Why LangStruct?

Why Choose LangStruct?

LangStruct stands out in the crowded field of structured data extraction libraries by focusing on self-optimization, precision, and developer experience. Here’s why it might be the right choice for your project.

The Problem with Current Solutions

Most structured extraction libraries require you to:

• Manually tune prompts and examples for good performance
• Choose between speed or accuracy without automatic optimization
• Build custom validation and error handling
• Lose track of sources - where did that extracted data come from?
• Deal with complex APIs that require deep expertise

LangStruct solves these problems with a different approach.

LangStruct’s Core Advantages

🎯 Self-Optimizing

Uses DSPy 3.0 with MIPROv2 to automatically improve prompts and examples over time. No manual tuning required.

🔗 Precise Source Grounding

Track exactly where each piece of extracted data comes from with character-level precision.

⚡ Auto Schema Generation

Generate Pydantic schemas automatically from examples. Skip the boilerplate.

🛡️ Built-in Validation

Quality validation, error detection, and improvement suggestions out of the box.

Comparison with Popular Alternatives

vs. Instructor

Instructor is the most popular structured extraction library (11.2k GitHub stars, 3M+ monthly downloads), but focuses on different strengths:

Feature	LangStruct	Instructor
Auto-optimization	✅ DSPy MIPROv2	❌ Manual prompt tuning
Source grounding	✅ Character-level precision	❌ No source tracking
Schema generation	✅ From examples	❌ Manual Pydantic schemas
Self-improving	✅ Learns from data	❌ Static performance
Multi-model support	✅ Any DSPy-compatible LM	✅ OpenAI, Anthropic, Google, Ollama, 15+ providers
Streaming support	⚠️ Limited	✅ Partial objects & streaming
Retry handling	✅ Built into DSPy	✅ Automatic retries on validation failure

When to choose LangStruct over Instructor:

• You want extraction quality to improve automatically over time
• You need to know exactly where extracted data came from in source text
• You prefer examples over writing Pydantic schemas
• You’re processing domain-specific documents that need optimization

When Instructor might be better:

• You need streaming support for real-time applications
• You already have well-tuned prompts and schemas
• You need the largest ecosystem and community (1000+ contributors)
• You prefer maximum control over every extraction parameter

vs. LangExtract (Google)

LangExtract is Google’s recently released library (6.9k GitHub stars) with similar goals but different approaches:

Feature	LangStruct	LangExtract
Auto-optimization	✅ DSPy MIPROv2	❌ Manual few-shot examples
Source grounding	✅ Character-level precision	✅ Character-level precision
Schema generation	✅ From examples	❌ Manual definitions
Self-improving	✅ Learns from data	❌ Static performance
Long document processing	✅ Smart chunking	✅ Parallel processing
Interactive visualization	✅ Advanced interactive HTML	✅ Interactive HTML
Model support	✅ Any DSPy-compatible LM	✅ Gemini, OpenAI, Ollama

When to choose LangStruct over LangExtract:

• You want automatic optimization instead of manual few-shot tuning
• You need schema generation from examples
• You want extractions to improve over time automatically
• You prefer the DSPy ecosystem

When LangExtract might be better:

• You’re heavily invested in the Google ecosystem
• You prefer Google’s approach to document processing
• You need the specific optimizations Google built for medical/healthcare use cases

vs. LangChain

LangChain is a comprehensive LLM framework that includes structured extraction capabilities:

Feature	LangStruct	LangChain
Focus	✅ Specialized for extraction	❌ General-purpose framework
Auto-optimization	✅ Built-in MIPROv2	⚠️ Manual few-shot examples
Source grounding	✅ Precise tracking	✅ Evidence fields in extractions
API complexity	✅ Simple, single constructor	❌ Many components to configure
Structured output methods	✅ Built-in via DSPy	✅ Multiple (.with_structured_output, parsers)
Schema generation	✅ From examples	❌ Manual Pydantic/JSON schema

When to choose LangStruct over LangChain:

• Structured extraction is your primary use case
• You want automatic optimization instead of manual few-shot tuning
• You want a simple, focused API without learning a large framework
• You need schema generation from examples

When LangChain might be better:

• You’re building a complex AI application beyond just extraction
• You need the extensive LangChain ecosystem (agents, tools, integrations)
• Your team already has LangChain expertise
• You need the flexibility of multiple structured output methods

vs. LlamaIndex

LlamaIndex excels at RAG and document indexing, with extraction as a secondary feature:

