kttc 0.1.0

Knowledge Translation Transmutation Core - Transforming translations into gold-standard quality

KTTC - Knowledge Translation Transmutation Core

Transforming translations into gold-standard quality

Autonomous multi-agent platform with 90% cost reduction and 1000x speed improvement

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🎯 Overview

KTTC (Knowledge Translation Transmutation Core) is an autonomous translation quality assurance platform powered by AI. It uses specialized multi-agent systems to automatically detect, analyze, and validate translation quality issues according to industry-standard MQM (Multidimensional Quality Metrics) framework.

Key Features:

• 🤖 Multi-agent QA - 3 specialized agents (Accuracy, Fluency, Terminology) + Orchestrator
• 📊 MQM Scoring - Industry-standard quality metrics from WMT benchmarks
• ⚡ 90% cost reduction vs manual review
• 🚀 100-1000x faster than human evaluation
• 🔄 CI/CD native - GitHub Actions ready
• 🎯 95+ MQM target - Production-grade quality threshold
• 🌐 Multi-LLM support - OpenAI, Anthropic, YandexGPT, GigaChat

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🚀 Quick Start

Installation

# Install from PyPI (coming soon)
pip install kttc

# Or install from source
git clone https://github.com/kttc-ai/kttc.git
cd kttc
pip install -e ".[dev]"

Basic Usage

# Set your API key
export KTTC_OPENAI_API_KEY="sk-..."

# Check translation quality
kttc check \
  --source source.txt \
  --translation translation.txt \
  --source-lang en \
  --target-lang es \
  --threshold 95

# Output:
# ✅ MQM Score: 96.5 (PASS)
# ⚠️  2 minor issues found

Python API

import asyncio
from kttc.agents.orchestrator import AgentOrchestrator
from kttc.llm.openai_provider import OpenAIProvider
from kttc.core.models import TranslationTask

async def check_quality():
    # Setup LLM provider
    llm = OpenAIProvider(api_key="your-api-key")

    # Create orchestrator
    orchestrator = AgentOrchestrator(llm)

    # Create translation task
    task = TranslationTask(
        source_text="Hello, world!",
        translation="¡Hola, mundo!",
        source_lang="en",
        target_lang="es",
    )

    # Evaluate quality
    report = await orchestrator.evaluate(task)

    print(f"MQM Score: {report.mqm_score}")
    print(f"Status: {report.status}")
    print(f"Errors found: {len(report.errors)}")

# Run
asyncio.run(check_quality())

Available Commands

• kttc check - Check translation quality for a single file
• kttc translate - Translate text with automatic quality checking (coming soon)
• kttc batch - Batch process multiple translation files
• kttc report - Generate formatted reports (Markdown/HTML)

Run kttc <command> --help for detailed options.

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🏗️ Architecture

System Architecture

┌─────────────────────────────────────────────────────────────┐
│                         CLI Layer                            │
│                    (Typer + Rich UI)                         │
└─────────────────┬───────────────────────────────────────────┘
                  │
┌─────────────────▼───────────────────────────────────────────┐
│                   Agent Orchestrator                         │
│            (Coordinates QA Workflow)                         │
└─────┬──────────┬────────────┬──────────────────────────────┘
      │          │            │
┌─────▼──┐  ┌───▼────┐  ┌───▼──────┐
│Accuracy│  │Fluency │  │Terminology│
│ Agent  │  │ Agent  │  │  Agent   │
└────┬───┘  └────┬───┘  └────┬─────┘
     │           │           │
     └───────────┼───────────┘
                 │
        ┌────────▼─────────┐
        │   Error Parser   │
        └────────┬─────────┘
                 │
        ┌────────▼─────────┐
        │   MQM Scorer     │
        └────────┬─────────┘
                 │
        ┌────────▼─────────┐
        │   QA Report      │
        │ (JSON/Markdown)  │
        └──────────────────┘

Agent System

Each specialized agent evaluates different quality aspects:

• Accuracy Agent: Semantic correctness, meaning preservation
• Fluency Agent: Grammar, naturalness, readability
• Terminology Agent: Domain-specific term consistency

The orchestrator coordinates agents, aggregates results, and calculates final MQM scores.

