BioTrouble: A Multi-Agent Workflow for Troubleshooting Molecular Biology Techniques

Abstract

Troubleshooting is a critical yet often underdocumented aspect of molecular biology experiments across laboratories. Failures in core techniques such as PCR, qPCR, molecular cloning, and related assays can lead to experimental failure, wasted resources, and delays in research progress. Here, we present BioTrouble, a multi-agent AI workflow designed to assist researchers in troubleshooting a wide range of molecular biology experiments. It leverages a custom-designed troubleshooting knowledge base through a retrieval-augmented generation (RAG) framework. BioTrouble employs small language models to generate the troubleshooting plan and utilizes a smart model routing system to manage cost per request. User interactions and feedback are stored as structured cases, enabling BioTrouble to expand its troubleshooting knowledge base and improve response generation over time. Compared with single-model SOTA LLM, BioTrouble generated comparable troubleshooting recommendations using small language models.