# Instructor — Structured Outputs from LLMs

> Get structured, validated outputs from LLMs using Pydantic models. Works with OpenAI, Anthropic, Google, Ollama, and more. Retry logic, streaming, partial responses. 12.6K+ stars.

## Install

Save as a script file and run:

## Quick Use

```bash
pip install instructor
```

```python
import instructor
from pydantic import BaseModel
from openai import OpenAI

client = instructor.from_openai(OpenAI())

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

user = client.chat.completions.create(
    model="gpt-4o",
    response_model=User,
    messages=[{"role": "user", "content": "Extract: Jason is 25 and loves hiking."}],
)
print(user)  # User(name='Jason', age=25, bio='Loves hiking')
```

---

## Intro

Instructor makes it easy to get structured, validated data from LLMs. Define a Pydantic model, and Instructor handles prompting, parsing, validation, and retries automatically. Works with OpenAI, Anthropic, Google, Ollama, LiteLLM, and any OpenAI-compatible API. Supports streaming, partial responses, and complex nested schemas. 12,600+ GitHub stars, MIT licensed.

**Best for**: Developers who need reliable structured data extraction from LLMs — not free-text
**Works with**: OpenAI, Anthropic, Google, Ollama, LiteLLM, Mistral, Cohere

---

## Key Features

### Type-Safe Extraction
Define output schema with Pydantic, get validated objects back:
```python
class Contact(BaseModel):
    name: str
    email: str
    company: Optional[str] = None
```

### Automatic Retries
If the LLM returns invalid data, Instructor retries with the validation error as context:
```python
client.chat.completions.create(
    response_model=Contact,
    max_retries=3,  # auto-retry on validation failure
    ...
)
```

### Streaming & Partial Responses
Stream structured objects as they're generated — great for UIs.

### Multi-Provider
One API across OpenAI, Anthropic, Google Gemini, Ollama, Mistral, and more.

### Complex Schemas
Nested models, lists, enums, optional fields, custom validators — full Pydantic support.

---

### FAQ

**Q: What is Instructor?**
A: A Python library that gets structured, validated outputs from LLMs using Pydantic models. Handles prompting, parsing, validation, and retries. 12.6K+ stars.

**Q: Does it work with Claude?**
A: Yes, Instructor supports Anthropic Claude, OpenAI, Google Gemini, Ollama, and many more providers.

---

## Source & Thanks

> Created by [Jason Liu](https://github.com/instructor-ai). Licensed under MIT.
> [instructor-ai/instructor](https://github.com/instructor-ai/instructor) — 12,600+ GitHub stars

---
Source: https://tokrepo.com/en/workflows/4a86c01b-d4d2-4c0f-8152-393c5685e429
Author: Script Depot