langextract

Free

A Python library for extracting structured information from unstructured text using LLMs with precise source grounding and interactive visualization.

Text-to-SpeechFreeFree tier

Inputs: text

Type

Open Source

Visit Website GitHub (36.4k stars)

About langextract

LangExtract is a Python library that uses LLMs to extract structured information from unstructured text documents based on user-defined instructions. It processes materials such as clinical notes or reports, identifying and organizing key details while ensuring the extracted data corresponds to the source text. Key capabilities include precise source grounding that maps every extraction to its exact location, reliable structured outputs via few-shot examples and controlled generation, optimized handling of long documents through chunking and parallel processing, and interactive HTML visualization for reviewing extracted entities. It supports cloud-based models like Google Gemini and local open-source models via Ollama, making it adaptable to any domain without requiring fine-tuning.

Key Features

Precise source grounding: maps every extraction to its exact location in the source text for traceability

Reliable structured outputs: enforces consistent output schema using few-shot examples and controlled generation

Optimized for long documents: uses text chunking, parallel processing, and multiple passes for high recall

Interactive visualization: generates self-contained HTML files to review extracted entities in original context

Flexible LLM support: works with cloud models (e.g., Google Gemini) and local models via Ollama

Adaptable to any domain: define extraction tasks with few examples, no fine-tuning required

Leverages LLM world knowledge with precise prompt wording and examples

Pros & Cons

Pros

Precise source grounding ensures high traceability and verifiability
Reliable structured outputs via schema enforcement and few-shot examples
Optimized for long documents with chunking and parallel processing
Interactive visualization simplifies review of large extraction results
Supports both cloud and local LLMs for flexibility
Open source with Apache-2.0 license, no vendor lock-in
Adaptable to any domain without model fine-tuning

Cons

Requires Python 3.10 or higher
Cloud-based LLM usage requires API keys and may incur costs
Extraction quality depends on the chosen LLM, prompt clarity, and task complexity
Not a production-grade application; intended as a library for integration

Best For

Extracting structured data from clinical notes and medical reportsStructuring radiology reports and medication informationAnalyzing literary texts, e.g., character, emotion, and relationship extraction from plays like Romeo and JulietAny domain requiring conversion of unstructured text into structured, grounded data