How to Use the Lago MCP in LangChain

Q: How do I connect the Lago MCP Server to my LangChain agent?

Install langchain-mcp-adapters. Pass your endpoint URL into MultiServerMCPClient and fetch the tools.

Q: Does this integration support session state?

LangChain is stateless by default. You call client.session() to keep the context active while chaining customer creation and subscription steps.

Build composable billing workflows with LangChain. Chain usage events and invoice generation into observable pipelines.

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Connect Lago MCP to LangChain

Create your Vinkius account to connect Lago 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 Lago with LangChain

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LangChain MCP Server Billing Chains

The `create_billable_metric` and `create_plan` tools let your LangChain agent define pricing models on the fly. You feed it a feature matrix, and the agent translates those inputs into a functional billing structure. Because LangChain treats every tool call as a link, the output of your metric creation instantly feeds into the plan setup. You track the entire execution sequence through LangSmith to catch configuration errors before customers see them.

Batch Event Processing

The `batch_events` tool allows your ReAct agents to ingest thousands of usage records in a single payload. Your agent pulls raw logs from a database, formats them, and pushes them straight to Lago. You avoid rate limits that block standard pipelines. The MCP integration groups the data, executes the batch, and routes the success confirmation directly to your observability dashboard.

Automated Onboarding

Use `upsert_customer` and `create_subscription` to handle account provisioning without manual data entry. The agent reads a new signup event and instantly wires up their billing profile. If they need prepaid credits, the chain just calls `create_wallet` next. The framework handles the logic branching, so nobody gets access until their payment structure actually exists in Lago.

Setup guide

Set up Lago 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 Lago 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({
    "lago-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 Lago 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 Lago. 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 Lago MCP in LangChain

Install `langchain-mcp-adapters`. Pass your endpoint URL into `MultiServerMCPClient` and fetch the tools.

Yes. LangSmith records the exact execution time and token usage for every single tool call.

You write a chain that queries your database first. The output of that step becomes the direct input for the `send_event` tool.

LangChain is stateless by default. You call `client.session()` to keep the context active while chaining customer creation and subscription steps.

Your agent only processes the raw invoice amounts and external customer IDs you explicitly pass to it. Vinkius runs the server in an ephemeral V8 Isolate Sandbox, meaning the memory wipes the second the transaction finishes.

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