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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.
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.
Getting structured data from LLMs is hard. You need to:
- Write complex JSON schemas
- Handle validation errors
- Retry failed extractions
- Parse unstructured responses
- 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 |
pip install instructor
Or with your package manager:
uv add instructor poetry add instructor
Use the same code with any LLM provider:
import os # OpenAI client = instructor.from_provider("openai/gpt-4o") # Anthropic client = instructor.from_provider("anthropic/claude-sonnet-5") # Google client = instructor.from_provider("google/gemini-3.8-flash") # 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=os.environ["OPENAI_API_KEY"]) client = instructor.from_provider( "anthropic/claude-sonnet-5", api_key=os.environ["ANTHROPIC_API_KEY"] ) 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": "..."}], )
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, )
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)
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": "..."}], )
Trusted by over 100,000 developers and companies building AI applications:
- 3M+ monthly downloads
- 10K+ GitHub stars
- 1000+ community contributors
Companies using Instructor include teams at OpenAI, Google, Microsoft, AWS, and many YC startups.
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)
Instructor's simple API is available in many languages:
- Python - The original
- TypeScript - Full TypeScript support
- Ruby - Ruby implementation
- Go - Go implementation
- Elixir - Elixir implementation
- Rust - Rust implementation
- Documentation - Comprehensive guides
- Examples - Copy-paste recipes
- Blog - Tutorials and best practices
- Discord - Get help from the community
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.
We welcome contributions! Check out our good first issues to get started.
MIT License - see LICENSE for details.
Built by the Instructor community. Special thanks to Jason Liu and all contributors.