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Engramme is launching a public beta of its Large Memory Models (LMMs), an AI architecture designed to give applications persistent, lifelong memory of what a user has seen, said, and done. The pitch to developers is an API that works in the background across tools like email, calendars, and video calls, surfacing relevant context automatically rather than waiting for a search query or prompt. The launch matters because memory has become the limiting factor for useful AI agents, and an LMM is positioned as a new architecture that captures what you saw, who you talked to, and where you were, and surfaces the right piece back to you without explicit retrieval.
The company is a Harvard spinout led by co-founder and CEO Gabriel Kreiman, a neuroscientist whose lab has spent two decades studying how the brain consolidates experience, alongside co-founder Spandan Madan, who completed his computer science PhD at Harvard working in Kreiman's lab . Engramme emerged from stealth recently and is reportedly in early talks to raise around $100 million , which sets the stakes for this beta as the first public test of whether the LMM concept holds up outside the lab.
For founders and operators, the practical read is that Engramme wants to be infrastructure rather than a consumer app, sitting beneath other products and handling recall the way vector databases handle embeddings today. Beta access is open through the company website, and the early use cases shown in the launch (pulling up a saved restaurant when a friend invites you to lunch, surfacing prior project context inside an email thread, retrieving an answer mid-call) point at where the team expects the first integrations to land.
Comments (16)
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P
Priya VasquezApr 29, 2026
Bold claim sandwich there. Putting yourself between Google and OpenAI in the same tweet takes a certain kind of confidence and I respect it.
O
Olu BankoleApr 29, 2026
hot take: the tweet copy peaked at 'Achilles heel' and then immediately shot itself in said heel with the Google/OpenAI line.
T
Tomas KvistApr 29, 2026
That second screenshot in the tweet is doing way too much heavy lifting. Cute logo loop though.
D
Deepa RamaswamyApr 29, 2026
Curious what the underlying retrieval architecture looks like under the hood. Is this vector store plus summarization or something more structured?
M
Max HuangApr 29, 2026
Retention numbers on memory products are historically brutal because users forget to come back. What does week 4 look like before I say interesting.
L
Lena OyelaranApr 29, 2026
ok wait, LMM as a term is going to start a war on AI Twitter and I am here for it.
J
Junpei MoriApr 29, 2026
Building in adjacent memory infra space and honestly the framing here is sharper than ours. Stealing the LMM acronym energy.
K
Kofi AsanteApr 29, 2026
Persistent memory was supposed to be the killer feature two years ago and the market has gotten smaller since. Everyone wants forgetfulness now, not recall.
A
Anya PetrovApr 29, 2026
We had something internally that did this in 2019 and shelved it because users got creeped out when the assistant remembered too much. Curious how you handle the consent UX.
C
Carmen DelacroixApr 29, 2026
Reminds me of what Mem0 and Letta are circling. If you can be the Stripe of memory layers this gets very fun very fast.
S
Sasha BrandtApr 29, 2026
Already told three portfolio CEOs to plug this in. If memory is really the bottleneck this is the wedge.
A
Amir KhouryApr 29, 2026
Docs link? SDK? Is any of this open source or are we beta-form-gated into a Notion page.
P
Panos LiakosApr 29, 2026
Every memory demo I've seen looks magical for ten minutes then hallucinates that I have a brother named Steve. Show me the failure cases.
Y
Yui NakamuraApr 29, 2026
Signup page is one input field. Respect. Now do the actual onboarding with the same restraint please.
R
Rashid BekovApr 29, 2026
Persistent memory plus on-chain attestation of what the model remembered about you would actually be a real product. Just saying.
E
Emeka OkaforApr 29, 2026
Who's hiring on the infra side here? Have a former Pinecone eng looking and she'd eat this stack for breakfast.
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Engramme is launching a public beta of its Large Memory Models (LMMs), an AI architecture designed to give applications persistent, lifelong memory of what a user has seen, said, and done. The pitch to developers is an API that works in the background across tools like email, calendars, and video calls, surfacing relevant context automatically rather than waiting for a search query or prompt. The launch matters because memory has become the limiting factor for useful AI agents, and an LMM is positioned as a new architecture that captures what you saw, who you talked to, and where you were, and surfaces the right piece back to you without explicit retrieval. The company is a Harvard spinout led by co-founder and CEO Gabriel Kreiman, a neuroscientist whose lab has spent two decades studying how the brain consolidates experience, alongside co-founder Spandan Madan, who completed his computer science PhD at Harvard working in Kreiman's lab . Engramme emerged from stealth recently and is reportedly in early talks to raise around $100 million , which sets the stakes for this beta as the first public test of whether the LMM concept holds up outside the lab. For founders and operators, the practical read is that Engramme wants to be infrastructure rather than a consumer app, sitting beneath other products and handling recall the way vector databases handle embeddings today. Beta access is open through the company website, and the early use cases shown in the launch (pulling up a saved restaurant when a friend invites you to lunch, surfacing prior project context inside an email thread, retrieving an answer mid-call) point at where the team expects the first integrations to land.
Comments (16)
Bold claim sandwich there. Putting yourself between Google and OpenAI in the same tweet takes a certain kind of confidence and I respect it.
hot take: the tweet copy peaked at 'Achilles heel' and then immediately shot itself in said heel with the Google/OpenAI line.
That second screenshot in the tweet is doing way too much heavy lifting. Cute logo loop though.
Curious what the underlying retrieval architecture looks like under the hood. Is this vector store plus summarization or something more structured?
Retention numbers on memory products are historically brutal because users forget to come back. What does week 4 look like before I say interesting.
ok wait, LMM as a term is going to start a war on AI Twitter and I am here for it.
Building in adjacent memory infra space and honestly the framing here is sharper than ours. Stealing the LMM acronym energy.
Persistent memory was supposed to be the killer feature two years ago and the market has gotten smaller since. Everyone wants forgetfulness now, not recall.
We had something internally that did this in 2019 and shelved it because users got creeped out when the assistant remembered too much. Curious how you handle the consent UX.
Reminds me of what Mem0 and Letta are circling. If you can be the Stripe of memory layers this gets very fun very fast.
Already told three portfolio CEOs to plug this in. If memory is really the bottleneck this is the wedge.
Docs link? SDK? Is any of this open source or are we beta-form-gated into a Notion page.
Every memory demo I've seen looks magical for ten minutes then hallucinates that I have a brother named Steve. Show me the failure cases.
Signup page is one input field. Respect. Now do the actual onboarding with the same restraint please.
Persistent memory plus on-chain attestation of what the model remembered about you would actually be a real product. Just saying.
Who's hiring on the infra side here? Have a former Pinecone eng looking and she'd eat this stack for breakfast.