This open-source research-focused agent utility library is released by a development team that is pioneering the popularization of scientific domain agent technology. This tool serves as a kind of academic cognitive framework and interface specification, helping general-purpose coding agents and large language models think and handle tools like human scientists. Much like a skilled artisan carefully selecting and arranging tools optimized for the task at hand, this library acts as a guide, enabling agents to confidently choose and immediately execute complex analytical tools. Agents that utilize this library can function as intelligent scientific research assistants, capable of autonomously designing and executing scientific research pipelines that require advanced domain knowledge, such as in bioinformatics and cheminformatics, going beyond typical text summarization or basic coding tasks. Existing AI development tools have shown significant limitations in reliability due to a lack of understanding of the complex query formats of scientific databases or unique data ecosystems, leading to issues such as incorrect API calls or the creation of code based on inaccurate data schema assumptions. This library fundamentally addresses these reliability issues by providing agents with access to over 140 domain knowledge-based skills and deterministic paths to access over 100 prominent global academic and scientific databases. Notably, it adheres to the skill standard specifications of the latest AI coding tools, boasting unparalleled versatility as it can be immediately applied without additional development effort. This is an innovative differentiator, akin to equipping a novice developer with only encyclopedic programming knowledge with the experimental equipment manuals and core database connection protocols of a specific research lab, enabling them to achieve the work efficiency of a senior researcher immediately. Researchers in the biotechnology field can leverage this library to complete highly tedious and complex computational processes, such as single-cell ribonucleic acid sequencing data analysis, with just a few conversational interactions. For example, when a researcher wants to identify key biomarkers expressed in a specific cancer cell line, the agent automatically activates library skills to perform preprocessing and dimensionality reduction and connects to data hubs. Subsequently, it identifies significant genes, connects to databases, visualizes protein interaction networks, and identifies related biological pathways all at once. This complex pipeline would take a researcher several days to code manually, but an agent equipped with these specialized skills can complete all the analysis within minutes and automatically generate a final research report. This tool aims to be more than just a software package; it aspires to be the central hub of a vast AI science ecosystem involving scientists and developers worldwide. In addition to traditional machine learning and cheminformatics libraries, it supports API integration with advanced cloud laboratories and automated experimental equipment, accelerating the realization of digital laboratory automation. As a result, researchers are completely freed from repetitive coding tasks and cumbersome API reference searches, and the AI agent becomes a companion and digital collaborator that faithfully assists in the entire research cycle, from hypothesis generation to data acquisition, simulation, and paper writing, changing the paradigm of research.

A utility library that empowers general-purpose coding agents with scientific research capabilities.

Identification of EGFR-Targeting Inhibitors via Virtual Screening: By integrating the ChEMBL API and RDKit, 1,200 EGFR-inhibiting compounds with a molecular weight of 300-500 and a logP range of 2-4 are filtered within 3 seconds, and their binding affinities are calculated using DiffDock, resulting in the identification of the top 5 optimal inhibitors, thereby reducing the initial screening period by 80%.