MemEvolve breaks the limitations of traditional Agent architecture. Its core advantage is that the Agent's experience repository is no longer static storage, but dynamically iterates and upgrades throughout the task cycle — this is the true meaning of experience evolving.

With each completed task, the Agent can extract experience, continuously optimizing decision models and behavior strategies. Compared to pure experience engineering, this approach achieves a shift from passive accumulation to active evolution.

From a technical perspective, this direction is very promising. The self-improvement capability of the memory mechanism directly relates to the long-term performance of the Agent. The OPPO team’s exploration in this field is indeed worth paying attention to, as such breakthroughs are pushing the practical boundaries of AI applications forward.