HydraDB OSS
<p> Now open source: the fastest, cheapest graph DB </p> <p> <a href="https://www.producthunt.com/products/hydradb-oss?utm_campaign=producthunt-atom-posts-feed&utm_medium=rss-feed&utm_source=producthunt-atom-posts-feed">Discussion</a> | <a href="https://www.producthunt.com/r/p/1192700?app_id=339">Link</a> </p>
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What is HydraDB OSS?
HydraDB is a graph database built on object storage, purpose-built for AI workloads. It delivers 10x cheaper storage and ultra-fast performance, enabling developers to build agent memory systems, ontologies, company brains, and context graphs without third-party abstractions. Offers high-recall context retrieval (90%+) with temporal versioning and relational-first design.
HydraDB OSS pricing
Pricing model: Freemium
HydraDB OSS pros
- 10x cheaper than traditional graph databases via object storage architecture
- High recall accuracy (90%+ on LongMemEval-S) and maintains accuracy at scale (115K tokens)
- Built-in support for agent memory, entity resolution, temporal versioning, and preference-aware personalization
- Low-latency design (<200ms) suitable for real-time AI applications
- Tiered storage system (hot cache, NVMe SSD, cold archive) scales efficiently with context
- Open source with data connectors for Slack, Notion, GitHub, Gmail, and more
- Relational-first approach delivers relevant context, not just similar results like vector databases
HydraDB OSS cons
- Relatively new product with less established ecosystem than mature GraphDB solutions
- Pricing details not clearly specified in available documentation
- Accuracy still degrades as context scales beyond certain thresholds (71% at 32K tokens, 38% at 64K)
- Purpose-built for graph use cases; may not be optimal for non-graph data workloads
Frequently asked questions about HydraDB OSS
What makes HydraDB different from vector databases?
HydraDB connects context and builds structured graphs to deliver exact relevant context, versus vector databases which return similar but not necessarily related data. It's relational-first, preference-aware, and temporally versioned.
What can I build with HydraDB?
Agent memory systems, ontologies, company brains, agentic actions, and context engineering layers for AI applications.
Is HydraDB open source?
Yes, HydraDB is now open source. The code is available on GitHub at https://github.com/hydra-db/hydradb.
How does HydraDB achieve such high recall accuracy?
It uses graph-native context infrastructure with entity resolution, preference-aware retrieval, and temporal versioning to maintain high accuracy (90%+) even as context scales.
What data sources can HydraDB connect to?
Data connectors are available for Slack, Notion, GitHub, Gmail, and more, allowing assembly of context from business data, workplace apps, chat sessions, and documents.
How fast is HydraDB for retrieval?
Built for low-latency apps with recalls under 200ms, suitable for real-time AI applications.