Generate consensus-based answers using Claude, GPT, Grok and Gemini
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Yehor EGMS
Description
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:
Response B
Response A
Response D
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