How to integrate Datadog MCP with LangChain

This guide walks you through connecting Datadog to LangChain using the Composio tool router. By the end, you'll have a working Datadog agent that can create downtime for nightly maintenance window, list all monitors tracking cpu usage, create synthetic api test for login endpoint through natural language commands. This guide will help you understand how to give your LangChain agent real control over a Datadog account through Composio's Datadog MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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Introduction

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

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

Also integrate Datadog with

ChatGPT OpenAI Agents SDK Claude Agent SDK Claude Code Claude Cowork Codex OpenCode Cursor VS Code OpenClaw Hermes CLI Google ADK Vercel AI SDK Mastra AI LlamaIndex CrewAI

TL;DR

Here's what you'll learn:

Get and set up your OpenAI and Composio API keys
Connect your Datadog project to Composio
Create a Tool Router MCP session for Datadog
Initialize an MCP client and retrieve Datadog tools
Build a LangChain agent that can interact with Datadog
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 Datadog MCP server, and what's possible with it?

The Datadog MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Datadog account. It provides structured and secure access to your monitoring and observability platform, so your agent can perform actions like creating dashboards, managing monitors, scheduling downtimes, and tracking key events on your behalf.

Custom dashboard creation and management: Direct your agent to build new dashboards or retrieve detailed information about existing dashboards for unified infrastructure and application monitoring.
Monitor setup and deletion: Easily have your agent create new monitors to track critical metrics or remove outdated ones to keep your alerting system relevant.
Automated downtime scheduling: Let your agent schedule maintenance windows by creating downtimes that suppress alerts during planned outages or deployments.
Event tracking and logging: Ask your agent to create and log significant events—like deployments or configuration changes—so your team always stays informed.
Service level objectives and synthetic testing: Instruct your agent to define SLOs or set up synthetic API tests for continuous reliability and performance tracking.

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

What is Composio SDK?

Composio's Composio SDK 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 Composio SDK

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

How the Composio SDK works

The Composio SDK follows a three-phase workflow:

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

Step-by-step Guide

Step by step10 STEPS

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

npm install @composio/langchain @langchain/core @langchain/openai @langchain/mcp-adapters 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/core is the core agent framework
dotenv/config 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

import { Composio } from '@composio/core';
import { LangchainProvider } from '@composio/langchain';
import { MultiServerMCPClient } from "@langchain/mcp-adapters";
import { createAgent } from "langchain";
import * as readline from 'readline';
import 'dotenv/config';

dotenv.config();

What's happening:

We're importing LangChain's MCP adapter and Composio SDK
The dotenv/config import loads environment variables from your .env file
This setup prepares the foundation for connecting LangChain with Datadog functionality through MCP

Initialize Composio client

const composioApiKey = process.env.COMPOSIO_API_KEY;
const userId = process.env.COMPOSIO_USER_ID;

if (!composioApiKey) throw new Error('COMPOSIO_API_KEY is not set');
if (!userId) throw new Error('COMPOSIO_USER_ID is not set');

async function main() {
    const composio = new Composio({
        apiKey: composioApiKey as string,
        provider: new LangchainProvider()
    });

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 Datadog tools
Validating that COMPOSIO_USER_ID is also set before proceeding

Create a Tool Router session

const session = await composio.create(
    userId as string,
    {
        toolkits: ['datadog']
    }
);

const url = session.mcp.url;

What's happening:

We're creating a Tool Router session that gives your agent access to Datadog 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 Datadog tools as needed

Configure the agent with the MCP URL

const client = new MultiServerMCPClient({
    "datadog-agent": {
        transport: "http",
        url: url,
        headers: {
            "x-api-key": process.env.COMPOSIO_API_KEY
        }
    }
});

const tools = await client.getTools();

const agent = createAgent({ model: "gpt-5", tools });

What's happening:

We're creating a MultiServerMCPClient that connects to our Datadog MCP server via HTTP
The client is configured with a name and the URL from our Tool Router session
getTools() retrieves all available Datadog tools that the agent can use
We're creating a LangChain agent using the GPT-5 model

Set up interactive chat interface

let conversationHistory: any[] = [];

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

const rl = readline.createInterface({
    input: process.stdin,
    output: process.stdout,
    prompt: 'You: '
});

rl.prompt();

rl.on('line', async (userInput: string) => {
    const trimmedInput = userInput.trim();

    if (['exit', 'quit', 'bye'].includes(trimmedInput.toLowerCase())) {
        console.log("\nGoodbye!");
        rl.close();
        process.exit(0);
    }

    if (!trimmedInput) {
        rl.prompt();
        return;
    }

    conversationHistory.push({ role: "user", content: trimmedInput });
    console.log("\nAgent is thinking...\n");

    const response = await agent.invoke({ messages: conversationHistory });
    conversationHistory = response.messages;

    const finalResponse = response.messages[response.messages.length - 1]?.content;
    console.log(`Agent: ${finalResponse}\n`);
        
        rl.prompt();
    });

    rl.on('close', () => {
        console.log('\n👋 Session ended.');
        process.exit(0);
    });

