HydraDB ✓

HydraDB is building the fastest GraphDB built on object storage: extremely scalable, cheap, and agent native.

Most teams building stateful AI end up stitching together five systems: a vector database, a graph database, a relational store, filesystem primitives, and a cache. You spend months planning, write brittle code, maintain it constantly and everything still fails in production.

Either embeddings miscalculate similarity, results aren’t relevant or personalized, or costs scale exponentially. Ultimately, your agents and evals suffer.

HydraDB solves that by giving you one graph that learns every user preference, retains every agent experience, and delivers personalized context the moment your agent needs it.

We give you primitives so that you can build your own context stores, memory layers, and workflows that require context for your AI. Think of it as a graph-native context delivery mechanism for your agents.


The graph, the memory primitives, the retrieval pipeline, and the ranking knobs are yours to compose. Your context. Your opinions.

What are people building with HydraDB?

In-house memory systems : Graphs work better for storing user preferences, past interactions, and agent traces.

Structures on domain knowledge : An operational "digital twin" of the businesses that bridges the gap between raw data and real-world operations.

Agent orchestration and tool-use : Audit-ready traces for every decision and tool call.

HydraDB designed for AI applications that need persistent, structured, and traceable memory across complex workflows.

The use cases span any domain where agents need to recall past interactions, track evolving relationships, and reason over historical context.