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108. Hands-on ~ Tool Calling Agent

This passage explains how to build a simple tool-calling agent with LangGraph using the ReAct loop:

  • Define tools with @tool, such as:

    • calculate(expression)

    • get_weather(city)

    • search_web(query)

  • Define graph state so messages are appended, not overwritten, using:

    • messages: Annotated[list, add_messages]

  • Bind tools to the LLM:

    • llm_with_tools = llm.bind_tools(tools)

  • Create an agent node that sends the message history to the LLM and returns its response.

  • Route execution based on whether the LLM produced tool calls:

    • If yes, go to the ToolNode

    • If no, end the graph

  • Use ToolNode(tools) to automatically execute requested tools.

  • Build the graph:

    • Start at the agent

    • Agent decides whether to call tools

    • Tools run if needed

    • Return to agent for the final response

  • Debugging note:

    • If messages always stay at length 1, the state is probably not using add_messages.

Overall, it shows the standard reason → act → reason workflow:

  1. The model reasons about what tool to use,

  2. the tool is executed,

  3. the model reasons again to produce the final answer.