Question 1

What is multi-agent debate?

Accepted Answer

Multi-agent debate is an AI reasoning technique where multiple language models, or multiple instances of the same model with different roles, discuss a question across structured rounds. Research suggests it can improve some reasoning and evaluation tasks, though results depend on the models, format, and problem.

Question 2

How is Multi Agent Debates different from prompting ChatGPT or Claude?

Accepted Answer

A single prompt gives you one model's first-pass answer. Multi Agent Debates gives you 10 purpose-built formats: Decision stress-test, Expert panel, Judged debate, 1:1 Human Dialogue, Team Battle, Shark Tank, Focus group, TribeMind, Idea Tournament, and Custom discussion. Each format preserves the run and produces an output suited to the job.

Question 3

Which AI models can I use with Multi Agent Debates?

Accepted Answer

You can use models from every major provider — Anthropic, OpenAI, Google, xAI, Meta, DeepSeek, Qwen, Z.ai, MiniMax, Kimi, and more. Assign a different model to each Worker and configure an automatic fallback order in Settings.

Question 4

Is multi-agent debate scientifically validated?

Accepted Answer

The platform's debate workflows draw on published research from several institutions. The evidence is promising for some reasoning and evaluation tasks, but results remain model-, protocol-, and task-dependent. Key papers include:

Question 5

Where can I read more about multi-agent debate?

Accepted Answer

We publish free, in-depth guides on multi-agent debate methodology—no signup required. Start here:

Question 6

What can I use Multi Agent Debates for?

Accepted Answer

Use it to pressure-test decisions, gather specialist viewpoints, compare two arguments, observe a private AI-to-AI dialogue, stage Team Battles, evaluate pitches, run moderated Focus Groups, simulate audience response with TribeMind, select ideas in a tournament, or configure a discussion from scratch.

Question 7

How does the Focus group format work?

Accepted Answer

Choose 4–12 Workers with known personas, edit the discussion guide, and let a neutral moderator run the session. The resulting report is grounded in that simulated panel and includes themes and verified quotes rather than presenting the run as real population research.

Question 8

What do Shark Tank, TribeMind, and Idea Tournament produce?

Accepted Answer

Shark Tank returns a cited pitch scorecard and verdict. TribeMind runs 12–50 fictional participants through independent and social rounds, then produces descriptive metrics and a grounded Observer report. Idea Tournament compares 4–16 ideas head to head and produces a champion spec plus kill cards.

Question 9

What do Lab Experiments do?

Accepted Answer

Lab Experiments copy a draft discussion into hidden child runs, vary temperature and repetition, frequency, and presence penalties, then evaluate each transcript against your validation prompt and expected outcome. They can stop on a score threshold, iteration limit, or total cost cap.

Question 10

Is Multi Agent Debates an alternative to AutoGen, CrewAI, or LangGraph?

Accepted Answer

Multi Agent Debates focuses on purpose-built discussion formats, durable run monitoring, and format-specific reports rather than general-purpose agent orchestration. AutoGen, CrewAI, and LangGraph are broader frameworks for building custom agent applications and graphs.

Question 11

How much does a run cost?

Accepted Answer

Cost varies with the selected models, participant count, number of rounds, research calls, and report generation. Multi Agent Debates records token and cost metadata and supports turn, runtime, and observed-cost limits. Lab Experiments also support their own total cost cap.

Question 12

Can I use Multi Agent Debates without writing any code?

Accepted Answer

Yes. All 10 discussion formats, Workers, Teams, Personas, Playbooks, Lab Experiments, live monitors, and exports are available through the web interface.

Question 13

Is my data used to train AI models?

Accepted Answer

Multi Agent Debates does not train models on your transcripts. Prompts are sent to the model provider selected for that run, so provider-side data handling follows that provider's terms. The platform persists workspace records in its configured Supabase storage and provides archive, delete, and export controls.

Question 14

What is the Degeneration of Thought problem?

Accepted Answer

Degeneration of Thought, formalized by Liang et al. (EMNLP 2024), is a failure mode where an LLM commits to an answer and struggles to produce genuinely novel reasoning during self-reflection. Separating roles across agents may help surface different arguments, but it does not eliminate the underlying risk.

Multi-Agent Debate & LLM Reasoning Glossary

Multi-Agent Debate (MAD)

Self-Consistency (CoT-SC)

M-MAD

Degeneration of Thought (DoT)

Truth-Seeking Debate

Open Discussion

Team Discussion

Dimension-Sweep Arbiter

Mixture-of-Agents (MoA)

Chain-of-Thought (CoT)

Reflexion

Self-Refine

ChatEval

AutoGen

CrewAI

LangGraph

Model Context Protocol (MCP)

Sycophancy

Adaptive Stability Detection

GroupDebate

Sparse Communication Topology

Calibration

Steelman

Position Drift

Hallucination

LLM-as-Judge

Red-Teaming

Best-of-N

Worker