How to Use the Kajabi MCP in LangChain

Q: How do I pass Kajabi site IDs to LangChain agents?

Use the listsites tool first within your LangChain agent's tool-calling loop. Once the agent gets the site ID, it passes that value to listcontacts or listoffers to filter the data.

Q: Can LangChain manage Kajabi contact tags dynamically?

Yes, your LangChain chain can pull tags using listtags and apply them with addtagtocontact. The agent handles the intermediate decisions, checking if a contact already has a tag via getcontactdetails before running the update.

Q: How does LangChain trace Kajabi MCP Server tool latency?

Every call to getproductdetails or listorders goes through the LangChain MCP adapter, which automatically logs performance metrics to LangSmith. You get full visibility into execution times and token counts for each tool run.

Q: What is the setup for using this Kajabi integration?

Install langchain-mcp-adapters and use the MultiServerMCPClient pointing to your Vinkius endpoint. Retrieve the tools with client.gettools() and pass them directly to your agent executor.

Run multi-step Kajabi operations inside LangChain chains to manage courses and update contact tags dynamically.

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Connect Kajabi MCP to LangChain

Create your Vinkius account to connect Kajabi 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 Kajabi with LangChain

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Automate Kajabi customer tagging in LangChain pipelines

The `add_tag_to_contact` tool lets your agent tag users based on their activity without manual intervention. First, the chain uses `list_contacts` to find the target user, then pulls available tags via `list_tags`, and finally applies the tag. This sequence runs as a single, observable execution block. LangSmith tracks every step of this tagging pipeline, showing you the exact inputs and outputs of each tool. You see the latency of `add_tag_to_contact` and can debug failing tag assignments instantly.

Chain course and product audits with LangChain agents

The `list_courses` tool retrieves your active curriculum details directly into your LangChain agent's memory. Your agent then calls `get_course_details` and `get_product_details` sequentially to build a complete profile of what you sell. This lets you construct reasoning chains that compare product configurations against actual customer access. Combining these tools into a single chain prevents state fragmentation across your system. Your agent handles the multi-step reasoning, deciding when to query course details and when to pull product specs based on intermediate outputs.

Track purchases and orders via this MCP Server

The `list_orders` tool exposes purchase histories directly to your LangChain runtimes. Your agent queries `list_purchases` and matches those records against `list_offers` to verify payment statuses in real-time. This setup replaces brittle, hardcoded webhooks with dynamic, tool-calling chains. Because Vinkius hosts this MCP Server, your LangChain code connects using a single endpoint token. You don't have to manage complex authentication headers or rate-limit queues for the individual Kajabi API endpoints.

Setup guide

Set up Kajabi 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 Kajabi 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({
    "kajabi-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 Kajabi 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 Kajabi. 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 Kajabi MCP in LangChain

Use the `list_sites` tool first within your LangChain agent's tool-calling loop. Once the agent gets the site ID, it passes that value to `list_contacts` or `list_offers` to filter the data.

Yes, your LangChain chain can pull tags using `list_tags` and apply them with `add_tag_to_contact`. The agent handles the intermediate decisions, checking if a contact already has a tag via `get_contact_details` before running the update.

Every call to `get_product_details` or `list_orders` goes through the LangChain MCP adapter, which automatically logs performance metrics to LangSmith. You get full visibility into execution times and token counts for each tool run.

Install `langchain-mcp-adapters` and use the `MultiServerMCPClient` pointing to your Vinkius endpoint. Retrieve the tools with `client.get_tools()` and pass them directly to your agent executor.

The Vinkius sandbox isolates the server, protecting sensitive data like customer emails and transaction records from `list_customers` and `list_orders`. Your credentials never touch the client directly, running inside an ephemeral V8 sandbox instead.

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