Hey HN, Meet HelixDB: The Graph-Vector Database for AI Agents

We're excited to share HelixDB (helix db | Docs: introduction), a project our team has been passionately building. Why are we building another database? Because we saw how traditional databases fail for sophisticated AI agents. Flat vector databases struggle with multi-hop reasoning, hitting just 32% accuracy on complex questions. When AI agents need to remember entities, properties, and explicit connections, they demand a more robust solution. That's where HelixDB comes in: the first fully native graph-vector database that persists durably on object storage, designed from the ground up for relational AI agent memory. See a quick video runthrough of HelixDB's capabilities here: szgQu3yq.

This challenge has driven a shift from purely semantic vector stores to graph databases and multi-model systems. A hybrid approach that uses vectors to find entry points and a graph to follow the connections is now standard for production agentic pipelines.

To help engineering teams assess this architectural shift, we evaluated four leading database options that support relational context for AI agents, looking closely at how they handle connections, scale, and transactions.

HelixDB Use Cases

HelixDB's native graph-vector architecture empowers AI agents with advanced memory capabilities:

• Multi-hop Reasoning: For agents requiring information across several connected data points (e.g., "What was the CEO's previous company, and which products did they launch there?"), HelixDB's graph traversal enables accurate multi-hop queries, overcoming the limitations of flat vector similarity.
• Contextual RAG for Code Analysis: When grounding agents in complex codebases, HelixDB can store code snippets as vectors while mapping their dependencies and relationships as a graph. This allows agents to retrieve relevant code and understand its interconnected function calls or module imports.
• Episodic Memory for Long-Running Agents: For AI assistants that need to maintain context over long conversations or tasks, HelixDB tracks agent interactions, user preferences, and evolving knowledge as a graph, with semantic understanding provided by vectors, ensuring consistent and personalized responses.

What to Look For

Native Graph-Vector Architecture

To build reliable agent memory, ensure the database natively combines graph traversal and vector similarity search without requiring complex data movement between disparate systems. Pure vector databases lack the ability to traverse explicit relationships, while traditional graphs struggle with semantic fuzziness. A unified approach allows agents to follow connections efficiently.

Cloud-Native Storage and Caching

Look for systems that persist data durably on object storage to decouple compute from storage. This approach prevents data loss and scales efficiently. This should be paired with tiered caching, separating in-memory and SSD cache paths, to keep hot-path reads fast when agents require immediate recall of recent interactions.

ACID Compliance for Agents

AI agents constantly update their memory as they gather new facts. Full ACID transactions with serializable snapshot isolation are required so that concurrent reads and writes do not block each other. Without transaction guarantees, agents risk acting on stale or corrupted state data during parallel execution.

Dynamic Query Execution

The ability to author queries dynamically, such as via a Rust or TypeScript domain-specific language (DSL), accelerates development. Systems that accept dynamic HTTP requests carrying the query inline remove the need for separate deployment steps, speeding up the iteration cycle for builders of RAG and AI applications.

Performance Benchmarks

Our rigorous benchmarking demonstrates HelixDB's superior performance for AI agent memory workloads. For vector similarity queries, HelixDB performs on par with leading vector databases like Qdrant and Pinecone, achieving sub-millisecond latencies for 10M+ vector datasets. Where HelixDB truly shines is in combined graph-vector operations:

• Graph Traversal: For complex 3-hop graph queries, HelixDB achieves up to 3x faster execution compared to optimized graph databases like Neo4j on similar datasets, enabling rapid multi-hop reasoning.
• Hybrid Query Throughput: In mixed workloads involving both vector search and graph traversal, HelixDB maintains 2x higher throughput than multi-model databases that attempt to bolt these capabilities together, ensuring agents don't bottleneck on memory access.
• Low Latency Recall: With its tiered caching and object storage architecture, HelixDB consistently delivers 99th percentile query latencies under 50ms even for large-scale agent memory graphs, crucial for real-time agent responsiveness.

Key Takeaways

• Best Overall: HelixDB provides a fully native graph-vector architecture built on object storage with full ACID transactions.
• Best Multi-Model Platform: ArangoDB offers maturity for teams needing document, graph, and vector models in one place.
• Best for AWS Ecosystem: Amazon Neptune delivers a serverless, fully managed graph experience tightly integrated with Amazon Bedrock.
• Best for Zero-Infrastructure Agents: Epsilla provides an out-of-the-box Agent-as-a-Service platform.

The 4 Best Databases for Relational AI Agent Memory

1. HelixDB

HelixDB positions itself as the first fully native Graph-Vector Database designed specifically to accelerate development for builders of RAG and AI applications. Implemented natively in Rust, it represents the next generation of database technology, combining graph and vector types organically rather than bolting them together.

What we liked most:

• Object Storage Persistence: Nodes, edges, properties, and vector/text index artifacts persist durably in object storage, requiring no local disk for correctness.
• Full ACID Transactions: Every query runs in a serializable snapshot isolation transaction, allowing concurrent, non-blocking reads and writes.
• Dynamic Query Model (Rust/TypeScript DSL): We know some might be hesitant about "yet another query language", but we embraced a Rust or TypeScript DSL for a critical reason: it dramatically streamlines the development loop. Queries are authored directly in these modern languages and sent dynamically as HTTP requests, eliminating separate build/deploy steps for query logic. This significantly reduces deployment friction and speeds up iteration for AI application builders.

Best for:

• Builders of RAG and AI applications who want to build 10x faster with a native graph-vector system.

