打造可交互可扩展的科研智能体框架，让研究自动化更易用。
TinyScientist: An Interactive, Extensible, and Controllable Framework for Building Research Agents
- 构建模块化框架，支持工具动态接入与流程迭代
- 开源代码与网页演示，降低科研自动化使用门槛
- 适合想快速搭建智能科研助手的研究者与开发者
基于大语言模型的自动科研正快速发展，催生了包含多智能体系统、规划、工具调用、代码执行和人机交互的复杂工作流。然而，随着越来越多研究者使用并开发此类工具，现有工作流的扩展性与维护难度日益增加，尤其在算法架构持续演进背景下。为此，TinyScientist识别自动科研流程的核心组件，提出一个可交互、可扩展、可控制的框架，能轻松适配新工具并支持持续演化。项目提供开源代码库、交互式网页演示及 PyPI Python 包，使前沿的自动科研流水线对所有研究者和开发者均触手可及。
原文摘要 · Abstract (English)
Automatic research with Large Language Models (LLMs) is rapidly gaining importance, driving the development of increasingly complex workflows involving multi-agent systems, planning, tool usage, code execution, and human-agent interaction to accelerate research processes. However, as more researchers and developers begin to use and build upon these tools and platforms, the complexity and difficulty of extending and maintaining such agentic workflows have become a significant challenge, particularly as algorithms and architectures continue to advance. To address this growing complexity, TinyScientist identifies the essential components of the automatic research workflow and proposes an interactive, extensible, and controllable framework that easily adapts to new tools and supports iterative growth. We provide an open-source codebase, an interactive web demonstration, and a PyPI Python package to make state-of-the-art auto-research pipelines broadly accessible to every researcher and developer.
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