The fastest GraphDB built on object storage

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June 29, 2026

HydraDB

United States Databases

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The fastest GraphDB built on object storage

API Context Graphs Graph Database Context Retrieval Object Storage Diverse Data SourcesFree + from $25/mo

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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.

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June 29, 2026

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HydraDB

@hydradb

hydradb.com

🇺🇸 United States

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Pros and Cons

Pros

Supports both structured and unstructured data

Sub-200ms Latency

Graph-native and temporal awareness

Multi-hop reasoning

Multi-tenant architecture and tenant-isolation

Cons

No MVCC support

Ingestion latency

Q&A

What is HydraDB designed to do?

HydraDB is designed to support modern AI workloads by providing effective data storage solutions, offering fast performance and cost efficiency. It is essential in building various AI systems including ontologies, agent memory, company brains, and context graphs. It also aids in creating an operational 'digital twin' for a business, bridging the gap between raw data and business operations.

What are the key features of HydraDB?

Key features of HydraDB include optimal data storage for AI systems, high recall accuracy, fast performance and cost efficiency. It integrates with various tools, documents, chats, and applications and allows businesses to develop a unified knowledge graph. The graph evolves with the business as it generates more context. The graph traversal feature ensures that relevant context is surfaced, aiding in precise context retrieval. It also supports memory trace of agent actions and facilitates learning from past mistakes through audit-ready traces.

What is the technology platform that HydraDB built on?

HydraDB is built on a technology platform utilizing a graph database built on object storage.

What is a 'digital twin' in HydraDB terms?

A 'digital twin' in HydraDB terms refers to the creation of an operational replica of your business. It is designed to bridge the gap between raw data and business operations, offering a comprehensive understanding of the business procedures and processes.

What types of information does HydraDB ingest?

HydraDB is designed to ingest and manage structured information from various sources including tools, documents, chats, and applications.

What sources can HydraDB ingest information from?

HydraDB can ingest information from diverse sources including, but not limited to, Slack, Notion, and Gmail.

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What tasks can HydraDB perform with AI agents?

With HydraDB, AI agents can operate precisely over structured knowledge. HydraDB also supports agents in remembering their actions and why they occurred. This facilitates learning from past mistakes and ensures audit-ready traces for every decision.

What is the graph traversal feature in HydraDB?

The graph traversal feature in HydraDB ensures that relevant context is surfaced and not just similar text. This feature aids in precise context retrieval.

Does HydraDB support AI agents in learning from past mistakes?

Yes, HydraDB does support AI agents in learning from their past mistakes. It helps agents remember their actions and why they occurred, thereby allowing them to reflect on and learn from past actions.

Can HydraDB trace agent memory actions?

Yes, HydraDB can trace agent memory actions. It ensures audit-ready traces for every decision, allowing agents to remember their actions and the rationale behind them.

What do you mean by HydraDB ensures audit-ready traces?

Ensuring audit-ready traces refers to HydraDB's capability to record and retrieve the history of decisions made. These traces allow users to audit or review the actions and decisions made by agents, providing transparency and facilitating learning from past mistakes.

What is the storage optimization in HydraDB?

HydraDB ensures storage optimization for high throughput using tiered storage. It applies the concept of a hot in-memory cache, NVMe SSD for warm storage, and object storage for cold archival.

How does HydraDB support high throughput?

HydraDB is designed for high throughput. It uses a tiered storage system across a hot in-memory cache, NVMe SSD for warm storage, and object storage for cold archival. This design ensures efficient and quick delivery of contextual data to AI.

Can HydraDB evolve along with the business?

Yes, HydraDB can evolve along with the business. The graph database on which it is built is capable of evolving as the business generates more context, keeping the knowledge base up-to-date and relevant.

What AI systems can I build with HydraDB?

With HydraDB, you can build various AI systems including ontologies, agent memory, company brains, and context graphs. You can also use it to develop a unified knowledge graph representing decisions, people, and contexts within your business.

How does HydraDB ensure high recall accuracy?

HydraDB ensures high recall accuracy by retaining every decision made by an AI agent, allowing agents to learn from past mistakes. It is designed to streamline the retrieval process, ensuring relevant context is surfaced whenever needed.

How is data stored in HydraDB?

Data in HydraDB is stored using a tiered system across a hot in-memory cache for frequently accessed data, NVMe SSD for less frequently accessed data, and object storage for long-term archival. This system optimizes data recall and costs.

Does HydraDB integrate with other tools, like Slack or Gmail?

Yes, HydraDB can indeed integrate with other tools. It is capable of ingesting information from a variety of sources including Slack, Notion, and Gmail.

What is the purpose of the knowledge graph in HydraDB?

The purpose of the knowledge graph in HydraDB is to network decisions, people, and contexts within a business, providing a foundation for AI agents. This allows businesses to have a broad and interconnected view of their operations, facilitating better decision-making by AI agents.

How can HydraDB help provide structured knowledge for AI agents?

HydraDB helps provide structured knowledge for AI agents by creating a graph database built on object storage. This enables AI agents to operate precisely over structured knowledge, rather than just raw data. The database evolves with the business, generating more context over time.

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