google/langextract

Health v2 · maintenance only

Adoption not part of the score

38490 stars · 2707 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

LangExtract is a Python library that uses LLMs to extract structured information from unstructured text based on user-defined instructions and few-shot examples. It grounds every extraction to its exact character span in the source text and provides interactive visualization for traceability.

Use cases

• extract structured data from clinical notes with llm
• parse entities from unstructured text with source citations
• extract medications from doctor reports
• structure radiology reports into
• highlight extracted fields back in the original document
• run information extraction with gemini or openai models
• use local llms with ollama for text extraction

When to choose

• you need structured extractions that are verifiably grounded to exact source text spans
• you want schema-consistent LLM outputs enforced via few-shot examples
• you need to work with clinical or long documents requiring traceability
• you want provider flexibility across Gemini, OpenAI, or local Ollama models

When to avoid

• you need fully offline extraction without any LLM
• you want a general-purpose NLP pipeline beyond information extraction
• you cannot send text to cloud LLM APIs and lack local model infrastructure
• you need high-throughput extraction with minimal LLM cost

Facets

library · maturity active

nlp llm-inference data-science rag large-language-models artificial-intelligence data-science python information-extraction structured-output source-grounding gemini openai ollama few-shot natural-language-processing

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markdown · JSON · MCP: product_card(name="google/langextract")