HydraDB vs Neo4j
A Neo4j alternative for
Agent Context
Neo4j does have native temporal values, native vector search, hybrid retrieval, and first-party GraphRAG tooling, so I would not try to position HydraDB as better on basic graph or vector capabilities.
Dimension
HydraDB
Neo4j
Old facts
Keeps every version as queryable graph state
Usually updates the graph; history must be modeled separately
Time
Tracks when relationships were written and when they were actually valid
Supports dates and time, but validity rules are defined by the application
State history
Can rebuild what the graph looked like at an earlier point
Requires your own versioning model and Cypher queries
Agent context
Combines temporal state, graph traversal, retrieval, reranking, and final context
GraphRAG provides retrievers, but context logic is configured by the application
Context assembly
Returns reranked, temporally filtered, token-budgeted context
GraphRAG can format query results for an LLM, but budgeting and temporal filtering are application-defined
Storage
S3-compatible object storage is the durable source of truth
Graph data is stored and managed by Neo4j database servers
Scaling
Storage and compute are separate; query nodes and indexers scale independently
Scales through clustering, replicas, federation, and sharding
Compute
Query and indexing compute are separate and disposable
Database servers handle graph storage and query execution together
Best fit
Agents that need to reason over changing organizational relationships and state
General-purpose graph applications, analytics, and custom GraphRAG systems
// When HydraDB is the stronger fit //
Choose HydraDB when context—not just storage—is the product
Choose Neo4j when you need an adaptable graph foundation for many use cases. Choose HydraDB when your application must repeatedly assemble trustworthy, compact context for agents—without rebuilding temporal logic, semantic recall, graph ranking, and prompt budgeting around the database.
History is retained
Updates add a new state to the record, so agents can retrieve what was true before—not only the latest memory.
Relationships are first-class
Follow a customer across conversations, tickets, incidents, documents, and resolutions without custom joins.
Time is queryable
Ask what an agent knew at a specific point in time and surface the decision trail behind its answer.
Context arrives assembled
Rank graph and vector recall together into token-budgeted context for every response.
Everything you need to evaluate agent context
Why would I use HydraDB instead of Neo4j for AI agents?
Neo4j is a general-purpose graph database. HydraDB’s architecture is designed for large, persistent graph workloads where AI agents continuously retrieve connected context. Its object-store-native durability and independently scalable traversal infrastructure make it particularly suited to agent memory and context graphs.
Is HydraDB just a wrapper around Neo4j?
No. HydraDB is an independent graph database written in Rust, with its own storage engine, query planner, indexing system, and GraphBLAS-powered traversal engine. It supports Neo4j-compatible Bolt drivers so existing Neo4j applications can connect without HydraDB depending on Neo4j internally.
Do I still need to write Cypher if I use HydraDB?
Not necessarily for every interaction, but Cypher is HydraDB’s primary graph query language. HydraDB supports a practical OpenCypher subset and also exposes HTTP APIs, so applications or agent infrastructure can generate and execute queries without requiring users to write Cypher manually.
What is the biggest architectural difference between HydraDB and Neo4j?
Neo4j runs databases across allocated cluster servers. HydraDB makes S3-compatible object storage the durable source of truth, while query nodes, indexers, RAM, and local SSD are disposable. This separates the graph itself from the machines serving it.
Can my existing Neo4j application work with HydraDB?
HydraDB supports Neo4j-compatible Bolt 5.x drivers, so applications can connect using familiar Neo4j clients. It also supports a practical subset of OpenCypher and provides JSON/NDJSON HTTP APIs, reducing how much application code needs to change.
Build agent context that stays correct over time
Move beyond fast graph queries alone. HydraDB keeps evolving historical state, and retrieval logic together so agents receive context they can trust.
