Dynamic Multi-Agent AI Conversations with Contextual Memory
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Orchestrate intelligent, multi-agent AI conversations that adapt dynamically to user input. This workflow enables specialized AI assistants to collaborate, remember past interactions, and provide highly personalized responses based on real-time context and mentions.
About This Workflow
This n8n workflow revolutionizes your conversational AI strategy by enabling dynamic multi-agent interactions. It intelligently routes user queries to specific AI assistants based on @mentions in the chat, or orchestrates a collaborative response from multiple agents if no specific direction is given. Each AI agent operates with a deep understanding of the conversation's history, thanks to persistent session memory, and is empowered by personalized user context and distinct system messages. This allows for highly nuanced and context-aware responses, moving beyond single-agent limitations to truly collaborative and intelligent AI experiences.
Key Features
- Dynamic Agent Routing: Intelligently directs user requests to specific AI assistants using
@mentionsor distributes them across all available agents when no explicit mention is made. - Persistent Conversational Memory: AI agents retain context from previous interactions within the same session, ensuring fluid and coherent multi-turn conversations.
- Personalized User Context: Integrates user-specific data (name, location, notes) and global settings into the AI's system message for tailored responses.
- Customizable AI Roles: Define distinct system messages and models for each AI agent, allowing them to specialize in different tasks or knowledge domains.
- Extensible & Adaptable: Easily add new AI agents and configure their behavior and interaction patterns to suit evolving needs.
How To Use
- "When chat message received" Trigger: Set up this node to connect to your desired chat platform (e.g., Slack, Telegram, custom webhook) where users will interact with your AI.
- Define Global Settings (Implied): Create a 'Set' or 'Function' node named 'Define Global Settings' to hold global variables, user details, and a common system message for all agents.
- Define Agent Settings (Implied): Create another 'Set' or 'Function' node named 'Define Agent Settings'. Here, define each of your AI agents by name, model, and their unique
systemMessage(e.g., "You are an expert financial advisor."). - "Extract mentions" Node: This node is pre-configured. If you changed the names of your trigger or agent settings nodes, update the
chatMessageNodeNameandagentSetupNodeNamevariables within the JavaScript code to ensure correct agent identification. - "AI Agent" Node: This node dynamically receives agent details. Ensure your LangChain credentials are set up for the models you plan to use.
- "Simple Memory" Node: The
sessionKeyis pre-configured to use the chat session ID. AdjustcontextWindowLengthto control how much past conversation history the AI remembers. - Complete the Loop: After the "AI Agent" node, ensure you have steps to capture the AI's response (e.g., into a node named 'Set lastAssistantMessage') and send it back to the user via your chat platform's API.
Apps Used
Workflow JSON
{
"id": "3ed2eb16-9791-480b-a6df-edd16b668d95",
"name": "Dynamic Multi-Agent AI Conversations with Contextual Memory",
"nodes": 15,
"category": "Operations",
"status": "active",
"version": "1.0.0"
}Note: This is a sample preview. The full workflow JSON contains node configurations, credentials placeholders, and execution logic.
Get This Workflow
ID: 3ed2eb16-9791...
About the Author
SaaS_Connector
Integration Guru
Connecting CRM, Notion, and Slack to automate your life.
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