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Status License Python Neo4j React Docker

SYNIZ

TRIZ-Swarm Multi-Agent Invention Engine

50 AI agents with distinct cognitive roles debate in parallel clusters
to solve engineering problems using Altshuller's 40 inventive principles


The Problem

Large language models generate plausible answers, but they do not invent. Engineering breakthroughs require systematic exploration of contradictions, forced analogies across domains, and the deliberate harvesting of unconventional ideas.

TRIZ provides a proven methodology --- 40 inventive principles distilled from analysis of over 2 million patents --- but applying it demands structured multi-perspective thinking that a single-prompt interaction cannot sustain.

SYNIZ embeds TRIZ methodology into a multi-agent swarm architecture. Agents with different personality profiles (provocateur, systems thinker, materials scientist, patent analyst, devil's advocate) debate in parallel clusters, challenge assumptions, and synthesize ideas through meta-debates. A Neo4j knowledge graph captures every idea, contradiction, and relationship, while episodic memory ensures the system learns from each session.


Architecture

                         +---------------------------+
                         |     React + D3.js UI      |
                         |   Real-time WebSocket      |
                         |   Graph Visualization      |
                         +-------------+-------------+
                                       |
                         +-------------v-------------+
                         |     FastAPI Backend        |
                         +-------------+-------------+
                                       |
              +------------------------+------------------------+
              |                        |                        |
   +----------v----------+  +---------v---------+  +-----------v-----------+
   |  SwarmOrchestrator   |  |   TRIZ Engine     |  |   Data Loaders        |
   |  50 Agents           |  |  40 Principles    |  |  USPTO/EPO Patents    |
   |  Cluster Debates     |  |  Contradiction    |  |  Materials Project    |
   |  Meta-Debates        |  |  Matrix           |  |  Science APIs         |
   |  SuperAgent          |  |  VePoL Analysis   |  |  HuggingFace          |
   +----------+-----------+  +---------+---------+  +-----------------------+
              |                        |
   +----------v------------------------v----------+
   |              Neo4j Knowledge Graph            |
   |  Ideas -- Contradictions -- Principles        |
   |  Analogies -- Agents -- Debates               |
   |  Graphiti Episodic Memory Layer               |
   +-----------------------------------------------+

Core Features

Multi-Agent Swarm Debates

  • 50 agents organized into clusters of 6, each with distinct cognitive roles and personalities
  • Structured multi-round debates with belief tracking and argument scoring
  • SuperAgent performs cross-cluster synthesis of winning ideas
  • Hallucination Harvester runs agents at temperature 1.4 to extract creative wild ideas

TRIZ Methodology Engine

  • 40 inventive principles with application heuristics
  • 39x39 contradiction matrix mapping parameter conflicts to recommended principles
  • Substance-Field (VePoL) analysis for physical interaction modeling
  • Ideal Final Result (IFR) formulation and gap analysis
  • Laws of technical system evolution

Knowledge Graph and Memory

  • Neo4j stores ideas, contradictions, principles, analogies, and relationships
  • Graph evolution tracks how ideas mutate, merge, and improve across sessions
  • Episodic memory via Graphiti for cross-session learning

Creative Augmentation

  • Analogy Mapper: cross-domain analogies (e.g., bird wing morphing applied to hull design)
  • Anomaly Detector: flags unusual idea patterns that may represent breakthroughs
  • Idea DNA: genetic representation enabling crossover and mutation operations
  • Idea Marketplace: agents trade and bid on ideas, surfacing high-value concepts

Patent and Materials Intelligence

  • USPTO and EPO patent dataset loaders for prior art analysis
  • Materials Project API for material property lookups
  • Scientific data source connectors

Maritime and Naval Engineering Applications

SYNIZ is particularly suited to maritime engineering challenges where multiple conflicting requirements must be resolved simultaneously.

Technical Contradiction Resolution

Contradiction TRIZ Principles Applied Outcome
Fuel efficiency vs. engine complexity Segmentation, Preliminary Action, Dynamics Modular hybrid propulsion, waste heat recovery, variable-geometry turbocharging
Structural strength vs. weight Local Quality, Composite Materials Selective reinforcement at stress zones, sandwich panel structures
Speed vs. stability Asymmetry, Dynamics, Nesting Asymmetric hull forms, active stabilizers, retractable appendages

IMO 2030/2050 Decarbonization

Agents specializing in propulsion, materials, hydrodynamics, and regulations debate fuel alternatives (ammonia, methanol, hydrogen) against infrastructure constraints. The SuperAgent synthesizes across clusters to find solutions satisfying thermodynamic, economic, and regulatory requirements simultaneously.

Problem Domains

  • Hull Fouling Reduction --- Biomimetic coatings, electrochemical antifouling, ultrasonic systems. Analogy Mapper draws from aerospace ice-prevention and medical biofilm research.
  • Vibration Dampening --- VePoL analysis models engine-mounting-hull interactions. Agents apply Dynamics, Cushioning, and Converting Harm to Benefit (energy harvesting).
  • Corrosion-Resistant Materials --- Materials Project API queries material properties; TRIZ resolves contradictions between durability, weight, and cost.
  • Fuel System Optimization --- Hallucination Harvester generates unconventional blending strategies; contradiction matrix resolves combustion efficiency vs. emissions.

Quick Start

# Prerequisites: Docker, Docker Compose, Ollama with qwen2.5:7b
ollama pull qwen2.5:7b

git clone https://github.com/ORTODOX1/SYNIZ.git
cd SYNIZ
docker-compose up -d
Service URL
Frontend http://localhost:5173
API Docs http://localhost:8000/docs
Neo4j Browser http://localhost:7474

Configuration

Parameter Default Description
SWARM_AGENTS_COUNT 50 Total agents in swarm
SWARM_CLUSTER_SIZE 6 Agents per debate cluster
SWARM_ROUNDS 5 Argumentation rounds per debate
SWARM_TEMPERATURE 0.9 Base LLM temperature
SUPERAGENT_HALLUCINATION_TEMPERATURE 1.4 Wild idea generation temperature
LLM_MODEL qwen2.5:7b Default model (supports per-role assignment)

Tech Stack

Layer Technology
Backend Python 3.11+, FastAPI, WebSocket
LLM Ollama / OpenAI API (heterogeneous per role)
Graph DB Neo4j 5.x + Graphiti episodic memory
Frontend React 18, TypeScript 5, D3.js
Infrastructure Docker Compose
Patent Data USPTO, EPO, HuggingFace Datasets
Materials Materials Project API

License

MIT

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TRIZ-Swarm: 50 AI agents debate engineering problems using 40 inventive principles. Neo4j knowledge graph + patent analysis

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