Langextract
title: google/langextract radar_quadrant: Languages & Frameworks radar_ring: Assess radar_position: inner created: 2026-05-26 last_updated: 2026-05-26 tags: [llm, extraction, nlp, structured-data, grounding, python, google] source_url: https://github.com/google/langextract
A Python library from Google for structured extraction from unstructured text. Every extracted field is grounded — traced back to the exact source span in the original document. Designed to make LLM-based extraction auditable and verifiable rather than opaque.
Source grounding. Each extracted value carries a reference to the precise character span in the source text from which it was derived. This makes extraction results inspectable and enables downstream validation against the original document.
Structured output. Extraction targets are defined as schemas (Python dataclasses or typed dicts). The library maps LLM output onto the schema while preserving the grounding links.
Auditability. Because every field maps back to a source span, extraction errors are diagnosable: the span can be read to determine whether the LLM misread the text or the schema was ambiguous.
Complements Markitdown in the ingestion pipeline: markitdown converts documents to clean markdown, langextract extracts structured data from that markdown with source grounding. Feeds into RAG pipelines covered by RagChunkingStrategies, SearchBasedRAG, and EnterpriseRAGLessons — structured extraction with grounding reduces hallucination risk in retrieval-augmented systems.
Placed in Languages & Frameworks / Assess / inner. Source grounding is a meaningful differentiator over ungrounded LLM extraction: it enables auditability that production pipelines require. Inner position reflects applicability to any team building document-processing or RAG pipelines where extraction correctness must be verifiable. Trial gate: one extraction pipeline where extracted fields are validated against source spans and at least one extraction error is caught via span inspection rather than end-to-end testing.
