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.
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:
conda activate biomni_e1
then install the biomni official pip package:
pip install biomni --upgrade
For the latest update, install from the github source version, or do:
pip install git+https://github.com/snap-stanford/Biomni.git@main
Lastly, configure your API keys in bash profile ~/.bashrc
:
export ANTHROPIC_API_KEY="YOUR_API_KEY"
export OPENAI_API_KEY="YOUR_API_KEY" # optional if you just use Claude
export AWS_BEARER_TOKEN_BEDROCK="YOUR_BEDROCK_API_KEY" # optional for AWS Bedrock models
export AWS_REGION="us-east-1" # optional, defaults to us-east-1 for Bedrock
Once inside the environment, you can start using Biomni:
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")
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.
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.
Biomni 101 - Basic concepts and first steps
More to come!
Experience Biomni through our no-code web interface at biomni.stanford.edu.
- 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
- 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.
@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}
}