Dynamically switch between LLMs for AI agents using LangChain code

Dynamically switch between LLMs for AI Agents using LangChain Code

Purpose

This example workflow demonstrates a way to connect multiple LLMs to a single AI Agent/LangChain Node and programmatically use one – or in this case loop through them.

What it does

This AI workflow takes in customer complaints and generates a response that is being validated before returned. If the answer was not satisfactory, the response will be generated again with a more capable model.

How it works

- A LangChain Code Node allows multiple LLMs to be connected to a single Basic LLM Chain. On every call only one LLM is actually being connected to the Basic LLM Chain, which is determined by the index defined in a previous Node. - The AI output is later validated by a Sentiment Analysis Node - If the result was not satisfactory, it loops back to the beginning and executes the same query with the next available LLM - The loop ends either when the result passed the requirements or when all LLMs have been used before.

Setup - Clone the workflow and select the belonging credentials. You'll need an OpenAI Account, alternatively you can swap the LLM nodes with ones from a different provider like Anthropic after the import.