex queries, GraphRAG leverages the structured relationships in knowledge graphs to enable advanced reasoning and provide more relevant answers. GraphRAG systems […] - [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/): What is LLM and Knowledge Graph Integration? 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). LLMs, like OpenAI’s GPT series or Meta’s Llama series, have shown incredible potential in generating human-like text, answering complex questions, and creating content across diverse fields. Meanwhile, KGs help organize and integrate information in a structured way, allowing machines to understand and infer the relationships between real-world entities. They encode entities (such as people, places, and things) and the relationships between them, making them ideal for tasks […] - [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. Since the birth of the Transformer architecture introduced in the “Attention Is All You Need” paper, we have seen the emergence of increasingly powerful LLMs. However, Large Language Models (LLMs) by themselves are not enough. This is because of two key reasons. First, they tend to hallucinate, which means they can “make up” facts and information that are simply untrue. LLMs work by […] - [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. Columnar databases are optimized for aggregating attributes across very large datasets. Graph databases are optimized for traversing relationships between many connected entities. Which one you need depends on whether your dominant workload is entity-centric, analytics-centric, or relationship-centric. Most engineers are fluent in rows and columns. Relationships tend to get modeled around rather than designed for, and that is where query plans quietly fall apart. Think of this as a practical lens rather than a strict database taxonomy: each model reflects a different primary access pattern. Row-Based Storage: Built for Whole 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. In this blog post we'll be developing a state machine framework where a machine is represented by a graph. Whenever a FSM (finite state machine) is executed a copy of the initial graph is created and the execution is bound to that dedicated clone. This approach is extremely flexible, as one can easily adjust a machine and changes will be applied to all future executions seamlessly, or in case a modification needs to be A/B tested a clone of the graph is easily made and compared to a […] - [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. These frameworks aim to leverage the strengths of both LLMs and KGs to enhance AI’s inferential and interpretative abilities, address the construction and evolution challenges of KGs, and […] - [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/): Source: https://medium.com/@akriti.upadhyay/building-advanced-rag-applications-using-falkordb-langchain-diffbot-api-and-openai-083fa1b6a96c 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. These platforms use powerful recommendation systems to keep you interested — by tracking your browsing history, your recently watched shows, the movies you have rated, the languages in which you watch films, and more. A Movie Recommendation System is a fascinating blend of technology, psychology, and art. They are now an integral part of our movie-watching experience. During the search, a number of parameters are taken into consideration such as Genre, Language, Country of […] - [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/): Source: https://medium.com/@akriti.upadhyay/building-advanced-rag-applications-using-falkordb-langchain-diffbot-api-and-openai-083fa1b6a96c Introduction The introduction of the Knowledge Graph Database in the realm of evolving Large Language Models has changed the way RAG applications are getting built. Since RAG mitigates knowledge limitations like hallucinations and knowledge cut-offs, we use RAG to build QA chatbots. Knowledge Graphs store and query the original data and capture different entities and relations embedded in one’s data. With the help of a Knowledge Graph, we’ll build an advanced RAG application using the OpenAI base model. Before diving deeper, let’s understand more about Knowledge Graphs! What Is a Knowledge Graph? The knowledge graph is centered around a […] - [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. FalkorDB is a graph database that builds on the legacy of RedisGraph, which was discontinued by Redis a few months ago. FalkorDB aims to provide a fast, scalable and reliable graph solution for your data needs. Docker container: docker run -it -p 6379:6379 -p 7687:7687 falkordb/falkordb:v4.0.1   Try it on Docker:  sudo docker run -it -p 6379:6379 -p 7687:7687 falkordb/falkordb Free FalkorDB Cloud: https://app.falkordb.cloud/ FalkorDB 4.0.0 first beta introduces some major features and enhancements over RedisGraph: Vector indexing support: You can now index your graph nodes and edges based […] - [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. Check the new version docker container (we plan to release a cloud sandbox soon) docker run -it -p 6379:6379 -p 7687:7687 falkordb/falkordb:4.0.0-alpha.1 Notice: the examples bellow are in Java but if Java is not your cup of tea you can find the same examples in other languages here: https://github.com/FalkorDB/demos  Vector Index The first feature is Vector Index support, which allows you to find nodes using vector similarity search. This means you can store and query high-dimensional vectors, such as embeddings or image features, […] - [RAG battle: vector database vs knowledge graph](https://www.falkordb.com/blog/rag-battle-vector-database-vs-knowledge-graph/): LLMs today 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. data summarization, Q&A chat bots and entity extraction are just a few examples of what people are doing with these models. With this new technology new disciplines and challenges emerge: What’s the proper way to query my data? How can I reduce model hallucinations? Where should I store my data?   Current approach As It seems Vector databases became the default options for indexing, […] - [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. As a quick recap I should mention that FalkorDB is a native graph database, developed as a Redis module, Falkor can manage thousands of individual graphs on a single instance.   