MemoryLake vs Vilix AI: Two Ways to Share Context Between ChatGPT, Claude, and Other AI Tools

Quick answer: No AI assistant shares its built-in memory with a rival assistant. Sharing context between ChatGPT, Claude, and other AI tools means putting that context in a neutral layer outside all of them, in a form every tool can reach. MemoryLake and Vilix AI both do that job, in two different shapes. MemoryLake centres on a project you load with files, documents, and context, then expose over MCP. Vilix AI centres on a hosted account you connect once, which every tool reads from and writes to as you work. The useful question is not which one has memory. It is which shape matches how your context is created.

Why built-in memory stays in one app

Each major assistant now remembers things inside its own walls. That memory helps inside that product and stops at its border. A preference saved in one chat app is invisible in your coding editor. A decision explained to a desktop assistant is unknown to the scheduled automation that runs overnight. A summary built on your phone does not follow you to the tool you open on your laptop.

This is not a missing setting you can switch on. The memory belongs to the app that collected it. So every tool switch turns into a manual transfer: copy the summary, paste the background, re-upload the file, repeat the standing rules. You become the integration between your own tools, and the transfer is always incomplete, because the nuance of the original conversation rarely survives being summarised into a paragraph.

A shared layer fixes the structure instead of the symptom. Context lives in one place, outside any single assistant, and every connected tool draws from that same place.

Shape one: a project you load and govern

MemoryLake describes its approach as one memory across models. You create a project, load the context you want shared, from your role and preferences to reference files in formats such as PDF, Word, Excel, PowerPoint, Markdown, and images, and MCP-compatible tools read that same project on demand.

That project model suits context that already exists as material: documents, specifications, research files, and defined facts you want several tools to consult. Its published material emphasises portability across models and agents, user-owned storage, and governed, version-aware memory, the language of a memory system you set up deliberately and keep under your control.

Its pricing page is token-based. The free plan includes 300,000 tokens a month. Pro is $19 a month with 6,200,000 tokens a month, and Premium is $199 a month with 66,000,000 tokens a month, with extra usage handled through credits. That metering makes sense for a store that tools query by volume, especially where files and documents make up a large share of what gets recalled.

Shape two: a memory your tools build as they work

Vilix AI starts from the conversation itself. It is cloud-hosted, so there is no infrastructure to deploy, run, or maintain. You connect the AI tools you already use to one Vilix AI account over MCP, and the same memory is available everywhere that connection reaches: on a laptop, on a phone, and in the automation tools that support MCP clients.

What those tools share is more than a file store. Vilix AI stores full conversation history, not just extracted facts, alongside projects, tasks, and standing rules. When one tool learns how a project is structured, why a decision was made, or how a recurring job should run, the next tool can recall the actual exchange behind that knowledge, not only a compressed note about it. That matters for scheduled work. An agent that wakes up for a daily run can read what was decided in yesterday's conversation, what changed in the run before, and which rules were set for it, instead of starting from a blank briefing every time.

Keeping that memory current takes no separate loading step. Tools read and write the shared memory as part of normal work, so context created in a morning planning conversation is available to an afternoon coding session and to an overnight automation without anyone carrying it across by hand.

Vilix AI offers a free plan that stays free forever, with Starter at $10 a month, Pro at $20 a month, and Power at $49 a month. A 7-day Pro trial is available with no credit card required. Your data can be exported or deleted at any time.

The two shapes, side by side

Choosing by how your context is created

If your shared context mainly lives in documents that several models need to consult, a loaded project is a natural fit. The material already exists, you place it once, and each tool queries the same repository.

If your shared context mainly lives in conversations, decisions, corrections, and recurring work, a connected memory is the natural fit. That is the context that usually gets lost between tools, because it was never a document in the first place. It was something said once, in one app, that every other app then needed.

Many operators have both kinds. The deciding test is simple: think about the last time you re-explained something to an AI tool. If what you re-explained was a file, start with the project shape. If what you re-explained was a conversation, start with the connection shape. Either way, the goal is the same: one memory, outside any single assistant, that every tool you trust can reach.