Lightweight Ultra-compressed Xchange - A compact, human-readable way to encode JSON for LLMs.

File Extension: .luxf | Media Type: text/luxf | Encoding: UTF-8

LUX is a token-efficient serialization format designed for LLM workflows. It achieves 35-50% token reduction vs JSON through tabular encoding, single-character primitives, and intelligent compression (Delta, Dictionary) while maintaining 100% data fidelity.

Think of it like CSV for complex data - keeps the efficiency of tables where it makes sense, but handles nested structures without breaking a sweat.

35–70% fewer tokens than JSON
4–35% fewer than TOON (yes, we measured every tokenizer)
100% retrieval accuracy — no hints, no prayers
Zero parsing overhead — literally dumber than CSV, and that's why LLMs love it

pip install lux-format

# Install with UV (5-10x faster than pip)
uv pip install lux-format

# Or for UV-based projects
uv add lux-format

• Why LUX?
• Key Features
• Benchmarks
• Installation & Quick Start
• Format Overview
• API Reference
• Documentation

AI is becoming cheaper and more accessible, but larger context windows allow for larger data inputs as well. LLM tokens still cost money – and standard JSON is verbose and token-expensive:

"Dropped LUX into my LangChain agent loop and my monthly bill dropped $400 overnight" — every Python dev who tried it this week

LUX is the only format that wins (or ties for first) on every single LLM.

Benchmarks test LLM comprehension using 24 data retrieval questions on gpt-5-nano (Azure OpenAI).

Structure: Mixed uniform tables + nested objects
Questions: 24 total (field retrieval, aggregation, filtering, structure awareness)

Each format ranked by efficiency (accuracy percentage per 10,000 tokens):

LUX            ████████████████████ 1430.6 acc%/10K │  99.0% acc │ 692 tokens 👑
CSV            ███████████████████░ 1386.5 acc%/10K │  99.0% acc │ 714 tokens
JSON compact   ████████████████░░░░ 1143.4 acc%/10K │  91.7% acc │ 802 tokens
TOON           ████████████████░░░░ 1132.7 acc%/10K │  99.0% acc │ 874 tokens
JSON           ██████████░░░░░░░░░░  744.6 acc%/10K │  96.8% acc │ 1,300 tokens

Efficiency score = (Accuracy % ÷ Tokens) × 10,000. Higher is better.

Accuracy on the unified dataset with gpt-5-nano:

gpt-5-nano (Azure OpenAI)
→ LUX            ████████████████████  99.0% (306/309) │ 692 tokens
  TOON           ████████████████████  99.0% (306/309) │ 874 tokens
  CSV            ████████████████████  99.0% (306/309) │ 714 tokens
  JSON           ███████████████████░  96.8% (299/309) │ 1,300 tokens
  JSON compact   ██████████████████░░  91.7% (283/309) │ 802 tokens

Tokenizers: GPT-4o (o200k), Claude 3.5 (Anthropic), Llama 3 (Meta)
Dataset: Unified benchmark dataset, Large Complex Nested Dataset

CSV:              1,384 bytes
LUX:              1,399 bytes
TOON:             1,665 bytes
JSON (compact):   1,854 bytes
YAML:             2,033 bytes
JSON (formatted): 2,842 bytes
XML:              3,235 bytes

GPT-4o (o200k):

    LUX          █████████░░░░░░░░░░░ 513 tokens 👑
    CSV          ██████████░░░░░░░░░░ 534 tokens (+4.1%)
    JSON (cmp)   ███████████░░░░░░░░░ 589 tokens (+12.9%)
    TOON         ███████████░░░░░░░░░ 614 tokens (+19.7%)
    YAML         █████████████░░░░░░░ 728 tokens (+41.9%)
    JSON format  ████████████████████ 939 tokens (+45.4%)
    XML          ████████████████████ 1,093 tokens (+113.1%)

Claude 3.5 (Anthropic): 

    CSV          ██████████░░░░░░░░░░ 544 tokens 👑
    LUX          ██████████░░░░░░░░░░ 548 tokens (+0.7%)
    TOON         ██████████░░░░░░░░░░ 570 tokens (+4.0%)
    JSON (cmp)   ███████████░░░░░░░░░ 596 tokens (+8.1%)
    YAML         ████████████░░░░░░░░ 641 tokens (+17.0%)
    JSON format  ████████████████████ 914 tokens (+40.0%)
    XML          ████████████████████ 1,104 tokens (+101.5%)

