HydraDB
HydraDB is an open-source graph database built on object storage, designed to be 10x cheaper and ultra-fast for modern AI workloads. It enables developers to build agent memory, ontologies, and company brains with a unified, relational-first context layer. The platform emphasizes high recall accuracy and temporal versioning, making it suitable for applications requiring precise, stateful AI context.
- Artificial Intelligence
- AI Agents
- Developer Tools
- Software Only
Funding
Founders
Product
Problem
Traditional AI systems often rely on vector databases and relational stores that retrieve similar, but not necessarily relevant, context, leading to missed relationships between concepts and lost agent traces across sessions. This fragmentation forces developers to juggle multiple systems, hindering the creation of truly stateful and context-aware AI applications.
Solution
HydraDB provides a purpose-built graph database on object storage that is 10x cheaper and ultra-fast, designed to store and query complex relationships for AI. It unifies graph, vector, and temporal data into a single layer, enabling developers to build in-house memory systems, ontologies, and company brains. By using a relational-first, preference-aware, and temporally versioned approach, HydraDB delivers precise context, improving recall accuracy and enabling AI to compound intelligence over time. It supports high throughput with tiered storage, moving data fluidly between hot, warm, and cold tiers to scale with system demands.
Target Audience
Primary customers are AI engineers and developers building stateful AI applications, including those focused on agent memory, context engineering, and enterprise knowledge graphs.
Features
- Graph database built on object storage, offering a 10x cost reduction and high performance.
- Relational-first, preference-aware, and temporally versioned data model for precise context retrieval.
- Unified layer combining graph, vector, and temporal primitives to eliminate the need for multiple systems.
- Tiered storage architecture with in-memory cache, NVMe SSD, and object storage for scalable performance.
- Git-style temporal versioning to recall data states at any point in time.
- High recall accuracy, leading on benchmarks like LongMemEval-S (90%+), BEAM, and FinanceBench.