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Instructor

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Effortlessly structure large language model outputs with Python and TypeScript.

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TL;DR - Instructor

  • Structures LLM outputs using Pydantic/Zod schemas.
  • Reduces prompt engineering and parsing errors.
  • Supports automatic re-asking for invalid responses.
Pricing: Free plan available
Best for: Growing teams

Pros & Cons

Pros

  • Greatly simplifies LLM output parsing and validation
  • Reduces development time by eliminating manual parsing logic
  • Improves reliability and robustness of LLM integrations
  • Supports multiple programming languages and LLM providers
  • Open-source and actively maintained

Cons

  • Requires familiarity with Pydantic or Zod for schema definition
  • Adds another dependency to the project
  • Debugging issues with schema adherence might require understanding LLM behavior

Preview

Key Features

Define output schemas with Pydantic (Python) or Zod (TypeScript)Automatic re-asking for invalid LLM responsesSupports various LLM providers (OpenAI, Anthropic, Google, etc.)Type-safe output parsingIntegration with existing LLM client librariesDeclarative schema definition

Pricing Plans

Free Trial

Free

Free

  • 1 user
  • 1 project
  • 100 MB storage
  • Basic features

Basic

$10/month

  • 5 users
  • 5 projects
  • 1 GB storage
  • Advanced features

Pro

$25/month

  • Unlimited users
  • Unlimited projects
  • 10 GB storage
  • All features
  • Priority support

What is Instructor?

Editorial review
Instructor is an open-source library designed to simplify the process of structuring outputs from large language models (LLMs). It allows developers to define expected output schemas using standard Python Pydantic or TypeScript Zod models, ensuring that LLM responses conform to a predictable and usable format. This eliminates the need for complex prompt engineering to enforce structure and reduces the likelihood of parsing errors. The library is ideal for developers, data scientists, and AI engineers working with LLMs who need reliable, structured data from their models for downstream processing, database storage, or API integrations. By abstracting away the complexities of output parsing and validation, Instructor significantly streamlines LLM integration into applications, making it easier to build robust and reliable AI-powered features. It supports various LLM providers and offers features like automatic re-asking for invalid responses, making it a powerful tool for production-grade LLM applications.

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Instructor FAQ

What is Instructor?

Instructor is an open-source library that helps developers structure the outputs of large language models (LLMs) by defining expected schemas using Pydantic (Python) or Zod (TypeScript). It automatically validates and parses LLM responses, and can even re-ask the LLM if the output is invalid.

How much does Instructor cost?

Instructor is an open-source library and is completely free to use. You only pay for the underlying LLM API calls you make.

Is Instructor free?

Yes, Instructor is an open-source project and is free to use.

Who is Instructor for?

Instructor is for developers, data scientists, and AI engineers who are building applications with large language models and need to ensure that the LLM outputs are consistently structured and valid for further processing or integration.

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