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How to integrate Scrapfly MCP with Pydantic AI

Connect Pydantic AI to Scrapfly MCP. Extract product prices from amazon listings, scrape job postings from linkedin search, and more using natural language, with authentication handled for you.

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Scrapfly is a powerful web scraping API that extracts data from any website. It lets developers handle tough sites with JavaScript rendering, proxy rotation, and anti-bot protection.

12 Tools

Introduction

This guide walks you through connecting Scrapfly to Pydantic AI using the Composio tool router. By the end, you'll have a working Scrapfly agent that can scrape web pages, capture screenshots, extract structured data, and crawl entire websites through natural language commands.

This guide will help you understand how to give your Pydantic AI 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

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TL;DR

Here's what you'll learn:
  • How to set up your Composio API key and User ID
  • How to create a Composio Tool Router session for Scrapfly
  • How to attach an MCP Server to a Pydantic AI agent
  • How to stream responses and maintain chat history
  • How to build a simple REPL-style chat interface to test your Scrapfly workflows

What is Pydantic AI?

Pydantic AI is a Python framework for building AI agents with strong typing and validation. It leverages Pydantic's data validation capabilities to create robust, type-safe AI applications.

Key features include:

  • Type Safety: Built on Pydantic for automatic data validation
  • MCP Support: Native support for Model Context Protocol servers
  • Streaming: Built-in support for streaming responses
  • Async First: Designed for async/await patterns

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.

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 step09 STEPS
1

Prerequisites

Before starting, make sure you have:
  • Python 3.9 or higher
  • A Composio account with an active API key
  • Basic familiarity with Python and async programming
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 pydantic-ai python-dotenv

Install the required libraries.

What's happening:

  • composio connects your agent to external SaaS tools like Scrapfly
  • pydantic-ai lets you create structured AI agents with tool support
  • python-dotenv loads your environment variables securely from a .env file
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

Create a .env file in your project root.

What's happening:

  • COMPOSIO_API_KEY authenticates your agent to Composio's API
  • USER_ID associates your session with your account for secure tool access
  • OPENAI_API_KEY to access OpenAI LLMs
5

Import dependencies

python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerStreamableHTTP

load_dotenv()
What's happening:
  • We load environment variables and import required modules
  • Composio manages connections to Scrapfly
  • MCPServerStreamableHTTP connects to the Scrapfly MCP server endpoint
  • Agent from Pydantic AI lets you define and run the AI assistant
6

Create a Tool Router Session

python
async def main():
    api_key = os.getenv("COMPOSIO_API_KEY")
    user_id = os.getenv("USER_ID")
    if not api_key or not user_id:
        raise RuntimeError("Set COMPOSIO_API_KEY and USER_ID in your environment")

    # Create a Composio Tool Router session for Scrapfly
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["scrapfly"],
    )
    url = session.mcp.url
    if not url:
        raise ValueError("Composio session did not return an MCP URL")
What's happening:
  • We're creating a Tool Router session that gives your agent access to Scrapfly tools
  • The create method takes the user ID and specifies which toolkits should be available
  • The returned session.mcp.url is the MCP server URL that your agent will use
7

Initialize the Pydantic AI Agent

python
# Attach the MCP server to a Pydantic AI Agent
scrapfly_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
agent = Agent(
    "openai:gpt-5",
    toolsets=[scrapfly_mcp],
    instructions=(
        "You are a Scrapfly assistant. Use Scrapfly tools to help users "
        "with their requests. Ask clarifying questions when needed."
    ),
)
What's happening:
  • The MCP client connects to the Scrapfly endpoint
  • The agent uses GPT-5 to interpret user commands and perform Scrapfly operations
  • The instructions field defines the agent's role and behavior
8

Build the chat interface

python
# Simple REPL with message history
history = []
print("Chat started! Type 'exit' or 'quit' to end.\n")
print("Try asking the agent to help you with Scrapfly.\n")

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", flush=True)

    async with agent.run_stream(user_input, message_history=history) as stream_result:
        collected_text = ""
        async for chunk in stream_result.stream_output():
            text_piece = None
            if isinstance(chunk, str):
                text_piece = chunk
            elif hasattr(chunk, "delta") and isinstance(chunk.delta, str):
                text_piece = chunk.delta
            elif hasattr(chunk, "text"):
                text_piece = chunk.text
            if text_piece:
                collected_text += text_piece
        result = stream_result

    print(f"Agent: {collected_text}\n")
    history = result.all_messages()
What's happening:
  • The agent reads input from the terminal and streams its response
  • Scrapfly API calls happen automatically under the hood
  • The model keeps conversation history to maintain context across turns
9

Run the application

python
if __name__ == "__main__":
    asyncio.run(main())
What's happening:
  • The asyncio loop launches the agent and keeps it running until you exit