Baseline We start out with a single FalkorDB instance, let’s call it primary, this instance handles both READ and WRITE operations. # create primary database docker run --name primary --rm -p 6379:6379 falkordb/falkordb  As a next step we would like to isolate our reads queries from our writes, to do so we […] - [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. I will also give some examples of use cases that need RAG and how it can improve the quality and accuracy of the generated text. Large Language Models (LLMs) are powerful tools for natural language processing, capable of generating fluent and coherent text for various tasks. However, LLMs also have some limitations, such as their knowledge base being stale, incomplete, or inaccurate. To overcome these challenges, we can use a technique called Retrieval Augmented Generation (RAG), which […] - [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. Langchain allows you to easily query your Knowledge Graph using natural language.  In this blog post, I will show you in a simple 4 steps how to use Langchain query a knowledge graph based on FalkorDB. 1. Installing LangChain First, you need to install LangChain on your machine. You can download it from the official website or use the command line: > pip install langchain 2. Starting FalkorDB server locally Staring a local FalkorDB is as simple as running a local docker you can go […] - [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. LlamaIndex is a tool that allows you to easily build and search a knowledge graph using natural language queries.  In this blog post, I will show you in a simple 6 steps how to use LlamaIndex to create and explore a knowledge graph based on FalkorDB. Installing LlamaIndex   First, you need to install LlamaIndex on your machine. You can download it from the official website or use the command line: Copy > pip install llama-index Starting FalkorDB server locally Staring […] - [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. Diffbot API has a powerful API that can extract structured data from unstructured documents, such as web pages, PDFs, or emails. With Diffbot API, you can create a Knowledge graph that represents the entities and relationships in your documents, and store it in FalkorDB. Then, you can use Langchain, to query your Knowledge graph and get answers to your questions. Langchain can handle complex and natural queries, and return relevant and accurate answers from your Knowledge graph. 1. Installing LangChain First, you need to install LangChain and some dependencies on your machine. You […] - [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/): Webinar Recording Overview Roi Lipman, CTO & Co-founder, and Gabriel Gomez, Core Software Developer, break down how FalkorDB finds shortest paths at scale. Finding shortest paths in a graph seems simple, until the graph gets large enough that it isn't. Even a small, ordered search space produces millions of possible routes between two points. At real scale, most graph databases fall back on the same handful of algorithms, and all of them get slower as the two points get farther apart. We took a different approach. FalkorDB is the first graph database to ship Customizable Contraction Hierarchies (CCH) as a built-in […] - [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/): Webinar Recording Overview Roi Lipman, CTO & Co-founder, and Gabriel Gomez, Core Software Developer, break down how FalkorDB finds shortest paths at scale. Finding shortest paths in a graph seems simple, until the graph gets large enough that it isn't. Even a small, ordered search space produces millions of possible routes between two points. At real scale, most graph databases fall back on the same handful of algorithms, and all of them get slower as the two points get farther apart. We took a different approach. FalkorDB is the first graph database to ship Customizable Contraction Hierarchies (CCH) as a built-in […] - [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/): Webinar Recording Overview Roi Lipman, CTO & Co-founder, and Gabriel Gomez, Core Software Developer, break down how FalkorDB finds shortest paths at scale. Finding shortest paths in a graph seems simple, until the graph gets large enough that it isn't. Even a small, ordered search space produces millions of possible routes between two points. At real scale, most graph databases fall back on the same handful of algorithms, and all of them get slower as the two points get farther apart. We took a different approach. FalkorDB is the first graph database to ship Customizable Contraction Hierarchies (CCH) as a built-in […] - [Memory Meets Motion: The FalkorDB Standouts](https://www.falkordb.com/blog/memory-meets-motion-the-falkordb-standouts/): Webinar Recording Overview Roi Lipman, CTO & Co-founder, and Gabriel Gomez, Core Software Developer, break down how FalkorDB finds shortest paths at scale. Finding shortest paths in a graph seems simple, until the graph gets large enough that it isn't. Even a small, ordered search space produces millions of possible routes between two points. At real scale, most graph databases fall back on the same handful of algorithms, and all of them get slower as the two points get farther apart. We took a different approach. FalkorDB is the first graph database to ship Customizable Contraction Hierarchies (CCH) as a built-in […] - [Your Agent's Memory Problem Is a Retrieval Architecture Problem](https://www.falkordb.com/blog/ai-agent-memory-retrieval-architecture/): Webinar Recording Overview Roi Lipman, CTO & Co-founder, and Gabriel Gomez, Core Software Developer, break down how FalkorDB finds shortest paths at scale. Finding shortest paths in a graph seems simple, until the graph gets large enough that it isn't. Even a small, ordered search space produces millions of possible routes between two points. At real scale, most graph databases fall back on the same handful of algorithms, and all of them get slower as the two points get farther apart. We took a different approach. FalkorDB is the first graph database to ship Customizable Contraction Hierarchies (CCH) as a built-in […] - [10 Network Analysis Applications Shaping Industries in 2026](https://www.falkordb.com/blog/network-analysis-applications/): Webinar Recording Overview Roi Lipman, CTO & Co-founder, and Gabriel Gomez, Core Software Developer, break down how FalkorDB finds shortest paths at scale. Finding shortest paths in a graph seems simple, until the graph gets large enough that it isn't. Even a small, ordered search space produces millions of possible routes between two points. At real scale, most graph databases fall back on the same handful of algorithms, and all of them get slower as the two points get farther apart. We took a different approach. FalkorDB is the first graph database to ship Customizable Contraction Hierarchies (CCH) as a built-in […] - [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/): Webinar Recording Overview Roi Lipman, CTO & Co-founder, and Gabriel Gomez, Core Software Developer, break down how FalkorDB finds shortest paths at scale. Finding shortest paths in a graph seems simple, until the graph gets large enough that it isn't. Even a small, ordered search space produces millions of possible routes between two points. At real scale, most graph databases fall back on the same handful of algorithms, and all of them get slower as the two points get farther apart. We took a different approach. FalkorDB is the first graph database to ship Customizable Contraction Hierarchies (CCH) as a built-in […] - [Rewriting FalkorDB in Rust: Make It Work, Make It Stable, Then Make It Fast](https://www.falkordb.com/blog/rewriting-falkordb-in-rust/): Webinar Recording Overview Roi Lipman, CTO & Co-founder, and Gabriel Gomez, Core Software Developer, break down how FalkorDB finds shortest paths at scale. Finding shortest paths in a graph seems simple, until the graph gets large enough that it isn't. Even a small, ordered search space produces millions of possible routes between two points. At real scale, most graph databases fall back on the same handful of algorithms, and all of them get slower as the two points get farther apart. We took a different approach. FalkorDB is the first graph database to ship Customizable Contraction Hierarchies (CCH) as a built-in […] - [Master Data Management: A Practical Guide for AI](https://www.falkordb.com/blog/master-data-management/): Webinar Recording Overview Roi Lipman, CTO & Co-founder, and Gabriel Gomez, Core Software Developer, break down how FalkorDB finds shortest paths at scale. Finding shortest paths in a graph seems simple, until the graph gets large enough that it isn't. Even a small, ordered search space produces millions of possible routes between two points. At real scale, most graph databases fall back on the same handful of algorithms, and all of them get slower as the two points get farther apart. We took a different approach. FalkorDB is the first graph database to ship Customizable Contraction Hierarchies (CCH) as a built-in […] - [Maximal Independent Set: Graph Database Use Cases](https://www.falkordb.com/blog/maximal-independent-set/): Webinar Recording Overview Roi Lipman, CTO & Co-founder, and Gabriel Gomez, Core Software Developer, break down how FalkorDB finds shortest paths at scale. Finding shortest paths in a graph seems simple, until the graph gets large enough that it isn't. Even a small, ordered search space produces millions of possible routes between two points. At real scale, most graph databases fall back on the same handful of algorithms, and all of them get slower as the two points get farther apart. We took a different approach. FalkorDB is the first graph database to ship Customizable Contraction Hierarchies (CCH) as a built-in […] - [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/): Webinar Recording Overview Roi Lipman, CTO & Co-founder, and Gabriel Gomez, Core Software Developer, break down how FalkorDB finds shortest paths at scale. Finding shortest paths in a graph seems simple, until the graph gets large enough that it isn't. Even a small, ordered search space produces millions of possible routes between two points. At real scale, most graph databases fall back on the same handful of algorithms, and all of them get slower as the two points get farther apart. We took a different approach. FalkorDB is the first graph database to ship Customizable Contraction Hierarchies (CCH) as a built-in […] - [Supply Chain Database Guide for AI and Analytics Workloads](https://www.falkordb.com/blog/supply-chain-database/): Webinar Recording Overview Roi Lipman, CTO & Co-founder, and Gabriel Gomez, Core Software Developer, break down how FalkorDB finds shortest paths at scale. Finding shortest paths in a graph seems simple, until the graph gets large enough that it isn't. Even a small, ordered search space produces millions of possible routes between two points. At real scale, most graph databases fall back on the same handful of algorithms, and all of them get slower as the two points get farther apart. We took a different approach. FalkorDB is the first graph database to ship Customizable Contraction Hierarchies (CCH) as a built-in […] - [How to Build Chatbots That Actually Work in Production](https://www.falkordb.com/blog/how-to-build-chatbots/): Webinar Recording Overview Roi Lipman, CTO & Co-founder, and Gabriel Gomez, Core Software Developer, break down how FalkorDB finds shortest paths at scale. Finding shortest paths in a graph seems simple, until the graph gets large enough that it isn't. Even a small, ordered search space produces millions of possible routes between two points. At real scale, most graph databases fall back on the same handful of algorithms, and all of them get slower as the two points get farther apart. We took a different approach. FalkorDB is the first graph database to ship Customizable Contraction Hierarchies (CCH) as a built-in […] - [Encryption in Transit: A Practical Guide for Modern Systems](https://www.falkordb.com/blog/encryption-in-transit/): Webinar Recording Overview Roi Lipman, CTO & Co-founder, and Gabriel Gomez, Core Software Developer, break down how FalkorDB finds shortest paths at scale. Finding shortest paths in a graph seems simple, until the graph gets large enough that it isn't. Even a small, ordered search space produces millions of possible routes between two points. At real scale, most graph databases fall back on the same handful of algorithms, and all of them get slower as the two points get farther apart. We took a different approach. FalkorDB is the first graph database to ship Customizable