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Improved Biomni support
This commit is contained in:
Timothy Kassis
@@ -1,375 +1,316 @@
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---
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name: biomni
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description: "AI agent for autonomous biomedical task execution. CRISPR design, scRNA-seq, ADMET, GWAS, structure prediction, multi-omics, with automated planning/code generation, for complex workflows."
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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.
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---
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# Biomni
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## Overview
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Biomni is a general-purpose biomedical AI agent that autonomously executes research tasks across diverse biomedical subfields. Use Biomni to combine large language model reasoning with retrieval-augmented planning and code-based execution for scientific productivity and hypothesis generation. The system operates with an ~11GB biomedical knowledge base covering molecular, genomic, and clinical domains.
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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.
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## Core Capabilities
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Biomni excels at:
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1. **Multi-step biological reasoning** - Autonomous task decomposition and planning for complex biomedical queries
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2. **Code generation and execution** - Dynamic analysis pipeline creation for data processing
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3. **Knowledge retrieval** - Access to ~11GB of integrated biomedical databases and literature
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4. **Cross-domain problem solving** - Unified interface for genomics, proteomics, drug discovery, and clinical tasks
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## When to Use This Skill
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Use biomni for:
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- **CRISPR screening** - Design screens, prioritize genes, analyze knockout effects
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- **Single-cell RNA-seq** - Cell type annotation, differential expression, trajectory analysis
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- **Drug discovery** - ADMET prediction, target identification, compound optimization
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- **GWAS analysis** - Variant interpretation, causal gene identification, pathway enrichment
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- **Clinical genomics** - Rare disease diagnosis, variant pathogenicity, phenotype-genotype mapping
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- **Lab protocols** - Protocol optimization, literature synthesis, experimental design
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## Quick Start
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Initialize and use the Biomni agent with these basic steps:
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### Installation and Setup
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Biomni requires conda environment setup and API keys for LLM providers:
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```bash
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# Clone repository and set up environment
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git clone https://github.com/snap-stanford/biomni
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cd biomni
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bash setup.sh
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# Or install via pip
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conda activate biomni_e1
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pip install biomni --upgrade
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```
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Configure API keys (store in `.env` file or environment variables):
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```bash
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export ANTHROPIC_API_KEY="your-key-here"
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# Optional: OpenAI, Azure, Google, Groq, AWS Bedrock keys
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```
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Use `scripts/setup_environment.py` for interactive setup assistance.
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### Basic Usage Pattern
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```python
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from biomni.agent import A1
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# Initialize agent with data path and LLM model
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# Initialize agent with data path and LLM choice
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agent = A1(path='./data', llm='claude-sonnet-4-20250514')
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# Execute a biomedical research task
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agent.go("Your biomedical task description")
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# Execute biomedical task autonomously
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agent.go("Your biomedical research question or task")
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# Save conversation history and results
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agent.save_conversation_history("report.pdf")
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```
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The agent will autonomously decompose the task, retrieve relevant biomedical knowledge, generate and execute code, and provide results.
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## Working with Biomni
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## Installation and Setup
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### 1. Agent Initialization
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### Environment Preparation
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1. **Set up the conda environment:**
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- Follow instructions in `biomni_env/README.md` from the repository
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- Activate the environment: `conda activate biomni_e1`
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2. **Install the package:**
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```bash
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pip install biomni --upgrade
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```
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Or install from source:
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```bash
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git clone https://github.com/snap-stanford/biomni.git
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cd biomni
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pip install -e .
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```
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3. **Configure API keys:**
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Set up credentials via environment variables or `.env` file:
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```bash
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export ANTHROPIC_API_KEY="your-key-here"
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export OPENAI_API_KEY="your-key-here" # Optional
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```
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4. **Data initialization:**
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On first use, the agent will automatically download the ~11GB biomedical knowledge base.
