HydraDB vs Dgraph
A Dgraph alternative for agent context
Use HydraDB when the graph is persistent context for agents rather than primarily a distributed application database.
Dgraph is a distributed graph database built around GraphQL and DQL. It supports predicate-based sharding and HNSW vector search through @embedding fields.
Its durable state is stored in Badger on Alpha nodes, and a typical highly available deployment includes 3 Zero nodes and 3 Alpha nodes. As the durable graph grows, the infrastructure storing and serving that graph must grow with it.
HydraDB separates those concerns. Durable graph history stays in object storage, while query and indexing compute can scale with current workload instead of total historical context.
HydraDB also builds retrieval around agent context: entities and relationships can be extracted during ingestion, while recall combines semantic, keyword, graph, and recency signals.
| Dimension | HydraDB | Dgraph |
|---|---|---|
| Focus | AI context infrastructure | Distributed general-purpose graph |
| Ingestion | Entities and relationships from ingested context | Application writes the graph |
| Retrieval | Semantic + keyword + graph + recency | DQL/GraphQL + HNSW |
| Temporal | Versioned graph state | Application-defined |
| Storage | Object store with decoupled compute | Badger on Alpha nodes |
| Best fit | Persistent agent context | Scalable GraphQL-native applications |
Build agent context that stays correct over time
HydraDB keeps evolving historical state and retrieval logic together so agents receive context they can trust.
HydraDB
