# How to integrate Phantombuster MCP with Autogen

```json
{
  "title": "How to integrate Phantombuster MCP with Autogen",
  "toolkit": "Phantombuster",
  "toolkit_slug": "phantombuster",
  "framework": "AutoGen",
  "framework_slug": "autogen",
  "url": "https://composio.dev/toolkits/phantombuster/framework/autogen",
  "markdown_url": "https://composio.dev/toolkits/phantombuster/framework/autogen.md",
  "updated_at": "2026-05-12T10:21:54.664Z"
}
```

## Introduction

This guide walks you through connecting Phantombuster to AutoGen 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 AutoGen 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 SDK](https://composio.dev/toolkits/phantombuster/framework/open-ai-agents-sdk)
- [Claude Agent SDK](https://composio.dev/toolkits/phantombuster/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/phantombuster/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/phantombuster/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/phantombuster/framework/codex)
- [OpenClaw](https://composio.dev/toolkits/phantombuster/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/phantombuster/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/phantombuster/framework/cli)
- [Google ADK](https://composio.dev/toolkits/phantombuster/framework/google-adk)
- [LangChain](https://composio.dev/toolkits/phantombuster/framework/langchain)
- [Vercel AI SDK](https://composio.dev/toolkits/phantombuster/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/phantombuster/framework/mastra-ai)
- [LlamaIndex](https://composio.dev/toolkits/phantombuster/framework/llama-index)
- [CrewAI](https://composio.dev/toolkits/phantombuster/framework/crew-ai)

## TL;DR

Here's what you'll learn:
- Get and set up your OpenAI and Composio API keys
- Install the required dependencies for Autogen and Composio
- Initialize Composio and create a Tool Router session for Phantombuster
- Wire that MCP URL into Autogen using McpWorkbench and StreamableHttpServerParams
- Configure an Autogen AssistantAgent that can call Phantombuster tools
- Run a live chat loop where you ask the agent to perform Phantombuster operations

## What is AutoGen?

Autogen is a framework for building multi-agent conversational AI systems from Microsoft. It enables you to create agents that can collaborate, use tools, and maintain complex workflows.
Key features include:
- Multi-Agent Systems: Build collaborative agent workflows
- MCP Workbench: Native support for Model Context Protocol tools
- Streaming HTTP: Connect to external services through streamable HTTP
- AssistantAgent: Pre-built agent class for tool-using assistants

