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---
name: biomni
description: Autonomous biomedical AI agent framework for executing complex research tasks across genomics, drug discovery, molecular biology, and clinical analysis. Use this skill when conducting multi-step biomedical research including CRISPR screening design, single-cell RNA-seq analysis, ADMET prediction, GWAS interpretation, rare disease diagnosis, or lab protocol optimization. Leverages LLM reasoning with code execution and integrated biomedical databases.
---
# 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:
1. **Multi-step biological reasoning** - Autonomous task decomposition and planning for complex biomedical queries
2. **Code generation and execution** - Dynamic analysis pipeline creation for data processing
3. **Knowledge retrieval** - Access to ~11GB of integrated biomedical databases and literature
4. **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:
```bash
uv pip install biomni --upgrade
```
Configure API keys (store in `.env` file or environment variables):
```bash
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
```python
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:
```python
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:
```python
# 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
```python
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
```python
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
```python
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
```python
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:
```python
# 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:
```python
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
```python
# 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**
```python
# Manually trigger data lake download
agent = A1(path='./data', llm='your-llm')
# First .go() call will download data
```
**API key errors**
```bash
# Verify environment variables
echo $ANTHROPIC_API_KEY
# Or check .env file in working directory
```
**Timeout on complex tasks**
```python
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