instructor

Instructor: Structured Outputs for LLMs

Get reliable JSON from any LLM. Built on Pydantic for validation, type safety, and IDE support.

import instructor
from pydantic import BaseModel


# Define what you want
class User(BaseModel):
    name: str
    age: int


# Extract it from natural language
client = instructor.from_provider("openai/gpt-4o-mini")
user = client.chat.completions.create(
    response_model=User,
    messages=[{"role": "user", "content": "John is 25 years old"}],
)

print(user)  # User(name='John', age=25)

That's it. No JSON parsing, no error handling, no retries. Just define a model and get structured data.

Why Instructor?

Getting structured data from LLMs is hard. You need to:

• Write complex JSON schemas
• Handle validation errors
• Retry failed extractions
• Parse unstructured responses
• Deal with different provider APIs

Instructor handles all of this with one simple interface:

Without Instructor

With Instructor

response = openai.chat.completions.create( model="gpt-4", messages=[{"role": "user", "content": "..."}], tools=[ { "type": "function", "function": { "name": "extract_user", "parameters": { "type": "object", "properties": { "name": {"type": "string"}, "age": {"type": "integer"}, }, }, }, } ], ) # Parse response tool_call = response.choices[0].message.tool_calls[0] user_data = json.loads(tool_call.function.arguments) # Validate manually if "name" not in user_data: # Handle error... pass

client = instructor.from_provider("openai/gpt-4") user = client.chat.completions.create( response_model=User, messages=[{"role": "user", "content": "..."}], ) # That's it! user is validated and typed

Install in seconds

pip install instructor

Or with your package manager:

uv add instructor
poetry add instructor

Works with every major provider

Use the same code with any LLM provider:

# OpenAI
client = instructor.from_provider("openai/gpt-4o")

# Anthropic
client = instructor.from_provider("anthropic/claude-3-5-sonnet")

# Google
client = instructor.from_provider("google/gemini-pro")

# Ollama (local)
client = instructor.from_provider("ollama/llama3.2")

# With API keys directly (no environment variables needed)
client = instructor.from_provider("openai/gpt-4o", api_key="sk-...")
client = instructor.from_provider("anthropic/claude-3-5-sonnet", api_key="sk-ant-...")
client = instructor.from_provider("groq/llama-3.1-8b-instant", api_key="gsk_...")

# All use the same API!
user = client.chat.completions.create(
    response_model=User,
    messages=[{"role": "user", "content": "..."}],
)

Production-ready features

Automatic retries

Failed validations are automatically retried with the error message:

from pydantic import BaseModel, field_validator


class User(BaseModel):
    name: str
    age: int

    @field_validator('age')
    def validate_age(cls, v):
        if v < 0:
            raise ValueError('Age must be positive')
        return v


# Instructor automatically retries when validation fails
user = client.chat.completions.create(
    response_model=User,
    messages=[{"role": "user", "content": "..."}],
    max_retries=3,
)

Streaming support

Stream partial objects as they're generated:

from instructor import Partial

for partial_user in client.chat.completions.create(
    response_model=Partial[User],
    messages=[{"role": "user", "content": "..."}],
    stream=True,
):
    print(partial_user)
    # User(name=None, age=None)
    # User(name="John", age=None)
    # User(name="John", age=25)

Nested objects

Extract complex, nested data structures:

from typing import List


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


class User(BaseModel):
    name: str
    age: int
    addresses: List[Address]


# Instructor handles nested objects automatically
user = client.chat.completions.create(
    response_model=User,
    messages=[{"role": "user", "content": "..."}],
)

Used in production by

Trusted by over 100,000 developers and companies building AI applications:

• 3M+ monthly downloads
• 10K+ GitHub stars
• 1000+ community contributors

Companies using Instructor include teams at OpenAI, Google, Microsoft, AWS, and many YC startups.

Get started

Basic extraction

Extract structured data from any text:

from pydantic import BaseModel
import instructor

client = instructor.from_provider("openai/gpt-4o-mini")


class Product(BaseModel):
    name: str
    price: float
    in_stock: bool


product = client.chat.completions.create(
    response_model=Product,
    messages=[{"role": "user", "content": "iPhone 15 Pro, $999, available now"}],
)

print(product)
# Product(name='iPhone 15 Pro', price=999.0, in_stock=True)

Multiple languages

Instructor's simple API is available in many languages:

• Python - The original
• TypeScript - Full TypeScript support
• Ruby - Ruby implementation
• Go - Go implementation
• Elixir - Elixir implementation
• Rust - Rust implementation

Learn more

• Documentation - Comprehensive guides
• Examples - Copy-paste recipes
• Blog - Tutorials and best practices
• Discord - Get help from the community

Why use Instructor over alternatives?

vs Raw JSON mode: Instructor provides automatic validation, retries, streaming, and nested object support. No manual schema writing.

vs LangChain/LlamaIndex: Instructor is focused on one thing - structured extraction. It's lighter, faster, and easier to debug.

vs Custom solutions: Battle-tested by thousands of developers. Handles edge cases you haven't thought of yet.

Contributing

We welcome contributions! Check out our good first issues to get started.

License

MIT License - see LICENSE for details.

Built by the Instructor community. Special thanks to Jason Liu and all contributors.