How to integrate Fluxguard MCP with LangChain

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Introduction

This guide walks you through connecting Fluxguard to LangChain using the Composio tool router. By the end, you'll have a working Fluxguard agent that can add competitor's homepage for daily monitoring, list all recent alerts for my sites, acknowledge today's website change alert, create webhook for instant change notifications through natural language commands.

This guide will help you understand how to give your LangChain agent real control over a Fluxguard account through Composio's Fluxguard MCP server.

Before we dive in, let's take a quick look at the key ideas and tools involved.

TL;DR

Here's what you'll learn:
  • Get and set up your OpenAI and Composio API keys
  • Connect your Fluxguard project to Composio
  • Create a Tool Router MCP session for Fluxguard
  • Initialize an MCP client and retrieve Fluxguard tools
  • Build a LangChain agent that can interact with Fluxguard
  • Set up an interactive chat interface for testing

What is LangChain?

LangChain is a framework for developing applications powered by language models. It provides tools and abstractions for building agents that can reason, use tools, and maintain conversation context.

Key features include:

  • Agent Framework: Build agents that can use tools and make decisions
  • MCP Integration: Connect to external services through Model Context Protocol adapters
  • Memory Management: Maintain conversation history across interactions
  • Multi-Provider Support: Works with OpenAI, Anthropic, and other LLM providers

What is the Fluxguard MCP server, and what's possible with it?

The Fluxguard MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Fluxguard account. It provides structured and secure access to your website monitoring and alerting data, so your agent can perform actions like adding new monitored pages, categorizing sites, retrieving alerts, acknowledging changes, and managing webhooks on your behalf.

  • Automated website monitoring setup: Direct your agent to add new web pages or entire sites for continuous change detection and tracking with just a quick prompt.
  • Alert retrieval and analysis: Have your agent fetch detailed information about recent alerts, surfacing critical changes on any monitored page instantly.
  • Intelligent alert acknowledgment: Let your agent acknowledge and mark alerts as reviewed, helping your team stay organized and responsive.
  • Site and category management: Organize your monitored properties by creating, updating, or deleting site categories to keep your web asset monitoring streamlined.
  • Webhook automation: Set up or remove webhooks to automate notifications, ensuring you never miss an important website change event.

Supported Tools & Triggers

Tools
Acknowledge Fluxguard AlertTool to acknowledge an alert, marking it as reviewed.
Add FluxGuard PageTool to add a new page for monitoring.
Create FluxGuard Site CategoryTool to create a new site category in FluxGuard.
Create WebhookTool to create a new webhook for receiving notifications about monitored pages.
Delete Fluxguard PageTool to delete a monitored page.
Delete Fluxguard SiteTool to delete a monitored site.
Delete WebhookTool to delete a webhook.
Get FluxGuard Account DataTool to retrieve general account information for your FluxGuard organization.
Get Alert DetailsTool to retrieve details of a specific alert.
Get FluxGuard AlertsTool to retrieve all alerts generated by site changes.
Get FluxGuard Site CategoriesTool to retrieve all site categories.
Get Fluxguard ChangeTool to retrieve details of a change by its ID.
Get ChangesTool to retrieve a list of all detected changes across monitored sites.
Get Sample Webhook PayloadTool to retrieve a sample webhook payload.
Get FluxGuard Site DetailsTool to retrieve details of a specific monitored site by its ID.
Get FluxGuard SitesTool to retrieve a list of all monitored sites.
Get SnapshotTool to retrieve details of a specific snapshot by its ID.
Get Site SnapshotsTool to retrieve a list of all site snapshots.
Get FluxGuard User DetailsTool to retrieve details that represent the current FluxGuard account as a user-like object.
Get FluxGuard UsersTool to retrieve all users in the organization.
Get Webhook DetailsTool to retrieve details of a specific webhook by its ID.
Get FluxGuard WebhooksTool to retrieve all configured webhooks.
Fluxguard Webhook NotificationTool to send change data to your webhook endpoint.

What is the Composio tool router, and how does it fit here?

What is Tool Router?

Composio's Tool Router helps agents find the right tools for a task at runtime. You can plug in multiple toolkits (like Gmail, HubSpot, and GitHub), and the agent will identify the relevant app and action to complete multi-step workflows. This can reduce token usage and improve the reliability of tool calls. Read more here: Getting started with Tool Router

The tool router generates a secure MCP URL that your agents can access to perform actions.