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### LLM Provider Configuration
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Biomni supports multiple LLM providers. Configure the default provider using:
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The A1 class is the primary interface for biomni:
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```python
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from biomni.agent import A1
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from biomni.config import default_config
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# Set the default LLM model
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default_config.llm = "claude-sonnet-4-20250514" # Anthropic
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# default_config.llm = "gpt-4" # OpenAI
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# default_config.llm = "azure/gpt-4" # Azure OpenAI
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# default_config.llm = "gemini/gemini-pro" # Google Gemini
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# Set timeout (optional)
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default_config.timeout_seconds = 1200
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# Set data path (optional)
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default_config.data_path = "./custom/data/path"
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```
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Refer to `references/llm_providers.md` for detailed configuration options for each provider.
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## Core Biomedical Research Tasks
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### 1. CRISPR Screening and Design
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Execute CRISPR screening tasks including guide RNA design, off-target analysis, and screening experiment planning:
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```python
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agent.go("Design a CRISPR screening experiment to identify genes involved in cancer cell resistance to drug X")
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```
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The agent will:
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- Retrieve relevant gene databases
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- Design guide RNAs with specificity analysis
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- Plan experimental controls and readout strategies
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- Generate analysis code for screening results
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### 2. Single-Cell RNA-seq Analysis
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Perform comprehensive scRNA-seq analysis workflows:
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```python
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agent.go("Analyze this 10X Genomics scRNA-seq dataset, identify cell types, and find differentially expressed genes between clusters")
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```
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Capabilities include:
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- Quality control and preprocessing
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- Dimensionality reduction and clustering
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- Cell type annotation using marker databases
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- Differential expression analysis
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- Pathway enrichment analysis
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### 3. Molecular Property Prediction (ADMET)
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Predict absorption, distribution, metabolism, excretion, and toxicity properties:
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```python
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agent.go("Predict ADMET properties for these drug candidates: [SMILES strings]")
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```
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The agent handles:
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- Molecular descriptor calculation
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- Property prediction using integrated models
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- Toxicity screening
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- Drug-likeness assessment
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### 4. Genomic Analysis
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Execute genomic data analysis tasks:
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```python
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agent.go("Perform GWAS analysis to identify SNPs associated with disease phenotype in this cohort")
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```
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Supports:
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- Genome-wide association studies (GWAS)
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- Variant calling and annotation
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- Population genetics analysis
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- Functional genomics integration
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### 5. Protein Structure and Function
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Analyze protein sequences and structures:
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```python
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agent.go("Predict the structure of this protein sequence and identify potential binding sites")
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```
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Capabilities:
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- Sequence analysis and domain identification
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- Structure prediction integration
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- Binding site prediction
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- Protein-protein interaction analysis
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### 6. Disease Diagnosis and Classification
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Perform disease classification from multi-omics data:
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```python
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agent.go("Build a classifier to diagnose disease X from patient RNA-seq and clinical data")
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```
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### 7. Systems Biology and Pathway Analysis
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Analyze biological pathways and networks:
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```python
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agent.go("Identify dysregulated pathways in this differential expression dataset")
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```
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### 8. Drug Discovery and Repurposing
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Support drug discovery workflows:
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```python
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agent.go("Identify FDA-approved drugs that could be repurposed for treating disease Y based on mechanism of action")
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```
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## Advanced Features
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### Custom Configuration per Agent
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Override global configuration for specific agent instances:
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```python
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# Basic initialization
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agent = A1(
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path='./project_data',
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llm='gpt-4o',
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timeout=1800
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path='./data', # Path to data lake (~11GB downloaded on first use)
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llm='claude-sonnet-4-20250514' # LLM model selection
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)
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# Advanced configuration
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default_config.llm = "gpt-4"
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default_config.timeout_seconds = 1200
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default_config.max_iterations = 50
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```
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### Conversation History and Reporting
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**Supported LLM Providers:**
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- Anthropic Claude (recommended): `claude-sonnet-4-20250514`, `claude-opus-4-20250514`
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- OpenAI: `gpt-4`, `gpt-4-turbo`
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- Azure OpenAI: via Azure configuration
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- Google Gemini: `gemini-2.0-flash-exp`
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- Groq: `llama-3.3-70b-versatile`
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- AWS Bedrock: Various models via Bedrock API
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Save execution traces as formatted PDF reports:
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See `references/llm_providers.md` for detailed LLM configuration instructions.
