How to integrate Apify MCP with OpenAI Agents SDK

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Introduction

This guide walks you through connecting Apify to the OpenAI Agents SDK 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 OpenAI Agents SDK 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

TL;DR

Here's what you'll learn:
  • Get and set up your OpenAI and Composio API keys
  • Install the necessary dependencies
  • Initialize Composio and create a Tool Router session for Apify
  • Configure an AI agent that can use Apify as a tool
  • Run a live chat session where you can ask the agent to perform Apify operations

What is OpenAI Agents SDK?

The OpenAI Agents SDK is a lightweight framework for building AI agents that can use tools and maintain conversation state. It provides a simple interface for creating agents with hosted MCP tool support.

Key features include:

  • Hosted MCP Tools: Connect to external services through hosted MCP endpoints
  • SQLite Sessions: Persist conversation history across interactions
  • Simple API: Clean interface with Agent, Runner, and tool configuration
  • Streaming Support: Real-time response streaming for interactive applications

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.

Supported Tools & Triggers

Tools
Build ActorTool to build an Actor with specified configuration.
Abort Actor BuildTool to abort an Actor build that is starting or running.
Delete Actor BuildTool to delete an Actor build permanently.
Get Actor BuildTool to get detailed information about a specific Actor build.
Get Actor Build LogTool to retrieve the log file for a specific Actor build.
Get user builds listTool to get a paginated list of all builds for a user.
Abort Actor RunTool to abort a running or starting Actor run.
Delete Actor RunTool to delete a finished Actor run.
Get Actor RunTool to get details about a specific Actor run.
Update Actor Run Status MessageTool to update the status message of an Actor run.
Delete Actor TaskTool to delete an Actor task permanently.
Get Actor TaskTool to get complete details about an Actor task.
Update Actor TaskTool to update Actor task settings using JSON payload.
Get last actor task runTool 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 ItemsTool to run an actor task synchronously and retrieve its dataset items.
Run Task Sync with Input Override & Get Dataset ItemsTool 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 ActorTool to update Actor settings using JSON payload.
Get last actor runTool 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 ItemsTool to run Actor synchronously and get dataset items.
Get list of ActorsTool to get the list of all Actors that the user created or used.
Delete Actor VersionTool to delete a specific version of an Actor's source code.
Delete Actor Version Environment VariableTool to delete an environment variable from a specific Actor version.
Get Actor Version Environment VariableTool to get environment variable details for a specific Actor version.
Update Actor Version Environment VariableTool to update environment variable for a specific Actor version using JSON payload.
Get list of Actor version environment variablesTool to get the list of environment variables for a specific Actor version.
Create Actor Version Environment VariableTool to create an environment variable for a specific Actor version.
Get Actor versionTool to get details about a specific version of an Actor.
Update Actor VersionTool to update an Actor version's configuration and source code.
Get list of Actor versionsTool to get the list of versions of a specific Actor.
Create Actor VersionTool to create a new version of an Actor.
Get list of Actor webhooksTool to get a list of webhooks for a specific Actor.
Create ActorTool to create a new Actor with specified configuration.
Create DatasetTool to create a new dataset.
Create Actor TaskTool to create a new Actor task with specified settings.
Create Task WebhookTool to create a webhook for an Actor task.
Delete DatasetTool to delete a dataset permanently.
Get DatasetTool to retrieve dataset metadata by dataset ID.
Update DatasetTool to update a dataset's name via JSON payload.
Get list of datasetsTool to get list of datasets for a user.
Get Dataset StatisticsTool to get dataset field statistics by dataset ID.
Delete ActorTool to delete an Actor permanently.
Delete WebhookTool to delete a webhook by its ID.
Get Actor DetailsTool to get details of a specific Actor.
Get Actor Last Run Dataset ItemsTool to get dataset items from the last run of an Actor.
Get all webhooksTool to get a list of all webhooks created by the user.
Get dataset itemsTool to retrieve items from a dataset.
Get Default BuildTool to get the default build for an Actor.
Get Key-Value RecordTool to retrieve a record from a key-value store.
Get list of buildsTool to get a list of builds for a specific Actor.
Get list of runsTool to get a list of runs for a specific Actor.
Get list of task runsTool to get a list of runs for a specific Actor task.
Get list of tasksTool to fetch a paginated list of tasks belonging to the authenticated user.
Get list of task webhooksTool to get a list of webhooks for a specific Actor task.
Get logTool to retrieve logs for a specific Actor run or build.
Get OpenAPI DefinitionTool to get the OpenAPI definition for a specific Actor build.
Get Run Dataset ItemsTool to get dataset items from a specific Actor run.
Get Task InputTool to retrieve the input configuration of a specific task.
Get Task Last Run Dataset ItemsTool to get dataset items from the last run of an Actor task.
Delete Key-Value StoreTool to delete a key-value store permanently.
Get Key-Value StoreTool to retrieve key-value store metadata by store ID.
Get Key-Value Store KeysTool to retrieve a list of keys from a key-value store.
Delete Key-Value Store RecordTool to delete a record from a key-value store.
Check Key-Value Store Record ExistsTool to check if a record exists in a key-value store.
Get list of key-value storesTool to get the list of key-value stores owned by the user.
Create Key-Value StoreTool to create a new key-value store or retrieve an existing one by name.
List User Actor RunsTool to get a paginated list of all Actor runs for the authenticated user.
Delete Request QueueTool to delete a request queue permanently.
Get Request QueueTool to retrieve request queue metadata by queue ID.
Get Request Queue HeadTool to retrieve first requests from the queue for inspection.
Get Head and Lock Queue RequestsTool to get and lock head requests from the queue.
Update Request QueueTool to update request queue name using JSON payload.
Delete Request from QueueTool to delete a specific request from a request queue.
Get Request from QueueTool to retrieve a specific request from a request queue by its ID.
Delete Request LockTool to delete a request lock from a request queue.
Prolong Request LockTool to prolong request lock in a request queue.
Update Request in QueueTool to update a request in a request queue.
Batch Delete Requests from QueueTool to batch-delete up to 25 requests from a queue.
Batch Add Requests to QueueTool to batch-add up to 25 requests to a request queue.
List Request Queue RequestsTool to list requests in a request queue with pagination support.
Add Request to QueueTool to add a request to the queue.
Unlock Queue RequestsTool to unlock requests in a request queue that are currently locked by the client.
Get list of request queuesTool to get list of request queues for a user.
Create Request QueueTool to create a new request queue or retrieve an existing one by name.
Run Actor AsynchronouslyTool to run a specific Actor asynchronously.
Run Actor SyncTool to run a specific Actor synchronously with input and return its output record.
Run Actor Sync & Get Dataset ItemsTool to run an Actor synchronously and retrieve its dataset items.
Run Task AsynchronouslyTool to run a specific Actor task asynchronously.
Delete ScheduleTool to delete a schedule by its ID.
Get ScheduleTool to get schedule details by ID.
Get Schedule LogTool to get schedule log by ID.
Update ScheduleTool to update an existing schedule with new settings.
Get list of schedulesTool to get list of schedules created by the user.
Create ScheduleTool to create a new schedule with specified settings.
Store Data in DatasetTool to store data items in a dataset.
Store Data in Key-Value StoreTool to create or update a record in a key-value store.
Get list of Actors in StoreTool to get list of public Actors from Apify Store.
Update Key-Value StoreTool to update a key-value store's properties.
Update Task InputTool to update the input configuration of a specific Actor task.
Get Public User DataTool to get public user data.
Get Current User Account DataTool to get private user account information.
Get Account LimitsTool to get a complete summary of account limits and usage.
Update Account LimitsTool to update account limits manageable on the Limits page.
Get Monthly UsageTool to get monthly usage summary with daily breakdown.
Get list of webhook dispatchesTool to get list of webhook dispatches for the user.
Get Webhook DispatchTool to get webhook dispatch object with all details.
Get webhookTool to get webhook object with all details.
Update WebhookTool to update webhook using JSON payload.
Test WebhookTool to test a webhook by creating a test dispatch with a dummy payload.
Get webhook dispatchesTool to get list of webhook dispatches for a specific webhook.

