| name | multi-agent-debate |
| description | Multi-agent debate / Society of Mind — N agents × R rounds converge on consensus. Du et al. 2023 beats zero-shot CoT on MMLU/GSM8K/MATH. Independent contributions from agent count AND round count. Sycophancy cascade prevention, heterogeneous models, compute budget. Sources: rohitg00/ai-engineering-from-scratch (Apache-2.0). |
/multi-agent-debate
When to Use
- High-stakes reasoning where a single LLM gives confidently wrong answers
- Reducing correlated errors: one model family has systematic bias in a domain
- Factual tasks where self-consistency sampling has plateaued (debate breaks the saturation)
- Research synthesis, code review, or multi-perspective analysis
Do NOT use for
- Tasks with a single deterministic answer verifiable by tool (use CRITIC in [[self-refine-critic]])
- Latency-sensitive workloads (N × R LLM calls = N×R cost)
- Tasks where any model in the pool is clearly much weaker than others (weak models drag consensus)
Du et al. 2023 algorithm (ICML 2024)
Round 0 (initial): each agent i produces answer_i independently
Round r (debate): each agent i sees all agents' round r-1 answers
→ produces updated answer_i conditioned on the group
Final: majority-vote across agents' final answers
Two independent contributions to quality (from paper ablations):
- Multiple agents (1 round, majority vote): beats single-agent; plateaus
- Multiple rounds (1 agent, self-reflection): barely helps alone
- Both together: produces the big accuracy jumps
Debate loop
from typing import Callable
def debate(
question: str,
agents: list[Callable[[str, list[str] | None], str]],
rounds: int = 3,
) -> str:
"""
agents: list of callables (question, prior_answers) -> answer
Returns majority-voted final answer.
"""
answers: list[str] = [agent(question, None) for agent in agents]
for r in range(1, rounds):
new_answers: list[str] = []
for i, agent in enumerate(agents):
others = [answers[j] for j in range(len(agents)) if j != i]
updated = agent(question, others)
new_answers.append(updated)
answers = new_answers
return majority_vote(answers)
def majority_vote(answers: list[str]) -> str:
from collections import Counter
import re
extracted = []
for a in answers:
m = re.search(r'(?:answer|final answer)[:\s]+(.+?)(?:\.|$)', a, re.IGNORECASE)
extracted.append(m.group(1).strip() if m else a.strip())
counts = Counter(extracted)
return counts.most_common(1)[0][0]
Agent prompt with debate context
def build_debate_prompt(question: str, prior_answers: list[str] | None) -> str:
if not prior_answers:
return f"""Answer the following question. Show your reasoning.
Question: {question}
Answer:"""
others_block = "\n\n".join(
f"Agent {i+1}'s answer:\n{ans}"
for i, ans in enumerate(prior_answers)
)
return f"""Answer the following question. You have seen other agents' answers below.
Consider their reasoning critically. You may update your answer or maintain it with justification.
Question: {question}
Other agents' answers:
{others_block}
Your updated answer (with reasoning):"""
Sycophancy cascade prevention
def build_adversarial_debate_prompt(question: str, prior_answers: list[str]) -> str:
"""Force at least one agent to argue the counter-position."""
others_block = "\n\n".join(f"Agent {i+1}: {a}" for i, a in enumerate(prior_answers))
return f"""Question: {question}
Other agents have proposed the following answers:
{others_block}
Your role: identify the weakest point in the most popular answer and argue for an alternative.
Be specific. If you ultimately agree after analysis, say so and explain why.
Counter-analysis:"""
def assign_roles(n_agents: int, round_num: int) -> list[str]:
roles = ["standard"] * n_agents
adversarial_idx = round_num % n_agents
roles[adversarial_idx] = "adversarial"
return roles
Compute budget guide
N agents × R rounds = N×R LLM calls, each with growing context
5 agents × 5 rounds = 25 calls at increasing context size
Cost per question can exceed 10× single CoT call
Practical recommendations:
Low-stakes tasks: 3 agents × 2 rounds (6 calls) — diminishing returns past round 2
High-stakes tasks: 5 agents × 3 rounds (15 calls)
Research synthesis: 3 heterogeneous models × 3 rounds
Heterogeneous = different model families (Claude + GPT + Llama)
→ errors don't correlate → more diverse starting points → better convergence
Topic drift mitigation
def inject_question_every_round(question: str, prior_answers: list[str]) -> str:
"""Re-anchor agents to the original question every round to prevent drift."""
return f"""IMPORTANT: Keep your answer focused on the original question:
"{question}"
Do not let discussion drift to adjacent topics.
Other agents' latest answers:
{chr(10).join(f'- {a[:300]}' for a in prior_answers)}
Your focused answer to the original question:"""
Anti-Fake-Pass Checklist
❌ All agents from same model family → monoculture collapse; errors correlate; debate is just sampling
❌ No adversarial role → sycophancy cascade; all agents defer to most confident answer
❌ Round count > 3 without task-specific tuning → diminishing returns + context explosion
❌ Topic drift across rounds → re-inject the original question every round
❌ Weak model in the pool → drags consensus toward its wrong answer (Du et al. "MAD" follow-up)
❌ No majority vote normalization → agents phrase the same answer differently; vote splits