## 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.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `PHANTOMBUSTER_ABORT_AGENT_V1` | Abort Agent (v1) | Tool to abort all running instances of an agent using the legacy v1 API. Use when you need to immediately stop an agent's execution. Supports soft abort for graceful shutdown. |
| `PHANTOMBUSTER_DELETE_AGENT` | Delete Agent | Tool to delete an agent by id. Use when you need to remove a PhantomBuster agent. Ensure the agent is not currently running before deletion. |
| `PHANTOMBUSTER_DELETE_LEAD_OBJECTS` | Delete Lead Objects | Tool to delete one or more lead objects from organization storage. Use when you need to remove lead data. Provide either leadObjectId, or both slug and type parameters. |
| `PHANTOMBUSTER_DELETE_LEADS_MANY` | Delete Many Leads | Tool to delete multiple leads from organization storage. Use when you need to bulk delete leads by their IDs. |
| `PHANTOMBUSTER_DELETE_LIST` | Delete List | Tool to delete a storage list by id (Beta). Use when you need to remove a list from organization storage. |
| `PHANTOMBUSTER_DELETE_SCRIPT` | Delete Script | Tool to delete a script by id. Use when you need to remove a specific script from PhantomBuster. Optionally specify a branch and environment to delete a specific version. |
| `PHANTOMBUSTER_GET_AGENT` | Get Agent | Tool to get an agent by its ID. Use when you need to retrieve detailed information about a specific Phantombuster agent, including its configuration, schedule, and execution settings. |
| `PHANTOMBUSTER_GET_AGENT_CONTAINERS` | Get Agent Containers (v1) | Tool to get a list of ended containers for an agent, ordered by date. Use when you need to retrieve the last available output logs from an agent. This is a legacy v1 API endpoint. |
| `PHANTOMBUSTER_GET_AGENT_OUTPUT` | Get Agent Output (v1) | Tool to get incremental data from an agent including console output, status, progress and messages. This is a legacy v1 API endpoint designed for easy incremental data retrieval. Use outputPos to track position. |
| `PHANTOMBUSTER_GET_AGENTS_FETCH_ALL` | Get All Agents | Tool to fetch all agents associated with the current user or organization. Use after validating your Phantombuster API key to list available agents. |
| `PHANTOMBUSTER_GET_AGENTS_FETCH_DELETED` | Get Deleted Agents | Tool to get deleted agents for the current user or organization. Use when you need to audit recently removed agents. |
| `PHANTOMBUSTER_GET_BRANCHES_DIFF` | Get Branches Diff | Tool to get the length difference between the staging and release branch of all scripts. Use when assessing branch changes before deployment. |
| `PHANTOMBUSTER_GET_BRANCHES_FETCH_ALL` | Get All Branches | Tool to fetch all branches associated with the current organization. Use when you need to enumerate every branch across your scripts. |
| `PHANTOMBUSTER_GET_CONTAINERS_FETCH_ALL` | Get Containers Fetch All | Tool to get all containers associated with a specified agent. Use when you need to retrieve every container for a given agent, for monitoring or reporting. |
| `PHANTOMBUSTER_GET_LEADS_BY_LIST` | Get Leads By List | Tool to fetch leads by their list ID. Use when you need to retrieve leads from a specific list with optional pagination and filtering. |
| `PHANTOMBUSTER_GET_LOCATION_IP` | Get IP Location | Tool to retrieve the country of a given or environment IP address. Use when you need to geolocate an IP endpoint. Example: get_location_ip(ip="8.8.8.8") |
| `PHANTOMBUSTER_GET_ORGS_EXPORT_AGENT_USAGE` | Export Agent Usage CSV | Tool to export agent usage CSV for current organization. Use when you need a downloadable report of all agents' run statistics in CSV format. |
| `PHANTOMBUSTER_GET_ORGS_EXPORT_CONTAINER_USAGE` | Export Container Usage CSV | Tool to export container usage CSV for current organization. Use when you need a downloadable CSV report of container execution history up to 6 months; URL expires in 30 days. |
| `PHANTOMBUSTER_GET_ORGS_FETCH` | Get Organization | Tool to fetch current organization details. Use when you need to retrieve the organization associated with the provided API key. |
| `PHANTOMBUSTER_GET_ORGS_FETCH_AGENT_GROUPS` | Get Agent Groups | Tool to get agent groups and order for the current organization. Use when you need to review how agents are grouped and ordered. |
| `PHANTOMBUSTER_GET_ORGS_FETCH_RESOURCES` | Get Organization Resources | Tool to get current organization's resources and usage. Use when you need to monitor quotas. |
| `PHANTOMBUSTER_GET_ORGS_FETCH_RUNNING_CONTAINERS` | Get Org Running Containers | Tool to get the current organization's running containers. Use after listing agents to identify active containers across the organization. |
| `PHANTOMBUSTER_GET_ORG_STORAGE_LISTS_FETCH_ALL` | Get Org Storage Lists Fetch All | Tool to fetch all storage lists for the authenticated organization. Use when you need to enumerate every storage list available (Beta). |
| `PHANTOMBUSTER_GET_SCRIPT` | Get Script | Tool to fetch a script by its unique ID. Use when you need to retrieve detailed metadata about a specific script, including its branches, visibility, and optionally its source code. |
| `PHANTOMBUSTER_GET_SCRIPT_BY_NAME` | Get Script by Name | Tool to retrieve a script by its name from Phantombuster (Legacy v1 API). Use when you need to fetch script metadata or content by name. Supports both JSON (structured) and raw (plain text) response formats. |
| `PHANTOMBUSTER_GET_SCRIPTS_CODE` | Get Script Code | Tool to get the code of a script. Use when you need to retrieve the source code content of a specific script. |
| `PHANTOMBUSTER_GET_SCRIPTS_FETCH_ALL` | Get All Scripts | Tool to fetch all scripts for the current user. Use after authenticating your Phantombuster API key to list scripts without their code bodies. |
