Hegelion

"The True is the whole." — G.W.F. Hegel

Hegelion applies dialectical reasoning to LLMs: forcing models to argue with themselves before reaching conclusions. This produces better reasoning for questions and better code for implementations.

Hegelion is prompt-driven by default: it generates prompts for your editor or LLM to execute, with no API keys required. Use it via MCP in editors like Claude Desktop/Cursor/VS Code, or via the Python API in your own agents.

Two Modes

Both modes use the same principle: force the model to oppose itself before concluding. This catches blind spots that single-pass approaches miss.

Autocoding: Player-Coach Loop

Updated in v0.5.0 — Based on Block AI's g3 agent research.

The Problem

Single-agent coding tools often:

• Declare success prematurely ("I have successfully implemented all requirements!")
• Accumulate context pollution over long sessions
• Miss edge cases because they verify their own work

The Solution

Two roles iterate until requirements are verified:

REQUIREMENTS (Source of Truth)
        │
        ▼
┌───────────────┐     ┌───────────────┐     ┌───────────────┐
│    PLAYER     │────▶│     COACH     │────▶│    ADVANCE    │
│  Implements   │     │   Verifies    │     │    State      │
│  code & tests │     │ independently │     │               │
└───────────────┘     └───────────────┘     └───────┬───────┘
        ▲                                           │
        │              ┌───────────┐                │
        └──────────────│ APPROVED? │◀───────────────┘
                       └───────────┘
                         │       │
                        No      Yes
                         │       │
                         ▼       ▼
                     Continue   Done

Player: Implements requirements, writes tests, responds to feedback. Does NOT declare success.

Coach: Independently verifies each requirement, ignores player's self-assessment, outputs structured checklist.

Key Insight

"Discard the player's self-report of success. Have the coach perform independent evaluation."

The coach catches issues by re-reading requirements and actually running tests—not by trusting what the player says it did.

Quick Start (Autocoding)

In Claude Code, Cursor, or any MCP-enabled editor:

Tip: if your editor exposes slash commands, you can use /hegelion as a wrapper. The underlying MCP tools are autocode and autocode_turn.

You: Call autocode with mode=init and these requirements:
     - Add user authentication to src/api.py
     - Add tests in tests/test_auth.py
     - All tests must pass

[Session initializes]

You: Call autocode_turn with role=player and implement

[Player writes code and tests]

You: Call autocode_turn with role=coach and verify

[Coach: ✓ auth endpoint exists, ✗ missing password validation test]

You: Call autocode_turn with role=advance, coach_feedback, approved=false

[Loop until COACH APPROVED]

State passing note: autocode_turn with role=player returns a player prompt plus an updated state advanced to phase: "coach" for the next call. Prompt outputs include current_phase (prompt phase) and next_phase (returned state's phase). All structured outputs include schema_version for client stability.

MCP Tools

Codex Skill (optional)

This repo includes a Codex skill at skills/hegelion/SKILL.md. Install it with your skill installer (for example, install-skill-from-github.py --repo Hmbown/Hegelion --path skills/hegelion). It mirrors the /hegelion command and uses MCP tools when available.

Why It Works

Dialectical Reasoning: Thesis → Antithesis → Synthesis

For questions requiring deep analysis, Hegelion forces three separate LLM calls:

[Call 1] Thesis     → LLM commits to a position
[Call 2] Antithesis → LLM attacks that position (separate call, no hedging)
[Call 3] Synthesis  → LLM reconciles the opposition

Why Separate Calls Matter

When the model must commit to a thesis, then genuinely attack it in a separate call, the synthesis surfaces insights that single-call approaches shortcut.

