LangExtract
Extract structured data from unstructured text with precise source grounding.
Python library for LLM-based text extraction with exact source grounding and visual validation.
LangExtract is a Python library that processes unstructured documents using language models to extract structured entities. It is built for developers and data professionals needing verifiable data extraction from text.
What it is
It parses unstructured documents via instructions and few-shot examples, mapping every extracted entity back to its exact character interval in the source text. It handles long documents through text chunking, parallel processing, and outputs interactive HTML files for visual review.
Who it's for
It suits data engineers and developers building extraction pipelines for complex texts like clinical notes or public reports. It does not suit non-technical users who require a graphical point-and-click desktop application without writing Python code.
Key features
Precise Source Grounding
Maps every extraction to its exact location in the source text for easy verification.
Interactive Visualization
Generates self-contained HTML files to review thousands of extracted entities in context.
Long Document Support
Combines text chunking, parallel processing, and multiple passes to handle large files.
Flexible Model Providers
Supports cloud models like the Gemini family as well as local open-source models via Ollama.
Up and running in 3 steps
Our honest verdict
What's great
- Maps extractions to exact character intervals in source text
- Outputs self-contained interactive HTML visualization files
- Supports local execution via Ollama alongside cloud APIs
- Handles long documents with chunking and parallel processing
Where it falls short
- Requires writing Python code to define tasks and run extractions
- Cloud model usage requires managing API keys and incurs token costs
- Prompt alignment warnings require iterative tuning of few-shot examples
Pricing & license
Free under its Apache-2.0 license. The only operational costs depend on whether you run local models or pay for cloud-based LLM API usage like Gemini.
Open remote AI-training roles
870 open remote AI-training roles, updated November 14, 2026; most pay $20–$170 an hour. Zekai lists them free; the application goes to the employer’s platform.
Alternatives
Use it, or have us set it up
FAQ
Can I use LangExtract without sending data to the cloud?
Yes, you can run local LLMs using the built-in Ollama interface.
How do I verify the accuracy of extractions?
LangExtract provides character intervals for grounded data and generates interactive HTML visualizations to review extractions in context.
