Five Complete LangChain Applications

Every module in this course built one real skill. This final module builds nothing new — it combines everything, into five complete, real applications, each drawing on a genuinely different subset of what you’ve learned. If you can look at each one and name exactly which module taught each piece, you’ve genuinely completed this course.

Project 1: AI Support Ticket Analyzer

Draws on: Module 7 (prompt templates), Module 18 (structured output).

from typing import Literal
from pydantic import BaseModel
from langchain.chat_models import init_chat_model
from langchain_core.prompts import ChatPromptTemplate

class TicketAnalysis(BaseModel):
    category: Literal["billing", "technical", "account", "other"]
    urgency: Literal["low", "medium", "high"]
    summary: str

prompt = ChatPromptTemplate.from_messages([
    ("system", "Analyze this support ticket and classify it precisely."),
    ("human", "{ticket_text}"),
])

model = init_chat_model("openai:gpt-4o-mini").with_structured_output(TicketAnalysis)
analyzer = prompt | model

result = analyzer.invoke({"ticket_text": "I was charged twice for my subscription this month and need a refund urgently."})
print(result)

Every real support queue benefits from this exact pattern — automatic, consistent, structured triage before a human ever reads the ticket.

Project 2: Document Q&A

Draws on: Modules 22-26, the entire retrieval and RAG sequence.

from langchain_community.document_loaders import TextLoader
from langchain_text_splitters import RecursiveCharacterTextSplitter
from langchain_openai import OpenAIEmbeddings
from langchain_core.vectorstores import InMemoryVectorStore
from langchain.chat_models import init_chat_model
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser
from langchain_core.runnables import RunnablePassthrough

documents = TextLoader("company_handbook.txt").load()
chunks = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50).split_documents(documents)

vector_store = InMemoryVectorStore(OpenAIEmbeddings(model="text-embedding-3-small"))
vector_store.add_documents(chunks)
retriever = vector_store.as_retriever(search_kwargs={"k": 3})

prompt = ChatPromptTemplate.from_template("Answer using only this context:\n{context}\n\nQuestion: {question}")
model = init_chat_model("openai:gpt-4o-mini")

def format_docs(docs):
    return "\n\n".join(d.page_content for d in docs)

qa_chain = (
    RunnablePassthrough.assign(context=lambda x: format_docs(retriever.invoke(x["question"])))
    | prompt | model | StrOutputParser()
)

print(qa_chain.invoke({"question": "What is our remote work policy?"}))

Project 3: Tool-Using Customer Support Agent

Draws on: Modules 12-20, the entire agent sequence.

from langchain.chat_models import init_chat_model
from langchain.tools import tool
from langchain.agents import create_agent
from langchain.agents.middleware import PIIMiddleware

CUSTOMERS = {"c_1": "Priya"}
ORDERS = {"o_1": {"customer_id": "c_1", "days_since_purchase": 12}}

@tool
def get_customer(customer_id: str) -> str:
    """Look up a customer's name."""
    return CUSTOMERS.get(customer_id, "Not found.")

@tool
def get_order(order_id: str) -> str:
    """Look up order details."""
    return str(ORDERS.get(order_id, "Not found."))

@tool
def check_refund_policy(days_since_purchase: int) -> str:
    """Check refund eligibility (within 30 days)."""
    return "Eligible." if days_since_purchase <= 30 else "Not eligible."

support_agent = create_agent(
    model=init_chat_model("openai:gpt-4o-mini"),
    tools=[get_customer, get_order, check_refund_policy],
    system_prompt="You are a warm, professional support agent. Always check policy before answering refund questions.",
    middleware=[PIIMiddleware("email")],
)

result = support_agent.invoke({"messages": [{"role": "user", "content": "Can order o_1 be refunded?"}]})
print(result["messages"][-1].content)

Project 4: Research Assistant

Draws on: Module 10 (streaming), Module 16 (search agents), Module 18 (structured output), Module 27 (resilience).

from pydantic import BaseModel
from langchain.chat_models import init_chat_model
from langchain.tools import tool
from langchain.agents import create_agent

@tool
def web_search(query: str) -> str:
    """Search the web for current information."""
    return f"Search result for '{query}': relevant information found."

