HelixDB is a fully native Graph-Vector Database built in Rust, specifically designed for building Retrieval Augmented Generation (RAG) and AI applications. HelixDB combines graph and vector data types in a single platform, aiming to solve the performance, complexity, and maintenance challenges that arise when stitching together multiple databases for AI retrieval.
What products and services does HelixDB offer?
HelixDB's core product is its Graph-Vector Database, which enables unified management of graph and vector data for AI and RAG applications. Key features include:
- Hybrid Query Traversals: Seamlessly combine vector similarity search and graph traversals in a single query, allowing for more flexible and powerful data retrieval.
- Type-Safety with Advanced Static Analysis: Offers real-time feedback and error detection to ensure greater reliability in database operations.
- High Performance: Achieves fast query speeds, with vector similarity searches around 2ms and graph traversals in under 1ms.
- Cost Efficiency: Eliminates the need for separate databases and custom logic, reducing infrastructure and maintenance costs.
- Managed Cloud Service & Commercial Support: HelixDB is developing a managed cloud solution and offers support for commercial customers.
HelixDB has also introduced HelixMCP, a feature that allows agents to walk the graph directly—agents can use the MCP endpoint via the Python SDK to discover data in the graph, enabling more contextual responses without the need for human-readable queries.
Who uses HelixDB?
HelixDB is designed for developers and organizations building RAG (Retrieval Augmented Generation), AI, and agent-based applications. Its unified approach to graph and vector data appeals to teams seeking to simplify their tech stack and improve the performance of AI retrieval tasks. Companies or projects focused on advanced search, knowledge graphs, and AI agents may particularly benefit from HelixDB.
What makes HelixDB different?
HelixDB's main differentiation lies in its native integration of both graph and vector data types, enabling complex hybrid queries without the overhead of managing multiple databases. This architecture is particularly suited for modern AI workloads, reducing latency, complexity, and operational costs. The platform is often described as "Supabase for agents," emphasizing its developer-centric design and ease of use for building AI-powered applications.
How was HelixDB started and who leads it?
HelixDB was founded by Xavier Cochran (Founder) and George Curtis (Co-founder), with additional team members including Lukas Nitzsche (systems and machine learning software) and Gahn Wuwong (Data Science & Business Analytics). The team brings expertise in systems, machine learning, and analytics, shaping the product’s technical direction and focus on AI-native data infrastructure.
Who are HelixDB's competitors?
HelixDB operates in the database technology sector, competing with both traditional graph databases and modern vector databases. Its unique hybrid approach positions it against products that require separate graph and vector solutions, as well as platforms like Neo4j, Pinecone, and other AI-centric data infrastructure providers.
Latest news about HelixDB
HelixDB recently launched HelixMCP, an advancement that enables AI agents to directly traverse and discover data within the graph, further streamlining contextual data retrieval for AI applications. More details and updates can be found at their website.
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