Rushdb

RushDB's pitch is narrow and honest: it is the persistence layer under your agent or application, not a chat product. The strongest part is the write path. You send the JSON your product already owns — an ACCOUNT with a nested TICKET array, for instance — and RushDB splits nested objects into separate linked records, forms the relationships, registers the live schema with value domains, and generates embeddings, all in one import. That removes the sync code most teams hand-write between an operational database and a vector store, and it is why the 'one model, one API, no synchronization code' framing holds up. The second strong part is read-time awareness. Agents can inspect fields, observed values, and relationship paths before they construct a query, which is a real difference from guessing a filter against an opaque vector index. Smart Search generates an inspectable SearchQuery rather than a black-box answer, so you can see and correct what the model decided. The query surface itself is genuinely combined — similarity search, exact filters, and multi-hop graph traversal in a single call — backed by Neo4j and ACID transactions. Weaknesses are structural, not cosmetic. Everything is metered in Knowledge Units on the write side: 0.5 per record, 1 per property, 0.25 per link, 5 per embedding, and beyond the included allowance Pro charges $10 per million KU. Read-heavy agent recall is cheap; a firehose of nested writes is not. The Free tier caps at 100K KU/month and pauses writes when you hit it (reads continue, with email warnings at 75%, 90%, and 100%). You also inherit Neo4j: self-hosting means managing Neo4j infrastructure yourself, and teams not already on Neo4j take on that operational learning curve. There are no frontend components — you integrate through the API and SDKs. On defaults, the current documentation-backed release sets openai/text-embedding-3-small for embeddings and openai/gpt-5.6-luna for chat, both replaceable with bring-your-own vectors, and core 2.11.0 tightened datetime handling so date-only strings like 2026-07-23 are typed as datetime without configuration. Where it fits: agent memory across sessions and model calls, GraphRAG that needs related entities rather than nearest chunks, multi-agent coordination with shared durable state, and connected-data analytics such as transaction-flow tracing. Where it does not: teams that want a plain vector store with no relationships, teams that need rigid schemas and controlled migrations, and write-heavy pipelines where per-KU pricing will dominate the bill.