The field: a blue meadow, the recurring image of memory forming
The path to useful AI is to scale in-context continuous learning that works with any model, any harness, and for all the varieties of use cases. As open-source model adoption grows and the world becomes even more multi-model, learning and memory should be separated from the model providers and be highly interoperable (hence in-context!).
Pre-training and post-training, scaling on data and compute, will continue to push models’ intelligence, but will not enable them to learn and improve in real time.
Our model learner-1 helps any model continuously improve by extracting and dreaming on the context of any user, task, or tenant, and putting it on our vector-graph database. This is then brought to the model by injecting tokens into its context in real time.
supermemory
fileschatsurlsraw datalearner-1memory dbv12v13updatemergeinferforgetprior memories in contextprior memoriesinject via hooksyour agent's contextor
Figure 1. Raw data enters supermemory. learner-1 updates, merges, infers and forgets against the memory db, and the result is injected into your agent’s context.
What we do
Memory that keeps learning.
learner-1 extracts and dreams on the context of every user, task, and tenant, stores it in a vector-graph database, and brings it back to the model by injecting tokens into its context in real time.
Any model, any harness.
Learning and memory live outside the model providers, so they carry across models and harnesses and stay interoperable, in context, and yours.
The hard infrastructure, done.
Dense, interconnected, growing learnings with an understanding of time, already serving 100k+ organizations and over a trillion tokens a month.
Per-question token cost across 5 to 9,988 files (log scale). fs-only grows ~7× over the range; supermemory grows ~4×. The 299-file corpus is a notable outlier where fs-only thrashes. n = 220 (fs) + 220 (smfs) trials · smfs.ai/research/runs
“Reduced avg response time from 40s → 12s. Using about 40–50% fewer tokens.”
supermemory is SOC 2 certified, HIPAA compliant and GDPR aligned. We will sign your contracts and DPA, and deploy air-gapped where the data cannot leave at all.
We’re a small team based in San Francisco, building the memory layer for every model and every harness. If that is the problem you want to spend the next few years on, we would like to hear from you.
One of the things missing from today’s systems is the ability to online learn and continually learn. So, you know, we train these systems, we balance them, we post-train them, and then they don’t continue to learn out in the world like we would.
Reference 02
AI will transform every industry, power every company, and be built by every country. Open models strengthen safety and cybersecurity, accelerate innovation and diffusion, and enable sovereign AI. The world needs both frontier closed models and frontier open models.
