Instructor

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Instructor is a Coding & Development tool that provides structured outputs from LLMs. It uses Pydantic for validation, type safety, and IDE support, simplifying JSON extraction and error handling.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Yes

Skill level

Technical

About

What is Instructor?

Instructor is a powerful library designed to simplify the process of obtaining structured outputs from Large Language Models (LLMs). Built on Pydantic, it ensures robust validation, type safety, and seamless IDE support, eliminating the need for manual JSON parsing, error handling, and retries. The tool works with major LLM providers like OpenAI, Anthropic, Google, and Ollama, allowing developers to use the same codebase across different models. Key features include automatic retries for failed validations, streaming support for partial object generation, and the ability to extract complex, nested data structures. Instructor is trusted by over 100,000 developers and companies, boasting millions of monthly downloads and thousands of GitHub stars.

Best used for

Ideal for developers who need to reliably extract structured data from LLMs, handle validation errors automatically, and integrate various LLM providers seamlessly. Especially valuable for building applications that require consistent and type-safe JSON outputs from natural language inputs.

Common actions

extract structured data

validate LLM outputs

integrate LLMs

simplify LLM workflows

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Capabilities

Key features

Structured LLM outputs
Pydantic validation
Type safety
Automatic retries
Streaming support
Nested object extraction
Multi-provider compatibility

Target Audience

developer

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

Which LLM providers does Instructor support?

Instructor supports major LLM providers including OpenAI (GPT-4o, GPT-4o-mini), Anthropic (Claude-3-5-sonnet), Google (Gemini-pro), and Ollama (llama3.2). It allows developers to use a consistent API across these different models.

How does Instructor handle validation errors?

Instructor automatically retries failed extractions when validation errors occur. If a Pydantic model's field validator raises an error, Instructor will attempt to re-prompt the LLM with the error message, improving reliability and reducing manual intervention.

Can Instructor extract nested data structures?

Yes, Instructor is designed to handle complex and nested data structures. By defining Pydantic models with nested BaseModel or List types, developers can easily extract intricate JSON objects from LLM responses without additional parsing logic.

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