Routing with LangChain

Implementing routing in code involves defining the possible paths and the logic that decides which path to take. Frameworks like LangChain and LangGraph provide specific components and structures for this. LangGraph's state-based graph structure is particularly intuitive for visualizing and implementing routing logic.

This code demonstrates a simple agent-like system using LangChain and Google's Generative AI. It sets up a "coordinator" that routes user requests to different simulated "sub-agent" handlers based on the request's intent (booking, information, or unclear). The system uses a language model to classify the request and then delegates it to the appropriate handler function, simulating a basic delegation pattern often seen in multi-agent architectures. First, ensure you have the necessary libraries installed:

pip install langchain langgraph google-cloud-aiplatform langchain-google-genai google-adk deprecated pydantic

You will also need to set up your environment with your API key for the language model you choose (e.g., OpenAI, Google Gemini, Anthropic).

This Python code constructs a simple agent-like system using the LangChain library and Google's Generative AI model, specifically gemini-2.5-flash. In detail, It defines three simulated sub-agent handlers: booking_handler, info_handler, and unclear_handler, each designed to process specific types of requests.

A core component is the coordinator_router_chain, which utilizes a ChatPromptTemplate to instruct the language model to categorize incoming user requests into one of three categories: 'booker', 'info', or 'unclear'. The output of this router chain is then used by a RunnableBranch to delegate the original request to the corresponding handler function. The RunnableBranch checks the decision from the language model and directs the request data to either the booking_handler, info_handler, or unclear_handler. The coordinator_agent combines these components, first routing the request for a decision and then passing the request to the chosen handler. The final output is extracted from the handler's response.

The main function demonstrates the system's usage with three example requests, showcasing how different inputs are routed and processed by the simulated agents. Error handling for language model initialization is included to ensure robustness. The code structure mimics a basic multi-agent framework where a central coordinator delegates tasks to specialized agents based on intent

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