AI Architecture Comparison: Chat-Triggered Routing Between Conversational Agent & LangChain LLM Chain

Problem:

A development team needed a practical side-by-side demonstration of two core n8n AI architectures, a full Conversational Agent with memory and tool access versus a simple Basic LLM Chain, to evaluate which approach suited different use cases before committing to a production build.

Solution:

Built a chat-triggered n8n workflow that checks the user's input for the keyword 'chain' or 'agent' using a Switch node with Rules mode, the 'agent' path routes to a full Conversational AI Agent powered by OpenAI with Window Buffer Memory for session context and a Wikipedia tool for live knowledge retrieval; the 'chain' path routes to a Basic LLM Chain powered by a second OpenAI model for fast, stateless single-turn responses, both paths return results directly in the chat interface.

Result:

Delivered a live comparison tool that demonstrates the practical difference between agentic and chain-based AI architectures in real time, enabling developers and clients to experience memory retention, tool use, and response quality differences between the two approaches through a simple keyword routing mechanism.