How the Tool Router works

The Tool Router follows a three-phase workflow:

  1. Discovery: Searches for tools matching your task and returns relevant toolkits with their details.
  2. Authentication: Checks for active connections. If missing, creates an auth config and returns a connection URL via Auth Link.
  3. Execution: Executes the action using the authenticated connection.

Step-by-step Guide

Prerequisites

Before starting this tutorial, make sure you have:
  • Python 3.10 or higher installed on your system
  • A Composio account with an API key
  • An OpenAI API key
  • Basic familiarity with Python and async programming

Getting API Keys for OpenAI and Composio

OpenAI API Key
  • Go to the OpenAI dashboard and create an API key. You'll need credits to use the models, or you can connect to another model provider.
  • Keep the API key safe.
Composio API Key
  • Log in to the Composio dashboard.
  • Navigate to your API settings and generate a new API key.
  • Store this key securely as you'll need it for authentication.

Install dependencies

pip install composio-langchain langchain-mcp-adapters langchain python-dotenv

Install the required packages for LangChain with MCP support.

What's happening:

  • composio-langchain provides Composio integration for LangChain
  • langchain-mcp-adapters enables MCP client connections
  • langchain is the core agent framework
  • python-dotenv loads environment variables

Set up environment variables

bash
COMPOSIO_API_KEY=your_composio_api_key_here
COMPOSIO_USER_ID=your_composio_user_id_here
OPENAI_API_KEY=your_openai_api_key_here

Create a .env file in your project root.

What's happening:

  • COMPOSIO_API_KEY authenticates your requests to Composio's API
  • COMPOSIO_USER_ID identifies the user for session management
  • OPENAI_API_KEY enables access to OpenAI's language models

Import dependencies

from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain.agents import create_agent
from dotenv import load_dotenv
from composio import Composio
import asyncio
import os

load_dotenv()
What's happening:
  • We're importing LangChain's MCP adapter and Composio SDK
  • The dotenv import loads environment variables from your .env file
  • This setup prepares the foundation for connecting LangChain with Fluxguard functionality through MCP

Initialize Composio client

async def main():
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))

    if not os.getenv("COMPOSIO_API_KEY"):
        raise ValueError("COMPOSIO_API_KEY is not set")
    if not os.getenv("COMPOSIO_USER_ID"):
        raise ValueError("COMPOSIO_USER_ID is not set")
What's happening:
  • We're loading the COMPOSIO_API_KEY from environment variables and validating it exists
  • Creating a Composio instance that will manage our connection to Fluxguard tools
  • Validating that COMPOSIO_USER_ID is also set before proceeding

Create a Tool Router session

# Create Tool Router session for Fluxguard
session = composio.create(
    user_id=os.getenv("COMPOSIO_USER_ID"),
    toolkits=['fluxguard']
)

url = session.mcp.url
What's happening:
  • We're creating a Tool Router session that gives your agent access to Fluxguard tools
  • The create method takes the user ID and specifies which toolkits should be available
  • The returned session.mcp.url is the MCP server URL that your agent will use
  • This approach allows the agent to dynamically load and use Fluxguard tools as needed

Configure the agent with the MCP URL

client = MultiServerMCPClient({
    "fluxguard-agent": {
        "transport": "streamable_http",
        "url": session.mcp.url,
        "headers": {
            "x-api-key": os.getenv("COMPOSIO_API_KEY")
        }
    }
})

tools = await client.get_tools()

agent = create_agent("gpt-5", tools)
What's happening:
  • We're creating a MultiServerMCPClient that connects to our Fluxguard MCP server via HTTP
  • The client is configured with a name and the URL from our Tool Router session
  • get_tools() retrieves all available Fluxguard tools that the agent can use
  • We're creating a LangChain agent using the GPT-5 model

Set up interactive chat interface

conversation_history = []

print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
print("Ask any Fluxguard related question or task to the agent.\n")

while True:
    user_input = input("You: ").strip()

    if user_input.lower() in ['exit', 'quit', 'bye']:
        print("\nGoodbye!")
        break

    if not user_input:
        continue

    conversation_history.append({"role": "user", "content": user_input})
    print("\nAgent is thinking...\n")

    response = await agent.ainvoke({"messages": conversation_history})
    conversation_history = response['messages']
    final_response = response['messages'][-1].content
    print(f"Agent: {final_response}\n")
What's happening:
  • We initialize an empty conversation_history list to maintain context across interactions
  • A while loop continuously accepts user input from the command line
  • When a user types a message, it's added to the conversation history and sent to the agent
  • The agent processes the request using the ainvoke() method with the full conversation history
  • Users can type 'exit', 'quit', or 'bye' to end the chat session gracefully

