Vector Databases · HelixDB

helix-db

HelixDB is a Rust-based OLTP database combining graph, vector, and traditional data models, designed for AI applications and RAG systems. It runs locally or on object storage via a managed cloud service, with query SDKs in Rust, TypeScript, Python, and Go.

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Source: GitHub — github.com/HelixDB/helix-db

5.6k

GitHub stars

310

Forks

Rust

Primary language

Apache-2.0

License (OSI-approved)

Key facts

Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.

Field	Value
Repository	HelixDB/helix-db
Owner	HelixDB
Primary language	Rust
License	Apache-2.0 — OSI-approved
Stars	5.6k
Forks	310
Open issues	11
Latest release	v3.0.8 (2026-07-05)
Last updated	2026-07-05
Source	https://github.com/HelixDB/helix-db

What helix-db is

OLTP graph-vector database written in Rust, supporting hybrid data models (graph, vector, KV, document, relational). Executes queries via REST API (`POST /v1/query`) using a JSON AST, with DSL builders in multiple languages. Local instances use in-memory or disk storage; Cloud deployments scale across object storage with ACID transactions, read replicas, and high availability.

Quickstart

Get the helix-db source

Clone the repository and explore it locally.

terminalbash

1git clone https://github.com/HelixDB/helix-db.git2cd helix-db3# follow the project's README for install & configuration

Need it deployed, integrated, or customized instead? DEV.co ships production installs.

Best use cases

AI agent memory and knowledge graphs

Store structured knowledge, embedding vectors, and relationships in a single system without separate vector DB + graph DB layers. Ideal for RAG applications requiring both semantic search and entity relationships.

Rapid AI application prototyping

The `helix chef` bootstrapper with Claude/Codex integration enables single-command project scaffolding and agent-driven development, reducing setup overhead for proof-of-concepts.

Multi-model workloads in one data layer

Applications needing simultaneous graph queries, vector similarity search, KV lookups, and relational operations without managing multiple databases or ETL pipelines.

Implementation considerations

Default local instances are in-memory; use `--disk` flag to persist data across restarts, or plan for Cloud deployment for durability.
DSL builders (Rust, TypeScript, Python, Go) all produce the same JSON AST; choose based on existing team expertise and deployment runtime compatibility.
No mention of built-in authentication/authorization in local setup; for production, clarify RBAC and multi-tenant isolation requirements before committing.
Requires Node.js 20+ for TypeScript SDK and working Rust/Go/Python toolchains for respective SDKs; Docker-based local instances reduce platform friction but add container overhead.

When to avoid it — and what to weigh

Mature, battle-tested production database required — Project was created in Nov 2024 with v3.0.8 as latest release (Jul 2026 in data). Adoption metrics and long-term stability history are not yet established. Only 11 open issues, but production-grade assurance requires additional real-world deployment evidence.
Heavy OLAP / analytical workload dominance — HelixDB is explicitly OLTP-focused. If your use case is primarily large-scale columnar analytics, batch ETL, or read-heavy reporting, a data warehouse (Snowflake, BigQuery, DuckDB) is more appropriate.
No need for vector or graph capabilities — If your application uses only relational or KV data without semantic search or graph traversal, the overhead and complexity of HelixDB's hybrid model is unnecessary; simpler RDBMS or KV stores are more cost-effective.
Strict vendor independence / no cloud lock-in tolerance — Managed service is tightly integrated via CLI and `helix.toml` authentication. Local deployments are possible, but Cloud is the primary commercial offering, creating some operational coupling.

License & commercial use

Apache License 2.0 (Apache-2.0). This is a permissive OSI-approved license allowing commercial use, modification, and distribution with proper attribution and liability disclaimer.

Apache-2.0 permits commercial use. HelixDB Cloud is the primary commercial offering (managed service on object storage). Local open-source deployments can be used commercially under Apache-2.0 terms. No source code contribution requirements for proprietary applications. Recommend reviewing cloud service terms of service and support SLAs separately from the license.

DEV.co evaluation signals

Editorial assessment — not user reviews. Directional, with an explicit confidence level.

Signal	Assessment
Maintenance	Active
Documentation	Strong
License clarity	Clear
Deployment complexity	Low
DEV.co fit	Strong
Assessment confidence	High

Security considerations

Apache-2.0 license does not guarantee security audit or formal assurance. Local in-memory instances have no persistence-layer encryption mentioned. Cloud deployments claim ACID transactions and high availability but details on encryption-at-rest, encryption-in-transit, and audit logging are not provided in the source data. No mention of vulnerability disclosure policy or security update cadence. Assess threat model carefully before storing sensitive data; request security documentation from the team.

Alternatives to consider

Weaviate / Qdrant

Dedicated vector databases with mature production deployments and extensive ML integrations. Use if vector search is primary need; lack native graph database features but excel at semantic search + filtering.

Neo4j

Established graph database with native ACID, clustering, and production SLAs. Use if graph queries are the dominant workload; vector support is newer (Neo4j 5.x) and less tightly integrated than HelixDB.

PostgreSQL + pgvector + PostGIS

Mature RDBMS with vector extension (pgvector) and spatial/graph capabilities via PostGIS. Use if you need a battle-tested, single-engine solution with proven operational tooling and broad ecosystem.

Software development agency

Build on helix-db with DEV.co software developers

Start a local HelixDB instance in seconds with `helix start dev`, or explore managed Cloud deployments. See docs.helix-db.com for quickstart guides and SDK examples.

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helix-db FAQ

Can I use HelixDB locally without cloud?

Yes. `helix start dev` spins up a local instance on port 6969 (in-memory by default). Use `--disk` to persist data. Cloud is optional for managed deployments.

Do I need to choose between graph and vector storage?

No. HelixDB's hybrid data model stores both simultaneously. You can query relationships as graphs and embeddings as vectors in a single query without ETL.

What if I need to write in a language not in the SDK list?

The query API is REST (`POST /v1/query` with JSON AST). Any HTTP client (curl, JavaScript fetch, etc.) can send queries; SDKs are optional convenience layers.

Is there a query builder without SDKs?

Yes. The CLI tool supports `helix query dev --file examples/request.json` with hand-written JSON AST, though DSL builders are more ergonomic for complex queries.

Software developers & web developers for hire

From first prototype to production, DEV.co delivers software development services around tools like helix-db. Our software development agency staffs experienced software developers and web developers for custom software development, web development, integrations, and ongoing support across vector databases and beyond.

Ready to unify your AI data layer?

Start a local HelixDB instance in seconds with `helix start dev`, or explore managed Cloud deployments. See docs.helix-db.com for quickstart guides and SDK examples.

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