| `PHANTOMBUSTER_GET_USER` | Get User Information | Tool to get information about your PhantomBuster account and your agents using the legacy v1 API. Use when you need to check account quotas, remaining resources, or list associated agents. |
| `PHANTOMBUSTER_POST_AGENTS_UNSCHEDULE_ALL` | Unschedule All Agent Launches | Tool to unschedule all scheduled launches for agents. Use when you need to disable every automated agent run organization-wide after verifying your API key. |
| `PHANTOMBUSTER_POST_AI_COMPLETIONS` | Request AI Completion | Tool to request a text completion from the AI module. Use when you need to generate text based on a prompt. |
| `PHANTOMBUSTER_POST_BRANCHES_CREATE` | Create Branch | Tool to create a new branch. Use when you need to isolate updates by creating a separate branch. Use after authenticating with your Phantombuster API key and optionally specifying an organization. |
| `PHANTOMBUSTER_POST_BRANCHES_DELETE` | Delete Branch | Tool to delete a branch by id. Use when you need to remove obsolete or incorrect script branches; ensure the branch exists before calling. |
| `PHANTOMBUSTER_POST_HCAPTCHA` | Solve hCaptcha | Tool to solve an hCaptcha challenge. Use when you need a valid hCaptcha token for form submissions or automation flows. |
| `PHANTOMBUSTER_POST_IDENTITIES_GENERATE_TOKEN` | Generate Identity Token | Tool to generate an identity token for PhantomBuster. Use when you need to create a new identity token for authentication or session management. |
| `PHANTOMBUSTER_POST_ORG_STORAGE_LEADS_SAVE_MANY` | Save Many Leads | Tool to save multiple leads (1-20) to organization storage in a single batch operation (Beta). Use when you need to create or update leads with LinkedIn profile data and optional enrichment fields. |
| `PHANTOMBUSTER_POST_RECAPTCHA` | Solve reCAPTCHA | Tool to solve a reCAPTCHA challenge (v2 or v3). Use when you need a valid reCAPTCHA response token for form submissions or automation. Note: API returns 200 status even on errors; check the error field in the response. |
| `PHANTOMBUSTER_POST_SCRIPTS_VISIBILITY` | Update Script Visibility | Tool to update the visibility of a script. Use when you need to change whether a script is private, public, or open source. |
| `PHANTOMBUSTER_RELEASE_BRANCH` | Release Branch | Tool to release a script branch. Use when you need to deploy a branch to production or release changes to specified scripts. |
| `PHANTOMBUSTER_SAVE_AGENT` | Save Agent | Tool to create a new agent or update an existing one. Use when you need to configure an agent's launch schedule, settings, or behavior. If an agent ID is provided, the existing agent will be updated; otherwise, a new agent is created. |
| `PHANTOMBUSTER_SAVE_AGENT_GROUPS` | Save Agent Groups | Tool to update agent groups and order for the current user's organization. Use when you need to reorganize agents into groups or change their display order. |
| `PHANTOMBUSTER_SAVE_COMPANY_OBJECT` | Save Company Object | Tool to save one company object to the organization storage. Use when you need to create or update a company object with LinkedIn company data and custom properties. |
| `PHANTOMBUSTER_SAVE_COMPANY_OBJECTS_MANY` | Save Many Company Objects | Tool to save many company objects to organization storage. Use when you need to bulk insert or update company data with a minimum of 1 and maximum of 20 objects per request. |
| `PHANTOMBUSTER_SAVE_IDENTITY_EVENT` | Save Identity Event | Tool to save an identity event to Phantombuster. Use when you need to record user interactions or activities associated with specific social media profiles. |
| `PHANTOMBUSTER_SAVE_LEAD` | Save Lead | Tool to save or update a lead in Phantombuster org storage. Use when you need to store LinkedIn profile data or other lead information. |
| `PHANTOMBUSTER_SAVE_LEAD_OBJECT` | Save Lead Object | Tool to save a lead object to organization storage. Use when you need to store or update lead information with custom properties. |
| `PHANTOMBUSTER_SAVE_LEADS_OBJECTS_MANY` | Save Many Lead Objects | Tool to save multiple lead objects to Phantombuster's organization storage. Use when you need to create or update multiple lead objects in a single API call. |
| `PHANTOMBUSTER_SAVE_LIST` | Save List | Tool to save (create or update) a list with filter criteria. Use when you need to create a new list or update an existing one by providing a filter configuration. This is a Beta feature. |
| `PHANTOMBUSTER_SAVE_SCRIPT` | Save Script | Tool to create a new script or update an existing one. Provide an id to update; otherwise creates new script. Use when you need to save JavaScript automation code to Phantombuster. |
| `PHANTOMBUSTER_SEARCH_COMPANY_OBJECTS` | Search Company Objects | Tool to search company objects in Phantombuster's organizational storage. Use when you need to find companies by specific criteria or perform a global search. |
| `PHANTOMBUSTER_SEARCH_LEAD_OBJECTS` | Search Lead Objects | Tool to search lead objects in Phantombuster org storage. Use when you need to find leads based on search criteria or filter conditions. |
| `PHANTOMBUSTER_STOP_AGENT` | Stop Agent | Tool to stop a running agent. Use when you need to halt agent execution, optionally cascading to slave agents or switching to manual launch mode. |
| `PHANTOMBUSTER_UPDATE_SCRIPT` | Update Script (v1 API) | Tool to update an existing script or create a new one if it does not exist (Legacy v1 API). Use when you need to save script code to Phantombuster. If insertOnly is true, the operation will fail if a script with the same name already exists. |
| `PHANTOMBUSTER_UPDATE_SCRIPTS_ACCESS_LIST` | Update Script Access List | Tool to update the access list of a script. Use when you need to add or remove users/orgs from a script's access list in a specific branch. |

