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- **Browser Automation**: Utilize the Playwright framework for simulating browser interactions, including scrolling, clicking, input handling, downloading, navigation, and more.
- **Document Parsing**: Extract content from Word, Excel, PDF, and PowerPoint files, converting them into text or Markdown format.
- **Code Execution**: Write and execute Python code using interpreter.
- **Built-in Toolkits**: Access to a comprehensive set of built-in toolkits including ArxivToolkit, AudioAnalysisToolkit, CodeExecutionToolkit, DalleToolkit, DataCommonsToolkit, ExcelToolkit, GitHubToolkit, GoogleMapsToolkit, GoogleScholarToolkit, ImageAnalysisToolkit, MathToolkit, NetworkXToolkit, NotionToolkit, OpenAPIToolkit, RedditToolkit, SearchToolkit, SemanticScholarToolkit, SymPyToolkit, VideoAnalysisToolkit, WeatherToolkit, BrowserToolkit, and many more for specialized tasks.
- **Built-in Toolkits**: Access to a comprehensive set of built-in toolkits including:
- **Model Context Protocol (MCP)**: A universal protocol layer that standardizes AI model interactions with various tools and data sources
- **Core Toolkits**: ArxivToolkit, AudioAnalysisToolkit, CodeExecutionToolkit, DalleToolkit, DataCommonsToolkit, ExcelToolkit, GitHubToolkit, GoogleMapsToolkit, GoogleScholarToolkit, ImageAnalysisToolkit, MathToolkit, NetworkXToolkit, NotionToolkit, OpenAPIToolkit, RedditToolkit, SearchToolkit, SemanticScholarToolkit, SymPyToolkit, VideoAnalysisToolkit, WeatherToolkit, BrowserToolkit, and many more for specialized tasks
# 🛠️ Installation
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# 🚀 Quick Start
## Try MCP (Model Context Protocol) Integration
Experience the power of MCP by running our example that demonstrates multi-agent information retrieval and processing:
```bash
# Set up MCP servers (one-time setup)
npx -y @smithery/cli install @wonderwhy-er/desktop-commander --client claude
npx @wonderwhy-er/desktop-commander setup
# Run the MCP example
python owl/run_mcp.py
```
This example showcases how OWL agents can seamlessly interact with file systems, web automation, and information retrieval through the MCP protocol. Check out `owl/run_mcp.py` for the full implementation.
## Basic Usage
After installation and setting up your environment variables, you can start using OWL right away:
```bash
@@ -355,6 +374,14 @@ Here are some tasks you can try with OWL:
# 🧰 Toolkits and Capabilities
## Model Context Protocol (MCP)
OWL's MCP integration provides a standardized way for AI models to interact with various tools and data sources:
Try our comprehensive MCP example in `owl/run_mcp.py` to see these capabilities in action!
## Available Toolkits
> **Important**: Effective use of toolkits requires models with strong tool calling capabilities. For multimodal toolkits (Web, Image, Video), models must also have multimodal understanding abilities.
OWL supports various toolkits that can be customized by modifying the `tools` list in your script: