Do We Still Need GraphRAG? Benchmarking RAG and GraphRAG for Agentic Search Systems

This project addresses a high-signal architectural question: whether the high pre-computation cost of GraphRAG (pioneered by Microsoft Research) is rendered obsolete by the 'agentic' paradigm where LLMs perform iterative, multi-step searches. As a benchmark, its defensibility is low (3/10) because it is a research artifact rather than a platform with a moat. While it has 4 forks within 16 days, indicating immediate academic interest, its value lies in the data it provides to system architects rather than a proprietary technology. The frontier risk is high because labs like OpenAI (SearchGPT) and Google are natively optimizing the trade-offs between retrieval density and agentic reasoning at the model level. If frontier models become efficient enough at multi-step reasoning, the specialized indexing required for GraphRAG may become a niche requirement for specific 'global query' use cases rather than a general standard. The project competes intellectually with Microsoft's GraphRAG and LangChain's multi-agent templates, but is at risk of displacement as soon as the next generation of models (e.g., GPT-5, Claude 4) significantly expands context windows or internal reasoning paths.