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.show more

Introducing the supermemory memory graph every document becomes atomic... facts, and dreaming keeps building the graph in the background - extracting facts - linking related memories - resolving updates, the old version stays as history get one for yourself in the console ↓show more

supermemory makes agent context into one API. everything built... in 𝚗𝚙𝚖 𝚒 𝚜𝚞𝚙𝚎𝚛𝚖𝚎𝚖𝚘𝚛𝚢 memory, filesystems, retrieval, profile, connectors and data extraction. ask your agent to use supermemory :)show more

Introducing Memory 101 🧠 Every ai tool you use... is a goldfish. brilliant for one chat, close the tab and it forgets you completely. memory is the most underbuilt layer in ai right now. so we're explaining how it actually works, one episode at a time. coming up: → rag vs memory, why most "ai memory" is just search → the 5 things real memory needs: facts, profiles, graphs, time, forgetting → why good memory has to forget things follow along!show more

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-𝟷show more

Introducing API v5, the agent-first memory API - container... tags are now namespaces, and they live in the url - output from the API is now optimized for agent context - one prompt to migrate - Markdown negotiation in API resultsshow more

🎉 supermemory plugins are now FREE TO USE! Magical... unified memory that is automatic, that works across agents and teams. just do `npx supermemory plugin` to set up. This is powered by a new engine we're working on and many breakthroughs in making the plugins feel magical.show more

The Context Cloud for agents is here. Introducing the... new supermemory. A new way to do context, for your agents Everything you need for context - Memory, RAG, Filesystems, Profiles, are now available as blocks you can compose to build something for your use caseshow more

Introducing the supermemory CLI. MCP isn't dead - but... CLIs can be more powerful. So we built the most advanced CLI for agents. EVERY single thing you can do on the supermemory platform can be done by Agents. just prompt them. They are first class users. > npx supermemoryshow more

As soon as you try to run 2 agents... on the same data, everything breaks: • Agent 1 reads some data and starts working. • Two seconds later, Agent 2 changes that same data. This is a problem. Agents will waste tokens on stale data, overwrite each other's work, and redo tasks the other has already finished. You can't prompt your way out of this mess. Everything old in software engineering is new again. We need proper coordination when running multiple agents. Here is a good open-source solution for this: Omnigraph is a graph database for context assembly and multi-agent coordination. Here is how it works: • It sets one shared place where multiple agents will work from • Each agent gets their own copy of the data • Agents write their changes to their copy • When an agent is done, it merges its changes back Three huge benefits from this model: 1. We know it works. We've been doing this for decades already. 2. No more lost information or agents stepping on each other's toes. 3. You get a complete trail of which agent did what and when. I recorded a video to show you a bit more about this. Here is the link to the Omnigraph GitHub repository:show more

Sometimes, memories feel so real that you wish you... could wake up and discover that it was all just a dream. This captures the pain of letting go, the memories that refuse to disappear, and the hope that love might still have one more chance.show more

woah this went too viral. In a world of... agents, do humans even need to use a UI? Re-introducing the supermemory CLI! agents are now first class citizen. You can create scoped access controls for them, so you never have to visit our console. > npx supermemory setupshow more

STANFORD BUILT AN LLM MEMORY THAT WORKS LIKE A... BRAIN AND IT MADE STANDARD RAG LOOK ANCIENT no fine-tuning. no bigger model. just a smarter way to remember the insight: the brain doesn't store memories as isolated notes. it indexes them and links them - so one cue pulls back the whole memory so HippoRAG stops treating documents as separate chunks. it builds a knowledge graph, then spreads activation across it (Personalized PageRank) to pull a full answer from a single cue - the brain calls this "pattern completion" the result: > up to 20% better on hard multi-hop questions > 10-30x cheaper and 6-13x faster than iterative retrieval > connects facts split across documents in a single step most RAG still encodes every passage in isolation - so it breaks the moment the answer is scattered more retrieval was never the answer. brain-like memory is bookmark this. the next leap in AI agents is how they remember, not how big they getshow more

every AI tweet writer sounds like chatgpt… so i... built one that sounds like me with supermemory! it scrapes viral AI twitter every day, rewrites the bangers in my voice, and posts them straight to X. >> the stack: apify scrapes, perplexity writes, and supermemory is the memory layer that makes it actually sound like me. >> one container learns my voice as its own entity, another remembers everything i've ever shipped, so it writes like me and never repeats a take. want it? comment below ↓show more

The American Dream does not need a makeover. It... has worked for 250 years because America is still the place where, no matter who you are or where you came from, you have a real shot at building a better life. Every administration has a responsibility to protect that opportunity. The American Dream is not just part of this country, it is one of its greatest exports.show more

