instructor README

The project's README from github.com/567-labs/instructor. As one markdown file: README.md.

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.

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Use Instructor for fast extraction, reach for PydanticAI when you need agents. Instructor keeps schema-first flows simple and cheap. If your app needs richer agent runs, built-in observability, or shareable traces, try PydanticAI. PydanticAI is the official agent runtime from the Pydantic team, adding typed tools, replayable datasets, evals, and production dashboards while using the same Pydantic models. Dive into the PydanticAI docs to see how it extends Instructor-style workflows.

Why Instructor?

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

  1. Write complex JSON schemas
  2. Handle validation errors
  3. Retry failed extractions
  4. Parse unstructured responses
  5. 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-5.4-mini",
    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-5.4-mini")

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:

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:

Learn more

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.

instructor (567-labs/instructor)

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14K ·GitHub
Instructor
What is this?Instructor is a Python library that gets reliable structured JSON output from LLMs by defining Pydantic models instead of hand-writing JSON schemas and parsing logic.
Is it a library or a full agent framework?It's a lightweight library focused purely on structured extraction, not an agent framework. The authors explicitly point to PydanticAI if you need agent runtimes, tools, and observability.
How do I add it to a project?Install it via pip (or your package manager of choice), then define a Pydantic model and call the LLM client wrapped by Instructor to get a validated object back.
Which LLM providers does it work with?It works with every major LLM provider using the same code, since it wraps the provider's client and layers Pydantic validation on top.
What happens if the LLM returns something that doesn't match my schema?Instructor automatically retries the extraction, feeding the validation error message back to the model so it can correct itself.
Is this only for Python projects?The core library is Python, but the same Instructor-style API is also implemented in TypeScript, Ruby, Go, Elixir, and Rust.
Can it stream partial results as the LLM generates them?Yes, Instructor supports streaming partial objects as they are generated rather than waiting for the full response.
Can it handle nested or complex data structures?Yes, it supports extracting complex, nested object structures, not just flat fields.
How is it different from LangChain or LlamaIndex?Instructor focuses on just one job, structured extraction, making it lighter, faster, and easier to debug than full orchestration frameworks like LangChain or LlamaIndex.
How widely is this used in production?The project claims over 100,000 developers, 3M+ monthly downloads, 10K+ GitHub stars, and use by teams at OpenAI, Google, Microsoft, AWS, and various YC startups.
What are similar projects?pydantic-ai, pydantic, TypeLLM, langextract, cascadeflow, sqlmodel, TabNine, langfuse
Which topics is it in?structured-data, elixir, pydantic, validation, ruby, large-language-models, json, golang, sdk, openai, rust, api
Creator
567-labs
Size
77.4 MB
Stars
14K
Updated
CommitOct 10Releasev1.17.0Sep 09
Age
3y
Commits · 90 Days