Model Behavior: Multi-Model Socratic Debate for AI Mutual Answer Review

Model Behavior builds an AI committee that allows multiple large language models to challenge, review, and synthesize more reliable answers via a structured debate process. It supports two modes—Council and Debate—and is compatible with Ollama local models and cloud APIs (e.g., OpenRouter, Gemini, OpenAI). It delivers responses more robust than single-model outputs, addressing issues like hallucinations and biases caused by the lack of external review in single models.

Most current AI tools adopt a "single model → single answer" approach, which has fundamental issues: single-model answers lack external review, easily leading to hallucinations, biases, or blind spots that users can hardly detect. Model Behavior shifts this mindset: it forms a multi-model committee that outputs final answers through a structured deliberation process, with full transparency (allowing users to read each model's statements, anonymous peer review records, and the conclusion-forming process).

🏛️ Council Mode (Classic Three Stages)

1. Independent Response: All models give initial responses based on their own knowledge
2. Anonymous Peer Review: Models anonymously rank each other's answers to identify persuasiveness and flaws
3. Chair Synthesis: The chair model integrates inputs to produce the final answer Suitable for scenarios requiring multi-angle review but with limited time.

🔀 Debate Mode (Four-Stage Deep Debate)

1. Socratic Stage: Models independently analyze the problem to establish their views
2. Debate Stage: Models agree with, oppose, or supplement other views
3. Devil's Advocate Stage: A dedicated model challenges the consensus to expose potential weaknesses
4. Synthesis Stage: The chair delivers the final verdict based on the full debate Produces more robust answers through active challenge mechanisms.

Model Behavior extends the provider support of the original llm-council, with feature comparisons:

Feature	llm-council	Model Behavior
Providers	Only OpenRouter	OpenRouter, Ollama (local + cloud), Gemini, OpenAI
Local/Offline Models	❌	✅ Run on own PC via Ollama, fully private
Mixed Providers in Single Committee	❌	✅ e.g., Local Llama + Cloud Gemini + OpenRouter GPT participate simultaneously
Response Mode	Wait for all to complete	Streaming (show results in stages)
Freely combine models based on privacy, cost, and performance needs.

1. 📡 Model Connectivity Test: Built-in button to ping all configured LLMs, showing real-time status and latency
2. 📎 File Upload Support: Attach 8 types of files (PDF/DOCX/TXT, max 20MB), extract text as context; no file content stored
3. 💾 Result Export: Support archiving and sharing in Markdown and HTML formats

• Architecture: Separate front-end and back-end (back-end: Python + uv dependency management; front-end: Node.js browser interface)
• Deployment: Windows-friendly, provides installation guides for Git, Node.js, Python, and steps for API key configuration
• File Extraction Capability: PDF (pypdf), DOCX (python-docx), XLSX/XLS (openpyxl/xlrd), text files (raw UTF-8)

Suitable scenarios:

1. Important Decision Support: Reduce the risk of single-model hallucinations
2. Complex Problem Analysis: Multi-angle review of policy/technology/ethics issues
3. Model Capability Comparison: Intuitively compare performance of different models
4. Learning and Research: Observe how models think and respond to challenges
5. Document Review: Multiple models jointly analyze long documents for comprehensive understanding

Model Behavior is improved based on karpathy/llm-council:

• Expand multi-provider support
• Add Debate mode and Devil's Advocate mechanism
• Add practical features like streaming responses and file uploads
• Improve UI readability
• Support local models to protect privacy From an experimental tool to a practical multi-model collaboration platform, providing a new option for high-reliability AI-assisted scenarios.