How to Integrate LangChain for lending with Stripe for RAG

Combining LangChain for lending with Stripe gives you a clean path to build AI agents that can answer lending questions, retrieve policy or loan-doc context with RAG, and trigger billing or payment events when a workflow needs it. In practice, this means you can build an assistant that explains loan terms from your internal knowledge base, then creates a Stripe checkout session for application fees, document fees, or subscription access.

Prerequisites

• •Python 3.10+
• •A LangChain for lending setup with:
  • •access to your lending documents
  • •an embedding model
  • •a vector store
• •A Stripe account with:
  • •API secret key
  • •a product and price configured, or permission to create them via API
• •Installed packages:
  • •langchain
  • •langchain-openai or your preferred LLM provider package
  • •langchain-community
  • •stripe
  • •python-dotenv
• •Environment variables configured:
  • •OPENAI_API_KEY
  • •STRIPE_SECRET_KEY
  • •STRIPE_WEBHOOK_SECRET if you plan to verify webhooks

Integration Steps

1) Install dependencies and load secrets

Start by wiring the runtime. Keep keys in environment variables; do not hardcode them in agent code.

pip install langchain langchain-openai langchain-community stripe python-dotenv

import os
from dotenv import load_dotenv

load_dotenv()

OPENAI_API_KEY = os.environ["OPENAI_API_KEY"]
STRIPE_SECRET_KEY = os.environ["STRIPE_SECRET_KEY"]

2) Build the RAG layer for lending content

Use LangChain’s standard retrieval flow: load documents, split them, embed them, and query through a retriever. This is the part that gives your agent grounded answers about lending policies, eligibility rules, repayment schedules, and fee disclosures.

from langchain_openai import OpenAIEmbeddings, ChatOpenAI
from langchain_community.vectorstores import FAISS
from langchain_text_splitters import RecursiveCharacterTextSplitter
from langchain_core.documents import Document

docs = [
    Document(page_content="Personal loans require KYC verification and minimum income of $2,500/month."),
    Document(page_content="Application fee is $25 and must be paid before underwriting starts."),
    Document(page_content="Late payment fee applies after a 10-day grace period.")
]

splitter = RecursiveCharacterTextSplitter(chunk_size=300, chunk_overlap=50)
chunks = splitter.split_documents(docs)

embeddings = OpenAIEmbeddings(model="text-embedding-3-small")
vectorstore = FAISS.from_documents(chunks, embeddings)
retriever = vectorstore.as_retriever(search_kwargs={"k": 2})

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

Now create a retrieval chain so the agent can answer from your lending corpus instead of guessing.

from langchain.chains import RetrievalQA

qa_chain = RetrievalQA.from_chain_type(
    llm=llm,
    retriever=retriever,
    return_source_documents=True,
)

result = qa_chain.invoke({"query": "What fee do applicants pay before underwriting?"})
print(result["result"])

3) Add Stripe payment creation for fee collection

Stripe handles the money side. For lending workflows, the most common pattern is creating a Checkout Session for an application fee or document processing fee.

import stripe

stripe.api_key = STRIPE_SECRET_KEY

checkout_session = stripe.checkout.Session.create(
    mode="payment",
    success_url="https://yourapp.com/success?session_id={CHECKOUT_SESSION_ID}",
    cancel_url="https://yourapp.com/cancel",
    line_items=[
        {
            "price_data": {
                "currency": "usd",
                "product_data": {"name": "Loan Application Fee"},
                "unit_amount": 2500,
            },
            "quantity": 1,
        }
    ],
)

print(checkout_session.url)

If you already have a Stripe Price ID in production, use it directly. That is cleaner than creating ad hoc prices at runtime.

session = stripe.checkout.Session.create(
    mode="payment",
    success_url="https://yourapp.com/success?session_id={CHECKOUT_SESSION_ID}",
    cancel_url="https://yourapp.com/cancel",
    line_items=[{"price": "price_12345", "quantity": 1}],
)

4) Orchestrate both inside one agent flow

The integration point is simple: use RAG to decide whether payment is required, then call Stripe if the retrieved policy says so. In production, this usually sits behind an agent tool or service layer.

def get_lending_answer(question: str):
    response = qa_chain.invoke({"query": question})
    return response["result"], response["source_documents"]

def create_application_fee_checkout():
    return stripe.checkout.Session.create(
        mode="payment",
        success_url="https://yourapp.com/success?session_id={CHECKOUT_SESSION_ID}",
        cancel_url="https://yourapp.com/cancel",
        line_items=[
            {
                "price_data": {
                    "currency": "usd",
                    "product_data": {"name": "Loan Application Fee"},
                    "unit_amount": 2500,
                },
                "quantity": 1,
            }
        ],
    )

question = "Do I need to pay anything before underwriting?"
answer, sources = get_lending_answer(question)

if "Application fee is $25" in answer:
    session = create_application_fee_checkout()
    print({"answer": answer, "payment_url": session.url})
else:
    print({"answer": answer})

Testing the Integration

Run a simple end-to-end check: ask the lending assistant about fees, then verify Stripe returns a valid Checkout URL.

question = "What fee do applicants pay before underwriting?"
answer, _ = get_lending_answer(question)

print("RAG answer:", answer)

session = create_application_fee_checkout()
print("Stripe checkout URL:", session.url)
print("Session ID:", session.id)

Expected output:

RAG answer: Application fee is $25 and must be paid before underwriting starts.
Stripe checkout URL: https://checkout.stripe.com/c/pay/cs_test_...
Session ID: cs_test_...

If you want webhook validation next, use Stripe’s signature verification before marking any payment as complete.

payload = request.data.decode("utf-8")
sig_header = request.headers.get("Stripe-Signature")

event = stripe.Webhook.construct_event(
    payload=payload,
    sig_header=sig_header,
    secret=os.environ["STRIPE_WEBHOOK_SECRET"],
)

Real-World Use Cases

• •

  Loan application assistants

  • •Answer borrower questions from policy docs with RAG.
  • •Trigger Stripe Checkout when the borrower needs to pay an application or processing fee.
• •

  Broker or advisor portals

  • •Let staff query internal lending rules through LangChain.
  • •Collect subscription payments for premium access to underwriting tools or research.
• •

  Document fulfillment workflows

  • •Retrieve which documents are required for a specific loan type.
  • •Charge for expedited review or document packaging through Stripe when requested.

Keep learning

• •The complete AI Agents Roadmap — my full 8-step breakdown
• •Free: The AI Agent Starter Kit — PDF checklist + starter code
• •Work with me — I build AI for banks and insurance companies

By Cyprian Aarons, AI Consultant at Topiax.