Introducing the supermemory company brain A super employee with... all the knowledge of your company, lives in your slack. - Learns how the team works and talks - Works across all your tools - Permission controls built-in for big teams 2 mins to set up. just invite to slack 👇show more

Meet the minds building supermemory! 🧠 Our research engineer... Prasanna breaks down how memory actually works, in your head, and in AI. inside the episode: → why forgetting is a feature and not a failure → what your hippocampus inside brain is really doing all day → how "dreaming" turns a messy day into long-term memory → what the AI labs get right, and wrong, when they copy the brain → why a bigger context window still isn't memory the line worth sitting with: stop asking where memory is stored. start asking what gets kept, and why the brain chose it. full episode below ↓show more

Today, Dream Labs is out of stealth. In February... 2026, while we were at NVIDIA, Scott Reed youliang Yunhao Ge and I started calling a new kind of robot model a World Action Model, one that learns how the world moves from video, then uses that to decide how to act. Today, we’re releasing Dream-1 through a public API that anyone in the world can use. When you rent a car you’ve never driven, you don’t learn to drive all over again. Within a few minutes, it feels like your own. Robots should work the same way: a good model should be able to pick up a new robot and get to work, without being retrained from scratch. That’s what we’ve spent the last 6 months on, and it’s what Dream-1 does. It controls bimanual robots with parallel grippers it has never seen before, zero-shot, on unseen tasks in unseen environments. Generalization has been my North Star in robotics: first by distilling video world models into VLAs (DreamGen), then by learning to act from those world models directly (DreamZero), and now by transferring across robots (Dream-1). I’ve come to believe it’s the only direction that lasts. Pushing a narrow model from 90% to 99.9% on a single task is real work, but much of it gets washed away once a model that generalizes arrives. Since the start of the company we’ve stayed single-minded about generalization.That focus is what got Dream-1 to control robots it was never trained on, on tasks it was never shown. And it’s what makes a public API possible at all: a model that only does one task on one robot can’t serve millions of users. Dream-1 is still a research beta, and it will make mistakes. But it already drives cars it’s never seen. Generalization wins in the end, and the end is closer than it looks. Here’s Dream-1 taking the wheel of a robot it’s never seen. Full details 👇show more

IF YOU ARE STILL PROMPTING BY HAND IN 2026... YOU ARE ALREADY BEHIND the guy who built Claude Code at Anthropic said he stopped writing prompts a long time ago he runs loops instead, systems that write the prompts for him and keep working while he sleeps you type one request at a time and wait for the answer he built a machine that fires hundreds of requests, checks its own output and fixes itself with no hands on the keyboard here is how you make the jump step 1 stop treating the model like a chat box and start treating it like a worker you manage step 2 write a loop that takes a goal, breaks it into tasks and sends each one to the model on its own step 3 add a second pass where the model grades its own work and retries whatever failed step 4 let it run on a timer so it keeps shipping while you are away from the screen the person still typing prompts one by one is doing the work of a intern the person running loops is doing the work of a whole team off one laptop prompting was never the skill, building the system that prompts for you isshow more

context engineering vs graph engineering. every few months the... list gets a new word and everyone treats it as a replacement for the last one. these two are not on the same list. one decides what the model sees this turn, the other decides what exists at all. the cleanest way to tell them apart is to ask what a single unit of work looks like. > context engineering is the window the window opens empty, every single time. you assemble what goes in it. the prompt, the docs, the history, the tool results. the assembling is the work. the window only grows. it never shrinks on its own, so eventually something gets dropped. usually from the middle. usually without telling you. then the turn ends and the window is thrown away. not archived, thrown away. the next turn opens empty again and you re-explain what you already explained. good context engineering is knowing what to leave out, not what to pack in. the unit of work is one window. > graph engineering is the structure the same material arrives from the same sources. instead of packing it into a window, you pull entities out of it, resolve the duplicates into one node, and write typed edges between them. nothing here is stored as text you hope to find again. it is stored as a thing with a name and its connections to other things. when the turn ends, the graph is still there. the next turn does not start from zero. it starts by querying what already exists, and the query walks edges instead of guessing at similarity. good graph engineering is deciding what counts as the same thing twice. the unit of work is one relationship. > they are not alternatives the graph is what refills the window. context engineering decides what fits. graph engineering decides what there is to choose from. remove the graph and every session starts blind. remove the context work and the best structure in the world arrives as an unreadable dump. that also tells you which one broke. the answer drifted from what you actually said, or forgot something from this same session. that is the window. the answer is coherent but invents a connection that does not exist, or cannot join two facts it has clearly seen. that is the structure. people debug the prompt because the prompt is the easiest thing to edit. it keeps taking the blame for failures that live a layer down. save this - then read the full breakdown belowshow more