Dray or Vilix AI: Which Shared Memory Actually Fits Your Multi-Tool Workflow?

You plan a launch in ChatGPT on Monday. On Tuesday, Grok writes the launch post. On Wednesday, Claude writes the pricing page. If each assistant starts with a blank slate, you spend the week pasting the same backstory into new chat boxes. The question underneath: how do you share context between ChatGPT, Claude, and the other AI tools you use every day?

Two products answer that question from opposite directions. Dray (https://withdray.com/) watches your chats from a browser extension and makes earlier conversations available when you switch assistants. Vilix AI (https://vilix.ai/?utm_source=vilix-blog&utm_medium=article&utm_campaign=dray-vs-vilix-ai-shared-memory-chatgpt-claude-tools) takes the opposite route: one hosted memory that every tool reads and writes over MCP, the open protocol. Here is the honest breakdown, so you can match the architecture to the way you actually work.

What Dray does, in its own words

Dray's pitch is "different agents, shared context." It is a browser extension that reads your chats inside ChatGPT, Claude, and Grok, then makes project context and relevant past conversations available when you switch between them. Coding tools (Claude Code, Codex, Cursor, Antigravity, GitHub Copilot, Windsurf, OpenCode, Cline, Amp, Goose) connect through the Dray CLI, which saves coding sessions and connects those agents to the shared memory over MCP.

The workflow it sells is concrete: plan the launch in ChatGPT, and Grok finds that plan through Dray when you switch tools. The site notes the catch: what an assistant finds depends on what Dray has read, and on the assistant choosing to look.

Two practical notes from Dray's own documentation. First, it needs Chrome, Edge, or Brave on a computer; Safari, Firefox, phones, and tablets cannot install the extension. Chats from desktop and phone apps get saved while Chrome is running and signed in to the site, and the coding agents save through the CLI without a browser. Second, Dray's FAQ is refreshingly direct about accuracy: "Will it remember everything perfectly? No. Reading may miss or misread chats, and memory extraction can be wrong. Check important details rather than treating a remembered fact as verified evidence." Any memory system that reads chats has to extract meaning from text, and Dray tells you the extraction is fallible rather than pretending otherwise.

The standout feature is chat linking. Choose "Link this chat" from the Dray menu in two assistants, or type /link, and the two assistants work on the same thread together, passing drafts back and forth. Chat links are included in every plan. Linking is not the same thing as shared memory: shared memory lets connected agents look up stored context, while a chat link puts chats in a shared channel where they post and read each other's messages.

Where the extension model shines

If your AI life mostly happens in browser chat windows, this is the lowest-friction shape. There is no server to run, no API keys to manage, no separate service to configure. You install the extension, browse as usual, and context follows you between the assistants you already use. The memory controls are practical too: you approve each assistant separately, choose how far back Dray reads, and a single switch in the extension turns "Let agents use my memory" off. Turned off, assistants answer without your memory while Dray keeps saving new chats; starting a message with /dray lets that one message use memory anyway.

Pricing is straightforward. Every plan includes every feature; higher plans just add monthly usage. Pro is $7 a month billed yearly ($84) or $9 monthly, with Max 5x and Max 20x plans scaling usage for heavier users. There is a 30-day full refund if you ask. One cost to know before signing up: adding Dray to ChatGPT requires a paid ChatGPT plan.

Where the browser boundary shows

The extension model has a hard edge worth naming, because it decides which workflows fit. Dray's memory is what it has read through your browser: web chats in three assistants, plus coding sessions through the CLI. Memory follows the browser session, not you.

Switch to a phone-only day, a second laptop, or an agent running on a server on a schedule, and the memory is not there with you. This matters especially for scheduled and recurring AI agents, the kind that run at 6 AM on a server while you sleep. Those agents never open a browser tab. They wake up blank every run and need memory that lives outside any single machine: one store the agent can reach from wherever it executes. An extension that watches chat windows cannot be that store, no matter how good its extraction is.

This is the fault line the whole comparison turns on. Dray answers: "My AI work happens in browser chats, and I want context to follow me between them." That is a large and real audience, and the product fits it well. It does not answer: "My AI work happens everywhere, including places no browser ever opens."

Where a hosted memory layer fits

Vilix AI answers the second question. Instead of watching your browser, it holds one memory in a hosted service, and your tools connect to it over MCP. Claude, Codex, Cursor, your phone apps, and your scheduled agents all read and write the same store. Because the memory lives outside every tool, it is reachable from every device and every run, including the 6 AM server job that never touches a browser.

A few properties define the model. It is cloud-hosted, so you manage nothing: no server, no sync job, no extension to keep running. It stores full conversation history, not just extracted facts, so the actual thread can be revisited anytime. The free plan is free forever, the 7-day Pro trial needs no credit card, and everything is portable: export or delete your data at any time.

For the automation-operator audience, the property that matters most is reach. A scheduled n8n workflow, a cron job running an agent, a coding session on a second machine: all of them can read the same memory over MCP because the memory does not depend on any one of them being a browser tab. The extension model asks for nothing to set up; the hosted model asks for a one-time MCP connection per tool, usually a few lines of configuration for tools that already speak the protocol.

The decision in one question

Ask yourself: where does your AI work actually live?

If it lives in browser chats, and the goal is continuity between ChatGPT, Claude, and Grok without re-pasting context, the extension model is the least-effort answer. Dray's chat linking is genuinely unique, its memory controls are thoughtful, and its pricing is modest for what it does.

If your work spans coding tools, scheduled agents, phone apps, and chat assistants, you want the memory to live outside the browser in a layer every tool can reach. That is what the hosted MCP model is for: one memory, every tool, every device, every run.

The two are not really competitors. They are answers to two different versions of the same question, and the right pick is the one that matches the shape of your own workflow.