Agents need continual learning. At supermemory, we are doubling down and pushing the frontier for memory and in-context learning, for every agent and use case. Introducing 𝚕𝚎𝚊𝚛𝚗𝚎𝚛-𝟷

Usual memory systems learn the moment data flows in, then never look at it again. @supermemory 𝚍𝚛𝚎𝚊𝚖𝚜. Status done means the chunks are indexed. Memories come from a second phase, where related documents are grouped so memories form from coherent units, never one isolated write. When you go quiet, or enough new context has piled up, supermemory enters a dream cycle on its own. During a dream it: - merges fragments that belong together - reweights old facts against everything since - resolves contradictions - derives facts you never stated in one place, each traceable to its sources The output is new memories, a graph of derivations between them, and a picture of the user that updates as the evidence changes. If you use supermemory in any way, this already works. Dream away.

We tried using Jev across various parts of the memory pipeline. Here's what we found

We reverse-engineered Instinct's memory system and implemented it with supermemory's architecture

markdown vs @supermemory in markdown only memory systems, the agent may have to look through and traverse files to answer things. This is wasteful and slow! The files also get stale really fast, and there's no forgetfulness, updates, etc. The agent has to just look through things. supermemory is kinda like a dense interconnected markdown system, except that we automatically construct almost an 'instant' file that will directly one shot what the agent needs. That context has the whole history of the thing, removes forgotten info, and even related things that the model should probably know. This information is dense, fast, and dynamically constructed, so you have a living breathing improving system. So yes, you should use supermemory instead of markdown

Now converging Jev with @supermemory and @muse

every day I'm even more bullish on how beautifully simple and composable supermemory's architecture is. And this is with incremental improvements throughout years of being with customers and really really caring about THEIR products. @supermemory will be the default way to do memory in the agents era. There's nothing else that makes sense to me.

a bad memory is a pile of chat turns ranked by similarity. the fact from march sits next to the one from today and both look equally true. so what is a good memory? - scoped to a user, project, tenant, or any entity, with reference material kept as documents in that same scope - written when it's worth remembering, so casual chatter is less likely to stick - kept as a fact that holds until updated, a preference that strengthens with repetition, or an episode that fades unless it mattered - related with updates, extends, or derives, so the latest fact wins, history stays, and a guess ranks lower until confirmed - atomic, time-aware, and willing to forget when the window closes - attributed to source documents and traceable That is supermemory.

Introducing the supermemory plugin for Muse code!

Big shout out to @supermemory for supporting memories so that the conversational interactions can feel much more personal for returning users. Without their indexing this wouldn’t be possible.

In personal assistant and chat based agents, 𝚑𝚢𝚋𝚛𝚒𝚍 mode in supermemory can greatly improve your agents performance. Here's why: Typically in memory systems, the main model, or another model has to figure out what's important to learn - we do this too! But, it's impossible to be able to accurately predict everything important for learning. In supermemory, learnings are formed for things that are important for future context and needs to stay fresh, or forgotten. But, raw context is also indexed. If the memories don't include all the information that the model needs, supermemory will automatically add parts of the previous conversations to enrich the context! In the background, learnings are made with this info, now that supermemory knows that it has importance.

One thing that's interesting about our new console: an agents page. Detailed instructions for MCP, CLI, or native plugins into any integration :)

This is genuinely the best developer console you'll see... @MaheshtheDev and @Parthivdzns