How to Use the Keap MCP in LangChain

Q: How do I connect LangChain to the Keap MCP server?

Use MultiServerMCPClient with the HTTP transport URL. Call client.gettools() and pass the resulting list into your createagent function.

Q: Can LangChain agents trigger Keap email campaigns?

Yes. Your agent reads listcampaigns to find the correct internal ID, then uses applytagtocontact. Keap handles the actual email delivery based on that tag trigger.

Q: How do I handle persistent context across multiple runs?

Adapters are stateless by default. You need to use client.session() to keep the agent's memory intact between different tool calls.

Build multi-step CRM reasoning pipelines by connecting LangChain agents directly to your Keap MCP Server.

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Connect Keap MCP to LangChain

Create your Vinkius account to connect Keap 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 Keap with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Chain Keap MCP Server with databases

The Keap MCP Server exposes `list_contacts` and `get_contact` directly to your LangChain agents. You build ReAct pipelines where the agent pulls a contact record, checks an external PostgreSQL database for product usage, and decides what happens next. If the agent sees a drop in activity, it calls `apply_tag_to_contact` to trigger a re-engagement sequence. LangSmith tracks the exact latency and token usage of every API call across the entire flow.

Automate campaign and lead routing

Your agent reads `list_opportunities` to find stalled deals. It parses the output, identifies missing follow-ups, and uses `list_campaigns` to find the right marketing track. Instead of manual updates, the agent calls `create_contact` for new stakeholders found in email threads. The output of the creation tool feeds directly into the next link in your chain for instant tagging.

Audit ecommerce and invoice data

LangChain agents pull financial context using `list_orders` and `list_invoices`. You write the logic that tells the agent to aggregate total spend before assigning a VIP status. The system checks `list_tags` to ensure the VIP label exists, then applies it. You get full observability into why the agent made that specific routing decision.

Setup guide

Set up Keap 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 Keap 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({
    "keap-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 Keap transactions"
    })
    print(result["messages"][-1].content)

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Common questions about Keap MCP in LangChain

Use `MultiServerMCPClient` with the HTTP transport URL. Call `client.get_tools()` and pass the resulting list into your `create_agent` function.

Yes. Your agent reads `list_campaigns` to find the correct internal ID, then uses `apply_tag_to_contact`. Keap handles the actual email delivery based on that tag trigger.

LangSmith logs every interaction automatically. You see the exact inputs your agent sent to `create_contact` and the raw JSON response it got back.

Adapters are stateless by default. You need to use `client.session()` to keep the agent's memory intact between different tool calls.

The server processes raw PII like email addresses and physical locations from `get_contact`. Vinkius runs this connection inside a V8 Isolate Sandbox, destroying the environment the moment your script finishes execution.

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