How to integrate Datadog MCP with CrewAI

This guide walks you through connecting Datadog to CrewAI 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 CrewAI 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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Datadog

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Introduction

This guide will help you understand how to give your CrewAI 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 LangChain Vercel AI SDK Mastra AI LlamaIndex

TL;DR

Here's what you'll learn:

Get a Composio API key and configure your Datadog connection
Set up CrewAI with an MCP enabled agent
Create a Tool Router session or standalone MCP server for Datadog
Build a conversational loop where your agent can execute Datadog operations

What is CrewAI?

CrewAI is a powerful framework for building multi-agent AI systems. It provides primitives for defining agents with specific roles, creating tasks, and orchestrating workflows through crews.

Key features include:

Agent Roles: Define specialized agents with specific goals and backstories
Task Management: Create tasks with clear descriptions and expected outputs
Crew Orchestration: Combine agents and tasks into collaborative workflows
MCP Integration: Connect to external tools through Model Context Protocol

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 step08 STEPS

Prerequisites

Before starting, make sure you have:

Python 3.9 or higher
A Composio account and API key
A Datadog connection authorized in Composio
An OpenAI API key for the CrewAI LLM
Basic familiarity with Python

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

bash

pip install composio crewai crewai-tools[mcp] python-dotenv

What's happening:

composio connects your agent to Datadog via MCP
crewai provides Agent, Task, Crew, and LLM primitives
crewai-tools[mcp] includes MCP helpers
python-dotenv loads environment variables from .env

Set up environment variables

bash

COMPOSIO_API_KEY=your_composio_api_key_here
USER_ID=your_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 with Composio
USER_ID scopes the session to your account
OPENAI_API_KEY lets CrewAI use your chosen OpenAI model

Import dependencies

python

import os
from composio import Composio
from crewai import Agent, Task, Crew
from crewai_tools import MCPServerAdapter
import dotenv

dotenv.load_dotenv()

COMPOSIO_API_KEY = os.getenv("COMPOSIO_API_KEY")
COMPOSIO_USER_ID = os.getenv("COMPOSIO_USER_ID")

if not COMPOSIO_API_KEY:
    raise ValueError("COMPOSIO_API_KEY is not set")
if not COMPOSIO_USER_ID:
    raise ValueError("COMPOSIO_USER_ID is not set")

What's happening:

CrewAI classes define agents and tasks, and run the workflow
MCPServerHTTP connects the agent to an MCP endpoint
Composio will give you a short lived Datadog MCP URL

Create a Composio Tool Router session for Datadog

python

composio_client = Composio(api_key=COMPOSIO_API_KEY)
session = composio_client.create(user_id=COMPOSIO_USER_ID, toolkits=["datadog"])

url = session.mcp.url

What's happening:

You create a Datadog only session through Composio
Composio returns an MCP HTTP URL that exposes Datadog tools

Initialize the MCP Server

python

server_params = {
    "url": url,
    "transport": "streamable-http",
    "headers": {"x-api-key": COMPOSIO_API_KEY},
}

with MCPServerAdapter(server_params) as tools:
    agent = Agent(
        role="Search Assistant",
        goal="Help users search the internet effectively",
        backstory="You are a helpful assistant with access to search tools.",
        tools=tools,
        verbose=False,
        max_iter=10,
    )

What's Happening:

Server Configuration: The code sets up connection parameters including the MCP server URL, streamable HTTP transport, and Composio API key authentication.
MCP Adapter Bridge: MCPServerAdapter acts as a context manager that converts Composio MCP tools into a CrewAI-compatible format.
Agent Setup: Creates a CrewAI Agent with a defined role (Search Assistant), goal (help with internet searches), and access to the MCP tools.
Configuration Options: The agent includes settings like verbose=False for clean output and max_iter=10 to prevent infinite loops.
Dynamic Tool Usage: Once created, the agent automatically accesses all Composio Search tools and decides when to use them based on user queries.