Complete Code

Here's the complete code to get you started with Scrapfly and Pydantic AI:

python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerStreamableHTTP

load_dotenv()

async def main():
    api_key = os.getenv("COMPOSIO_API_KEY")
    user_id = os.getenv("USER_ID")
    if not api_key or not user_id:
        raise RuntimeError("Set COMPOSIO_API_KEY and USER_ID in your environment")

    # Create a Composio Tool Router session for Scrapfly
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["scrapfly"],
    )
    url = session.mcp.url
    if not url:
        raise ValueError("Composio session did not return an MCP URL")

    # Attach the MCP server to a Pydantic AI Agent
    scrapfly_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
    agent = Agent(
        "openai:gpt-5",
        toolsets=[scrapfly_mcp],
        instructions=(
            "You are a Scrapfly assistant. Use Scrapfly tools to help users "
            "with their requests. Ask clarifying questions when needed."
        ),
    )

    # Simple REPL with message history
    history = []
    print("Chat started! Type 'exit' or 'quit' to end.\n")
    print("Try asking the agent to help you with Scrapfly.\n")

    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", flush=True)

        async with agent.run_stream(user_input, message_history=history) as stream_result:
            collected_text = ""
            async for chunk in stream_result.stream_output():
                text_piece = None
                if isinstance(chunk, str):
                    text_piece = chunk
                elif hasattr(chunk, "delta") and isinstance(chunk.delta, str):
                    text_piece = chunk.delta
                elif hasattr(chunk, "text"):
                    text_piece = chunk.text
                if text_piece:
                    collected_text += text_piece
            result = stream_result

        print(f"Agent: {collected_text}\n")
        history = result.all_messages()

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

Conclusion

You've built a Pydantic AI agent that can interact with Scrapfly through Composio's Tool Router. With this setup, your agent can perform real Scrapfly actions through natural language. You can extend this further by:
  • Adding other toolkits like Gmail, HubSpot, or Salesforce
  • Building a web-based chat interface around this agent
  • Using multiple MCP endpoints to enable cross-app workflows (for example, Gmail + Scrapfly for workflow automation)
This architecture makes your AI agent "agent-native", able to securely use APIs in a unified, composable way without custom integrations.
TOOLS

Supported Tools

Every Scrapfly action and event your agent gets out of the box.

Capture Website Screenshot

Capture a full-page, viewport, or selected-element screenshot of a website, with options such as custom resolution and dark mode.

Capture Screenshot Metadata (HEAD)

Tool to capture screenshot metadata without downloading the image body.

Create Scrapfly Crawler

Create a web crawler to recursively crawl pages from a starting URL, with configurable limits and content formats.

Extract Structured Data

Tool to extract structured data from HTML or other content using AI models, LLM prompts, or custom templates.

Get Scrapfly Account Information

Retrieve Scrapfly account information, including subscription, usage, billing, and project settings.

Get Crawler Artifact

Tool to download crawler artifact files in WARC or HAR format.

Get Crawler Contents

Tool to retrieve extracted content from crawled pages.

Get Crawler Status

Tool to get the current status of a crawler including progress, pages crawled, and completion state.

Get Crawler URLs

Retrieve discovered and crawled URLs from a crawler, optionally filtered by status.

Scrapfly Scrape

Request a web scrape with options such as JavaScript rendering, proxy location, and extraction.

Scrapfly Scrape POST

Tool to scrape web pages using POST method to send data in the request body.

Scrape With PUT

Tool to scrape web pages using PUT method with body payload.

FRAMEWORKS

How to build Scrapfly MCP Agent with another framework

Claude Code logo

Claude Code

Use Scrapfly MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Scrapfly MCP with Claude Cowork

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Scrapfly MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Scrapfly MCP with Claude Agent SDK

Codex logo

Codex

Use Scrapfly MCP with Codex

OpenClaw logo

OpenClaw

Use Scrapfly MCP with OpenClaw

Hermes logo

Hermes

Use Scrapfly MCP with Hermes

Atomic Agent logo

Atomic Agent

Use Scrapfly MCP with Atomic Agent

Google ADK logo

Google ADK

Use Scrapfly MCP with Google ADK

LangChain logo

LangChain

Use Scrapfly MCP with LangChain

Vercel AI SDK logo

Vercel AI SDK

Use Scrapfly MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Scrapfly MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Scrapfly MCP with LlamaIndex

CrewAI logo

CrewAI

Use Scrapfly MCP with CrewAI

AutoGen logo

AutoGen

Use Scrapfly MCP with AutoGen

FAQ

Frequently asked questions

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.

Yes, you can. Pydantic AI 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.

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.

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.

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