# How to integrate Scrapfly MCP with Autogen

```json
{
  "title": "How to integrate Scrapfly MCP with Autogen",
  "toolkit": "Scrapfly",
  "toolkit_slug": "scrapfly",
  "framework": "AutoGen",
  "framework_slug": "autogen",
  "url": "https://composio.dev/toolkits/scrapfly/framework/autogen",
  "markdown_url": "https://composio.dev/toolkits/scrapfly/framework/autogen.md",
  "updated_at": "2026-05-12T10:24:48.991Z"
}
```

## Introduction

This guide walks you through connecting Scrapfly to AutoGen using the Composio tool router. By the end, you'll have a working Scrapfly agent that can extract product prices from amazon listings, scrape job postings from linkedin search, get latest news headlines from bbc homepage through natural language commands.
This guide will help you understand how to give your AutoGen agent real control over a Scrapfly account through Composio's Scrapfly MCP server.
Before we dive in, let's take a quick look at the key ideas and tools involved.

## Also integrate Scrapfly with

- [OpenAI Agents SDK](https://composio.dev/toolkits/scrapfly/framework/open-ai-agents-sdk)
- [Claude Agent SDK](https://composio.dev/toolkits/scrapfly/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/scrapfly/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/scrapfly/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/scrapfly/framework/codex)
- [OpenClaw](https://composio.dev/toolkits/scrapfly/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/scrapfly/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/scrapfly/framework/cli)
- [Google ADK](https://composio.dev/toolkits/scrapfly/framework/google-adk)
- [LangChain](https://composio.dev/toolkits/scrapfly/framework/langchain)
- [Vercel AI SDK](https://composio.dev/toolkits/scrapfly/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/scrapfly/framework/mastra-ai)
- [LlamaIndex](https://composio.dev/toolkits/scrapfly/framework/llama-index)
- [CrewAI](https://composio.dev/toolkits/scrapfly/framework/crew-ai)

## 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 Scrapfly
- Wire that MCP URL into Autogen using McpWorkbench and StreamableHttpServerParams
- Configure an Autogen AssistantAgent that can call Scrapfly tools
- Run a live chat loop where you ask the agent to perform Scrapfly 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 Scrapfly MCP server, and what's possible with it?

The Scrapfly MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Scrapfly account. It provides structured and secure access to powerful web scraping capabilities, so your agent can perform actions like extracting website data, bypassing anti-bot protection, rendering JavaScript content, and rotating proxies—all on your behalf.
- Dynamic web data extraction: Instruct your agent to fetch and extract content from almost any website, even those with heavy client-side rendering or complex structures.
- JavaScript rendering support: Enable your agent to scrape websites that require full JavaScript execution for content loading, making dynamic sites accessible for data extraction.
- Anti-bot protection bypass: Allow your agent to automatically navigate sites with CAPTCHAs, bot detection, or rate limiting using Scrapfly's built-in countermeasures.
- Automatic proxy rotation: Let your agent leverage Scrapfly's proxy network to rotate requests, reduce blocks, and ensure more reliable scraping at scale.
- Custom request handling: Have your agent specify advanced options like custom headers or cookies for targeted and flexible scraping sessions.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `SCRAPFLY_CAPTURE_SCREENSHOT` | Capture Website Screenshot | Tool to capture a full-page or viewport screenshot of a website. Use when you need to take a screenshot with options like JS rendering, custom resolution, or accessibility testing. Returns the screenshot image directly. Supports vision deficiency simulations and dark mode. |
| `SCRAPFLY_CAPTURE_SCREENSHOT_HEAD` | Capture Screenshot Metadata (HEAD) | Tool to capture screenshot metadata without downloading the image body. Use this for async screenshot workflows where you need the URL to retrieve the image later. Returns the screenshot URL in response, saving bandwidth compared to full screenshot retrieval. |
| `SCRAPFLY_CREATE_CRAWLER` | Create Scrapfly Crawler | Tool to create a new web crawler to recursively crawl an entire website. Returns a crawler UUID for tracking progress. Use when you need to crawl multiple pages from a website with configurable limits and extraction rules. |
| `SCRAPFLY_EXTRACT_DATA` | Extract Structured Data | Tool to extract structured data from HTML or other content using AI models, LLM prompts, or custom templates. Use when you need to parse web pages or documents into structured JSON data. Supports predefined extraction models for common types (articles, products, events) or custom extraction via prompts/templates. |
| `SCRAPFLY_GET_ACCOUNT_INFO` | Get Scrapfly Account Information | Tool to retrieve Scrapfly account information. Use after authenticating to get API credit balance and usage stats. Returns comprehensive account data including subscription plan, usage statistics, billing info, and project settings. |
| `SCRAPFLY_GET_CRAWLER_ARTIFACT` | Get Crawler Artifact | Tool to download crawler artifact files in WARC or HAR format. Use when you need to retrieve the complete crawl results as an archive file. WARC format is recommended for large crawls as it includes gzip compression. |
| `SCRAPFLY_GET_CRAWLER_CONTENTS` | Get Crawler Contents | Tool to retrieve extracted content from crawled pages. Supports multiple output formats including markdown, text, HTML, and JSON. Use when you need to access the actual content extracted during a crawl, with optional filtering by URL and format selection. |
| `SCRAPFLY_GET_CRAWLER_STATUS` | Get Crawler Status | Tool to get the current status of a crawler including progress, pages crawled, and completion state. Use for polling workflow to monitor crawl progress. |
| `SCRAPFLY_GET_CRAWLER_URLS` | Get Crawler URLs | Tool to retrieve the list of discovered and crawled URLs from a crawler. Use when you need to get all URLs found during a crawl or filter by status to analyze failed URLs with error codes. Supports pagination for large result sets. |
| `SCRAPFLY_SCRAPE` | Scrapfly Scrape | Tool to perform a web scraping request. Use when you need to fetch a page with custom configuration like JS rendering, proxies, and extraction. |
| `SCRAPFLY_SCRAPE_POST` | Scrapfly Scrape POST | Tool to scrape web pages using POST method to send data in the request body. Use when you need to scrape endpoints that require POST requests, such as form submissions or APIs that expect data payload. |
| `SCRAPFLY_SCRAPE_WITH_PUT` | Scrape With PUT | Tool to scrape web pages using PUT method with body payload. Use when the target API requires PUT requests with data in the request body. Forwards PUT request with custom body to the target URL. If not specified, content-type defaults to application/x-www-form-urlencoded. |

