Browser Use
MCP server for browser-use.
Overview
This repository contains the server for the browser-use library, which provides a powerful browser automation system that enables AI agents to interact with web browsers through natural language. The server is built on Anthropic's Model Context Protocol (MCP) and provides a seamless integration with the browser-use library.
Features
- Browser Control
- Automated browser interactions via natural language
- Navigation, form filling, clicking, and scrolling capabilities
- Tab management and screenshot functionality
- Cookie and state management
- Agent System
- Custom agent implementation in custom_agent.py
- Vision-based element detection
- Structured JSON responses for actions
- Message history management and summarization
- Configuration
- Environment-based configuration for API keys and settings
- Chrome browser settings (debugging port, persistence)
- Model provider selection and parameters
Dependencies
This project relies on the following Python packages:
| Package | Version | Description |
|---|---|---|
| Pillow | >=10.1.0 | Python Imaging Library (PIL) fork that adds image processing capabilities to your Python interpreter. |
| browser-use | ==0.1.19 | A powerful browser automation system that enables AI agents to interact with web browsers through natural language. The core library that powers this project's browser automation capabilities. |
| fastapi | >=0.115.6 | Modern, fast (high-performance) web framework for building APIs with Python 3.7+ based on standard Python type hints. Used to create the server that exposes the agent's functionality. |
| fastmcp | >=0.4.1 | A framework that wraps FastAPI for building MCP (Model Context Protocol) servers. |
| instructor | >=1.7.2 | Library for structured output prompting and validation with OpenAI models. Enables extracting structured data from model responses. |
| langchain | >=0.3.14 | Framework for developing applications with large language models (LLMs). Provides tools for chaining together different language model components and interacting with various APIs and data sources. |
| langchain-google-genai | >=2.1.1 | LangChain integration for Google GenAI models, enabling the use of Google's generative AI capabilities within the LangChain framework. |
| langchain-openai | >=0.2.14 | LangChain integrations with OpenAI's models. Enables using OpenAI models (like GPT-4) within the LangChain framework. Used in this project for interacting with OpenAI's language and vision models. |
| langchain-ollama | >=0.2.2 | Langchain integration for Ollama, enabling local execution of LLMs. |
| openai | >=1.59.5 | Official Python client library for the OpenAI API. Used to interact directly with OpenAI's models (if needed, in addition to LangChain). |
| python-dotenv | >=1.0.1 | Reads key-value pairs from a .env file and sets them as environment variables. Simplifies local development and configuration management. |
| pydantic | >=2.10.5 | Data validation and settings management using Python type annotations. Provides runtime enforcement of types and automatic model creation. Essential for defining structured data models in the agent. |
| pyperclip | >=1.9.0 | Cross-platform Python module for copy and paste clipboard functions. |
| uvicorn | >=0.22.0 | ASGI web server implementation for Python. Used to serve the FastAPI application. |
Components
Resources
The server implements a browser automation system with:
- Integration with browser-use library for advanced browser control
- Custom browser automation capabilities
- Agent-based interaction system with vision capabilities
- Persistent state management
- Customizable model settings
Requirements
- Operating Systems (Linux, macOS, Windows; we haven't tested for Docker or Microsoft WSL)
- Python 3.11 or higher
- uv (fast Python package installer)
- Chrome/Chromium browser
- Claude Desktop
Quick Start
Claude Desktop
On MacOS: ~/Library/Application\ Support/Claude/claude_desktop_config.json
On Windows: %APPDATA%/Claude/claude_desktop_config.json
Installing via Smithery
To install Browser Use for Claude Desktop automatically via Smithery:
npx -y @smithery/cli install @JovaniPink/mcp-browser-use --client claude
Environment Variables
Key environment variables:
# API Keys
ANTHROPIC_API_KEY=anthropic_key
# Chrome Configuration
# Optional: Path to Chrome executable
CHROME_PATH=/path/to/chrome
# Optional: Chrome user data directory
CHROME_USER_DATA=/path/to/user/data
# Default: 9222
CHROME_DEBUGGING_PORT=9222
# Default: localhost
CHROME_DEBUGGING_HOST=localhost
# Keep browser open between tasks
CHROME_PERSISTENT_SESSION=false
# Model Settings
# Options: anthropic, openai, azure, deepseek
MCP_MODEL_PROVIDER=anthropic
# Model name
MCP_MODEL_NAME=claude-3-5-sonnet-20241022
MCP_TEMPERATURE=0.3
MCP_MAX_STEPS=30
MCP_USE_VISION=true
MCP_MAX_ACTIONS_PER_STEP=5
Development
Setup
- Clone the repository:
git clone https://github.com/JovaniPink/mcp-browser-use.git
cd mcp-browser-use
- Create and activate virtual environment:
python -m venv .venv
source .venv/bin/activate # On Windows: .venv\Scripts\activate
- Install dependencies:
uv sync
- Start the server
uv run mcp-browser-use
Debugging
For debugging, use the MCP Inspector:
npx @modelcontextprotocol/inspector uv --directory /path/to/project run mcp-server-browser-use
The Inspector will display a URL for the debugging interface.
Browser Actions
The server supports various browser actions through natural language:
- Navigation: Go to URLs, back/forward, refresh
- Interaction: Click, type, scroll, hover
- Forms: Fill forms, submit, select options
- State: Get page content, take screenshots
- Tabs: Create, close, switch between tabs
- Vision: Find elements by visual appearance
- Cookies & Storage: Manage browser state
Security
I want to note that their are some Chrome settings that are set to allow for the browser to be controlled by the server. This is a security risk and should be used with caution. The server is not intended to be used in a production environment.
Security Details: SECURITY.MD
Contributing
We welcome contributions to this project. Please follow these steps:
- Fork this repository.
- Create your feature branch:
git checkout -b my-new-feature. - Commit your changes:
git commit -m 'Add some feature'. - Push to the branch:
git push origin my-new-feature. - Submit a pull request.
For major changes, open an issue first to discuss what you would like to change. Please update tests as appropriate to reflect any changes made.