How to integrate Apify MCP with CrewAI

This guide walks you through connecting Apify to CrewAI using the Composio tool router. By the end, you'll have a working Apify agent that can create a new dataset for scraped results, fetch items from a specific apify dataset, get details of your latest apify actor through natural language commands. This guide will help you understand how to give your CrewAI agent real control over a Apify account through Composio's Apify MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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Apify is a cloud platform for building, deploying, and managing web scraping and automation tools called Actors. It lets you automate data extraction and workflow tasks at scale—no infrastructure headaches.

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Introduction

This guide walks you through connecting Apify to CrewAI using the Composio tool router. By the end, you'll have a working Apify agent that can create a new dataset for scraped results, fetch items from a specific apify dataset, get details of your latest apify actor through natural language commands.

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

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

Also integrate Apify with

OpenAI Agents SDKClaude Agent SDKClaude CodeClaude CoworkCodexOpenClawHermesCLIGoogle ADKLangChainVercel AI SDKMastra AILlamaIndex

TL;DR

Here's what you'll learn:
  • Get a Composio API key and configure your Apify connection
  • Set up CrewAI with an MCP enabled agent
  • Create a Tool Router session or standalone MCP server for Apify
  • Build a conversational loop where your agent can execute Apify 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 Apify MCP server, and what's possible with it?

The Apify MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Apify account. It provides structured and secure access to your web scraping and automation workflows, so your agent can create actors, manage datasets, fetch scraped data, schedule tasks, and maintain webhooks on your behalf.

  • Automated Actor Creation and Management: Easily instruct your agent to programmatically create, configure, or delete Apify actors for custom web automation or scraping jobs.
  • Dataset Handling and Data Retrieval: Let your agent spin up new datasets, organize scraped results, and pull items from datasets for downstream analysis or reporting.
  • Task Scheduling and Automation: Have your agent create and manage recurring actor tasks, making it simple to automate data extraction or browser automation at set intervals.
  • Webhook Integration and Event Handling: Direct your agent to set up or remove webhooks for actor tasks, enabling real-time notifications or downstream integrations when a task completes or fails.
  • Actor and Build Metadata Access: Empower your agent to fetch detailed metadata about actors, including build information and configuration details, for monitoring or troubleshooting purposes.

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:

  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

Step by step08 STEPS
1

Prerequisites

Before starting, make sure you have:
  • Python 3.9 or higher
  • A Composio account and API key
  • A Apify connection authorized in Composio
  • An OpenAI API key for the CrewAI LLM
  • Basic familiarity with Python
2

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.
3

Install dependencies

bash
pip install composio crewai crewai-tools[mcp] python-dotenv
What's happening:
  • composio connects your agent to Apify via MCP
  • crewai provides Agent, Task, Crew, and LLM primitives
  • crewai-tools[mcp] includes MCP helpers
  • python-dotenv loads environment variables from .env
4

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
5

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 Apify MCP URL
6

Create a Composio Tool Router session for Apify

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

url = session.mcp.url
What's happening:
  • You create a Apify only session through Composio
  • Composio returns an MCP HTTP URL that exposes Apify tools
7

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.
8

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 Apify 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=["apify"],
)
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 Apify through Composio's Tool Router. The agent can perform Apify 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 Apify action and event your agent gets out of the box.

Build Actor

Tool to build an Actor with specified configuration.

Abort Actor Build

Tool to abort an Actor build that is starting or running.

Delete Actor Build

Tool to delete an Actor build permanently.

Get Actor Build

Tool to get detailed information about a specific Actor build.

Get Actor Build Log

Tool to retrieve the log file for a specific Actor build.

Get user builds list

Tool to get a paginated list of all builds for a user.

Abort Actor Run

Tool to abort a running or starting Actor run.

Delete Actor Run

Tool to delete a finished Actor run.

Get Actor Run

Tool to get details about a specific Actor run.

Update Actor Run Status Message

Tool to update the status message of an Actor run.

Delete Actor Task

Tool to delete an Actor task permanently.

Get Actor Task

Tool to get complete details about an Actor task.

