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Generate consensus answers with multiple AI models & peer review system

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Important notice

This workflow is provided as-is. Please review and test before using in production.

1. Workflow Overview

AI Council: Multi Model Consensus with Peer Review Inspired by Andrej Karpathy's LLM Council , but rebuilt in n8n. This workflow creates a "council" of AI...

Best for

  • Engineering automation workflows
  • AI Summarization automation workflows
  • advanced n8n builders looking for reusable templates

Tools used

@n8n/n8n-nodes-langchain.chattrigger, @n8n/n8n-nodes-langchain.lmchatopenrouter, n8n-nodes-base.merge, n8n-nodes-base.aggregate, @n8n/n8n-nodes-langchain.agent, n8n-nodes-base.stickynote, @n8n/n8n-nodes-langchain.chainllm

Source and attribution

This workflow is cataloged by N8N Workflows and links back to its original n8n.io source page by Guido X Jansen.

Original n8n.io source

1.1 Workflow description

Title
Generate consensus answers with multiple AI models & peer review system
Workflow name
Generate consensus answers with multiple AI models & peer review system

AI Council: Multi-Model Consensus with Peer Review

Inspired by Andrej Karpathy's LLM Council, but rebuilt in n8n.

This workflow creates a "council" of AI models that independently answer your question, then peer-review each other's responses before a final arbiter synthesizes the best answer.


Who is this for?

  • If you want to prepare for an upcoming meeting with different people and prep for their different views
  • find any "blind spots" in your view on a certain subject
  • Researchers wanting more robust AI-generated answers
  • Developers exploring multi-model architectures
  • Anyone seeking higher-quality responses through AI consensus, potentially with faster/cheaper models.
  • Teams evaluating different LLM capabilities side-by-side

How it works

  1. Ask a Question — Submit your query via the Chat Trigger
  2. Individual Answers — Four different models (Gemini, Llama, Gemma, Mistral) independently generate responses
  3. Peer Review — Each model reviews ALL answers, identifying pros, cons, and overall assessment
  4. Final Synthesis — DeepSeek R1 analyzes all peer reviews and produces a refined, consensus-based final answer

Setup Instructions

Prerequisites

  • Access to an LLM (e.g. OpenRouter account with API credits)

Steps

  1. Create OpenRouter credentials in n8n:
    • Go to Settings → Credentials → Add Credential
    • Select "OpenRouter" and paste your API key
  2. Connect all model nodes to your OpenRouter credential. In this example I used Gemini, Llama, Gemma, Mistral and Deepseek, but you can use whatever you want. You can also use the same models, but change their parameters. Play around to find out what suits you best.
  3. Activate the workflow and open the Chat interface to test

Customization Ideas

  • You can add as many answer and review models as you want. Do note that each AI node is executed in series, so each will add to the total duration.
  • Swap models via OpenRouter's model selector (e.g., use Claude, GPT-4, etc.)
  • Adjust the peer review prompt to represent a certain persona or with domain-specific evaluation criteria
  • Add memory nodes for multi-turn conversations
  • Connect to Slack/Discord instead of the Chat Trigger

1.2 Logical Blocks

This catalog entry is organized from the workflow JSON. The node-level section below shows the executable blocks available for review before importing the template.

2. Block-by-Block Analysis

Block 1 - Chat Trigger

Type / Role
@n8n/n8n-nodes-langchain.chatTrigger - chatTrigger
Config choices
Version 1.4

Block 2 - Gemini Model

Type / Role
@n8n/n8n-nodes-langchain.lmChatOpenRouter - lmChatOpenRouter
Config choices
Version 1