Quick Start (Dialectical)

pip install hegelion

# MCP setup (auto-detects OS)
hegelion-setup-mcp --host claude-desktop

# MCP setup for Claude Desktop (macOS)
hegelion-setup-mcp --write "$HOME/Library/Application Support/Claude/claude_desktop_config.json"

Or use the prompt-driven Python API (you run the prompt with your LLM of choice):

from hegelion.core.prompt_dialectic import create_single_shot_dialectic_prompt

prompt = create_single_shot_dialectic_prompt(
    "Is AI conscious?",
    use_council=True,
    response_style="sections",
)
print(prompt)

Health check (lists tools + generates a sample prompt):

hegelion-server --self-test

Feature Toggles

Installation

pip install hegelion

For MCP integration (works with any MCP-enabled editor):

# Shortcuts
hegelion-setup-mcp --host claude-desktop
hegelion-setup-mcp --host cursor
hegelion-setup-mcp --host vscode
hegelion-setup-mcp --host windsurf

# Claude Desktop (macOS)
hegelion-setup-mcp --write "$HOME/Library/Application Support/Claude/claude_desktop_config.json"

# Claude Desktop (Windows)
hegelion-setup-mcp --write "%APPDATA%\\Claude\\claude_desktop_config.json"

# Claude Desktop (Linux)
hegelion-setup-mcp --write "$HOME/.config/Claude/claude_desktop_config.json"

# Then restart your MCP host

Claude Code (no MCP setup needed):

# Use /hegelion command directly in any Hegelion repo clone
# Or add MCP server for tool access
claude mcp add hegelion python -- -m hegelion.mcp.server

Manual config (any MCP host):

{
  "mcpServers": {
    "hegelion": {
      "command": "python",
      "args": ["-m", "hegelion.mcp.server"]
    }
  }
}

If you run from source (not site-packages), set PYTHONPATH to the repo root. The hegelion-setup-mcp command writes this automatically. If your host expects a full command path, use python -m hegelion.mcp.server instead of hegelion-server.

Supported editors: Claude Desktop, Claude Code, Cursor, VS Code + GitHub Copilot, Windsurf, Google Antigravity, Gemini CLI

See MCP Integration Guide for setup instructions.

Documentation

• MCP Integration — Setup for Claude Desktop, Cursor, VS Code + Copilot, Windsurf, Antigravity, Gemini CLI
• Python API — Prompt-driven API reference
• CLI Reference — MCP server and setup commands
• Configuration — Backends and feature toggles
• Technical Specification — Output schemas, phase specs

Contributing

Issues and PRs welcome. For significant changes, open a discussion first.

Recent Changes

v0.5.0 (March 2026)

• MCP tool consolidation: 14 tools simplified to 4 unified tools: dialectic, autocode, autocode_turn, autocode_session
• State machine simplification: AutocodingStatus removed; phase is now the only state indicator
• Schema migration: schema_version bumped to 2 with backward-compatible v1-to-v2 loading
• Validation cleanup: Added @validated decorator + typed specs to remove repetitive handler boilerplate
• Dead code removal: Removed unused judge path, unused conversation state, and deprecated response styles
• Python 3.13 support: Added to CI and classifiers
• Public API exports: hegelion now exports key classes directly (DialecticalPrompt, PromptDrivenDialectic, AutocodingState, etc.)
• PEP 561 py.typed marker: Enables type-checker support for library consumers
• --version flag: Both hegelion-server and hegelion-setup-mcp now support --version
• Updated model references: .env.example modernized (Claude Sonnet 4.6, GPT-5.2, Gemini 3 Pro)
• CHANGELOG fix: Corrected malformed duplicate section headers

v0.4.6 (February 4, 2026)

• CI fix: Fixed CI pipeline and automated release workflow

v0.4.5 (February 4, 2026)

• Single-call CLI execution for dialectical_single_shot: execute, timeout_seconds, max_retries
• hegelion-setup-mcp flags: --llm-command-json, --llm-command, --auto-execute

v0.4.4 (January 21, 2026)

• Simplified skill/command: Condensed to minimal routing tables, MCP-first approach

v0.4.3 (January 12, 2026)

• MCP refactor: Split tooling, handlers, and validation to make the server easier to extend and maintain
• Codex skill: Added skills/hegelion/SKILL.md for the /hegelion workflow

License: MIT