danfiru

Let Agents Design Agents Memento-Skills is a self-evolving agent framework where agents learn from failures and rewrite their own skills. Most agent frameworks treat skills as static. You write them once, load them into context, and hope they work. When they fail, you debug manually or try again with the same broken skill. Memento-Skills takes a different approach. When a skill fails, the system reflects on why it failed, locates the broken skill, rewrites it, and stores the improved version back into the skill library. Here's how it works: The framework runs a continuous Read → Execute → Reflect → Write loop. Read: Retrieve candidate skills from the local library instead of loading every skill into context. Execute: Run skills in a local sandbox with actual tool calling - file operations, web search, scripts, external systems. Reflect: When execution fails, the system records what went wrong, updates the skill's utility score, and attributes the failure to specific skills. Write: Rewrite broken skills, optimize weak ones, or create new skills when nothing suitable exists. This isn't about accumulating more skills. It's about building a skill library that improves through task experience. The system was tested on HLE (Humanity's Last Exam) and GAIA (General AI Assistants) benchmarks. Performance improved over multiple learning rounds as the skill library grew from basic atomic skills into a richer set of learned capabilities. Built for open-source LLM ecosystems - works with Kimi, MiniMax, GLM, and other OpenAI-compatible endpoints. Comes with 9 built-in skills (filesystem, web-search, PDF, docx, xlsx, pptx, image analysis, skill-creator, dependency install) that serve as the starting point for the evolving library. It's 100% open source Link to Memento-Skills in comments!

Meet Zookeeper, our conversational CAD agent. ✨