How to integrate Phantombuster MCP with Pydantic AI

This guide walks you through connecting Phantombuster to Pydantic AI using the Composio tool router. By the end, you'll have a working Phantombuster agent that can download agent usage csv for last month, list all active agents in your account, get the country for this ip address through natural language commands. This guide will help you understand how to give your Pydantic AI agent real control over a Phantombuster account through Composio's Phantombuster MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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Api Key

Phantombuster is a cloud-based automation platform for extracting web data and automating actions online. It helps you automate lead generation, web scraping, and social media workflows at scale.

53 Tools

Introduction

This guide walks you through connecting Phantombuster to Pydantic AI using the Composio tool router. By the end, you'll have a working Phantombuster agent that can download agent usage csv for last month, list all active agents in your account, get the country for this ip address through natural language commands.

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

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

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

The Phantombuster MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Phantombuster account. It provides structured and secure access to your web automation and data extraction tools, so your agent can perform actions like running agents, fetching reports, exporting usage data, and managing your automations on your behalf.

  • Agent management and monitoring: Instantly list, audit, or fetch details about all your Phantombuster agents and see which are active, deleted, or grouped together.
  • Data extraction and export: Have your agent export detailed usage reports or download CSVs of agent and container activity for analytics and compliance.
  • Automation workflow insight: Get visibility into branches, containers, and deployment differences—helping you track automation changes and resource usage.
  • Organization and account overview: Let your agent retrieve comprehensive organization information or check current API key associations for security and collaboration.
  • IP geolocation support: Enable your agent to look up the physical location of specific IP addresses for auditing or compliance checks.

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

Abort Agent (v1)

Tool to abort all running instances of an agent using the legacy v1 API.

Delete Agent

Tool to delete an agent by id.

Delete Lead Objects

Tool to delete one or more lead objects from organization storage.

Delete Many Leads

Tool to delete multiple leads from organization storage.

Delete List

Tool to delete a storage list by id (Beta).

Delete Script

Tool to delete a script by id.

Get Agent

Tool to get an agent by its ID.

Get Agent Containers (v1)

Tool to get a list of ended containers for an agent, ordered by date.

Get Agent Output (v1)

Tool to get incremental data from an agent including console output, status, progress and messages.

Get All Agents

Tool to fetch all agents associated with the current user or organization.

Get Deleted Agents

Tool to get deleted agents for the current user or organization.

Get Branches Diff

Tool to get the length difference between the staging and release branch of all scripts.

Get All Branches

Tool to fetch all branches associated with the current organization.

Get Containers Fetch All

Tool to get all containers associated with a specified agent.

Get Leads By List

Tool to fetch leads by their list ID.

Get IP Location

Tool to retrieve the country of a given or environment IP address.

Export Agent Usage CSV

Tool to export agent usage CSV for current organization.

Export Container Usage CSV

Tool to export container usage CSV for current organization.

Get Organization

Tool to fetch current organization details.

Get Agent Groups

Tool to get agent groups and order for the current organization.

Get Organization Resources

Tool to get current organization's resources and usage.

Get Org Running Containers

Tool to get the current organization's running containers.

Get Org Storage Lists Fetch All

Tool to fetch all storage lists for the authenticated organization.

Get Script

Tool to fetch a script by its unique ID.

Get Script by Name

Tool to retrieve a script by its name from Phantombuster (Legacy v1 API).

Get Script Code

Tool to get the code of a script.

Get All Scripts

Tool to fetch all scripts for the current user.

Get User Information

Tool to get information about your PhantomBuster account and your agents using the legacy v1 API.

Unschedule All Agent Launches

Tool to unschedule all scheduled launches for agents.

Request AI Completion

Tool to request a text completion from the AI module.

Create Branch

Tool to create a new branch.

Delete Branch

Tool to delete a branch by id.

Solve hCaptcha

Tool to solve an hCaptcha challenge.

Generate Identity Token

Tool to generate an identity token for PhantomBuster.

Save Many Leads

Tool to save multiple leads (1-20) to organization storage in a single batch operation (Beta).

Solve reCAPTCHA

Tool to solve a reCAPTCHA challenge (v2 or v3).

Update Script Visibility

Tool to update the visibility of a script.

Release Branch

Tool to release a script branch.

Save Agent

Tool to create a new agent or update an existing one.

Save Agent Groups

Tool to update agent groups and order for the current user's organization.

Save Company Object

Tool to save one company object to the organization storage.

Save Many Company Objects

Tool to save many company objects to organization storage.

Save Identity Event

Tool to save an identity event to Phantombuster.

Save Lead

Tool to save or update a lead in Phantombuster org storage.

Save Lead Object

Tool to save a lead object to organization storage.

Save Many Lead Objects

Tool to save multiple lead objects to Phantombuster's organization storage.

Save List

Tool to save (create or update) a list with filter criteria.

Save Script

Tool to create a new script or update an existing one.

Search Company Objects

Tool to search company objects in Phantombuster's organizational storage.

Search Lead Objects

Tool to search lead objects in Phantombuster org storage.

Stop Agent

Tool to stop a running agent.

Update Script (v1 API)

Tool to update an existing script or create a new one if it does not exist (Legacy v1 API).

Update Script Access List

Tool to update the access list of a script.

FAQ

Frequently asked questions

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

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

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