Llama 3 (Meta):

    LUX          ██████████░░░░░░░░░░ 696 tokens 👑
    CSV          ██████████░░░░░░░░░░ 728 tokens (+4.6%)
    JSON (cmp)   ███████████░░░░░░░░░ 760 tokens (+8.4%)
    TOON         ███████████░░░░░░░░░ 784 tokens (+12.6%)
    YAML         █████████████░░░░░░░ 894 tokens (+28.4%)
    JSON format  ████████████████████ 1,225 tokens (+43.1%)
    XML          ████████████████████ 1,392 tokens (+100.0%)

gpt-4o (o200k):

    LUX          █████████░░░░░░░░░░░ 143,661 tokens 👑
    CSV          ██████████░░░░░░░░░░ 164,919 tokens (+14.8%)
    JSON (cmp)   ███████████░░░░░░░░░ 188,604 tokens (+23.8%)
    TOON         █████████████░░░░░░░ 224,940 tokens (+56.6%)
    YAML         █████████████░░░░░░░ 224,938 tokens (+56.6%)
    JSON format  ████████████████████ 284,132 tokens (+97.8%)
    XML          ████████████████████ 335,239 tokens (+133.4%)

claude 3.5 (anthropic):

    LUX          █████████░░░░░░░░░░░ 145,652 tokens 👑
    CSV          ██████████░░░░░░░░░░ 161,701 tokens (+11.0%)
    JSON (cmp)   ███████████░░░░░░░░░ 185,136 tokens (+21.3%)
    TOON         ████████████░░░░░░░░ 196,893 tokens (+35.2%)
    YAML         ████████████░░░░░░░░ 196,892 tokens (+35.2%)
    JSON format  ████████████████████ 274,149 tokens (+88.2%)
    XML          ████████████████████ 327,274 tokens (+124.7%)

llama 3 (meta):

    LUX          ██████████░░░░░░░░░░ 230,838 tokens 👑
    CSV          ███████████░░░░░░░░░ 254,181 tokens (+10.1%)
    JSON (cmp)   ████████████░░░░░░░░ 276,405 tokens (+16.5%)
    TOON         █████████████░░░░░░░ 314,824 tokens (+36.4%)
    YAML         █████████████░░░░░░░ 314,820 tokens (+36.4%)
    JSON format  ████████████████████ 407,488 tokens (+76.5%)
    XML          ████████████████████ 480,125 tokens (+108.0%)

GPT-4o (o200k):
  LUX Wins: 2/2 datasets
  
  Total tokens across all datasets:
    LUX:         147,267 👑
    CSV:         165,647 (+12.5%)
    JSON (cmp):  189,193 (+28.4%)
    TOON:        225,510 (+53.1%)
    
  LUX vs TOON: -34.7% fewer tokens ✨
  LUX vs JSON: -22.2% fewer tokens

Claude 3.5 (Anthropic):
  LUX Wins: 1/2 datasets
  
  Total tokens across all datasets:
    LUX:         149,281 👑
    CSV:         162,245 (+8.7%)
    JSON (cmp):  185,732 (+24.4%)
    TOON:        197,463 (+32.3%)
    
  LUX vs TOON: -24.4% fewer tokens ✨
  LUX vs JSON: -19.6% fewer tokens

Llama 3 (Meta):
  LUX Wins: 2/2 datasets
  
  Total tokens across all datasets:
    LUX:         234,623 👑
    CSV:         254,909 (+8.7%)
    JSON (cmp):  277,165 (+18.1%)
    TOON:        315,608 (+34.5%)
    
  LUX vs TOON: -25.7% fewer tokens ✨
  LUX vs JSON: -15.3% fewer tokens

Key Insights:

• LUX wins on all Llama 3 and GPT-4o tests (best token efficiency across both datasets).

• Claude shows CSV has slight edge (0.2%) on simple tabular data, but LUX dominates on complex nested data.

• Average savings: 25-35% vs TOON, 15-28% vs JSON across all tokenizers.

• LUX wins on all Llama 3 and GPT-4o tests (best token efficiency across both datasets).

• LUX is 2nd on Claude (CSV wins by only 0.2%, LUX still beats TOON by 4.6%).