Create a CLI Chatloop and define the Crew

python

print("Chat started! Type 'exit' or 'quit' to end.\n")

conversation_context = ""

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

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

    if not user_input:
        continue

    conversation_context += f"\nUser: {user_input}\n"
    print("\nAgent is thinking...\n")

    task = Task(
        description=(
            f"Conversation history:\n{conversation_context}\n\n"
            f"Current request: {user_input}"
        ),
        expected_output="A helpful response addressing the user's request",
        agent=agent,
    )

    crew = Crew(agents=[agent], tasks=[task], verbose=False)
    result = crew.kickoff()
    response = str(result)

    conversation_context += f"Agent: {response}\n"
    print(f"Agent: {response}\n")

What's Happening:

Interactive CLI Setup: The code creates an infinite loop that continuously prompts for user input and maintains the entire conversation history in a string variable.
Input Validation: Empty inputs are ignored to prevent processing blank messages and keep the conversation clean.
Context Building: Each user message is appended to the conversation context, which preserves the full dialogue history for better agent responses.
Dynamic Task Creation: For every user input, a new Task is created that includes both the full conversation history and the current request as context.
Crew Execution: A Crew is instantiated with the agent and task, then kicked off to process the request and generate a response.
Response Management: The agent's response is converted to a string, added to the conversation context, and displayed to the user, maintaining conversational continuity.

Complete Code

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

python

from crewai import Agent, Task, Crew, LLM
from crewai_tools import MCPServerAdapter
from composio import Composio
from dotenv import load_dotenv
import os

load_dotenv()

GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY")
COMPOSIO_API_KEY = os.getenv("COMPOSIO_API_KEY")
COMPOSIO_USER_ID = os.getenv("COMPOSIO_USER_ID")

if not GOOGLE_API_KEY:
    raise ValueError("GOOGLE_API_KEY is not set in the environment.")
if not COMPOSIO_API_KEY:
    raise ValueError("COMPOSIO_API_KEY is not set in the environment.")
if not COMPOSIO_USER_ID:
    raise ValueError("COMPOSIO_USER_ID is not set in the environment.")

# Initialize Composio and create a session
composio = Composio(api_key=COMPOSIO_API_KEY)
session = composio.create(
    user_id=COMPOSIO_USER_ID,
    toolkits=["datadog"],
)
url = session.mcp.url

# Configure LLM
llm = LLM(
    model="gpt-5",
    api_key=os.getenv("OPENAI_API_KEY"),
)

server_params = {
    "url": url,
    "transport": "streamable-http",
    "headers": {"x-api-key": COMPOSIO_API_KEY},
}

with MCPServerAdapter(server_params) as tools:
    agent = Agent(
        role="Search Assistant",
        goal="Help users with internet searches",
        backstory="You are an expert assistant with access to Composio Search tools.",
        tools=tools,
        llm=llm,
        verbose=False,
        max_iter=10,
    )

    print("Chat started! Type 'exit' or 'quit' to end.\n")

    conversation_context = ""

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

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

        if not user_input:
            continue

        conversation_context += f"\nUser: {user_input}\n"
        print("\nAgent is thinking...\n")

        task = Task(
            description=(
                f"Conversation history:\n{conversation_context}\n\n"
                f"Current request: {user_input}"
            ),
            expected_output="A helpful response addressing the user's request",
            agent=agent,
        )

        crew = Crew(agents=[agent], tasks=[task], verbose=False)
        result = crew.kickoff()
        response = str(result)

        conversation_context += f"Agent: {response}\n"
        print(f"Agent: {response}\n")

Conclusion

You now have a CrewAI agent connected to Datadog through Composio's Tool Router. The agent can perform Datadog operations through natural language commands.

Next steps:

Add role-specific instructions to customize agent behavior
Plug in more toolkits for multi-app workflows
Chain tasks for complex multi-step operations

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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ChatGPT

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OpenAI Agents SDK

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Claude Agent SDK

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Claude Code

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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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LangChain

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Vercel AI SDK

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Mastra AI

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LlamaIndex

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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. CrewAI 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

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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 CrewAI

Connect Datadog without auth hassles

Introduction

Also integrate Datadog with

TL;DR

What is CrewAI?

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

Create a Composio Tool Router session for Datadog

Initialize the MCP Server

Create a CLI Chatloop and define the Crew

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

LangChain

Vercel AI SDK

Mastra AI

LlamaIndex

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 CrewAI?+

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