HelixDB

graph + vector + OLTP in one engine is pretty wild. most teams still duct-tape 3 different systems together. if helixdb pulls this off, that removes a lot of ugly infra. definitely one to watch.

Agents walking a graph through MCP instead of generating queries is a better primitive than most RAG stacks offer. HelixDB combining vector search and graph traversals in one engine skips the sync layer between your vector store and graph DB... that glue code is where production pipelines break. HelixQL being type-safe matters more than it sounds. When an agent autonomously queries data, a runtime type error hits harder than in a human-driven flow. Worth watching whether the custom query language creates friction for teams on Cypher. A compatibility layer there would ease adoption.

Graph + vector in a single engine built in Rust is exactly what the agent ecosystem needs right now. Most people are duct-taping Postgres + Pinecone together — having native support for both traversal and similarity search in one place should make agentic workflows way cleaner. Excited to see what the HelixQL language evolves into.

Really impressive journey especially scaling to billions of queries Curious — with this kind of workload, how are you handling security around data access and isolation? Especially if teams are using it in multi-tenant or production environments

Who is HelixDB *not* for right now? Concretely, which workload types or operational requirements (multi-region HA, strict compliance, massive batch analytics, etc.) are you intentionally deprioritizing—and what principles are guiding what you build next?

Love the helix DB team and product. Congrats!

Graph + vector + OLTP in one engine is interesting. Are you targeting agent memory use cases primarily, or positioning this as a general-purpose database long term?

In college, whilst dealing with the hardships of graph databases, my co-founder, Xav, and I set out to build something new which was easy to use, learn, and scale. Despite not having the proper qualifications, we quickly attracted the attention of developers from X and companies like United Healthcare.

After dropping-out of college and moving to SF to attend Y Combinator, we've grown the repo to nearly 4k stars, executed billions of queries, and out-performed industry leading competitors.

P.S: We love feedback, and criticism. Please share your thoughts and questions below!