# FalkorDB

> Ultra-fast, Multi-tenant Graph Database


## Posts

- [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, […]
- [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 […]
- [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 […]
- [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 […]
- [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 […]
- [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 […]
- [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 […]
- [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), […]
- [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 […]
- [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 […]
- [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. […]
- [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.
- [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. 
- [Multigraph Topology for Scalable, Isolated Graph Workloads](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.
- [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.
- [Migrate from Relational Database to Graph Database](https://www.falkordb.com/blog/relational-database-to-graph-database-2/): 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.
- [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, […]
- [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: Key Technical Differences](https://www.falkordb.com/blog/vector-database-vs-graph-database/): Unstructured data is all the data that isn’t organized in a predefined format but is stored in its native form. Due to this lack of organization, it becomes more challenging to sort, extract, and analyze. More than 80% of all enterprise data is unstructured, and this number is growing. This type of data comes from various sources such as emails, social media, customer reviews, support queries, or product descriptions, which businesses seek to extract meaningful insights from. The rapid growth of unstructured data presents both a challenge and an opportunity for businesses. To extract insights from unstructured data, the modern […]
- [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 […]
- [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 […]
- [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 […]
- [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 […]
- [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 […]
- [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 […]
- [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 […]
- [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 […]
- [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 […]
- [Beyond Rows and Columns: Exploring the Missing Third Dimension](https://www.falkordb.com/blog/beyond-rows-and-columns-exploring-the-missing-third-dimension/): If you are working with data, you might be familiar with the concepts of rows and columns, which are the basic building blocks of most database models. However, there is another dimension that is often overlooked or ignored, which can offer new possibilities and insights for your data analysis. In this blog post, I will compare three common database storage models: row-based, column-based and network-based. These models affect how data is stored, accessed and manipulated in a database system along three dimensions. The first dimension The first dimension, and the most common one, is the row-based (aka Relational) model, where data […]
- [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 […]

