A computational argumentation system for exploring debate structure
Human debates are often decided by preparation, rhetoric, and charisma rather than argument quality. Judges can be swayed by fallacies and performance. The better debater wins, not necessarily the better position.
Dialectic removes this imbalance. The same AI agent argues both sides with identical capabilities. No rhetorical tricks, no charisma bias, no skill gap. What remains is the raw structure of argumentation itself.
The goal here isn't to determine which side "wins." It's to explore the natural geometry of argument space. Which claims are inherently defensible? What attacks work against what defenses? Where do positions become unassailable, and where do they crumble? By grounding debates in formal structure, these patterns become visible and quantifiable.
Debate Theory
Toulmin Framework
Every argument follows a structured schema:
| Component | Purpose |
|---|---|
| Claim | Central assertion |
| Grounds | Evidence supporting the claim (1-3 pieces) |
| Warrant | Logical connection between grounds and claim |
| Backing | Support for the warrant (optional) |
| Qualifier | Scope limitations (optional) |
Attack Types
Arguments can target specific components of opponent arguments:
- claim_attack: Challenge the assertion directly
- grounds_attack: Undermine the evidence
- warrant_attack: Break the logical connection
- backing_attack: Weaken warrant support
Defense Types
Arguments can defend previously made arguments:
- reinforce: Strengthen with additional support
- clarify: Address misinterpretation
- concede_and_pivot: Acknowledge weakness, redirect
Zero-Sum Scoring
Arguments compete for a fixed score pool. The judge distributes scores that sum to exactly 0 across all arguments in each exchange.
- 0 = Neutral (neither winning nor losing)
- Positive = Winning (took score from weaker arguments)
- Negative = Losing (gave score to stronger arguments)
This forces comparative evaluation. Arguments don't exist in isolation, they win or lose relative to each other.
Prerequisites
Dialectic requires Claude Code, Anthropic's agentic coding tool.
Installation
Clone and Go (Recommended)
git clone https://github.com/urav06/dialectic.git
cd dialectic
claude
Debates work immediately. No additional setup required.
Plugin Marketplace
/plugin → Add Marketplace → urav06/dialectic
/plugin → Browse and install plugins → dialectic@dialectic-marketplace
With the plugin installed, you can create debates in any project.
Optional: Graph Visualization
For automatic argument graph generation, install mermaid-cli:
npm install -g @mermaid-js/mermaid-cli
Or via Docker:
docker pull minlag/mermaid-cli
[!NOTE] Docker requires setting up a shell alias for
mmdcto work with Dialectic.alias mmdc="docker run --rm -v \"\$(pwd):/data\" minlag/mermaid-cli"
Usage
Commands
/debate-new: Create a new debate
/debate-new # Interactive setup
/debate-new climate-policy # With slug
/debate-new climate-policy "This house..." # With slug and motion
/debate-run <slug> <exchanges>: Run debate rounds
/debate-run climate-policy 5
/debate-report <slug>: Generate analysis and visualization
/debate-report climate-policy
Output Structure
pineapples-on-pizza/
├── debate.md # Motion and state
├── scores.json # Score history
├── arguments/
│ ├── prop_000a.md # Opening arguments
│ ├── prop_000b.md
│ ├── prop_000c.md
│ ├── opp_000a.md
│ ├── opp_000b.md
│ ├── opp_000c.md
│ ├── prop_001.md # Rebuttal arguments
│ ├── opp_001.md
│ └── ...
├── argument-graph.mmd # Mermaid source
├── argument-graph.png # Visual graph (if mmdc installed)
└── README.md # Debate analysis
Example Debates
Halt AI Development — This house would pause the development of artificial general intelligence until robust safety frameworks are established.
PhD Research Autonomy — PhD students should follow their gut, not their supervisor, when choosing their research topic.