Minimal React loop agent in python

agent_loop_langchain_tool_calling.py

"""
Minimal ReAct-style loop with LangChain tool calling.

ReAct = "Reason + Act": the model thinks, decides whether to call a tool,
observes the result, and repeats until it has a final answer.

This uses LangChain's tool-calling primitives directly (no AgentExecutor
magic) so you can see exactly what's happening at each step.
"""

from langchain_core.tools import tool
from langchain_core.messages import HumanMessage, ToolMessage
from langchain_anthropic import ChatAnthropic
# Swap for: from langchain_openai import ChatOpenAI


# ---- 1. Define tools -------------------------------------------------

@tool
def get_weather(city: str) -> str:
    """Get the current weather for a given city."""
    # Replace with a real API call
    fake_data = {"toronto": "18°C, cloudy", "waterloo": "17°C, rainy"}
    return fake_data.get(city.lower(), "No data for that city")


@tool
def add(a: float, b: float) -> float:
    """Add two numbers together."""
    return a + b


tools = [get_weather, add]
tools_by_name = {t.name: t for t in tools}


# ---- 2. Bind tools to the model ---------------------------------------

llm = ChatAnthropic(model="claude-haiku-4-5-20251001", temperature=0)
llm_with_tools = llm.bind_tools(tools)


# ---- 3. The ReAct loop --------------------------------------------------

def run_agent(user_input: str, max_steps: int = 5) -> str:
    messages = [HumanMessage(content=user_input)]

    for step in range(max_steps):
        ai_msg = llm_with_tools.invoke(messages)
        messages.append(ai_msg)

        # No tool calls -> model gave a final answer, stop looping
        if not ai_msg.tool_calls:
            return ai_msg.content

        # Otherwise, execute each requested tool call and feed results back
        for call in ai_msg.tool_calls:
            tool_fn = tools_by_name[call["name"]]
            result = tool_fn.invoke(call["args"])
            messages.append(
                ToolMessage(content=str(result), tool_call_id=call["id"])
            )

    return "Max steps reached without a final answer."


if __name__ == "__main__":
    answer = run_agent(
        "What's the weather in Waterloo, and what is 18 plus 24?"
    )
    print(answer)