Contraction Hierarchies (CCH) as a built-in […] - [10 Top Python Graph Libraries: A 2026 Guide](https://www.falkordb.com/blog/python-graph-libraries/): Webinar Recording Overview Roi Lipman, CTO & Co-founder, and Gabriel Gomez, Core Software Developer, break down how FalkorDB finds shortest paths at scale. Finding shortest paths in a graph seems simple, until the graph gets large enough that it isn't. Even a small, ordered search space produces millions of possible routes between two points. At real scale, most graph databases fall back on the same handful of algorithms, and all of them get slower as the two points get farther apart. We took a different approach. FalkorDB is the first graph database to ship Customizable Contraction Hierarchies (CCH) as a built-in […] - [Pattern Matching in SQL](https://www.falkordb.com/blog/pattern-matching-in-sql/): Webinar Recording Overview Roi Lipman, CTO & Co-founder, and Gabriel Gomez, Core Software Developer, break down how FalkorDB finds shortest paths at scale. Finding shortest paths in a graph seems simple, until the graph gets large enough that it isn't. Even a small, ordered search space produces millions of possible routes between two points. At real scale, most graph databases fall back on the same handful of algorithms, and all of them get slower as the two points get farther apart. We took a different approach. FalkorDB is the first graph database to ship Customizable Contraction Hierarchies (CCH) as a built-in […] - [What Is Agentic Workflows](https://www.falkordb.com/blog/what-is-agentic-workflows/): Webinar Recording Overview Roi Lipman, CTO & Co-founder, and Gabriel Gomez, Core Software Developer, break down how FalkorDB finds shortest paths at scale. Finding shortest paths in a graph seems simple, until the graph gets large enough that it isn't. Even a small, ordered search space produces millions of possible routes between two points. At real scale, most graph databases fall back on the same handful of algorithms, and all of them get slower as the two points get farther apart. We took a different approach. FalkorDB is the first graph database to ship Customizable Contraction Hierarchies (CCH) as a built-in […] - [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/): Webinar Recording Overview Roi Lipman, CTO & Co-founder, and Gabriel Gomez, Core Software Developer, break down how FalkorDB finds shortest paths at scale. Finding shortest paths in a graph seems simple, until the graph gets large enough that it isn't. Even a small, ordered search space produces millions of possible routes between two points. At real scale, most graph databases fall back on the same handful of algorithms, and all of them get slower as the two points get farther apart. We took a different approach. FalkorDB is the first graph database to ship Customizable Contraction Hierarchies (CCH) as a built-in […] - [Payment Fraud Prevention: A Graph-Based Architectural Guide](https://www.falkordb.com/blog/payment-fraud-prevention/): Webinar Recording Overview Roi Lipman, CTO & Co-founder, and Gabriel Gomez, Core Software Developer, break down how FalkorDB finds shortest paths at scale. Finding shortest paths in a graph seems simple, until the graph gets large enough that it isn't. Even a small, ordered search space produces millions of possible routes between two points. At real scale, most graph databases fall back on the same handful of algorithms, and all of them get slower as the two points get farther apart. We took a different approach. FalkorDB is the first graph database to ship Customizable Contraction Hierarchies (CCH) as a built-in […] - [Topological Sort Algorithm: A Practical Guide for 2026](https://www.falkordb.com/blog/topological-sort-algorithm-2/): Webinar Recording Overview Roi Lipman, CTO & Co-founder, and Gabriel Gomez, Core Software Developer, break down how FalkorDB finds shortest paths at scale. Finding shortest paths in a graph seems simple, until the graph gets large enough that it isn't. Even a small, ordered search space produces millions of possible routes between two points. At real scale, most graph databases fall back on the same handful of algorithms, and all of them get slower as the two points get farther apart. We took a different approach. FalkorDB is the first graph database to ship Customizable Contraction Hierarchies (CCH) as a built-in […] - [GraphRAG by FalkorDB: A Knowledge Graph App You Can Actually Question](https://www.falkordb.com/blog/knowledge-graph-rag-app/): Webinar Recording Overview Roi Lipman, CTO & Co-founder, and Gabriel Gomez, Core Software Developer, break down how FalkorDB finds shortest paths at scale. Finding shortest paths in a graph seems simple, until the graph gets large enough that it isn't. Even a small, ordered search space produces millions of possible routes between two points. At real scale, most graph databases fall back on the same handful of algorithms, and all of them get slower as the two points get farther apart. We took a different approach. FalkorDB is the first graph database to ship Customizable Contraction Hierarchies (CCH) as a built-in […] - [Talk to Your Tables: Agentic Graph Analytics Inside Snowflake](https://www.falkordb.com/blog/talk-to-your-tables-agentic-graph-analytics-inside-snowflake/): Webinar Recording Overview Roi Lipman, CTO & Co-founder, and Gabriel Gomez, Core Software Developer, break down how FalkorDB finds shortest paths at scale. Finding shortest paths in a graph seems simple, until the graph gets large enough that it isn't. Even a small, ordered search space produces millions of possible routes between two points. At real scale, most graph databases fall back on the same handful of algorithms, and all of them get slower as the two points get farther apart. We took a different approach. FalkorDB is the first graph database to ship Customizable Contraction Hierarchies (CCH) as a built-in […] - [Build GraphRAG with FalkorDB, LangChain & LangGraph](https://www.falkordb.com/blog/graphrag-langchain-langgrap/): Webinar Recording Overview Roi Lipman, CTO & Co-founder, and Gabriel Gomez, Core Software Developer, break down how FalkorDB finds shortest paths at scale. Finding shortest paths in a graph seems simple, until the graph gets large enough that it isn't. Even a small, ordered search space produces millions of possible routes