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.
This post is a hands-on walkthrough for developers who want to get up and running with Graphiti and FalkorDB, fast.
Graphiti now supports FalkorDB backend for multi-agent environments, addressing performance and isolation requirements in production AI agent deployments with sub-10ms queries.
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.
The latest KPMG AI Report highlights data quality and privacy as major roadblocks. Discover why GraphRAG is essential for enterprise AI agents.
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.
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.
Learn to build AI agents with memory using LangChain and FalkorDB. This integration enables context-aware AI applications, leveraging graph databases for enhanced capabilities.
Process documents directly using our string loader feature. Integrate LangChain and LlamaIndex to chunk and load data, building tailored knowledge graphs.
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.