GAIM

As someone running a marketing agency, I switch between ChatGPT, Claude and Gemini all day, and re-pasting brand guidelines, tone notes and client background into every new chat is a real time sink. A single MCP memory layer that all of them can read from would remove a lot of that. Do memories get saved only when I add them manually, or can GAIM pick up important context from my chats on its own? And is there a way to import the memory I've already built up inside ChatGPT or Claude so I'm not starting from zero?

As someone running a marketing agency, I switch between ChatGPT, Claude and Gemini all day, and re-pasting brand guidelines, tone notes and client background into every new chat is a real time sink. A single MCP memory layer that all of them can read from would remove a lot of that. Do memories get saved only when I add them manually, or can GAIM pick up important context from my chats on its own? And is there a way to import the memory I’ve already built up inside ChatGPT or Claude so I’m not starting from zero?

I got tired of explaining myself to AI over and over again. I use multiple AI tools in my daily work, and every one of them had a different version of my context. ChatGPT knew one thing, Claude knew another, and coding agents had their own isolated context. So I started building GAIM as a memory layer that sits outside the AI itself. The idea is simple: save the context I want to keep, and make it available to the AI tools I choose through MCP. I've been using GAIM myself for the past two months, and that's what pushed me to launch it publicly. I'm curious whether other people have the same problem - and, more importantly, whether persistent AI memory is something people will actually use long-term.

When two connected AI clients write to the same memory at once, say Claude and Codex in one afternoon, how do you handle conflicting or duplicate entries? I'd find it useful to see which client created each memory and when, so I can prune stale ones before they confuse the other tools.

Persistent memory is the most-requested missing piece in AI assistants, so this hits a real pain point. Two questions: can users see and edit what GAIM remembers about them, and does the memory carry across different AI models or stay tied to one? Transparency and control over stored memories would be a big trust signal, especially for anyone sharing personal details like goals or routines.

We run most of our work through MCP agents too, so the "start from zero every chat" problem is very real. For memories that conflict across tools (e.g. a preference saved from Claude that ChatGPT would phrase differently), does GAIM resolve that automatically or surface the conflict for the user to pick?

Giving AI a memory that stays with the user is useful, but memory can also hold wrong facts. When GAIM stores something the user said or the model inferred, can the user see where each memory came from and correct or delete it? Source-linked, editable memories would make this much easier to trust.

Really clean UI! Curious about your onboarding flow—how are you handling latency when processing user requests in real time? Also, do you plan to add direct API webhooks or third-party integrations soon?

A portable memory layer that keeps your context across different AI tools seamlessly

Nice! what language did you use?

I am tired of explaining myself to AI over and over again. thanks

This is such a relatable problem—re-explaining project stacks and personal preferences to Claude, ChatGPT, and coding agents every single day becomes exhausting. Having a portable, tool-agnostic memory layer via MCP makes total sense. Love the focus on data control and exportability too. Great launch

Memory is the part most AI tools get wrong, so this is interesting. How do you handle letting users see and delete what it remembers? And does the memory carry across different AI models, or is it tied to one?

This is such a relatable problem—re-explaining project stacks and personal preferences to Claude, ChatGPT, and coding agents every single day becomes exhausting. Having a portable, tool-agnostic memory layer via MCP makes total sense. Love the focus on data control and exportability too. Great launch

This is such a relatable problem—re-explaining project stacks and personal preferences to Claude, ChatGPT, and coding agents every single day becomes exhausting. Having a portable, tool-agnostic memory layer via MCP makes total sense. Love the focus on data control and exportability too. Great launch

Rebuilding project context every time I switch between Claude, ChatGPT and a coding agent is a real pain, so one MCP-based memory layer makes a lot of sense. How do you deal with stale memories? If a project decision changes, can older entries be marked as replaced so an agent doesn't act on outdated context?

AI is very famous today, you made a good choice to make this sort of product, I really liked it

The persistent-memory angle is what makes AI assistants actually useful day to day, since re-explaining context every chat gets old fast. Curious how you let users review or edit what GAIM remembers?

This tackles one of the most frustrating parts of using multiple AI tools daily, constantly re-explaining your context from scratch.

The portable-memory idea is compelling because it addresses a real friction point: rebuilding context when switching between Claude, ChatGPT, and coding agents. I am curious how you handle conflicts when the same fact changes across tools, and whether team memory permissions let a user keep personal memories fully separate from shared project knowledge.

The portable memory idea solves a real pain point of re-explaining context across ChatGPT, Claude and others. I'd love to see a short note on how memory conflicts or outdated entries are handled, and whether MCP access can be scoped per client for privacy.

Having to explain the same context to different AI tools over and over is genuinely frustrating. A portable memory layer that works across multiple AI platforms could make workflows so much more seamless. Love the focus on keeping memory under the user's control. Excited to see how GAIM evolves!

Keeping context in one memory layer instead of re-explaining yourself in each chat solves a real annoyance, and exposing it through MCP means it can work across clients. I like that memories are exportable. How does retrieval decide which saved facts to surface when the question is vague?

Having to explain the same context to different AI tools over and over is genuinely frustrating. A portable memory layer that works across multiple AI platforms could make workflows so much more seamless. Love the focus on keeping memory under the user's control. Excited to see how GAIM evolves!

Portable memory across Claude, ChatGPT and Gemini via MCP solves a real annoyance. I'm curious how you handle stale or conflicting memories (e.g. a decision that changed later), and whether memories can be scoped per project so client context doesn't leak into personal chats. The 1,000-memory free tier seems generous for trying it out.

One memory shared by Claude, ChatGPT and Codex over MCP fills a real gap. How do you handle stale memories, like a project decision that changed last month? Can I mark a memory as superseded so agents stop pulling the old one?

How do you handle conflicting memories when a project decision changes? Being able to see where a memory came from and replace outdated context would be especially useful when several coding agents share it

This addresses a real pain point. The "start from zero in every new chat" problem gets worse the more AI tools you use, and a portable memory layer is a smart way to solve it — especially the MCP-compatible angle, since that means it works with the tools people already have rather than forcing them into a new ecosystem. Two things I'd want to know more about: how memory relevance is determined during search (is it semantic retrieval, or more like tagged snippets?), and how the team-memory feature handles permissions between shared project context and private memories. The export-anytime promise is a good trust signal too — that's often the first thing people ask about with memory products. Solid concept.

A single portable memory layer across Claude, ChatGPT, Gemini and Codex via MCP is the right idea — rebuilding project context in every chat is the friction that makes AI assistants feel forgetful. The exportable, user-controlled angle matters too. One question: how does GAIM handle stale or conflicting memories when a project decision changes, and can a memory be corrected or deleted from every connected client at once?

Keeping personal memory and team memory in separate stores, with team records staying when someone leaves, is a smart call for teams that use coding agents. How do you handle stale memories, like an architecture decision that later gets reversed? Does a coding agent see the old record next to the new one, or is there a way to mark a memory as replaced?