instructor — quality + safety report

---
name: instructor
description: Extract structured data from LLM responses with Pydantic validation, retry failed extractions automatically, parse complex JSON with type safety, and stream partial results with Instructor - battle-tested structured output library
version: 1.0.0
author: Orchestra Research
license: MIT
tags: [Prompt Engineering, Instructor, Structured Output, Pydantic, Data Extraction, JSON Parsing, Type Safety, Validation, Streaming, OpenAI, Anthropic]
dependencies: [instructor, pydantic, openai, anthropic]
---

# 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 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

```python
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

```python
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

```python
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

```python
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

```python
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

```python
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

```python
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.

```python
# 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:**
1. LLM generates output
2. Pydantic validates
3. If invalid: Error message sent back to LLM
4. LLM tries again with error feedback
5. Repeats up to max_retries

### 4. Streaming

Stream partial results for real-time processing.

#### Streaming Partial Objects

```python
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

```python
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

```python
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

```python
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)

```python
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

```python
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

```python
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

```python
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

```python
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

```python
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

```python
from typing import Union

class TextContent(BaseModel):

… (truncated)