TL;DR: Memory engines like Mem0 and Zep automatically extract memory from conversations. Claude Projects pins instructions and files inside one app. UseMyContext.ai is a third architecture: an independent, client-agnostic personal context layer - your profile, projects, and core files managed in one web dashboard and served over MCP to whatever AI tool you are in. You curate what it holds, every client reads the same picture, and the only write an AI can make is a suggestion that waits for your approval.
Three architectures for the same problem
If you use more than one AI tool, you know the symptom: the memory lives in multiple places. Claude holds one version of you, ChatGPT another, your editor a third, and a folder of loose text files holds the rest. The tools in this comparison all attack that problem, but from three different architectural positions, and the position decides the practical questions: who writes the context, who can read it, and what happens when the AI learns something new.
Mem0 and Zep are memory layers a developer integrates: a pipeline watches conversations, extracts what looks durable, and retrieves it when the app asks. Claude Projects is application memory: instructions and files you pin inside Claude, applied to the chats in that project. A context layer is neither. It is an independent service that sits between you and all of your AI tools:
[ your master context + files ] one dashboard: usemycontext.ai
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v
[ remote MCP server ] https://mcp.usemycontext.ai/mcp
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+---------+---------+
v v v
[ Claude ] [ Claude Code ] [ Cursor ] ... any MCP-capable clientFive architectural differences follow from that position.
1. One identity, every client
When memory is bound to an application, switching applications means starting over. A Claude Project's instructions and knowledge apply inside Claude; move to your editor and they do not follow. A memory store built with Mem0 or Zep lives with the application a team integrated it into; sharing it across products is its own integration project.
A context layer inverts that: the profile is central and the clients are interchangeable. You manage your profile, projects, and core files at usemycontext.ai, and any MCP-capable environment signs in - over OAuth 2.1, or with an access token where the client cannot do the browser flow - and reads the same layer. The Connect docs walk through Claude on web and desktop, ChatGPT, Claude Code, Cursor, VS Code, Codex CLI, and more - each is a different door into the same one server, so there is nothing to re-introduce and nothing to keep in sync.
2. You curate; no pipeline infers
Automatic extraction is the memory engines' core convenience: nothing to write down, the store fills itself from conversation. The structural trade-off: a pipeline, not you, decides what is worth keeping, and auditing what has accumulated becomes a second job. The failure mode to watch for is one giant memory blob where it is hard to tell what is true and what was merely guessed.
UseMyContext is explicit and human-owned. You write the profile, organize the projects, upload the files; what a connected AI knows is exactly what you can read on your dashboard. The AI queries that structured context through explicit read tools - profile, list_files, search_files, ask_docs - rather than a similarity search over things you never wrote. Similarity can retrieve; it cannot promote something to being true about you. Deciding what is true stays your job, on purpose.
3. Writes are suggestions, not edits
Most integrations pick one of two write postures: full write access, where the tool can alter the files or the memory store directly, or strictly read-only, where nothing the AI learns is ever kept.
UseMyContext takes a third posture: read-only by default, with an asynchronous approval loop. Of the thirteen tools a connected AI sees, twelve are reads. The single write tool, suggest_update, files a pending suggestion; nothing touches your profile or files until you review and approve it in the web app. If the AI learned something wrong, you decline the suggestion and it never becomes part of your context.
4. A hosted remote server, not a local process
Many MCP servers run locally over stdio: a Node or Python runtime on your machine, a config file per client, a process that has to be running. UseMyContext is a cloud-hosted Streamable HTTP MCP server at https://mcp.usemycontext.ai/mcp. Clients that speak OAuth over MCP open a browser sign-in - your email and a one-time code, no API key to copy; the few that cannot do the browser flow use an access token you generate on the Connect page. Nothing runs on your machine, and in Claude and ChatGPT the connection is one click from their directories.
5. Folders that pick the right profile
For coding tools, there is one more piece: the right context should load because of where you are, not because you remembered to say so. In Claude Code and Cursor, a .umc file in a repo root binds that folder to one of your profiles, so a work repo reads your work profile and a side project reads your personal one. Details: Folder mapping.
What happens after you connect?
Control does not end at setup. Every client you connect appears on the Connect page in your account, and you can revoke any one of them, or disconnect everything at once, at any time. Every access an AI makes leaves a record in your audit trail, and the service is built so that the public, internet-facing parts cannot read your private context.
How do they compare side by side?
| Claude Projects | Mem0 / Zep | UseMyContext | |
|---|---|---|---|
| Scope | One app (chats inside Claude) | The app a team integrates it into | Any MCP client that lets you add a custom remote server |
| Primary mechanism | Instructions and files pinned by hand | Vector or graph memory extracted from conversations | Hosted profile and file server |
| Multi-app sync | No | Partial (per integration a team builds) | Yes |
| Data control | Manual (you edit the project) | API-level (the integrating team edits or deletes memories) | Explicit curation (you write and approve everything) |
| Memory writes | Manual (you edit the project by hand) | Immediate (the pipeline writes as it extracts) | Pending suggestions you approve |
So which one do you need?
The three are not really competitors, so the honest answer is a routing question. If you are building an AI product and its users need memory, Mem0 and Zep are the right shelf - serious developer tools, evaluated on their own merits. If you live inside one assistant, its built-in project memory may be all you need.
UseMyContext is a context hub, not a memory engine. It earns its place when you work across multiple AI tools and want a single dashboard controlling what those assistants know about you: what it holds is what you wrote, and the only write an AI can make is a suggestion you approve.
FAQ
Is UseMyContext a competitor to Mem0 or Zep?
Mostly no. They are memory infrastructure for developers building AI products and agents; UseMyContext is a consumer product, a personal context layer your existing AI tools read over MCP. A developer building with Mem0 or Zep can still use UseMyContext personally - the categories do not exclude each other.
Can I use Claude Projects and UseMyContext together?
Yes. A Claude Project is a good home for instructions that only matter inside Claude; the context layer is where the durable, portable picture of you lives, so every other tool you connect reads it too.
Which AI tools can read my context layer?
Any MCP client that lets you add a custom remote server. The Connect docs cover Claude on web and desktop, ChatGPT, Claude Code, Cursor, VS Code, Codex CLI, Cline, Gemini CLI, and Goose, and in Claude and ChatGPT the install is one click from their directories.
Can an AI change my profile or files through this connection?
No. Twelve of the thirteen tools are reads; the one write, suggest_update, files a pending suggestion that waits in your app for review. Nothing is saved to your context until you approve it.