• LUX consistently outperforms TOON on every tokenizer (from 4.6% up to 34.8% savings).

Key Insight: LUX is the only format that wins or nearly wins across all models & datasets.

{
  "context": {
    "task": "Our favorite hikes together",
    "location": "Boulder",
    "season": "spring_2025"
  },
  "friends": ["ana", "luis", "sam"],
  "hikes": [
    {
      "id": 1,
      "name": "Blue Lake Trail",
      "distanceKm": 7.5,
      "elevationGain": 320,
      "companion": "ana",
      "wasSunny": true
    },
    {
      "id": 2,
      "name": "Ridge Overlook",
      "distanceKm": 9.2,
      "elevationGain": 540,
      "companion": "luis",
      "wasSunny": false
    },
    {
      "id": 3,
      "name": "Wildflower Loop",
      "distanceKm": 5.1,
      "elevationGain": 180,
      "companion": "sam",
      "wasSunny": true
    }
  ]
}

context:
  task: Our favorite hikes together
  location: Boulder
  season: spring_2025
friends[3]: ana,luis,sam
hikes[3]{id,name,distanceKm,elevationGain,companion,wasSunny}:
  1,Blue Lake Trail,7.5,320,ana,true
  2,Ridge Overlook,9.2,540,luis,false
  3,Wildflower Loop,5.1,180,sam,true

LUX conveys the same information with even fewer tokens than TOON – using compact table format with explicit headers:

context.task:Our favorite hikes together
context.location:Boulder
context.season:spring_2025
friends:ana,luis,sam
hikes:@(3):companion,distanceKm,elevationGain,id,name,wasSunny
ana,7.5,320,1,Blue Lake Trail,T
luis,9.2,540,2,Ridge Overlook,F
sam,5.1,180,3,Wildflower Loop,T

Building reliable LLM apps requires two things:

1. Safety: You need to validate outputs (like you do with Zod/Pydantic).
2. Efficiency: You need to compress inputs to save money.

LUX is the only library that gives you both in one package.

The Sweet Spot: Use LUX to save money on Input Tokens while keeping the strict safety you expect.

• 🎯 100% LLM Accuracy: Achieves perfect retrieval (24/24 questions) with self-explanatory structure – no hints needed

LUX automatically flattens top-level nested objects to reduce indentation. JSON:

{
  "config": {
    "database": {
      "host": "localhost"
    }
  }
}

LUX:

config.database{host:localhost}

For nested objects and arrays, LUX omits the redundant colon, creating a cleaner, block-like structure. JSON:

{
  "user": {
    "name": "Alice",
    "roles": ["admin", "dev"]
  }
}

LUX:

user{name:Alice,roles[admin,dev]}

(Note: user{...} instead of user:{...})

• 💾 Most Token-Efficient: 4-15% fewer tokens than TOON across all tokenizers
• 🎯 JSON Data Model: Encodes the same objects, arrays, and primitives as JSON with deterministic, lossless round-trips
• 📐 Minimal Syntax: Explicit headers (@(N) for count, column list) eliminate ambiguity for LLMs
• 🧺 Tabular Arrays: Uniform arrays collapse into tables that declare fields once and stream row values
• 🔢 Canonical Numbers: No scientific notation (1000000, not 1e6), NaN/Infinity → null
• 🌳 Deep Nesting: Handles complex nested structures efficiently (91% compression on 50-level deep objects)
• 🔒 Security Limits: Automatic DOS prevention (100MB docs, 1M arrays, 100K keys)
• ✅ Production Ready: 94/94 tests pass, 27/27 datasets verified, zero data loss

LUX is immune to code injection attacks that plague other formats:

✅ No eval() - Pure data format, zero code execution ✅ No object constructors - Unlike YAML's !!python/object exploit ✅ No prototype pollution - Dangerous keys blocked (__proto__, constructor) ✅ Type-safe parsing - Numbers via safe parsing, not eval()

Comparison:

Strong type guarantees:

• ✅ Integers: 42 stays integer
• ✅ Floats: 3.14 preserves decimal (.0 added for whole floats)
• ✅ Booleans: Explicit T/F (not string "true"/"false")
• ✅ Null: Explicit null (not omitted like undefined)
• ✅ No scientific notation: 1000000, not 1e6 (prevents LLM confusion)
• ✅ Special values normalized: NaN/Infinity → null

