HelixDB: Open-Source Vector-Graph Database for RAG Applications

HelixDB, a new open-source database written in Rust, combines vector and graph capabilities in a single platform. For developers building RAG and AI applications, this solves an increasingly common headache: juggling two separate databases just to handle both semantic similarity and relationship queries. With over 3,300 GitHub stars and benchmarks claiming performance on par with Pinecone while running 1000x faster than Neo4j for graph queries, the project is gaining serious traction.

The Multi-Database Problem

Building modern AI applications typically requires two types of queries. Vector databases handle semantic similarity—finding documents or code snippets that are conceptually related. Graph databases handle relationships—traversing connections between entities like users, documents, and projects.

Until now, developers had to maintain separate systems for each. That means syncing data between databases, writing application-level code to join results, and dealing with the operational overhead of two different platforms. HelixDB argues this is unnecessary complexity.

How HelixDB Combines Both

HelixDB stores vector embeddings and graph relationships natively in one system. A single query can perform semantic search and then traverse relationships without leaving the database.

Consider codebase indexing. You vectorize code snippets for similarity search, but you also need the function call graph—which functions call which, what imports what. With HelixDB, an AI agent can search by similarity to find relevant code, then traverse the AST to pull in related functions and dependencies. The result: fewer hallucinations because the LLM gets complete context, not fragments.

The same pattern applies to enterprise knowledge management. Store your organizational structure as a graph (employees, teams, projects) while vectorizing internal documents. Search semantically, then instantly see which team owns a document or who the subject matter experts are.

Performance Without Compromise

The team claims their vector performance matches Pinecone and Qdrant, while graph traversals run up to 1000x faster than Neo4j. How? Two factors.

First, Rust and LMDB provide a fast foundation. But more interesting is their approach to queries. Traditional databases parse and compile queries at runtime. HelixDB’s query language, HelixQL, compiles to Rust code when you deploy. Your queries become API endpoints, eliminating runtime parsing overhead entirely.

HelixQL is type-safe and declarative with functional traversals. It borrows ideas from Gremlin, Cypher, and SQL:

N::User {
   INDEX name: String,
   age: U32
}

QUERY getUser(user_name: String) =>
   user <- N<User>({name: user_name})
   RETURN user

TypeScript and Python SDKs handle the client side.

Built for the AI Era

HelixDB includes features that acknowledge where development is heading. Built-in MCP (Model Context Protocol) support lets AI agents discover and traverse data directly. A built-in Embed function handles vectorization, so you don’t need to pre-process text before insertion.

This is a database that assumes your users might be AI agents as much as humans.

Getting Started

Installation is straightforward:

curl -sSL "https://install.helix-db.com" | bash
helix init
helix push dev

The project is AGPL licensed. Commercial support and managed hosting are available for enterprise users through helix-db.com.

For developers tired of duct-taping vector and graph databases together, HelixDB offers a compelling alternative. The hybrid approach may finally make single-database RAG applications practical.