## Supported Triggers

None listed.

## Creating MCP Server - Stand-alone vs Composio SDK

The Phantombuster MCP server is an implementation of the Model Context Protocol that connects your AI agents and assistants directly to Phantombuster. Instead of manually wiring Phantombuster APIs, OAuth, and scopes yourself, you get a structured, tool-based interface that an LLM can call safely.
With Composio's managed implementation, you don't have to create your own developer app. For production, if you're building an end product, we recommend using your own credentials. The managed server helps you prototype fast and go from 0-1 faster.

## Step-by-step Guide

### 1. Prerequisites

You will need:
- A Composio API key
- An OpenAI API key (used by Autogen's OpenAIChatCompletionClient)
- A Phantombuster account you can connect to Composio
- Some basic familiarity with Autogen and Python async

### 1. Getting API Keys for OpenAI and Composio

OpenAI API Key
- Go to the [OpenAI dashboard](https://platform.openai.com/settings/organization/api-keys) 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](https://dashboard.composio.dev?utm_source=toolkits&utm_medium=framework_docs).
- Navigate to your API settings and generate a new API key.
- Store this key securely as you'll need it for authentication.

### 2. Install dependencies

Install Composio, Autogen extensions, and dotenv.
What's happening:
- composio connects your agent to Phantombuster via MCP
- autogen-agentchat provides the AssistantAgent class
- autogen-ext-openai provides the OpenAI model client
- autogen-ext-tools provides MCP workbench support
```bash
pip install composio python-dotenv
pip install autogen-agentchat autogen-ext-openai autogen-ext-tools
```

### 3. Set up environment variables

Create a .env file in your project folder.
What's happening:
- COMPOSIO_API_KEY is required to talk to Composio
- OPENAI_API_KEY is used by Autogen's OpenAI client
- USER_ID is how Composio identifies which user's Phantombuster connections to use
```bash
COMPOSIO_API_KEY=your-composio-api-key
OPENAI_API_KEY=your-openai-api-key
USER_ID=your-user-identifier@example.com
```

### 4. Import dependencies and create Tool Router session

What's happening:
- load_dotenv() reads your .env file
- Composio(api_key=...) initializes the SDK
- create(...) creates a Tool Router session that exposes Phantombuster tools
- session.mcp.url is the MCP endpoint that Autogen will connect to
```python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio

from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient
from autogen_ext.tools.mcp import McpWorkbench, StreamableHttpServerParams

load_dotenv()

async def main():
    # Initialize Composio and create a Phantombuster session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["phantombuster"]
    )
    url = session.mcp.url
```

### 5. Configure MCP parameters for Autogen

Autogen expects parameters describing how to talk to the MCP server. That is what StreamableHttpServerParams is for.
What's happening:
- url points to the Tool Router MCP endpoint from Composio
- timeout is the HTTP timeout for requests
- sse_read_timeout controls how long to wait when streaming responses
- terminate_on_close=True cleans up the MCP server process when the workbench is closed
```python
# Configure MCP server parameters for Streamable HTTP
server_params = StreamableHttpServerParams(
    url=url,
    timeout=30.0,
    sse_read_timeout=300.0,
    terminate_on_close=True,
    headers={"x-api-key": os.getenv("COMPOSIO_API_KEY")}
)
```

### 6. Create the model client and agent

What's happening:
- OpenAIChatCompletionClient wraps the OpenAI model for Autogen
- McpWorkbench connects the agent to the MCP tools
- AssistantAgent is configured with the Phantombuster tools from the workbench
```python
# Create model client
model_client = OpenAIChatCompletionClient(
    model="gpt-5",
    api_key=os.getenv("OPENAI_API_KEY")
)

# Use McpWorkbench as context manager
async with McpWorkbench(server_params) as workbench:
    # Create Phantombuster assistant agent with MCP tools
    agent = AssistantAgent(
        name="phantombuster_assistant",
        description="An AI assistant that helps with Phantombuster operations.",
        model_client=model_client,
        workbench=workbench,
        model_client_stream=True,
        max_tool_iterations=10
    )
```

