Samyama Graph aims to unify vector search and graph traversal in one database

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Most GraphRAG systems rely on separate vector and graph databases, forcing application code to manually join results from both layers. This split architecture means no single query planner can optimize vector search, graph traversal, and ranking together, adding complexity and reducing efficiency. Samyama Graph is a new Rust-native database designed to address this by combining OpenCypher-style graph querying, vector search, and graph algorithms in a single engine. The project targets GraphRAG workloads, knowledge graphs, AI agent memory, and large-scale relationship analytics. The core argument is that keeping semantic retrieval and graph reasoning in one system removes the need for custom orchestration logic and enables unified query optimization.

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