How to Create a Routing System

Imagine you have multiple possible actions that your AI can take. Sometimes, it replies directly, while other times, it calls a tool, such as performing a mathematical calculation, before responding. In the previous lesson, we learned about maintaining state and building a single-node graph where the node itself handles both processing and deciding whether to call a tool based on the input. This linear and straightforward approach works well for simple tasks but can become cumbersome as workflows become complex.

We’ll take this a step further by introducing a dynamic routing system that transforms our AI model into a kind of “router.” Like a traffic signal directs cars straight ahead or off to a side street, our routing system uses conditional edges to determine which node to run next based on the AI’s response. By externalizing decision-making to these conditional edges, we create a scalable and branched workflow where multiple nodes work together, enabling the AI to efficiently manage the workflow’s direction. This cleaner separation of concerns makes our system more flexible and enhances its ability to handle a wider variety of tasks seamlessly, paving the way for more sophisticated and intelligent AI workflows with LangGraph.

How does the routing mechanism work?

As established in the previous lesson, we already have a state and an AI model that can recall the entire conversation. We introduce a routing mechanism to examine the AI’s response and determine the next step. If the response indicates that a tool should be called, for instance, to perform a calculation, we direct the workflow to a specialized ...