### 7. Run the interactive chat loop

What's happening:
- The script prompts you in a loop with You:
- Autogen passes your input to the model, which decides which Phantombuster tools to call via MCP
- agent.run_stream(...) yields streaming messages as the agent thinks and calls tools
- Typing exit, quit, or bye ends the loop
```python
print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
print("Ask any Phantombuster related question or task to the agent.\n")

# Conversation loop
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")

    # Run the agent with streaming
    try:
        response_text = ""
        async for message in agent.run_stream(task=user_input):
            if hasattr(message, "content") and message.content:
                response_text = message.content

        # Print the final response
        if response_text:
            print(f"Agent: {response_text}\n")
        else:
            print("Agent: I encountered an issue processing your request.\n")

    except Exception as e:
        print(f"Agent: Sorry, I encountered an error: {str(e)}\n")
```

## Complete Code

```python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio

from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient
from autogen_ext.tools.mcp import McpWorkbench, StreamableHttpServerParams

load_dotenv()

async def main():
    # Initialize Composio and create a Phantombuster session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["phantombuster"]
    )
    url = session.mcp.url

    # Configure MCP server parameters for Streamable HTTP
    server_params = StreamableHttpServerParams(
        url=url,
        timeout=30.0,
        sse_read_timeout=300.0,
        terminate_on_close=True,
        headers={"x-api-key": os.getenv("COMPOSIO_API_KEY")}
    )

    # Create model client
    model_client = OpenAIChatCompletionClient(
        model="gpt-5",
        api_key=os.getenv("OPENAI_API_KEY")
    )

    # Use McpWorkbench as context manager
    async with McpWorkbench(server_params) as workbench:
        # Create Phantombuster assistant agent with MCP tools
        agent = AssistantAgent(
            name="phantombuster_assistant",
            description="An AI assistant that helps with Phantombuster operations.",
            model_client=model_client,
            workbench=workbench,
            model_client_stream=True,
            max_tool_iterations=10
        )

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

        # Conversation loop
        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")

            # Run the agent with streaming
            try:
                response_text = ""
                async for message in agent.run_stream(task=user_input):
                    if hasattr(message, 'content') and message.content:
                        response_text = message.content

                # Print the final response
                if response_text:
                    print(f"Agent: {response_text}\n")
                else:
                    print("Agent: I encountered an issue processing your request.\n")

            except Exception as e:
                print(f"Agent: Sorry, I encountered an error: {str(e)}\n")

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

## Conclusion

You now have an Autogen assistant wired into Phantombuster through Composio's Tool Router and MCP. From here you can:
- Add more toolkits to the toolkits list, for example notion or hubspot
- Refine the agent description to point it at specific workflows
- Wrap this script behind a UI, Slack bot, or internal tool
Once the pattern is clear for Phantombuster, you can reuse the same structure for other MCP-enabled apps with minimal code changes.

## How to build Phantombuster MCP Agent with another framework

- [OpenAI Agents SDK](https://composio.dev/toolkits/phantombuster/framework/open-ai-agents-sdk)
- [Claude Agent SDK](https://composio.dev/toolkits/phantombuster/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/phantombuster/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/phantombuster/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/phantombuster/framework/codex)
- [OpenClaw](https://composio.dev/toolkits/phantombuster/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/phantombuster/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/phantombuster/framework/cli)
- [Google ADK](https://composio.dev/toolkits/phantombuster/framework/google-adk)
- [LangChain](https://composio.dev/toolkits/phantombuster/framework/langchain)
- [Vercel AI SDK](https://composio.dev/toolkits/phantombuster/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/phantombuster/framework/mastra-ai)
- [LlamaIndex](https://composio.dev/toolkits/phantombuster/framework/llama-index)
- [CrewAI](https://composio.dev/toolkits/phantombuster/framework/crew-ai)

## Related Toolkits

- [Excel](https://composio.dev/toolkits/excel) - Microsoft Excel is a robust spreadsheet application for organizing, analyzing, and visualizing data. It's the go-to tool for calculations, reporting, and flexible data management.
