Context and memory for LLMs and agents
GraphRAG
Ground LLM answers in your knowledge graphAgentic AI
Agents grounded in how your domain connectsAgentic memory
Queryable memory that carries context across sessionsChatbots
Context-aware chatbots with long-term memoryProven patterns on connected data
Fraud detection
Trace rings of accounts, devices and transactions liveCybersecurity
Assets, findings and threats queried as one graphSupply chain
See how one supplier delay ripples downstreamReal-time recommendations
Suggestions based on how things actually relate
Security teams need real-time attack path discovery across multi-cloud environments. Graph databases process entity relationships for sub-100ms threat analysis.
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.
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.
Graph Query Language demystified: learn how FalkorDB simplifies querying for LLM and GraphRAG workflows using Cypher-like syntax.
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.
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.
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.
Discover how to leverage LlamaIndex RAG with FalkorDB to create efficient GraphRAG systems. Enhance LLM performance with knowledge graph-powered retrieval and generation.
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.
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
