Is it safe to let Biomni execute code on my system?

Biomni executes LLM-generated code to perform analysis. For security, it is highly recommended to run Biomni within isolated environments like Docker or virtual machines and review generated code for sensitive tasks.

Which LLMs are compatible with the Biomni skill?

While Anthropic Claude models like Sonnet and Opus are recommended for their reasoning quality, Biomni also supports OpenAI, Google Gemini, Azure OpenAI, Groq, and AWS Bedrock.

Does Biomni require a local database download?

Yes, Biomni utilizes an integrated biomedical knowledge lake of approximately 11GB, which is automatically downloaded to your specified path on first use to facilitate local reasoning and retrieval.

What kind of biomedical data can Biomni analyze?

Biomni can handle a wide variety of biological data types, including single-cell RNA-seq (h5ad files), GWAS summary statistics, SMILES strings for drug candidates, and genomic variant files.

Biomni Biomedical AI Agent

Name: Biomni Biomedical AI Agent
Author: Activer007

byActiver007

Data Science & ML

Automates complex biomedical research tasks including drug discovery, genomics analysis, and CRISPR screening design using an autonomous AI agent framework.

About

Biomni is an open-source biomedical AI agent framework designed to execute multi-step research tasks autonomously. Developed at Stanford's SNAP lab, it integrates sophisticated LLM reasoning with code execution and a massive 11GB biomedical database to handle specialized workflows from single-cell RNA-seq analysis to ADMET prediction. It is an essential tool for bioinformaticians and researchers needing to bridge the gap between high-level biological hypotheses and complex data processing pipelines across genomics, proteomics, and clinical analysis.

Key Features

Dynamic Python code generation and execution for data analysis
Cross-domain support for genomics, drug discovery, and lab protocol optimization
0 GitHub stars
Autonomous multi-step biological reasoning and task decomposition
Integrated access to 11GB of biomedical databases including Gene, Protein, and Clinical data
Built-in evaluation framework for benchmarking agent performance on biomedical tasks

Use Cases

Automating single-cell RNA-seq workflows from quality control to cell type annotation
Designing genome-wide CRISPR knockout screens and prioritizing genes based on pathway relevance
Predicting ADMET properties and identifying causal variants in GWAS interpretation

About

Key Features

Dynamic Python code generation and execution for data analysis
Cross-domain support for genomics, drug discovery, and lab protocol optimization
0 GitHub stars
Autonomous multi-step biological reasoning and task decomposition
Integrated access to 11GB of biomedical databases including Gene, Protein, and Clinical data
Built-in evaluation framework for benchmarking agent performance on biomedical tasks

Use Cases

Automating single-cell RNA-seq workflows from quality control to cell type annotation
Designing genome-wide CRISPR knockout screens and prioritizing genes based on pathway relevance
Predicting ADMET properties and identifying causal variants in GWAS interpretation