How to integrate Phantombuster MCP with CrewAI

This guide walks you through connecting Phantombuster to CrewAI 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 CrewAI 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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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.

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Introduction

This guide walks you through connecting Phantombuster to CrewAI 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 CrewAI 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.

Also integrate Phantombuster with

OpenAI Agents SDKClaude Agent SDKClaude CodeClaude CoworkCodexOpenClawHermesCLIGoogle ADKLangChainVercel AI SDKMastra AILlamaIndex

TL;DR

Here's what you'll learn:
  • Get a Composio API key and configure your Phantombuster connection
  • Set up CrewAI with an MCP enabled agent
  • Create a Tool Router session or standalone MCP server for Phantombuster
  • Build a conversational loop where your agent can execute Phantombuster operations

What is CrewAI?

CrewAI is a powerful framework for building multi-agent AI systems. It provides primitives for defining agents with specific roles, creating tasks, and orchestrating workflows through crews.

Key features include:

  • Agent Roles: Define specialized agents with specific goals and backstories
  • Task Management: Create tasks with clear descriptions and expected outputs
  • Crew Orchestration: Combine agents and tasks into collaborative workflows
  • MCP Integration: Connect to external tools through Model Context Protocol

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 step08 STEPS
1

Prerequisites

Before starting, make sure you have:
  • Python 3.9 or higher
  • A Composio account and API key
  • A Phantombuster connection authorized in Composio
  • An OpenAI API key for the CrewAI LLM
  • Basic familiarity with Python
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 crewai crewai-tools[mcp] python-dotenv
What's happening:
  • composio connects your agent to Phantombuster via MCP
  • crewai provides Agent, Task, Crew, and LLM primitives
  • crewai-tools[mcp] includes MCP helpers
  • python-dotenv loads environment variables from .env
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_here

Create a .env file in your project root.

What's happening:

  • COMPOSIO_API_KEY authenticates with Composio
  • USER_ID scopes the session to your account
  • OPENAI_API_KEY lets CrewAI use your chosen OpenAI model
5

Import dependencies

python
import os
from composio import Composio
from crewai import Agent, Task, Crew
from crewai_tools import MCPServerAdapter
import dotenv

dotenv.load_dotenv()

COMPOSIO_API_KEY = os.getenv("COMPOSIO_API_KEY")
COMPOSIO_USER_ID = os.getenv("COMPOSIO_USER_ID")

if not COMPOSIO_API_KEY:
    raise ValueError("COMPOSIO_API_KEY is not set")
if not COMPOSIO_USER_ID:
    raise ValueError("COMPOSIO_USER_ID is not set")
What's happening:
  • CrewAI classes define agents and tasks, and run the workflow
  • MCPServerHTTP connects the agent to an MCP endpoint
  • Composio will give you a short lived Phantombuster MCP URL
6

Create a Composio Tool Router session for Phantombuster

python
composio_client = Composio(api_key=COMPOSIO_API_KEY)
session = composio_client.create(user_id=COMPOSIO_USER_ID, toolkits=["phantombuster"])

url = session.mcp.url
What's happening:
  • You create a Phantombuster only session through Composio
  • Composio returns an MCP HTTP URL that exposes Phantombuster tools
7

Initialize the MCP Server

python
server_params = {
    "url": url,
    "transport": "streamable-http",
    "headers": {"x-api-key": COMPOSIO_API_KEY},
}

with MCPServerAdapter(server_params) as tools:
    agent = Agent(
        role="Search Assistant",
        goal="Help users search the internet effectively",
        backstory="You are a helpful assistant with access to search tools.",
        tools=tools,
        verbose=False,
        max_iter=10,
    )
What's Happening:
  • Server Configuration: The code sets up connection parameters including the MCP server URL, streamable HTTP transport, and Composio API key authentication.
  • MCP Adapter Bridge: MCPServerAdapter acts as a context manager that converts Composio MCP tools into a CrewAI-compatible format.
  • Agent Setup: Creates a CrewAI Agent with a defined role (Search Assistant), goal (help with internet searches), and access to the MCP tools.
  • Configuration Options: The agent includes settings like verbose=False for clean output and max_iter=10 to prevent infinite loops.
  • Dynamic Tool Usage: Once created, the agent automatically accesses all Composio Search tools and decides when to use them based on user queries.
8

Create a CLI Chatloop and define the Crew

python
print("Chat started! Type 'exit' or 'quit' to end.\n")

conversation_context = ""

