# BioAgents: Democratizing Bioinformatics Analysis with Multi-Agent Systems

arXiv: https://arxiv.org/abs/2501.06314 · HTML: https://mlsanity.com/paper/2501.06314/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Placed in Medical and scientific domain agents, one of 16 directions in LLM agents and tool use.
- Builds on 28 of its 56 references that are in the map.
- First posted on arXiv 10 Jan 2025.

## Placed in

- LLM agents and tool use / [Medical and scientific domain agents](https://mlsanity.com/agents/medical-and-scientific-domain-agents/)

## Builds on (most cited first, 28 of 28)

- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Toolformer: Language Models Can Teach Themselves to Use Tools](https://mlsanity.com/paper/2302.04761/) (2023)
- [A Survey on Large Language Model based Autonomous Agents](https://mlsanity.com/paper/2308.11432/) (2023)
- [Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone](https://mlsanity.com/paper/2404.14219/) (2024)
- [Large Language Models Encode Clinical Knowledge](https://mlsanity.com/paper/2212.13138/) (2022)
- [CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society](https://mlsanity.com/paper/2303.17760/) (2023)
- [AgentBench: Evaluating LLMs as Agents](https://mlsanity.com/paper/2308.03688/) (2023)
- [ChemCrow: Augmenting large-language models with chemistry tools](https://mlsanity.com/paper/2304.05376/) (2023)
- [When Can LLMs Actually Correct Their Own Mistakes? A Critical Survey of Self-Correction of LLMs](https://mlsanity.com/paper/2406.01297/) (2024)
- [Automatically Correcting Large Language Models: Surveying the landscape of diverse self-correction strategies](https://mlsanity.com/paper/2308.03188/) (2023)
- [Agent-as-a-Judge: Evaluate Agents with Agents](https://mlsanity.com/paper/2410.10934/) (2024)
- [What can Large Language Models do in chemistry? A comprehensive benchmark on eight tasks](https://mlsanity.com/paper/2305.18365/) (2023)
- [Empowering Biomedical Discovery with AI Agents](https://mlsanity.com/paper/2404.02831/) (2024)
- [A Survey on Self-Evolution of Large Language Models](https://mlsanity.com/paper/2404.14387/) (2024)
- [Metacognitive Capabilities of LLMs: An Exploration in Mathematical Problem Solving](https://mlsanity.com/paper/2405.12205/) (2024)
- [Self-Evaluation Improves Selective Generation in Large Language Models](https://mlsanity.com/paper/2312.09300/) (2023)
- [Internal Consistency and Self-Feedback in Large Language Models: A Survey](https://mlsanity.com/paper/2407.14507/) (2024)
- [What is the Role of Small Models in the LLM Era: A Survey](https://mlsanity.com/paper/2409.06857/) (2024)
- [CellAgent: An LLM-driven Multi-Agent Framework for Automated Single-cell Data Analysis](https://mlsanity.com/paper/2407.09811/) (2024)
- [Two Failures of Self-Consistency in the Multi-Step Reasoning of LLMs](https://mlsanity.com/paper/2305.14279/) (2023)
- [Towards Understanding the Characteristics of Code Generation Errors Made by Large Language Models](https://mlsanity.com/paper/2406.08731/) (2024)
- [BioCoder: A Benchmark for Bioinformatics Code Generation with Large Language Models](https://mlsanity.com/paper/2308.16458/) (2023)
- [Simulating Human Strategic Behavior: Comparing Single and Multi-agent LLMs](https://mlsanity.com/paper/2402.08189/) (2024)
- [Enhancing Trust in LLM-Based AI Automation Agents: New Considerations and Future Challenges](https://mlsanity.com/paper/2308.05391/) (2023)
- [Enhancing Trust in LLMs: Algorithms for Comparing and Interpreting LLMs](https://mlsanity.com/paper/2406.01943/) (2024)
- [Revolutionizing Mental Health Care through LangChain: A Journey with a Large Language Model](https://mlsanity.com/paper/2403.05568/) (2024)
- [An Evaluation of Large Language Models in Bioinformatics Research](https://mlsanity.com/paper/2402.13714/) (2024)

## Built on by (most cited first, 8 of 8)

- [From AI for Science to Agentic Science: A Survey on Autonomous Scientific Discovery](https://mlsanity.com/paper/2508.14111/) (2025)
- [LLM-based Agentic Reasoning Frameworks: A Survey from Methods to Scenarios](https://mlsanity.com/paper/2508.17692/) (2025)
- [Autonomous Agents for Scientific Discovery: Orchestrating Scientists, Language, Code, and Physics](https://mlsanity.com/paper/2510.09901/) (2025)
- [BioDSA-1K: Benchmarking Data Science Agents for Biomedical Research](https://mlsanity.com/paper/2505.16100/) (2025)
- [BioinfoMCP: A Unified Platform Enabling MCP Interfaces in Agentic Bioinformatics](https://mlsanity.com/paper/2510.02139/) (2025)
- [A Systematic Review on the Generative AI Applications in Human Medical Genomics](https://mlsanity.com/paper/2508.20275/) (2025)
- [Biomedical reasoning in action: Multi-agent System for Auditable Biomedical Evidence Synthesis](https://mlsanity.com/paper/2510.05335/) (2025)
- [Generative Artificial Intelligence in Bioinformatics: A Systematic Review of Models, Applications, and Methodological Advances](https://mlsanity.com/paper/2511.03354/) (2025)

## Abstract (arXiv)

Creating end-to-end bioinformatics workflows requires diverse domain expertise, which poses challenges for both junior and senior researchers as it demands a deep understanding of both genomics concepts and computational techniques. While large language models (LLMs) provide some assistance, they often fall short in providing the nuanced guidance needed to execute complex bioinformatics tasks, and require expensive computing resources to achieve high performance. We thus propose a multi-agent system built on small language models, fine-tuned on bioinformatics data, and enhanced with retrieval augmented generation (RAG). Our system, BioAgents, enables local operation and personalization using proprietary data. We observe performance comparable to human experts on conceptual genomics tasks, and suggest next steps to enhance code generation capabilities.