Block 3 - Merge Answers

Type / Role
n8n-nodes-base.merge - merge
Config choices
Version 3.2

Block 4 - Aggregate Answers

Type / Role
n8n-nodes-base.aggregate - aggregate
Config choices
Version 1

Block 5 - Merge Reviews

Type / Role
n8n-nodes-base.merge - merge
Config choices
Version 3.2

Block 6 - Aggregate Reviews

Type / Role
n8n-nodes-base.aggregate - aggregate
Config choices
Version 1

Block 7 - Final Analysis Agent

Type / Role
@n8n/n8n-nodes-langchain.agent - agent
Config choices
Version 3

Block 8 - Sticky Note

Type / Role
n8n-nodes-base.stickyNote - stickyNote
Config choices
Version 1

Block 9 - Sticky Note1

Type / Role
n8n-nodes-base.stickyNote - stickyNote
Config choices
Version 1

Block 10 - Sticky Note2

Type / Role
n8n-nodes-base.stickyNote - stickyNote
Config choices
Version 1

Block 11 - Sticky Note3

Type / Role
n8n-nodes-base.stickyNote - stickyNote
Config choices
Version 1

Block 12 - Sticky Note4

Type / Role
n8n-nodes-base.stickyNote - stickyNote
Config choices
Version 1

Block 13 - Mistral Model

Type / Role
@n8n/n8n-nodes-langchain.lmChatOpenRouter - lmChatOpenRouter
Config choices
Version 1

Block 14 - Gemma Model

Type / Role
@n8n/n8n-nodes-langchain.lmChatOpenRouter - lmChatOpenRouter
Config choices
Version 1

Block 15 - Llama Model

Type / Role
@n8n/n8n-nodes-langchain.lmChatOpenRouter - lmChatOpenRouter
Config choices
Version 1

Block 16 - Answer Agent #4

Type / Role
@n8n/n8n-nodes-langchain.chainLlm - chainLlm
Config choices
Version 1.7

Block 17 - Answer Agent #3

Type / Role
@n8n/n8n-nodes-langchain.chainLlm - chainLlm
Config choices
Version 1.7

Block 18 - Answer Agent #2

Type / Role
@n8n/n8n-nodes-langchain.chainLlm - chainLlm
Config choices
Version 1.7

Block 19 - Answer Agent #1

Type / Role
@n8n/n8n-nodes-langchain.chainLlm - chainLlm
Config choices
Version 1.7

Block 20 - Review Agent #1

Type / Role
@n8n/n8n-nodes-langchain.chainLlm - chainLlm
Config choices
Version 1.7

Block 21 - Review Agent #2

Type / Role
@n8n/n8n-nodes-langchain.chainLlm - chainLlm
Config choices
Version 1.7

Block 22 - Review Agent #3

Type / Role
@n8n/n8n-nodes-langchain.chainLlm - chainLlm
Config choices
Version 1.7

Block 23 - Review Agent #4

Type / Role
@n8n/n8n-nodes-langchain.chainLlm - chainLlm
Config choices
Version 1.7

Block 24 - Deepseek R1 model

Type / Role
@n8n/n8n-nodes-langchain.lmChatOpenRouter - lmChatOpenRouter
Config choices
Version 1

Showing the first 24 of 25 workflow blocks. Download the JSON for the full node graph.

3. Summary Table

Workflow Generate consensus answers with multiple AI models & peer review system
Complexity advanced
Nodes 25
Categories Engineering, AI Summarization
Author Guido X Jansen
Published 10 Dec 2025

4. Reproducing the Workflow from Scratch

  1. 1. Download the workflow JSON

    Use the JSON export at /data/workflows/11660/11660.json as the source template for this automation.

  2. 2. Import the template into n8n

    Open n8n, import the downloaded JSON, and review each node before activating the workflow.

  3. 3. Configure credentials and variables

    Replace placeholder credentials, API keys, webhook URLs, account IDs, and environment-specific values with your own settings.

  4. 4. Test with sample data

    Run the workflow manually or in a staging workspace, inspect node output, and confirm downstream systems receive the expected data.

  5. 5. Activate and monitor

    Enable the workflow only after testing, then monitor executions, errors, and rate limits during the first production runs.

5. General Notes & Resources

Review imported nodes carefully before activation. This catalog entry is intended to help you inspect the workflow structure, understand required services, and find related templates faster.

Node names, credentials, schedules, webhook paths, and external service limits may need adjustment for your workspace.

Frequently asked questions

What does Generate consensus answers with multiple AI models & peer review system do?

AI Council: Multi Model Consensus with Peer Review Inspired by Andrej Karpathy's LLM Council , but rebuilt in n8n. This workflow creates a "council" of AI...

What do I need before importing this workflow?

Review the workflow JSON, configure any required credentials in n8n, and test the automation in a safe workspace before using it in production.

Can I customize this workflow?

Yes. Use the block-by-block analysis and the downloadable JSON to inspect each node, then adjust credentials, prompts, schedules, filters, or destinations for your Engineering, AI Summarization use case.