You’re probably staring at a bot that looks fine in a demo and then falls apart the moment real users ask messy questions, switch topics
You’re probably dealing with the same kind of mess most AI teams hit early on. A workflow works in staging, then a vendor changes a
GraphRAG · FalkorDB From documents to answers: a knowledge graph you can question Upload your documents, watch a knowledge graph build itself in real time,
Most retrieval systems in production today rely on a single search modality, and each one fails in predictable ways. Keyword search misses semantic intent. Vector
AI agents that forget everything between invocations are operationally useless for complex workflows. Multi-step reasoning, personalized recommendations, and enterprise knowledge retrieval all demand persistent, structured
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.
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.