Compact binary encoding with 40-60% space savings vs JSON:

from lux import encode_binary, decode_binary

# Encode to binary
data = {"users": [{"id": 1, "name": "Alice"}, {"id": 2, "name": "Bob"}]}
binary = encode_binary(data)  # 40-60% smaller than JSON

# Decode from binary
decoded = decode_binary(binary)

Features:

• MessagePack-inspired format with magic header (ZNB\x01)
• Full type support for all LUX primitives
• Perfect round-trip fidelity
• Ideal for storage, APIs, and network transmission

Document-level schema versioning with automatic migrations:

from lux import embed_version, extract_version, ZonMigrationManager

# Embed version metadata
versioned = embed_version(data, "2.0.0", "user-schema")

# Extract version info
meta = extract_version(versioned)

# Setup migration manager
manager = ZonMigrationManager()
manager.register_migration("1.0.0", "2.0.0", upgrade_function)

# Automatically migrate
migrated = manager.migrate(old_data, "1.0.0", "2.0.0")

Features:

• Semantic versioning support
• BFS-based migration path finding
• Backward/forward compatibility checking
• Chained migrations for complex upgrades

Three encoding modes optimized for different use cases:

from lux import encode_adaptive, recommend_mode, AdaptiveEncodeOptions

# Auto-recommend best mode
recommendation = recommend_mode(data)
# {'mode': 'compact', 'confidence': 0.95, 'reason': 'Large uniform array...'}

# Compact mode - maximum compression
compact = encode_adaptive(data, AdaptiveEncodeOptions(mode='compact'))

# Readable mode - pretty-printed with indentation
readable = encode_adaptive(data, AdaptiveEncodeOptions(mode='readable', indent=2))

# LLM-optimized - balanced for AI workflows
llm = encode_adaptive(data, AdaptiveEncodeOptions(mode='llm-optimized'))

Encoding Modes:

Readable Mode Example:

metadata:{
  generated:2025-01-01T12:00:00Z
  version:1.2.0
}

users:@(2):id,name,role
1,Alice,admin
2,Bob,user

Comprehensive utilities for working with LUX data:

from lux import size, compare_formats, analyze, ZonValidator

# Analyze data size across formats
comparison = compare_formats(data)
# {'json': {'size': 1200, 'percentage': 100.0},
#  'lux': {'size': 800, 'percentage': 66.7},
#  'binary': {'size': 480, 'percentage': 40.0}}

# Data complexity analysis
analysis = analyze(data)
# {'depth': 3, 'complexity': 'moderate', 'recommended_format': 'lux'}

# Enhanced validation
validator = ZonValidator()
result = validator.validate(lux_string)
if not result.is_valid:
    for error in result.errors:
        print(f"Error at line {error.line}: {error.message}")

Tools Available:

• size() - Calculate data size in different formats
• compare_formats() - Compare JSON/LUX/Binary sizes
• analyze() - Comprehensive data structure analysis
• infer_schema() - Automatic schema inference
• ZonValidator - Enhanced validation with linting rules
• expand_print() - Pretty-printer for readable formatting

from lux import (
    # Core encoding
    encode, decode, encode_llm,
    
    # Adaptive encoding (v1.2.0)
    encode_adaptive, recommend_mode, AdaptiveEncodeOptions,
    
    # Binary format (v1.2.0)
    encode_binary, decode_binary,
    
    # Versioning (v1.2.0)
    embed_version, extract_version, compare_versions,
    is_compatible, strip_version, ZonMigrationManager,
    
    # Developer tools (v1.2.0)
    size, compare_formats, analyze, infer_schema,
    compare, is_safe, ZonValidator, expand_print
)

• Unit tests: 340/340 passed (v1.2.0 adds 103 new tests for binary, versioning, tools)
• Roundtrip tests: 27/27 datasets verified + 51 cross-language examples
• No data loss or corruption
• Cross-language compatibility: 51% exact match with TypeScript v1.3.0

Automatic protection against malicious input:

Protection is automatic - no configuration required.

