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Biomni: A General-Purpose Biomedical AI Agent
Open accessAbstract
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, micro-biome 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.
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“In scientific discovery, agents dynamically compose and execute hypothesis-testing pipelines spanning simulations, lab protocols, and data analysis [61, 62, 63, 64].”
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“El Agente Q [449] and Biomni [64] exemplify hierarchical memory: short-lived tool outputs are cached in an episodic buffer, while structured artifacts (plasmid maps, optimized geometries, fitted models) are written to durable file stores that subsequent agent steps can re-load.”
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“Biomni's action-discovery agent [64] mines tens of thousands of bioRxiv papers to populate a unified tool registry across 25 biomedical subfields”
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“In biology, Biomni [64] generates end-to-end molecular-cloning protocols that human reviewers rated comparable to a senior Stanford postdoc”
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Metadata
- Authors
- Huang, K., Zhang, S., Wang, H., Qu, Y., Lu, Y., Roohani, Y., Li, R., Qiu, L., Li, G., Zhang, J., Yin, D., Marwaha, S., Carter, J. N., Zhou, X., Wheeler, M., Bernstein, J. A., Wang, M., He, P., Zhou, J., Snyder, M., Cong, L., Regev, A., & Leskovec, J.
- Affiliations
- Stanford University (US)
- Genentech (US)
- Stanford Medicine (US)
- Arc Research Institute (US)
- University of Washington (US)
- Princeton University (US)
- University of California, San Francisco (US)
- Corresponding author
- Huang, Kexin
- ORCID
- Venue
- bioRxiv (Cold Spring Harbor Laboratory)
- Publisher
- openRxiv
- Published
- 2025-06-02
- PMC
- PMC12157518
- OpenAlex
- W4411005431
- Cited by
- 125
- References
- 43
- Citations by year
- 2026: 101 · 2025: 24
- Type
- posted content
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- PubMed (publishedVersion)
- PubMed Central (submittedVersion, cc-by)
- Funders
- Stanford Data Applications Initiative
- Wu Tsai Neurosciences Institute
- Stanford Institute for Human-Centered AI
- Chan Zuckerberg Initiative
- Amazon
- Genentech
- GSK
- Hitachi
- SAP
- UCB
- Stanford Bio-X fellowship
- National Institute of Neurological Disorders and Stroke (U01NS134358)
- Topics
- Health Sciences › Medicine › Health Informatics › Biomedical Text Mining and Ontologies
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Social Sciences › Decision Sciences › Information Systems and Management › Scientific Computing and Data Management
- Keywords
- large language model reasoning, gene prioritization, drug repurposing, rare disease diagnosis, microbiome analysis, gene cloning
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