How to integrate Phantombuster MCP with LangChain

This guide walks you through connecting Phantombuster to LangChain 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 LangChain 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 LangChain 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 LangChain 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 ADKVercel AI SDKMastra AILlamaIndexCrewAI

TL;DR

Here's what you'll learn:
  • Get and set up your OpenAI and Composio API keys
  • Connect your Phantombuster project to Composio
  • Create a Tool Router MCP session for Phantombuster
  • Initialize an MCP client and retrieve Phantombuster tools
  • Build a LangChain agent that can interact with Phantombuster
  • Set up an interactive chat interface for testing

What is LangChain?

LangChain is a framework for developing applications powered by language models. It provides tools and abstractions for building agents that can reason, use tools, and maintain conversation context.

Key features include:

  • Agent Framework: Build agents that can use tools and make decisions
  • MCP Integration: Connect to external services through Model Context Protocol adapters
  • Memory Management: Maintain conversation history across interactions
  • Multi-Provider Support: Works with OpenAI, Anthropic, and other LLM providers

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 step10 STEPS
1

Prerequisites

Before starting this tutorial, make sure you have:
  • Python 3.10 or higher installed on your system
  • A Composio account with an API key
  • An OpenAI 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

npm install @composio/langchain @langchain/core @langchain/openai @langchain/mcp-adapters dotenv

Install the required packages for LangChain with MCP support.

What's happening:

  • @composio/langchain provides Composio integration for LangChain
  • @langchain/mcp-adapters enables MCP client connections
  • @langchain/core is the core agent framework
  • dotenv/config loads environment variables
4

Set up environment variables

bash
COMPOSIO_API_KEY=your_composio_api_key_here
COMPOSIO_USER_ID=your_composio_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 your requests to Composio's API
  • COMPOSIO_USER_ID identifies the user for session management
  • OPENAI_API_KEY enables access to OpenAI's language models
5

Import dependencies

import { Composio } from '@composio/core';
import { LangchainProvider } from '@composio/langchain';
import { MultiServerMCPClient } from "@langchain/mcp-adapters";
import { createAgent } from "langchain";
import * as readline from 'readline';
import 'dotenv/config';

dotenv.config();
What's happening:
  • We're importing LangChain's MCP adapter and Composio SDK
  • The dotenv/config import loads environment variables from your .env file
  • This setup prepares the foundation for connecting LangChain with Phantombuster functionality through MCP
6

Initialize Composio client

const composioApiKey = process.env.COMPOSIO_API_KEY;
const userId = process.env.COMPOSIO_USER_ID;

if (!composioApiKey) throw new Error('COMPOSIO_API_KEY is not set');
if (!userId) throw new Error('COMPOSIO_USER_ID is not set');

async function main() {
    const composio = new Composio({
        apiKey: composioApiKey as string,
        provider: new LangchainProvider()
    });
What's happening:
  • We're loading the COMPOSIO_API_KEY from environment variables and validating it exists
  • Creating a Composio instance that will manage our connection to Phantombuster tools
  • Validating that COMPOSIO_USER_ID is also set before proceeding
7

Create a Tool Router session

const session = await composio.create(
    userId as string,
    {
        toolkits: ['phantombuster']
    }
);

const url = session.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
  • This approach allows the agent to dynamically load and use Phantombuster tools as needed
8

Configure the agent with the MCP URL

const client = new MultiServerMCPClient({
    "phantombuster-agent": {
        transport: "http",
        url: url,
        headers: {
            "x-api-key": process.env.COMPOSIO_API_KEY
        }
    }
});

const tools = await client.getTools();

const agent = createAgent({ model: "gpt-5", tools });
What's happening:
  • We're creating a MultiServerMCPClient that connects to our Phantombuster MCP server via HTTP
  • The client is configured with a name and the URL from our Tool Router session
  • getTools() retrieves all available Phantombuster tools that the agent can use
  • We're creating a LangChain agent using the GPT-5 model
9

