Hello! Temporal modeling is one piece, but the key things are performance (neo4j is a pain as murmansk said) and the AI-agent-centered design. It means everything has been tested from an agent point of view and we automatically gather agents' feedback to close self-improvement loops. Beyond the MCP, there are several agent-specific features, since long-term we want to compete with context layers like Mem0 or Cognee ,but natively, without the glue they need.

Say you want to build a context layer for your company with BlitzGraph. Agents would use ephemeral subspaces to gather as much info as possible prior to re-categorize it, store files natively, keep episodic memories, track everything that happens to each memory or record with the native $history. Also the query language is in JSON, so it's easy to build programatically, it'sis strongly typed, and errors are written to guide agents to fix them. Agents can also batch-query docs, which are dense and straight to the point for token optimization (and the discovery tree for related docs is also optimized for them)

The long term objective is to become the "brain" or logic/storage layer of AI agents

Not the blitz author. Neo4j was freaking slow when I tried to use it.