Biomni

Overview

Biomni is an open-source biomedical AI agent framework from Stanford's SNAP lab that autonomously executes complex research tasks across biomedical domains. Use this skill when working on multi-step biological reasoning tasks, analyzing biomedical data, or conducting research spanning genomics, drug discovery, molecular biology, and clinical analysis.

Core Capabilities

Biomni excels at:

• Multi-step biological reasoning - Autonomous task decomposition and planning for complex biomedical queries

• Code generation and execution - Dynamic analysis pipeline creation for data processing

• Knowledge retrieval - Access to ~11GB of integrated biomedical databases and literature

• Cross-domain problem solving - Unified interface for genomics, proteomics, drug discovery, and clinical tasks

When to Use This Skill

Use biomni for:

• CRISPR screening - Design screens, prioritize genes, analyze knockout effects

• Single-cell RNA-seq - Cell type annotation, differential expression, trajectory analysis

• Drug discovery - ADMET prediction, target identification, compound optimization

• GWAS analysis - Variant interpretation, causal gene identification, pathway enrichment

• Clinical genomics - Rare disease diagnosis, variant pathogenicity, phenotype-genotype mapping

• Lab protocols - Protocol optimization, literature synthesis, experimental design

Quick Start

Installation and Setup

Install Biomni and configure API keys for LLM providers:

uv pip install biomni --upgrade

Configure API keys (store in .env file or environment variables):

export ANTHROPIC_API_KEY="your-key-here"

Optional: OpenAI, Azure, Google, Groq, AWS Bedrock keys

Use scripts/setup_environment.py for interactive setup assistance.

Basic Usage Pattern

from biomni.agent import A1

Initialize agent with data path and LLM choice

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

Execute biomedical task autonomously

agent.go("Your biomedical research question or task")

Save conversation history and results

agent.save_conversation_history("report.pdf")

Working with Biomni

1. Agent Initialization

The A1 class is the primary interface for biomni:

from biomni.agent import A1 from biomni.config import default_config

Basic initialization

agent = A1( path='./data', # Path to data lake (~11GB downloaded on first use) llm='claude-sonnet-4-20250514' # LLM model selection )

Advanced configuration

default_config.llm = "gpt-4" default_config.timeout_seconds = 1200 default_config.max_iterations = 50

Supported LLM Providers:

• Anthropic Claude (recommended): claude-sonnet-4-20250514 , claude-opus-4-20250514

• OpenAI: gpt-4 , gpt-4-turbo

• Azure OpenAI: via Azure configuration

• Google Gemini: gemini-2.0-flash-exp

• Groq: llama-3.3-70b-versatile

• AWS Bedrock: Various models via Bedrock API

See references/llm_providers.md for detailed LLM configuration instructions.

2. Task Execution Workflow

Biomni follows an autonomous agent workflow:

Step 1: Initialize agent

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

Step 2: Execute task with natural language query

result = agent.go(""" Design a CRISPR screen to identify genes regulating autophagy in HEK293 cells. Prioritize genes based on essentiality and pathway relevance. """)

Step 3: Review generated code and analysis

Agent autonomously:

- Decomposes task into sub-steps

- Retrieves relevant biological knowledge

- Generates and executes analysis code

- Interprets results and provides insights

Step 4: Save results

agent.save_conversation_history("autophagy_screen_report.pdf")

3. Common Task Patterns

CRISPR Screening Design

agent.go(""" Design a genome-wide CRISPR knockout screen for identifying genes affecting [phenotype] in [cell type]. Include:

1. sgRNA library design
2. Gene prioritization criteria
3. Expected hit genes based on pathway analysis """)

Single-Cell RNA-seq Analysis

agent.go(""" Analyze this single-cell RNA-seq dataset:

• Perform quality control and filtering
• Identify cell populations via clustering
• Annotate cell types using marker genes
• Conduct differential expression between conditions File path: [path/to/data.h5ad] """)

Drug ADMET Prediction

agent.go(""" Predict ADMET properties for these drug candidates: [SMILES strings or compound IDs] Focus on:

• Absorption (Caco-2 permeability, HIA)
• Distribution (plasma protein binding, BBB penetration)
• Metabolism (CYP450 interaction)
• Excretion (clearance)
• Toxicity (hERG liability, hepatotoxicity) """)

GWAS Variant Interpretation

agent.go(""" Interpret GWAS results for [trait/disease]:

• Identify genome-wide significant variants
• Map variants to causal genes
• Perform pathway enrichment analysis
• Predict functional consequences Summary statistics file: [path/to/gwas_summary.txt] """)

See references/use_cases.md for comprehensive task examples across all biomedical domains.

