How to integrate Better stack MCP with LangChain

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Introduction

This guide walks you through connecting Better stack to LangChain using the Composio tool router. By the end, you'll have a working Better stack agent that can show uptime percentage for api monitor, create escalation policy for on-call team, list heartbeat availability for last week, delete unused source group from logging through natural language commands.

This guide will help you understand how to give your LangChain agent real control over a Better stack account through Composio's Better stack 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 Better stack project to Composio
  • Create a Tool Router MCP session for Better stack
  • Initialize an MCP client and retrieve Better stack tools
  • Build a LangChain agent that can interact with Better stack
  • 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 Better stack MCP server, and what's possible with it?

The Better stack MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Better Stack account. It provides structured and secure access to your monitoring, logging, and incident management tools, so your agent can perform actions like retrieving uptime metrics, managing escalation policies, checking heartbeat statuses, and organizing log sources on your behalf.

  • Monitor health checks and availability: Let your agent fetch uptime percentages, availability summaries, and incident details for any monitor in your stack.
  • Automated escalation policy management: Instruct your agent to create or delete escalation policies, keeping your incident response workflows up-to-date without manual effort.
  • Heartbeat tracking and organization: Have your agent fetch specific heartbeat data, check heartbeat availability, or group related heartbeats for easier monitoring.
  • Log source grouping and management: Enable your agent to create or delete source groups, helping you organize log streams and maintain a tidy observability structure.
  • Webhook integration setup: Direct your agent to register outgoing webhooks so your stack can notify external systems of important events automatically.

Supported Tools & Triggers

Tools
Create Escalation PolicyTool to create a new escalation policy.
Create Heartbeat GroupTool to create a new heartbeat group.
Create Outgoing Webhook IntegrationTool to create a new outgoing webhook integration.
Create Source GroupTool to create a new source group.
Delete Escalation PolicyTool to delete an escalation policy by id.
Delete Source GroupTool to delete a source group by id.
Get HeartbeatTool to get a single heartbeat by id.
Get Heartbeat AvailabilityTool to retrieve availability summary for a specific heartbeat.
Get MonitorTool to get a single monitor.
Get Monitor AvailabilityTool to return an availability summary for a specific monitor.
Get Monitor Response TimesTool to return response times for a specific monitor.
Get Status PageTool to get a single status page by id.
Get Telemetry API TokenTool to retrieve the telemetry api token from the integration configuration.
Get Uptime API TokenTool to retrieve the configured uptime api token.
List Catalog RelationsTool to list all catalog relations.
List Google Monitoring IntegrationsTool to list all google monitoring integrations.
List Grafana IntegrationsTool to list all grafana integrations.
List HeartbeatsTool to list all heartbeats.
List MonitorsTool to list all monitors.
List New Relic IntegrationsTool to list new relic integrations.
List On-Call SchedulesTool to list all on-call schedules.
List Status Page ReportsTool to list all reports on a status page.
List Status PagesTool to list all your status pages.
Update HeartbeatTool to update an existing heartbeat configuration.
Update Heartbeat GroupTool to update an existing heartbeat group.
Update Source GroupTool to update an existing source group.

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 Better stack 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 Better stack tools
  • Validating that COMPOSIO_USER_ID is also set before proceeding

Create a Tool Router session

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

url = session.mcp.url
What's happening:
  • We're creating a Tool Router session that gives your agent access to Better stack 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 Better stack tools as needed

Configure the agent with the MCP URL

client = MultiServerMCPClient({
    "better_stack-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 Better stack MCP server via HTTP
  • The client is configured with a name and the URL from our Tool Router session
  • get_tools() retrieves all available Better stack 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 Better stack 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 Better stack 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=['better_stack']
    )

    url = session.mcp.url
    
    client = MultiServerMCPClient({
        "better_stack-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 Better stack 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 Better stack 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 Better stack MCP Agent with another framework

OpenAI Agents SDK

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Claude Agent SDK

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Google ADK

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Vercel AI SDK

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Mastra AI

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LlamaIndex

LlamaIndex

Use Better stack MCP with LlamaIndex

CrewAI

CrewAI

Use Better stack MCP with CrewAI

Claude Code

Claude Code

Use Better stack MCP with Claude Code

FAQ

What are the differences in Tool Router MCP and Better stack MCP?

With a standalone Better stack MCP server, the agents and LLMs can only access a fixed set of Better stack tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Better stack 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 Better stack tools.

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

Yes, absolutely. You can configure which Better stack 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 Better stack data and credentials are handled as safely as possible.

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