浏览器自动化代理

# Using curl
curl -LsSf https://astral.sh/uv/install.sh | sh

# Or using pip
pip install uv

cd mcp-agent

# Create virtual environment and install dependencies with uv
uv venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

# Install dependencies
uv pip install -r requirements.txt
# Or use uv sync if you have a pyproject.toml
uv sync

GROQ_API_KEY=your_groq_api_key_here

npx playwright install

mcp-agent/
├── main.py              # Main agent implementation
├── browser_mcp.json     # MCP server configuration
├── pyproject.toml       # Python project configuration
├── requirements.txt     # Python dependencies
├── uv.lock             # uv lock file
├── .env                # Environment variables (create this)
└── README.md           # This file

# Activate virtual environment
source .venv/bin/activate

# Run the agent
python main.py

You: Search for Airbnb listings in San Francisco for next weekend

You: Take a screenshot of the first search result

You: Navigate to google.com and search for "Python MCP"

You: What was the first thing I asked you?

{
  "mcpServers": {
    "playwright": {
      "command": "npx",
      "args": ["@playwright/mcp@latest"]
    },
    "airbnb": {
      "command": "npx",
      "args": ["-y", "@openbnb/mcp-server-airbnb"]
    },
    "duckduckgo-search": {
      "command": "npx",
      "args": ["-y", "duckduckgo-mcp-server"]
    }
  }
}

agent = MCPAgent(
    llm=llm,
    client=client,
    max_steps=15,        # Maximum reasoning steps
    memory_enabled=True, # Enable conversation memory
)