Introduction
Instructor focuses on extracting reliable structured outputs from LLMs. Built on Pydantic, it offers validation, type-safety and a developer-friendly API to convert natural language into validated JSON objects.
Key features
- Define response models with Pydantic and automatically validate outputs.
- Supports multiple providers (OpenAI, Anthropic, Google, etc.), streaming, automatic retries and nested object parsing.
- Cross-language SDKs, extensive examples and documentation for quick adoption.
Use cases
- Stable extraction of structured information from free text (user profiles, product data, forms).
- Streaming or partial-object scenarios where progressive validation is required.
- Integrating structured extraction into data pipelines, API gateways or downstream validation systems.
Technical details
- Primarily a Python implementation; the repo includes examples, docs and test suites and is MIT licensed.
- Built-in retry and error handling for validation failures, streaming support, and compatibility with many LLM provider APIs.
- Active community and frequent releases suitable for production and research use.