Biomni: A General-Purpose Biomedical AI Agent

Biomni: универсальный биомедицинский агент на основе ИИ
Aviv Regev, Xin‐Fu Zhou, Yingzhou Lu, Junze Zhang, M Snyder, Jure Leskovec, Matthew T. Wheeler, Le Cong, Kexin Huang, Shruti Marwaha, Jonathan A. Bernstein, Hanchen Wang, Jingtian Zhou, Yusuf Roohani, Yuanhao Qu, Di Yin, Serena Zhang, Ryan J. Li, Lin Qiu, G. L. Li, Jennefer N. Carter, Mengdi Wang, Peng He
2025-06-02

BiomniLLM reasoning and code-based executionaction discovery agentbiomedical AI agentretrieval-augmented planning
Biomedical research underpins progress in our understanding of human health and disease, drug discovery, and clinical care. However, with the growth of complex lab experiments, large datasets, many analytical tools, and expansive literature, biomedical research is increasingly constrained by repetitive and fragmented workflows that slow discovery and limit innovation, underscoring the need for a fundamentally new way to scale scientific expertise. Here, we introduce Biomni, a general-purpose biomedical AI agent designed to autonomously execute a wide spectrum of research tasks across diverse biomedical subfields. To systematically map the biomedical action space, Biomni first employs an action discovery agent to create the first unified agentic environment - mining essential tools, databases, and protocols from tens of thousands of publications across 25 biomedical domains. Built on this foundation, Biomni features a generalist agentic architecture that integrates large language model (LLM) reasoning with retrieval-augmented planning and code-based execution, enabling it to dynamically compose and carry out complex biomedical workflows - entirely without relying on predefined templates or rigid task flows. Systematic benchmarking demonstrates that Biomni achieves strong generalization across heterogeneous biomedical tasks - including causal gene prioritization, drug repurposing, rare disease diagnosis, microbiome analysis, and molecular cloning - without any task-specific prompt tuning. Real-world case studies further showcase Biomni's ability to interpret complex, multi-modal biomedical datasets and autonomously generate experimentally testable protocols. Biomni envisions a future where virtual AI biologists operate alongside and augment human scientists to dramatically enhance research productivity, clinical insight, and healthcare. Biomni is ready to use at https://biomni.stanford.edu, and we invite scientists to explore its capabilities, stress-test its limits, and co-create the next era of biomedical discoveries.
1
An action discovery agent was used to create a unified agentic environment by mining essential tools, databases, and protocols from tens of thousands of publications across 25 biomedical domains.
2
Biomni is a general-purpose biomedical AI agent that autonomously executes a wide spectrum of research tasks across diverse biomedical subfields.
3
Biomni's architecture integrates LLM reasoning with retrieval-augmented planning and code-based execution, enabling dynamic composition and execution of complex biomedical workflows without predefined templates.
4
Real-world case studies demonstrate Biomni can interpret complex, multi-modal biomedical datasets and autonomously generate experimentally testable protocols.
5
Systematic benchmarking shows Biomni generalizes strongly across heterogeneous biomedical tasks (causal gene prioritization, drug repurposing, rare disease diagnosis, microbiome analysis, molecular cloning) without task-specific prompt tuning.

Biomni, a general-purpose biomedical AI agent and its unified agentic environment for executing biomedical research tasks

Autonomous composition and execution of diverse biomedical workflows (including action discovery, LLM-based reasoning, retrieval-augmented planning, and code-based execution) to generalize across tasks such as gene prioritization, drug repurposing, rare disease diagnosis, microbiome analysis, and molecular cloning

Publication Details
Publication Date
2025-06-02
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Authors
Aviv Regev
Xin‐Fu Zhou
Yingzhou Lu
Junze Zhang
M Snyder
Jure Leskovec
Matthew T. Wheeler
Le Cong
Kexin Huang
Shruti Marwaha
Jonathan A. Bernstein
Hanchen Wang
Jingtian Zhou
Yusuf Roohani
Yuanhao Qu
Di Yin
Serena Zhang
Ryan J. Li
Lin Qiu
G. L. Li
Jennefer N. Carter
Mengdi Wang
Peng He
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