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### 2. Task Execution Workflow
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Biomni follows an autonomous agent workflow:
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```python
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# After executing tasks
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agent.save_conversation_history(
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output_path='./reports/experiment_log.pdf',
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format='pdf'
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# Step 1: Initialize agent
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agent = A1(path='./data', llm='claude-sonnet-4-20250514')
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# Step 2: Execute task with natural language query
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result = agent.go("""
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Design a CRISPR screen to identify genes regulating autophagy in
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HEK293 cells. Prioritize genes based on essentiality and pathway
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relevance.
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""")
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# Step 3: Review generated code and analysis
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# Agent autonomously:
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# - Decomposes task into sub-steps
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# - Retrieves relevant biological knowledge
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# - Generates and executes analysis code
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# - Interprets results and provides insights
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# Step 4: Save results
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agent.save_conversation_history("autophagy_screen_report.pdf")
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```
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### 3. Common Task Patterns
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#### CRISPR Screening Design
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```python
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agent.go("""
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Design a genome-wide CRISPR knockout screen for identifying genes
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affecting [phenotype] in [cell type]. Include:
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1. sgRNA library design
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2. Gene prioritization criteria
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3. Expected hit genes based on pathway analysis
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""")
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```
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#### Single-Cell RNA-seq Analysis
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```python
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agent.go("""
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Analyze this single-cell RNA-seq dataset:
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- Perform quality control and filtering
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- Identify cell populations via clustering
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- Annotate cell types using marker genes
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- Conduct differential expression between conditions
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File path: [path/to/data.h5ad]
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""")
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```
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#### Drug ADMET Prediction
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```python
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agent.go("""
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Predict ADMET properties for these drug candidates:
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[SMILES strings or compound IDs]
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Focus on:
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- Absorption (Caco-2 permeability, HIA)
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- Distribution (plasma protein binding, BBB penetration)
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- Metabolism (CYP450 interaction)
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- Excretion (clearance)
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- Toxicity (hERG liability, hepatotoxicity)
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""")
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```
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#### GWAS Variant Interpretation
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```python
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agent.go("""
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Interpret GWAS results for [trait/disease]:
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- Identify genome-wide significant variants
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- Map variants to causal genes
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- Perform pathway enrichment analysis
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- Predict functional consequences
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Summary statistics file: [path/to/gwas_summary.txt]
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""")
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```
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See `references/use_cases.md` for comprehensive task examples across all biomedical domains.
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### 4. Data Integration
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Biomni integrates ~11GB of biomedical knowledge sources:
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- **Gene databases** - Ensembl, NCBI Gene, UniProt
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- **Protein structures** - PDB, AlphaFold
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- **Clinical datasets** - ClinVar, OMIM, HPO
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- **Literature indices** - PubMed abstracts, biomedical ontologies
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- **Pathway databases** - KEGG, Reactome, GO
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Data is automatically downloaded to the specified `path` on first use.
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### 5. MCP Server Integration
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Extend biomni with external tools via Model Context Protocol:
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```python
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# MCP servers can provide:
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# - FDA drug databases
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# - Web search for literature
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# - Custom biomedical APIs
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# - Laboratory equipment interfaces
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# Configure MCP servers in .biomni/mcp_config.json
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```
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### 6. Evaluation Framework
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Benchmark agent performance on biomedical tasks:
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```python
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from biomni.eval import BiomniEval1
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evaluator = BiomniEval1()
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# Evaluate on specific task types
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score = evaluator.evaluate(
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task_type='crispr_design',
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instance_id='test_001',
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answer=agent_output
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)
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# Access evaluation dataset
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dataset = evaluator.load_dataset()
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```
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Requires one of: WeasyPrint, markdown2pdf, or Pandoc.