## Pages

- [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/): AWS Neptune vs FalkorDB The best AWS Neptune Alternative Ready to leave behind ballooning costs, poor latency and inadequate performance? Licensing FalkorDB Neptune Open Source ✓ ✗ Architecture FalkorDB Neptune Data Storage In-memory On disk Written in C, Rust Java / C++ Enterprise Features FalkorDB Neptune Multi-tenancy ✓ All plans X Replication ✓ ✓ Performance FalkorDB Neptune Latency Ultra-low Poor Memory Efficiency Highly efficient Poor Horizontal Scaling Flexible X Query & Processing FalkorDB Neptune Query language Cypher Gremlin, Cypher (limited) Query execution Plans ✓ ✓ Vector & Full-text Search ✓ X Deployment Options FalkorDB Neptune On-prem ✓ X Cloud ✓ […]
- [Graph-Powered Cyber Security](https://www.falkordb.com/cyber-security/): The Leading Graph Database for Cybersecurity The only open-source, multi-tenant and ultra-fast security graph for CNAPP, CSPM & CIEM that’s ready to scale with you.  Get Private Demo 10K+ Multi-Tenant Graph Real-Time Detection Cypher support “Defenders think in lists. Attackers think in graphs. As long as this is true, attackers win.” John Lambert Distinguished Engineer at Microsoft Graph modeling of your cyber data Your security data is already interconnected, making graph representation ideal for advanced Cypher querying. Built-in multi-tenancy Manage 10K+ tenants effortlessly with fully isolated tenant data, zero wasted infrastructure, and out-of-the-box scalability for future workloads. Real-time detection Current […]
- [Use Cases](https://www.falkordb.com/use-cases/): GraphRAG & Graph Use Cases Build Reliable, Scalable AI and Security Systems on FalkorDB’s Graph Technology Stop choosing between scale, latency, and accuracy. Use our high-performance, graph-native database enabling precise generative AI, fraud detection, access management, and advanced GraphRAG solutions. Start Free 10K+ Multi-Graph (Tenants) Horizontal Scaling Vector Capabilities Open-source Generative AI Precise Context for AI Models FalkorDB connects datasets to deliver precise context, significantly improving the accuracy and relevance of generative AI outputs. Developers leverage seamless data retrieval to optimize model performance. Accurate data retrieval Enhanced output relevance Seamless dataset linking Fraud Detection Detect Fraud in Real-time Identify fraudulent activities […]
- [FalkorDB vs Neo4j](https://www.falkordb.com/falkordb-vs-neo4j/): Neo4j vs FalkorDB The best Neo4j Alternative Ready to leave behind ballooning costs, poor latency and inadequate performance? FalkorDB vs Neo4j Comparison Licensing FalkorDB Neo4j Open Source ✓ ✗ Architecture FalkorDB Neo4j Data Storage In-memory On disk Written in C, Rust Java Enterprise Features FalkorDB Neo4j Multi-tenancy ✓ All plans Enterprise plan only Replication ✓ ✓ Performance FalkorDB Neo4j Latency 496x faster Poor Memory Efficiency Highly efficient Baseline Horizontal Scaling Flexible Limited Query & Processing FalkorDB Neo4j Query language Cypher Cypher Query execution Plans ✓ ✓ Vector & Full-text Search ✓ ✓ Deployment Options FalkorDB Neo4j On-prem ✓ Single-instance only […]
- [GraphRAG-SDK](https://www.falkordb.com/graphrag-sdk/): Get GraphRAG Right with Our SDK A specialized toolkit for building Graph Retrieval-Augmented Generation (GraphRAG) systems, integrating knowledge graphs, ontology management, and large language models (LLMs) to deliver accurate, efficient, and customizable RAG workflows. Start Free OPEN-SOURCE STRUCTURED & UNSTRUCTURED DATA OPTIMIZED FOR AI ONTOLOGY DISCOVERY KNOWLEDGE GRAPH CONSTRUCTION MULTI-AGENT RAG Remove the barrier of ontology creation Transform raw data into structured knowledge models automatically. Use generative AI to detect and construct ontologies from your datasets Review, modify and iterate on detected ontologies to optimize graph structures Define custom parameters to scope and control ontology detection processes Handle structured and unstructured […]
- [Media](https://www.falkordb.com/media-center/): FalkorDB Media Center Download FalkorDB’s sales and marketing collateral and assets for promotional use.  Chat on Discord Join the discussion Contact us FalkorDB’s logo Download FalkorDB’s set of logos (both color and dark versions) Download Do not change, modify, alter, or otherwise change the logo in any way. Need assistance? reach out to us. FalkorDB’s ‘F’ Icon Download FalkorDB’s set of icons (both color and dark versions) Download Do not change, modify, alter, or otherwise change the logo in any way. Need assistance? reach out to us. #7466FF #FF804D #191919 #8051E30D #FCFCFC Our story Our mission is clear: to empower […]
- [Plans](https://www.falkordb.com/plans/): Plans built to go to production faster. View Pricing information inside the cloud instance. For more info, please reach out to Sales. FalkorDB Pricing Table FREE Build a Powerful MVP Multi-Graph / Multi-Tenancy ✓ Graph Access Control ✓ TLS ✗ VPC ✗ Cluster Deployment ✗ High Availability ✗ Multi-zone Deployment ✗ Scalability ✗ Continuous Persistence ✗ Automated Backups ✗ Advanced Monitoring ✗ Support Community Dedicated Account Manager ✗ Cloud Providers Sign up STARTUP From $73/1GB/Month Multi-Graph / Multi-Tenancy ✓ Graph Access Control ✓ TLS ✓ VPC ✗ Cluster Deployment ✗ High Availability ✗ Multi-zone Deployment ✗ Scalability ✗ Continuous Persistence […]
- [Homepage](https://www.falkordb.com/): Ultra-fast, Multi-tenant Graph Database Powering GenAI Start Free See Repo Available On All Major Platforms FalkorDB Integrations Slider 10K+ MULTI-GRAPHS(TENANTS) OPEN SOURCE LINEAR SCALABILITY ULTRA-LOW LATENCY GRAPHRAG & AGENTMEMORY OPTIMIZED REDIS-BASED Purpose-built to Your Use Case GraphRAG Agentic AI Chatbots Fraud Detection Security Graph AI Retrieval GraphRAG Combine LLMs with domain-specific knowledge graphs to reduce hallucinations and enrich AI responses. Enable natural language queries, traceable retrieval logic, and hidden insight discovery for smarter decision-making and faster AI deployment. Work with structured and unstructured data Ontology auto-detection Built-in agent orchestration Personalized Systems Agentic AI Combine graph traversal with vector search to […]
