How to Use the CHATFLY MCP in LangChain

Chain CHATFLY tools in your LangChain agents to automate support workflows without leaving your Python code.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect CHATFLY MCP to LangChain

Create your Vinkius account to connect CHATFLY 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 CHATFLY with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Sequence CHATFLY actions in LangChain

Trigger `trigger_bot_training` directly from your LangGraph pipeline to keep knowledge bases current after data updates. Your chain handles the logic, moving from data ingestion to model updates in one execution flow. Pass the output of `list_chatfly_bots` into subsequent nodes to dynamically select which bot receives specific customer queries. You build complex, multi-step reasoning agents that manage your support infrastructure with code.

Observe CHATFLY traces in LangSmith

Monitor every `send_bot_message` call with native LangSmith tracing to see exactly what your agent sends to the API. You catch latency spikes and token overages before they hit your production logs. Debug tool inputs and outputs side-by-side to ensure your agent's reasoning matches the bot's responses. This MCP server logs every interaction, giving you a clear view of how your chain handles real-time customer data.

Persistent state for LangChain agents

Use `get_conversation_history` to pull past interactions into your agent's memory bank. You maintain context across long-running chains, allowing the agent to reference previous tickets during a live conversation. Store these histories in your database or vector store to build long-term memory for your support agents. The server handles the connection, so your agent stays focused on synthesizing past conversations into actionable insights.

Setup guide

Set up CHATFLY 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 CHATFLY 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({
    "chatfly-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 CHATFLY 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 CHATFLY. 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 CHATFLY 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 CHATFLY MCP in LangChain

Install the MCP adapters and point your client to the Vinkius endpoint. You initialize the server, fetch the tool list, and bind them to your LangChain agent definitions.

Yes, you use the conversation history tools to pull data directly into your agent's state. It keeps your bot's memory grounded in actual account data.

It does. You can share the tool definition across multiple agents within your graph to delegate support tasks based on specific bot requirements.

Every call is fully observable. You see the request and response payloads inside your trace, making it easy to track bot performance.

The server manages all authentication via a single endpoint token, keeping your API keys out of your codebase. It only processes chatbot metadata and conversation logs, ensuring your core account credentials stay isolated.

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