M4A

Instructor

Get structured outputs from LLMs with Pydantic validation

open-sourceproductionpythonstructured-outputspydanticopen-sourcevalidation
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Memory Types

Integrations

openai, anthropic, cohere, gemini, ollama


Overview


Instructor is a popular library that makes it simple to get structured, validated outputs from language models. Built on top of Pydantic, it allows developers to define output schemas using Python type hints and automatically coerces LLM outputs to match those schemas. Instructor has become essential for production applications requiring reliable, structured data extraction.


The library handles the complexity of function calling, parsing, validation, and retries, providing a clean interface that feels like native Python. It supports streaming, async operations, and works with all major LLM providers, making it a versatile choice for any project needing structured outputs.


Key Features


  • **Pydantic Integration**: Define schemas with Python type hints
  • **Automatic Validation**: Validates and retries until schema matches
  • **Multi-Model Support**: Works with OpenAI, Anthropic, Cohere, etc.
  • **Streaming**: Stream partial objects as they're generated
  • **Async Support**: Full async/await support
  • **Retry Logic**: Automatic retries with validation feedback
  • **Type Safety**: Full type checking with mypy
  • **Simple API**: Minimal boilerplate, feels like native Python

    • When to Use Instructor


    Instructor is ideal for:

  • Extracting structured data from text
  • Building reliable data extraction pipelines
  • Applications requiring validated LLM outputs
  • Projects using Pydantic for data validation
  • Type-safe LLM integrations
  • Production systems needing consistent outputs

    • Pros


  • Extremely simple and intuitive API
  • Excellent type safety with Pydantic
  • Works across all major LLM providers
  • Handles retries and validation automatically
  • Active development and community
  • Great documentation with examples
  • Production-ready and reliable
  • Minimal dependencies

    • Cons


  • Python-only (no other language support)
  • Focused scope - not a full framework
  • Requires understanding of Pydantic
  • Can use more tokens due to retries
  • Less suitable for unstructured outputs
  • Not designed for complex multi-step workflows
  • Limited to function calling-capable models

    • Pricing


  • **Open Source**: Free, MIT license
  • **Self-Hosted**: Free to use anywhere
  • **LLM Costs**: Standard API costs for LLM providers

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