What's happening:

We initialize an empty conversationHistory list to maintain context across interactions
A readline interface is used to continuously accept 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 invoke() method with the full conversation history
Users can type 'exit', 'quit', or 'bye' to end the chat session gracefully

Run the application

main().catch((err) => {
    console.error('Fatal error:', err);
    process.exit(1);
});

What's happening:

We call the main() function to start the application

Complete Code

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

import { Composio } from '@composio/core';
import { LangchainProvider } from '@composio/langchain';
import { MultiServerMCPClient } from "@langchain/mcp-adapters";  
import { createAgent } from "langchain";
import * as readline from 'readline';
import 'dotenv/config';

const composioApiKey = process.env.COMPOSIO_API_KEY;
const userId = process.env.COMPOSIO_USER_ID;

if (!composioApiKey) throw new Error('COMPOSIO_API_KEY is not set');
if (!userId) throw new Error('COMPOSIO_USER_ID is not set');

async function main() {
    const composio = new Composio({
        apiKey: composioApiKey as string,
        provider: new LangchainProvider()
    });

    const session = await composio.create(
        userId as string,
        {
            toolkits: ['datadog']
        }
    );

    const url = session.mcp.url;
    
    const client = new MultiServerMCPClient({
        "datadog-agent": {
            transport: "http",
            url: url,
            headers: {
                "x-api-key": process.env.COMPOSIO_API_KEY
            }
        }
    });
    
    const tools = await client.getTools();
  
    const agent = createAgent({ model: "gpt-5", tools });
    
    let conversationHistory: any[] = [];
    
    console.log("Chat started! Type 'exit' or 'quit' to end the conversation.\n");
    console.log("Ask any Datadog related question or task to the agent.\n");
    
    const rl = readline.createInterface({
        input: process.stdin,
        output: process.stdout,
        prompt: 'You: '
    });

    rl.prompt();

    rl.on('line', async (userInput: string) => {
        const trimmedInput = userInput.trim();
        
        if (['exit', 'quit', 'bye'].includes(trimmedInput.toLowerCase())) {
            console.log("\nGoodbye!");
            rl.close();
            process.exit(0);
        }
        
        if (!trimmedInput) {
            rl.prompt();
            return;
        }
        
        conversationHistory.push({ role: "user", content: trimmedInput });
        console.log("\nAgent is thinking...\n");
        
        const response = await agent.invoke({ messages: conversationHistory });
        conversationHistory = response.messages;
        
        const finalResponse = response.messages[response.messages.length - 1]?.content;
        console.log(`Agent: ${finalResponse}\n`);
        
        rl.prompt();
    });

    rl.on('close', () => {
        console.log('\nSession ended.');
        process.exit(0);
    });
}

main().catch((err) => {
    console.error('Fatal error:', err);
    process.exit(1);
});

Conclusion

You've successfully built a LangChain agent that can interact with Datadog 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.

TOOLS

Supported Tools

Every Datadog action and event your agent gets out of the box.

Create Dashboard

Create a dashboard in Datadog.

Create downtime

Creates a new downtime in Datadog to suppress alerts during maintenance windows or planned outages.

Create event

Creates a new event in Datadog.

Create monitor

Creates a new Datadog monitor to track metrics, logs, or other data sources with configurable alerting thresholds and notifications.

Create SLO

Create a Service Level Objective (SLO) in Datadog.

Create Synthetic API Test

Create a synthetic API test in Datadog.

Create Webhook

Create a webhook in Datadog.

Delete Dashboard

Delete a dashboard in Datadog.

Delete monitor

Deletes a Datadog monitor permanently.

Get Dashboard

Get a specific dashboard from Datadog.

Get monitor

Retrieves detailed information about a specific Datadog monitor, including its current state, configuration, and any active downtimes.

Get Service Dependencies

Get service dependency mapping from Datadog APM.

Get Synthetics Locations

Tool to retrieve all available public and private locations for Synthetic tests in Datadog.

Get host tags

Retrieves all tags associated with a specific host in Datadog.

Get usage summary

Retrieves usage summary information from Datadog including API calls, hosts, containers, and other billable usage metrics.

List All Tags

List all tags from Datadog.

List API Keys

List API keys in Datadog.

List APM Services

List APM services from Datadog.

