Agent memory database
Read the category page before comparing vendors. It defines the retrieval signals an agent memory store needs and the tradeoff between a service and an embedded engine.
These pages compare MongrelDB with agent-memory frameworks and embedded vector stores on the questions that decide production fit: who owns storage, which retrieval signals are native, how writes stay consistent, and what happens when the workload grows past a demo.
The category page for agents that need hybrid recall, not only vectors.
FlagshipEvaluation criteria for in-process ANN, SQL, filters, and recovery.
Agent memoryMemory framework and API versus an embedded engine you own.
Agent memoryManaged memory service versus local hybrid storage.
Vector storeEmbedding collections versus SQL, transactions, and hybrid retrieval.
Vector storeColumnar vector data versus operational hybrid memory.
Vector extensionSQLite extension simplicity versus a multi-index Rust engine.
Cloud serviceManaged cloud document service versus an embedded in-process engine.
RoundupFrameworks, managed services, DIY stacks, and the embedded engine option for agent memory.
RoundupEmbedded libraries, extensions, services, and engines for local vector search.
RoundupVector services, embedded engines, and the Postgres extension path.
RoundupManaged, self-hosted, compatible, and embedded document database options.
RoundupEmbedded vector retrieval five ways.
RoundupServer, embedded, and in-Postgres vector database options.
Read the category page before comparing vendors. It defines the retrieval signals an agent memory store needs and the tradeoff between a service and an embedded engine.
Every MongrelDB capability claim on these pages comes from the public engine repository, benchmarks, or the Hermes memory plugin documentation.