How to integrate WebCrawlerAPI MCP with Pydantic AI

This guide walks you through connecting WebCrawlerAPI to Pydantic AI using the Composio tool router. By the end, you'll have a working WebCrawlerAPI agent that can scrape web pages, start site crawls, monitor feed changes, manage feeds, and check costs through natural language commands. This guide will help you understand how to give your Pydantic AI agent real control over a WebCrawlerAPI account through Composio's WebCrawlerAPI MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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WebCrawlerAPI is a unified API for crawling websites, scraping pages, running AI web agents, and monitoring website feeds. It helps you extract fresh web data without building crawler infrastructure yourself.

10 Tools

Introduction

This guide walks you through connecting WebCrawlerAPI to Pydantic AI using the Composio tool router. By the end, you'll have a working WebCrawlerAPI agent that can scrape web pages, start site crawls, monitor feed changes, manage feeds, and check costs through natural language commands.

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

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

Also integrate WebCrawlerAPI with

OpenAI Agents SDKClaude Agent SDKClaude CodeClaude CoworkCodexOpenClawHermesCLIGoogle ADKLangChainVercel AI SDKMastra AILlamaIndexCrewAI

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 WebCrawlerAPI
  • 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 WebCrawlerAPI 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 WebCrawlerAPI MCP server, and what's possible with it?

The WebCrawlerAPI MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your WebCrawlerAPI account. It provides structured and secure access to website crawling, page scraping, monitored feeds, and usage data, so your agent can crawl sites, extract page content, monitor website changes, manage feeds, and review costs on your behalf.

  • Website crawling: Have your agent start site crawls and check their progress, page results, errors, and observed costs.
  • Page scraping and extraction: Direct your agent to scrape a web page and return its content or structured information.
  • Website change monitoring: Let the agent create recurring feeds and review detected changes and recent monitoring activity.
  • Feed management: Instruct your agent to list monitored feeds, inspect their status, pause or resume monitoring, and permanently cancel feeds.
  • Usage and cost tracking: Have your agent check your available balance, request volume, and usage costs for a chosen date range.

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 WebCrawlerAPI
  • 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 WebCrawlerAPI
  • MCPServerStreamableHTTP connects to the WebCrawlerAPI 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 WebCrawlerAPI
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["webcrawlerapi"],
    )
    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 WebCrawlerAPI 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
webcrawlerapi_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
agent = Agent(
    "openai:gpt-5",
    toolsets=[webcrawlerapi_mcp],
    instructions=(
        "You are a WebCrawlerAPI assistant. Use WebCrawlerAPI tools to help users "
        "with their requests. Ask clarifying questions when needed."
    ),
)
What's happening:
  • The MCP client connects to the WebCrawlerAPI endpoint
  • The agent uses GPT-5 to interpret user commands and perform WebCrawlerAPI 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 WebCrawlerAPI.\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
  • WebCrawlerAPI 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 WebCrawlerAPI 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 WebCrawlerAPI
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["webcrawlerapi"],
    )
    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
    webcrawlerapi_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
    agent = Agent(
        "openai:gpt-5",
        toolsets=[webcrawlerapi_mcp],
        instructions=(
            "You are a WebCrawlerAPI assistant. Use WebCrawlerAPI 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 WebCrawlerAPI.\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 WebCrawlerAPI through Composio's Tool Router. With this setup, your agent can perform real WebCrawlerAPI 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 + WebCrawlerAPI 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 WebCrawlerAPI action and event your agent gets out of the box.

Create Crawl

Start a metered asynchronous crawl over a site and return its job ID.

Create Feed

Create a recurring website-change feed.

Delete Feed

Permanently cancel a feed so it cannot be resumed.

Get Crawl Job

Get a crawl job's status, configuration, per-page results, content URLs, errors, and observed costs.

Get Feed

Get one feed's configuration, lifecycle status, and recent run history, including per-run crawl counts and cost.

Get Organization Costs

Return current spendable balance plus request count and USD usage for a date range.

List Feed Changes

Return one page of detected feed changes as structured JSON, with an opaque continuation cursor for older pages.

List Feeds

List active and paused feeds for the connected organization, newest first.

Scrape Page

Scrape one web page synchronously and return requested content or structured extraction.

Set Feed State

Pause an active feed's future scheduled runs or resume a paused feed.

FRAMEWORKS

How to build WebCrawlerAPI MCP Agent with another framework

OpenAI Agents SDK logo

OpenAI Agents SDK

Use WebCrawlerAPI MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use WebCrawlerAPI MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use WebCrawlerAPI MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use WebCrawlerAPI MCP with Claude Cowork

Codex logo

Codex

Use WebCrawlerAPI MCP with Codex

OpenClaw logo

OpenClaw

Use WebCrawlerAPI MCP with OpenClaw

Hermes logo

Hermes

Use WebCrawlerAPI MCP with Hermes

CLI logo

CLI

Use WebCrawlerAPI MCP with CLI

Google ADK logo

Google ADK

Use WebCrawlerAPI MCP with Google ADK

LangChain logo

LangChain

Use WebCrawlerAPI MCP with LangChain

Vercel AI SDK logo

Vercel AI SDK

Use WebCrawlerAPI MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use WebCrawlerAPI MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use WebCrawlerAPI MCP with LlamaIndex

CrewAI logo

CrewAI

Use WebCrawlerAPI MCP with CrewAI

FAQ

Frequently asked questions

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

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

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