A
securityA
licenseA
qualityA MCP server that provides browser automation tools, allowing users to navigate websites, take screenshots, click elements, fill forms, and execute JavaScript through Playwright.
Last updated -
8
1
Apache 2.0
Enables browser automation with Databricks language models for web-based tasks via API.
Allows loading environment variables from .env files for configuring API keys and other sensitive information.
Enables interaction with GitHub through browser automation, including searching for repositories and accessing project information.
Provides integration with Google's language models for web browsing and automation tasks.
Supports using Hugging Face models for browser automation and web interaction tasks.
Integrates with LangChain components for creating browser automation agents that can execute web-based tasks.
Enables using NVIDIA's language models for browser automation through the MCP server.
Allows using Ollama's local language models for browser automation and web interaction.
Integrates with OpenAI's language models to enable browser automation and web interaction through natural language commands.
Provides Python client support for utilizing the browser automation capabilities of the MCP server.
Browser Use MCP Server
A FastMCP server that enables browser automation through natural language commands. This server allows Language Models to browse the web, fill out forms, click buttons, and perform other web-based tasks via a simple API.
Quick Start
1. Install the package
Install with a specific provider (e.g., OpenAI)
pip install -e "git+https://github.com/yourusername/browser-use-mcp.git#egg=browser-use-mcp[openai]"
Or install all providers
pip install -e "git+https://github.com/yourusername/browser-use-mcp.git#egg=browser-use-mcp[all-providers]"
Install Playwright browsers
playwright install chromium
2. Configure your MCP client
Add the browser-use-mcp server to your MCP client configuration:
{
"mcpServers": {
"browser-use-mcp": {
"command": "browser-use-mcp",
"args": ["--model", "gpt-4o"],
"env": {
"OPENAI_API_KEY": "your-openai-api-key", // Or any other provider's API key
"DISPLAY": ":0" // For GUI environments
}
}
}
}
Replace "your-openai-api-key" with your actual API key or use an environment variable reference like process.env.OPENAI_API_KEY.
3. Use it with your favorite MCP client
Example using mcp-use with Python
import asyncio
import os
from dotenv import load_dotenv
from langchain_openai import ChatOpenAI
from mcp_use import MCPAgent, MCPClient
async def main():
# Load environment variables
load_dotenv()
# Create MCPClient from config file
client = MCPClient(
config={
"mcpServers": {
"browser-use-mcp": {
"command": "browser-use-mcp",
"args": ["--model", "gpt-4o"],
"env": {
"OPENAI_API_KEY": os.getenv("OPENAI_API_KEY"),
"DISPLAY": ":0",
},
}
}
}
)
# Create LLM
llm = ChatOpenAI(model="gpt-4o")
# Create agent with the client
agent = MCPAgent(llm=llm, client=client, max_steps=30)
# Run the query
result = await agent.run(
"""
Navigate to https://github.com, search for "browser-use-mcp", and summarize the project.
""",
max_steps=30,
)
print(f"\nResult: {result}")
if __name__ == "__main__":
asyncio.run(main())
Using Claude for Desktop
Open Claude for Desktop
Go to Settings → Experimental features
Enable Claude API Beta and OpenAPI schema for API
Add the following configuration to your Claude Desktop config file:
Mac:
~/Library/Application Support/Claude/claude_desktop_config.jsonWindows:
%AppData%\Claude\claude_desktop_config.json
{
"mcpServers": {
"browser-use": {
"command": "browser-use-mcp",
"args": ["--model", "claude-3-opus-20240229"]
}
}
}
Start a new conversation with Claude and ask it to perform web tasks
Supported LLM Providers
The following LLM providers are supported for browser automation:
Provider | API Key Environment Variable |
OpenAI |
|
Anthropic |
|
| |
Cohere |
|
Mistral AI |
|
Groq |
|
Together AI |
|
AWS Bedrock |
and
|
Fireworks |
|
Azure OpenAI |
and
|
Vertex AI |
|
NVIDIA |
|
AI21 |
|
Databricks |
and
|
IBM watsonx.ai |
|
xAI |
|
Upstage |
|
Hugging Face |
|
Ollama |
|
Llama.cpp |
|
For more information check out: https://python.langchain.com/docs/integrations/chat/
You can create a .env file in the project directory with your API keys:
OPENAI_API_KEY=your_openai_key_here
# Or any other provider key
Troubleshooting
API Key Issues: Ensure your API key is correctly set in your environment variables or
.envfile.Provider Not Found: Make sure you've installed the required provider package.
Browser Automation Errors: Check that Playwright is correctly installed with
playwright install chromium.Model Selection: If you get errors about an invalid model, try using the
--modelflag to specify a valid model for your provider.Debug Mode: Use
--debugto enable more detailed logging that can help identify issues.MCP Client Configuration: Make sure your MCP client is correctly configured with the right command and environment variables.
License
MIT # browser-use-mcp
This server cannot be installed
hybrid server
The server is able to function both locally and remotely, depending on the configuration or use case.
A FastMCP server that enables browser automation through natural language commands, allowing Language Models to browse the web, fill out forms, click buttons, and perform other web-based tasks via a simple API.
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