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 [email protected]"
    }],
    response_model=User
)

print(user.name)   # "John Doe"
print(user.age)    # 30
print(user.email)  # "[email protected]"

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, [email protected]"}]
)

Core Concepts

1. Response Models (Pydantic)

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

Basic Model

from pydantic import BaseModel, Field

class Article(BaseModel):
    title: str = Field(description="Article title")
    author: str = Field(description="Author name")
    word_count: int = Field(description="Number of words", gt=0)
    tags: list[str] = Field(description="List of relevant tags")

article = client.messages.create(
    model="claude-sonnet-4-5-20250929",
    max_tokens=1024,
    messages=[{
        "role": "user",
        "content": "Analyze this article: [article text]"
    }],
    response_model=Article
)

Benefits:

• Type safety with Python type hints
• Automatic validation (word_count > 0)
• Self-documenting with Field descriptions
• IDE autocomplete support

Nested Models

class Address(BaseModel):
    street: str
    city: str
    country: str

class Person(BaseModel):
    name: str
    age: int
    address: Address  # Nested model

person = client.messages.create(
    model="claude-sonnet-4-5-20250929",
    max_tokens=1024,
    messages=[{
        "role": "user",
        "content": "John lives at 123 Main St, Boston, USA"
    }],
    response_model=Person
)

print(person.address.city)  # "Boston"

Optional Fields

from typing import Optional

class Product(BaseModel):
    name: str
    price: float
    discount: Optional[float] = None  # Optional
    description: str = Field(default="No description")  # Default value

# LLM doesn't need to provide discount or description

Enums for Constraints

from enum import Enum

class Sentiment(str, Enum):
    POSITIVE = "positive"
    NEGATIVE = "negative"
    NEUTRAL = "neutral"

class Review(BaseModel):
    text: str
    sentiment: Sentiment  # Only these 3 values allowed

review = client.messages.create(
    model="claude-sonnet-4-5-20250929",
    max_tokens=1024,
    messages=[{
        "role": "user",
        "content": "This product is amazing!"
    }],
    response_model=Review
)

print(review.sentiment)  # Sentiment.POSITIVE

2. Validation

Pydantic validates LLM outputs automatically. If validation fails, Instructor retries.

Built-in Validators

from pydantic import Field, EmailStr, HttpUrl

class Contact(BaseModel):
    name: str = Field(min_length=2, max_length=100)
    age: int = Field(ge=0, le=120)  # 0 <= age <= 120
    email: EmailStr  # Validates email format
    website: HttpUrl  # Validates URL format

# If LLM provides invalid data, Instructor retries automatically

Custom Validators

from pydantic import field_validator

class Event(BaseModel):
    name: str
    date: str
    attendees: int

    @field_validator('date')
    def validate_date(cls, v):
        """Ensure date is in YYYY-MM-DD format."""
        import re
        if not re.match(r'\d{4}-\d{2}-\d{2}', v):
            raise ValueError('Date must be YYYY-MM-DD format'