Update Actor Task

Tool to update Actor task settings using JSON payload.

Get last actor task run

Tool to get the most recent run of a specific Actor task.

Run Task Sync (GET)

Tool to run a specific task synchronously and return its output.

Run Task Sync & Get Dataset Items

Tool to run an actor task synchronously and retrieve its dataset items.

Run Task Sync with Input Override & Get Dataset Items

Tool to run an actor task synchronously with input overrides and retrieve its dataset items.

Run Task Sync (POST)

Tool to run an Actor task synchronously with input override and return its output.

Update Actor

Tool to update Actor settings using JSON payload.

Get last actor run

Tool to get the most recent run of a specific Actor.

Run Actor Sync without Input (GET)

Tool to run a specific Actor synchronously without input and return its output.

Run Actor Sync & Get Dataset Items

Tool to run Actor synchronously and get dataset items.

Get list of Actors

Tool to get the list of all Actors that the user created or used.

Delete Actor Version

Tool to delete a specific version of an Actor's source code.

Delete Actor Version Environment Variable

Tool to delete an environment variable from a specific Actor version.

Get Actor Version Environment Variable

Tool to get environment variable details for a specific Actor version.

Update Actor Version Environment Variable

Tool to update environment variable for a specific Actor version using JSON payload.

Get list of Actor version environment variables

Tool to get the list of environment variables for a specific Actor version.

Create Actor Version Environment Variable

Tool to create an environment variable for a specific Actor version.

Get Actor version

Tool to get details about a specific version of an Actor.

Update Actor Version

Tool to update an Actor version's configuration and source code.

Get list of Actor versions

Tool to get the list of versions of a specific Actor.

Create Actor Version

Tool to create a new version of an Actor.

Get list of Actor webhooks

Tool to get a list of webhooks for a specific Actor.

Create Actor

Tool to create a new Actor with specified configuration.

Create Dataset

Tool to create a new dataset.

Create Actor Task

Tool to create a new Actor task with specified settings.

Create Task Webhook

Tool to create a webhook for an Actor task.

Delete Dataset

Tool to delete a dataset permanently.

Get Dataset

Tool to retrieve dataset metadata by dataset ID.

Update Dataset

Tool to update a dataset's name via JSON payload.

Get list of datasets

Tool to get list of datasets for a user.

Get Dataset Statistics

Tool to get dataset field statistics by dataset ID.

Delete Actor

Tool to delete an Actor permanently.

Delete Webhook

Tool to delete a webhook by its ID.

Get Actor Details

Tool to get details of a specific Actor.

Get Actor Last Run Dataset Items

Tool to get dataset items from the last run of an Actor.

Get all webhooks

Tool to get a list of all webhooks created by the user.

Get dataset items

Tool to retrieve items from a dataset.

Get Default Build

Tool to get the default build for an Actor.

Get Key-Value Record

Tool to retrieve a record from a key-value store.

Get list of builds

Tool to get a list of builds for a specific Actor.

Get list of runs

Tool to get a list of runs for a specific Actor.

Get list of task runs

Tool to get a list of runs for a specific Actor task.

Get list of tasks

Tool to fetch a paginated list of tasks belonging to the authenticated user.

Get list of task webhooks

Tool to get a list of webhooks for a specific Actor task.

Get log

Tool to retrieve logs for a specific Actor run or build.

Get OpenAPI Definition

Tool to get the OpenAPI definition for a specific Actor build.

Get Run Dataset Items

Tool to get dataset items from a specific Actor run.

Get Task Input

Tool to retrieve the input configuration of a specific task.

Get Task Last Run Dataset Items

Tool to get dataset items from the last run of an Actor task.

Delete Key-Value Store

Tool to delete a key-value store permanently.

Get Key-Value Store

Tool to retrieve key-value store metadata by store ID.

Get Key-Value Store Keys

Tool to retrieve a list of keys from a key-value store.

Delete Key-Value Store Record

Tool to delete a record from a key-value store.

Check Key-Value Store Record Exists

Tool to check if a record exists in a key-value store.

