Unabyss for Claude - Shared memory across all apps and LLMs. In Claude

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Claude doesn't know what happens in GPT. Neither one really knows who you are or what your company does. Now they can. Unabyss gives Claude memories from your other AI agents and everyday apps: email, Drive, GitHub, Notion, meeting recorders, and 20+ more. It saves new memories too, so GPT and Cursor stay in sync with the exact same context - sharper than wiring each tool into Claude one by one. Finally, a real memory that follows you. Private. Portable.

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The portable memory angle is the strongest part of this. One thing I haven't seen addressed though ,what happens when you disconnect a source? If I connected Notion six months ago and Unabyss built context from it, then I disconnect Notion or delete those docs, does that memory get cleaned up automatically or does stale context from a deleted source keep surfacing in other tools indefinitely?

Β Hi Jasnoor - when you disconnect the app, there are two options: one to disconnect only, and the second to remove all the data associated with it.

Β The two option approach also solves a trust problem, if disconnecting always deleted everything automatically, people would hesitate to connect sensitive sources in the first place knowing one accidental disconnect wipes months of structured context. Keeping those as separate actions is the right call.

Following this one because I trust the team behind it. Genuine question for anyone who's set it up: how deep does the context actually go on day one vs. after a week of syncing? That's the part I'd want to see before I move my whole stack over. Either way, congrats on the relaunch πŸŽ‰

Β thanks a lot!

This new version looks cool. How does permission layer works? Can I give access to my context to someone else?

Β of course, you can give an access to your context to your brother for example! :))

Thanks for doing this and congrats on its success , a cross platform memory means more connected workflow than building/importing the context every-time a targeted action needs to be completed.. just one question though, how do you tackle the problem of lost in the middle for most of the unabyss builds..

So clever! Congrats!

Β thanks!

The continuous flow part is cool, I think this is most useful for someone who does their research on chatgpt/gemini and move to actual implementation on claude code. Upvoted.

qq: Can we restrict the data to be sync'd at conversation level between the platforms ? For ex, I liked to design frontend on chatgpt due to its powerful imaggen models and then code it in claude code, can i only restrict the connection to select that particular conversation from chatgpt ?

Β Thanks! Yes, you can adjust what is stored by tweaking the global instructions prompt you receive during agent connection setup.

congrats team for the launch!

Β thanks a lot! Did you try Unabyss?

Cross-app shared memory is one of those things that sounds simple and is deceptively hard once you get into consistency and staleness β€” how are you handling conflicting updates when two apps touch the same memory at roughly the same time? That's been a recurring headache in anything we've built that needs state to persist across separate tools.

Β we have a conflict resolution engine that handles all edge cases. It compares all of the similar memories and updates. If there is any misalignment, our retrieval agent will notify you about this and will give you an option to choose/fix or update the memory. I hope this answers your question :).

Β That's a smart approach β€” surfacing the conflict to the user rather than silently picking a winner is the right call, especially early on when trust in the system matters most. Curious whether that retrieval-agent notification happens in real time or in a batch/background pass β€” real-time feels like it'd add latency, but batching risks the user acting on stale data in the meantime. Nice launch, by the way :)

shared memory across apps and models is the thing I actually want, I context-switch between 3 different assistants all day and lose the thread every time. how do you handle memory conflicts when two apps write contradicting facts about the same thing

Β we have a conflict resolution engine that handles this in the background. But if it misses something, you can always correct the context in the context chat.

This is a massive quality-of-life upgrade for anyone bouncing between tools. Since you're essentially a shared memory layer between LLMs, how do you handle conflicting context? Can we as users set priority rules?

Β at the moment, we don't have any manual priority rules. Our conflict resolution engine handles all of that.

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