Set up interactive chat interface

let conversationHistory: any[] = [];

console.log("Chat started! Type 'exit' or 'quit' to end the conversation.\n");
console.log("Ask any Phantombuster related question or task to the agent.\n");

const rl = readline.createInterface({
    input: process.stdin,
    output: process.stdout,
    prompt: 'You: '
});

rl.prompt();

rl.on('line', async (userInput: string) => {
    const trimmedInput = userInput.trim();

    if (['exit', 'quit', 'bye'].includes(trimmedInput.toLowerCase())) {
        console.log("\nGoodbye!");
        rl.close();
        process.exit(0);
    }

    if (!trimmedInput) {
        rl.prompt();
        return;
    }

    conversationHistory.push({ role: "user", content: trimmedInput });
    console.log("\nAgent is thinking...\n");

    const response = await agent.invoke({ messages: conversationHistory });
    conversationHistory = response.messages;

    const finalResponse = response.messages[response.messages.length - 1]?.content;
    console.log(`Agent: ${finalResponse}\n`);
        
        rl.prompt();
    });

    rl.on('close', () => {
        console.log('\n👋 Session ended.');
        process.exit(0);
    });
What's happening:
  • We initialize an empty conversationHistory list to maintain context across interactions
  • A readline interface is used to continuously accept user input from the command line
  • When a user types a message, it's added to the conversation history and sent to the agent
  • The agent processes the request using the invoke() method with the full conversation history
  • Users can type 'exit', 'quit', or 'bye' to end the chat session gracefully
10

Run the application

main().catch((err) => {
    console.error('Fatal error:', err);
    process.exit(1);
});
What's happening:
  • We call the main() function to start the application

Complete Code

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

import { Composio } from '@composio/core';
import { LangchainProvider } from '@composio/langchain';
import { MultiServerMCPClient } from "@langchain/mcp-adapters";  
import { createAgent } from "langchain";
import * as readline from 'readline';
import 'dotenv/config';

const composioApiKey = process.env.COMPOSIO_API_KEY;
const userId = process.env.COMPOSIO_USER_ID;

if (!composioApiKey) throw new Error('COMPOSIO_API_KEY is not set');
if (!userId) throw new Error('COMPOSIO_USER_ID is not set');

async function main() {
    const composio = new Composio({
        apiKey: composioApiKey as string,
        provider: new LangchainProvider()
    });

    const session = await composio.create(
        userId as string,
        {
            toolkits: ['phantombuster']
        }
    );

    const url = session.mcp.url;
    
    const client = new MultiServerMCPClient({
        "phantombuster-agent": {
            transport: "http",
            url: url,
            headers: {
                "x-api-key": process.env.COMPOSIO_API_KEY
            }
        }
    });
    
    const tools = await client.getTools();
  
    const agent = createAgent({ model: "gpt-5", tools });
    
    let conversationHistory: any[] = [];
    
    console.log("Chat started! Type 'exit' or 'quit' to end the conversation.\n");
    console.log("Ask any Phantombuster related question or task to the agent.\n");
    
    const rl = readline.createInterface({
        input: process.stdin,
        output: process.stdout,
        prompt: 'You: '
    });

    rl.prompt();

    rl.on('line', async (userInput: string) => {
        const trimmedInput = userInput.trim();
        
        if (['exit', 'quit', 'bye'].includes(trimmedInput.toLowerCase())) {
            console.log("\nGoodbye!");
            rl.close();
            process.exit(0);
        }
        
        if (!trimmedInput) {
            rl.prompt();
            return;
        }
        
        conversationHistory.push({ role: "user", content: trimmedInput });
        console.log("\nAgent is thinking...\n");
        
        const response = await agent.invoke({ messages: conversationHistory });
        conversationHistory = response.messages;
        
        const finalResponse = response.messages[response.messages.length - 1]?.content;
        console.log(`Agent: ${finalResponse}\n`);
        
        rl.prompt();
    });

    rl.on('close', () => {
        console.log('\nSession ended.');
        process.exit(0);
    });
}

main().catch((err) => {
    console.error('Fatal error:', err);
    process.exit(1);
});

Conclusion

You've successfully built a LangChain agent that can interact with Phantombuster through Composio's Tool Router.

Key features of this implementation:

  • Dynamic tool loading through Composio's Tool Router
  • Conversation history maintenance for context-aware responses
  • Async Python provides clean, efficient execution of agent workflows
You can extend this further by adding error handling, implementing specific business logic, or integrating additional Composio toolkits to create multi-app workflows.
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

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

CrewAI logo

CrewAI

Use Phantombuster MCP with CrewAI

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. LangChain 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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Abhi Arya
Co-founder, Opennote

We chose Composio over Pipedream because it delivered depth where it mattered — niche tools and tricky edge cases other platforms simply ignored. That gave us confidence to scale without compromising.

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CEO, Zams

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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