List AWS Integration

List AWS integrations in Datadog.

List dashboards

Lists all Datadog dashboards with basic information.

List events

Lists events from Datadog within a specified time range.

List hosts

Lists all hosts in your Datadog infrastructure with detailed information including metrics, tags, and status.

List Incidents

List incidents from Datadog.

List Log Indexes

Tool to retrieve a list of all log indexes configured in Datadog, including their names and configurations.

List active metrics

Discover metric names by listing actively reporting metrics since a given timestamp.

List monitors

Get all monitor details.

List Roles

List roles from Datadog organization.

List service checks

Lists service checks from Datadog.

List SLOs

List Service Level Objectives (SLOs) from Datadog.

List Synthetics Tests

List Synthetics tests from Datadog.

List Users

List users from Datadog organization.

List Webhooks

List webhooks from Datadog.

Mute Monitor

Mute a monitor in Datadog.

Query metrics

Queries Datadog metrics and returns time series data.

Search logs

Searches Datadog logs with advanced filtering capabilities.

Search Spans Analytics

Search and analyze span data with aggregations in Datadog.

Search Traces

Search for traces in Datadog APM.

Submit metrics

Submits custom metrics to Datadog.

Unmute Monitor

Unmute a monitor in Datadog.

Update Dashboard

Update a dashboard in Datadog.

Update host tags

Updates tags for a specific host in Datadog.

Update monitor

Updates an existing Datadog monitor with new configuration, thresholds, or notification settings.

FRAMEWORKS

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Claude Cowork

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Codex

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Cursor

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OpenClaw

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Hermes

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CLI

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Google ADK

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Vercel AI SDK

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Mastra AI

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LlamaIndex

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CrewAI

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OpenCode

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VS Code

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Ably

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FAQ

Frequently asked questions

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

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 Datadog tools.

Yes, absolutely. You can configure which Datadog 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.

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 Datadog data and credentials are handled as safely as possible.

Start with Datadog.It takes 30 seconds.

Managed auth, hosted MCP servers, and every Datadog tool your agent needs.Free to start.

Start building

Customer stories

Teams ship real agents on Composio

GET STARTED FOR FREE BOOK A DEMO

Karan skipped his own birthday party to fix our critical issue. It was 10pm and he diverted his Waymo to help us instead. This really sets the bar — it shows the commitment you need when users rely on your software.

Harsha Gaddipati

Co-founder, Slashy

A lot of students tell us that the moment their connected tools start talking to each other inside Opennote feels almost magical. The agent just knows them, and it's immensely helped keep new users on the platform.

Abhi Arya

Co-founder, Opennote

We chose Composio over Pipedream because it delivered depth where it mattered — niche tools and tricky edge cases other platforms simply ignored. That gave us confidence to scale without compromising.

Nirman Dave

CEO, Zams

As a solo builder, shipping fast is life or death. The only way I can outcompete incumbents is by outmanoeuvring them. Getting bogged down managing agent auth would have been a death sentence.

Ryan Yu

Founder, Extra Thursday

Before Composio, adding tool integrations was slow and resource-intensive. Each one could take weeks of engineering time, and maintaining them meant constantly keeping up with API changes.

Tomisin Jenrola

Founder & CEO, SwarmZero

With hands-on help from their founder, we integrated Gmail and Google Drive in just 30 minutes. This level of personal support and commitment is exactly what startups should strive for.

Jerome Leclanche

Co-Founder, Ingram Technologies

How to integrate Datadog MCP with LangChain

Connect Datadog without auth hassles

Introduction

Also integrate Datadog with

TL;DR

What is LangChain?

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

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

What is Composio SDK?

How the Composio SDK works

Step-by-step Guide

Prerequisites

Getting API Keys for OpenAI and Composio

Install dependencies

Set up environment variables

Import dependencies

Initialize Composio client

Create a Tool Router session

Configure the agent with the MCP URL

Set up interactive chat interface

Run the application

Complete Code

Conclusion

Supported Tools

How to build Datadog MCP Agent with another framework

ChatGPT

OpenAI Agents SDK

Claude Agent SDK

Claude Code

Claude Cowork

Codex

Cursor

OpenClaw

Hermes

CLI

Google ADK

Vercel AI SDK

Mastra AI

LlamaIndex

CrewAI

OpenCode

VS Code

Explore Other Toolkits

Supabase

Codeinterpreter

GitHub

Ably

Frequently asked questions

What are the differences in Tool Router MCP and Datadog MCP?+

Can I use Tool Router MCP with LangChain?+

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

How safe is my data with Composio Tool Router?+

Start with Datadog.It takes 30 seconds.

Teams ship real agents on Composio