What is HelixDB ?
Building applications that understand both complex relationships and semantic meaning often means wrestling with separate graph and vector databases. This can complicate your architecture, slow down development, and increase operational overhead. HelixDB offers a streamlined alternative: a high-performance, graph-vector database designed from the ground up in Rust for speed, simplicity, and developer productivity, especially for AI-driven projects.
HelixDB unifies these critical data models, allowing you to manage intricate graph connections and dense vector embeddings within a single, cohesive system. Powered by the robust LMDB storage engine (via Heed), it provides ACID compliance and millisecond query latency, helping you build sophisticated, data-intensive applications more efficiently.
Key Features
🔗 Unified Graph-Vector Model: Manage complex relationships (graph) and semantic similarity data (vectors) within one database. This eliminates the need to synchronize or integrate separate, specialized databases, simplifying your stack.
🚀 High Performance: Achieve millisecond query latency. HelixDB is built in Rust for speed and compiles your queries directly into optimized API endpoints, minimizing runtime overhead. Its foundation on LMDB ensures efficient data persistence.
💻 Developer-Focused Experience: Interact with HelixDB using HelixQL, an intuitive query language designed for clarity (often requiring significantly less code than Cypher or Gremlin). Benefit from compile-time type checking and IDE support. Use the straightforward Helix CLI (
helix install,helix init,helix check,helix deploy) for easy local setup, development, and deployment.🧠 Native Vector Support: Seamlessly store, index, and query vector embeddings alongside your graph data. Perform combined operations like graph traversals filtered or enhanced by vector similarity searches, ideal for advanced AI tasks.
🔒 Reliable & Flexible: Ensure data integrity with ACID compliance. Choose between deploying the open-source version on your own infrastructure (Unix-ready) or using the fully managed cloud service for simplified operations and scaling.
Use Cases
Building Advanced RAG Systems: Go beyond standard vector retrieval. With HelixDB, you can query for semantically relevant information using vectors, and simultaneously traverse the graph to understand the context, connections, and provenance of that information, leading to more accurate and insightful AI responses.
Powering AI Code Assistants: Index your entire codebase, capturing not just the semantic meaning of code snippets (vectors) but also their relationships like file structure, dependencies, and call graphs (graph). This allows AI agents to retrieve highly relevant code examples and documentation with full contextual understanding.
Creating Intelligent Knowledge Bases: Structure technical documentation, product catalogs, or research data as a graph to represent connections and hierarchies. Enrich nodes with vector embeddings representing their content. This enables powerful semantic search capabilities that understand user intent and navigate complex relationships simultaneously (e.g., "Find documents similar to X that are dependencies of project Y").
Conclusion
HelixDB tackles the complexity inherent in managing both graph and vector data for modern applications. By providing a unified, high-performance database built with developer experience in mind, it empowers you to build sophisticated AI features and data-intensive applications faster. Its Rust foundation ensures speed and reliability, while the intuitive HelixQL and supporting tools streamline the development process. Whether you're building next-generation RAG systems, AI coding tools, or complex knowledge graphs, HelixDB offers a powerful and efficient data foundation.
More information on HelixDB
Launched
2025-02
Pricing Model
Free
Starting Price
Global Rank
10428349
Follow
Month Visit
<5k
Tech used
Next.js,Vercel,Gzip,Webpack,HSTS
HelixDB was manually vetted by our editorial team and was first featured on 2025-04-13.
