Heinrich The Inventing Machine MCP Server

Heinrich: The Inventing Machine

An open-source AI system for TRIZ-based inventive problem solving with interpretable, step-by-step reasoning flow

🌍 Languages: English | 中文 | Русский | العربية

Overview

Heinrich is an open-source AI engine that combines classical TRIZ (Theory of Inventive Problem Solving) methodology with modern Large Language Models to provide systematic, interpretable inventive problem-solving capabilities. Named after Genrich Altshuller, the creator of TRIZ, Heinrich embodies the systematic thinking approach of the original methodology while leveraging contemporary AI advances.

Key Features

• Systematic TRIZ Pipeline: 7-module workflow following classical TRIZ methodology
• Interpretable Reasoning: Every step is logged and traceable to TRIZ principles
• Knowledge Base: Complete 39 parameters, 40 principles, contradiction matrix
• Multi-LLM Support: Google Gemini, Ollama (local), and more coming soon
• Multi-modal Support: Text problems, technical specifications, patent analysis
• Evaluation Suite: Curated case base with reference solutions
• Multilingual: Full documentation in English, Chinese, Russian, and Arabic
• Ethical AI: Transparent persona with safety guidelines

Getting Started

Installation

Prerequisites

• Python 3.8 or higher
• pip (Python package manager)

Install from source

1. Clone the repository:

git clone https://github.com/NickScherbakov/Heinrich-The-Inventing-Machine.git
cd Heinrich-The-Inventing-Machine

2. Install dependencies:

pip install -r requirements.txt

3. Install Heinrich in development mode:

pip install -e .

Dependencies

Heinrich requires the following Python packages:

• PyYAML>=6.0 - For TRIZ knowledge base (YAML files)
• numpy>=1.21.0 - For numerical computations
• dataclasses-json>=0.5.0 - For data serialization
• google-generativeai>=0.3.0 - For Google Gemini LLM integration
• requests>=2.31.0 - For HTTP requests (Ollama)

All dependencies are automatically installed when using pip install.

LLM Providers

Heinrich supports multiple LLM backends:

Provider	Setup	Best For
Google Gemini	API key from AI Studio	Production, cloud deployment
Ollama	Local installation from ollama.ai	Local development, privacy
Vertex AI	GCP project with Vertex AI enabled	Enterprise, GCP integration

Using Google Gemini (Recommended)

from heinrich.llm.adapters import GeminiAdapter

adapter = GeminiAdapter({
    "api_key": "your-gemini-api-key",
    "model": "gemini-1.5-pro",
    "temperature": 0.7,
})

response = adapter.generate("Analyze this TRIZ problem...")

See examples/gemini_usage.py for a complete example.

Usage

Quick Example

Here's a simple example to get started with Heinrich:

from problem_parser import ProblemParser

# Create a problem parser
parser = ProblemParser()

# Analyze a problem
problem = "We need to make a car faster, but increasing engine power makes it consume more fuel."
result = parser.parse(problem)

# View the analysis
print(f"Technical System: {result.technical_system}")
print(f"Desired Improvement: {result.desired_improvement}")
print(f"Undesired Consequence: {result.undesired_consequence}")

Output:

Technical System: car
Desired Improvement: faster
Undesired Consequence: increasing engine power makes it consume more fuel

Run the Basic Example

Try the included basic usage example:

python3 examples/basic_usage.py

This demonstrates problem parsing with multiple examples.

Command-Line Interface

For full TRIZ pipeline analysis, use the interactive CLI:

# Interactive problem-solving session
python3 heinrich_cli.py interactive

# Batch processing (coming soon)
python3 heinrich_cli.py batch problems.txt

# API server mode (coming soon)
python3 heinrich_cli.py api --port 8080

The interactive mode guides you through the complete TRIZ methodology:

1. Problem description and parsing
2. Contradiction identification using 39 TRIZ parameters
3. Principle selection from 40 inventive principles
4. Scientific effects integration
5. Solution concept generation
6. Context-aware adaptation
7. Comprehensive report generation

Example Problem Analysis

Input: "We need to make a car faster, but increasing engine power makes it consume more fuel."

Heinrich Output:

Step 1: Problem Analysis
- Technical system: Automotive propulsion
- Desired improvement: Speed (Parameter 9)
- Harmful consequence: Energy consumption (Parameter 19)

Step 2: Contradiction Identification
- Physical contradiction: Engine must be powerful AND energy-efficient
- Technical contradiction: Speed vs Energy consumption

Step 3: Principle Selection
- Principle 15: Dynamics (variable characteristics)
- Principle 2: Taking out (separate conflicting properties)
- Principle 35: Parameter change (different states/properties)

Step 4: Solution Concepts
1. Variable compression ratio engine (Principle 15)
2. Hybrid powertrain with mode switching (Principle 2)
3. Active aerodynamics adaptation (Principle 35)
...