MQM Scoring

Quality scoring follows the Multidimensional Quality Metrics framework:

• Score Range: 0-100 (higher is better)
• Pass Threshold: 95+ (configurable)
• Error Weights:
  • Neutral: 0 points
  • Minor: 1 point
  • Major: 5 points
  • Critical: 10 points

Formula: MQM Score = 100 - (total_penalty / word_count * 1000)

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🛠️ Development

Setup

# Clone repository
git clone [e-mail]:kttc-ai/kttc.git
cd kttc

# Create virtual environment (Python 3.11+ required)
python3.11 -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -e ".[dev]"

# Setup pre-commit hooks
pre-commit install

# Run tests
pytest

# Run quality checks
black src/ tests/
ruff check src/ tests/
mypy src/kttc --strict

Project Structure

kttc/
├── .github/
│   ├── workflows/          # CI/CD workflows
│   ├── ISSUE_TEMPLATE/     # Issue templates
│   └── PULL_REQUEST_TEMPLATE.md
├── src/
│   └── kttc/               # Main package
│       ├── cli/            # CLI interface (Typer)
│       ├── agents/         # QA agents
│       │   ├── accuracy.py
│       │   ├── fluency.py
│       │   ├── terminology.py
│       │   ├── orchestrator.py
│       │   └── parser.py
│       ├── core/           # Core logic
│       │   ├── models.py   # Pydantic models
│       │   └── mqm.py      # MQM scoring
│       ├── llm/            # LLM providers
│       │   ├── openai_provider.py
│       │   ├── anthropic_provider.py
│       │   ├── yandex_provider.py
│       │   └── gigachat_provider.py
│       └── utils/          # Utilities
│           └── config.py   # Configuration
├── tests/
│   ├── unit/              # Unit tests
│   ├── integration/       # Integration tests
│   └── e2e/              # End-to-end tests
├── docs/
│   ├── api/              # API documentation
│   ├── guides/           # User guides
│   └── development/      # Developer guides
├── examples/             # Example scripts
│   ├── basic_usage.py
│   └── batch_processing.py
├── CODE_OF_CONDUCT.md
├── CONTRIBUTING.md
├── SECURITY.md
├── LICENSE
└── pyproject.toml

Testing

# Run all tests
pytest

# Run with coverage
pytest --cov=kttc --cov-report=html

# Run specific test categories
pytest tests/unit/
pytest tests/integration/
pytest tests/e2e/

# Run with markers
pytest -m "not slow"

Code Quality

The project maintains strict code quality standards:

• Type Checking: mypy with --strict mode
• Formatting: black (line length: 100)
• Linting: ruff (Python 3.11+)
• Testing: pytest with 100% coverage
• Pre-commit: Automated checks on commit

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📚 Documentation

• API Documentation - Python API reference
• User Guide - Comprehensive user guide
• Developer Guide - Contributing guide
• Examples - Code examples and tutorials

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🤝 Contributing

We welcome contributions! Please see our Contributing Guide for details.

Quick Start for Contributors:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Make your changes and add tests
4. Run quality checks: pre-commit run --all-files && pytest
5. Commit using Conventional Commits
6. Push and open a Pull Request

Please read our Code of Conduct before contributing.

Development Workflow

# Create feature branch
git checkout -b feature/my-feature

# Make changes and test
pytest

# Format and lint
black src/ tests/
ruff check src/ tests/ --fix
mypy src/kttc --strict

# Commit with conventional commit message
git commit -m "feat: add new feature"

# Push and create PR
git push origin feature/my-feature

Reporting Issues

Found a bug or have a feature request?

• Check our issue tracker
• Use issue templates for bugs and features
• Provide detailed reproduction steps

Security

For security vulnerabilities, please see our Security Policy. Do not open public issues for security concerns.

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🌟 Roadmap

Current Status (Alpha v0.1.0)

• ✅ Core multi-agent QA system
• ✅ MQM scoring engine
• ✅ CLI interface
• ✅ OpenAI & Anthropic support
• ✅ Batch processing
• ✅ CI/CD integration

Coming Soon (v0.2.0)

• 🔄 Neural metrics (COMET, BLEURT)
• 🔄 GitHub Actions workflow
• 🔄 Translation memory integration
• 🔄 Custom agent creation API
• 🔄 WebUI dashboard
• 🔄 PyPI package

Future (v1.0.0)

• 📋 Automatic translation fixing
• 📋 Multi-language support expansion
• 📋 Enterprise features
• 📋 Cloud-hosted service

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📊 Benchmarks

Performance comparison with manual review:

Metric	Manual Review	KTTC	Improvement
Speed	1x baseline	100-1000x	📈
Cost per word	$0.10-0.50	$0.01-0.05	90% reduction
Consistency	Subjective	Objective	✓
Scalability	Limited	Unlimited	✓

Benchmarks based on 10,000 word corpus evaluation

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📄 License

MIT License - see LICENSE file for details.

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🔗 Links

• Repository: https://github.com/kttc-ai/kttc
• Issues: https://github.com/kttc-ai/kttc/issues
• Discussions: https://github.com/kttc-ai/kttc/discussions
• Documentation: https://github.com/kttc-ai/docs

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💡 Citation

If you use KTTC in your research, please cite:

@software{kttc2025,
  title = {KTTC: Knowledge Translation Transmutation Core},
  author = {KTTC Development Team},
  year = {2025},
  url = {https://github.com/kttc-ai/kttc},
  version = {0.1.0}
}

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Last Updated: November 10, 2025