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LangExtract
LangExtract is a Python library for extracting structured information from unstructured text using large language models.
LangExtract is a Python library that uses large language models 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. The library supports cloud-based models like the Google Gemini family, OpenAI models, and local open-source models via Ollama. It features precise source grounding, controlled output schemas, text chunking for long documents, and an interactive HTML visualization generation. Users can install it from PyPI or source, configure API keys via environment variables or a .env file, and add custom model providers via a lightweight plugin system.
LangExtract at a glance
- Best for: Software engineer, Researcher
- Runs on: API, self-hosted, open source
- Pricing: Free
- Released: July 2025
- Category: AI coding tool
- Website: github.com/google/langextract
Key features
- Precise source grounding with visual highlighting
- Reliable structured outputs via few-shot examples
- Optimized text chunking and parallel processing for long documents
- Interactive HTML visualization generation
- Flexible support for cloud and local LLMs
- Adaptable to any domain without model fine-tuning
- Custom model provider support via a plugin system
Use it for
- Extracting structured medication information from clinical text
- Structuring radiology reports
- Extracting entities from full literary texts like Romeo and Juliet
Alternatives to LangExtract
- GLiNER — Enables zero-shot entity extraction without labeled training data on consumer CPUs.
- edgextract — Runs entirely in the browser with local model execution and strict data privacy.
- Hyper-Extract — Extracts complex knowledge graphs and hypergraphs from documents with one command.
- DeepKE — It supports multi-modal, few-shot, and document-level extraction scenarios.
- Trafilatura — It extracts main texts and metadata while removing boilerplate noise.
- ScrapeGraphAI — Extracts structured data via natural language prompts with no selectors or proxies.
- deepdoctection — Orchestrates layout analysis, OCR, and model fine-tuning in PyTorch pipelines.
- CTranslate2 — Provides fast and efficient inference execution for Transformer models.
- docTR — Extracts text and layout details from documents using deep learning.
- Unstract — It uses dual LLMs to eliminate hallucinations and ensure extraction accuracy.
- MedicalGPT — Provides a comprehensive pipeline for full-cycle LLM training and alignment.
- xTuring — Provides complete control over customizing open-source LLMs.
All LangExtract alternatives