This product was not featured by Product Hunt yet. It will not be visible on their landing page and won't be ranked (cannot win product of the day regardless of upvotes).
LoraDB
Embedded Rust graph database for AI, Cypher & Vectors Search
LoraDB is an embedded graph database for connected systems. Built in Rust, it runs in-process, speaks a Cypher-like query language, and now supports first-class vector values for graph-shaped AI retrieval. Store relationships, entities, and embeddings in one engine across Rust, Node.js, Python, WASM, Go, Ruby, or HTTP.
I started LoraDB because I kept reaching for a graph database in places where the existing options felt too heavy for the job.
The workloads were always similar: connected entities, typed relationships, short traversals, ranking and filtering over neighborhoods, and state that needed to stay close to the application instead of across a slow boundary. Sometimes it was product data, sometimes internal tools, sometimes agent memory. The model kept becoming a graph, but I did not want a large database stack before I even knew the model fit.
So I built LoraDB to feel different on day one: local, in-process, fast to start, small enough to understand, and queryable with a Cypher-like language.
LoraDB is an embedded graph database written in Rust. It runs in memory, speaks a pragmatic Cypher-like query language, and works across Rust, Node.js, Python, WASM, Go, Ruby, and HTTP.
The reason that matters is simple: a lot of AI retrieval systems need both similarity and structure. Similarity finds candidates, but the graph explains them. I wanted embeddings to live next to the relationships that give them meaning, not in a separate system glued together by application code.
If you try it, I’d love to hear what graph you’d load first, what kind of retrieval or relationship-heavy workflow you’d test, and most important; tell me what you think!
No comment highlights available yet. Please check back later!
About LoraDB on Product Hunt
“Embedded Rust graph database for AI, Cypher & Vectors Search”
LoraDB was submitted on Product Hunt and earned 3 upvotes and 1 comments, placing #85 on the daily leaderboard. LoraDB is an embedded graph database for connected systems. Built in Rust, it runs in-process, speaks a Cypher-like query language, and now supports first-class vector values for graph-shaped AI retrieval. Store relationships, entities, and embeddings in one engine across Rust, Node.js, Python, WASM, Go, Ruby, or HTTP.
LoraDB was featured in Open Source (68.5k followers), GitHub (41.3k followers) and Database (2.1k followers) on Product Hunt. Together, these topics include over 36.8k products, making this a competitive space to launch in.
Who hunted LoraDB?
LoraDB was hunted by Joost van Berkel. 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.
Want to see how LoraDB stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.
Joost van Berkel
I started LoraDB because I kept reaching for a graph database in places where the existing options felt too heavy for the job.
The workloads were always similar: connected entities, typed relationships, short traversals, ranking and filtering over neighborhoods, and state that needed to stay close to the application instead of across a slow boundary. Sometimes it was product data, sometimes internal tools, sometimes agent memory. The model kept becoming a graph, but I did not want a large database stack before I even knew the model fit.
So I built LoraDB to feel different on day one: local, in-process, fast to start, small enough to understand, and queryable with a Cypher-like language.
LoraDB is an embedded graph database written in Rust. It runs in memory, speaks a pragmatic Cypher-like query language, and works across Rust, Node.js, Python, WASM, Go, Ruby, and HTTP.
The reason that matters is simple: a lot of AI retrieval systems need both similarity and structure. Similarity finds candidates, but the graph explains them. I wanted embeddings to live next to the relationships that give them meaning, not in a separate system glued together by application code.
If you try it, I’d love to hear what graph you’d load first, what kind of retrieval or relationship-heavy workflow you’d test, and most important; tell me what you think!