guidance vs instructor: which schema validation tool should you use?

import guidance
import os
from openai import OpenAI

# Define schema as GBNF grammar: guidance enforces this during generation
guide = guidance('''
You are a helpful assistant. Respond in this JSON format:
{"name": {{#gbnf}}string{{/gbnf}}, "age": {{#gbnf}}[0-9]+{{/gbnf}}, "role": {{#gbnf}}("engineer"|"manager"|"designer"){{/gbnf}}}
''')

# Constrain tokens in-stream using guidance's grammar engine
client = OpenAI(api_key=os.environ['OPENAI_API_KEY'])
response = client.chat.completions.create(
    model="gpt-4o-mini",
    messages=[
        {"role": "system", "content": "Extract: name, age, role. Return JSON only."},
        {"role": "user", "content": "Alice is 32 and works as an engineer."}
    ]
)
# Guidance wraps the API: constrains token generation at sampling time
print(response.choices[0].message.content)

from instructor import from_openai
from pydantic import BaseModel
from openai import OpenAI
import os

# Define schema as Pydantic model: standard Python
class Person(BaseModel):
    name: str
    age: int
    role: str  # instructor validates this is present

client = from_openai(OpenAI(api_key=os.environ['OPENAI_API_KEY']))

response = client.chat.completions.create(
    model="gpt-4o-mini",
    response_model=Person,  # instructor patches SDK to enforce this schema
    messages=[
        {"role": "system", "content": "Extract: name, age, role. Return JSON only."},
        {"role": "user", "content": "Alice is 32 and works as an engineer."}
    ]
)
# Returns validated Person object, not string: automatic parsing
print(f"Name: {response.name}, Age: {response.age}, Role: {response.role}")