Memory that thinks in relationships.
A high-performance graph engine for the most valuable question in your data: how is everything connected? MANTIS answers it in the blink of an eye — the memory layer for intelligent systems and the connective tissue for data that refuses to sit in rows and columns.
Tables are good at storing facts. They are poor at storing relationships — who knows whom, what depends on what, how one event led to another. Yet relationships are exactly what intelligent systems need to reason: to recall context, to trace consequences, to understand a situation rather than just look up a value.
MANTIS is built for that. It stores information as a living network of entities and the links between them, and answers questions about those links with startling speed. Ask it how two things relate, what a change ripples out to, or which paths connect a start to a goal — and it returns in a fraction of a millisecond, without the weight of a heavy database sitting between you and your data.
For an AI agent, that is memory: not a log to scroll through, but a structure it can reason over.
Model your world as it actually is — people, things, and events, and every meaningful connection between them.
Traversals, shortest paths, and influence scores return instantly — fast enough to sit inside a live decision loop.
Built-in analytics surface what matters most, the communities hidden in your data, and the paths that connect any two points.
A minimal footprint that runs right alongside your application — the power of a graph platform without the overhead of one.
MANTIS is the recall layer for autonomous systems — the place a goal-driven agent stores what it has learned and retrieves it by relevance instead of recomputing it. Beyond AI, it powers knowledge graphs, recommendations, fraud and network analysis, and any application whose value lies in the connections. It is part of the FluxDB data line.
Named for the mantis shrimp — one of nature's most sophisticated sensory systems — built to see what connects.
