A month ago, my collaborator Ismael Faro and I started exploring an idea: what if we could build an AI agentic framework that was not only powerful but also completely transparent and human-readable? This led to LLMunix, an experimental "Operating System" where agents and tools are defined as simple Markdown files. The goal is to democratize agentic AI. You give it a high-level goal in natural language, and it orchestrates its Markdown-defined agents to achieve it. The main idea is that anyone, not just a developer, should be able to understand—and even modify—the system's capabilities by just reading or editing a Markdown file. The really interesting part is that the system can evolve. Powered by a capable LLM (like Claude or Gemini), it can create new agents and tools from scratch during a task. We recently tested this by asking it to analyze the SakanaAI "AI-Scientist-v2" project from GitHub. LLMunix not only produced a detailed analysis but also autonomously created five new components for itself (like a TreeSearchTool and a ReflectionAgent) based on the concepts it found in the SakanaAI repo. It essentially learned from other AI research and upgraded its own architecture on the fly. This approach was recently validated when Claude Code launched its "sub-agents" feature, which natively supports a very similar, modular, file-based agent architecture. We believe this is a step towards making AI less of a "black box." The next step is to make it even more accessible for any user by exposing it as a server for desktop apps to use. I wrote a more detailed article about the journey and the concepts behind it here: https://www.linkedin.com/pulse/dawn-new-ai-era-how-human-rea... The project is on GitHub, and we would love to get your thoughts and feedback on this approach. GitHub Repo: https://github.com/EvolvingAgentsLabs/llmunix What do you think? Is a human-readable, self-evolving system a viable path for the future of agentic frameworks?