HelixDB
HelixDB is an open-source graph-vector database built in Rust for RAG, agents, and knowledge graphs.
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What is HelixDB?
HelixDB is an open-source graph-vector database built from scratch in Rust, designed as the storage backend for AI applications, agents, and RAG systems. HelixDB combines graph traversal, vector search, and HelixQL (a strongly typed compiled query language) in a single platform, eliminating separate application DB, vector DB, and graph DB components; it also supports KV, documents, and relational data.
Key features include built-in MCP tools for agent data discovery, built-in embeddings via the Embed function, knowledge-graph tooling with Zep-AI Graphiti integrations, keyword and hybrid vector search for RAG, full ACID transactions with serializable snapshot isolation, and ultra-low latency from its Rust implementation. The open-source HelixDB core can be built locally, while Helix Cloud provides managed deployment for everyone from indie hackers prototyping agent memory to large-scale production graph applications.
HelixDB pricing
Pricing model: Freemium
HelixDB is free and open-source under the AGPL-3.0 license with no licensing fees for the engine itself. The open-source version can be self-hosted locally. Helix Cloud is the managed service offering with a freemium model - a free tier is available for getting started without upfront costs. Enterprise support and commercial managed service options are available upon contact for teams needing enterprise SLAs and deployment options. Exact pricing for paid tiers is not publicly disclosed and requires contacting the company.
HelixDB pros
- Open-source under AGPL-3.0 license with no licensing fees
- Built in Rust for ultra-low latency and high performance
- Combines graph and vector database in a single engine
- Built-in MCP support for agent data discovery
- Built-in embeddings function to vectorize text directly
- Full ACID transactions with serializable snapshot isolation
- Supports graph, vector, KV, documents, and relational data
- Auto-scales reader nodes horizontally on demand
- Object-storage-backed architecture for unlimited data storage
- Secure by default with logical VPC isolation
- Built-in vector search, keyword search, and hybrid search
- Strongly typed compiled HelixQL query language
- SDKs available for Python, TypeScript, Go, and Rust
- Tiered caching with SSD and in-memory caches
- Automatic sharding based on vertex degree for scalability
HelixDB cons
- Pricing not publicly disclosed for managed service
- Not intended for OLAP or massive batch analytics
- Fine-grained user access controls require developer implementation
- Ecosystem and management tooling still maturing
- Open-source v1 used LMDB limited to sequential writes
- Multi-tenant authorization needs additional engineering
- Managed service availability limited to selected users
- Third-party integrations still expanding
Frequently asked questions about HelixDB
What is HelixDB?
HelixDB is an open-source graph-vector database built from scratch in Rust. It combines graph database relationships with vector search capabilities for AI/ML workloads in a single platform, eliminating the need for separate application DB, vector DB, and graph DB components.
Is HelixDB open source?
Yes, HelixDB is completely free and open-source under the AGPL-3.0 license. The open-source core can be built and run locally. Helix Cloud offers managed cloud services, and enterprise support is available for teams needing enterprise SLAs.
How does HelixDB handle scalability for large graph datasets?
HelixDB uses automatic sharding based on vertex degree, distributing high-traffic graph partitions across nodes while maintaining cross-shard ACID compliance via Raft. Reader nodes auto-scale horizontally to handle query load, and the object-storage-backed architecture allows virtually unlimited data storage.
Can HelixDB integrate with existing AI/ML workflows?
Yes, its Python SDK supports PyTorch/TensorFlow embeddings, and HelixQL allows joint graph-vector operations like MATCH patterns combined with vector_search. Built-in embeddings function lets you vectorize text directly without pre-embedding.
What makes HelixDB suitable for real-time OLTP workloads?
Rust's async I/O and lock-free data structures enable 99.99% uptime with under 5ms P99 latency, validated under Jepsen tests for distributed consistency. The database handles concurrent reads and writes without blocking each other.
How does vector indexing work in HelixDB?
HelixDB uses disk-optimized HNSW with incremental indexing, supporting up to 2048-dimensional vectors and GPU-accelerated similarity searches via CUDA kernels. It includes built-in vector search with approximate vector search capabilities.
What SDKs are available for HelixDB?
HelixDB provides SDKs for Python, TypeScript/JavaScript, Go, and Rust. The HelixCLI tool can be used to check, compile, and deploy Helix locally, and queries are invoked through these SDKs after deployment.
What is HelixQL?
HelixQL is a strongly typed compiled query language for HelixDB. Queries are authored in a Rust DSL, deployed as stored procedures, and invoked by name over HTTP. This eliminates query parsing at runtime for better performance.
What is the difference between HelixDB open-source and Helix Cloud?
The open-source v1 version used LMDB which was limited to sequential writes and could handle relatively small amounts of data. Helix Cloud uses a new LSM-based storage engine backed by object storage that handles concurrent writes and allows virtually unlimited data storage with high availability.
Who should use HelixDB?
HelixDB is for developers and teams building AI applications, agent memory systems, recommendation systems, knowledge graphs, and low-latency graph applications. It serves indie hackers prototyping custom agent memory as well as Fortune 500 companies needing infinitely scalable OLTP graph/vector databases for RAG and AI workloads.