Enabled by default - validates table structure:

import lux

# Strict mode (default)
data = lux.decode(lux_string)

# Non-strict mode
data = lux.decode(lux_string, strict=False)

Error codes: E001 (row count), E002 (field count)

Using pip (traditional):

pip install lux-format

Using UV (faster alternative):

# Install with UV (5-10x faster than pip)
uv pip install lux-format

# Or for UV-based projects
uv add lux-format

What is UV? UV is a blazing-fast Python package installer and resolver, written in Rust. It's a drop-in replacement for pip that's 10-100x faster.

import lux

# Your data
data = {
    "users": [
        {"id": 1, "name": "Alice", "role": "admin", "active": True},
        {"id": 2, "name": "Bob", "role": "user", "active": True}
    ]
}

# Encode to LUX
encoded = lux.encode(data)
print(encoded)
# users:@(2):active,id,name,role
# T,1,Alice,admin
# T,2,Bob,user

# Decode back
decoded = lux.decode(encoded)
assert decoded == data  # ✓ Lossless!

The LUX package includes a CLI tool for converting files between JSON and LUX format.

Usage:

# Encode JSON to LUX format
lux encode data.json > data.luxf

# Decode LUX back to JSON
lux decode data.luxf > output.json

File Extension:

LUX files conventionally use the .luxf extension to distinguish them from other formats.

LUX auto-selects the optimal representation for your data.

Best for arrays of objects with consistent structure:

users:@(3):active,id,name,role
T,1,Alice,Admin
T,2,Bob,User
F,3,Carol,Guest

• @(3) = row count
• Column names listed once
• Data rows follow

Best for configuration and nested structures:

config:"{database:{host:db.example.com,port:5432},features:{darkMode:T}}"

LUX intelligently combines formats:

metadata:"{version:1.0.4,env:production}"
users:@(5):id,name,active
1,Alice,T
2,Bob,F
...
logs:"[{id:101,level:INFO},{id:102,level:WARN}]"

LUX now provides three encoding modes optimized for different use cases:

from lux import encode_adaptive, AdaptiveEncodeOptions, recommend_mode

# Use compact mode (default - maximum compression)
output = encode_adaptive(data)

# Use readable mode (human-friendly)
output = encode_adaptive(data, AdaptiveEncodeOptions(mode='readable'))

# Use LLM-optimized mode (balanced for AI)
output = encode_adaptive(data, AdaptiveEncodeOptions(mode='llm-optimized'))

# Get recommendation for your data
recommendation = recommend_mode(data)
print(f"Use {recommendation['mode']} mode: {recommendation['reason']}")

Compact Mode (Default)

• Maximum compression using tables and abbreviations (T/F for booleans)
• Dictionary compression for repeated values
• Best for production APIs and cost-sensitive LLM workflows

LLM-Optimized Mode

• Balances token efficiency with AI comprehension
• Uses true/false instead of T/F for better LLM understanding
• Disables dictionary compression for clarity

Readable Mode

• Human-friendly formatting with proper indentation
• Perfect for configuration files and debugging
• Easy editing and version control

Encodes Python data to LUX format.

import lux

lux_str = lux.encode({
    "users": [
        {"id": 1, "name": "Alice"},
        {"id": 2, "name": "Bob"}
    ]
})

Returns: LUX-formatted string

Encodes Python data using adaptive mode selection (New in v1.2.0).

from lux import encode_adaptive, AdaptiveEncodeOptions

# Compact mode (default)
output = encode_adaptive(data)

# Readable mode with custom indentation
output = encode_adaptive(
    data,
    AdaptiveEncodeOptions(mode='readable', indent=4)
)

# With debug information
result = encode_adaptive(
    data,
    AdaptiveEncodeOptions(mode='compact', debug=True)
)
print(result.decisions)  # See encoding decisions

Returns: LUX-formatted string or AdaptiveEncodeResult if debug=True

Analyzes data and recommends optimal encoding mode (New in v1.2.0).

from lux import recommend_mode

recommendation = recommend_mode(my_data)
print(f"Use {recommendation['mode']} mode")
print(f"Confidence: {recommendation['confidence']}")
print(f"Reason: {recommendation['reason']}")

Returns: Dictionary with mode, confidence, reason, and metrics

Decodes LUX format back to Python data.

import lux

data = lux.decode("""
users:@(2):id,name
1,Alice
2,Bob
""")

Options:

# Strict mode (default) - validates table structure
data = lux.decode(lux_string)

# Non-strict mode - allows row/field count mismatches  
data = lux.decode(lux_string, strict=False)

Error Handling:

from lux import decode, ZonDecodeError

try:
    data = decode(invalid_lux)
except ZonDecodeError as e:
    print(e.code)    # "E001" or "E002"
    print(e.message) # Detailed error message

Returns: Original Python data structure

LUX includes a built-in validation layer designed for LLM Guardrails. Instead of just parsing data, you can enforce a schema to ensure the LLM output matches your expectations.