- [21risk](https://composio.dev/toolkits/_21risk) - 21RISK is a web app built for easy checklist, audit, and compliance management. It streamlines risk processes so teams can focus on what matters.
- [Abstract](https://composio.dev/toolkits/abstract) - Abstract provides a suite of APIs for automating data validation and enrichment tasks. It helps developers streamline workflows and ensure data quality with minimal effort.
- [Addressfinder](https://composio.dev/toolkits/addressfinder) - Addressfinder is a data quality platform for verifying addresses, emails, and phone numbers. It helps you ensure accurate customer and contact data every time.
- [Agenty](https://composio.dev/toolkits/agenty) - Agenty is a web scraping and automation platform for extracting data and automating browser tasks—no coding needed. It streamlines data collection, monitoring, and repetitive online actions.
- [Ambee](https://composio.dev/toolkits/ambee) - Ambee is an environmental data platform providing real-time, hyperlocal APIs for air quality, weather, and pollen. Get precise environmental insights to power smarter decisions in your apps and workflows.
- [Ambient weather](https://composio.dev/toolkits/ambient_weather) - Ambient Weather is a platform for personal weather stations with a robust API for accessing local, real-time, and historical weather data. Get detailed environmental insights directly from your own sensors for smarter apps and automations.
- [Anonyflow](https://composio.dev/toolkits/anonyflow) - Anonyflow is a service for encryption-based data anonymization and secure data sharing. It helps organizations meet GDPR, CCPA, and HIPAA data privacy compliance requirements.
- [Api ninjas](https://composio.dev/toolkits/api_ninjas) - Api ninjas offers 120+ public APIs spanning categories like weather, finance, sports, and more. Developers use it to supercharge apps with real-time data and actionable endpoints.
- [Api sports](https://composio.dev/toolkits/api_sports) - Api sports is a comprehensive sports data platform covering 2,000+ competitions with live scores and 15+ years of stats. Instantly access up-to-date sports information for analysis, apps, or chatbots.
- [Apify](https://composio.dev/toolkits/apify) - Apify is a cloud platform for building, deploying, and managing web scraping and automation tools called Actors. It lets you automate data extraction and workflow tasks at scale—no infrastructure headaches.
- [Autom](https://composio.dev/toolkits/autom) - Autom is a lightning-fast search engine results data platform for Google, Bing, and Brave. Developers use it to access fresh, low-latency SERP data on demand.
- [Beaconchain](https://composio.dev/toolkits/beaconchain) - Beaconchain is a real-time analytics platform for Ethereum 2.0's Beacon Chain. It provides detailed insights into validators, blocks, and overall network performance.
- [Big data cloud](https://composio.dev/toolkits/big_data_cloud) - BigDataCloud provides APIs for geolocation, reverse geocoding, and address validation. Instantly access reliable location intelligence to enhance your applications and workflows.
- [Bigpicture io](https://composio.dev/toolkits/bigpicture_io) - BigPicture.io offers APIs for accessing detailed company and profile data. Instantly enrich your applications with up-to-date insights on 20M+ businesses.
- [Bitquery](https://composio.dev/toolkits/bitquery) - Bitquery is a blockchain data platform offering indexed, real-time, and historical data from 40+ blockchains via GraphQL APIs. Get unified, reliable access to complex on-chain data for analytics, trading, and research.
- [Brightdata](https://composio.dev/toolkits/brightdata) - Brightdata is a leading web data platform offering advanced scraping, SERP APIs, and anti-bot tools. It lets you collect public web data at scale, bypassing blocks and friction.
- [Builtwith](https://composio.dev/toolkits/builtwith) - BuiltWith is a web technology profiler that uncovers the technologies powering any website. Gain actionable insights into analytics, hosting, and content management stacks for smarter research and lead generation.
- [Byteforms](https://composio.dev/toolkits/byteforms) - Byteforms is an all-in-one platform for creating forms, managing submissions, and integrating data. It streamlines workflows by centralizing form data collection and automation.
- [Cabinpanda](https://composio.dev/toolkits/cabinpanda) - Cabinpanda is a data collection platform for building and managing online forms. It helps streamline how you gather, organize, and analyze responses.

## Frequently Asked Questions

### What are the differences in Tool Router MCP and Phantombuster MCP?

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.

### Can I use Tool Router MCP with Autogen?

Yes, you can. Autogen 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.

### Can I manage the permissions and scopes for Phantombuster while using Tool Router?

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.

### How safe is my data with Composio Tool Router?

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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[See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)