Instructor

Overview

Instructor is the most popular Python library for extracting structured data from Large Language Models (LLMs), with over 3 million monthly downloads, 11k+ GitHub stars, and 100+ contributors. It patches the OpenAI Python SDK to add a response_model parameter that maps LLM outputs to Pydantic models.

Features

• Type-Safe Extraction: Built on Pydantic for automatic validation, type safety, and IDE support
• Multi-Provider Support: Works with OpenAI GPT models, Anthropic Claude, Google Gemini, open source models via Ollama, DeepSeek, and 15+ other providers
• Automatic Validation: Ensures LLM outputs conform to your defined schema
• Retry Logic: Automatically retries failed extractions with error feedback
• Streaming Support: Process responses in real-time as they're generated
• Zero-Cost Abstraction: Simple patch over existing SDK without performance overhead

Installation

pip install instructor                    # OpenAI by default
pip install "instructor[anthropic]"       # For Claude
pip install "instructor[google-genai]"    # For Gemini

Basic Usage

import instructor
from pydantic import BaseModel

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

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)

Use Cases

• Extracting structured information from unstructured text
• Building reliable data pipelines with LLMs
• Creating type-safe API responses from AI models
• Parsing and validating complex nested data structures

Pricing

Free and open-source under the MIT license.