## Supported Triggers

None listed.

## Creating MCP Server - Stand-alone vs Composio SDK

The Scrapfly MCP server is an implementation of the Model Context Protocol that connects your AI agents and assistants directly to Scrapfly. Instead of manually wiring Scrapfly APIs, OAuth, and scopes yourself, you get a structured, tool-based interface that an LLM can call safely.
With Composio's managed implementation, you don't have to create your own developer app. For production, if you're building an end product, we recommend using your own credentials. The managed server helps you prototype fast and go from 0-1 faster.

## Step-by-step Guide

### 1. Prerequisites

You will need:
- A Composio API key
- An OpenAI API key (used by Autogen's OpenAIChatCompletionClient)
- A Scrapfly account you can connect to Composio
- Some basic familiarity with Autogen and Python async

### 1. Getting API Keys for OpenAI and Composio

OpenAI API Key
- Go to the [OpenAI dashboard](https://platform.openai.com/settings/organization/api-keys) 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](https://dashboard.composio.dev?utm_source=toolkits&utm_medium=framework_docs).
- Navigate to your API settings and generate a new API key.
- Store this key securely as you'll need it for authentication.

### 2. Install dependencies

Install Composio, Autogen extensions, and dotenv.
What's happening:
- composio connects your agent to Scrapfly via MCP
- autogen-agentchat provides the AssistantAgent class
- autogen-ext-openai provides the OpenAI model client
- autogen-ext-tools provides MCP workbench support
```bash
pip install composio python-dotenv
pip install autogen-agentchat autogen-ext-openai autogen-ext-tools
```

### 3. Set up environment variables

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 Scrapfly connections to use
```bash
COMPOSIO_API_KEY=your-composio-api-key
OPENAI_API_KEY=your-openai-api-key
USER_ID=your-user-identifier@example.com
```

### 4. Import dependencies and create Tool Router session

What's happening:
- load_dotenv() reads your .env file
- Composio(api_key=...) initializes the SDK
- create(...) creates a Tool Router session that exposes Scrapfly tools
- session.mcp.url is the MCP endpoint that Autogen will connect to
```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 Scrapfly session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["scrapfly"]
    )
    url = session.mcp.url
```

### 5. Configure MCP parameters for Autogen

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
```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")}
)
```

### 6. Create the model client and agent

What's happening:
- OpenAIChatCompletionClient wraps the OpenAI model for Autogen
- McpWorkbench connects the agent to the MCP tools
- AssistantAgent is configured with the Scrapfly tools from the workbench
```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 Scrapfly assistant agent with MCP tools
    agent = AssistantAgent(
        name="scrapfly_assistant",
        description="An AI assistant that helps with Scrapfly operations.",
        model_client=model_client,
        workbench=workbench,
        model_client_stream=True,
        max_tool_iterations=10
    )
```

### 7. Run the interactive chat loop

What's happening:
- The script prompts you in a loop with You:
- Autogen passes your input to the model, which decides which Scrapfly 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
```python
print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
print("Ask any Scrapfly 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")
```

