Loading repository data…
XMUDeepLIT / repository
A Survey of Self-Evolving Agents | A curated list of resources (surveys, papers, benchmarks, and opensource projects) on Self-Evolving Agents.
A transparent discovery signal based on current public GitHub metadata.
This score does not audit code, security, maintainers, documentation quality, or suitability. Verify the repository and its current documentation before adoption.
This repository provides a comprehensive collection of research papers, benchmarks, and open-source projects on Self-Evolving Agents. It includes contents from our survey paper 📖"A Systematic Survey of Self-Evolving Agents: From Model-Centric to Environment-Driven Co-Evolution" and will be continuously updated.
🤗 You're very welcome to contribute to this repository. If you find any missing resources or come across interesting new research works, please don’t hesitate to launch an issue or submit a pull request!
📫 Contact us via emails: {xiangzhishang,yangchengyi}@stu.xmu.edu.cn, qinggangzhang@jlu.edu.cn
📃 Please cite our paper if you find our survey or repository helpful!
@article{xiang2026systematic,
title={A Systematic Survey of Self-Evolving Agents: From Model-Centric to Environment-Driven Co-Evolution},
author={Xiang, Zhishang and Yang, Chengyi and Chen, Zerui and Wei, Zhimin and Tang, Yunbo and Teng, Zongpei and Peng, Zexi and Li, Zongxia and Huang, Chengsong and He, Yicheng and others},
journal={Available at SSRN 6626878},
year={2026}
}
Agentic Self-Evolving represents a paradigm shift in AI development, enabling systems to autonomously improve through three key dimensions:
Model-Centric Self-Evolution: Focuses on improving the model itself through inference-based evolution (parallel sampling, sequential self-correction, structured reasoning) and training-based evolution (synthesis-driven offline and exploration-driven online self-evolving).
Environment-Centric Self-Evolution: Enhances the agent's interaction with external knowledge and experience through static knowledge evolution, dynamic experience evolution, modular architecture evolution, and agentic topology evolution.
Selected from shared topics, language and repository description—not editorial ratings.
DataArcTech /
The repo of survey paper "A Survey of Agent Skills: Toward Procedural Infrastructure for LLM Agents"
73/100 healthwmmthu /
A curated list of papers, blog posts, and systems on skills for LLM agents — reusable, named capability units that an agent can store, retrieve, compose, and improve over time — together with closely adjacent research on tool use, function calling, procedural memory, and skill induction.
71/100 healthyashonwu /
Model-Environment Co-Evolution: Enables simultaneous evolution of both the model and its environment through environment training and multi-agent policy co-evolution.
Agent Skills from the Perspective of Procedural Memory: A Survey