LangStruct vs LangExtract

LangStruct vs LangExtract

Both LangStruct and Google’s LangExtract solve structured extraction with character-level source tracking. This page helps you choose between them based on your specific needs.

Core Philosophy Differences

LangStruct: Self-Optimizing

Uses DSPy to automatically improve prompts. No manual tuning needed - the system learns from your data.

LangExtract: Manual Optimization

Requires manual few-shot examples and prompt engineering. You control and tune every aspect.

This fundamental difference drives all other design decisions in both libraries.

Feature Comparison

Feature	LangStruct	LangExtract
Optimization approach	✅ Automatic (DSPy MIPROv2)	⚠️ Manual prompts/examples
Query parsing for RAG	✅ Query parsing included	❌ Extraction only
Schema definition	✅ From examples or Pydantic	⚠️ Prompt + examples (task spec)
Source grounding	✅ Character-level precision	✅ Character-level precision
Performance improvement	✅ Self-improving with data	⚠️ Depends on prompt/example tuning
Document chunking	✅ Smart semantic chunking	✅ Parallel processing
Interactive visualization	✅ HTML with highlighting	✅ HTML with highlighting
Model portability	✅ Auto-reoptimize for any model	⚠️ Manual prompt retuning needed
Model support	✅ Any DSPy-compatible LM	✅ Gemini, OpenAI, Ollama
GitHub stars	~500 (new)	6.9k
Backed by	Community	Google

Comparison verified on 2025-09-10 against the latest LangExtract docs. For fair context, see LangExtract’s README and example guides:

• Repository: https://github.com/google/langextract
• Longer text example: https://github.com/google/langextract/blob/main/docs/examples/longer_text_example.md

Code Comparison

LangStruct Approach

from langstruct import LangStruct

# Define by example - no manual prompts
extractor = LangStruct(example={
    "company": "Apple Inc.",
    "revenue": 125.3,
    "quarter": "Q3 2024"
})

# Automatically optimizes with DSPy
result = extractor.extract(text)
print(result.sources)  # Character-level tracking

LangExtract Approach

from langextract import LangExtract

# Manual prompt engineering required
extractor = LangExtract(
    model="gemini-1.5-flash",
    schema={
        "company": "string",
        "revenue": "number",
        "quarter": "string"
    },
    examples=[  # Manual few-shot examples
        {"text": "...", "output": {...}},
        {"text": "...", "output": {...}}
    ]
)

result = extractor.extract(text)
print(result.extractions[0].provenance)  # Character tracking

When to Choose LangStruct

✅ Choose LangStruct when you need:

Automatic Optimization

• Your extraction quality should improve over time
• You don’t want to manually tune prompts
• You’re processing domain-specific documents

Query Parsing for RAG

• You need to parse natural language queries into filters
• You want bidirectional RAG (documents + queries)
• You’re building advanced search systems

Schema from Examples

• You prefer showing examples over writing schemas
• You want quick prototyping
• Your schema evolves frequently

Self-Improving System

• You have training data available
• Accuracy matters more than initial setup speed
• You want to optimize for your specific domain

❌ Choose LangExtract when you need:

Maximum Control

• You want to control every prompt detail
• You prefer manual optimization
• You have prompt engineering expertise

Google Ecosystem

• You’re heavily invested in Google Cloud
• You primarily use Gemini models
• You want Google’s support and backing

Medical/Healthcare Focus

• LangExtract has specific optimizations for medical data
• You need their pre-tuned medical examples
• You’re processing clinical documents

Immediate Production Use

• You need a mature, battle-tested library (6.9k stars)
• You can’t wait for DSPy optimization
• You want extensive documentation and examples

Setup Comparison

Setup Time

• LangStruct: Fast setup with automatic optimization
• LangExtract: Manual example preparation needed

Accuracy

• LangStruct: Self-optimizing with automatic performance tuning
• LangExtract: Depends on manual tuning and examples

Token Efficiency

• LangStruct: Optimizes token usage automatically
• LangExtract: Token usage depends on manual prompt crafting

Model Switching: The Hidden Cost

Scenario: Your company starts with OpenAI, then switches to Claude for cost reasons, then moves to local Llama for compliance.

With Traditional Libraries (LangExtract, etc.)

# Month 1: Carefully tune prompts for OpenAI
extractor = LangExtract(...)
# Spend days crafting examples and prompt engineering

# Month 6: Switch to Claude - everything breaks!
# ❌ Prompts don't work the same way
# ❌ Few-shot examples need rewriting
# ❌ Back to manual tuning for weeks

# Month 12: Move to local Llama - start over again!
# ❌ Different prompt format requirements
# ❌ Re-engineer everything from scratch

With LangStruct

# Month 1: Set up once
extractor = LangStruct(example=schema)
extractor.optimize(training_data)

# Month 6: Switch to Claude
extractor = LangStruct(example=schema, model="claude-3-7-sonnet-latest")
extractor.optimize(training_data)  # ✅ Same workflow

# Month 12: Move to local Llama
extractor = LangStruct(example=schema, model="ollama/llama3.2")
extractor.optimize(training_data)

Time saved: Weeks → Minutes per model switch

The Bottom Line

LangStruct and LangExtract both provide character-level source tracking and interactive visualizations. The key differences:

• LangStruct: Auto-optimization + Model portability (future-proof)
• LangExtract: Manual tuning + Vendor lock-in (technical debt)

Choose LangStruct if you value your engineering time and want to avoid vendor lock-in.

Complementary Tools

Note that other popular tools serve different purposes and work well WITH LangStruct:

• Instructor: Great for general structured output from LLMs
• LangChain: Comprehensive LLM application framework
• LlamaIndex: Excellent for RAG and document indexing
• Unstructured: Best for parsing complex document formats

These aren’t competitors - they solve different problems. LangStruct specifically competes with LangExtract in the “extraction with source tracking” space.

Try Both Libraries

# Try LangStruct
pip install langstruct

# Try LangExtract
pip install langextract

Both are excellent libraries. Your choice depends on whether you prefer automatic optimization (LangStruct) or manual control (LangExtract).