Instructor
Library for getting reliable, structured data output from LLMs using function-calling APIs and Pydantic models for validation.
- Actively maintained
- Well documented
- Permissive license
Overview
Instructor wraps an LLM's native function-calling or tool-use capability so a developer can request a response that matches a Pydantic model, and automatically retries the request if the model's output doesn't validate against that schema - turning unreliable free-text LLM output into dependable structured data. It supports OpenAI, Anthropic, and many other model providers through one consistent interface.
Instructor is a fit for teams that need structured data extraction from LLMs, achieving that through function-calling APIs and retry logic - a different technical approach than Outlines (also in this catalog), which constrains generation at the token level instead.
Why it's in this catalog
Catalog rating: ★★★☆☆ (3/5)
Popular structured-output library for LLMs with a distinctive validation-first approach.
- Mature
- Community-driven
- Categories
- AI & Machine Learning
- Languages
- Python
- License
- MIT
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