How to Use the Klarna MCP in LangChain

Q: How do I handle Klarna session timeouts in LangChain?

Use a conditional router in your chain to call getpaymentsession when a timeout is suspected. If the session expired, your agent triggers createpaymentsession to refresh the token.

Q: Can I trace Klarna tool inputs inside LangChain using LangSmith?

Yes, every argument passed to createklarnaorder is logged in your LangSmith dashboard. You can monitor the exact JSON payload, including line items and authorization tokens, for debugging.

Q: How does LangChain manage multiple MCP servers alongside Klarna?

You initialize the MultiServerMCPClient with the Klarna endpoint and your other servers. This allows your agent to fetch warehouse data from one server and immediately call updateordershipping on this MCP server.

Q: What happens if a Klarna payment capture fails in a chain?

Your agent catches the error from captureklarnaorder and routes the payload to a retry loop or flags it for human review. You define this fallback behavior directly inside your LangGraph state definition.

Run multi-step checkout chains and track every transaction state inside your LangChain pipelines.

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Connect Klarna MCP to LangChain

Create your Vinkius account to connect Klarna to LangChain and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Klarna with LangChain

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Build multi-step Klarna checkout chains in LangChain

`create_payment_session` initiates the checkout flow directly from your LLM chain using this MCP Server. Your agent grabs the output payload, extracts the session ID, and passes it to the next node to handle the buyer's journey. LangSmith tracks the entire state machine as your chain moves from session to order creation. You see exact latency numbers and token costs for every payment step without guessing.

Capture payments based on shipping events

`capture_klarna_order` executes the moment your shipping agent confirms a package left the warehouse. This tool acts as the final step in your fulfillment chain, converting pre-authorizations into captured funds. Combining this tool with `update_order_shipping` keeps your ledger accurate. The agent coordinates these calls sequentially, preventing uncaptured shipments and keeping finance happy.

Handle returns through LangChain reasoning loops

`refund_klarna_order` triggers when a customer support agent flags a return request in your chat chain. The model evaluates the refund amount, checks the original order details, and processes the transaction. If the order is authorized but not yet captured, the chain routes to `cancel_authorized_order` instead. This conditional logic runs autonomously, saving manual processing time.

Setup guide

Set up Klarna MCP in LangChain

Prerequisites

Python 3.10+ installed
langchain-mcp-adapters + langgraph packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install langchain-mcp-adapters langgraph langchain-openai. The MCP adapters package converts MCP tools into native LangChain BaseTool objects.
2

Connect via HTTP transport
Use MultiServerMCPClient with "transport": "http" pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create a ReAct agent
Pass the discovered tools to create_react_agent() from LangGraph. The agent automatically routes Klarna tool calls through the MCP protocol.
4

Run with any LLM
Swap ChatOpenAI for ChatAnthropic, ChatGoogleGenerativeAI, or any LangChain-compatible model. The MCP tools work identically across all providers.

agent.py

from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI

async with MultiServerMCPClient({
    "klarna-mcp": {
        "transport": "http",
        "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
    }
}) as client:
    tools = client.get_tools()

    agent = create_react_agent(
        ChatOpenAI(model="gpt-4o"),
        tools,
    )
    result = await agent.ainvoke({
        "messages": "List recent Klarna transactions"
    })
    print(result["messages"][-1].content)

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Klarna. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Klarna MCP in LangChain

Use a conditional router in your chain to call `get_payment_session` when a timeout is suspected. If the session expired, your agent triggers `create_payment_session` to refresh the token.

Yes, every argument passed to `create_klarna_order` is logged in your LangSmith dashboard. You can monitor the exact JSON payload, including line items and authorization tokens, for debugging.

You initialize the `MultiServerMCPClient` with the Klarna endpoint and your other servers. This allows your agent to fetch warehouse data from one server and immediately call `update_order_shipping` on this MCP server.

Your agent catches the error from `capture_klarna_order` and routes the payload to a retry loop or flags it for human review. You define this fallback behavior directly inside your LangGraph state definition.

This MCP Server processes session IDs and transaction amounts inside ephemeral V8 isolates. No raw credit card details or customer credentials ever touch the Vinkius hosting layer or your LangChain environment.

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