How to Create a Routing System
Discover how to create a dynamic routing system in LangGraph that enables your AI applications to make intelligent decisions. Learn to implement conditional routing based on AI responses, integrate tool execution nodes, and build scalable multi-agent workflows that adapt seamlessly to user inputs.
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 how to maintain state and build 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 becomes cumbersome as workflows grow more complex.
We will now 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 that directs cars straight ahead or onto 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, branched workflow where multiple nodes work together. This cleaner separation of concerns makes the system more flexible and enhances its ability to handle a wider variety of tasks, paving the way for more sophisticated 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. The routing mechanism examines the AI’s response and determines the next step. If the response indicates that a tool should be called, for instance to perform a calculation, the workflow is directed to a specialized node that executes this tool. Otherwise, the system produces the AI’s ...