{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence"}],"enrichment":{"capability":"Instructor wraps LLM APIs to extract validated, typed structured data from natural language by defining Pydantic models and letting the package handle schema generation, validation, and retries.","skillfed_tags":["llm-integration","data-validation","structured-extraction"],"use_cases":["Extract product details (name, price, availability) from unstructured product descriptions or web scrapes.","Parse customer feedback into structured fields (sentiment, category, action items) for downstream processing.","Convert natural language requirements into typed configuration objects for application setup.","Batch-extract entities (people, places, organizations) from documents with automatic validation and retry.","Build chatbot intents and slot-filling workflows where LLM responses must conform to a known schema."],"what_it_does":"Instructor is a Python library that bridges Pydantic models and LLM APIs to extract structured, validated data from natural language. Instead of writing JSON schemas, parsing responses, and handling validation errors manually, you define a Pydantic model and pass it to Instructor; the library generates the schema, sends it to the LLM, validates the response, and automatically retries on validation failure. It works with OpenAI, Anthropic, Google, Ollama, Groq, and other providers through a unified interface.\n\nThe package is built on Pydantic for type safety and IDE support, includes automatic retries with error feedback, supports streaming partial objects, handles nested data structures, and eliminates boilerplate around JSON parsing and manual validation. It's designed for developers who need reliable structured extraction without the complexity of writing custom parsing logic or managing provider-specific APIs.","worth_installing":"Yes. Instructor is actively maintained, has no known vulnerabilities, low install friction, and a permissive license. It solves a real problem\u2014reliable structured extraction from LLMs\u2014with a clean API and broad provider support. Install it if you need to extract validated structured data from LLM responses; skip it if you're building agents or need richer observability (the docs suggest PydanticAI for that)."},"id":"instructor","links":{"html":"https://skillfed.io/packages/instructor","md":"https://skillfed.io/packages/instructor.md","pypi":"https://pypi.org/project/instructor/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-06-28","license_spdx":null,"license_treatment":"permissive","name":"instructor","python_support":"supports_current","summary":"structured outputs for llm"},"popularity":{"monthly_downloads":19880373,"position":1053,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"1.15.4"}