— 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