About SuperMemo API
About us
We’re turning decades of memory research into developer infrastructure
SuperMemo API gives product teams access to advanced spaced repetition technology in a form that can be integrated into their own products. It helps learning systems decide what a user should review, when the next review should happen, and how often knowledge should return to support long-term retention.
Our goal is simple: make proven memory optimisation available beyond the SuperMemo ecosystem. Language apps, exam platforms, flashcard tools, corporate training systems, AI tutors, and other learning products can now build smarter review experiences without developing a scheduling algorithm from scratch.
Decades of memory research
SuperMemo has been developing spaced repetition technology since the 1980s. What began as a practical answer to one of the hardest problems in learning – forgetting – became a long-running research and product journey focused on helping people remember more with less wasted effort. SuperMemo API turns this experience into infrastructure that other learning products can use.
Built for learning products
SuperMemo API is designed for EdTech platforms, language apps, flashcard tools, exam preparation systems, corporate training solutions, and AI-powered learning products. It can support many types of knowledge, from vocabulary and facts to professional concepts and exam material. The API provides the memory scheduling layer, so product teams can focus on content, user experience, and learner outcomes.
Focused on long-term retention
A completed lesson does not always mean retained knowledge. That is why intelligent review timing matters. SuperMemo API uses learner interaction data to help calculate when each item should return. The aim is to bring knowledge back at the right moment – before it fades too far, but not so often that learning becomes repetitive or inefficient.
Powered by SM-20
At the heart of SuperMemo API is SM-20, the latest generation of SuperMemo’s spaced repetition technology. It is designed to support precise, adaptive scheduling based on learner performance and repetition history. Instead of relying on fixed review patterns, SM-20 helps create a more personal learning path for every user, item by item.