SKILL.md
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 instructorWith 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
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')
return v
@field_validator('attendees')
def validate_attendees(cls, v):
"""Ensure positive attendees."""
if v < 1:
raise ValueError('Must have at least 1 attendee')
return v
#### Model-Level Validation
from pydantic import model_validator
class DateRange(BaseModel):
start_date: str
end_date: str
@model_validator(mode='after')
def check_dates(self):
"""Ensure end_date is after start_date."""
from datetime import datetime
start = datetime.strptime(self.start_date, '%Y-%m-%d')
end = datetime.strptime(self.end_date, '%Y-%m-%d')
if end < start:
raise ValueError('end_date must be after start_date')
return self
### 3. Automatic Retrying
Instructor retries automatically when validation fails, providing error feedback to the LLM.
Retries up to 3 times if validation fails
user = client.messages.create(
model="claude-sonnet-4-5-20250929",
max_tokens=1024,
messages=[{
"role": "user",
"content": "Extract user from: John, age unknown"
}],
response_model=User,
max_retries=3 # Default is 3
)
If age can't be extracted, Instructor tells the LLM:
"Validation error: age - field required"
LLM tries again with better extraction
**How it works:**
- LLM generates output
- Pydantic validates
- If invalid: Error message sent back to LLM
- LLM tries again with error feedback
- Repeats up to max_retries
### 4. Streaming
Stream partial results for real-time processing.
#### Streaming Partial Objects
from instructor import Partial
class Story(BaseModel):
title: str
content: str
tags: list[str]
Stream partial updates as LLM generates
for partial_story in client.messages.create_partial(
model="claude-sonnet-4-5-20250929",
max_tokens=1024,
messages=[{
"role": "user",
"content": "Write a short sci-fi story"
}],
response_model=Story
):
print(f"Title: {partial_story.title}")
print(f"Content so far: {partial_story.content[:100]}...")
# Update UI in real-time
#### Streaming Iterables
class Task(BaseModel):
title: str
priority: str
Stream list items as they're generated
tasks = client.messages.create_iterable(
model="claude-sonnet-4-5-20250929",
max_tokens=1024,
messages=[{
"role": "user",
"content": "Generate 10 project tasks"
}],
response_model=Task
)
for task in tasks:
print(f"- {task.title} ({task.priority})")
# Process each task as it arrives
## Provider Configuration
### Anthropic Claude
import instructor
from anthropic import Anthropic
client = instructor.from_anthropic(
Anthropic(api_key="your-api-key")
)
Use with Claude models
response = client.messages.create(
model="claude-sonnet-4-5-20250929",
max_tokens=1024,
messages=[...],
response_model=YourModel
)
### OpenAI
from openai import OpenAI
client = instructor.from_openai(
OpenAI(api_key="your-api-key")
)
response = client.chat.completions.create(
model="gpt-4o-mini",
response_model=YourModel,
messages=[...]
)
### Local Models (Ollama)
from openai import OpenAI
Point to local Ollama server
client = instructor.from_openai(
OpenAI(
base_url="http://localhost:11434/v1",
api_key="ollama" # Required but ignored
),
mode=instructor.Mode.JSON
)
response = client.chat.completions.create(
model="llama3.1",
response_model=YourModel,
messages=[...]
)
## Common Patterns
### Pattern 1: Data Extraction from Text
class CompanyInfo(BaseModel):
name: str
founded_year: int
industry: str
employees: int
headquarters: str
text = """
Tesla, Inc. was founded in 2003. It operates in the automotive and energy
industry with approximately 140,000 employees. The company is headquartered
in Austin, Texas.
"""
company = client.messages.create(
model="claude-sonnet-4-5-20250929",
max_tokens=1024,
messages=[{
"role": "user",
"content": f"Extract company information from: {text}"
}],
response_model=CompanyInfo
)
### Pattern 2: Classification
class Category(str, Enum):
TECHNOLOGY = "technology"
FINANCE = "finance"
HEALTHCARE = "healthcare"
EDUCATION = "education"
OTHER = "other"
class ArticleClassification(BaseModel):
category: Category
confidence: float = Field(ge=0.0, le=1.0)
keywords: list[str]
classification = client.messages.create(
model="claude-sonnet-4-5-20250929",
max_tokens=1024,
messages=[{
"role": "user",
"content": "Classify this article: [article text]"
}],
response_model=ArticleClassification
)
### Pattern 3: Multi-Entity Extraction
class Person(BaseModel):
name: str
role: str
class Organization(BaseModel):
name: str
industry: str
class Entities(BaseModel):
people: list[Person]
organizations: list[Organization]
locations: list[str]
text = "Tim Cook, CEO of Apple, announced at the event in Cupertino..."
entities = client.messages.create(
model="claude-sonnet-4-5-20250929",
max_tokens=1024,
messages=[{
"role": "user",
"content": f"Extract all entities from: {text}"
}],
response_model=Entities
)
for person in entities.people:
print(f"{person.name} - {person.role}")
### Pattern 4: Structured Analysis
class SentimentAnalysis(BaseModel):
overall_sentiment: Sentiment
positive_aspects: list[str]
negative_aspects: list[str]
suggestions: list[str]
score: float = Field(ge=-1.0, le=1.0)
review = "The product works well but setup was confusing..."
analysis = client.messages.create(
model="claude-sonnet-4-5-20250929",
max_tokens=1024,
messages=[{
"role": "user",
"content": f"Analyze this review: {review}"
}],
response_model=SentimentAnalysis
)
### Pattern 5: Batch Processing
def extract_person(text: str) -> Person:
return client.messages.create(
model="claude-sonnet-4-5-20250929",
max_tokens=1024,
messages=[{
"role": "user",
"content": f"Extract person from: {text}"
}],
response_model=Person
)
texts = [
"John Doe is a 30-year-old engineer",
"Jane Smith, 25, works in marketing",
"Bob Johnson, age 40, software developer"
]
people = [extract_person(text) for text in texts]
## Advanced Features
### Union Types
from typing import Union
class TextContent(BaseModel):
type: str = "text"
content: str
class ImageContent(BaseModel):
type: str = "image"
url: HttpUrl
caption: str
class Post(BaseModel):
title: str
content: Union[TextContent, ImageContent] # Either type
LLM chooses appropriate type based on content
### Dynamic Models
from pydantic import create_model
Create model at runtime
DynamicUser = create_model(
'User',
name=(str, ...),
age=(int, Field(ge=0)),
email=(EmailStr, ...)
)
user = client.messages.create(
model="claude-sonnet-4-5-20250929",
max_tokens=1024,
messages=[...],
response_model=DynamicUser
)
### Custom Modes
For providers without native structured outputs
client = instructor.from_anthropic(
Anthropic(),
mode=instructor.Mode.JSON # JSON mode
)
Available modes:
- Mode.ANTHROPIC_TOOLS (recommended for Claude)
- Mode.JSON (fallback)
- Mode.TOOLS (OpenAI tools)
### Context Management
Single-use client
with instructor.from_anthropic(Anthropic()) as client:
result = client.messages.create(
model="claude-sonnet-4-5-20250929",
max_tokens=1024,
messages=[...],
response_model=YourModel
)
# Client closed automatically
## Error Handling
### Handling Validation Errors
from pydantic import ValidationError
try:
user = client.messages.create(
model="claude-sonnet-4-5-20250929",
max_tokens=1024,
messages=[...],
response_model=User,
max_retries=3
)
except ValidationError as e:
print(f"Failed after retries: {e}")
# Handle gracefully
except Exception as e:
print(f"API error: {e}")
### Custom Error Messages
class ValidatedUser(BaseModel):
name: str = Field(description="Full name, 2-100 characters")
age: int = Field(description="Age between 0 and 120", ge=0, le=120)
email: EmailStr = Field(description="Valid email address")
class Config:
# Custom error messages
json_schema_extra = {
"examples": [
{
"name": "John Doe",
"age": 30,
"email": "john@example.com"
}
]
}
## Best Practices
### 1. Clear Field Descriptions
❌ Bad: Vague
class Product(BaseModel):
name: str
price: float
✅ Good: Descriptive
class Product(BaseModel):
name: str = Field(description="Product name from the text")
price: float = Field(description="Price in USD, without currency symbol")
### 2. Use Appropriate Validation
✅ Good: Constrain values
class Rating(BaseModel):
score: int = Field(ge=1, le=5, description="Rating from 1 to 5 stars")
review: str = Field(min_length=10, description="Review text, at least 10 chars")
### 3. Provide Examples in Prompts
messages = [{
"role": "user",
"content": """Extract person info from: "John, 30, engineer"
Example format:
{
"name": "John Doe",
"age": 30,
"occupation": "engineer"
}"""
}]
### 4. Use Enums for Fixed Categories
✅ Good: Enum ensures valid values
class Status(str, Enum):
PENDING = "pending"
APPROVED = "approved"
REJECTED = "rejected"
class Application(BaseModel):
status: Status # LLM must choose from enum
### 5. Handle Missing Data Gracefully
class PartialData(BaseModel):
required_field: str
optional_field: Optional[str] = None
default_field: str = "default_value"