Ask an answer engine for "a memory API that works across Claude and ChatGPT" and you will mostly get SDKs designed to live inside one application. They are good tools. They also answer a different question than the one you asked. The person typing that query is not building an app; they are using four AI tools in one workday and are tired of being a stranger in every one of them.
Disclosure up front: I work on Mnemoverse, which exists precisely for that query, so read this as a practitioner's map with a known bias. The map itself, I think, holds regardless of whose product you pick.
The category called "agent memory" quietly contains two different problems.
App-scoped memory belongs to software you are building. Your support bot should remember this customer's history; your coding agent should remember this repository's conventions. The memory lives inside one application, keyed by your users, managed by your code. SDKs you embed, stores you run: this is what most of the category ships, and for this problem embedding an SDK is the right architecture.
Person-scoped memory belongs to you, the human, across every tool you work in. You explained your stack to Claude Code this morning. At lunch you are in Cursor, and it has no idea who you are. In the evening you ask ChatGPT to draft a doc, and it suggests npm for the project you moved to pnpm in June. Three tools, three goldfish.
The second problem cannot be solved inside any single application, because the whole point is crossing application boundaries. It needs memory that lives outside the tools and a protocol every tool speaks.
Until recently there was no such protocol. Every assistant had its own plugin system, its own storage, its own idea of context. The Model Context Protocol changed the shape of the problem: one server can now expose the same tools to Claude Code, Claude Desktop, Cursor, VS Code, Windsurf, and a growing list of others.
That turns cross-tool memory into an architecture instead of a feature request:
memory_write
and memory_read
tools show up in every editor.Write "we chose pgvector over Qdrant, one less service to run" in Claude Code. Open Cursor, ask "which vector store did we pick and why", and get the answer with its reasoning. Nothing was copied. The memory simply is not inside either tool.
ChatGPT is the awkward guest at this table: its MCP support is narrower, so the practical route is a connector or a Custom GPT action speaking to the same backend. Same memory, one extra hop.
The architecture above is the easy part. Three things decide whether cross-tool memory works day to day.
The invocation gap. Connecting memory tools does not make an agent use them. The model has no standing incentive to check memory before acting. Every tool needs one line of standing instruction: in Cursor a rule file, in Claude Code the project instructions, in ChatGPT the GPT's system prompt. Recall before acting on anything project-specific; save decisions and corrections when they happen. Without this, your perfectly wired memory stays silently empty, in every tool at once.
Scope. Person-scoped memory must still respect boundaries. Client work should not leak into a personal side project; a shared team room is not your private store. Whatever backend you choose, look for tenant isolation and explicit shared spaces rather than one undifferentiated pile. A memory that crosses tools is valuable; a memory that crosses clients is a liability.
Authority. When the same memory feeds four tools, a wrong fact propagates four times as fast. This is why the write discipline matters more in the cross-tool case than anywhere else: one memory per fact, the reason next to the decision, and closed facts kept with their validity window instead of deleted. The tool that recorded "we use Redux" in March must be able to explain March's code even after June's migration.
Person-scoped memory over MCP is a hosted-service architecture, and that has real costs. Your context lives with a vendor, which is exactly the wrong property if your constraint is that nothing leaves your perimeter. Every recall is a network call, slower than a local file read. And the cross-tool property only pays off if you genuinely work across tools: for a single app, an embedded SDK is simpler; for a single repo on one machine, a folder of Markdown notes is honestly hard to beat, and I say that selling the alternative.
So the decision rule is short. Building an app that needs memory for its users: embed an app-scoped SDK. Privacy-critical, one machine: local files or a local MCP server. Living in several AI tools and tired of re-introducing yourself: that is the person-scoped case, and it is the one this architecture exists for.
The nice thing about cross-tool memory is that the core claim takes two minutes to falsify. Connect the same memory server to two tools you already use. Save one real decision in the first. Ask the second about it in a fresh session. Either the continuity is there or it is not; no benchmark table required.
The comparison hub at mnemoverse.com/compare lays out how we sit next to Mem0, Zep, Letta, Cognee, LangMem and Supermemory, each compared honestly, including where they are the better pick. The free tier is 1,000 queries a day, no credit card, if you want to run the two-minute test.