How to Use the Bot9 MCP in LangChain

Build complex reasoning chains for Bot9 within LangChain, linking agent decisions directly to your customer support data.

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Connect Bot9 MCP to LangChain

Create your Vinkius account to connect Bot9 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 Bot9 with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Chain Bot9 actions in LangChain

Feed the output of `list_conversations` directly into your next processing step. You can chain multi-step reasoning where the agent decides if it needs to trigger `send_message` based on previous interaction history. This MCP Server fits right into your existing LangGraph setup. Your agents handle the logic while the tools manage the heavy lifting of bot configuration.

Trace Bot9 calls with LangSmith

Monitor every `create_bot` or `add_data_source` call with full observability. You see the latency and token usage for every interaction in your pipeline. Debugging becomes simple when you track exactly how the agent interacts with your knowledge base. It provides a clear audit trail for every automated support decision.

Manage support bots in sequences

Sequence `list_bots` and `get_bot` to build dynamic support workflows. Your agent updates configurations on the fly as it gathers more information from users. It removes the need for manual oversight in your support automation. The agent adapts the bot's behavior by calling these tools in the specific order you define.

Setup guide

Set up Bot9 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 Bot9 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({
    "bot9-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 Bot9 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 Bot9. 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.

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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 Bot9 MCP in LangChain

Use the MultiServerMCPClient to connect the server. You pass the tools returned by the client directly into your agent definition.

Yes, use the client session to maintain context. This ensures your agent remembers previous messages during long-running chains.

The server supports multi-server aggregation. You can connect several instances and let your agent choose the right one for the job.

Bot9 stores everything on its own managed infrastructure. You just manage the state through the tool calls provided by the server.

Your customer messages and bot configurations remain private. The connection uses scoped tokens and the Vinkius sandbox to isolate your data.

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

Mastra AI sdk-typescript

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