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A dialectical framework for augmented intelligence. AI reasoning powered with dialectics supports humans in: system optimization (psychology, engineering, business, politics, etc.); dispute resolution (mediation, conflicts, negotiations, etc.); decision-making (dilemmas, challenging situations, win-win, etc.).

Project description

Dialectical Framework

A reasoning framework for AI applications that need structured dialectical analysis. It curates a graph database through LLM-guided conversation, building up thesis-antithesis-synthesis structures from any domain.

The graph database is the state. Every interaction — extracting theses, finding oppositions, building wheels — writes semantic nodes and relationships into the graph. The framework is essentially a curation engine: an LLM orchestrator that progressively structures user input into dialectical knowledge graphs.

How It Works

  1. Input — User provides text, URLs, or ideas
  2. Analysis — LLM extracts theses, finds antitheses, generates aspects (T+, T-, A+, A-)
  3. Graph curation — Each insight is committed as nodes/relationships in the graph database
  4. Exploration — Perspectives are combined into Cycles, arranged into Wheels, and Transformations reveal paths toward synthesis

The graph accumulates structured reasoning over time. Applications query it, visualize it, or build on it.

Architecture

Host Application (Chainlit, API, CLI)
        │
        ▼
    Orchestrator (LLM + tools)
        │
        ▼
    Graph Database (Memgraph / Neo4j)

The Orchestrator is the main entry point. It manages an LLM conversation with tools that read and write the graph. The host app controls persona and session identity; the framework handles reasoning and graph curation.

Core Graph Structure

At the heart is the Dialectical Wheel — a semantic graph where nodes are statements and edges encode dialectical relationships (opposition, complementarity, transformation).

Structure Role
Statement Atomic unit of meaning
Perspective T/A opposition with aspects (T+, T-, A+, A-)
Cycle Ordered sequence of Perspectives
Wheel Concrete T-A arrangement implementing a Cycle
Transformation Action-Reflection paths between segments
Synthesis Emergent S+/S- from the Wheel's circular causality

Think of a Wheel as a pizza: segments are slices (T, T+, T-), Perspectives are half-pizzas (thesis + opposing antithesis), and Transitions are the cuts between slices.

Simple Detailed
Wheel Wheel

Integration

The framework is designed as a drop-in reasoning engine for AI applications that need dialectical analysis — decision support, systems thinking, mediation, ethical modeling.

from dialectical_framework.dialectical_reasoning import DialecticalReasoning
from dialectical_framework.settings import Settings
from dialectical_framework.agents.orchestrator.orchestrator import Orchestrator

# Initialize once
DialecticalReasoning.setup(Settings.from_env())

# Per-session usage
orchestrator = Orchestrator(app_preamble="You are a systems thinking coach...")

async for event in orchestrator.chat_stream("Analyze the tension between growth and sustainability"):
    # ThinkingDelta, TextDelta, ToolStart, ToolResult, ResponseComplete
    handle(event)

Setup

Requirements

  • Python 3.11+
  • Memgraph or Neo4j
  • An LLM provider (OpenAI, Anthropic, or any LiteLLM-compatible)

Install

poetry install

Environment Variables

Variable Description Example
DIALEXITY_DEFAULT_MODEL Model in provider/model format bedrock/anthropic.claude-sonnet-4-20250514-v1:0
DIALEXITY_GRAPH_DB_VENDOR Graph database memgraph (default) or neo4j
DIALEXITY_GRAPH_DB_HOST Database host 127.0.0.1
DIALEXITY_GRAPH_DB_PORT Database port 7687
DIALEXITY_THINKING_LEVEL Extended thinking budget medium, high, max (optional)

Store in .env or export in your environment.

Run Tests

poetry run pytest              # All tests (LLM mocked)
poetry run pytest -m llm       # Only LLM-path tests (mocked)
poetry run pytest --real-llm   # Hit real LLM provider

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