SciToolAgent provides a framework for integrating diverse scientific tools with Large Language Models (LLMs) to overcome limitations in current scientific research workflows. It empowers researchers and power users by enabling autonomous planning, execution, and summarization of complex, multi-domain scientific tasks, significantly enhancing research efficiency and intelligence.

How It Works

The system utilizes LLMs as Planners, Executors, and Summarizers, guided by a comprehensive scientific tool knowledge graph (SciToolKG). This KG models relationships, dependencies, and compatibility among hundreds of scientific tools across biology, chemistry, and materials science. This approach allows SciToolAgent to autonomously devise optimal tool sequences, execute them reliably, and synthesize results, offering a novel and robust method for scientific problem-solving.

Quick Start & Requirements

• Installation: Clone the repository, create a Python 3.10 Conda environment (conda create -n SciToolAgent python=3.10), activate it (conda activate SciToolAgent), and install dependencies (pip install -r requirements.txt).
• Prerequisites: Requires Python 3.10 and OpenAI API credentials (OPENAI_API_BASE, OPENAI_API_KEY) configured in .env files.
• Execution:
  1. Start the tool service: cd tools && bash run.sh.
  2. Run the agent: cd ../test && PYTHONPATH=. python test_run_SciToolAgent.py.
  • Four example cases are available in Cases.ipynb.
• Setup Footprint: Requires environment setup and API key configuration; specific resource footprints are not detailed.

Highlighted Details

• Access to over 500 scientific tools, including web APIs, ML models, Python functions, and knowledge databases.
• SciToolKG: A detailed knowledge graph modeling relationships, dependencies, and compatibility among scientific tools.
• LLM-based Planner, Executor, and Summarizer for autonomous workflow management.
• Built-in safety checking system for responsible tool execution.

Maintenance & Community

The project was released on GitHub in December 2024 and has a publication in Nature Computational Science (August 2025), indicating active development and research backing. Specific community links (Discord, Slack) or roadmap details are not provided in the README.

Licensing & Compatibility

The README does not specify a license type, which is a critical omission for assessing commercial use or closed-source integration.

Limitations & Caveats

Users must configure API keys and potentially model files for AI-driven tools. Package conflicts may arise during installation, potentially requiring alternative installation methods. The README does not detail specific unsupported platforms or known bugs.