OpenClaw with and without MemOS: What has really changed?

After a couple of weeks of working with MemOS, I noticed something strange. My agent started finishing my thoughts. Not in a general sense, but specifically — the agent was pulling facts from conversations a week ago and connecting them to what I was asking now. What’s more, it started making deeper and more personal conclusions based on all this data.

To understand why this is important, you need to see the problem. The agent in OpenClaw stores memory in static markdown files. After each session, a file is written. There is a search function for these files, but the agent has to decide for itself whether it needs to look there. Most of the time, it doesn’t. And the problem goes deeper than that.

The context window in top models such as Claude Opus is about 200,000 (200 thousands) tokens. A significant part of this is consumed by system prompts and OpenClaw’s own instructions. And over time, these prompts only increase, because the agent saves what is important to it there. The more context you load, the worse the model performs. It loses focus, starts to hallucinate, and forgets instructions. In general, this is a separate major pain point.

However, MemOS solves this problem. Each message first passes through the MemOS system. It scans the entire history in its memory database across all sessions and projects. It uses semantic search, RAG, a graph of connections, and internal language models. The output is a short context injection that is attached directly to the message. The agent always sees the essence. Moreover, everything is described briefly within the context of each message that we send to the agent. No unnecessary tokens, only what is needed right now.

MemOS search pipeline:

But the most interesting thing happened later. I set up a separate agent on PicoClaw. I gave it different settings and registered it as a different personality. I connected the same MemOS account, and the agent immediately knew everything we had discussed with the other agent. But thanks to the different settings, he had a different interpretation of the same facts from memory. Like two colleagues who worked on the same project but see it in their own way.

MemCubes memory layers:

Then I connected MemOS to Claude Codeand even to Gemini via a Chrome extension. Now my code agent, content agent, and planning agent share one memory, which is VERY convenient, as I don’t have to retell anything to anyone anymore.

Now for the most important part.

Without MemOS, the more data accumulates, the worse the agent performs. The context fills up like water in a bucket, and once the bucket is full, the model degrades. With MemOS, the more data there is, the more connections there are in memory, and the smarter and more capable the agent becomes.

Every bug that is fixed and every solution that is found is saved and pops up exactly when needed.

In other words, MemOS is like human memory: over time, new neural connections, new experiences, and new skills appear. Memory is divided into different types for convenience and time. Separate agents work inside MemOS, cleaning up and distributing data, monitoring the frequency of what is used most often, and enriching this data with new experiences.