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Structured outputs with LiteLLM, a complete guide w/ instructor¶

LiteLLM provides a unified interface for multiple LLM providers, making it easy to switch between different models and providers. This guide shows you how to use Instructor with LiteLLM for type-safe, validated responses across various LLM providers.

Quick Start¶

Install Instructor with LiteLLM support:

pip install "instructor[litellm]"

Simple User Example (Sync)¶

from litellm import completion
import instructor
from pydantic import BaseModel

# Enable instructor patches
client = instructor.from_litellm(completion)

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

# Create structured output
user = client.completion(
    model="gpt-3.5-turbo",  # Can use any supported model
    messages=[
        {"role": "user", "content": "Extract: Jason is 25 years old"},
    ],
    response_model=User,
)

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

Simple User Example (Async)¶

from litellm import acompletion
import instructor
from pydantic import BaseModel
import asyncio

# Enable instructor patches for async
client = instructor.from_litellm(acompletion)

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

async def extract_user():
    user = await client.acompletion(
        model="gpt-3.5-turbo",
        messages=[
            {"role": "user", "content": "Extract: Jason is 25 years old"},
        ],
        response_model=User,
    )
    return user

# Run async function
user = asyncio.run(extract_user())
print(user)  # User(name='Jason', age=25)

Updates and Compatibility¶

Instructor maintains compatibility with LiteLLM's latest releases. Check the changelog for updates.

Note: Always verify provider-specific features and limitations in their respective documentation before implementation.