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

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

    if not user_input:
        continue

    conversation_context += f"\nUser: {user_input}\n"
    print("\nAgent is thinking...\n")

    task = Task(
        description=(
            f"Conversation history:\n{conversation_context}\n\n"
            f"Current request: {user_input}"
        ),
        expected_output="A helpful response addressing the user's request",
        agent=agent,
    )

    crew = Crew(agents=[agent], tasks=[task], verbose=False)
    result = crew.kickoff()
    response = str(result)

    conversation_context += f"Agent: {response}\n"
    print(f"Agent: {response}\n")
What's Happening:
  • Interactive CLI Setup: The code creates an infinite loop that continuously prompts for user input and maintains the entire conversation history in a string variable.
  • Input Validation: Empty inputs are ignored to prevent processing blank messages and keep the conversation clean.
  • Context Building: Each user message is appended to the conversation context, which preserves the full dialogue history for better agent responses.
  • Dynamic Task Creation: For every user input, a new Task is created that includes both the full conversation history and the current request as context.
  • Crew Execution: A Crew is instantiated with the agent and task, then kicked off to process the request and generate a response.
  • Response Management: The agent's response is converted to a string, added to the conversation context, and displayed to the user, maintaining conversational continuity.

Complete Code

Here's the complete code to get you started with Phantombuster and CrewAI:

python
from crewai import Agent, Task, Crew, LLM
from crewai_tools import MCPServerAdapter
from composio import Composio
from dotenv import load_dotenv
import os

load_dotenv()

GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY")
COMPOSIO_API_KEY = os.getenv("COMPOSIO_API_KEY")
COMPOSIO_USER_ID = os.getenv("COMPOSIO_USER_ID")

if not GOOGLE_API_KEY:
    raise ValueError("GOOGLE_API_KEY is not set in the environment.")
if not COMPOSIO_API_KEY:
    raise ValueError("COMPOSIO_API_KEY is not set in the environment.")
if not COMPOSIO_USER_ID:
    raise ValueError("COMPOSIO_USER_ID is not set in the environment.")

# Initialize Composio and create a session
composio = Composio(api_key=COMPOSIO_API_KEY)
session = composio.create(
    user_id=COMPOSIO_USER_ID,
    toolkits=["phantombuster"],
)
url = session.mcp.url

# Configure LLM
llm = LLM(
    model="gpt-5",
    api_key=os.getenv("OPENAI_API_KEY"),
)

server_params = {
    "url": url,
    "transport": "streamable-http",
    "headers": {"x-api-key": COMPOSIO_API_KEY},
}

with MCPServerAdapter(server_params) as tools:
    agent = Agent(
        role="Search Assistant",
        goal="Help users with internet searches",
        backstory="You are an expert assistant with access to Composio Search tools.",
        tools=tools,
        llm=llm,
        verbose=False,
        max_iter=10,
    )

    print("Chat started! Type 'exit' or 'quit' to end.\n")

    conversation_context = ""

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

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

        if not user_input:
            continue

        conversation_context += f"\nUser: {user_input}\n"
        print("\nAgent is thinking...\n")

        task = Task(
            description=(
                f"Conversation history:\n{conversation_context}\n\n"
                f"Current request: {user_input}"
            ),
            expected_output="A helpful response addressing the user's request",
            agent=agent,
        )

        crew = Crew(agents=[agent], tasks=[task], verbose=False)
        result = crew.kickoff()
        response = str(result)

        conversation_context += f"Agent: {response}\n"
        print(f"Agent: {response}\n")

Conclusion

You now have a CrewAI agent connected to Phantombuster through Composio's Tool Router. The agent can perform Phantombuster operations through natural language commands.

Next steps:

  • Add role-specific instructions to customize agent behavior
  • Plug in more toolkits for multi-app workflows
  • Chain tasks for complex multi-step operations
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.

FRAMEWORKS

How to build Phantombuster MCP Agent with another framework

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Phantombuster MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Phantombuster MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use Phantombuster MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Phantombuster MCP with Claude Cowork

Codex logo

Codex

Use Phantombuster MCP with Codex

OpenClaw logo

OpenClaw

Use Phantombuster MCP with OpenClaw

Hermes logo

Hermes

Use Phantombuster MCP with Hermes

CLI logo

CLI

Use Phantombuster MCP with CLI

Google ADK logo

Google ADK

Use Phantombuster MCP with Google ADK

LangChain logo

LangChain

Use Phantombuster MCP with LangChain

Vercel AI SDK logo

Vercel AI SDK

Use Phantombuster MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Phantombuster MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Phantombuster MCP with LlamaIndex

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. CrewAI 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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Co-founder, Slashy

A lot of students tell us that the moment their connected tools start talking to each other inside Opennote feels almost magical. The agent just knows them, and it's immensely helped keep new users on the platform.

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As a solo builder, shipping fast is life or death. The only way I can outcompete incumbents is by outmanoeuvring them. Getting bogged down managing agent auth would have been a death sentence.

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Founder, Extra Thursday

Before Composio, adding tool integrations was slow and resource-intensive. Each one could take weeks of engineering time, and maintaining them meant constantly keeping up with API changes.

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Founder & CEO, SwarmZero

With hands-on help from their founder, we integrated Gmail and Google Drive in just 30 minutes. This level of personal support and commitment is exactly what startups should strive for.

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