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### Model Context Protocol (MCP) Integration
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Extend agent capabilities with external tools:
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```python
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# Add MCP-compatible tools
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agent.add_mcp(config_path='./mcp_config.json')
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```
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MCP enables integration with:
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- Laboratory information management systems (LIMS)
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- Specialized bioinformatics databases
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- Custom analysis pipelines
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- External computational resources
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### Using Biomni-R0 (Specialized Reasoning Model)
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Deploy the 32B parameter Biomni-R0 model for enhanced biological reasoning:
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```bash
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# Install SGLang
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pip install "sglang[all]"
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# Deploy Biomni-R0
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python -m sglang.launch_server \
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--model-path snap-stanford/biomni-r0 \
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--port 30000 \
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--trust-remote-code
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```
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Then configure the agent:
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```python
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from biomni.config import default_config
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default_config.llm = "openai/biomni-r0"
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default_config.api_base = "http://localhost:30000/v1"
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```
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Biomni-R0 provides specialized reasoning for:
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- Complex multi-step biological workflows
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- Hypothesis generation and evaluation
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- Experimental design optimization
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- Literature-informed analysis
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## Best Practices
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### Task Specification
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Provide clear, specific task descriptions:
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✅ **Good:** "Analyze this scRNA-seq dataset (file: data.h5ad) to identify T cell subtypes, then perform differential expression analysis comparing activated vs. resting T cells"
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❌ **Vague:** "Analyze my RNA-seq data"
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### Data Organization
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Structure data directories for efficient retrieval:
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```
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project/
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├── data/ # Biomni knowledge base
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├── raw_data/ # Your experimental data
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├── results/ # Analysis outputs
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└── reports/ # Generated reports
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```
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### Iterative Refinement
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Use iterative task execution for complex analyses:
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```python
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# Step 1: Exploratory analysis
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agent.go("Load and perform initial QC on the proteomics dataset")
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# Step 2: Based on results, refine analysis
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agent.go("Based on the QC results, remove low-quality samples and normalize using method X")
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# Step 3: Downstream analysis
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agent.go("Perform differential abundance analysis with adjusted parameters")
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```
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### Task Formulation
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- **Be specific** - Include biological context, organism, cell type, conditions
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- **Specify outputs** - Clearly state desired analysis outputs and formats
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- **Provide data paths** - Include file paths for datasets to analyze
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- **Set constraints** - Mention time/computational limits if relevant
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### Security Considerations
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⚠️ **Important**: Biomni executes LLM-generated code with full system privileges. For production use:
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- Run in isolated environments (Docker, VMs)
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- Avoid exposing sensitive credentials
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- Review generated code before execution in sensitive contexts
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- Use sandboxed execution environments when possible
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**CRITICAL:** Biomni executes LLM-generated code with full system privileges. For production use:
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### Performance Optimization
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- **Choose appropriate LLMs** - Claude Sonnet 4 recommended for balance of speed/quality
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- **Set reasonable timeouts** - Adjust `default_config.timeout_seconds` for complex tasks
