How to Use the Twenty CRM MCP in LangChain

Q: Can I list all accounts with LangChain and Twenty CRM?

Absolutely. The listcompanies tool fetches every record in your system. This means you can build chains that process hundreds of companies sequentially.

Run complex, multi-step logic with LangChain and Twenty CRM.

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Connect Twenty CRM MCP to LangChain

Create your Vinkius account to connect Twenty CRM 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 Twenty CRM with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Automating follow-up tasks

Need to manage a full sales cycle? Your AI client runs a sequence: first, it finds the correct contact using `get_person`. Next, it checks related accounts with `list_companies` and finally updates the deal status by calling `update_opportunity`. This chain handles everything from discovery to action.

Building data pipelines

The MCP Server lets you build complex workflows. Start by retrieving a list of all open accounts using `list_companies` and then iterate through them, creating necessary follow-up records via `create_task`. This process makes it simple to manage entire groups of leads in Twenty CRM.

Full record lifecycle management

It's not just about adding data. Your agent can run a complete cleanup cycle. For example, you might call `get_opportunity` to review details, then use `delete_note` if the notes are outdated, and finally update the associated person via `update_person`. This level of control keeps your Twenty CRM clean.

Setup guide

Set up Twenty CRM 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 Twenty CRM 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({
    "twenty-crm-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 Twenty CRM transactions"
    })
    print(result["messages"][-1].content)

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Common questions about Twenty CRM MCP in LangChain

LangChain lets you string together multiple tool calls. You don't just read a record; you build an agent that decides the order of operations, maybe getting a company ID then creating a related task.

Absolutely. The `list_companies` tool fetches every record in your system. This means you can build chains that process hundreds of companies sequentially.

While AutoGen is great for debate, LangChain excels at structured pipelines. You'd use `update_company` or `update_opportunity` within a defined sequence of steps.

Yes. Because the output from one tool call (like a person's ID) becomes the direct input for the next, you can create deep, multi-step reasoning pipelines.

This MCP Server touches core contact information, including `Person` records. You can use the agent to confirm who owns which accounts before running any large-scale updates.

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