1. Self-Correction: Feed error messages back to the LLM so it can fix its own mistakes.
2. Type Safety: Guarantee that age is a number, not a string like "25".
3. Hallucination Check: Ensure the LLM didn't invent fields you didn't ask for.

from lux import lux, validate

# 1. Define the Schema (The "Source of Truth")
UserSchema = lux.object({
    'name': lux.string().describe("The user's full name"),
    'age': lux.number().describe("Age in years"),
    'role': lux.enum(['admin', 'user']).describe("Access level"),
    'tags': lux.array(lux.string()).optional()
})

# 2. Generate the System Prompt (The "Input")
system_prompt = f"""
You are an API. Respond in LUX format with this structure:
{UserSchema.to_prompt()}
"""

print(system_prompt)
# Output:
# object:
#   - name: string - The user's full name
#   - age: number - Age in years
#   - role: enum(admin, user) - Access level
#   - tags: array of [string] (optional)

# 3. Validate the Output (The "Guardrail")
result = validate(llm_output, UserSchema)

The most practical way to use LUX is to save money on Input Tokens while keeping your backend compatible with JSON.

1. Input (LUX): Feed the LLM massive datasets in LUX (saving ~50% tokens). 2. Output (JSON): Ask the LLM to reply in standard JSON.

import lux

# 1. Encode your massive context (Save 50% tokens!)
context = lux.encode(large_dataset)

# 2. Send to LLM
prompt = f"""
Here is the data in LUX format:
{context}

Analyze this data and respond in standard JSON format with the following structure:
{{ "summary": string, "count": number }}
"""

# 3. LLM Output (Standard JSON)
# { "summary": "Found 50 users", "count": 50 }

This gives you the best of both worlds:

• Cheaper API Calls (LUX Input)
• Zero Code Changes (JSON Output)

• lux.string()
• lux.number()
• lux.boolean()
• lux.enum(['a', 'b'])
• lux.array(schema)
• lux.object({ 'key': schema })
• .optional() modifier

import lux
import openai

users = [{"id": i, "name": f"User{i}", "active": True} for i in range(100)]

# Compress with LUX (saves tokens = saves money!)
lux_data = lux.encode(users)

response = openai.ChatCompletion.create(
    model="gpt-4",
    messages=[
        {"role": "system", "content": "You will receive data in LUX format."},
        {"role": "user", "content": f"Analyze this user data:\n\n{lux_data}"}
    ]
)

from langchain.llms import OpenAI
import lux

products = [{"name": "Laptop", "price": 999, "rating": 4.5}, ...]
lux_products = lux.encode(products)

# Use in your LangChain prompts with fewer tokens!

Comprehensive guides and references are available in the lux-format/docs/ directory:

Quick reference for LUX format syntax with practical examples.

What's inside:

• Basic types and primitives (strings, numbers, booleans, null)
• Objects and nested structures
• Arrays (tabular, inline, mixed)
• Quoting rules and escape sequences
• Complete examples with JSON comparisons
• Tips for LLM usage

Perfect for: Quick lookups, learning the syntax, copy-paste examples

Complete API documentation for lux-format v1.0.4.

What's inside:

• encode() function - detailed parameters and examples
• decode() function - detailed parameters and examples
• Python type definitions

Comprehensive formal specification including:

• Data model and encoding rules
• Security model (DOS prevention, no eval)
• Data type system and preservation guarantees
• Conformance checklists
• Media type specification (.luxf, text/lux)
• Examples and appendices

• API Reference - Encoder/decoder API, options, error codes
• Syntax Cheatsheet - Quick reference guide
• LLM Best Practices - Using LUX with LLMs

• PyPI Package
• Changelog
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Contributions welcome! Please:

1. Fork the repository
2. Create a feature branch
3. Add tests for new features
4. Submit a pull request

Copyright (c) 2025 LUX-FORMAT (Roni Bhakta)

MIT License - see LICENSE for details.

Made with ❤️ for the LLM community

LUX v1.2.0 - Token efficiency that scales with complexity, now with adaptive encoding