# Instructor

> Get structured, validated output from LLMs using plain type definitions, with automatic retries on validation failure.

Instructor turns an LLM into a typed function: define a Pydantic model (or a Zod/equivalent schema in its ports), and Instructor coerces the model's output into that shape, validating it and automatically re-asking on failure. The simplest way to get reliable structured data out of an LLM.

Website: https://python.useinstructor.com

Instructor makes structured output from LLMs feel like calling a typed function. You define the shape you want as a Pydantic model (Python — with ports for TypeScript, Go, and others), pass it in, and Instructor handles the rest: it instructs the model, parses the response into your type, **validates** it, and **automatically retries** with the validation errors fed back if the output doesn't conform.

It is aimed at developers who want data, not prose, from an LLM — extraction, classification, form-filling — without writing brittle JSON parsing and retry loops by hand. Because it builds on the providers' native function-calling/structured-output capabilities, it's thin and reliable rather than a heavy framework.

## Highlights

- **Types as the schema** — define output with Pydantic (or the language port's equivalent); no hand-written JSON Schema.
- **Validation + auto-retry** — invalid output is re-requested with the errors, so you get conforming data or a clear failure.
- **Provider-agnostic** — works across OpenAI, Anthropic, and many other models.
- **Streaming partials** — stream structured objects as they're produced.
- **Minimal footprint** — a focused library, not a framework you build your app around.

## In an AI-assisted workflow

```python
import instructor
from pydantic import BaseModel

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

client = instructor.from_provider("anthropic/claude")
user = client.chat.completions.create(response_model=User, messages=[...])  # -> validated User
```

> [!TIP]
> Let your types do the prompting: a well-named model with field descriptions and constraints (enums, ranges) often beats paragraphs of instructions for getting the exact structure you want.

## Good to know

Instructor is free and open source (MIT); you pay your model provider for tokens. For a cross-language, schema-first approach with its own DSL, compare [BAML](/tools/baml); for a TypeScript-native app toolkit that also does structured output, see the [Vercel AI SDK](/tools/vercel-ai-sdk). Background on the techniques: [Structured Output vs JSON Mode vs Function Calling](/guides/concepts/structured-output-2026).

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_Source: https://agentscamp.com/tools/instructor — Tool on AgentsCamp._