instructor

# Instructor: Structured LLM Outputs ## When to Use This Skill Use Instructor when you need to: - **Extract structured data** from LLM responses reliably - **Validate outputs** against Pydantic schemas automatically - **Retry failed extractions** with automatic error handling - **Parse complex JSON** with type safety and validation - **Stream partial results** for real-time processing - **Support multiple LLM providers** with consistent API **GitHub Stars**: 15,000+ | **Battle-tested**: 100,000+ developers ## Installation ```bash # Base installation pip install instructor # With specific providers pip install "instructor[anthropic]" # Anthropic Claude pip install "instructor[openai]" # OpenAI pip install "instructor[all]" # All providers ``` ## Quick Start ### Basic Example: Extract User Data ```python import instructor from pydantic import BaseModel from anthropic import Anthropic # Define output structure class User(BaseModel): name: str age: int email: str # Create instructor client client = instructor.from_anthropic(Anthropic()) # Extract structured data user = client.messages.create( model="claude-sonnet-4-5-20250929", max_tokens=1024, messages=[{ "role": "user", "content": "John Doe is 30 years old. His email is john@example.com" }], response_model=User ) print(user.name) # "John Doe" print(user.age) # 30 print(user.email) # "john@example.com" ``` ### With OpenAI ```python from openai impor...