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.
Replies
Your pitch sounds compelling, but what's the actual technical moat compared to existing MCP servers, RAG pipelines, and personal knowledge systems? For example, I can already connect Claude/GPT to Gmail, Google Drive, GitHub, Notion, Slack, and meeting transcripts using OpenMemory, Mem0, Zep, Graphlit, LangChain/LlamaIndex, or even custom MCP servers. Beyond packaging and UX, what is genuinely difficult to replicate? Why couldn't a competent engineer build an equivalent system in 7–14 days using existing APIs and open-source infrastructure?
How do you resolve conflicting memories across different sources?
How do you decide what deserves to become a long-term memory versus transient context?
Is your advantage primarily data ingestion, memory consolidation, retrieval quality, or simply convenience?
One more question: doesn't your business model create a "double payment" problem? Users already pay subscriptions for GPT, Claude, or Cursor. If Unabyss sits in the middle and consumes additional tokens to build and maintain shared memory, doesn't that mean users end up paying twice for essentially the same AI stack? How do you address that concern?
Unabyss
@piotr_wasaznik a short answer is: you need a solid team of top-notch engineers to build and maintain a memory system, plus implement efficient retrieval.
And that's not even the hardest part. The real challenge is parsing and structuring the data, not just pulling it. You can't simply dump your entire GDrive into a RAG pipeline (or any memory system) and expect good results without proper parsing.
But let's break it down.
MCP servers
-> a single MCP server isn't a memory system. It's just a way to access data.
-> agents struggle with cognitively demanding (summaries, analysis, reports, brainstorms, decision-making) when they're limited to a single MCP (single source) because they lack context from the rest of your stack. Imagine analyzing an ad campaign without understanding the user journey after the click, your ICP, or your value proposition - surely you're going to draw wrong conclusions.
-> cross-source reasoning is also extremely expensive. Most agents pull far too much data, still miss the relevant context, and end up reasoning over noisy inputs. (re double payment, too)
RAG
-> realistically, 99% of people can't set up a production-quality RAG.
-> even when implemented correctly, RAG alone doesn't work for context retrieval in most real-world workflows. (we tested it early on, and it just fails)
Memory systems
-> setup requires senior engineers.
-> BUT once you get into conflict resolution, deduplication, ranking, and optimization, it starts looking less like a weekend project and more like a PhD thesis.
Double-payment
-> we use LLMs once to ingest and structure data into memory.
-> without a memory layer, your agent has to repeatedly process the same raw data every time you ask a question. In practice, can get 10× or 100x more expensive if you do it often.
So our core advantage is ingestion breadth × consolidation × retrieval precision. Essentially, means democratizing access to high-quality AI memory for everyday use cases.
We're making something that normally requires an elite engineering team available to everyone :)
Would love to see a quick preview of exactly what context each AI tool will receive before sharing, like a diff view so I can tweak it without digging through settings every time.
Unabyss
@dursunltxo preview the whole context; it's tricky because there is a lot of it. But there is a simpler solution that we implemented - a permission layer. When you connect to the agent, you can choose what type of information and from which app the agent will get it.
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.
TapRefer
openclaw alternative??
Unabyss
@jiteshghanchi not at all! We are usefull tool for openclaw or any other agent ;)
TapRefer
@dominik_bartosik awesome product, man.
What is the difference vs something like basic memory?
Portable context across tools is important. The hard problem seems to begin when those tools create conflicting memories or interpretations. How does Unabyss decide which context remains authoritative when Claude, GPT, and Cursor update the same subject differently?
ClinicFrame
Congrats on launch number two, Philip. Shared memory across tools is the right problem, re-explaining context to every model is a real tax on anyone working across Claude, Cursor and GPT. The flip side is that a memory layer is also where sensitive data quietly accumulates. I saw the granular per-tool controls in the description, so my question goes one level deeper: is there automatic redaction of sensitive data before a memory gets stored, and can memories expire or be purged by topic? In healthcare, my world, those two features decide whether a tool like this is usable at all.
The self-updating part is what makes this interesting. Static context files go stale the week after you write them, and re-explaining yourself to every AI tool is the tax everyone quietly pays. Congrats on taking #1!