Run the application

if __name__ == "__main__":
    asyncio.run(main())
What's happening:
  • We call the main() function using asyncio.run() to start the application

Complete Code

Here's the complete code to get you started with Fluxguard and LangChain:

from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain.agents import create_agent
from dotenv import load_dotenv
from composio import Composio
import asyncio
import os

load_dotenv()

async def main():
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    
    if not os.getenv("COMPOSIO_API_KEY"):
        raise ValueError("COMPOSIO_API_KEY is not set")
    if not os.getenv("COMPOSIO_USER_ID"):
        raise ValueError("COMPOSIO_USER_ID is not set")
    
    session = composio.create(
        user_id=os.getenv("COMPOSIO_USER_ID"),
        toolkits=['fluxguard']
    )

    url = session.mcp.url
    
    client = MultiServerMCPClient({
        "fluxguard-agent": {
            "transport": "streamable_http",
            "url": url,
            "headers": {
                "x-api-key": os.getenv("COMPOSIO_API_KEY")
            }
        }
    })
    
    tools = await client.get_tools()
  
    agent = create_agent("gpt-5", tools)
    
    conversation_history = []
    
    print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
    print("Ask any Fluxguard related question or task to the agent.\n")
    
    while True:
        user_input = input("You: ").strip()
        
        if user_input.lower() in ['exit', 'quit', 'bye']:
            print("\nGoodbye!")
            break
        
        if not user_input:
            continue
        
        conversation_history.append({"role": "user", "content": user_input})
        print("\nAgent is thinking...\n")
        
        response = await agent.ainvoke({"messages": conversation_history})
        conversation_history = response['messages']
        final_response = response['messages'][-1].content
        print(f"Agent: {final_response}\n")

if __name__ == "__main__":
    asyncio.run(main())

Conclusion

You've successfully built a LangChain agent that can interact with Fluxguard through Composio's Tool Router.

Key features of this implementation:

  • Dynamic tool loading through Composio's Tool Router
  • Conversation history maintenance for context-aware responses
  • Async Python provides clean, efficient execution of agent workflows
You can extend this further by adding error handling, implementing specific business logic, or integrating additional Composio toolkits to create multi-app workflows.

How to build Fluxguard MCP Agent with another framework

OpenAI Agents SDK

OpenAI Agents SDK

Use Fluxguard MCP with OpenAI Agents SDK

Claude Agent SDK

Claude Agent SDK

Use Fluxguard MCP with Claude Agent SDK

Google ADK

Google ADK

Use Fluxguard MCP with Google ADK

Vercel AI SDK

Vercel AI SDK

Use Fluxguard MCP with Vercel AI SDK

Mastra AI

Mastra AI

Use Fluxguard MCP with Mastra AI

LlamaIndex

LlamaIndex

Use Fluxguard MCP with LlamaIndex

CrewAI

CrewAI

Use Fluxguard MCP with CrewAI

FAQ

What are the differences in Tool Router MCP and Fluxguard MCP?

With a standalone Fluxguard MCP server, the agents and LLMs can only access a fixed set of Fluxguard tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Fluxguard and many other apps based on the task at hand, all through a single MCP endpoint.

Can I use Tool Router MCP with LangChain?

Yes, you can. LangChain fully supports MCP integration. You get structured tool calling, message history handling, and model orchestration while Tool Router takes care of discovering and serving the right Fluxguard tools.

Can I manage the permissions and scopes for Fluxguard while using Tool Router?

Yes, absolutely. You can configure which Fluxguard scopes and actions are allowed when connecting your account to Composio. You can also bring your own OAuth credentials or API configuration so you keep full control over what the agent can do.

How safe is my data with Composio Tool Router?

All sensitive data such as tokens, keys, and configuration is fully encrypted at rest and in transit. Composio is SOC 2 Type 2 compliant and follows strict security practices so your Fluxguard data and credentials are handled as safely as possible.

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