biomni

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# 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

From Zaoqu-Liu/ScienceClaw, MIT.

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