Agents: An Open-source Framework for Autonomous Language Agents

Wangchunshu Zhou, Yuchen Eleanor Jiang, Long Li, Jialong Wu, Tiannan Wang, Shuai Wang, Jiamin Chen, Jintian Zhang, Jing Chen, Xiangru Tang, Peng Cui, Ningyu Zhang, Huajun Chen, Mrinmaya Sachan

Keywords: large language models, language agents

TL;DR: We propose Agents, an SOP-based framework for building controllable language agents and mult-agent systems.

Abstract: Recent advances on large language models (LLMs) enable researchers and developers to build autonomous language agents that can automatically solve various tasks and interact with environments, humans, and other agents using natural language interfaces. We consider language agents as a promising direction towards artificial general intelligence and release Agents, an open-source library with the goal of opening up these advances to a wider non-specialist audience. Agents is carefully engineered to support important features including planning, memory, tool usage, multi-agent communication, and fine-grained symbolic control. Agents is user-friendly as it enables non-specialists to build, customize, test, tune, and deploy state-of-the-art autonomous language agents without much coding. The library is also research-friendly as its modularized design makes it easily extensible for researchers.

Submission Number: 84