- [Book a Demo](https://www.falkordb.com/get-a-demo/): BOOK A DEMO Schedule a time that works for you. FalkorDB’s graph database is designed for development teams managing structured and unstructured interconnected data in real-time or interactive environments. Go from concept to enterprise-grade in weeks, not months. Multi-tenant: Manage thousands of graphs in a single instance Ultra-low latency, enabling Real-Time applications Cloud, On-prem and BYOC Fully open-source SOC 2 Type II Compliant “FalkorDB has been a game-changer for our startup. Disappointed by Neo4j’s lack of support and slow performance, we turned to FalkorDB as a robust backend solution. Our wish? To run non-traversal queries with performance rivaling MongoDB or […]
- [Graph Database Graph Size Calculator](https://www.falkordb.com/graph-database-graph-size-calculator/): Graph Size Calculator Graph Size Calculator Estimate the memory requirements for your graph database based on nodes, edges, and properties. Number of nodes Number of edges Properties per node Properties per edge Recommended Size: 3326 MB Common use cases’ estimates: Number of nodes: 1,000,000 | Number of edges: 50,000,000 | Properties/node: 8 | Properties/edge: 1 Need help estimating costs? Need to speak to our Solution Architects?Book here Chat on Discord Join the discussion Email us
- [Community Projects](https://www.falkordb.com/community-projects/): GraphRAG Community Projects Check out community projects with FalkorDB Get Started Free View Documentation POPULAR HackerNoon The One Tool You Absolutely Need to Efficiently Scale Retrieval-Augmented Generation To further enhance the efficiency and scalability of RAG workflows, integrating a high-performance database like FalkorDB is essential. FalkorDB provides a reliable and scalable storage solution for the dynamic knowledge bases that RAG systems depend on, ensuring rapid data retrieval and seamless integration with messaging systems like KubeMQ. TrustGraph Podcast Knowledge Graphs and the Future of Hybrid RAG | Our Agentic Future TrustGraph Podcast: The CTO and Cofounder of FalkorDB, Roi Lipman, describes how […]
- [Try Free](https://www.falkordb.com/try-free/): Try FalkorDB’s Graph Database Run on Cloud Get instant access to cloud resources and advanced capabilities to handle complex graph-based AI applications with ease. Launch a Free Instance ↗ Run via Docker docker pull falkordb/falkordb:latest-alpine See Full Guide FalkorDB Integrations Slider Enhance your project with powerful frameworks and integrations AI AGENT MEMORY Graphiti (by ZEP) A Python framework for building temporally-aware, multi-tenant live knowledge graph designed for multi-agent AI systems with memory. Explore KNOWLEDGE GRAPH VISUALIZATION g.v() All-in-one graph database client to write, debug, test and analyze results for your property-graph database. Explore AI AGENT MEMORY Cognee Cognee’s mapping of […]
- [CR Lobby](https://www.falkordb.com/cr-lobby/)
- [Privacy Policy](https://www.falkordb.com/privacy-policy/): Last updated: July 22, 2024   This Privacy Policy describes Our policies and procedures on the collection, use and disclosure of Your information when You use the Service and tells You about Your privacy rights and how the law protects You. We use Your Personal data to provide and improve the Service. By using the Service, You agree to the collection and use of information in accordance with this Privacy Policy. This Privacy Policy has been created with the help of the Free Privacy Policy Generator. Interpretation and Definitions Interpretation The words of which the initial letter is capitalized have […]
- [Blog](https://www.falkordb.com/blog/): LATEST NEWS 08 / 12 : Talk with Your Graph Database: Natural Language Queries with FalkorDB and Text-to-Cypher 08 / 10 : String Interning in Graph Databases: Save Memory, Boost Performance 08 / 04 : Building Temporal Knowledge Graphs with Graphiti 07 / 31 : Neo4j to FalkorDB: Migrate for Speed & Scale 07 / 30 : 6 Reasons you need a graph to store your security data Blog Latest news on GraphRAG, Security Graph and Graph Databases Subscribe for how-to guides, workshop invitations, product announcements and occasional opinions. Upcoming workshops & events Want to participate, co-host or be a guest in […]
- [Contact Us](https://www.falkordb.com/contact-us/): CONTACT US Let’s direct your inquiry Setting up your own integration is the fastest and most efficient way to start using FalkorDB. However, if your organization manages a high volume of data and has advanced integration needs, our support team would be happy to guide you. Tell us about your project and we’ll get you to the right place. Chat on Discord Join the discussion Need to speak to our Solution Architects?Book here
- [Jobs](https://www.falkordb.com/jobs/): Careers Jobs & Open Positions Join the team building the world’s fastest graph database for AI. R&D Core Developer Tel Aviv, Israel (Hybrid) Full-Time Design, implement, test, and optimize core components of the FalkorDB graph database engine. Collaborate to integrate with LLMs and AI applications. View Position → Overview As a core developer at FalkorDB, you will be responsible for: Designing, implementing, testing, and optimizing core components of the FalkorDB graph database engine Collaborating with other developers and researchers to integrate FalkorDB with LLMs and other applications Troubleshooting and resolving issues and bugs related to FalkorDB performance and functionality Researching […]
- [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)