EverMind's open-source Raven V0.2.0 breaks away from the traditional approach of recursive self-improvement solely at the model parameter level, proposing instead to analogize the entire Agent to the human brain. In this analogy, model parameters correspond to the cerebral cortex, responsible for slow-paced capability consolidation; while the Harness, composed of memory, skills, prompts, and more, corresponds to the hippocampus, enabling rapid adaptation. This concept allows recursive self-improvement to be implemented at the Harness layer. Raven V0.2.0 incorporates two core designs: First, it supports AI in independently rewriting four types of content within the Harness: modules, code, prompts, and strategies. The experimental feature, Curator, has achieved a closed loop from feedback collection to modification validation and installation. Second, as a unified orchestration layer for the Harness, this version can uniformly schedule four types of specialized Agents developed in-house—research, coding, design, and continuous execution—as well as external specialized Agents such as Claude Code and Codex. It breaks down complex tasks, manages dependencies and parallel branches using directed acyclic graphs, and leverages EverOS to retain cross-session context and experience, enabling cross-model and cross-framework capability combinations. Multi-dimensional evaluations show that, with the same base model, Raven outperforms similar Harness frameworks in tasks such as multi-Agent orchestration, in-depth research, coding, design, and continuous execution. Currently, Raven has been applied in various practical scenarios, including AI-driven autonomous optimization of pre-training schemes, autonomous creation and release of materials, and full-cycle development of Godot game projects. Users can save reusable Playbook processes and, combined with feedback, allow Curator to continuously adjust Harness strategies, gradually cultivating a digital partner tailored to their work habits. Additionally, this version opens up a full set of extensible Harness interfaces and experimental self-improvement reference implementations, facilitating developers to directly reuse relevant capabilities.