class ResearchSummary(BaseModel):
    topic: str
    key_findings: list[str]
    sources_consulted: int

resilient_model = init_chat_model("openai:gpt-4o-mini").with_retry(stop_after_attempt=2)

research_agent = create_agent(
    model=resilient_model,
    tools=[web_search],
    system_prompt="Research the topic thoroughly using search before summarizing.",
    response_format=ResearchSummary,
)

result = research_agent.invoke({"messages": [{"role": "user", "content": "Research the current state of RAG techniques."}]})
print(result["structured_response"])

Project 5: A Production-Oriented Application, File by File

This final project is laid out the way a genuine, real project actually lives on disk — not one script, but a properly organized application.

support-agent-app/
├── .env.example
├── requirements.txt
├── config.py
├── models.py
├── prompts.py
├── tools/
│   ├── __init__.py
│   └── customer_tools.py
├── services/
│   └── agent.py
├── tests/
│   └── test_agent.py
└── main.py

config.py — recall Module 3.

from dotenv import load_dotenv
import os

load_dotenv()

OPENAI_API_KEY = os.environ.get("OPENAI_API_KEY")
if not OPENAI_API_KEY:
    raise ValueError("Missing OPENAI_API_KEY — check your .env file.")

models.py — recall Module 18.

from typing import Literal
from pydantic import BaseModel

class TicketResolution(BaseModel):
    resolved: bool
    category: Literal["billing", "technical", "account", "other"]
    summary: str

prompts.py — recall Module 7.

from langchain_core.prompts import ChatPromptTemplate

SUPPORT_SYSTEM_PROMPT = (
    "You are a warm, professional customer support agent. "
    "Always check policy eligibility before taking any action."
)

tools/customer_tools.py — recall Module 12.

from langchain.tools import tool

ORDERS = {"o_1": {"customer_id": "c_1", "days_since_purchase": 12}}

@tool
def get_order(order_id: str) -> str:
    """Look up order details by order ID."""
    order = ORDERS.get(order_id)
    return str(order) if order else f"No order found with ID {order_id}."

@tool
def check_refund_policy(days_since_purchase: int) -> str:
    """Check whether an order is eligible for a refund (within 30 days)."""
    return "Eligible for refund." if days_since_purchase <= 30 else "Not eligible — past 30 days."

services/agent.py — recall Modules 15, 20, 27, 28.

import config
from langchain.chat_models import init_chat_model
from langchain.agents import create_agent
from langchain.agents.middleware import PIIMiddleware
from prompts import SUPPORT_SYSTEM_PROMPT
from tools.customer_tools import get_order, check_refund_policy

def build_agent():
    model = init_chat_model("openai:gpt-4o-mini").with_retry(stop_after_attempt=2)
    return create_agent(
        model=model,
        tools=[get_order, check_refund_policy],
        system_prompt=SUPPORT_SYSTEM_PROMPT,
        middleware=[PIIMiddleware("email")],
    )

tests/test_agent.py — recall Module 30.

from tools.customer_tools import get_order, check_refund_policy

def test_get_order_found():
    assert "c_1" in get_order.invoke({"order_id": "o_1"})

def test_check_refund_policy_eligible():
    assert "Eligible" in check_refund_policy.invoke({"days_since_purchase": 10})

def test_check_refund_policy_not_eligible():
    assert "Not eligible" in check_refund_policy.invoke({"days_since_purchase": 45})

main.py — everything wired together.

from services.agent import build_agent

def main():
    agent = build_agent()
    config = {"configurable": {"thread_id": "session-1"}, "recursion_limit": 10}

    result = agent.invoke(
        {"messages": [{"role": "user", "content": "Can order o_1 be refunded?"}]},
        config=config,
    )
    print(result["messages"][-1].content)

if __name__ == "__main__":
    main()

Notice how data actually flows through this real structure: main.py calls build_agent(), which reads validated configuration from config.py, assembles tools from tools/customer_tools.py, applies a system prompt from prompts.py, and wraps everything in the resilience and safety middleware from Modules 27 and 28 — every file doing exactly one job, genuinely testable in isolation, exactly the discipline Module 30 taught.

Closing this entire course

You began Module 1 watching a two-line script answer one question, and asking why an entire framework needed to exist for something that simple. Thirty-four modules later, you’ve built agents that reason across multiple tool calls, RAG systems grounded in real documents, resilient applications that survive real failures, and a properly structured, production-shaped project like the one above.

The honest measure of this course was never “can you recite what LangChain is.” It’s this: given a real, unfamiliar LLM application problem, can you figure out which components you need, build them, understand what’s actually happening underneath, debug them when they misbehave, and know — genuinely — when LangChain is the right tool, and when it isn’t. That’s what you now have.