Description

n8n-nodes-roundrobin

This is a custom node for n8n that allows you to store and retrieve messages in a round-robin fashion, particularly useful for LLM conversation loops with multiple personas.

Features

• Store messages from different roles/personas in a stateful way during workflow execution
• Enhanced persona profiles with tone, expertise areas, colors, and system prompts
• Round counter tracks conversation turns and allows setting limits
• Reliable storage using binary data attachments for persistence
• LLM platform integrations with formatting for OpenAI, Anthropic Claude, and Google Gemini
• System prompt management for guiding AI behavior
• Retrieve messages in various formats (array, object by role, or conversation history for LLMs)
• Simplify outputs to create clean, minimal data structures for AI models
• LLM-ready defaults pre-configured for ChatGPT-style conversations
• Clear stored messages when needed

Installation

Local Installation (Development)

1. Clone this repository:

git clone https://github.com/JamesFincher/n8n-nodes-roundrobin.git

2. Navigate to the project directory:

cd n8n-nodes-roundrobin

3. Install dependencies:

npm install

4. Build the project:

npm run build

5. Link the package to your n8n installation:

npm link

6. In your n8n installation directory, link this package:

cd YOUR_N8N_INSTALLATION_DIRECTORY
npm link n8n-nodes-roundrobin

Global Installation (Production)

To install the node globally, run:

npm install -g n8n-nodes-roundrobin@0.9.2

Docker Installation

If you're using Docker to run n8n, you can include this custom node by:

1. Creating a custom Dockerfile:

FROM n8nio/n8n

RUN npm install -g n8n-nodes-roundrobin@0.9.2

2. Building your custom image:

docker build -t n8n-with-roundrobin .

3. Running n8n with your custom image:

docker run -it --rm \
  --name n8n \
  -p 5678:5678 \
  n8n-with-roundrobin

Usage

Basic Workflow Example

1. Initial Setup: Start by configuring the Round Robin node in "Store" mode.

   • Set the number of spots (default: 3 for User, Assistant, System)
   • Define the roles for each spot with enhanced persona details
   • Configure colors, tones, expertise areas, and system prompts

2. Storing Messages: For each message in your workflow:

   • Configure the Round Robin node to "Store" mode
   • Specify which role's spot to store the message (e.g., spot index 0 for "User")
   • Set the input field name that contains the message (default: "output")

3. Retrieving Messages: When you need to retrieve the conversation:

   • Configure the Round Robin node to "Retrieve" mode
   • Choose the output format (array, object, or conversation history)
   • Select the target LLM platform (OpenAI, Claude, etc.)
   • Configure system prompt options
   • Enable "Simplify Output" for clean, minimal data

LLM Conversation Loop Example

Here's a typical workflow for managing multi-persona conversations with LLMs:

1. Initialize with the Round Robin in "Clear" mode to start fresh
2. Store Initial Message from User (spot index 0)
3. Loop:
   • Retrieve the conversation history (format: conversationHistory, platform: openai)
   • Send to LLM for Assistant response
   • Store the Assistant response (spot index 1)
   • Retrieve updated conversation history
   • Process or display the results
   • Store the next user input (spot index 0)
   • Continue the loop

Node Reference

Store Mode

• Number of Spots: Define how many distinct roles/personas you have (default: 3)
• Roles: Configure enhanced persona details:
  • Name: Name of the role/persona
  • Description: Description of the role/persona
  • Color: Visual color indicator for the role
  • Tone: Tone of voice (friendly, professional, technical, etc.)
  • Expertise Areas: Comma-separated list of expertise domains
  • System Prompt Template: Role-specific system instructions
  • Enabled: Whether this role should be included in conversations
• Input Message Field: Field name containing the message to store (default: "output")
• Spot Index: Which spot to store the message in (0-based index)

Retrieve Mode

• Output Format:
  • array: Returns all messages as an array
  • object: Groups messages by role name
  • conversationHistory: Formats for LLM input (default)
• LLM Platform: Format specifically for different AI platforms
  • OpenAI (ChatGPT): Standard OpenAI format with user/assistant/system roles
  • Anthropic (Claude): Claude-specific format with Human/Assistant markers
  • Google (Gemini): Google's conversation format
  • Generic: Generic format compatible with most LLMs
• System Prompt Options:
  • Include System Prompt: Whether to include system instructions
  • System Prompt Position: Place at start or end of conversation
• Simplify Output: Returns clean, minimal data structures (default: true)
• Maximum Messages: Limit the number of messages returned (0 = all)

Clear Mode

Resets all stored messages while preserving role configurations.

Important Notes

• Data is stored in workflow memory and will be lost if:
  • The n8n instance is restarted
  • The workflow is updated and redeployed

Version History

v0.9.2 – UI Improvements & Round Counter Optimization

• Improved round counter implementation with better error handling
• Enhanced user interface with more intuitive parameter organization
• Added round count information to all output formats
• Further improved storage reliability across workflow executions

v0.9.1 – Storage Improvements & Round Counter

• Added conversation round counter to track and limit conversation turns
• Made binary storage the default for better persistence across executions
• Improved UI with better organization, hints, and notices
• Enhanced role configuration interface

v0.8.9 – Code Quality & Project Structure Improvements

• Fixed TypeScript configuration for better development experience
• Added ESLint for code quality assurance
• Improved project structure following n8n community node standards
• Enhanced gitignore patterns for cleaner development
• Fixed type inference issues in storage management

v0.8.0 – Storage System Refactoring

• Complete refactoring of the storage system for maximum reliability
• Added proper static data handling with namespace isolation for multiple nodes
• Split monolithic code into modular functions for better maintainability
• Improved type safety and error handling throughout the codebase
• Removed redundant serialization for better performance

v0.7.1 – Critical Data Persistence Fix

• Fixed critical issue with data persistence between node executions
• Improved static data storage by using unique node identifiers
• Fixed type handling for expertise fields
• Optimized data structures to ensure reliable storage

v0.7.0 – Phase 2: Enhanced Persona System & LLM Integration

• Added rich persona profiles with tone, expertise, and color
• Implemented platform-specific formatting for OpenAI, Claude, and Gemini
• Added system prompt management with positioning options
• Enhanced filtering of messages by role enablement

v0.6.1 – Storage Reliability Fix

• Fixed critical issue with data persistence between workflow executions
• Implemented robust serialization to ensure messages and roles are properly stored
• Added more thorough type checking and initialization of static data
• Improved error handling for serialization/deserialization

v0.5.0 – Phase 1 Release

• Fixed storage implementation for reliable persistence
• Added "Simplify Output" option for cleaner data
• Implemented better defaults for LLM use cases
• Improved error handling and debugging
• Added helpful notices and documentation

License

MIT

Author

James Fincher