Gödel Agent: A Self-Referential Agent Framework for Recursively Self-Improvement

The rapid advancement of large language models (LLMs) has significantly enhanced the capabilities of agents across various tasks.However, existing agentic systems, whether based on fixed pipeline algorithms or pre-defined meta-learning frameworks, cannot search the whole agent design space due to the restriction of human-designed components, and thus might miss the more optimal agent design.In this paper, we introduce Gdel Agent, a selfevolving framework inspired by the Gdel machine, enabling agents to recursively improve themselves without relying on predefined routines or fixed optimization algorithms.Gdel Agent leverages LLMs to dynamically modify its own logic and behavior, guided solely by high-level objectives through prompting.Experimental results on multiple domains demonstrate that implementation of Gdel Agent can achieve continuous self-improvement, surpassing manually crafted agents in performance, efficiency, and generalizability.