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
Explore RedisGraph EOL in this dev guide with practical migration steps and technical validation tips for switching to FalkorDB.
FalkorDB’s integration with Cognee empowers developers to build smarter AI systems with improved query precision and reduced hallucinations.
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
When building AI-driven systems, FalkorDB vs Neo4j graph databases offer different advantages. Find the best fit for your AI needs.
Knowledge graph visualization offers deep insights, enhancing decision-making for AI applications with FalkorDB.
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
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
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
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
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
