TinyScientist: An Interactive, Extensible, and Controllable Framework for Building Research Agents
This addresses the challenge of complexity in extending and maintaining agentic workflows for researchers and developers in automatic research with LLMs, though it appears incremental as it builds on existing tools and platforms.
The paper tackles the growing complexity of building and maintaining automatic research workflows with LLMs by proposing TinyScientist, an interactive, extensible, and controllable framework that adapts to new tools and supports iterative growth, providing an open-source codebase, web demo, and Python package to make state-of-the-art auto-research pipelines accessible.
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.