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

# 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

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

from openai import OpenAI

client = instructor.from_openai(OpenAI())

user = client.chat.completions.create(
    model="gpt-4o-mini",
    response_model=User,
    messages=[{"role": "user", "content": "Extract: Alice, 25, alice@email.com"}]
)

Core Concepts

1. Response Models (Pydantic)

Response models define the structure and validation rules for LLM outputs.

Basic Model

from pydantic import BaseModel, Field