Instructor Has a Free API You Should Know About

Instructor is a library for getting structured, validated outputs from LLMs. Instead of parsing messy text, define a Pydantic model and get a typed object back — every time.

Why Instructor Fixes LLM Output

A developer was using regex to parse LLM responses into JSON. It broke 30% of the time — hallucinated fields, wrong types, missing data. Instructor guarantees structured output with automatic retries.

Key Features:

• Structured Output — Get Pydantic models from any LLM
• Validation — Automatic retry on validation errors
• Streaming — Stream partial objects as they're generated
• Multi-Provider — OpenAI, Anthropic, Cohere, Mistral, local models
• Type-Safe — Full IDE support with type inference

Quick Start

pip install instructor

import instructor
from openai import OpenAI
from pydantic import BaseModel

client = instructor.from_openai(OpenAI())

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

user = client.chat.completions.create(
    model="gpt-4",
    response_model=User,
    messages=[{"role": "user", "content": "Extract: John is 30, email [e-mail]"}]
)

print(user.name)   # "John"
print(user.age)    # 30
print(user.email)  # "[e-mail]"

Complex Extraction

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

class Company(BaseModel):
    name: str
    employees: int
    address: Address
    tags: list[str]

company = client.chat.completions.create(
    model="gpt-4",
    response_model=Company,
    messages=[{"role": "user", "content": article_text}]
)

Why Choose Instructor

1. Guaranteed structure — no more parsing failures
2. Validation + retries — automatic error correction
3. Any provider — works with all major LLMs

Check out Instructor docs to get started.

---

Building AI data pipelines? Check out my Apify actors or email [e-mail] for custom solutions.