snap-stanford/Biomni

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Biomni: A General-Purpose Biomedical AI Agent

Overview

Biomni is a general-purpose biomedical AI agent designed to autonomously execute a wide range of research tasks across diverse biomedical subfields. By integrating cutting-edge large language model (LLM) reasoning with retrieval-augmented planning and code-based execution, Biomni helps scientists dramatically enhance research productivity and generate testable hypotheses.

Quick Start

Installation

Our software environment is massive and we provide a single setup.sh script to setup. Follow this file to setup the env first.

Then activate the environment E1:

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conda activate biomni_e1

then install the latest biomni package:

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pip install biomni --upgrade

Or install from the github source version.

Lastly, configure your API keys in bash profile ~/.bashrc:

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export ANTHROPIC_API_KEY="YOUR_API_KEY"
export OPENAI_API_KEY="YOUR_API_KEY" # optional if you just use Claude

Basic Usage

Once inside the environment, you can start using Biomni:

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from biomni.agent import A1

# Initialize the agent with data path, Data lake will be automatically downloaded on first run (~11GB)
agent = A1(path='./data', llm='claude-sonnet-4-20250514')

# Execute biomedical tasks using natural language
agent.go("Plan a CRISPR screen to identify genes that regulate T cell exhaustion, generate 32 genes that maximize the perturbation effect.")
agent.go("Perform scRNA-seq annotation at [PATH] and generate meaningful hypothesis")
agent.go("Predict ADMET properties for this compound: CC(C)CC1=CC=C(C=C1)C(C)C(=O)O")

🤝 Contributing to Biomni

Biomni is an open-science initiative that thrives on community contributions. We welcome:

🔧 New Tools: Specialized analysis functions and algorithms
📊 Datasets: Curated biomedical data and knowledge bases
💻 Software: Integration of existing biomedical software packages
📋 Benchmarks: Evaluation datasets and performance metrics
📚 Misc: Tutorials, examples, and use cases
🔧 Update existing tools: many current tools are not optimized - fix and replacements are welcome!

Check out this Contributing Guide on how to contribute to the Biomni ecosystem.

If you have particular tool/database/software in mind that you want to add, you can also submit to this form and the biomni team will implement them.

🔬 Call for Contributors: Help Build Biomni-E2

Biomni-E1 only scratches the surface of what’s possible in the biomedical action space.

Now, we’re building Biomni-E2 — a next-generation environment developed with and for the community.

We believe that by collaboratively defining and curating a shared library of standard biomedical actions, we can accelerate science for everyone.

Join us in shaping the future of biomedical AI agent.

Contributors with significant impact (e.g., 10+ significant & integrated tool contributions or equivalent) will be invited as co-authors on our upcoming paper in a top-tier journal or conference.
All contributors will be acknowledged in our publications.
More contributor perks…

Let’s build it together.

Tutorials and Examples

Biomni 101 - Basic concepts and first steps

More to come!

🌐 Web Interface

Experience Biomni through our no-code web interface at biomni.stanford.edu.

Release schedule

8 Real-world research task benchmark/leaderboard release
A tutorial on how to contribute to Biomni
A tutorial on baseline agents
Biomni A1+E1 release

Note

This release was frozen as of April 15 2025, so it differs from the current web platform.
Biomni itself is Apache 2.0-licensed, but certain integrated tools, databases, or software may carry more restrictive commercial licenses. Review each component carefully before any commercial use.

Cite Us

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@article{huang2025biomni,
  title={Biomni: A General-Purpose Biomedical AI Agent},
  author={Huang, Kexin and Zhang, Serena and Wang, Hanchen and Qu, Yuanhao and Lu, Yingzhou and Roohani, Yusuf and Li, Ryan and Qiu, Lin and Zhang, Junze and Di, Yin and others},
  journal={bioRxiv},
  pages={2025--05},
  year={2025},
  publisher={Cold Spring Harbor Laboratory}
}