How to integrate Browseai MCP with CrewAI

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Introduction

This guide walks you through connecting Browseai to CrewAI using the Composio tool router. By the end, you'll have a working Browseai agent that can extract product prices from competitor websites, monitor job postings for new remote roles, get detailed status of last robot run, bulk scrape news article headlines daily through natural language commands.

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

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

TL;DR

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

The Browseai MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Browseai account. It provides structured and secure access to your robots, tasks, and monitoring workflows, so your agent can perform actions like running robots, extracting website data, monitoring for changes, and managing web automation tasks on your behalf.

  • On-demand web data extraction: Trigger your Browseai robots to collect data from any website instantly, using custom input parameters for targeted web scraping.
  • Automated website monitoring: Set up and manage recurring monitors that watch websites for changes, so your agent can alert you to updates or new information as soon as they happen.
  • Bulk task execution: Launch thousands of data extraction tasks at once for large-scale or repeated scraping jobs, streamlining high-volume automation for your workflows.
  • Task and robot management: Retrieve lists of your robots, fetch detailed results for specific tasks, check execution history, and delete obsolete tasks or monitors to keep your automation setup organized.
  • Real-time notifications and workflow integration: Configure webhooks so your agent receives instant updates when tasks complete, fail, or when website changes are detected, enabling seamless integration with your broader systems.

Supported Tools & Triggers

Tools
Bulk Run TasksThis action allows users to bulk run up to 50,000 tasks using a specified robot.
Create MonitorThis tool creates a new monitor for a specific robot in browse ai.
Create WebhookThis tool creates a new webhook for a browseai robot.
Delete a specific monitorThis tool allows users to delete a specific monitor from their browse ai account.
Delete a specific taskThis tool allows you to delete a specific task in browseai by its task id.
Get Robots ListThis tool retrieves a list of all robots under your account in browse ai.
Get Robot TasksThis tool retrieves all tasks associated with a specific robot in browse ai.
Get Task DetailsThis tool retrieves detailed information about a specific task in browse ai by its task id.
Run RobotThis tool allows you to trigger the execution of a browseai robot on demand.

What is the Composio tool router, and how does it fit here?

What is Tool Router?

Composio's Tool Router 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 Tool Router

The tool router generates a secure MCP URL that your agents can access to perform actions.

How the Tool Router works

The Tool Router 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

Prerequisites

Before starting, make sure you have:
  • Python 3.9 or higher
  • A Composio account and API key
  • A Browseai connection authorized in Composio
  • An OpenAI API key for the CrewAI LLM
  • Basic familiarity with Python

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.

Install dependencies

bash
pip install composio crewai crewai-tools python-dotenv
What's happening:
  • composio connects your agent to Browseai via MCP
  • crewai provides Agent, Task, Crew, and LLM primitives
  • crewai-tools includes MCP helpers
  • python-dotenv loads environment variables from .env

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

Import dependencies

python
from crewai import Agent, Task, Crew, LLM
from crewai_tools import MCPServerAdapter  # optional import if you plan to adapt tools
from composio import Composio
from dotenv import load_dotenv
import os
from crewai.mcp import MCPServerHTTP

load_dotenv()
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 Browseai MCP URL

Create a Composio Tool Router session for Browseai

python
composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
session = composio.create(
    user_id=os.getenv("USER_ID"),
    toolkits=["browseai"],
)
url = session.mcp.url
What's happening:
  • You create a Browseai only session through Composio
  • Composio returns an MCP HTTP URL that exposes Browseai tools

Configure the LLM

python
llm = LLM(
    model="gpt-5-mini",
    api_key=os.getenv("OPENAI_API_KEY"),
)
What's happening:
  • CrewAI will call this LLM for planning and responses
  • You can swap in a different model if needed

