Detailed Introduction
AgentEvolver is an end-to-end training framework for efficient self-evolving agents. It combines three mechanisms—self-questioning, self-navigating, and self-attributing—to enable agents to autonomously discover tasks, accumulate cross-task experience, and continuously optimize policies. The system integrates environment sandboxes, large language models (LLM, Large Language Model), and experience management through a modular service-oriented dataflow architecture.
Main Features
- Automatic task generation: Agents can autonomously create diverse tasks during interaction, reducing manual dataset construction.
- Experience-guided exploration: Summarizes and reuses experience across tasks to guide higher-quality rollouts and improve exploration efficiency.
- Attribution-based credit assignment: Analyzes long trajectories to assign fine-grained credit, improving convergence and stability.
Use Cases
Suitable for long-running training and continuous capability evolution scenarios, such as training agents for complex interactive applications, optimizing policies in simulated environments, and research platforms requiring multi-task or multi-stage adaptation. AgentEvolver is useful for teams seeking to improve agent performance under constrained compute budgets.
Technical Features
- Service-oriented dataflow: Decoupled components for easier extension and secondary development.
- Environment compatibility: Standardized interfaces to connect diverse external environments and tool APIs.
- Pluggable experience management: Supports summarization, indexing, and reusable storage for cross-task transfer learning.
- Open and reproducible: Released under Apache-2.0, with quick-start examples and comprehensive documentation for research and engineering reproduction.