Sisteme de baze de date care integrează modele de stocare de tip graf, document și cheie-valoare într-o singură arhitectură unificată.
SurrealDB is a multi-model database engine designed to store and query document, graph, relational, and vector data within a single ACID-compliant platform. It functions as an AI-native data store, integrating vector search, graph traversal, and machine learning model execution directly into its query layer. By providing a unified declarative query language, the platform eliminates the need for external middleware to synchronize data across different storage models. The platform distinguishes itself through its ability to manage agent memory and complex workflows natively. It allows developer
SurrealDB is a multi-model database engine that natively stores and queries graph, document, relational, and key-value data within a single ACID-compliant platform, using a unified declarative query language, which directly matches your need for an integrated graph+document+key-value database with schema flexibility.
This project is a multi-model database system designed to store and manage information as documents, graphs, and key-value pairs within a single engine. It functions as a graph database and knowledge graph platform, providing the infrastructure to build, query, and visualize structured data models. By integrating vector search capabilities, the system serves as a vector database that supports retrieval-augmented generation for artificial intelligence applications. The platform distinguishes itself through a unified query language that allows users to perform document lookups, graph traversals
ArangoDB is a native multi-model database that supports graph, document, and key-value data models in a single engine with a unified query language (AQL), ACID transactions, and schema-free JSON storage, directly matching every required feature for this search.