How to Use the ChatGen MCP in LangChain

Q: How do I use ChatGen tools within a LangChain agent?

Use the langchain-mcp-adapters library to get the tools. After initializing the client, call client.gettools() and pass that list to your agent's constructor. LangChain then knows how to call createbot or listleads based on the prompt.

Q: Can a LangChain agent manage multiple ChatGen bots at once?

Yes. Your agent can call listbots to get all their IDs. Then, it can loop through them to perform batch operations, like updating a welcome message on every bot with updatebot.

Q: What's the best way to handle ChatGen lead data in LangChain?

Start by having your agent listleads. For each lead, use getleaddetails and listconversations to get the full picture. You can then chain this data into other LangChain tools for analysis, storage, or routing to a CRM.

Build autonomous lead-gen agents with ChatGen and LangChain. Chain bot management and lead analysis into a single workflow.

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Connect ChatGen MCP to LangChain

Create your Vinkius account to connect ChatGen 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 ChatGen with LangChain

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Manage Your Bots Programmatically

Your LangChain agent can now run your entire fleet of ChatGen bots. It can spin up new ones with `create_bot`, tweak their settings using `update_bot`, and take them down with `delete_bot` when a campaign ends. This isn't about single commands; it's about building chains that react to events. For example, build a chain that checks performance metrics from another system. If a bot is underperforming, the agent can automatically update its script or even replace it entirely. You can `list_bots` to get a full inventory, and then have the agent decide what to do next, all without you lifting a finger.

Automate Lead Analysis with this MCP Server

Connect LangChain to your ChatGen leads pipeline. Your agent can `list_leads` to get a fresh batch of prospects, then pull specifics on each one using `get_lead_details`. It's a direct line into your sales funnel, giving your agent the raw data needed to make decisions. The real power comes from chaining these actions. An agent could fetch a new lead, analyze the `list_conversations` transcript to score its quality, and then push it to the right sales team's CRM using another LangChain integration. This MCP connection gives your agent the facts to work with.

Build Custom Monitoring and Reporting Chains

Stop manually checking dashboards. Use LangChain to build agents that monitor ChatGen for you. Your agent can periodically `list_conversations` to look for negative sentiment or pull `list_teams` to generate reports on bot assignments. Imagine an agent that runs every morning. It lists all bots, checks their latest conversations for errors, and sends a summary to your Slack channel. LangChain handles the logic, and this server provides the tools to get the data from ChatGen.

Setup guide

Set up ChatGen 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 ChatGen 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({
    "chatgen-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 ChatGen 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 ChatGen. 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 ChatGen MCP in LangChain

Use the `langchain-mcp-adapters` library to get the tools. After initializing the client, call `client.get_tools()` and pass that list to your agent's constructor. LangChain then knows how to call `create_bot` or `list_leads` based on the prompt.

Yes. Your agent can call `list_bots` to get all their IDs. Then, it can loop through them to perform batch operations, like updating a welcome message on every bot with `update_bot`.

Start by having your agent `list_leads`. For each lead, use `get_lead_details` and `list_conversations` to get the full picture. You can then chain this data into other LangChain tools for analysis, storage, or routing to a CRM.

That's exactly what LangChain is for. You can create a chain where your agent first gets lead data from ChatGen, then enriches it with another service, and finally creates a ticket in a project management tool. The ChatGen tools become one part of a much larger workflow.

Your ChatGen lead details and conversation transcripts are only passed through the Vinkius ephemeral sandbox during a tool call. Vinkius doesn't store your data; it's proxied directly to the ChatGen API through a secure connection. Your access is controlled by a single endpoint token.

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