Building a ReAct Agent

By combining reasoning and tool execution, we can construct a ReAct Agent that runs in a loop to solve problems. This page covers building a complete working agent project.

1. The Modern LangChain Agent Stack

To build agents in modern LangChain, we use LangGraph, which models agents as state graphs where the LLM decides node transitions (e.g. calling tools vs. returning the final response).

User Input ──> [ Agent Loop (LLM) ] ──(decides)──> [ Call Tool ] ──(execute)──> [ Return to Loop ]
                      │
                   (finish)
                      ▼
                 Final Answer

2. In-Memory Working Agent Project

Let’s build a working agent equipped with a math evaluation tool and a dictionary database look-up tool.

2.1 Install Dependencies

Run in your terminal:

uv add langgraph langchain-google-genai

2.2 Complete Code Implementation

Save and run this script:

import os
from dotenv import load_dotenv
from langchain_core.tools import tool
from langchain.chat_models import init_chat_model
from langgraph.prebuilt import create_react_agent

# 1. Load environment variables
load_dotenv()

# 2. Define custom tools
@tool
def calculate_math(expression: str) -> str:
    """Evaluates basic mathematical expressions (addition, subtraction, multiplication, division)."""
    try:
        # Using eval safely by restricting scope
        result = eval(expression, {"__builtins__": None}, {})
        return str(result)
    except Exception as e:
        return f"Error evaluating expression: {str(e)}"

@tool
def get_user_email(employee_name: str) -> str:
    """Queries the internal HR directory to find an employee's email address."""
    directory = {
        "alice": "[email protected]",
        "bob": "[email protected]",
        "siva": "[email protected]"
    }
    name_lower = employee_name.lower().strip()
    return directory.get(name_lower, f"User {employee_name} not found in HR directory.")

# 3. Initialize the Chat Model
llm = init_chat_model("gemini-2.5-flash", model_provider="google_genai")

# 4. Bind tools and create the ReAct Agent
tools = [calculate_math, get_user_email]
agent = create_react_agent(llm, tools)

# 5. Run the agent with a multi-step query
# The agent must: 
# 1. Look up Siva's email.
# 2. Calculate the math expression.
# 3. Answer both in a single response.
query = "Find siva's email and calculate 345 * 12."
print(f"Goal: '{query}'\n")

print("Executing Agent Loop...")
inputs = {"messages": [("user", query)]}
response = agent.invoke(inputs)

# 6. Print final conversation trace
print("\n--- Final Agent Response ---")
print(response["messages"][-1].content)

Expected Trace Behavior:

The agent notices "Find siva's email" and runs get_user_email(employee_name="siva").
The agent notices "calculate 345 * 12" and runs calculate_math(expression="345 * 12").
The agent merges both observations and outputs: “Siva’s email is [email protected] and 345 * 12 is 4140.”

3. Practice Exercises

Practice 1: Add a Currency Converter Tool

Extend the working agent by adding a new tool convert_usd_to_eur(amount: float) -> float that multiplies the USD amount by 0.92. Run the query: "Find siva's email and convert 150 USD to EUR." and print the output. Instructions:

Write the convert_usd_to_eur tool.
Add it to the tools list.
Call create_react_agent(llm, tools).
Invoke the agent and print the final message content.

Solution

from langchain_core.tools import tool
from langchain.chat_models import init_chat_model
from langgraph.prebuilt import create_react_agent

@tool
def convert_usd_to_eur(amount: float) -> float:
    """Converts USD currency amount to EUR using a fixed exchange rate."""
    return amount * 0.92

# Existing tools
tools = [calculate_math, get_user_email, convert_usd_to_eur]
llm = init_chat_model("gemini-2.5-flash", model_provider="google_genai")

agent = create_react_agent(llm, tools)

query = "Find siva's email and convert 150 USD to EUR."
inputs = {"messages": [("user", query)]}
response = agent.invoke(inputs)

print(response["messages"][-1].content)

Foundations

Agentic Implementation

1. The Modern LangChain Agent Stack

2. In-Memory Working Agent Project

2.1 Install Dependencies

2.2 Complete Code Implementation

3. Practice Exercises

Practice 1: Add a Currency Converter Tool

​1. The Modern LangChain Agent Stack

​2. In-Memory Working Agent Project

​2.1 Install Dependencies

​2.2 Complete Code Implementation

​3. Practice Exercises

​Practice 1: Add a Currency Converter Tool

1. The Modern LangChain Agent Stack

2. In-Memory Working Agent Project

2.1 Install Dependencies

2.2 Complete Code Implementation

3. Practice Exercises

Practice 1: Add a Currency Converter Tool