Agents and Tools — ReAct Agents in LangChain

from langchain_core.tools import tool
from langchain_openai import ChatOpenAI
from langchain.agents import create_tool_calling_agent, AgentExecutor
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder

# Define tools with the @tool decorator
@tool
def calculate(expression: str) -> str:
    """Evaluate a math expression. Input should be a valid Python math expression like '2 + 2' or '100 * 1.15'."""
    try:
        result = eval(expression, {"__builtins__": {}}, {})
        return str(result)
    except Exception as e:
        return f"Error: {e}"

@tool
def get_company_info(company_name: str) -> str:
    """Look up information about a company. Returns founding year, headquarters, and main products."""
    # In real code, this would query a database or API
    companies = {
        "openai": "Founded 2015, San Francisco, AI research and GPT models",
        "anthropic": "Founded 2021, San Francisco, Claude AI models",
        "langchain": "Founded 2022, LangChain Inc., AI application framework",
    }
    return companies.get(company_name.lower(), "Company not found in database")

# Build the agent
model = ChatOpenAI(model="gpt-4o-mini")
tools = [calculate, get_company_info]

prompt = ChatPromptTemplate.from_messages([
    ("system", "You are a helpful research assistant. Use tools when needed."),
    ("human", "{input}"),
    MessagesPlaceholder(variable_name="agent_scratchpad")
])

agent = create_tool_calling_agent(model, tools, prompt)
executor = AgentExecutor(agent=agent, tools=tools, verbose=True)

# Run the agent — watch it reason and use tools
result = executor.invoke({
    "input": "Anthropic was founded how many years after OpenAI? Calculate the difference."
})
print(result["output"])