Generate consensus-based answers using Claude, GPT, Grok and Gemini

The original LLM Council concept was introduced by Andrej Karpathy and published as an open-source repository demonstrating multi-model consensus and ranking. This workflow is my adaptation of that original idea, reimplemented and structured as a production-ready n8n template. Original repository - https://github.com/karpathy/llm-council

This n8n template implements the LLM Council pattern: a single user question is processed in parallel by multiple large language models, independently evaluated by peer models, and then synthesized into one high-quality, consensus-driven final answer. It is designed for use cases where answer quality, balance, and reduced single-model bias are critical.

📌 Section 1: Trigger & Input

⚡ When Chat Message Received (Chat Trigger) Purpose: Receives a user’s message and initiates the entire workflow.

How it works:

A user sends a chat message

The message is stored as the Original Question

The same input is forwarded simultaneously to multiple LLM pipelines

Why it matters: Provides a clean, unified entry point for all downstream multi-model logic.

📌 Section 2: Stage 1 — Parallel LLM Responses

🤖 Basic LLM Chains (x4) Models used:

Anthropic Claude

OpenAI GPT

xAI Grok

Google Gemini

Purpose: Each model independently generates its own response to the same question.

Key characteristics:

Identical prompt structure for all models

Independent reasoning paths

No shared context between models

Why it matters: Produces diverse perspectives, reasoning styles, and solution approaches.

📌 Section 3: Stage 2 — Response Anonymization

🧾 Set Nodes (Response A / B / C / D) Purpose: Stores model outputs in an anonymized format:

Response A

Response B

Response C

Response D

Why it matters: Prevents evaluator models from knowing which LLM authored which response, reducing bias during evaluation.

📌 Section 4: Stage 3 — Peer Evaluation & Ranking

📊 Evaluation Chains (Claude / GPT / Grok / Gemini) Purpose: Each model acts as a reviewer and:

Analyzes all four anonymized responses

Describes strengths and weaknesses of each

Produces a strict FINAL RANKING from best to worst

Ranking format (strict):

FINAL RANKING:

1. Response B
2. Response A
3. Response D
4. Response C

Why it matters: Creates multiple independent quality assessments from different model perspectives.

📌 Section 5: Stage 4 — Ranking Aggregation

🧮 Code Node (JavaScript) Purpose: Aggregates all peer rankings by:

Parsing ranking positions

Calculating average position per response

Counting evaluation occurrences

Sorting responses by best average score

Output includes:

Aggregated rankings

Best response label

Best average score

Why it matters: Transforms subjective rankings into a structured, quantitative consensus.

📌 Section 6: Stage 5 — Final Consensus Answer

🧠 Chairman LLM Chain Purpose: One model acts as the Council Chairman and:

Reviews all original responses

Considers peer rankings and aggregated scores

Identifies consensus patterns and disagreements

Produces a single, clear, high-quality final answer

Why it matters: Delivers a refined response that reflects collective model intelligence rather than a simple average.

📊 Workflow Overview Stage Node / Logic Purpose 1 Chat Trigger Receive user question 2 LLM Chains Generate independent responses 3 Set Nodes Anonymize outputs 4 Evaluation Chains Peer review & ranking 5 Code Node Aggregate rankings 6 Chairman LLM Final synthesized answer 🎯 Key Benefits

🧠 Multi-model intelligence — avoids reliance on a single LLM ⚖️ Reduced bias — anonymized peer evaluation 📊 Quality-driven selection — ranking-based consensus 🔁 Modular architecture — easy to add or replace models 🌍 Language-flexible — input and output languages configurable 🧩 Production-ready logic — clear stages, deterministic ranking

🚀 Ideal Use Cases

High-stakes decision support

Complex technical or architectural questions

Strategy and research synthesis

AI assistants requiring higher trust and reliability

Comparing and selecting the best LLM-generated answers