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

Prerequisites

Before starting, make sure you have:
  • Composio API Key and OpenAI API Key
  • Primary know-how of OpenAI Agents SDK
  • A live Apify project
  • Some knowledge of Python or Typescript

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

Install dependencies

pip install composio_openai_agents openai-agents python-dotenv

Install the Composio SDK and the OpenAI Agents SDK.

Set up environment variables

bash
OPENAI_API_KEY=sk-...your-api-key
COMPOSIO_API_KEY=your-api-key
USER_ID=composio_user@gmail.com

Create a .env file and add your OpenAI and Composio API keys.

Import dependencies

import asyncio
import os
from dotenv import load_dotenv

from composio import Composio
from composio_openai_agents import OpenAIAgentsProvider
from agents import Agent, Runner, HostedMCPTool, SQLiteSession
What's happening:
  • You're importing all necessary libraries.
  • The Composio and OpenAIAgentsProvider classes are imported to connect your OpenAI agent to Composio tools like Apify.

Set up the Composio instance

load_dotenv()

api_key = os.getenv("COMPOSIO_API_KEY")
user_id = os.getenv("USER_ID")

if not api_key:
    raise RuntimeError("COMPOSIO_API_KEY is not set. Create a .env file with COMPOSIO_API_KEY=your_key")

# Initialize Composio
composio = Composio(api_key=api_key, provider=OpenAIAgentsProvider())
What's happening:
  • load_dotenv() loads your .env file so OPENAI_API_KEY and COMPOSIO_API_KEY are available as environment variables.
  • Creating a Composio instance using the API Key and OpenAIAgentsProvider class.

