Instructor
Structured outputs from language models using Python type hints
Quick Verdict
4.7/5
Rating
202
Reviews
free
Pricing
Structured outputs from language models using Python type hints
Instructor is a Python library that ensures LLM responses conform to specified data structures using Pydantic models. It adds validation, retries, and type safety to API calls with minimal code changes.
Instructor intercepts LLM API calls and enforces structured outputs by wrapping provider SDKs. It supports OpenAI, Anthropic, Cohere, and local models. Features include automatic retries on validation failures, streaming support, partial responses, and detailed error feedback. Reduces boilerplate for extracting typed data from unstructured LLM outputs.
Pros
- Define output schemas as Python types—no custom prompting syntax required
- Automatically retry failed validations without manual error handling
- Works with multiple LLM providers through a unified interface
- Stream responses while maintaining type guarantees
- Minimal overhead—wraps existing client code with ~3 lines
Cons
- Adds latency for validation and potential retries on complex schemas
- Performance depends on model compliance—some models struggle with strict constraints
- Limited to Python ecosystem; no native support for other languages
Best For
Python developers building production systems that need reliable, typed data extraction from LLM outputs without manual JSON parsing and validation.
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Frequently Asked Questions
What is Instructor?
Instructor is a Python library that ensures LLM responses conform to specified data structures using Pydantic models. It adds validation, retries, and type safety to API calls with minimal code changes.
Is Instructor free?
Yes — Instructor is free to use. See the pricing section above for details on any paid tiers or add-ons.
Who is Instructor for?
Python developers building production systems that need reliable, typed data extraction from LLM outputs without manual JSON parsing and validation.
What are the main benefits of Instructor?
Define output schemas as Python types—no custom prompting syntax required. Automatically retry failed validations without manual error handling. Works with multiple LLM providers through a unified interface.
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