EvoAgent: An Evolvable Agent Framework with Skill Learning and Multi-Agent Delegation
This paper proposes EvoAgent--an evolvable large language model (LLM) agent framework that integrates structured skill learning with a hierarchical sub-agent delegation mechanism. EvoAgent models skills as multi-file structured capability units equipped with triggering mechanisms and evolutionary metadata, and enables continuous skill generation and optimization through a user-feedback-driven closed-loop process. In addition, by incorporating a three-stage skill matching strategy and a three-layer memory architecture, the framework supports dynamic task decomposition for complex problems and long-term capability accumulation. Experimental results based on real-world foreign trade scenario...
| Authors | Aimin Zhang, Jiajing Guo, Fuwei Jia et al. |
|---|---|
| Primary category | cs.AI |
| Published | 22 April 2026 |
| Last updated at source | 24 June 2026 |
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