HelixDB

Unified graph-and-vector database built for AI agent memory and GraphRAG.

Freemium· GW-10: $86.87 · GW-20: $173.74 · GW-40: $348.21 · GW-80: $696.42 · GW-160: $1,392.84RAG7.0 / 10

Best for

Pick HelixDB if you're building an agent or RAG system that needs graph traversal and vector recall in the same query without stitching two databases together.

Skip if

Skip it if you only need a simple vector store for embeddings search and don't care about graph relationships or temporal memory.

HelixDB is a database purpose-built for AI memory infrastructure, combining knowledge graphs, vector search, full-text search, and temporal awareness in a single system backed by object storage. It targets the increasingly common pattern where teams end up duct-taping a vector DB to a graph DB to a search engine just to give an agent something that resembles long-term memory.

The pitch is GraphRAG without the integration tax: entities, threads, and episodic timelines live alongside per-user/tenant vector recall and full-text indexes, so a single query can blend semantic similarity with graph traversal. It is aimed at developers building agentic systems, RAG pipelines, or internal 'company brain' knowledge bases, with SDKs for Rust, Go, TypeScript, and Python. The project is open source, with a managed cloud offering and auto-scaling reader nodes for production workloads.

The object-storage foundation is the differentiator the team leans on: cheaper than keeping everything in hot memory, with buffer-based durability and lower latency than gluing several specialized stores together. Pricing isn't surfaced clearly on the homepage beyond a referenced pricing page, so plan on a conversation with the team for serious deployments.

Editor's take

HelixDB is betting that the next wave of RAG is graph-shaped, and that combining vectors, graphs, and full-text under one roof beats the current Frankenstein stacks. It's a credible technical bet, but the project is still earning its production scars; we'd prototype before committing a critical agent memory layer to it.

— The AI Tool Bible editorial team

Pros

✅ Unifies graph, vector, and full-text search in one query layer
✅ Object-storage backend keeps costs and ops overhead lower than hot-memory stores
✅ Open source with SDKs in Rust, Go, TypeScript, and Python
✅ Temporal awareness for facts that change over time, useful for agent memory

Cons

⚠️ Younger project than Pinecone/Weaviate/Neo4j; smaller ecosystem and tooling
⚠️ Pricing for managed tier not transparent on the marketing site
⚠️ Object-storage tradeoffs may add latency vs in-memory vector DBs for hot paths