FalkorDB shipped graphrag-sdk v1.0.0rc1 and I wanted to see how it feels on real content, not a toy dataset. An afternoon of "let me just try it" turned into a few days of "if I'm going to have an opinion, I should measure it against something." The something, obviously, was neo4j-graphrag. Same corpus, same LLM, same embedder, same 25-question set, same blind judge. The whole thing — ingest, 25 queries, and the judge rubric across both stacks — costs about $0.15 to reproduce end-to-end. This is a write-up of what I did, what broke, what the numbers actually say, and what I'd do differently. I'm not here to crown a winner. I'm here to show what it took to compare them honestly. Repo: github.com/FalkorDB/graphrag-sdk-demo . The corpus and the pipeline The corpus is 8 FalkorDB blog posts and case studies, roughly 140 KB of Markdown. Topics range from "what is GraphRAG" to the Securin threat-intel case study to a March 2026 cybersecurity webinar announcement.…