openclaw - 💡(How to fix) Fix [Feature request]: Advanced Persistent Memory & Knowledge Management System for Autonomous Agents [2 comments, 2 participants]

Summary

Introduce a structured and evolving memory system so agents can actually remember, organize and reuse information across sessions instead of starting from scratch every time.

Problem to solve

Right now the system behaves like it has almost no real memory continuity. As soon as a session ends, most of the useful context is basically gone or becomes very hard to reuse in a reliable way. This makes long workflows fragile, forces users to repeat themselves, and creates a kind of “stateless loop” where the agent never really improve over time.

In practice, this means agents struggle to follow long-running projects, forget decisions that were already made, and cannot build any form of accumulated knowledge. It also makes advanced use cases way harder than they should be, because the user has to compensate manually for something that should be handled by the system.

The need here is not just “more memory”, but a system that is structured, persistent and able to reorganize itself over time. Without that, autonomy will always hit a ceiling no matter how good the model is.

Proposed solution

The goal is to introduce a real knowledge layer, not just a buffer of past messages. The system should behave like a second brain, where information is stored, categorized, updated and reused continuously.

At a high level, the agent should be able to write, read, and refine its own knowledge base while working. This includes tracking ongoing projects, storing useful patterns, remembering user preferences and keeping a clear history of what has been done.

A key part of the solution is also a consolidation loop. Instead of just storing raw data, the system should regularly process what happened, extract what matters, and improve the structure of its knowledge. This is what makes it “self improving” rather than just persistent.

Below is a concrete example of how this behavior can be defined at the prompt/system level. This example should remain exactly as is, since it already reflects a working implementation logic:

💡 Reference Implementation (Prompt)

First exemple :

**

I want you to implement a knowledge management system based on the work of Thiago Forte, incorporating a daily note and a prioritization system, where you are actively logging the important information related to everything that we are working on and doing together, and also create a nightly job where you review every single thing we talked about today and update your information accordingly.

**

Below is a working prompt used successfully to implement this system manually:

We are having trouble remembering important information across sessions.

What this shows in a more concrete way is the expected behavior: the agent is not just reacting, it is building something persistent. The system should support this natively instead of relying on manual prompts like this.

Alternatives considered

Some lighter approaches like just increasing context window or storing raw conversation logs were considered, but they don’t really solve the core issue. They store more data, but they don’t structure it or make it reusable in a smart way. So the problem stays the same, just slightly delayed.

Impact

This affects basically anyone trying to use OpenClaw for something more than simple one-shot interactions. The severity is high because it directly limits autonomy and makes complex workflows unreliable. It happens all the time as soon as you work across multiple sessions or try to build something iterative.

The consequence is pretty clear: users repeat themselves, agents forget important things, tasks get partially done or inconsistent, and overall trust in the system goes down. It also slows everything because you constantly need to rebuild context manually.

Evidence/examples

The prompt above already demonstrates that when this kind of system is emulated manually, the agent becomes significantly more consistent and useful over time. Without it, the difference is very noticeable, especially on long or multi-step tasks.

Additional information

This is less about adding a feature and more about unlocking a different level of behavior. Without structured memory, autonomy will always feel limited, even if the models themselves are strong. With it, the system can actually evolve with usage, which is kinda the whole point of building agents in the first place.