Fast graphDB, built on object storage, for modern AI
Graphs are more accurate while retrieving context for agents than other search methods.
HydraDB is the graph infrastructure that lets you build AI apps like graphRAG, ontology, agent memory, company brain, multiplayer AI apps, context graphs, and more. The world’s fastest and most scalable graph database.
The GraphDB purpose built
for agents
High recall accuracy
We built several applications with HydraDB Most achieved strong benchmark results for accuracy
94%
Search accuracy
For cost efficiency & scale
Built for high throughput, with context managed between in-memory, NVMe SSD, and object storage
Recall everything
Assemble context from business data, workplace apps, chats, documents, emails from one system of record for agents
Built for low latency
So you can build real-time applications
< 200ms
Architecture
Built on object storage, with separate compute for writes, indexing, and reads. A graph core designed for speed, affordability, and scale.
Agents are as smart as the context they have
- A customer’s history lives in your CRM, decisions in Slack, and blockers in project tools, leaving your agents without the connections needed to explain what’s happening
- Use HydraDB to connect documents, conversations, and decisions around shared customers, people, and projects so your agents can follow context across tools
- Unlock complete context for vague queries: “What’s blocking this launch?” with the owners, dependencies, and everything in between
SOTA for agent applications
We built various retrieval applications on HydraDB that achieved good performance
Category
HydraDB
mem0-oSS
ZEP
Single Session (User)
100.00
38.71
92.90
81.40
Single Session (Assistant)
100.00
8.93
80.40
94.60
Preference Extraction
96.67
40.00
56.70
20.00
Knowledge updates
97.43
52.56
83.30
78.20
Temporal Reasoning
90.97
25.56
62.40
45.10
Multi-session reasoning
76.69
20.30
57.90
44.30
Overall Score
90.79
29.07
71.20
60.20
Built by the community 12k+ Stars on Github & Counting
Of everything agent-related I’ve seen lately, HydraDB is the only one my first reaction said is solid Knowledge graph + log structure You can tell at a glance it was designed by someone who understands both algorithms and computer systems
Menci
@lcMenci · translated from Chinese
I’ve literally rebuilt the same memory workaround on three different agent projects always ends in a sloppy Postgres table and a context-injection script nobody wants to maintain about time someone made this simple
Zephyr
@Zephyr_hg
got curious about the actual engine, so I went into the HydraDB OSS repo, ran graph-node locally, and dogfooded it with a small Rust API
nikhil
@nick_realm_01
Sometimes you see a new technology so disruptive that it can kill an entire industry HydraDB is not just the replacement for a vector DB but will also completely change how the world thinks of databases
Sudarshan Kamath
@kamath_sutra
I built my own mini @hydra_db Spent the last few weeks rebuilding an AI memory system from scratch, this time based on the HydraDB paper
Karthiknadar1204
@uchiha_katsuki_
Built Lazaret for Hack Hydra A supply-chain incident response system that compiles a package compromise into HydraDB once, then replays the blast radius from graph state, showing exactly what was exposed, when, and through which dependency path
Tim
@winsznx
pulling off sub-200ms recall on a full relational graph is an INSANE engineering flex props to @contextkingceo and @hydra_db - If these latency numbers hold up, this launch is pretty massive
Charly Wargnier
@DataChaz
Really impressed with HydraDB for agent memory Now integrated into cognivern.persidian.com
papajams
@papajimjams
Memory layers are becoming so important these days I recently read the HydraDB paper, and the performance of their product is seriously impressive Watch out!!
Qian Jiang
@qianjiang___
Frequently asked questions
What makes HydraDB different?
HydraDB is a graph database built on object storage, so your graph can grow without tying storage to database servers. Query and indexing capacity scale independently, giving you infrastructure built for large, connected datasets and the agents that depend on them.
Who is HydraDB for?
HydraDB is for engineers building products that need rich, connected context. Context can be scattered across emails, chats, docs, transcripts, PDFs, code, and memories. Graphs are used connect it around so your product stays on top.
Why can't I just use Postgres for this? Or pgGraph?
You can. Postgres with a graph extension is a practical starting point. As your graph and query volume grow, graph indexes and traversals can compete with your core database for memory and CPU, slowing everyday transactions. HydraDB runs the graph workload on separate, purpose-built infrastructure, so you can scale graph queries without consuming the resources your application depends on.
When should I use HydraDB instead of a vector database?
Use a vector database when you mainly need to find similar documents or passages. Use HydraDB when your application needs to follow relationships, traces, and context across all multiple places: who owns a project, what depends on it, or which past decisions explain its current state. We don't ask you to replace your vector database. Both graph search and vector search serve different purposes. In fact, HydraDB comes with built-in native support for vector database.
Does HydraDB work with GraphRAG?
Yes. HydraDB can serve as the graph database behind your GraphRAG application, storing entities and relationships your retrieval pipeline can query. Keep your models and extraction pipeline, then use HydraDB to retrieve connected context for questions that require following relationships across multiple sources.
