Not only supports Gremlin graph query language and RESTful API but also provides commonly used graph algorithm APIs. To help users easily implement various queries and analyses, HugeGraph has a full range of accessory tools, such as supporting distributed storage, data replication, scaling horizontally, and supports many built-in backends of storage engines.

Has been deeply optimized in graph storage and graph computation. It provides multiple batch import tools that can easily complete the fast-import of tens of billions of data, achieves millisecond-level response for graph retrieval through ameliorated queries, and supports concurrent online and real-time operations for thousands of users.

Adapts to the Apache Gremlin standard graph query language and the Property Graph standard modeling method, and both support graph-based OLTP and OLAP schemes. Furthermore, HugeGraph can be integrated with Hadoop and Spark’s big data platforms, and easily extend the back-end storage engine through plug-ins.

Integrates LLM for GraphRAG intelligent Q&A, automated knowledge graph construction, with 20+ built-in graph machine learning algorithms to easily build AI-driven graph applications.

Supports horizontal scaling and distributed deployment, seamlessly migrating from standalone to PB-level clusters, with multiple storage engine options for different scale and performance requirements.

Adheres to Apache TinkerPop standards, provides multi-language clients (Java, Python, Go), compatible with mainstream big data platforms, with an active and continuously evolving community.