HydraDB 推出面向 AI 智能体的图原生上下文基础设施，用一个 API 实现跨会话持久化、跨智能体知识累积。它把内存、NVMe 和对象存储合并为单一图层，主打上下文交付更快、更便宜、更精准。其 650 万美元融资方并非前沿实验室，创始人认为智能体跨会话失忆才是真问题，RAG 和手动注入只是补丁。
For two years the whole conversation was about context window size.
Meanwhile the actual problem never moved: agents don't remember anything between sessions. We kept patching it with RAG and manual context injection and calling that memory.
HydraDB is going at the layer everyone routed around.
One API, sessions that persist, knowledge that compounds across agents. The tell in the $6.5M is who raised it: not a frontier lab. They had the compute to solve persistence and spent it on scaling, so memory became a startup's whole thesis instead of a line item in theirs. Fantastic!
Introducing HydraDB. The graph native context infrastructure for agents. Purpose built to deliver precise context & observability into why agents act the way they do. We've always believed graphs are the best way to manage AI context, but they've been too expensive to scale or impractical for storing full context. Until now. @hydra_db combines in memory, NVMe, and object storage into a single graph layer, making context delivery faster, cheaper, and more precise. We want context delivery to be extremely fast, 1000x cheap, and highly precise. Give your agents a brain. Video在 X 查看被引用的帖子
来源：@kimmonismus · x.com