NornicDB

It speaks Neo4j's language through Bolt and Cypher, exposes REST, GraphQL, and gRPC interfaces, and can preserve Qdrant-style client workflows where that helps migration.

It is built for knowledge systems, agent memory, Graph-RAG, and canonical truth stores where semantic search is only part of the query.

Built for AI-native workloads: vector search, memory decay, and auto-relationships are first-class features.

Agent and Graph-RAG systems: replacing a Neo4j + Qdrant + embeddings stack with a single deployment for task tracking, dependency graphs, and retrieval pipelines.

Translation and evaluation workflows: replacing a document store plus embeddings pipeline with a single deployment for graph-native retrieval and faster aggregation paths.

Search remains current-state focused: current search paths are intentionally separate from historical MVCC state.

Hybrid retrieval is where NornicDB is materially different from vector-only stacks: the query shape is vector search followed by graph expansion in the same engine.

This point is: once vector search plus one-hop traversal stays in low single-digit milliseconds locally, the bottleneck shifts from retrieval logic to deployment topology.