Considered it, but it didn't fit our constraints-driven design ("this must have exactly one author"), first-class edges, polymorphism and JSON DX. SPARQL is a string query language which is exactly the kind of "build the query as txt and pray" that I wanted agents to avoid. BQL is more like graphQL but with a fully typed JSON shape, and batched, nested, topologically-ordered mutations built in.
We did borrow the inference ideas tho, the inference/recursive relations are on the oven!
Fair! We haven't wiped storage so far and we do multiple daily backups, to speed up development I'd rather keep that warning there until we implemente a proper migration engine and enable payments.
On the issues: those were filed by hand over a couple of years trying to ship products on these DBs. Probably came across as bragging but the point was that I spent a big chunk of my life digging into graph and modern databases, and the gaps pushed me to give up and build one that gathers all their best ideas
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
Hello! I would say about 50% of them were bugs, 25% were features/mechanisms I loved from the the other ones, and 25% trying to push a better way to model things, which is what I never got in none of them. Typedb would be close if entities could evolve and belong to multiple types, as well as if they focused on making it adapted for app creation, but it wasn't their priority. In surrealdb I opened several issues around enhancing the graph db part, add some topological ordering to mutations the way typedb does it and several other topics. The most recent ones are in surrealdb's repo: https://github.com/surrealdb/surrealdb/issues?q=is%3Aissue%2.... Half of them have been achieved but the other half remains. With blitzgraph I tried to take the best ideas and pracrices from each, keep the tradeoffs loww, and lean as hard as possible into being AI-agent-first