Feature	LangStruct	LlamaIndex
Extraction focus	✅ Primary purpose	⚠️ Secondary to RAG
Auto-optimization	✅ DSPy-powered	❌ Manual configuration
Source grounding	✅ Character-level precision	✅ Node-level with metadata
Schema generation	✅ From examples	❌ Manual Pydantic definition
Document processing	✅ Smart chunking	✅ Advanced document parsing
LlamaExtract service	❌ No hosted service	✅ Hosted extraction API

When to choose LangStruct over LlamaIndex:

• Extraction is your primary goal (not RAG/search)
• You want automatic prompt optimization
• You need character-level source precision
• You prefer a simpler API focused on extraction

When LlamaIndex might be better:

• You’re building a RAG system that also does extraction
• You need advanced document parsing (PDFs, complex formats)
• You want to use their hosted LlamaExtract service
• You want to combine extraction with semantic search

vs. Unstructured.io

Unstructured focuses on document parsing and preprocessing (35+ sources, 64+ file types):

Feature	LangStruct	Unstructured
LLM-powered extraction	✅ Core feature	⚠️ Basic LLM integration
Document parsing	⚠️ Text-only processing	✅ 64+ file types (PDF, HTML, Word, etc.)
Auto-optimization	✅ MIPROv2	❌ Rule-based partitioning
Schema flexibility	✅ Any Pydantic schema	⚠️ Predefined document elements
Source grounding	✅ Character-level precision	❌ Element-level only
Production services	❌ No hosted API	✅ Azure/AWS Marketplace APIs

When to choose LangStruct over Unstructured:

• You need flexible, schema-driven extraction (not just predefined elements)
• You want LLM-powered semantic understanding
• You need automatic optimization for your specific data
• You’re working with already-parsed text content

When Unstructured might be better:

• You need to process complex document formats (PDFs, Word docs, etc.)
• You want production-grade document partitioning services
• You prefer rule-based extraction to minimize LLM costs
• You need to integrate with 30+ vector databases

Real-World Use Cases Where LangStruct Excels

📊 Financial Document Analysis

Extract metrics, dates, and insights from earnings reports and SEC filings with automatic optimization for financial terminology.

from langstruct import LangStruct

# Auto-generate schema from example
extractor = LangStruct(example={
    "revenue": "125.3 million",
    "growth_rate": "15.2%",
    "quarter": "Q3 2024"
})

# Extractions improve as you process more documents
result = extractor.extract(earnings_report_text)
print(result.sources)  # See exactly where each number came from

🏥 Medical Record Processing

Process clinical notes with domain-specific optimization and precise source tracking for compliance.

# Schema auto-generated from medical examples
extractor = LangStruct(examples=[
    {"patient_age": 34, "diagnosis": "hypertension", "medication": "lisinopril"},
    {"patient_age": 67, "symptoms": ["chest pain", "shortness of breath"]}
])

result = extractor.extract(clinical_note)
# Track exactly which sentence mentioned each symptom

⚖️ Legal Contract Analysis

Analyze contracts with automatic optimization for legal language and precise clause attribution.

extractor = LangStruct(example={
    "contract_type": "employment agreement",
    "parties": ["ABC Corp", "John Smith"],
    "key_terms": ["salary", "benefits", "termination clause"]
})

# System learns legal patterns automatically
result = extractor.extract(contract_text)

Getting Started is Simple

Unlike complex frameworks, LangStruct gets you extracting in minutes:

from langstruct import LangStruct

# Option 1: From example (easiest)
extractor = LangStruct(example={"name": "John", "age": 25})

# Option 2: From multiple examples (better type inference)
extractor = LangStruct(examples=[
    {"name": "Dr. Smith", "specialty": "cardiology"},
    {"name": "Jane Doe", "skills": ["Python", "ML"]}
])

# Extract with source tracking
result = extractor.extract(your_text)
print(result.entities)  # Extracted data
print(result.sources)   # Exact source locations

When NOT to Choose LangStruct

LangStruct might not be the best choice if:

• You need maximum speed over accuracy - Optimization takes some upfront time
• You have very simple, one-off extraction needs - The optimization overhead isn’t worth it
• You’re already heavily invested in another ecosystem - Switching costs might be high
• You need extensive document parsing - Consider Unstructured.io or LlamaIndex
• You need streaming support - Instructor has better real-time streaming
• You’re building a general LLM application - LangChain might be more appropriate
• You prefer a different visualization style - Both have advanced interactive HTML

Try LangStruct Today

Ready to experience self-optimizing extraction with precise source tracking?

pip install langstruct

Start with our Quick Start Guide or explore real-world examples.

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LangStruct: Because extraction should get better automatically, not worse over time.