between two points. At real scale, most graph databases fall back on the same handful of algorithms, and all of them get slower as the two points get farther apart. We took a different approach. FalkorDB is the first graph database to ship Customizable Contraction Hierarchies (CCH) as a built-in […] - [Using FalkorDB to Give Engineers X-Ray Vision Into Their Data Pipelines](https://www.falkordb.com/blog/falkordb-x-ray-vision-data-pipelines/): Webinar Recording Overview Roi Lipman, CTO & Co-founder, and Gabriel Gomez, Core Software Developer, break down how FalkorDB finds shortest paths at scale. Finding shortest paths in a graph seems simple, until the graph gets large enough that it isn't. Even a small, ordered search space produces millions of possible routes between two points. At real scale, most graph databases fall back on the same handful of algorithms, and all of them get slower as the two points get farther apart. We took a different approach. FalkorDB is the first graph database to ship Customizable Contraction Hierarchies (CCH) as a built-in […] - [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/): Webinar Recording Overview Roi Lipman, CTO & Co-founder, and Gabriel Gomez, Core Software Developer, break down how FalkorDB finds shortest paths at scale. Finding shortest paths in a graph seems simple, until the graph gets large enough that it isn't. Even a small, ordered search space produces millions of possible routes between two points. At real scale, most graph databases fall back on the same handful of algorithms, and all of them get slower as the two points get farther apart. We took a different approach. FalkorDB is the first graph database to ship Customizable Contraction Hierarchies (CCH) as a built-in […] - [Give Your Agents a Brain: Mastering Knowledge Graphs and Agentic Memory](https://www.falkordb.com/news-updates/give-your-agents-a-brain/): Webinar Recording Overview Roi Lipman, CTO & Co-founder, and Gabriel Gomez, Core Software Developer, break down how FalkorDB finds shortest paths at scale. Finding shortest paths in a graph seems simple, until the graph gets large enough that it isn't. Even a small, ordered search space produces millions of possible routes between two points. At real scale, most graph databases fall back on the same handful of algorithms, and all of them get slower as the two points get farther apart. We took a different approach. FalkorDB is the first graph database to ship Customizable Contraction Hierarchies (CCH) as a built-in […] - [Curing AI Amnesia: A Memory Your Agent Builds by Chatting](https://www.falkordb.com/blog/curing-ai-amnesia/): Webinar Recording Overview Roi Lipman, CTO & Co-founder, and Gabriel Gomez, Core Software Developer, break down how FalkorDB finds shortest paths at scale. Finding shortest paths in a graph seems simple, until the graph gets large enough that it isn't. Even a small, ordered search space produces millions of possible routes between two points. At real scale, most graph databases fall back on the same handful of algorithms, and all of them get slower as the two points get farther apart. We took a different approach. FalkorDB is the first graph database to ship Customizable Contraction Hierarchies (CCH) as a built-in […] - [Cypher Query Cheatsheet for Developers: Essential Patterns and Syntax](https://www.falkordb.com/blog/cypher-query-cheatsheet-for-developers/): Webinar Recording Overview Roi Lipman, CTO & Co-founder, and Gabriel Gomez, Core Software Developer, break down how FalkorDB finds shortest paths at scale. Finding shortest paths in a graph seems simple, until the graph gets large enough that it isn't. Even a small, ordered search space produces millions of possible routes between two points. At real scale, most graph databases fall back on the same handful of algorithms, and all of them get slower as the two points get farther apart. We took a different approach. FalkorDB is the first graph database to ship Customizable Contraction Hierarchies (CCH) as a built-in […] - [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/): Webinar Recording Overview Roi Lipman, CTO & Co-founder, and Gabriel Gomez, Core Software Developer, break down how FalkorDB finds shortest paths at scale. Finding shortest paths in a graph seems simple, until the graph gets large enough that it isn't. Even a small, ordered search space produces millions of possible routes between two points. At real scale, most graph databases fall back on the same handful of algorithms, and all of them get slower as the two points get farther apart. We took a different approach. FalkorDB is the first graph database to ship Customizable Contraction Hierarchies (CCH) as a built-in […] - [What Is Hybrid Search in AI? Combining Vectors, Graphs, and Keywords](https://www.falkordb.com/blog/what-is-hybrid-search-in-ai/): Webinar Recording Overview Roi Lipman, CTO & Co-founder, and Gabriel Gomez, Core Software Developer, break down how FalkorDB finds shortest paths at scale. Finding shortest paths in a graph seems simple, until the graph gets large enough that it isn't. Even a small, ordered search space produces millions of possible routes between two points. At real scale, most graph databases fall back on the same handful of algorithms, and all of them get slower as the two points get farther apart. We took a different approach. FalkorDB is the first graph database to ship Customizable Contraction Hierarchies (CCH) as a built-in […] - [Memgraph vs Neo4j: Performance and Architecture for Production Workloads](https://www.falkordb.com/comparison/memgraph-vs-neo4j-performance-and-architecture-for-production-workloads/): Webinar Recording Overview Roi Lipman, CTO & Co-founder, and Gabriel Gomez, Core Software Developer, break down how FalkorDB finds shortest paths at scale. Finding shortest paths in a graph seems simple, until the graph gets large enough that it isn't. Even a small, ordered search space produces millions of possible routes between two points. At real scale, most graph databases fall back on the same handful of algorithms, and all of them get slower as the two points get farther apart. We took a different approach. FalkorDB is the first graph database to ship Customizable Contraction Hierarchies (CCH) as a built-in […] - [Cypher Query Language: The Complete Guide for Graph Database