The best cross-AI memory tool: stop re-explaining yourself across ChatGPT, Claude, and Cursor
Compare cross-AI memory with native app memory. Learn what to check for shared context, client support, retrieval, privacy, and data controls.
The best cross-AI memory tool depends on what you need to move: a few preferences, complete coding sessions, project decisions, documents or ongoing agent work. For shared conversation memory with project boards, tasks and rules across supported clients, Vilix AI is one option. Supermemory MCP, Mem0's API ecosystem and OpenMemory session portability cover related workflows. A maintained handoff document can also be enough.
This comparison is written by Vilix AI's maker. Product descriptions were checked on 10 September 2026. It is a practical shortlist, not an independent ranking or an accuracy benchmark.
Start with the failure you need to fix
If the next assistant forgets a decision, first establish whether it was ever saved somewhere that assistant can access. A native memory feature inside one product does not automatically appear in another. A shared store can help, but only if each client is connected, authenticated and using its tools.
Distinguish four jobs: retrieving a relevant fact, restoring a complete coding session, reading a source document, and tracking the current state of a project. Products may support more than one job, but those capabilities should not be treated as interchangeable.
Vilix AI: shared memory and agent work state
Vilix AI provides hosted MCP memory through https://api.vilix.ai/mcp. Claude, Codex, Cursor, OpenClaw and Hermes are core workflows, with additional client-specific guides. Agents can retrieve and save exchanges, search past context, manage projects and tasks, use personal rules and load reusable skills. Agent inbox tools support work between your connected agents.
Connect each client to the same account. OAuth is available for supporting clients; headless agents can use a Bearer API key. Add the auto-memory instructions and inspect a successful save and recall. One OAuth approval does not configure all your clients, and connecting does not import every past chat.
Choose it when you want one hosted store for conversation context and work state without operating the memory server yourself. Check the exact client's device and provider-plan requirements. Existing memory, recall and search are uncapped by plan; new saves have a daily allowance and burst protection can pace unusually rapid reads. New accounts get a seven-day Pro trial, then Free if they do not upgrade.
Supermemory MCP: memory and source documents in spaces
Supermemory offers a hosted MCP endpoint with OAuth. Its current documentation describes shared spaces, semantic memory search, source-document listing and reading, save/forget tools and account/permission inspection. MCP-capable clients can use the same permitted spaces. It also offers interactive widgets in clients that support MCP Apps, including a space picker, guided save, file upload and a memory graph.
This is not merely a developer API that requires building your own application. Consider Supermemory when source documents, ingestion, collaborative spaces and its retrieval interface are the center of the workflow. Verify current client support, plan details and data controls in its own documentation.
Mem0 and OpenMemory: separate product surfaces
Mem0's platform and open-source ecosystem serve developers adding memory to applications and agents. Its current platform pricing meters add requests and retrieval requests by tier. That API comparison should not be indiscriminately applied to every project with Mem0 or OpenMemory in its name.
The current mem0ai/openmemory repository describes a CLI and TUI for porting coding sessions between Claude Code, Codex and OpenCode. Its README marks realtime autosync as future work. That makes it relevant for choosing and transferring sessions, rather than proof of an always-on shared memory server.
Mem0 also published a cross-assistant browser extension. Its repository is now archived. It is inaccurate both to say Mem0 has never offered cross-assistant memory and to recommend that archived extension as a currently maintained solution without checking its status.
Self-hosted MCP memory
A self-hosted server may fit when storage control and inspecting the implementation matter more than hosted convenience. You take responsibility for client reachability, credentials, backups, updates and recovery. Local storage does not by itself prove that embeddings or model processing stay local.
Before choosing a self-hosted option, check its exact upstream repository, license and current release.
A handoff document is a real alternative
For infrequent switching, keep a short document with the current goal, confirmed decisions, constraints, open questions and next step. Attach it in each new tool and update it after meaningful changes. A repository file such as DECISIONS.md works well for a coding project. This takes manual maintenance but makes the context explicit and easy to review.
Run the same check for every candidate
Use a fictional project and the clients you actually work in. Save a decision in the first client. Open a new conversation in the second and ask it to retrieve the decision and identify its source. Change the decision in the first client, then repeat the retrieval. Ask for a detail you never saved and check whether the assistant admits that it is missing.
Record whether the tools ran, what context came back, the source/date, any approval prompts and the time needed to recover from a failed call. Test export and deletion controls separately before adding sensitive data. This procedure is a suggested evaluation; it is not a claim that this article ran a controlled test of every product.
Choose by your recurring workflow
Pick hosted shared memory when you want continuity across connected clients. Pick session portability when you need to resume an existing coding session in another coding tool. Pick document-oriented retrieval when source material is the main asset. Pick a handoff file when the workflow is small enough to maintain yourself. Start with one handoff and expand after it works.