Keyan Ding at Zhejiang University wants to solve the scientific software mess:

While Large Language Models show promise in tool automation, they struggle to seamlessly integrate and orchestrate multiple tools for complex scientific workflows. Here, we present SciToolAgent, an LLM-powered agent that automates hundreds of scientific tools across biology, chemistry, and materials science. At its core, SciToolAgent leverages a scientific tool knowledge graph that enables intelligent tool selection and execution through graph-based retrieval-augmented generation.

Scientific software is a collection of brilliant, disconnected islands. We have incredibly powerful and specialised computational tools for everything from materials science to protein folding, but each exists in its own silo, demanding deep expertise to operate. This fragmentation is a massive barrier, effectively preventing researchers from easily combining these tools to solve complex, multi-step problems. It’s like having a workshop full of advanced machinery, but with no common language or system for making the machines work together.

Instead of building an agent that's expert in everything, build an expert orchestrator. The innovation isn't the agent - it's the knowledge graph that maps the entire scientific software ecosystem.

What each tool does, what data it needs, what it produces, how they connect. A rich map instead of just a list.