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The fastest & most scalable graph-vector database on the market
As AI agents replace traditional software, they need a way to store, recall, and reason over contextual data. HelixDB gives them all of that in one system. We’re building the knowledge infrastructure every AI agent will depend on.
HelixDB is a native graph-vector database built in Rust, designed for RAG, semantic search, knowledge graphs, and AI agent workflows. It emphasizes scalability and production-ready deployment, including a cloud offering and on-demand auto-scaling.
A native graph-vector database implemented in Rust that supports graph structures and vector embeddings for AI workloads. It offers production deployment with Helix Cloud, high availability across gateways and DB nodes, and auto-scaling of reader nodes based on load to optimize costs. The platform claims to handle graph and vector data at scale, with a focus on RAG, semantic search, knowledge graphs, and AI agent workflows, and provides documentation, demos, and a pricing page for details.
Who it’s for: Teams building large-scale graph and vector AI applications, including RAG systems, semantic search engines, knowledge graphs, and AI agent workflows, who need scalable, production-grade storage and cloud deployment.
pricing page available; cloud deployment (Helix Cloud) mentioned; production deployment noted; growth-focused messaging
CEO & Co-Founder of HelixDB, a graph-vector database for building RAG.
HelixDB is a graph-vector database that brings structure to your un-structured data for RAG and AI applications.
HelixDB launches as an open-source graph-vector database that integrates vector types with graph relationships to support AI retrieval engines for agents and LLMs. It offers a type-safe query language, fast performance, and upcoming MCP tools, ingestion pipelines, and embedded embedding-models for multi-modal data to improve RAG workflows.
Open-source, serverless vectordb for production-scale generative AI
Give AI agents accurate knowledge search that scales