Architecture

Heinrich implements a modular, interpretable TRIZ reasoning pipeline:

Problem Input
     ↓
[Problem Parser] → Normalized problem description
     ↓
[Contradiction Identifier] → Technical/Physical contradictions
     ↓
[Principle Selector] → Relevant TRIZ principles (1-40)
     ↓
[Effects Lookup] → Scientific effects and evolution patterns
     ↓
[Concept Generator] → Solution concepts with reasoning
     ↓
[Adaptation Planner] → Context-aware recommendations
     ↓
[Report Builder] → Structured solution report

Core Modules

• Problem Parser: Extracts essence from unstructured problem descriptions
• Contradiction Identifier: Maps to 39 TRIZ parameters and identifies contradictions
• Principle Selector: Selects relevant principles from contradiction matrix
• Effects Lookup: References scientific effects and evolution patterns
• Concept Generator: Creates solution concepts with TRIZ traceability
• Adaptation Planner: Contextualizes solutions for real-world constraints
• Report Builder: Generates structured, traceable solution reports

The Heinrich Persona

Heinrich operates as an AI mentor inspired by Genrich Altshuller's methodical thinking style. This persona:

• Follows systematic TRIZ methodology rigorously
• Provides educational explanations for each step
• Maintains ethical boundaries and transparency
• Important: This is an AI system inspired by TRIZ principles, not a simulation of the real person

Knowledge Base

Heinrich includes comprehensive TRIZ knowledge:

• 39 Technical Parameters: Complete parameter definitions and relationships
• 40 Inventive Principles: Detailed principles with sub-principles and examples
• Contradiction Matrix: 39×39 matrix mapping contradictions to principles
• Scientific Effects Database: Physics, chemistry, and biological effects
• Evolution Patterns: 8 patterns of technical system evolution
• Case Base: Curated educational cases with reference solutions

Evaluation and Quality

Heinrich includes rigorous evaluation capabilities:

• Case-based Testing: Validated against classical TRIZ educational cases
• Consistency Metrics: Measures contradiction identification accuracy
• Completeness Metrics: Evaluates principle coverage and reasoning depth
• Expert Review: Structured templates for TRIZ expert evaluation
• Comparative Analysis: Benchmarks against other AI problem-solving approaches

Multilingual Support

Full documentation and interface support for 4 languages:

• English (en): Primary development language
• 中文 Chinese (zh): Complete translation with technical terminology
• Русский Russian (ru): Complete translation honoring TRIZ origins
• العربية Arabic (ar): NEW - Complete translation for Arabic-speaking innovators

All translations include:

• ✅ TRIZ terminology glossaries (30+ terms each)
• ✅ Complete documentation structure
• ✅ RTL (Right-to-Left) support for Arabic
• ✅ Automated translation synchronization workflows

See i18n/README.md for translation guidelines and contribution workflow.

AI Integration via MCP

Heinrich supports seamless AI-assisted workflows through the GitHub MCP (Model Context Protocol) Server. This integration enables AI assistants to directly interact with the repository, eliminating the need for manual copy-pasting and creating a true bridge between human intention and AI execution.

What is MCP?

MCP (Model Context Protocol) is an open standard that provides AI assistants with standardized access to external tools and services. With MCP integration, AI can:

• 🤖 Comment on issues and pull requests directly
• 🌿 Create and manage branches
• 🔍 Search code and issues
• 📝 Update repository metadata
• 👥 Manage collaborators and reviews
• And much more!

Getting Started with MCP

We offer two setup options:

1. Hosted MCP Server (Recommended) - Uses GitHub's official endpoint with OAuth authentication
2. Local Docker MCP Server - Self-hosted for more control and customization

For detailed setup instructions, see our comprehensive guide:

📖 MCP Integration Guide

Quick Setup (Hosted)

1. Create a .mcp.json configuration file (see .mcp.json.example)
2. Authorize via GitHub OAuth when prompted by your AI assistant
3. Start using AI-assisted workflows immediately!

Why MCP for Heinrich?

This integration aligns perfectly with Heinrich's mission of automation and improving creative processes. MCP creates a seamless workflow where:

• AI becomes a true collaborator in the development process
• Friction is removed from repetitive tasks
• You focus on creative problem-solving while AI handles execution
• The system learns and adapts to your workflow

"Heinrich + MCP: Where systematic invention meets seamless AI collaboration" 🤝

Contributing

We welcome contributions! See CONTRIBUTING.md for:

• Code style and testing requirements
• Documentation standards for all three languages
• Translation workflow and terminology management
• Issue templates and PR guidelines

Research and Citations

If you use Heinrich in your research, please cite:

@software{heinrich_triz_2025,
  title={Heinrich: The Inventing Machine - Open Source TRIZ AI Engine},
  author={Heinrich Development Team},
  year={2025},
  url={https://github.com/your-org/heinrich},
  license={Apache-2.0}
}

License

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.

Acknowledgments

• Genrich Altshuller and the TRIZ community for the foundational methodology
• Open source TRIZ databases and educational materials
• Contributors and maintainers of this project
• The broader AI and problem-solving research communities

Roadmap

• v0.1 (November 2025): Core pipeline with CLI interface
• v0.5 (January 2026): Complete knowledge base and evaluation suite
• v1.0 (March 2026): Production-ready with full multilingual support
• v1.5 (June 2026): Advanced agent orchestration and plugin system

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Heinrich - Where systematic invention meets artificial intelligence 🚀

"The best problems are those that seem impossible to solve... until you find the right principle." - Inspired by TRIZ methodology