Key Points
1. Large Language Models (LLMs) enable the development of autonomous language agents that can solve complex tasks and interact with the environment, humans, and other agents using natural language interfaces.
2. AGENTS is an open-source framework designed to address the limitations of existing language agent frameworks and make the development, customization, and deployment of autonomous language agents accessible to non-specialists.
3. AGENTS supports important features such as long-short term memory, tool usage, web navigation, multi-agent communication, human-agent interaction, and controllability through symbolic plans (SOPs).
4. The framework focuses on enabling autonomous agents to maintain long-term and short-term memory, use external tools, navigate the web, engage in multi-agent communication, and interact with human users.
5. AGENTS provides a novel approach by introducing symbolic plans (SOPs) to provide fine-grained control over an agent's behavior, making its actions more stable and predictable while facilitating tuning and optimization.
6. AGENTS is the only framework in a comparison with other platforms that simultaneously supports tool usage, long-short term memory, and multi-agent communication, while also offering human-agent interaction and controllability through SOPs.
7. The framework is designed to be used as an API, allowing the deployment of language agents in real-world applications and reducing the effort of building, testing, and tuning language agents from scratch.
8. AGENTS introduces AGENT HUB, a platform for sharing fine-tuned language agents and facilitating the customization of language agents by starting with community agents.
9. The framework provides a pipeline for automatic SOP generation based on retrieval-augmented generation (RAG), reducing the effort required for users to manually specify the SOP from scratch.
Summary
The paper discusses the development of AGENTS, an open-source library and framework for language agents, which aims to make recent advances in large language models (LLMs) accessible to a wider audience. AGENTS is designed to support key features including memory components, tool usage and web navigation, multi-agent communication, human-agent interaction, and controllability. It allows non-specialists to build, customize, test, tune, and deploy advanced autonomous language agents without extensive coding. Additionally, the paper introduces an automated SOP generation pipeline to reduce manual effort in customizing and tuning language agents. AGENTS is positioned as a versatile and research-friendly framework that provides controllability through symbolic plans (SOPs).
The framework is compared to existing open-source projects and frameworks for language agents, highlighting its unique capabilities in supporting memory, tool usage, and multi-agent communication simultaneously. The paper also presents code examples and the execution logic of a (multi) agent system using AGENTS, as well as case studies demonstrating the development of single-agent systems, multi-agent systems, and systems requiring human-agent interaction. Furthermore, AGENTS offers the capability to deploy language agents as APIs through FastAPI, and introduces AGENT HUB, a platform for sharing and accessing fine-tuned language agents.
The paper concludes by underscoring the importance of AGENTS in making recent advances in language agents accessible and impactful for both technical and non-technical audiences, thus contributing to the advancement of language agents and their potential applications.
Reference: https://arxiv.org/abs/2309.07870