One major challenge in deploying autonomous agents is building systems that can adapt to changes in their environments without the need to retrain the underlying large language models (LLMs). Memento-Skills, a new framework developed by researchers at multiple universities, addresses this bottleneck by giving agents the ability to develop their skills by themselves. “It adds its continual learning capability to the existing offering in the current market, such as OpenClaw and Claude Code,” Jun Wang, co-author of the paper, told VentureBeat. Memento-Skills acts as an evolving external memory, allowing the system to progressively improve its capabilities without modifying the underlying model. The framework provides a set of skills that can be updated and expanded as the agent receives feedback from its environment. For enterprise teams running agents in production, that matters. The alternative — fine-tuning model weights or manually building skills — carries significant operational overhead and data requirements. Memento-Skills sidesteps both. The challenges of building self-evolving agents Self-evolving agents are crucial because they overcome the limitations of frozen language models. Once a model is …