How to Use the Cliengo MCP in LangChain

Q: How do I connect the Cliengo MCP Server to LangChain?

Install langchain-mcp-adapters. Initialize a MultiServerMCPClient pointing to your Vinkius endpoint, call client.gettools(), and pass that array to your ReAct agent.

Q: Can LangChain agents read specific Cliengo WhatsApp chats?

Yes. The agent uses listchatconversations to find the thread ID, then calls getchathistory to pull the full transcript into the context window.

Build conversational marketing pipelines that fetch Cliengo leads and feed them directly into LangChain agents.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect Cliengo MCP to LangChain

Create your Vinkius account to connect Cliengo 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.

GDPR Free for Subscribers

Setup Cliengo with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

LangChain ReAct agents for lead qualification

Your agent needs context before talking to a prospect. Using `list_cliengo_leads`, a LangChain ReAct loop pulls recent captures and decides which ones need immediate follow-up based on your custom criteria. Once the system identifies a priority target, the chain calls `get_lead_details` to grab the specific contact info. The output from that tool feeds straight into your next prompt template, completely bypassing manual CRM lookups.

Chain chat history into support workflows

Stop making agents guess what a customer said on WhatsApp. You string together `list_chat_conversations` and `get_chat_history` to pull the exact transcript into your working memory before generating a reply. Because LangChain treats every tool call as a discrete step, you get full observability in LangSmith. You see exactly how long the MCP Server took to fetch those messages and what the LLM decided to do with them.

Audit configurations across projects

Managing multiple chat widgets gets messy fast. A scheduled chain runs `list_cliengo_websites` to map out every active installation on your account without human intervention. From there, the agent checks `list_cliengo_webhooks` to ensure lead data is routing correctly. If something looks off, the pipeline flags the broken configuration before you lose valuable prospect information.

Setup guide

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

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Cliengo. 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.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

Connect Cliengo now

Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about Cliengo MCP in LangChain

Install `langchain-mcp-adapters`. Initialize a `MultiServerMCPClient` pointing to your Vinkius endpoint, call `client.get_tools()`, and pass that array to your ReAct agent.

Yes. The agent uses `list_chat_conversations` to find the thread ID, then calls `get_chat_history` to pull the full transcript into the context window.

Every interaction logs automatically. You get exact latency numbers and token counts for operations like fetching user lists or polling webhooks.

That is the whole point of composability. Your chain pulls a transcript via `get_contact_messages` and immediately embeds it into your Pinecone index.

Vinkius runs this MCP Server in an ephemeral V8 isolate sandbox. Your agent pulls sensitive contact details and chat histories into memory only when requested, and the server destroys the environment the moment the connection drops.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript