LangStruct

Why LangStruct?

Optimized Prompts

Automatically optimize extraction prompts using DSPy for higher accuracy and lower costs

Source Grounding

Track exactly where each extracted piece of information comes from in the original text

Self-Improving

Continuously improve performance as you process more documents

Interactive Visualization

Beautiful HTML visualizations with search, filtering, and entity highlighting

Production Ready

Built-in validation, error handling, and compliance features for enterprise use

Quick Example

Extract structured data from any text with just a few lines of code:

from langstruct import LangStruct, Schema, Field

class PersonSchema(Schema):
    name: str = Field(description="Full name")
    age: int = Field(description="Age in years")
    location: str = Field(description="Current location")

extractor = LangStruct(schema=PersonSchema)
result = extractor.extract("Dr. Sarah Johnson, 34, works in San Francisco")

print(result.entities)  # {'name': 'Dr. Sarah Johnson', 'age': 34, 'location': 'San Francisco'}
print(result.confidence)  # 0.94

Production Use Cases

Financial Documents

Extract metrics, dates, and insights from earnings reports, SEC filings, and financial statements

Medical Records

Process clinical notes, lab reports, and medical documents

Legal Contracts

Analyze contracts, agreements, and legal documents for key terms and risks

Research Papers

Extract methodology, results, and citations from scientific literature

Next Steps

Ready to start extracting structured data?

1. Why LangStruct? - See how LangStruct compares to alternatives
2. Installation - Install LangStruct in your project
3. Quick Start - Build your first extraction pipeline
4. Examples - Explore real-world use cases
5. Optimization - Maximize accuracy and minimize costs