Get list of key-value stores

Tool to get the list of key-value stores owned by the user.

Create Key-Value Store

Tool to create a new key-value store or retrieve an existing one by name.

List User Actor Runs

Tool to get a paginated list of all Actor runs for the authenticated user.

Delete Request Queue

Tool to delete a request queue permanently.

Get Request Queue

Tool to retrieve request queue metadata by queue ID.

Get Request Queue Head

Tool to retrieve first requests from the queue for inspection.

Get Head and Lock Queue Requests

Tool to get and lock head requests from the queue.

Update Request Queue

Tool to update request queue name using JSON payload.

Delete Request from Queue

Tool to delete a specific request from a request queue.

Get Request from Queue

Tool to retrieve a specific request from a request queue by its ID.

Delete Request Lock

Tool to delete a request lock from a request queue.

Prolong Request Lock

Tool to prolong request lock in a request queue.

Update Request in Queue

Tool to update a request in a request queue.

Batch Delete Requests from Queue

Tool to batch-delete up to 25 requests from a queue.

Batch Add Requests to Queue

Tool to batch-add up to 25 requests to a request queue.

List Request Queue Requests

Tool to list requests in a request queue with pagination support.

Add Request to Queue

Tool to add a request to the queue.

Unlock Queue Requests

Tool to unlock requests in a request queue that are currently locked by the client.

Get list of request queues

Tool to get list of request queues for a user.

Create Request Queue

Tool to create a new request queue or retrieve an existing one by name.

Run Actor Asynchronously

Tool to run a specific Actor asynchronously.

Run Actor Sync

Tool to run a specific Actor synchronously with input and return its output record.

Run Actor Sync & Get Dataset Items

Tool to run an Actor synchronously and retrieve its dataset items.

Run Task Asynchronously

Tool to run a specific Actor task asynchronously.

Delete Schedule

Tool to delete a schedule by its ID.

Get Schedule

Tool to get schedule details by ID.

Get Schedule Log

Tool to get schedule log by ID.

Update Schedule

Tool to update an existing schedule with new settings.

Get list of schedules

Tool to get list of schedules created by the user.

Create Schedule

Tool to create a new schedule with specified settings.

Store Data in Dataset

Tool to store data items in a dataset.

Store Data in Key-Value Store

Tool to create or update a record in a key-value store.

Get list of Actors in Store

Tool to get list of public Actors from Apify Store.

Update Key-Value Store

Tool to update a key-value store's properties.

Update Task Input

Tool to update the input configuration of a specific Actor task.

Get Public User Data

Tool to get public user data.

Get Current User Account Data

Tool to get private user account information.

Get Account Limits

Tool to get a complete summary of account limits and usage.

Update Account Limits

Tool to update account limits manageable on the Limits page.

Get Monthly Usage

Tool to get monthly usage summary with daily breakdown.

Get list of webhook dispatches

Tool to get list of webhook dispatches for the user.

Get Webhook Dispatch

Tool to get webhook dispatch object with all details.

Get webhook

Tool to get webhook object with all details.

Update Webhook

Tool to update webhook using JSON payload.

Test Webhook

Tool to test a webhook by creating a test dispatch with a dummy payload.

Get webhook dispatches

Tool to get list of webhook dispatches for a specific webhook.

FRAMEWORKS

How to build Apify MCP Agent with another framework

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Apify MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Apify MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use Apify MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Apify MCP with Claude Cowork

Codex logo

Codex

Use Apify MCP with Codex

OpenClaw logo

OpenClaw

Use Apify MCP with OpenClaw

Hermes logo

Hermes

Use Apify MCP with Hermes

CLI logo

CLI

Use Apify MCP with CLI

Google ADK logo

Google ADK

Use Apify MCP with Google ADK

LangChain logo

LangChain

Use Apify MCP with LangChain

Vercel AI SDK logo

Vercel AI SDK

Use Apify MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Apify MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Apify MCP with LlamaIndex

FAQ

Frequently asked questions

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

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

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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.

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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.

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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.

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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.

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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.

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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.

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