Resolution criteria

This market resolves YES if at least one major LLM provider (OpenAI, Anthropic, Google, Meta, Mistral, or Cohere) publicly documents graph-based RAG as a core part of its production architecture in 2026. "Publicly documents" means the company explicitly describes graph-based RAG in official technical documentation, architecture diagrams, blog posts, or research papers. "Core part of production architecture" means the company indicates graph-based RAG is a fundamental component of how their LLM systems operate in production, not merely an optional feature or research prototype.

The market resolves NO if no major LLM provider meets these criteria by December 31, 2026.

Background

Microsoft Research's GraphRAG uses the LLM to create a knowledge graph based on the private dataset. The knowledge graph construction landscape reached production maturity in 2024–2025, with organizations achieving 300–320% ROI and measurable business impact across finance, healthcare, and manufacturing. In 2026, LangChain Community Graphs' Neo4jCypher QA is transforming enterprise AI, powering 65% of graph-based RAG systems according to the 2025 GraphML Summit report. However, most current implementations are third-party frameworks and enterprise deployments rather than core LLM provider architectures. Claude automatically switches to a faster mode powered by RAG that keeps response times quick while maintaining quality responses, expanding project capacity by up to 10x. Google's Gemini Enterprise documentation describes RAG workflows but does not explicitly highlight graph-based retrieval as a core architectural component.

Considerations

Graph-based RAG differs from traditional vector-based RAG by using structured knowledge graphs to enable relationship-based reasoning and multi-hop queries. GraphRAG addresses Vanilla RAG's core limitations through enhanced relational reasoning, enabling LLMs to retrieve information not explicitly mentioned in datasets by analyzing interconnected data relationships. The distinction matters because many LLM providers currently document vector-based RAG but not graph-based approaches. Additionally, "core part of production architecture" is subjective—a company might use graph-based RAG internally without publicly positioning it as core to their architecture, or they might mention it in research contexts without integrating it into production systems.