Unabyss
MCP-native self-updating context layer for your AI
2.4K followers
MCP-native self-updating context layer for your AI
2.4K followers
Set it up once and never re-explain yourself to AI again. Connect the apps you use daily - Unabyss will extract, structure, and update your context automatically. Share it with any AI tool via MCP, with granular control over what each tool can see.
This is the 2nd launch from Unabyss. View more
Unabyss for Claude
Launched this week
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.
Free Options
Launch Team / Built With
When local version? I need to start pushing this to my clients.
Unabyss
@greenparrotnow we're shipping local memory next week. I'll share closed beta with you via DM!
Unabyss
@greenparrotnow already in progress ;) local version is launching soon
Really interesting approach to portable AI memory. How does Unabyss handle conflicting or outdated information when different connected apps contain different versions of the same context? Can users review, correct, or delete a memory before it is shared across Claude and their other AI tools?
Unabyss
@j_mehta1 conflicts are resolved both automatically and by you when interacting with our context chat or in your favorite agent like Claude or Hermes.
Cross-app shared memory is one of those things that sounds simple and is deceptively hard once you get into consistency and staleness — 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.
Unabyss
@abhineetarora 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 :).
@dominik_bartosik That's a smart approach — 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 — 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
Unabyss
@omri_ben_shoham1 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?
Unabyss
@denitsapenchevavaltchanova, at the moment, we don't have any manual priority rules. Our conflict resolution engine handles all of that.
Hey, congrats on the launch! I’ve been following the AI memory space for a while, so I’m curious how you see Unabyss alongside tools like Mem0, Zep, Cognee, and claude-mem.
What feels like the biggest difference to you today?
I’m especially interested in how you approach conflicting or outdated memories when several apps and agents contribute to the same context. It seems like one of the hardest and most interesting problems in this space.
P.S. Saw you followed me on linkedin. Thumbs up and followed!
Unabyss
@nik_shilov Hi Nik, conflicts are handled by our dedicated engine. Each new memory item is checked against existing ones; if any differences appear, then all of the memories are judged against the origin, date, author, etc. In most cases, the resolution engine can handle it, but if it is unsure, it will leave a note next to the memory change.
How do you handle data consistency and integrity across different AI tools and apps connected through Unabyss, especially when updates are made to the context layer?
Unabyss
@aymnart Once you update the context, it is available in the same form everywhere, no matter which agent is fetching it.