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HydraDB OSS
Now open source: the fastest, cheapest graph DB
HydraDB is a native graph database built for AI infrastructure — memory, ontologies, and agent context. Today, we're open sourcing the core: the fastest, cheapest graph DB on the market, built directly on object storage. One API call gets you core primitives, sub-200ms latency, and none of the operational overhead of legacy graph databases. We built HydraDB because AI applications need graph-native context, not bolted-on workarounds. Try it, break it, contribute — it's yours now.
Our goal is to support modern AI use cases like agent memory, company brain, and ontologies. A lot of agents today are built with vectorDBs or stitch together multiple around semantic similarity: embed something, retrieve closest chunks, and pass to models.
But similarity isn’t always relevance. Moreover, information in isolation doesn’t work well for agents. They come back with more questions about that single chunk than answers or work done.
HydraDB is a graph database, built on object storage, written in Rust, for intelligent AI that requires context extremely fast, maintains state over time, all while making it extremely cost efficient.
Context an agent needs is a notion page connected to your emails, messages, and 10 other documents so that your agent gets to choose how much context is enough to complete some work. That’s when it gets a “brain” that hold a relationship, a dependency, a sequence of events, a previous decision, or a fact that has since been superseded. It’s an intelligent graph.
Why did we build it?
As we worked on AI systems, we kept running into the same problem: models were getting better and eventually reaching a point where intelligence is no longer a bottleneck, but the context they received was still fragmented and lossy.
Enterprise data lives across Slack, Jira, Gmail, GitHub, Drive and dozens of other systems. Agents need to understand not just the individual pieces of data, but how those pieces relate to each other. The same thing shows up in agent memory, code intelligence, knowledge systems, security, and other domains.
We think graphs are going to become an increasingly important part of the context layer for AI. We built HydraDB to make your AI relationally aware, give it connected context easier to store, retrieve, and reason over.
So what are we launching today?
Today we’re open-sourcing HydraDB.
You can now run it yourself, build on top of it, inspect how it works, and tell us where it breaks.
We’re especially excited to see what people build around:
agent memory and long-term context
GraphRAG and knowledge retrieval
enterprise knowledge and ontologies
code and dependency graphs
multi-hop reasoning
Why open source?
Because we don’t think the next generation of AI infrastructure should be something developers have to take on faith. AI needs to be sovereign and something you own.
If HydraDB is going to become useful infrastructure, developers should be able to run it themselves, understand it, extend it, benchmark it, and push it into use cases we haven’t thought of.
We also know we won’t figure this out alone.
A big reason for this launch is to get HydraDB into the hands of people building real systems and learn from what they do with it.
So if you try it, we’d love to hear:
What did you build?
What worked?
What was painful?
And what would make HydraDB dramatically more useful for you?
Thanks for checking us out and special s/o to everyone who has helped us get here ❤️
About HydraDB OSS on Product Hunt
“Now open source: the fastest, cheapest graph DB”
HydraDB OSS launched on Product Hunt on September 2nd, 2026 and earned 95 upvotes and 3 comments, placing #19 on the daily leaderboard. HydraDB is a native graph database built for AI infrastructure — memory, ontologies, and agent context. Today, we're open sourcing the core: the fastest, cheapest graph DB on the market, built directly on object storage. One API call gets you core primitives, sub-200ms latency, and none of the operational overhead of legacy graph databases. We built HydraDB because AI applications need graph-native context, not bolted-on workarounds. Try it, break it, contribute — it's yours now.
On the analytics side, HydraDB OSS competes within Developer Tools, Artificial Intelligence and Tech — topics that collectively have 1.6M followers on Product Hunt. The dashboard above tracks how HydraDB OSS performed against the three products that launched closest to it on the same day.
Who hunted HydraDB OSS?
HydraDB OSS was hunted by Chris Messina. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of HydraDB OSS including community comment highlights and product details, visit the product overview.
Chris Messina
Hello Product Hunt community 👋
We’re excited to open source HydraDB today.
What is HydraDB?
Our goal is to support modern AI use cases like agent memory, company brain, and ontologies. A lot of agents today are built with vectorDBs or stitch together multiple around semantic similarity: embed something, retrieve closest chunks, and pass to models.
But similarity isn’t always relevance. Moreover, information in isolation doesn’t work well for agents. They come back with more questions about that single chunk than answers or work done.
HydraDB is a graph database, built on object storage, written in Rust, for intelligent AI that requires context extremely fast, maintains state over time, all while making it extremely cost efficient.
Context an agent needs is a notion page connected to your emails, messages, and 10 other documents so that your agent gets to choose how much context is enough to complete some work. That’s when it gets a “brain” that hold a relationship, a dependency, a sequence of events, a previous decision, or a fact that has since been superseded. It’s an intelligent graph.
Why did we build it?
As we worked on AI systems, we kept running into the same problem: models were getting better and eventually reaching a point where intelligence is no longer a bottleneck, but the context they received was still fragmented and lossy.
Enterprise data lives across Slack, Jira, Gmail, GitHub, Drive and dozens of other systems. Agents need to understand not just the individual pieces of data, but how those pieces relate to each other. The same thing shows up in agent memory, code intelligence, knowledge systems, security, and other domains.
We think graphs are going to become an increasingly important part of the context layer for AI. We built HydraDB to make your AI relationally aware, give it connected context easier to store, retrieve, and reason over.
So what are we launching today?
Today we’re open-sourcing HydraDB.
You can now run it yourself, build on top of it, inspect how it works, and tell us where it breaks.
We’re especially excited to see what people build around:
agent memory and long-term context
GraphRAG and knowledge retrieval
enterprise knowledge and ontologies
code and dependency graphs
multi-hop reasoning
Why open source?
Because we don’t think the next generation of AI infrastructure should be something developers have to take on faith. AI needs to be sovereign and something you own.
If HydraDB is going to become useful infrastructure, developers should be able to run it themselves, understand it, extend it, benchmark it, and push it into use cases we haven’t thought of.
We also know we won’t figure this out alone.
A big reason for this launch is to get HydraDB into the hands of people building real systems and learn from what they do with it.
So if you try it, we’d love to hear:
What did you build?
What worked?
What was painful?
And what would make HydraDB dramatically more useful for you?
Thanks for checking us out and special s/o to everyone who has helped us get here ❤️