How to Use the AppLovin MCP in LangChain

Q: How do I use the AppLovin tools with a LangChain agent?

After installing the adapter, you get the tools by calling client.gettools(). Pass that list directly to LangChain's createagent function. The agent will then have access to all the AppLovin operations.

Q: Does the AppLovin MCP server support session persistence for LangChain?

Yes. While the client is stateless by default, you can use client.session() to maintain context across multiple calls to the MCP server. This is useful for building conversational agents that remember previous questions about your ad performance.

Build agents that manage your AppLovin ad performance using LangChain chains.

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Connect AppLovin MCP to LangChain

Create your Vinkius account to connect AppLovin 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 AppLovin with LangChain

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ChatGPT

Claude

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Chain Ad Reports Together

Use tools like `list_campaigns` and `get_app_discovery_report` to create multi-step ad analysis agents. Your agent can find active campaigns, then pull performance data for each one automatically. It's all part of a single, observable chain. This isn't just about calling one API. LangChain lets your agent decide the next step based on the last result. If a campaign's revenue from `get_user_ad_revenue_report` is low, the agent can dig deeper without you writing a single line of procedural code.

Monitor Ad Revenue and Cohorts

The `get_max_report` and `get_max_cohort_report` tools give your agent direct access to MAX mediation and cohort data. You can build chains that compare recent aggregated performance against historical cohort behavior. Because LangChain tracks every tool input and output, you get a clear trace of how your agent arrived at its conclusions. You'll see exactly which date ranges and columns it requested from the AppLovin MCP server to build its analysis.

Build Custom AppLovin Agents with this MCP Server

The AppLovin tools cover everything from high-level app listings (`list_apps`) to granular user revenue (`get_user_ad_revenue_report`). You can combine these tools in LangChain to build specialized agents for different tasks—one for user acquisition, another for monetization. Start with a simple connection check using `get_account_check`. Then, give your agent the full suite of tools. It can autonomously track campaign performance, report on MAX revenue, and answer questions about your ad business.

Setup guide

Set up AppLovin 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 AppLovin 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({
    "applovin-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 AppLovin 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 AppLovin. 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 AppLovin MCP in LangChain

After installing the adapter, you get the tools by calling `client.get_tools()`. Pass that list directly to LangChain's `create_agent` function. The agent will then have access to all the AppLovin operations.

Yes, that's what LangChain is for. You can create a chain that pulls campaign data from AppLovin with `list_campaigns`, then joins it with your internal CRM data using another integration.

Use LangSmith. Every call your agent makes to the AppLovin MCP server, including the inputs and outputs, is traced. This makes it easy to see why an agent chose a specific tool or what data it got back.

Yes. While the client is stateless by default, you can use `client.session()` to maintain context across multiple calls to the MCP server. This is useful for building conversational agents that remember previous questions about your ad performance.

Your AppLovin ad performance data is streamed directly to the agent through an ephemeral, sandboxed environment on Vinkius. The connection is secured by your unique endpoint token, and the data is only held in memory for the duration of the tool call.

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