Membase or Vilix AI: Which AI Memory Tool Actually Fits How You Work?

Short answer: both tools solve the same pain, agent amnesia, but they are built for opposite jobs. Membase is a builder's memory layer: SDKs, scoped memory containers, and agent hooks for shipping memory inside products. Vilix AI is an operator's memory layer: a cloud-hosted service your tools connect to over MCP, so one memory follows you across every agent with nothing to build. Pick the one that matches the job you are actually doing.

What Membase is

Membase, built by Unibase, is a unified AI memory layer aimed at developers building agentic products. Its stated principles are worth reading carefully, because they describe what "unified memory" has to mean once it lives outside the agents: model-agnostic storage so you are not locked to one vendor, memory written by one agent and readable by all, scoped access so not every agent sees everything, and attribution so a wrong memory can be traced back to the agent, session, and time that created it.

The shape of the product shows its audience. You reach it through a Python SDK (pip install unibaseio-membase), a TypeScript SDK (npm install @unibaseio/membase), a CLI (membase --local), REST routes, and an MCP server. Memory is organized into containers, so several agents can share one store while each agent only sees the projects it has been given. Keys can be scoped per agent, so revoking one agent's access does not touch the others. A local mode runs the store on your own machine in ~/.membase using your own OpenAI key, and the hosted tier is in beta, listed on their site as totally free with no credit card required.

Their own use-case guides walk through a coding-agent setup: install hooks into Claude Code, and memory recall plus saving run outside the model, so a memory built in Claude Code is also available in Codex. Per-project memory is a first-class idea: give each project or client its own container, so one client's notes never leak into another client's answers. They also publish their own benchmark numbers, including 93.1% on LoCoMo and 92.6% on LongMemEval_S, using a fraction of the tokens of full history. Those are the vendor's own numbers from their package page, and they should be read as the vendor's claim, not an independent test.

The honest read: Membase is designed for people who ship. If memory is a feature inside your product or your agent system, the SDKs, containers, and per-agent scoping are the controls you want to have.

What Vilix AI is

Vilix AI takes the other side of the same problem. It is a cloud-hosted memory layer, so there is no infrastructure to run, nothing to provision, nothing to integrate into a codebase. Your AI tools connect to it over MCP, the open protocol, and the same memory store is reachable from Claude, Codex, Cursor, your phone apps, and your scheduled agents.

Two design choices define it. First, it stores full conversation history, not just extracted facts, so the real conversation can be revisited anytime instead of trusting a summary of what the agent thought mattered at the time. Second, the same memory follows you everywhere: what your coding agent learns on your laptop is visible to your automation agent, because both read from one place.

For an operator, the practical surface is small on purpose. Connect your tools over MCP once, and every scheduled run starts with your context instead of waking up blind. The free plan is free forever, the 7-day Pro trial needs no credit card, and you can export or delete everything at any time.

Four questions to ask any memory tool before you commit

Marketing pages for memory tools all sound the same, so use these four questions to separate them:

1. Where does the memory live, and who operates it? Some tools hand you libraries and expect you to run the store; others host it and you connect over a protocol. Neither is wrong, but it decides who pages you at 2am.
2. Who can see what? Per-agent and per-project scoping is the difference between one shared brain and one shared liability. If agents serve different clients, you need containers or an equivalent boundary.
3. What exactly gets stored? Extracted facts are compact but lossy. Full conversation history is faithful but heavier. Know which one you are getting, because it changes what you can audit later.
4. Can you leave? Export and delete are the exit door. A memory layer you cannot leave becomes a tax on everything you build.

Membase answers these with SDKs, containers, scoped keys, and a delete-anytime policy on their security page. Vilix AI answers them with a hosted layer, MCP access from every tool, full conversation history, and export-or-delete at any time.

The decision in one question

The comparison only makes sense against a real job. Two shapes of work, two answers:

You are building a product. Your app has agents that serve many end users, each needing their own memory, with access scoped per agent and per project. Memory is a feature you ship. This is Membase territory: containers, scoped keys, SDKs your codebase calls directly, a local mode for development.

You are operating. You run scheduled agents, automations in tools like n8n or Make, a coding agent on your laptop, and a chat assistant on your phone, and you want all of them to remember the same things. Nothing to provision, nothing to ship, just tools connecting to one memory over MCP and every run starting informed. That is the operator's job, and it is what a hosted memory layer is built for.

The question that settles it: are you shipping memory as a feature, or are you running agents that need to remember? Answer that honestly and the "best AI memory tool" question answers itself. And if the answer is the second one, you can start with Vilix AI: connect your tools, and stop re-briefing your agents every run.