Scalable Multi-Agent Chat Automation with AI Mentions and Memory

Overview

This workflow enables scalable, automated multi-agent chat interactions by leveraging AI agents, mention extraction, and contextual memory. It is designed to process incoming chat messages, identify mentioned agents, and coordinate intelligent responses.

Key Features

• Chat Trigger: Initiates workflow when a new chat message is received.
• Mention Extraction: Parses messages to identify and route to relevant AI agents.
• AI Agent Coordination: Utilizes multiple AI agents to generate context-aware responses.
• Memory Buffer: Maintains conversation context for coherent, ongoing interactions.
• Batch Processing: Efficiently handles multiple mentions and responses in parallel.
• Custom Logic: Employs code and conditional nodes for advanced message handling and formatting.

Benefits

• Enhanced Productivity: Automates complex chat workflows, reducing manual intervention.
• Scalability: Supports multiple agents and high message volumes seamlessly.
• Consistency: Ensures accurate, context-rich responses using memory buffers.
• Time Savings: Streamlines chat management, freeing up human resources.

Use Cases

• Customer support automation with specialized AI agents.
• Internal team chatbots for HR, IT, or sales queries.
• Multi-departmental communication hubs.

Integrations & Processes

• Integrates with LangChain for AI agent orchestration and memory management.
• Utilizes n8n's batch, code, and logic nodes for flexible automation.