## Complete Code

```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 Scrapfly session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["scrapfly"]
    )
    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 Scrapfly assistant agent with MCP tools
        agent = AssistantAgent(
            name="scrapfly_assistant",
            description="An AI assistant that helps with Scrapfly 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 Scrapfly 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 Scrapfly 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 Scrapfly, you can reuse the same structure for other MCP-enabled apps with minimal code changes.

## How to build Scrapfly MCP Agent with another framework

- [OpenAI Agents SDK](https://composio.dev/toolkits/scrapfly/framework/open-ai-agents-sdk)
- [Claude Agent SDK](https://composio.dev/toolkits/scrapfly/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/scrapfly/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/scrapfly/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/scrapfly/framework/codex)
- [OpenClaw](https://composio.dev/toolkits/scrapfly/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/scrapfly/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/scrapfly/framework/cli)
- [Google ADK](https://composio.dev/toolkits/scrapfly/framework/google-adk)
- [LangChain](https://composio.dev/toolkits/scrapfly/framework/langchain)
- [Vercel AI SDK](https://composio.dev/toolkits/scrapfly/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/scrapfly/framework/mastra-ai)
- [LlamaIndex](https://composio.dev/toolkits/scrapfly/framework/llama-index)
- [CrewAI](https://composio.dev/toolkits/scrapfly/framework/crew-ai)

## Related Toolkits

- [Excel](https://composio.dev/toolkits/excel) - Microsoft Excel is a robust spreadsheet application for organizing, analyzing, and visualizing data. It's the go-to tool for calculations, reporting, and flexible data management.
- [21risk](https://composio.dev/toolkits/_21risk) - 21RISK is a web app built for easy checklist, audit, and compliance management. It streamlines risk processes so teams can focus on what matters.
- [Abstract](https://composio.dev/toolkits/abstract) - Abstract provides a suite of APIs for automating data validation and enrichment tasks. It helps developers streamline workflows and ensure data quality with minimal effort.
- [Addressfinder](https://composio.dev/toolkits/addressfinder) - Addressfinder is a data quality platform for verifying addresses, emails, and phone numbers. It helps you ensure accurate customer and contact data every time.
- [Agenty](https://composio.dev/toolkits/agenty) - Agenty is a web scraping and automation platform for extracting data and automating browser tasks—no coding needed. It streamlines data collection, monitoring, and repetitive online actions.
- [Ambee](https://composio.dev/toolkits/ambee) - Ambee is an environmental data platform providing real-time, hyperlocal APIs for air quality, weather, and pollen. Get precise environmental insights to power smarter decisions in your apps and workflows.
- [Ambient weather](https://composio.dev/toolkits/ambient_weather) - Ambient Weather is a platform for personal weather stations with a robust API for accessing local, real-time, and historical weather data. Get detailed environmental insights directly from your own sensors for smarter apps and automations.
- [Anonyflow](https://composio.dev/toolkits/anonyflow) - Anonyflow is a service for encryption-based data anonymization and secure data sharing. It helps organizations meet GDPR, CCPA, and HIPAA data privacy compliance requirements.
- [Api ninjas](https://composio.dev/toolkits/api_ninjas) - Api ninjas offers 120+ public APIs spanning categories like weather, finance, sports, and more. Developers use it to supercharge apps with real-time data and actionable endpoints.
- [Api sports](https://composio.dev/toolkits/api_sports) - Api sports is a comprehensive sports data platform covering 2,000+ competitions with live scores and 15+ years of stats. Instantly access up-to-date sports information for analysis, apps, or chatbots.
- [Apify](https://composio.dev/toolkits/apify) - 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.
- [Autom](https://composio.dev/toolkits/autom) - Autom is a lightning-fast search engine results data platform for Google, Bing, and Brave. Developers use it to access fresh, low-latency SERP data on demand.
- [Beaconchain](https://composio.dev/toolkits/beaconchain) - Beaconchain is a real-time analytics platform for Ethereum 2.0's Beacon Chain. It provides detailed insights into validators, blocks, and overall network performance.
- [Big data cloud](https://composio.dev/toolkits/big_data_cloud) - BigDataCloud provides APIs for geolocation, reverse geocoding, and address validation. Instantly access reliable location intelligence to enhance your applications and workflows.
- [Bigpicture io](https://composio.dev/toolkits/bigpicture_io) - BigPicture.io offers APIs for accessing detailed company and profile data. Instantly enrich your applications with up-to-date insights on 20M+ businesses.
- [Bitquery](https://composio.dev/toolkits/bitquery) - Bitquery is a blockchain data platform offering indexed, real-time, and historical data from 40+ blockchains via GraphQL APIs. Get unified, reliable access to complex on-chain data for analytics, trading, and research.
- [Brightdata](https://composio.dev/toolkits/brightdata) - Brightdata is a leading web data platform offering advanced scraping, SERP APIs, and anti-bot tools. It lets you collect public web data at scale, bypassing blocks and friction.
- [Builtwith](https://composio.dev/toolkits/builtwith) - BuiltWith is a web technology profiler that uncovers the technologies powering any website. Gain actionable insights into analytics, hosting, and content management stacks for smarter research and lead generation.
- [Byteforms](https://composio.dev/toolkits/byteforms) - Byteforms is an all-in-one platform for creating forms, managing submissions, and integrating data. It streamlines workflows by centralizing form data collection and automation.
- [Cabinpanda](https://composio.dev/toolkits/cabinpanda) - Cabinpanda is a data collection platform for building and managing online forms. It helps streamline how you gather, organize, and analyze responses.

## Frequently Asked Questions

### What are the differences in Tool Router MCP and Scrapfly MCP?

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

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

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

---
[See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)
