LangExtract

LangExtract is a Python library that uses LLMs to extract structured information from unstructured text based on user-defined instructions, with precise source grounding that links each extracted entity to its location in the original document. It processes domain-specific materials such as clinical notes and reports. A key strength is source traceability, which ensures each extracted data point can be traced back to its source. The extracted entities can be exported as a JSONL file, a standard format for language model data, and visualized through an interactive HTML interface for contextual review. Teams considering structured output from LLMs for document processing should evaluate LangExtract alongside schema-enforcement approaches such as Pydantic AI. LangExtract is better suited to long-form, unstructured source material, while Pydantic AI excels at constraining output formats for shorter, more predictable inputs.

LangExtract is a Python library that uses LLMs to extract structured information from unstructured text based on user-defined instructions. It processes domain-specific materials — such as clinical notes and reports — identifying and organizing key details while keeping each extracted data point traceable to its source. The extracted entities can be exported as a .jsonl file, a standard format for language model data and visualized through an interactive HTML interface for contextual review. Our teams evaluated LangExtract for extracting entities to populate a domain knowledge graph and found it effective for transforming complex documents into structured, machine-readable representations.