Developers](https://www.falkordb.com/blog/cypher-query-language/): Webinar Recording Overview Roi Lipman, CTO & Co-founder, and Gabriel Gomez, Core Software Developer, break down how FalkorDB finds shortest paths at scale. Finding shortest paths in a graph seems simple, until the graph gets large enough that it isn't. Even a small, ordered search space produces millions of possible routes between two points. At real scale, most graph databases fall back on the same handful of algorithms, and all of them get slower as the two points get farther apart. We took a different approach. FalkorDB is the first graph database to ship Customizable Contraction Hierarchies (CCH) as a built-in […] - [AWS Neptune vs Neo4j: Architectural Differences for Production Workloads](https://www.falkordb.com/comparison/aws-neptune-vs-neo4j-architectural-differences-for-production-workloads/): Webinar Recording Overview Roi Lipman, CTO & Co-founder, and Gabriel Gomez, Core Software Developer, break down how FalkorDB finds shortest paths at scale. Finding shortest paths in a graph seems simple, until the graph gets large enough that it isn't. Even a small, ordered search space produces millions of possible routes between two points. At real scale, most graph databases fall back on the same handful of algorithms, and all of them get slower as the two points get farther apart. We took a different approach. FalkorDB is the first graph database to ship Customizable Contraction Hierarchies (CCH) as a built-in […] - [TigerGraph vs Neo4j: Architectural Trade-Offs for Production Workloads](https://www.falkordb.com/comparison/neo4j-vs-tigergraph/): Webinar Recording Overview Roi Lipman, CTO & Co-founder, and Gabriel Gomez, Core Software Developer, break down how FalkorDB finds shortest paths at scale. Finding shortest paths in a graph seems simple, until the graph gets large enough that it isn't. Even a small, ordered search space produces millions of possible routes between two points. At real scale, most graph databases fall back on the same handful of algorithms, and all of them get slower as the two points get farther apart. We took a different approach. FalkorDB is the first graph database to ship Customizable Contraction Hierarchies (CCH) as a built-in […] - [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/): Webinar Recording Overview Roi Lipman, CTO & Co-founder, and Gabriel Gomez, Core Software Developer, break down how FalkorDB finds shortest paths at scale. Finding shortest paths in a graph seems simple, until the graph gets large enough that it isn't. Even a small, ordered search space produces millions of possible routes between two points. At real scale, most graph databases fall back on the same handful of algorithms, and all of them get slower as the two points get farther apart. We took a different approach. FalkorDB is the first graph database to ship Customizable Contraction Hierarchies (CCH) as a built-in […] - [Graph Database Anti-Patterns That Kill AI Application Performance](https://www.falkordb.com/blog/graph-database-anti-patterns-ai-performance/): Webinar Recording Overview Roi Lipman, CTO & Co-founder, and Gabriel Gomez, Core Software Developer, break down how FalkorDB finds shortest paths at scale. Finding shortest paths in a graph seems simple, until the graph gets large enough that it isn't. Even a small, ordered search space produces millions of possible routes between two points. At real scale, most graph databases fall back on the same handful of algorithms, and all of them get slower as the two points get farther apart. We took a different approach. FalkorDB is the first graph database to ship Customizable Contraction Hierarchies (CCH) as a built-in […] - [FalkorDB vs TigerGraph: Which Graph Database Is Best for AI Workloads?](https://www.falkordb.com/blog/falkordb-vs-tigergraph/): Webinar Recording Overview Roi Lipman, CTO & Co-founder, and Gabriel Gomez, Core Software Developer, break down how FalkorDB finds shortest paths at scale. Finding shortest paths in a graph seems simple, until the graph gets large enough that it isn't. Even a small, ordered search space produces millions of possible routes between two points. At real scale, most graph databases fall back on the same handful of algorithms, and all of them get slower as the two points get farther apart. We took a different approach. FalkorDB is the first graph database to ship Customizable Contraction Hierarchies (CCH) as a built-in […] - [Graph Database Explained: A Beginner's Guide for Developers](https://www.falkordb.com/blog/graph-database-explained/): Webinar Recording Overview Roi Lipman, CTO & Co-founder, and Gabriel Gomez, Core Software Developer, break down how FalkorDB finds shortest paths at scale. Finding shortest paths in a graph seems simple, until the graph gets large enough that it isn't. Even a small, ordered search space produces millions of possible routes between two points. At real scale, most graph databases fall back on the same handful of algorithms, and all of them get slower as the two points get farther apart. We took a different approach. FalkorDB is the first graph database to ship Customizable Contraction Hierarchies (CCH) as a built-in […] - [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. - [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. ## Pages - [GraphRAG](https://www.falkordb.com/graphrag/): Webinar Recording Overview Roi Lipman, CTO & Co-founder, and Gabriel Gomez, Core Software Developer, break down how FalkorDB finds shortest paths at scale. Finding shortest paths in a graph seems simple, until the graph gets large enough that it isn't. Even a small, ordered search space produces millions of possible routes between two points. At real scale, most graph databases fall back on the same handful of algorithms, and all of them get slower as the two points get farther apart. We took a different approach. FalkorDB is the first graph database to ship Customizable Contraction Hierarchies (CCH) as a built-in […] - [QueryWeaver: Graph-Powered Text-to-SQL](https://www.falkordb.com/queryweaver-text-to-sql/): Webinar Recording Overview Roi Lipman, CTO & Co-founder, and Gabriel Gomez, Core Software Developer, break down how FalkorDB finds shortest paths at scale. Finding shortest paths in a graph seems simple, until the graph gets large enough that it isn't. Even a