Real World Applications - Build Something People Use

# Handles customer questions automatically
tools = [
    "check_order_status",
    "process_refund", 
    "answer_faq",
    "escalate_to_human"
]

# Handles 80% of questions without humans

# Finds and summarizes information
tools = [
    "web_search",
    "read_papers",
    "create_summary",
    "generate_citations"
]

# Saves hours of research time

# Writes blog posts, social media, emails
tools = [
    "research_topic",
    "write_draft",
    "generate_images",
    "schedule_post"
]

# Creates content in minutes, not hours

# Analyzes data and creates reports
tools = [
    "query_database",
    "run_python",
    "create_charts",
    "generate_report"
]

# Turns raw data into insights

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

# --- Tools ---

@tool
def check_order(order_id: str) -> str:
    """Check the status of an order"""
    orders = {
        "ORD001": {"status": "shipped", "eta": "2 days"},
        "ORD002": {"status": "processing", "eta": "5 days"},
        "ORD003": {"status": "delivered", "eta": "delivered"},
    }
    order = orders.get(order_id)
    if order:
        return f"Order {order_id}: {order['status']}, ETA: {order['eta']}"
    return f"Order {order_id} not found"

@tool
def process_refund(order_id: str, reason: str) -> str:
    """Process a refund for an order"""
    return f"Refund of $49.99 processed for {order_id}. Reason: {reason}. Refund will appear in 3-5 business days."

@tool
def get_faq(question: str) -> str:
    """Get answer to frequently asked question"""
    faqs = {
        "shipping": "We offer free shipping on orders over $50. Standard shipping takes 3-5 days.",
        "returns": "Returns accepted within 30 days. Items must be unused.",
        "payment": "We accept all major credit cards, PayPal, and Apple Pay.",
    }
    for key, answer in faqs.items():
        if key in question.lower():
            return answer
    return "Please contact [email protected] for more help."

# --- Agent ---

llm = ChatOpenAI(model="gpt-4o", temperature=0)

agent = create_react_agent(
    model=llm,
    tools=[check_order, process_refund, get_faq],
)

# --- Chat Loop ---

print("Customer Support Agent (type 'quit' to exit)")
print("-" * 50)

while True:
    user_input = input("You: ")
    if user_input.lower() == "quit":
        break
    
    result = agent.invoke({
        "messages": [{"role": "user", "content": user_input}]
    })
    
    print(f"Agent: {result['messages'][-1].content}")
    print()

from langchain_openai import ChatOpenAI
from langchain_core.tools import tool
from langgraph.prebuilt import create_react_agent
from duckduckgo_search import DDGS

@tool
def search_web(query: str) -> str:
    """Search the web for current information"""
    with DDGS() as ddgs:
        results = list(ddgs.text(query, max_results=5))
    return "\n".join([f"- {r['title']}: {r['body'][:200]}" for r in results])

@tool
def summarize(text: str) -> str:
    """Create a summary of the given text"""
    llm = ChatOpenAI(model="gpt-4o")
    response = llm.invoke(f"Summarize this in 3 bullet points:\n{text}")
    return response.content

@tool
def create_outline(topic: str) -> str:
    """Create an outline for a research topic"""
    llm = ChatOpenAI(model="gpt-4o")
    response = llm.invoke(f"Create a research outline for: {topic}")
    return response.content

agent = create_react_agent(
    model=ChatOpenAI(model="gpt-4o"),
    tools=[search_web, summarize, create_outline],
)

# Research a topic
result = agent.invoke({
    "messages": [{"role": "user", "content": "Research the latest trends in AI agents"}]
})
print(result["messages"][-1].content)