Fast graph database, built on object storage, for modern agents
We let you build fast, cost-efficient, and scalable enterprise graphs for AI
Build ontologies, agent memory, company brains, and enterprise graphs.
Trusted by teams building world class AI Agents
Architecture Overview
Everything you need to Compound Intelligence
High Recall Accuracy
Learn how we lead on LongMemEval-S (90%+), BEAM, and FinanceBench.
Accuracy
Scales with your Systems
Designed for high throughput using tiered storage: a hot in-memory cache, NVMe SSD for warm storage, and object storage for cold archival. Context moves fluidly between tiers.
Recall Everything
Assemble context from business data, workplace apps, chat sessions, documents. Remember user preferences while retrieving.
Data
Chat
Preference
Built for Low
Latency Apps
Built for low-latency apps — so you can build real-time applications with HydraDB.
< 200ms
State of the art on various benchmarks
HydraDB outperforms various context applications on five of six LongMemEval-S categories. Last updated: March 2026
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
Accuracy vs Context length
HydraDB
VectorDB
Full Context
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. Hydra DB 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 Hydra DB 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 Hydra DB 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 is HydraDB? How is it different from other graph databases?
HydraDB is an object-store-native distributed graph database built in Rust, designed to serve as the context layer for AI systems. Unlike traditional graph databases, where storage is tied more closely to database servers or dedicated cluster volumes, HydraDB makes object storage itself the source of truth. This lets query nodes and indexers scale, restart, or be replaced independently without moving or replicating the graph. It also supports snapshot-consistent OpenCypher, GraphBLAS-accelerated traversal, Neo4j-compatible Bolt, and HTTP APIs.
Why use a graph database like HydraDB for AI agents?
A graph database like HydraDB gives AI agents something vector search alone cannot: relationships and state. Instead of retrieving isolated chunks that merely look similar to a query, an agent can follow explicit connections between people, projects, events, policies, documents, and past actions to understand how the current situation fits together.
When should I use HydraDB instead of a vector database?
Use a vector database when your main problem is semantic search over mostly static content: finding documents, chunks, tickets, or products that are similar to a query. Use HydraDB when your AI agent needs to understand how information connects and changes over time. If the agent needs to answer questions like who owns this project, what is blocking it, which policy applies to this customer, or what changed since the last session, similarity search alone is not enough. HydraDB lets the agent traverse entities and relationships and work with structured state instead of only retrieving nearby embeddings.
Does HydraDB work with GraphRAG?
Yes. HydraDB can be used as the graph database behind a GraphRAG system. GraphRAG is a retrieval approach rather than a specific database: it builds or uses a knowledge graph, then retrieves context by traversing relationships instead of relying only on vector similarity. Microsoft’s GraphRAG architecture is explicitly designed around a storage-agnostic knowledge model and allows custom storage and workflow implementations.
How does HydraDB improve retrieval for AI agents?
HydraDB improves retrieval by combining vector search with graph traversal, exact-match search, and temporal context. Instead of returning isolated similar chunks, it reconstructs the relationships, dependencies, and latest valid state around a query, which gives AI agents a smaller, more relevant set of evidence to reason over.
Can I use HydraDB with my existing AI stack?
Yes. HydraDB plugs into your existing AI stack through Neo4j-compatible Bolt and HTTP APIs. Keep your current models, agents, and retrieval pipeline, and add HydraDB as the graph layer for connected, stateful context.
How does HydraDB use context graphs for AI agents?
HydraDB turns fragmented agent memory into a connected context graph. This lets agents retrieve not only the relevant fact, but also how that fact relates to other entities, where it came from, and what the current state is, which gives multiple agents and workflows a consistent view of the same domain.
What AI use cases is HydraDB best suited for?
HydraDB is best for AI agents that need persistent, connected context such as support agents, coding agents, research agents, GraphRAG systems, and enterprise copilots. It is especially useful when the agent needs to remember history, follow relationships, and understand how state changes over time.