small, ordered search space produces millions of possible routes between two points. At real scale, most graph databases fall back on the same handful of algorithms, and all of them get slower as the two points get farther apart. We took a different approach. FalkorDB is the first graph database to ship Customizable Contraction Hierarchies (CCH) as a built-in […] - [FalkorDB | Graph Database](https://www.falkordb.com/falkordb-graph-database/): Webinar Recording Overview Roi Lipman, CTO & Co-founder, and Gabriel Gomez, Core Software Developer, break down how FalkorDB finds shortest paths at scale. Finding shortest paths in a graph seems simple, until the graph gets large enough that it isn't. Even a small, ordered search space produces millions of possible routes between two points. At real scale, most graph databases fall back on the same handful of algorithms, and all of them get slower as the two points get farther apart. We took a different approach. FalkorDB is the first graph database to ship Customizable Contraction Hierarchies (CCH) as a built-in […] - [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/): Webinar Recording Overview Roi Lipman, CTO & Co-founder, and Gabriel Gomez, Core Software Developer, break down how FalkorDB finds shortest paths at scale. Finding shortest paths in a graph seems simple, until the graph gets large enough that it isn't. Even a small, ordered search space produces millions of possible routes between two points. At real scale, most graph databases fall back on the same handful of algorithms, and all of them get slower as the two points get farther apart. We took a different approach. FalkorDB is the first graph database to ship Customizable Contraction Hierarchies (CCH) as a built-in […] - [Graph-Powered Cyber Security](https://www.falkordb.com/cyber-security/): Webinar Recording Overview Roi Lipman, CTO & Co-founder, and Gabriel Gomez, Core Software Developer, break down how FalkorDB finds shortest paths at scale. Finding shortest paths in a graph seems simple, until the graph gets large enough that it isn't. Even a small, ordered search space produces millions of possible routes between two points. At real scale, most graph databases fall back on the same handful of algorithms, and all of them get slower as the two points get farther apart. We took a different approach. FalkorDB is the first graph database to ship Customizable Contraction Hierarchies (CCH) as a built-in […] - [Use Cases](https://www.falkordb.com/use-cases/): Webinar Recording Overview Roi Lipman, CTO & Co-founder, and Gabriel Gomez, Core Software Developer, break down how FalkorDB finds shortest paths at scale. Finding shortest paths in a graph seems simple, until the graph gets large enough that it isn't. Even a small, ordered search space produces millions of possible routes between two points. At real scale, most graph databases fall back on the same handful of algorithms, and all of them get slower as the two points get farther apart. We took a different approach. FalkorDB is the first graph database to ship Customizable Contraction Hierarchies (CCH) as a built-in […] - [FalkorDB vs Neo4j](https://www.falkordb.com/falkordb-vs-neo4j/): Webinar Recording Overview Roi Lipman, CTO & Co-founder, and Gabriel Gomez, Core Software Developer, break down how FalkorDB finds shortest paths at scale. Finding shortest paths in a graph seems simple, until the graph gets large enough that it isn't. Even a small, ordered search space produces millions of possible routes between two points. At real scale, most graph databases fall back on the same handful of algorithms, and all of them get slower as the two points get farther apart. We took a different approach. FalkorDB is the first graph database to ship Customizable Contraction Hierarchies (CCH) as a built-in […] - [GraphRAG-SDK](https://www.falkordb.com/graphrag-sdk/): Webinar Recording Overview Roi Lipman, CTO & Co-founder, and Gabriel Gomez, Core Software Developer, break down how FalkorDB finds shortest paths at scale. Finding shortest paths in a graph seems simple, until the graph gets large enough that it isn't. Even a small, ordered search space produces millions of possible routes between two points. At real scale, most graph databases fall back on the same handful of algorithms, and all of them get slower as the two points get farther apart. We took a different approach. FalkorDB is the first graph database to ship Customizable Contraction Hierarchies (CCH) as a built-in […] - [Media](https://www.falkordb.com/media-center/): Webinar Recording Overview Roi Lipman, CTO & Co-founder, and Gabriel Gomez, Core Software Developer, break down how FalkorDB finds shortest paths at scale. Finding shortest paths in a graph seems simple, until the graph gets large enough that it isn't. Even a small, ordered search space produces millions of possible routes between two points. At real scale, most graph databases fall back on the same handful of algorithms, and all of them get slower as the two points get farther apart. We took a different approach. FalkorDB is the first graph database to ship Customizable Contraction Hierarchies (CCH) as a built-in […] - [Plans](https://www.falkordb.com/plans/): Webinar Recording Overview Roi Lipman, CTO & Co-founder, and Gabriel Gomez, Core Software Developer, break down how FalkorDB finds shortest paths at scale. Finding shortest paths in a graph seems simple, until the graph gets large enough that it isn't. Even a small, ordered search space produces millions of possible routes between two points. At real scale, most graph databases fall back on the same handful of algorithms, and all of them get slower as the two points get farther apart. We took a different approach. FalkorDB is the first graph database to ship Customizable Contraction Hierarchies (CCH) as a built-in […] - [Homepage](https://www.falkordb.com/): Webinar Recording Overview Roi Lipman, CTO & Co-founder, and Gabriel Gomez, Core Software Developer, break down how FalkorDB finds shortest paths at scale. Finding shortest paths in a graph seems simple, until the graph gets large enough that it isn't. Even a small, ordered search space produces millions of possible routes between two points. At real scale, most graph databases fall back on the same handful of algorithms, and all of them get slower as the two points get