Attach the MCP server and create the agent

python
toolkit_agent = Agent(
    role="Browseai Assistant",
    goal="Help users interact with Browseai through natural language commands",
    backstory=(
        "You are an expert assistant with access to Browseai tools. "
        "You can perform various Browseai operations on behalf of the user."
    ),
    mcps=[
        MCPServerHTTP(
            url=url,
            streamable=True,
            cache_tools_list=True,
            headers={"x-api-key": os.getenv("COMPOSIO_API_KEY")},
        ),
    ],
    llm=llm,
    verbose=True,
    max_iter=10,
)
What's happening:
  • MCPServerHTTP connects the agent to the Browseai MCP endpoint
  • cache_tools_list saves a tools catalog for faster subsequent runs
  • verbose helps you see what the agent is doing

Add a REPL loop with Task and Crew

python
print("Chat started! Type 'exit' or 'quit' to end.\n")
print("Try asking the agent to perform Browseai operations.\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"Based on the conversation history:\n{conversation_context}\n\n"
            f"Current user request: {user_input}\n\n"
            f"Please help the user with their Browseai related request."
        ),
        expected_output="A helpful response addressing the user's request",
        agent=toolkit_agent,
    )

    crew = Crew(
        agents=[toolkit_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:
  • You build a simple chat loop and keep a running context
  • Each user turn becomes a Task handled by the same agent
  • Crew executes the task and returns a response

Run the application

python
if __name__ == "__main__":
    main()
What's happening:
  • Standard Python entry point so you can run python crewai_browseai_agent.py

Complete Code

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

python
# file: crewai_browseai_agent.py
from crewai import Agent, Task, Crew, LLM
from crewai_tools import MCPServerAdapter  # optional
from composio import Composio
from dotenv import load_dotenv
import os
from crewai.mcp import MCPServerHTTP

load_dotenv()

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

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

    # Create Browseai assistant agent
    toolkit_agent = Agent(
        role="Browseai Assistant",
        goal="Help users interact with Browseai through natural language commands",
        backstory=(
            "You are an expert assistant with access to Browseai tools. "
            "You can perform various Browseai operations on behalf of the user."
        ),
        mcps=[
            MCPServerHTTP(
                url=url,
                streamable=True,
                cache_tools_list=True,
                headers={"x-api-key": os.getenv("COMPOSIO_API_KEY")},
            ),
        ],
        llm=llm,
        verbose=True,
        max_iter=10,
    )

    print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
    print("Try asking the agent to perform Browseai operations.\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"Based on the conversation history:\n{conversation_context}\n\n"
                f"Current user request: {user_input}\n\n"
                f"Please help the user with their Browseai related request."
            ),
            expected_output="A helpful response addressing the user's request",
            agent=toolkit_agent,
        )

        crew = Crew(
            agents=[toolkit_agent],
            tasks=[task],
            verbose=False,
        )

        result = crew.kickoff()
        response = str(result)

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

if __name__ == "__main__":
    main()

Conclusion

You now have a CrewAI agent connected to Browseai through Composio's Tool Router. The agent can perform Browseai 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

How to build Browseai MCP Agent with another framework

OpenAI Agents SDK

OpenAI Agents SDK

Use Browseai MCP with OpenAI Agents SDK

Claude Agent SDK

Claude Agent SDK

Use Browseai MCP with Claude Agent SDK

Google ADK

Google ADK

Use Browseai MCP with Google ADK

LangChain

LangChain

Use Browseai MCP with LangChain

Vercel AI SDK

Vercel AI SDK

Use Browseai MCP with Vercel AI SDK

Mastra AI

Mastra AI

Use Browseai MCP with Mastra AI

LlamaIndex

LlamaIndex

Use Browseai MCP with LlamaIndex

FAQ

What are the differences in Tool Router MCP and Browseai MCP?

With a standalone Browseai MCP server, the agents and LLMs can only access a fixed set of Browseai tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Browseai and many other apps based on the task at hand, all through a single MCP endpoint.

Can I use Tool Router MCP with CrewAI?

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 Browseai tools.

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

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

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