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.

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