farther apart. We took a different approach. FalkorDB is the first graph database to ship Customizable Contraction Hierarchies (CCH) as a built-in […] - [Book a Demo](https://www.falkordb.com/get-a-demo/): Webinar Recording Overview Roi Lipman, CTO & Co-founder, and Gabriel Gomez, Core Software Developer, break down how FalkorDB finds shortest paths at scale. Finding shortest paths in a graph seems simple, until the graph gets large enough that it isn't. Even a small, ordered search space produces millions of possible routes between two points. At real scale, most graph databases fall back on the same handful of algorithms, and all of them get slower as the two points get farther apart. We took a different approach. FalkorDB is the first graph database to ship Customizable Contraction Hierarchies (CCH) as a built-in […] - [Graph Database Graph Size Calculator](https://www.falkordb.com/graph-database-graph-size-calculator/): Webinar Recording Overview Roi Lipman, CTO & Co-founder, and Gabriel Gomez, Core Software Developer, break down how FalkorDB finds shortest paths at scale. Finding shortest paths in a graph seems simple, until the graph gets large enough that it isn't. Even a small, ordered search space produces millions of possible routes between two points. At real scale, most graph databases fall back on the same handful of algorithms, and all of them get slower as the two points get farther apart. We took a different approach. FalkorDB is the first graph database to ship Customizable Contraction Hierarchies (CCH) as a built-in […] - [Community Projects](https://www.falkordb.com/community-projects/): Webinar Recording Overview Roi Lipman, CTO & Co-founder, and Gabriel Gomez, Core Software Developer, break down how FalkorDB finds shortest paths at scale. Finding shortest paths in a graph seems simple, until the graph gets large enough that it isn't. Even a small, ordered search space produces millions of possible routes between two points. At real scale, most graph databases fall back on the same handful of algorithms, and all of them get slower as the two points get farther apart. We took a different approach. FalkorDB is the first graph database to ship Customizable Contraction Hierarchies (CCH) as a built-in […] - [Try Free](https://www.falkordb.com/try-free/): Webinar Recording Overview Roi Lipman, CTO & Co-founder, and Gabriel Gomez, Core Software Developer, break down how FalkorDB finds shortest paths at scale. Finding shortest paths in a graph seems simple, until the graph gets large enough that it isn't. Even a small, ordered search space produces millions of possible routes between two points. At real scale, most graph databases fall back on the same handful of algorithms, and all of them get slower as the two points get farther apart. We took a different approach. FalkorDB is the first graph database to ship Customizable Contraction Hierarchies (CCH) as a built-in […] - [CR Lobby](https://www.falkordb.com/cr-lobby/): Webinar Recording Overview Roi Lipman, CTO & Co-founder, and Gabriel Gomez, Core Software Developer, break down how FalkorDB finds shortest paths at scale. Finding shortest paths in a graph seems simple, until the graph gets large enough that it isn't. Even a small, ordered search space produces millions of possible routes between two points. At real scale, most graph databases fall back on the same handful of algorithms, and all of them get slower as the two points get farther apart. We took a different approach. FalkorDB is the first graph database to ship Customizable Contraction Hierarchies (CCH) as a built-in […] - [Privacy Policy](https://www.falkordb.com/privacy-policy/): Webinar Recording Overview Roi Lipman, CTO & Co-founder, and Gabriel Gomez, Core Software Developer, break down how FalkorDB finds shortest paths at scale. Finding shortest paths in a graph seems simple, until the graph gets large enough that it isn't. Even a small, ordered search space produces millions of possible routes between two points. At real scale, most graph databases fall back on the same handful of algorithms, and all of them get slower as the two points get farther apart. We took a different approach. FalkorDB is the first graph database to ship Customizable Contraction Hierarchies (CCH) as a built-in […] - [Blog](https://www.falkordb.com/blog/): Webinar Recording Overview Roi Lipman, CTO & Co-founder, and Gabriel Gomez, Core Software Developer, break down how FalkorDB finds shortest paths at scale. Finding shortest paths in a graph seems simple, until the graph gets large enough that it isn't. Even a small, ordered search space produces millions of possible routes between two points. At real scale, most graph databases fall back on the same handful of algorithms, and all of them get slower as the two points get farther apart. We took a different approach. FalkorDB is the first graph database to ship Customizable Contraction Hierarchies (CCH) as a built-in […] - [Contact Us](https://www.falkordb.com/contact-us/): Webinar Recording Overview Roi Lipman, CTO & Co-founder, and Gabriel Gomez, Core Software Developer, break down how FalkorDB finds shortest paths at scale. Finding shortest paths in a graph seems simple, until the graph gets large enough that it isn't. Even a small, ordered search space produces millions of possible routes between two points. At real scale, most graph databases fall back on the same handful of algorithms, and all of them get slower as the two points get farther apart. We took a different approach. FalkorDB is the first graph database to ship Customizable Contraction Hierarchies (CCH) as a built-in […] - [Jobs](https://www.falkordb.com/jobs/): Webinar Recording Overview Roi Lipman, CTO & Co-founder, and Gabriel Gomez, Core Software Developer, break down how FalkorDB finds shortest paths at scale. Finding shortest paths in a graph seems simple, until the graph gets large enough that it isn't. Even a small, ordered search space produces millions of possible routes between two points. At real scale, most graph databases fall back on the same handful of algorithms, and all of them get slower as the two points get farther apart. We took a different approach. FalkorDB is the first graph database to ship Customizable Contraction Hierarchies (CCH) as a built-in […] - [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)