RAGU introduces multi-step GraphRAG with compact Meno-Lite-0.1 extractor

RAGU is an open-source modular GraphRAG engine that improves structured knowledge integration by separating extraction from consolidation through a two-stage process involving typed extraction, deduplication, summarization, and community detection.

The system utilizes Meno-Lite-0.1, a 7B model optimized for language skills, which outperforms Qwen2.5-32B on knowledge-graph construction by +12.5% relative harmonic mean while matching it on English GraphRAG tasks. On GraphRAG-Bench (Medical), RAGU achieves evidence recall up to 0.84 and overtakes HippoRAG2 on synthesis tasks, demonstrating that previous advantages of competitors were largely answer-format artifacts.

RAGU is installable via pip, runs on a single GPU, and is released under the MIT license with source code available on GitHub.