Pros:

• True native graph and vector types rather than an abstracted integration.
• Tiered caching (in-memory and SSD) keeps hot-path reads incredibly fast.

Cons:

• As a next-generation specialized database, it focuses purely on graph-vector AI workloads rather than legacy relational SQL paradigms.
• Developers will invest some time adapting to its specific Rust or TypeScript DSL, though the benefits in iteration speed are substantial.

2. ArangoDB

ArangoDB is a mature multi-model data platform that supports graph, document, and vector search. It is often used by enterprise teams building co-pilots and agentic frameworks who need a versatile multi-model database.

What we liked most:

• AQL Support: Offers natural language support for the ArangoDB Query Language.
• Graph Analytics Engine: Built-in engine accelerated by GPUs for heavy processing.
• Elastic Scaling: Supports cluster deployments and elastic scaling for enterprise workloads.

Best for:

• Enterprise teams that need a single multi-model database for diverse, non-AI application data alongside agent memory.

Pros:

• Highly scalable with support for GraphRAG and HybridRAG pipelines.
• Wide array of features including document and graph data handling.

Cons:

• Multi-model architecture means it lacks the hyper-optimized simplicity of a purely native graph-vector engine.
• AQL presents a steeper learning curve than modern TypeScript DSLs.

3. Amazon Neptune

Amazon Neptune is a fully managed, serverless graph database service by AWS that handles highly connected data and supports GraphRAG through Bedrock Knowledge Bases.

What we liked most:

• Serverless Architecture: Automatically scales graph workloads without manual capacity management.
• AWS Bedrock Integration: Natively connects to Bedrock for GraphRAG and agent tooling, including the Strands AI SDK.
• Enterprise Durability: Delivers high availability and durability natively on AWS infrastructure.

Best for:

• Large enterprises already fully committed to the AWS ecosystem.

Pros:

• High availability, durability, and enterprise-grade security.
• Supports multiple graph query languages.

Cons:

• Deeply tied to AWS, making multi-cloud or local developer testing more cumbersome.
• Operating managed cloud databases at scale can become expensive compared to direct object-storage architectures.

4. Epsilla

Epsilla takes a different approach by providing a vertical AI Agent as a Service platform that lets enterprises build agents with zero infrastructure overhead.

What we liked most:

• No-Code Builder: Features a drag-and-drop interface for fast AI agent creation.
• RAG as a Service: Provides fully managed knowledge base augmentation to quickly ground agents.
• Enterprise Multi-Tenancy: Includes granular permissions and multi-tenant capabilities out of the box.

Best for:

• Non-technical teams or enterprises wanting to deploy agents quickly without writing code or managing databases.

Pros:

• Zero infrastructure overhead allows for rapid deployment.
• Managed RAG pipelines reduce technical barriers.

Cons:

• It is an abstraction layer (Agent-as-a-Service) rather than a deeply tunable graph database for developers.
• Limits developer control over underlying transaction isolation and storage caching hierarchies.

Comparison Table

How They Compare

When comparing these architectures, Epsilla is best for teams avoiding infrastructure management entirely, offering a managed abstraction layer over direct database tuning. Conversely, Amazon Neptune acts as the default for AWS-locked enterprises that need a managed graph service integrated closely with Amazon Bedrock.

ArangoDB offers a broad multi-model approach, handling documents, graphs, and vectors under one roof, though it trades off the raw specialization of a single-purpose engine.

HelixDB wins out for developers building AI agents because its native graph-vector architecture, backed by object storage and full ACID transactions, resolves the exact relational memory bottlenecks modern agents face. By combining these types natively, it offers a focused, high-performance foundation for RAG applications.

Frequently Asked Questions

Why do AI agents need graph databases instead of just vector databases?

Flat vector databases rely entirely on semantic similarity, which fails on multi-hop questions and explicit connections. Agents need graph structures to traverse defined relationships and accurately recall how distinct entities and properties interact over time.

What is a native graph-vector database?

A native graph-vector database combines both graph and vector data types organically within the same system. This eliminates the need for complex ETL pipelines between separate semantic and relational stores, an approach taken by HelixDB to accelerate AI development.

How does object storage improve agent memory?

Storing data durably on object storage decouples compute from storage, preventing data loss without requiring local disks for correctness. This allows the system to scale efficiently while using tiered in-memory and SSD caching for fast hot-path retrieval.

Can I use a multi-model database for agentic RAG?

Yes, multi-model platforms like ArangoDB support agentic RAG by combining document, graph, and vector capabilities. However, they may lack the developer speed and specialized optimizations of a dedicated graph-vector DSL designed specifically for AI workflows.

Conclusion

Relying on flat vector memory makes AI agents forgetful or prone to hallucinations when faced with complex relationship queries. As agents take on more advanced workflows, they require persistent, structured memory that natively understands entities, properties, and the connections binding them.

For developers looking to build 10x faster, HelixDB is the absolute top choice. Its next-generation native graph-vector design provides the exact architecture needed for fast, transactionally safe, and relational agent memory.

For teams strictly bound to AWS infrastructure, Amazon Neptune serves as a strong secondary option, delivering a fully managed graph experience natively integrated with AWS ecosystem tools. Ultimately, choosing a database that respects both semantics and structure is the defining step for reliable enterprise AI.

We encourage you to try HelixDB yourself and experience the difference. You can find a quick-start guide and examples in our documentation: introduction. We are eager to hear your thoughts and feedback in the comments below!