Create a Tool Router session

# Create a Apify Tool Router session
session = composio.create(
    user_id=user_id,
    toolkits=["apify"]
)

mcp_url = session.mcp.url

What is happening:

  • You give the Tool Router the user id and the toolkits you want available. Here, it is only apify.
  • The router checks the user's Apify connection and prepares the MCP endpoint.
  • The returned session.mcp.url is the MCP URL that your agent will use to access Apify.
  • This approach keeps things lightweight and lets the agent request Apify tools only when needed during the conversation.

Configure the agent

# Configure agent with MCP tool
agent = Agent(
    name="Assistant",
    model="gpt-5",
    instructions=(
        "You are a helpful assistant that can access Apify. "
        "Help users perform Apify operations through natural language."
    ),
    tools=[
        HostedMCPTool(
            tool_config={
                "type": "mcp",
                "server_label": "tool_router",
                "server_url": mcp_url,
                "headers": {"x-api-key": api_key},
                "require_approval": "never",
            }
        )
    ],
)
What's happening:
  • We're creating an Agent instance with a name, model (gpt-5), and clear instructions about its purpose.
  • The agent's instructions tell it that it can access Apify and help with queries, inserts, updates, authentication, and fetching database information.
  • The tools array includes a HostedMCPTool that connects to the MCP server URL we created earlier.
  • The headers dict includes the Composio API key for secure authentication with the MCP server.
  • require_approval: 'never' means the agent can execute Apify operations without asking for permission each time, making interactions smoother.

Start chat loop and handle conversation

print("\nComposio Tool Router session created.")

chat_session = SQLiteSession("conversation_openai_toolrouter")

print("\nChat started. Type your requests below.")
print("Commands: 'exit', 'quit', or 'q' to end\n")

async def main():
    try:
        result = await Runner.run(
            agent,
            "What can you help me with?",
            session=chat_session
        )
        print(f"Assistant: {result.final_output}\n")
    except Exception as e:
        print(f"Error: {e}\n")

    while True:
        user_input = input("You: ").strip()
        if user_input.lower() in {"exit", "quit", "q"}:
            print("Goodbye!")
            break

        result = await Runner.run(
            agent,
            user_input,
            session=chat_session
        )
        print(f"Assistant: {result.final_output}\n")

asyncio.run(main())
What's happening:
  • The program prints a session URL that you visit to authorize Apify.
  • After authorization, the chat begins.
  • Each message you type is processed by the agent using Runner.run().
  • The responses are printed to the console, and conversations are saved locally using SQLite.
  • Typing exit, quit, or q cleanly ends the chat.

Complete Code

Here's the complete code to get you started with Apify and OpenAI Agents SDK:

import asyncio
import os
from dotenv import load_dotenv

from composio import Composio
from composio_openai_agents import OpenAIAgentsProvider
from agents import Agent, Runner, HostedMCPTool, SQLiteSession

load_dotenv()

api_key = os.getenv("COMPOSIO_API_KEY")
user_id = os.getenv("USER_ID")

if not api_key:
    raise RuntimeError("COMPOSIO_API_KEY is not set. Create a .env file with COMPOSIO_API_KEY=your_key")

# Initialize Composio
composio = Composio(api_key=api_key, provider=OpenAIAgentsProvider())

# Create Tool Router session
session = composio.create(
    user_id=user_id,
    toolkits=["apify"]
)
mcp_url = session.mcp.url

# Configure agent with MCP tool
agent = Agent(
    name="Assistant",
    model="gpt-5",
    instructions=(
        "You are a helpful assistant that can access Apify. "
        "Help users perform Apify operations through natural language."
    ),
    tools=[
        HostedMCPTool(
            tool_config={
                "type": "mcp",
                "server_label": "tool_router",
                "server_url": mcp_url,
                "headers": {"x-api-key": api_key},
                "require_approval": "never",
            }
        )
    ],
)

print("\nComposio Tool Router session created.")

chat_session = SQLiteSession("conversation_openai_toolrouter")

print("\nChat started. Type your requests below.")
print("Commands: 'exit', 'quit', or 'q' to end\n")

async def main():
    try:
        result = await Runner.run(
            agent,
            "What can you help me with?",
            session=chat_session
        )
        print(f"Assistant: {result.final_output}\n")
    except Exception as e:
        print(f"Error: {e}\n")

    while True:
        user_input = input("You: ").strip()
        if user_input.lower() in {"exit", "quit", "q"}:
            print("Goodbye!")
            break

        result = await Runner.run(
            agent,
            user_input,
            session=chat_session
        )
        print(f"Assistant: {result.final_output}\n")

asyncio.run(main())

Conclusion

This was a starter code for integrating Apify MCP with OpenAI Agents SDK to build a functional AI agent that can interact with Apify.

Key features:

  • Hosted MCP tool integration through Composio's Tool Router
  • SQLite session persistence for conversation history
  • Simple async chat loop for interactive testing
You can extend this by adding more toolkits, implementing custom business logic, or building a web interface around the agent.

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FAQ

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

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.

Can I use Tool Router MCP with OpenAI Agents SDK?

Yes, you can. OpenAI Agents SDK 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.

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

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.

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 Apify data and credentials are handled as safely as possible.

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