instructor

Structured LLM output is extracted into validated Pydantic models by defining a schema and processing the response through instructor, which enforces type safety and reliable JSON parsing automatically.

LLM extraction fails when responses lack required fields because the output is not validated. Instructor catches invalid fields, enforces schema constraints, and applies automatic retries with corrective feedback until the output matches.

Anthropic models are fully supported for structured data extraction. Instructor provides multi-provider workflow support across Anthropic, OpenAI, and local model backends to enforce type safety and reliable JSON parsing.

Partial Pydantic model results are streamed in real time by configuring instructor for streaming. This allows you to process iterable results and nested entities progressively as the LLM generates the response.

The best way to extract entities from text is schema-guided extraction using Pydantic models. Instructor converts free-form LLM responses into validated Python objects for users, companies, reviews, and other structured records.

Pydantic schemas are required to classify documents and enforce constraints on LLM outputs. Instructor uses these explicit schemas to validate fields and generate analysis objects that downstream Python code can trust.