Arango

Arango, formerly ArangoDB, is a graph-native contextual data platform that unifies graph, vector, document, key-value, and full-text search data models in a single system to serve as the data foundation for agentic AI applications. The platform version 4.0 introduces AutoGraph, which automatically ingests fragmented enterprise data and builds a governed knowledge graph by extracting entities, resolving relationships, and maintaining schema as data changes, reducing knowledge graph build time from months to weeks. Auto Ingestion and Retrieval eliminates runtime pipeline overhead by ingesting structured, semi-structured, and unstructured data into a unified contextual data layer. The AQL query language provides declarative access across all data models, while the Deep Search feature delivers hybrid vector and graph retrieval for GraphRAG and HybridRAG implementations. Pre-built MCP integrations connect AI agents directly to the contextual data layer without runtime context rebuilding. Performance benchmarks demonstrate 2000x faster workloads compared to multi-system Frankenstacks and 70-percent infrastructure simplification by replacing separate vector store, graph database, search index, and governance layer deployments. The platform provides enterprise-grade high availability, disaster recovery, role-based access control, elastic horizontal scaling, and graph-native lineage for auditable decision tracing. Proven in over 200 production environments worldwide, customers include Articul8 for enterprise LLM platforms, Cloudera for hybrid data management, Cloud Imperium Games for Star Citizen telemetry, and Cycode for application security analysis. Official drivers cover Python, JavaScript, Java, Go, Rust, and Csharp. Arango University provides certification programs.