How to Use the Trengo MCP in LangChain

Q: Can I list team members with Trengo and LangChain?

Yes. You call listteammembers to get a roster of users who work on the account. This is useful if you're building an agent that needs to know which internal user should be assigned a new ticket via createticket.

Build multi-step reasoning pipelines for Trengo using LangChain.

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Connect Trengo MCP to LangChain

Create your Vinkius account to connect Trengo 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 Trengo with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Building Chains with MCP Server

You can create complex workflows where the agent makes decisions based on live data. For example, you might first use `list_tickets` to check for open issues. Then, if a ticket is found, your agent calls `get_ticket` to get the details. It uses that output to decide whether it needs to call `send_message` or just update the status with `update_ticket`.

Managing Customer Context

Need to figure out who you're talking to? The agent handles this by chaining lookups. You can start by listing all contacts using `list_contacts`. Once the user is identified, the agent calls `list_messages` to pull up the conversation history and decide what information needs to be included in a new ticket via `create_ticket`.

Automating Communication Setup

The MCP Server lets you build automated setup routines. You can first call `get_account_profile` just to verify the credentials. After that, the agent uses the results to check for existing connections using `list_webhooks`. If nothing is found, it proceeds with calling `create_webhook` to set up the necessary data pipeline.

Setup guide

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

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Common questions about Trengo MCP in LangChain

LangChain treats the MCP Server as a source of actions. You pass the server's tool definitions, and your agent can then decide when and how to call them using standard Python function calls.

This server handles customer conversation data, including ticket details, messages from various channels like WhatsApp, and contact information. The agent can process all of this structured data through its chains.

Yes. You call `list_team_members` to get a roster of users who work on the account. This is useful if you're building an agent that needs to know which internal user should be assigned a new ticket via `create_ticket`.

The server manages conversations across WhatsApp, email, and chat. Your agent can use tools like `send_message` to send out communication, regardless of the source channel used in the initial ticket.

The server touches customer conversation data. Because you're building multi-step reasoning chains, it's critical to track which tool inputs (like `get_ticket` payload) the agent uses for every action.

Use it with your favorite AI tools

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