HelixDB: The Native Graph-Vector Database Built in Rust for AI at Millisecond Speed

As AI applications demand faster response times, better semantic reasoning, and lower infrastructure complexity, a new database is emerging as a serious alternative to traditional vector and graph engines: HelixDB.

Built in Rust and backed by the ultra-fast LMDB storage engine, HelixDB offers native support for both graph traversal and vector search — unlocking hybrid RAG (Retrieval-Augmented Generation) workflows without stitching together multiple systems like Neo4j + Qdrant + Redis.

What is HelixDB?

HelixDB is a next-generation graph-vector database that combines structured knowledge (via graph relationships) with semantic similarity (via embeddings) — all in a single, blazing-fast system.

It’s purpose-built for modern AI use cases: code assistants, knowledge bases, semantic search, and intelligent agents. By compiling your queries into the database itself and exposing them as API endpoints, HelixDB eliminates overhead and maximizes speed.

Key Features

🚀 Millisecond Latency
HelixDB compiles your queries directly into the runtime for ultra-fast execution.

🧠 Vector-First & Graph-Native
Combine vector similarity with graph traversal in one seamless query.

🧩 Unified Language: HelixQL
HelixQL offers a modern, type-safe syntax that’s 70% more concise than Cypher or SQL, with built-in support for graph and vector semantics.

🧰 Developer-Friendly CLI
A full CLI toolkit lets you write, check, deploy, and manage local or cloud instances easily.

🔗 API Autogeneration
Every query becomes a standalone, optimized REST API endpoint with caching and low-latency responses.

🔒 ACID-Compliant
Data consistency and reliability guaranteed via the LMDB engine.

☁️ Managed Cloud Option
Fully managed HelixDB instances are available with usage-based pricing starting at $0.10/hour.

Sample HelixQL Query

QUERY findFriends(userID: String) =>
  user <- V<User>(userID)
  posts <- user::Out<Posts>::RANGE(20)
  RETURN user::|usr|{
    ID, name, age,
    following: usr::In<Follows>,
    posts: posts::{
      postID: ID,
      creatorID: usr::ID,
      ..
    },
  }

This is automatically compiled into:

POST https://api.helix-db.com/myproject/findFriends

With full type safety and auto-optimization during build time.

Use Cases

• 🔍 Semantic Search: Combine embeddings with relational filtering.
• 🤖 AI Code Assistants: Retrieve related code blocks and context.
• 📚 Technical Knowledge Bases: Index documentation and its dependencies.
• 🧠 Hybrid RAG: Fuse symbolic graphs and vector recall for more accurate AI output.
• 🏗️ Product Matching: Find compatible parts via vector and graph similarity.

Why HelixDB?

Getting Started

HelixDB runs natively on Unix and is easy to set up:

curl -sSL "https://install.helix-db.com" | bash
helix init --path ./project
helix deploy --local

Manage instances with:

helix start <id>
helix stop <id>
helix instances

Roadmap Highlights

• Advanced vector type support (e.g., cosine, dot-product, hybrid)
• Improved build tools and IDE integrations
• Embedded testing framework via CLI
• Dedicated query profiling and explain plans

Community & Support

HelixDB is open source under GPLv3 and backed by a growing community of AI and database developers. You can join discussions, open issues, or contribute via:

• GitHub: github.com/HelixDB/helix-db
• Docs & roadmap: helix-db.com

HelixDB is not just another database — it’s the first of its kind to natively unify graph and vector computing in one fast, simple, and developer-first platform.

Start building smarter AI pipelines with Helix today.