# Biomni: A General-Purpose Biomedical AI Agent for Research

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Biomni is a general-purpose biomedical AI agent designed to autonomously execute a wide range of research tasks across diverse biomedical subfields. It integrates cutting-edge large language model reasoning with retrieval-augmented planning and code-based execution. This powerful tool helps scientists significantly enhance research productivity and generate testable hypotheses.

GitHub: https://github.com/snap-stanford/Biomni
OSRepos URL: https://osrepos.com/repo/snap-stanford-biomni

## Summary

Biomni is a general-purpose biomedical AI agent designed to autonomously execute a wide range of research tasks across diverse biomedical subfields. It integrates cutting-edge large language model reasoning with retrieval-augmented planning and code-based execution. This powerful tool helps scientists significantly enhance research productivity and generate testable hypotheses.

## Topics

- agent
- ai
- biomedicine
- python
- llm
- research
- bioinformatics
- stanford

## Repository Information

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## Content

## Introduction
Biomni is an innovative, general-purpose biomedical AI agent developed by SNAP Stanford. It is designed to autonomously execute a wide range of research tasks across diverse biomedical subfields, significantly enhancing research productivity. By integrating cutting-edge large language model (LLM) reasoning with retrieval-augmented planning and code-based execution, Biomni empowers scientists to generate testable hypotheses and streamline complex workflows.

## Installation
To get started with Biomni, follow these steps for installation and environment setup.
First, set up the environment by following the instructions in the `biomni_env/README.md` file within the repository.
Then, activate the environment:
bash
conda activate biomni_e1

Install the Biomni pip package:
bash
pip install biomni --upgrade

For the latest updates, install directly from the GitHub source:
bash
pip install git+https://github.com/snap-stanford/Biomni.git@main

Finally, configure your API keys for LLM providers like Anthropic or OpenAI. You can do this by creating a `.env` file or setting shell environment variables, as detailed in the official [Biomni repository](https://github.com/snap-stanford/Biomni){:target="_blank"}.

## Examples
Once installed and configured, Biomni can be used to execute biomedical tasks using natural language.
Here are some basic usage examples:
python
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 also provides a Gradio-based web interface for interactive use. You can launch it with:
python
from biomni.agent import A1

agent = A1(path='./data', llm='claude-sonnet-4-20250514')
agent.launch_gradio_demo()

Remember to install Gradio separately: `pip install "gradio>=5.0,<6.0"`.

## Why Use Biomni?
Biomni offers several compelling reasons for researchers and developers in the biomedical field:
*   **General-Purpose AI Agent**: It can handle a wide array of research tasks across various biomedical subfields, from experimental planning to data analysis.
*   **LLM-Powered Reasoning**: Leverages advanced large language models for intelligent planning, retrieval-augmented generation, and code-based execution.
*   **Enhanced Productivity**: Automates complex, multi-step research workflows, allowing scientists to focus on higher-level problem-solving.
*   **Know-How Library**: Integrates a curated collection of best practices, protocols, and troubleshooting guides, providing domain expertise on demand.
*   **Biomni-R0 Model**: Features a specialized reasoning model, Biomni-R0, built on Qwen-32B, optimized for tool use and complex biological problem-solving.
*   **Biomni-Eval1 Benchmark**: Provides a comprehensive evaluation benchmark for assessing biological reasoning capabilities across 10 diverse tasks.
*   **Community-Driven Development**: Welcomes contributions for new tools, datasets, software integrations, benchmarks, and know-how documents, fostering an open-science ecosystem.

## Links
Explore Biomni further through these official resources:
*   **GitHub Repository**: [https://github.com/snap-stanford/Biomni](https://github.com/snap-stanford/Biomni){:target="_blank"}
*   **Official Web UI**: [https://biomni.stanford.edu](https://biomni.stanford.edu){:target="_blank"}
*   **Research Paper**: [https://www.biorxiv.org/content/10.1101/2025.05.30.656746v1](https://www.biorxiv.org/content/10.1101/2025.05.30.656746v1){:target="_blank"}
*   **Join Slack**: [https://join.slack.com/t/biomnigroup/shared_invite/zt-3avks4913-dotMBt8D_apQnJ3mG~ak6Q](https://join.slack.com/t/biomnigroup/shared_invite/zt-3avks4913-dotMBt8D_apQnJ3mG~ak6Q){:target="_blank"}
*   **Follow on X**: [https://x.com/ProjectBiomni](https://x.com/ProjectBiomni){:target="_blank"}
*   **Follow on LinkedIn**: [https://www.linkedin.com/company/project-biomni](https://www.linkedin.com/company/project-biomni){:target="_blank"}