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- **Monitor iterations** - Track `max_iterations` to prevent runaway loops
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- **Cache data** - Reuse downloaded data lake across sessions
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1. **Use sandboxed environments:** Deploy in Docker containers or VMs with restricted permissions
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2. **Validate sensitive operations:** Review code before execution for file access, network calls, or credential usage
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3. **Limit data access:** Restrict agent access to only necessary data directories
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||||
4. **Monitor execution:** Log all executed code for audit trails
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### Result Documentation
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||||
```python
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# Always save conversation history for reproducibility
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||||
agent.save_conversation_history("results/project_name_YYYYMMDD.pdf")
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||||
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||||
Never run Biomni with:
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||||
- Unrestricted file system access
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||||
- Direct access to sensitive credentials
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||||
- Network access to production systems
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||||
- Elevated system privileges
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||||
# Include in reports:
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# - Original task description
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||||
# - Generated analysis code
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||||
# - Results and interpretations
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||||
# - Data sources used
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||||
```
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||||
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||||
### Model Selection Guidelines
|
||||
## Resources
|
||||
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||||
Choose models based on task complexity:
|
||||
### References
|
||||
Detailed documentation available in the `references/` directory:
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||||
|
||||
- **Claude Sonnet 4:** Recommended for most biomedical tasks, excellent biological reasoning
|
||||
- **GPT-4/GPT-4o:** Strong general capabilities, good for diverse tasks
|
||||
- **Biomni-R0:** Specialized for complex biological reasoning, multi-step workflows
|
||||
- **Smaller models:** Use for simple, well-defined tasks to reduce cost
|
||||
- **`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
|
||||
|
||||
## Evaluation and Benchmarking
|
||||
### Scripts
|
||||
Helper scripts in the `scripts/` directory:
|
||||
|
||||
Biomni-Eval1 benchmark contains 433 evaluation instances across 10 biological tasks:
|
||||
- **`setup_environment.py`** - Interactive environment and API key configuration
|
||||
- **`generate_report.py`** - Enhanced PDF report generation with custom formatting
|
||||
|
||||
- GWAS analysis
|
||||
- Disease diagnosis
|
||||
- Gene detection and classification
|
||||
- Molecular property prediction
|
||||
- Pathway analysis
|
||||
- Protein function prediction
|
||||
- Drug response prediction
|
||||
- Variant interpretation
|
||||
- Cell type annotation
|
||||
- Biomarker discovery
|
||||
|
||||
Use the benchmark to:
|
||||
- Evaluate custom agent configurations
|
||||
- Compare LLM providers for specific tasks
|
||||
- Validate analysis pipelines
|
||||
### 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
|
||||
|
||||
**Issue:** Data download fails or times out
|
||||
**Solution:** Manually download the knowledge base or increase timeout settings
|
||||
**Data download fails**
|
||||
```python
|
||||
# Manually trigger data lake download
|
||||
agent = A1(path='./data', llm='your-llm')
|
||||
# First .go() call will download data
|
||||
```
|
||||
|
||||
**Issue:** Package dependency conflicts
|
||||
**Solution:** Some optional dependencies cannot be installed by default due to conflicts. Install specific packages manually and uncomment relevant code sections as documented in the repository
|
||||
**API key errors**
|
||||
```bash
|
||||
# Verify environment variables
|
||||
echo $ANTHROPIC_API_KEY
|
||||
# Or check .env file in working directory
|
||||
```
|
||||
|
||||
**Issue:** LLM API errors
|
||||
**Solution:** Verify API key configuration, check rate limits, ensure sufficient credits
|
||||
**Timeout on complex tasks**
|
||||
```python
|
||||
from biomni.config import default_config
|
||||
default_config.timeout_seconds = 3600 # 1 hour
|
||||
```
|
||||
|
||||
**Issue:** Memory errors with large datasets
|
||||
**Solution:** Process data in chunks, use data subsampling, or deploy on higher-memory instances
|
||||
**Memory issues with large datasets**
|
||||
- Use streaming for large files
|
||||
- Process data in chunks
|
||||
- Increase system memory allocation
|
||||
|
||||
### Getting Help
|
||||
|
||||
For detailed troubleshooting:
|
||||
- Review the Biomni GitHub repository issues
|
||||
- Check `references/api_reference.md` for detailed API documentation
|
||||
- Consult `references/task_examples.md` for comprehensive task patterns
|
||||
|
||||
## Resources
|
||||
|
||||
### references/
|
||||
Detailed reference documentation for advanced usage:
|
||||
|
||||
- **api_reference.md:** Complete API documentation for A1 agent, configuration objects, and utility functions
|
||||
- **llm_providers.md:** Comprehensive guide for configuring all supported LLM providers (Anthropic, OpenAI, Azure, Gemini, Groq, Ollama, AWS Bedrock)
|
||||
- **task_examples.md:** Extensive collection of biomedical task examples with code patterns
|
||||
|
||||
### scripts/
|
||||
Helper scripts for common operations:
|
||||
|
||||
- **setup_environment.py:** Automated environment setup and validation
|
||||
- **generate_report.py:** Enhanced PDF report generation with custom formatting
|
||||
|
||||
Load reference documentation as needed:
|
||||
```python
|
||||
# Claude can read reference files when needed for detailed information
|
||||
# Example: "Check references/llm_providers.md for Azure OpenAI configuration"
|
||||
```
|
||||
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
|
||||
|
||||
Reference in New Issue
Block a user