# Awesome-Self-Evolving-Agents: A Curated List for AI Agent Research This repository profile is provided by osrepos.com, an open source repository discovery platform. Source: osrepos.com Repository profile: https://osrepos.com/repo/xmudeeplit-awesome-self-evolving-agents Generated for open source discovery and AI-assisted research. Awesome-Self-Evolving-Agents is a comprehensive GitHub repository offering a curated collection of resources on self-evolving agents. It includes a systematic survey, research papers, benchmarks, and open-source projects, providing valuable insights into this rapidly advancing field of AI. This repository serves as an essential guide for researchers and developers exploring model-centric, environment-centric, and co-evolutionary approaches. GitHub: https://github.com/XMUDeepLIT/Awesome-Self-Evolving-Agents OSRepos URL: https://osrepos.com/repo/xmudeeplit-awesome-self-evolving-agents ## Summary Awesome-Self-Evolving-Agents is a comprehensive GitHub repository offering a curated collection of resources on self-evolving agents. It includes a systematic survey, research papers, benchmarks, and open-source projects, providing valuable insights into this rapidly advancing field of AI. This repository serves as an essential guide for researchers and developers exploring model-centric, environment-centric, and co-evolutionary approaches. ## Topics - agent - self-evolving-agents - AI - machine-learning - research - survey - github - awesome-list ## Repository Information Last analyzed by OSRepos: Mon Sep 14 2026 21:24:55 GMT+0100 (Western European Summer Time) Detail views: 1 GitHub clicks: 1 ## Safety Notice OSRepos shares public repositories for knowledge and discovery only. Review source code, dependencies, licenses, and security implications before running or installing anything. ## Content ## Introduction The `Awesome-Self-Evolving-Agents` repository, maintained by XMUDeepLIT, is a meticulously curated collection of resources dedicated to the burgeoning field of self-evolving agents. This repository stems from a systematic survey paper, "A Systematic Survey of Self-Evolving Agents: From Model-Centric to Environment-Driven Co-Evolution," and aims to provide a continuously updated hub for researchers and practitioners. It covers a wide array of materials, including foundational survey papers, cutting-edge research articles, performance benchmarks, and practical open-source projects. Self-evolving agents represent a transformative paradigm in AI, enabling systems to autonomously enhance their capabilities across three primary dimensions: * **Model-Centric Self-Evolution**: Focuses on improving the agent's internal model through inference-based and training-based evolution. * **Environment-Centric Self-Evolution**: Enhances agent interaction with external knowledge and experience, encompassing static knowledge, dynamic experience, modular architecture, and agentic topology evolution. * **Model-Environment Co-Evolution**: Explores the simultaneous evolution of both the agent's model and its environment, including multi-agent policy co-evolution and environment training. ## Why Use and Key Benefits This repository offers immense value for anyone interested in the latest advancements in agentic AI. Key benefits include: * **Comprehensive Resource**: It provides a single, organized location for a vast amount of research, making it easier to navigate the complex landscape of self-evolving agents. * **Structured Taxonomy**: The content is categorized into clear sections, following a comprehensive taxonomy that helps users understand the different facets and approaches within self-evolution. * **Up-to-Date Information**: The repository is continuously updated, ensuring access to the newest papers, benchmarks, and projects as the field evolves. * **Foundation for Research**: Researchers can leverage this collection to identify key trends, understand existing methodologies, and pinpoint areas for future exploration. * **Practical Applications**: Beyond theoretical papers, it links to open-source projects and real-world applications, bridging the gap between academic research and practical implementation in areas like automated scientific discovery and autonomous software engineering. ## Installation `Awesome-Self-Evolving-Agents` is a curated list of resources, not a software library that requires installation. To "use" this repository, simply clone it or browse its contents directly on GitHub. All resources, including papers, benchmarks, and links to open-source projects, are accessible through the repository's markdown files. bash git clone https://github.com/XMUDeepLIT/Awesome-Self-Evolving-Agents.git cd Awesome-Self-Evolving-Agents You can then explore the `README.md` file and linked documents to delve into the various categories of self-evolving agents. ## Examples The repository is structured to provide clear examples and references across its various sections: * **Research Papers**: Find extensive lists of papers categorized by evolution mechanism, such as "Inference-Based Evolution" (e.g., Self-consistency, Self-refine) and "Training-Based Evolution" (e.g., Self-instruct, Self-play fine-tuning). * **Benchmarks**: Discover benchmarks like GPQA for scientific reasoning, GSM8K for mathematical reasoning, and SWE-bench for software engineering, each with links to their respective projects and papers. * **Open Source Libraries**: Explore foundational agent orchestration tools like LangGraph and AutoGen, as well as libraries for distributed training, post-training, and efficient fine-tuning. * **Applications**: See real-world implementations in "Automated Scientific Discovery" (e.g., Agon, The AI Scientist) and "Autonomous Software Engineering" (e.g., SWE-agent, Devin). Each entry typically includes a direct link to the paper or project, facilitating easy access to the original work. ## Links * **GitHub Repository**: [XMUDeepLIT/Awesome-Self-Evolving-Agents](https://github.com/XMUDeepLIT/Awesome-Self-Evolving-Agents){target=_blank} * **Survey Paper**: ["A Systematic Survey of Self-Evolving Agents: From Model-Centric to Environment-Driven Co-Evolution"](https://doi.org/10.36227/techrxiv.177203250.05832634/v2){target=_blank}