Minimal Agent Example: Calculator

The simplest possible agent: an LLM with a calculator tool.

Code: github.com/agents4science/agents4science.github.io/tree/main/Capabilities/local-agents/AgentsCalculator

What It Does

1. User asks a math question
2. LLM recognizes it needs to calculate
3. LLM calls the calculate tool
4. Tool returns the result
5. LLM responds with the answer

The Code

from langchain_openai import ChatOpenAI
from langchain_core.tools import tool
from langchain_core.messages import HumanMessage
from langgraph.prebuilt import create_react_agent


@tool
def calculate(expression: str) -> str:
    """Evaluate a mathematical expression."""
    return str(eval(expression, {"__builtins__": {}}, {}))


llm = ChatOpenAI(model="gpt-4o-mini")
agent = create_react_agent(llm, [calculate])

for step in agent.stream({"messages": [HumanMessage(content="What is 347 * 892?")]}):
    print(step)

Running the Example

cd Capabilities/local-agents/AgentsCalculator
python -m venv .venv && source .venv/bin/activate
pip install -r requirements.txt
python langgraph_calculator.py

LLM Configuration

Supports OpenAI, FIRST (HPC inference), Ollama (local), or mock mode.

See LLM Configuration for details on configuring LLM backends, including Argonne’s FIRST service.

Key Points

• Tool definition: The @tool decorator turns a function into an LLM-callable tool
• Agent creation: create_react_agent wires up the LLM with tools in a ReAct loop
• Execution: agent.stream() runs the agent and yields intermediate steps

For more complex workflows with branching, cycles, or custom state, use LangGraph’s StateGraph directly (see AgentsLangGraph).

Requirements

• Python 3.10+
• LangGraph 1.0+
• OpenAI API key, FIRST token, or run in mock mode