Memento-Skills: Let Agents Design Agents
We introduce Memento-Skills, a generalist, continually-learnable LLM agent system that functions as an agent-designing agent: it autonomously constructs, adapts, and improves task-specific agents through experience. The system is built on a memory-based reinforcement learning framework with stateful prompts, where reusable skills (stored as structured markdown files) serve as persistent, evolving memory. These skills encode both behaviour and context, enabling the agent to carry forward knowledge across interactions.
  Starting from simple elementary skills (like Web search and terminal operations), the agent continually improves via the Read--Write Reflective Learning mechanism introduced in Memento~2~wang2025memento2. In the read phase, a behaviour-trainable skill router selects the most relevant skill conditioned on the current stateful prompt; in the write phase, the agent updates and expands its skill library based on new experience. This closed-loop design enables continual learning without updating LLM parameters, as all adaptation is realised through the evolution of externalised skills and prompts.
  Unlike prior approaches that rely on human-designed agents, Memento-Skills enables a generalist agent to design agents end-to-end for new tasks. Through iterative skill generation and refinement, the system progressively improves its own capabilities. Experiments on the General AI Assistants benchmark and Humanity's Last Exam demonstrate sustained gains, achieving 26.2\% and 116.2\% relative improvements in overall accuracy, respectively. Code is available at https://github.com/Memento-Teams/Memento-Skills.
69bcb692081b9ad49af57275
Huichi Zhou
69bcb692081b9ad49af57276
Siyuan Guo
69bcb692081b9ad49af57277
Anjie Liu
69bcb692081b9ad49af57278
Zhongwei Yu
69bcb692081b9ad49af57279
Ziqin Gong
69bcb692081b9ad49af5727a
Bowen Zhao
69bcb692081b9ad49af5727b
Zhixun Chen
69bcb692081b9ad49af5727c
Menglong Zhang
69bcb692081b9ad49af5727d
Yihang Chen
69bcb692081b9ad49af5727e
Jinsong Li
69bcb692081b9ad49af5727f
Runyu Yang
69bcb692081b9ad49af57280
Qiangbin Liu
69bcb692081b9ad49af57281
Xinlei Yu
69bcb692081b9ad49af57282
Jianmin Zhou
69bcb692081b9ad49af57283
Na Wang
69bcb692081b9ad49af57284
Chunyang Sun
69bcb692081b9ad49af57285
Jun Wang
Memento-Skills: Let Agents Design Agents
We introduce Memento-Skills, a generalist, continually-learnable LLM agent system that functions as an agent-designing agent: it autonomously constructs, adapts, and improves task-specific agents through experience. The system is built on a memory-based reinforcement learning framework with stateful prompts, where reusable skills (stored as structured markdown files) serve as persistent, evolving memory. These skills encode both behaviour and context, enabling the agent to carry forward knowledge across interactions.
  Starting from simple elementary skills (like Web search and terminal operations), the agent continually improves via the Read--Write Reflective Learning mechanism introduced in Memento~2~wang2025memento2. In the read phase, a behaviour-trainable skill router selects the most relevant skill conditioned on the current stateful prompt; in the write phase, the agent updates and expands its skill library based on new experience. This closed-loop design enables continual learning without updating LLM parameters, as all adaptation is realised through the evolution of externalised skills and prompts.
  Unlike prior approaches that rely on human-designed agents, Memento-Skills enables a generalist agent to design agents end-to-end for new tasks. Through iterative skill generation and refinement, the system progressively improves its own capabilities. Experiments on the General AI Assistants benchmark and Humanity's Last Exam demonstrate sustained gains, achieving 26.2\% and 116.2\% relative improvements in overall accuracy, respectively. Code is available at https://github.com/Memento-Teams/Memento-Skills.
A generalist language model agent system autonomously designs and improves task-specific agents through memory-based reinforcement learning with stateful prompts and skill libraries.
LLM agent system memory-based reinforcement learning stateful prompts skill router Read--Write Reflective Learning continual learning agent-designing agent skill library behavior-trainable skill router externalised skills