How to integrate Apify MCP with Autogen

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Introduction

This guide walks you through connecting Apify to AutoGen 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 AutoGen 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 required dependencies for Autogen and Composio
  • Initialize Composio and create a Tool Router session for Apify
  • Wire that MCP URL into Autogen using McpWorkbench and StreamableHttpServerParams
  • Configure an Autogen AssistantAgent that can call Apify tools
  • Run a live chat loop where you ask the agent to perform Apify operations

What is AutoGen?

Autogen is a framework for building multi-agent conversational AI systems from Microsoft. It enables you to create agents that can collaborate, use tools, and maintain complex workflows.

Key features include:

  • Multi-Agent Systems: Build collaborative agent workflows
  • MCP Workbench: Native support for Model Context Protocol tools
  • Streaming HTTP: Connect to external services through streamable HTTP
  • AssistantAgent: Pre-built agent class for tool-using assistants

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

You will need:

  • A Composio API key
  • An OpenAI API key (used by Autogen's OpenAIChatCompletionClient)
  • A Apify account you can connect to Composio
  • Some basic familiarity with Autogen and Python async

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 python-dotenv
pip install autogen-agentchat autogen-ext-openai autogen-ext-tools

Install Composio, Autogen extensions, and dotenv.

What's happening:

  • composio connects your agent to Apify via MCP
  • autogen-agentchat provides the AssistantAgent class
  • autogen-ext-openai provides the OpenAI model client
  • autogen-ext-tools provides MCP workbench support

Set up environment variables

bash
COMPOSIO_API_KEY=your-composio-api-key
OPENAI_API_KEY=your-openai-api-key
USER_ID=your-user-identifier@example.com

Create a .env file in your project folder.

What's happening:

  • COMPOSIO_API_KEY is required to talk to Composio
  • OPENAI_API_KEY is used by Autogen's OpenAI client
  • USER_ID is how Composio identifies which user's Apify connections to use

Import dependencies and create Tool Router session

python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio

from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient
from autogen_ext.tools.mcp import McpWorkbench, StreamableHttpServerParams

load_dotenv()

async def main():
    # Initialize Composio and create a Apify session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["apify"]
    )
    url = session.mcp.url
What's happening:
  • load_dotenv() reads your .env file
  • Composio(api_key=...) initializes the SDK
  • create(...) creates a Tool Router session that exposes Apify tools
  • session.mcp.url is the MCP endpoint that Autogen will connect to

Configure MCP parameters for Autogen

python
# Configure MCP server parameters for Streamable HTTP
server_params = StreamableHttpServerParams(
    url=url,
    timeout=30.0,
    sse_read_timeout=300.0,
    terminate_on_close=True,
    headers={"x-api-key": os.getenv("COMPOSIO_API_KEY")}
)

Autogen expects parameters describing how to talk to the MCP server. That is what StreamableHttpServerParams is for.

What's happening:

  • url points to the Tool Router MCP endpoint from Composio
  • timeout is the HTTP timeout for requests
  • sse_read_timeout controls how long to wait when streaming responses
  • terminate_on_close=True cleans up the MCP server process when the workbench is closed

Create the model client and agent

python
# Create model client
model_client = OpenAIChatCompletionClient(
    model="gpt-5",
    api_key=os.getenv("OPENAI_API_KEY")
)

# Use McpWorkbench as context manager
async with McpWorkbench(server_params) as workbench:
    # Create Apify assistant agent with MCP tools
    agent = AssistantAgent(
        name="apify_assistant",
        description="An AI assistant that helps with Apify operations.",
        model_client=model_client,
        workbench=workbench,
        model_client_stream=True,
        max_tool_iterations=10
    )

What's happening:

  • OpenAIChatCompletionClient wraps the OpenAI model for Autogen
  • McpWorkbench connects the agent to the MCP tools
  • AssistantAgent is configured with the Apify tools from the workbench

Run the interactive chat loop

python
print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
print("Ask any Apify related question or task to the agent.\n")

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

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

    if not user_input:
        continue

    print("\nAgent is thinking...\n")

    # Run the agent with streaming
    try:
        response_text = ""
        async for message in agent.run_stream(task=user_input):
            if hasattr(message, "content") and message.content:
                response_text = message.content

        # Print the final response
        if response_text:
            print(f"Agent: {response_text}\n")
        else:
            print("Agent: I encountered an issue processing your request.\n")

    except Exception as e:
        print(f"Agent: Sorry, I encountered an error: {str(e)}\n")
What's happening:
  • The script prompts you in a loop with You:
  • Autogen passes your input to the model, which decides which Apify tools to call via MCP
  • agent.run_stream(...) yields streaming messages as the agent thinks and calls tools
  • Typing exit, quit, or bye ends the loop

Complete Code

Here's the complete code to get you started with Apify and AutoGen:

python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio

from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient
from autogen_ext.tools.mcp import McpWorkbench, StreamableHttpServerParams

load_dotenv()

async def main():
    # Initialize Composio and create a Apify session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["apify"]
    )
    url = session.mcp.url

    # Configure MCP server parameters for Streamable HTTP
    server_params = StreamableHttpServerParams(
        url=url,
        timeout=30.0,
        sse_read_timeout=300.0,
        terminate_on_close=True,
        headers={"x-api-key": os.getenv("COMPOSIO_API_KEY")}
    )

    # Create model client
    model_client = OpenAIChatCompletionClient(
        model="gpt-5",
        api_key=os.getenv("OPENAI_API_KEY")
    )

    # Use McpWorkbench as context manager
    async with McpWorkbench(server_params) as workbench:
        # Create Apify assistant agent with MCP tools
        agent = AssistantAgent(
            name="apify_assistant",
            description="An AI assistant that helps with Apify operations.",
            model_client=model_client,
            workbench=workbench,
            model_client_stream=True,
            max_tool_iterations=10
        )

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

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

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

            if not user_input:
                continue

            print("\nAgent is thinking...\n")

            # Run the agent with streaming
            try:
                response_text = ""
                async for message in agent.run_stream(task=user_input):
                    if hasattr(message, 'content') and message.content:
                        response_text = message.content

                # Print the final response
                if response_text:
                    print(f"Agent: {response_text}\n")
                else:
                    print("Agent: I encountered an issue processing your request.\n")

            except Exception as e:
                print(f"Agent: Sorry, I encountered an error: {str(e)}\n")

if __name__ == "__main__":
    asyncio.run(main())

Conclusion

You now have an Autogen assistant wired into Apify through Composio's Tool Router and MCP. From here you can:
  • Add more toolkits to the toolkits list, for example notion or hubspot
  • Refine the agent description to point it at specific workflows
  • Wrap this script behind a UI, Slack bot, or internal tool
Once the pattern is clear for Apify, you can reuse the same structure for other MCP-enabled apps with minimal code changes.

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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 Autogen?

Yes, you can. Autogen 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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