AgentEvolver - Ali Tongyi Labs Open Source System for Intelligent Body Evolution

What is AgentEvolver?

AgentEvolver is an open source intelligent body evolution system from Alibaba Tongyi Lab. Through the three mechanisms of self-questioning, self-navigation and self-attribution, it realizes autonomous learning and evolution of intelligences.AgentEvolver adopts a service-oriented architecture, modularizes the environment sandbox, LLM, and experience management, and supports the seamless integration of multiple external environments and tool APIs. Its advantages are efficient learning, cost-effectiveness and continuous evolution capability. Compared with traditional reinforcement learning methods, AgentEvolver excels in exploration efficiency, sample utilization, and adaptation speed, reducing the reliance on manual datasets and random exploration costs.AgentEvolver's framework code is open-sourced, providing developers with flexible customization and development space.

Features of AgentEvolver

• Self-Questioning: Intelligent bodies can autonomously explore the environment, generate diverse tasks, reduce the reliance on manual datasets, and enhance the diversity and autonomy of task generation.
• Self-Navigating (Self-Navigating): By summarizing and reusing cross-task experience, intelligences can generate high-quality exploration trajectories more efficiently, improve exploration efficiency, and avoid repeated exploration.
• Self-Attributing.: Causal contribution analysis of intermediate steps in long trajectories to achieve fine-grained reward allocation, improve sample efficiency, and optimize the learning process.
• Modular Architecture: Adopting service-oriented data flow architecture, integrating environment sandbox, LLM and experience management into modular services, supporting seamless integration of multiple external environments and tool APIs for easy customization and secondary development.
• Efficient Learning and Evolution: Through autonomous learning and evolutionary mechanisms, AgentEvolver outperforms traditional methods in terms of exploration efficiency, sample utilization, and adaptation speed, and is able to continuously improve the capabilities of the intelligences.
• Open Source and Scalability: The framework code has been open-sourced, providing developers with flexible customization and development space for future algorithm upgrades and application scenario expansion.

AgentEvolver's core strengths

• Autonomous evolutionary capacity: Through the three mechanisms of self-questioning, self-navigation and self-attribution, AgentEvolver is able to autonomously generate tasks, optimize exploration paths, and perform fine-grained reward allocation, realizing the self-evolution of intelligences and reducing the dependence on human intervention.
• Efficient Learning Efficiency: With excellent performance in task exploration and sample utilization, AgentEvolver is able to utilize data more efficiently than traditional reinforcement learning methods, improving the speed and quality of learning and significantly increasing exploration efficiency and sample utilization.
• Significant cost-effectiveness: Reduces the high cost of manually constructing datasets and stochastic exploration, reduces development and operational costs through autonomous task generation and experience reuse, and improves overall economics.
• Flexible Architecture Design: Adopting modular service architecture, it supports seamless integration of multiple external environments and tool APIs with good scalability and customization, which is convenient for developers to carry out secondary development and future upgrading according to their needs.

What is AgentEvolver's official website?

• Github repository:: https://github.com/modelscope/AgentEvolver
• arXiv Technical Paper:: https://arxiv.org/pdf/2511.10395

Who AgentEvolver is for

• Artificial intelligence researchers: It can be used to explore cutting-edge technologies for autonomous learning and evolution of intelligences, and to advance the development of AI theory and practice.
• Algorithm engineers and developers: It can be used to develop and optimize intelligent body systems, customize development using its open source framework, and improve project efficiency.
• Corporate Technical Team: It can be used to build intelligent automation tools to enhance the intelligence of internal processes, such as intelligent customer service and automated office.
• Universities and research institutions: Can be used for teaching and research to help students and researchers gain a deeper understanding of the mechanisms of autonomous evolution of intelligences.
• Individual developers interested in AI technology: Open source frameworks can be used to learn and practice and explore innovative applications of AI technology.
• Technology enthusiasts and innovators: It can be used to explore the future direction of AI and inspire new ideas and application scenarios.