4. Data Integration

Biomni integrates ~11GB of biomedical knowledge sources:

• Gene databases - Ensembl, NCBI Gene, UniProt

• Protein structures - PDB, AlphaFold

• Clinical datasets - ClinVar, OMIM, HPO

• Literature indices - PubMed abstracts, biomedical ontologies

• Pathway databases - KEGG, Reactome, GO

Data is automatically downloaded to the specified path on first use.

5. MCP Server Integration

Extend biomni with external tools via Model Context Protocol:

MCP servers can provide:

- FDA drug databases

- Web search for literature

- Custom biomedical APIs

- Laboratory equipment interfaces

Configure MCP servers in .biomni/mcp_config.json

6. Evaluation Framework

Benchmark agent performance on biomedical tasks:

from biomni.eval import BiomniEval1

evaluator = BiomniEval1()

Evaluate on specific task types

score = evaluator.evaluate( task_type='crispr_design', instance_id='test_001', answer=agent_output )

Access evaluation dataset

dataset = evaluator.load_dataset()

Best Practices

Task Formulation

• Be specific - Include biological context, organism, cell type, conditions

• Specify outputs - Clearly state desired analysis outputs and formats

• Provide data paths - Include file paths for datasets to analyze

• Set constraints - Mention time/computational limits if relevant

Security Considerations

⚠️ Important: Biomni executes LLM-generated code with full system privileges. For production use:

• Run in isolated environments (Docker, VMs)

• Avoid exposing sensitive credentials

• Review generated code before execution in sensitive contexts

• Use sandboxed execution environments when possible

Performance Optimization

• Choose appropriate LLMs - Claude Sonnet 4 recommended for balance of speed/quality

• Set reasonable timeouts - Adjust default_config.timeout_seconds for complex tasks

• Monitor iterations - Track max_iterations to prevent runaway loops

• Cache data - Reuse downloaded data lake across sessions

Result Documentation

Always save conversation history for reproducibility

agent.save_conversation_history("results/project_name_YYYYMMDD.pdf")

Include in reports:

- Original task description

- Generated analysis code

- Results and interpretations

- Data sources used

Resources

References

Detailed documentation available in the references/ directory:

• api_reference.md

• Complete API documentation for A1 class, configuration, and evaluation

• llm_providers.md

• LLM provider setup (Anthropic, OpenAI, Azure, Google, Groq, AWS)

• use_cases.md

• Comprehensive task examples for all biomedical domains

Scripts

Helper scripts in the scripts/ directory:

• setup_environment.py

• Interactive environment and API key configuration

• generate_report.py

• Enhanced PDF report generation with custom formatting

External Resources

• GitHub: https://github.com/snap-stanford/biomni

• Web Platform: https://biomni.stanford.edu

• Paper: https://www.biorxiv.org/content/10.1101/2025.05.30.656746v1

• Model: https://huggingface.co/biomni/Biomni-R0-32B-Preview

• Evaluation Dataset: https://huggingface.co/datasets/biomni/Eval1

Troubleshooting

Common Issues

Data download fails

Manually trigger data lake download

agent = A1(path='./data', llm='your-llm')

First .go() call will download data

API key errors

Verify environment variables

echo $ANTHROPIC_API_KEY

Or check .env file in working directory

Timeout on complex tasks

from biomni.config import default_config default_config.timeout_seconds = 3600 # 1 hour

Memory issues with large datasets

• Use streaming for large files

• Process data in chunks

• Increase system memory allocation

Getting Help

For issues or questions:

• GitHub Issues: https://github.com/snap-stanford/biomni/issues

• Documentation: Check references/ files for detailed guidance

• Community: Stanford SNAP lab and biomni contributors